From f04255c6941bbeaac594562050f7ebc8148613ed Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Fri, 29 Mar 2024 18:49:36 +0100 Subject: [PATCH 01/74] feat: Add dbrx support (#1685) Close #1679 --- .../text_generation_server/models/__init__.py | 24 + .../custom_modeling/flash_dbrx_modeling.py | 1055 +++++++++++++++++ .../custom_modeling/flash_mixtral_modeling.py | 2 + .../models/flash_dbrx.py | 99 ++ 4 files changed, 1180 insertions(+) create mode 100644 server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py create mode 100644 server/text_generation_server/models/flash_dbrx.py diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index 6adbe580..06219e7c 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -71,6 +71,7 @@ try: from text_generation_server.models.flash_mixtral import FlashMixtral from text_generation_server.models.flash_phi import FlashPhi from text_generation_server.models.flash_starcoder2 import FlashStarcoder2 + from text_generation_server.models.flash_dbrx import FlashDbrx from text_generation_server.utils.flash_attn import HAS_FLASH_ATTN_V2_CUDA except ImportError as e: @@ -86,6 +87,7 @@ if FLASH_ATTENTION: __all__.append(IDEFICSSharded) __all__.append(FlashMistral) __all__.append(FlashMixtral) + __all__.append(FlashDbrx) __all__.append(FlashPhi) __all__.append(FlashQwen2) __all__.append(FlashStarcoder2) @@ -381,6 +383,28 @@ def get_model( trust_remote_code=trust_remote_code, ) + if model_type == "dbrx": + if FLASH_ATTENTION: + return FlashDbrx( + model_id, + revision, + quantize=quantize, + use_medusa=use_medusa, + dtype=dtype, + trust_remote_code=trust_remote_code, + ) + elif sharded: + raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded DBRX")) + else: + return CausalLM( + model_id, + revision, + quantize=quantize, + use_medusa=use_medusa, + dtype=dtype, + trust_remote_code=trust_remote_code, + ) + if model_type in ["RefinedWeb", "RefinedWebModel", "falcon"]: if sharded: if FLASH_ATTENTION: diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py new file mode 100644 index 00000000..dd0bcca5 --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py @@ -0,0 +1,1055 @@ +# coding=utf-8 +# Copyright 2022 HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.distributed + +import numpy as np + +from torch import nn +from transformers.activations import ACT2FN +from transformers.configuration_utils import PretrainedConfig +from typing import Optional, List, Tuple, Any +from loguru import logger + +from text_generation_server.utils import paged_attention, flash_attn +from text_generation_server.utils.layers import ( + FastLinear, + FastLayerNorm, + TensorParallelRowLinear, + TensorParallelColumnLinear, + TensorParallelEmbedding, + PositionRotaryEmbedding, + SpeculativeHead, + get_linear, +) +from text_generation_server.utils.log import log_once + +HAS_MEGABLOCKS = True +try: + import stk + import megablocks.ops as ops +except ImportError: + logger.warning("Dbrx: megablocks is not installed") + HAS_MEGABLOCKS = False + + +class DbrxAttentionConfig(PretrainedConfig): + def __init__( + self, + attn_pdrop: float = 0, + clip_qkv: Optional[float] = None, + kv_n_heads: int = 1, + rope_theta: float = 10000.0, + **kwargs: Any, + ): + super().__init__(**kwargs) + self.attn_pdrop = attn_pdrop + self.clip_qkv = clip_qkv + self.kv_n_heads = kv_n_heads + self.rope_theta = rope_theta + + for k in ["model_type"]: + if k in kwargs: + kwargs.pop(k) + if len(kwargs) != 0: + raise ValueError(f"Found unknown {kwargs=}") + + +class DbrxFFNConfig(PretrainedConfig): + def __init__( + self, + ffn_act_fn: Optional[dict] = None, + ffn_hidden_size: int = 3584, + moe_num_experts: int = 4, + moe_top_k: int = 1, + moe_jitter_eps: Optional[float] = None, + moe_loss_weight: float = 0.01, + moe_normalize_expert_weights: Optional[float] = 1, + uniform_expert_assignment: bool = False, + **kwargs: Any, + ): + super().__init__() + if ffn_act_fn is None: + ffn_act_fn = {"name": "silu"} + self.ffn_act_fn = ffn_act_fn + self.ffn_hidden_size = ffn_hidden_size + self.moe_num_experts = moe_num_experts + self.moe_top_k = moe_top_k + self.moe_jitter_eps = moe_jitter_eps + self.moe_loss_weight = moe_loss_weight + self.moe_normalize_expert_weights = moe_normalize_expert_weights + self.uniform_expert_assignment = uniform_expert_assignment + + if uniform_expert_assignment: + raise ValueError("`uniform_expert_assignment = True` is not supported") + + for k in ["model_type"]: + if k in kwargs: + kwargs.pop(k) + if len(kwargs) != 0: + raise ValueError(f"Found unknown {kwargs=}") + + +class DbrxConfig(PretrainedConfig): + def __init__( + self, + d_model: int = 2048, + n_heads: int = 16, + n_layers: int = 24, + max_seq_len: int = 2048, + vocab_size: int = 32000, + resid_pdrop: float = 0.0, + emb_pdrop: float = 0.0, + attn_config: Optional[DbrxAttentionConfig] = None, + ffn_config: Optional[DbrxFFNConfig] = None, + use_cache: bool = True, + initializer_range: float = 0.02, + output_router_logits: bool = False, + router_aux_loss_coef: float = 0.05, + **kwargs: Any, + ): + if attn_config is None: + self.attn_config = DbrxAttentionConfig() + elif isinstance(attn_config, dict): + self.attn_config = DbrxAttentionConfig(**attn_config) + else: + self.attn_config = attn_config + + if ffn_config is None: + self.ffn_config = DbrxFFNConfig() + elif isinstance(ffn_config, dict): + self.ffn_config = DbrxFFNConfig(**ffn_config) + else: + self.ffn_config = ffn_config + + self.d_model = d_model + self.n_heads = n_heads + self.n_layers = n_layers + self.max_seq_len = max_seq_len + self.vocab_size = vocab_size + self.resid_pdrop = resid_pdrop + self.emb_pdrop = emb_pdrop + self.use_cache = use_cache + self.initializer_range = initializer_range + self.output_router_logits = output_router_logits + self.router_aux_loss_coef = router_aux_loss_coef + + tie_word_embeddings = kwargs.pop("tie_word_embeddings", False) + if tie_word_embeddings: + raise ValueError("tie_word_embeddings is not supported for Dbrx models.") + + super().__init__( + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + +def promote_scalar(x: torch.Tensor) -> torch.Tensor: + return x.view(1) if len(x.size()) == 0 else x + + +def load_attention(config, prefix, weights): + if config.n_heads != config.attn_config.kv_n_heads: + return _load_gqa(config, prefix, weights) + else: + return TensorParallelColumnLinear.load_qkv( + config, + prefix=f"{prefix}.Wqkv", + weights=weights, + bias=False, + ) + + +def _load_gqa(config, prefix: str, weights): + assert config.d_model % config.n_heads == 0 + assert config.n_heads % weights.process_group.size() == 0 + + head_dim = config.d_model // config.n_heads + world_size = weights.process_group.size() + rank = weights.process_group.rank() + + q_block_size = config.d_model // world_size + q_start = rank * q_block_size + q_stop = (rank + 1) * q_block_size + + kv_block_size = (config.attn_config.kv_n_heads * head_dim) // world_size + k_offset = config.d_model + k_start = k_offset + rank * kv_block_size + k_stop = k_offset + (rank + 1) * kv_block_size + + v_offset = config.d_model + config.attn_config.kv_n_heads * head_dim + v_start = v_offset + rank * kv_block_size + v_stop = v_offset + (rank + 1) * kv_block_size + + if config.quantize in ["gptq", "awq"]: + try: + qweight_slice = weights._get_slice(f"{prefix}.qweight") + q_qweight = qweight_slice[:, q_start:q_stop] + k_qweight = qweight_slice[:, k_start:k_stop] + v_qweight = qweight_slice[:, v_start:v_stop] + + qweight = torch.cat([q_qweight, k_qweight, v_qweight], dim=1) + except RuntimeError: + raise RuntimeError( + f"Cannot load `{config.quantize}` weight, make sure the model is already quantized" + ) + + qzeros_slice = weights._get_slice(f"{prefix}.qzeros") + q_qzeros = qzeros_slice[:, q_start:q_stop] + k_qzeros = qzeros_slice[:, k_start:k_stop] + v_qzeros = qzeros_slice[:, v_start:v_stop] + + qzeros = torch.cat([q_qzeros, k_qzeros, v_qzeros], dim=1) + + scales_slice = weights._get_slice(f"{prefix}.scales") + q_scales = scales_slice[:, q_start:q_stop] + k_scales = scales_slice[:, k_start:k_stop] + v_scales = scales_slice[:, v_start:v_stop] + + scales = torch.cat([q_scales, k_scales, v_scales], dim=1) + + bits, groupsize, desc_act, quant_method = weights._get_gptq_params() + + from text_generation_server.utils.layers import HAS_EXLLAMA + + use_exllama = ( + bits == 4 and HAS_EXLLAMA and config.quantize == "gptq" and not desc_act + ) + + if config.quantize == "gptq" and quant_method == "gptq": + g_idx_slice = weights._get_slice(f"{prefix}.g_idx") + q_g_idx = g_idx_slice[:, q_start:q_stop] + k_g_idx = g_idx_slice[:, k_start:k_stop] + v_g_idx = g_idx_slice[:, v_start:v_stop] + + w = [q_g_idx, k_g_idx, v_g_idx] + for w2 in w[1:]: + torch.testing.assert_close(w2, w[0]) + g_idx = w[0] + elif config.quantize == "gptq" and quant_method == "awq": + log_once( + logger.info, "Converting AWQ model to Exllama/GPTQ packing format." + ) + from text_generation_server.utils.awq.conversion_utils import ( + fast_awq_to_gptq, + ) + + qweight, qzeros = fast_awq_to_gptq(qweight, qzeros) + if use_exllama: + g_idx = None + else: + g_idx = ( + torch.arange(qweight.shape[0] * (32 // bits), device=qweight.device) + // groupsize + ).to(dtype=torch.int32) + else: + g_idx = None + + weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama) + else: + qkv_slice = weights._get_slice(f"{prefix}.Wqkv.weight") + q = qkv_slice[q_start:q_stop] + k = qkv_slice[k_start:k_stop] + v = qkv_slice[v_start:v_stop] + + weight = torch.cat([q, k, v], dim=0) + weight = weight.to(dtype=weights.dtype).to(device=weights.device) + + return TensorParallelColumnLinear( + get_linear(weight, bias=None, quantize=config.quantize) + ) + + +def _load_experts(config, prefix, weights): + world_size = weights.process_group.size() + rank = weights.process_group.rank() + + assert ( + config.ffn_config.ffn_hidden_size % world_size == 0 + ), f"The chosen size {config.ffn_config.ffn_hidden_size} is not compatible with sharding on {world_size} shards" + + expert_size = config.ffn_config.ffn_hidden_size + block_size = expert_size // world_size + start = rank * block_size + stop = (rank + 1) * block_size + + tensor = torch.empty( + (config.ffn_config.moe_num_experts * block_size, config.d_model), + dtype=weights.dtype, + device=weights.device, + ) + + slice_ = weights._get_slice(f"{prefix}") + + for i in range(config.ffn_config.moe_num_experts): + offset = i * expert_size + expert_slice = slice_[start + offset : stop + offset] + + tensor[i * block_size : (i + 1) * block_size] = expert_slice.to( + dtype=weights.dtype + ).to(device=weights.device) + return tensor + + +def _load_experts_quantized(config, prefix, weights, cls): + world_size = weights.process_group.size() + rank = weights.process_group.rank() + + assert ( + config.ffn_config.ffn_hidden_size % world_size == 0 + ), f"The chosen size {config.ffn_config.ffn_hidden_size} is not compatible with sharding on {world_size} shards" + + expert_size = config.ffn_config.ffn_hidden_size + block_size = expert_size // world_size + start = rank * block_size + stop = (rank + 1) * block_size + + slice_ = weights._get_slice(f"{prefix}") + + experts = [] + for i in range(config.ffn_config.moe_num_experts): + if config.quantize in ["gptq", "awq"]: + raise NotImplementedError( + "Dbrx does not support gptq/awq quantization yet." + ) + else: + offset = i * expert_size + expert_slice = ( + slice_[start + offset : stop + offset] + .to(dtype=weights.dtype) + .to(device=weights.device) + ) + + if cls == TensorParallelRowLinear: + expert_slice = expert_slice.t().contiguous() + linear = get_linear(expert_slice, None, config.quantize) + experts.append(cls(linear, weights.process_group)) + else: + linear = get_linear(expert_slice, None, config.quantize) + experts.append(cls(linear)) + + return experts + + +class DbrxAttention(torch.nn.Module): + def __init__( + self, + prefix: str, + config, + weights, + ): + super().__init__() + self.clip_qkv = config.attn_config.clip_qkv + self.num_heads = config.n_heads + self.hidden_size = config.d_model + self.head_size = self.hidden_size // self.num_heads + + self.rotary_emb = PositionRotaryEmbedding.static( + config=config, + dim=self.head_size, + base=config.attn_config.rope_theta, + device=weights.device, + ) + + self.softmax_scale = self.head_size**-0.5 + + if self.num_heads % weights.process_group.size() != 0: + raise ValueError( + f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} " + f"and `num_shards`: {weights.process_group.size()}" + ) + self.num_heads = self.num_heads // weights.process_group.size() + self.num_key_value_heads = ( + config.attn_config.kv_n_heads // weights.process_group.size() + ) + + self.query_key_value = load_attention(config, prefix, weights) + + self.o_proj = TensorParallelRowLinear.load( + config, + prefix=f"{prefix}.out_proj", + weights=weights, + bias=False, + ) + self.num_groups = self.num_heads // self.num_key_value_heads + self.kv_head_mapping = torch.arange( + 0, self.num_key_value_heads, dtype=torch.int32, device=weights.device + ).repeat_interleave(self.num_groups) + + def forward( + self, + hidden_states, + cos, + sin, + cu_seqlen_prefill, + kv_cache, + block_tables, + slots, + input_lengths, + max_s, + ): + qkv = self.query_key_value(hidden_states) + if self.clip_qkv is not None: + qkv = qkv.clamp(min=-self.clip_qkv, max=self.clip_qkv) + + query, kv = qkv.split( + [ + self.head_size * self.num_heads, + 2 * self.head_size * self.num_key_value_heads, + ], + dim=1, + ) + query = query.view(-1, self.num_heads, self.head_size) + kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size) + + self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin) + + paged_attention.reshape_and_cache( + kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots + ) + + # output tensor + attn_output = torch.empty_like(query) + + # Prefill + if cu_seqlen_prefill is not None: + # flash attention + flash_attn.attention( + query, + torch.select(kv, dim=1, index=0), + torch.select(kv, dim=1, index=1), + attn_output, + cu_seqlen_prefill, + max_s, + self.softmax_scale, + ) + # Decode + else: + paged_attention.attention( + attn_output, + query, + kv_cache[0], + kv_cache[1], + self.kv_head_mapping, + self.softmax_scale, + block_tables, + input_lengths, + max_s, + ) + + return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size)) + + +class DbrxNormAttentionNorm(nn.Module): + def __init__( + self, + prefix: str, + config, + weights, + ): + super().__init__() + self.norm_1 = FastLayerNorm.load_no_bias( + prefix=f"{prefix}.norm_1", weights=weights, eps=1e-5 + ) + self.self_attn = DbrxAttention( + prefix=f"{prefix}.attn", config=config, weights=weights + ) + self.norm_2 = FastLayerNorm.load_no_bias( + prefix=f"{prefix}.norm_2", + weights=weights, + eps=1e-5, + ) + + def forward( + self, + hidden_states, + residual, + cos, + sin, + cu_seqlen_prefill, + kv_cache, + block_tables, + slots, + input_lengths, + max_s, + ): + normed_hidden_states, res = self.norm_1(hidden_states, residual) + + # Self Attention + attn_output = self.self_attn( + normed_hidden_states, + cos, + sin, + cu_seqlen_prefill, + kv_cache, + block_tables, + slots, + input_lengths, + max_s, + ) + + # faster post attention rms norm + normed_attn_res_output, attn_res = self.norm_2(attn_output, res) + + return normed_attn_res_output, attn_res + + +@torch.jit.script +def select_experts( + gate_logits: torch.Tensor, top_k: int, moe_normalize_expert_weights: int +): + # all_probs: (sequence_length, n_experts) and upcast for softmax + all_probs = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float) + # weights, selected_experts: (sequence_length, top-k) + weights, selected_experts = torch.topk(all_probs, top_k, dim=-1) + if moe_normalize_expert_weights: + weights = weights / torch.norm( + weights, p=moe_normalize_expert_weights, dim=-1, keepdim=True + ) + weights = weights.view(-1) + selected_experts = selected_experts.view(-1) + + return selected_experts, weights + + +@torch.jit.script +def round_up(x: torch.Tensor, value: int): + return torch.div(x + (value - 1), value, rounding_mode="trunc") * value + + +class BlockSparseMoE(nn.Module): + """ + Built on the paper and library Megablocks as described in + https://arxiv.org/abs/2211.15841. This implementation is + strictly equivalent to standard MoE with full capacity (no + dropped tokens). It's faster since it formulates MoE operations + in terms of block-sparse operations to accomodate imbalanced + assignments of tokens to experts, whereas standard MoE either + (1) drop tokens at the cost of reduced performance or (2) set + capacity factor to number of experts and thus waste computation + and memory on padding. + """ + + def __init__(self, prefix, config: DbrxConfig, weights): + super().__init__() + self.moe_normalize_expert_weights = ( + config.ffn_config.moe_normalize_expert_weights + ) + self.hidden_dim = config.d_model + self.ffn_dim = config.ffn_config.ffn_hidden_size // weights.process_group.size() + self.num_experts = config.ffn_config.moe_num_experts + self.top_k = config.ffn_config.moe_top_k + + act = config.ffn_config.ffn_act_fn["name"] + if "gelu" in act: + self.act = lambda x: torch.nn.functional.gelu( + x, + approximate=( + "tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none" + ), + ) + elif "silu" in act: + self.act = torch.nn.functional.silu + else: + self.act = ACT2FN[act] + + # gating + self.gate = FastLinear.load( + config, f"{prefix}.router.layer", weights, bias=False + ) + + # merged expert weights, all of size (n_experts * ffn_dim, hidden_dim) + self.w1 = _load_experts(config, f"{prefix}.experts.mlp.w1", weights) + self.w2 = _load_experts(config, f"{prefix}.experts.mlp.w2", weights) + self.v1 = _load_experts(config, f"{prefix}.experts.mlp.v1", weights) + + self.offsets = None + self.offsets_block_rows = 0 + + self.process_group = weights.process_group + + # Calculate the number of bits needed to represent the expert indices + # so that we can pass it to radix sort. + self.sort_end_bit = max(int(np.ceil(np.log2(self.num_experts))), 1) + self.blocking = 128 + self.quantize_scatter_num_bits = -1 + + def topology(self, x: torch.Tensor, padded_bins: torch.Tensor): + padded_tokens, _ = x.size() + assert padded_tokens % self.blocking == 0 + assert self.ffn_dim % self.blocking == 0 + + # Offsets for the sparse matrix. All rows have the + # same number of nonzero blocks dictated by the + # dimensionality of a single expert. + block_rows = padded_tokens // self.blocking + blocks_per_row = self.ffn_dim // self.blocking + if self.offsets is None or block_rows > self.offsets_block_rows: + self.offsets = torch.arange( + 0, + block_rows * blocks_per_row + 1, + blocks_per_row, + dtype=torch.int32, + device=x.device, + ) + self.offsets_block_rows = block_rows + offsets = self.offsets + else: + offsets = self.offsets[: block_rows + 1] + + # Indices for the sparse matrix. The indices for + # the intermediate matrix are dynamic depending + # on the mapping of tokens to experts. + column_indices = ops.topology( + padded_bins, self.blocking, block_rows, blocks_per_row + ) + + # For now, use meta init to save the device memory. + data = torch.empty( + column_indices.numel(), + self.blocking, + self.blocking, + dtype=x.dtype, + device="meta", + ) + shape = (padded_tokens, self.ffn_dim * self.num_experts) + row_indices = stk.ops.row_indices(shape, data, offsets, column_indices) + return stk.Matrix( + shape, + data, + row_indices, + column_indices, + offsets, + False, + False, + False, + ) + + def indices_and_padded_bins(self, selected_experts: torch.Tensor): + # Sort the expert ids to produce the scatter/gather + # indices for the permutation. + # selected_experts = selected_experts.int() + + # returns bin_ids == num of experts for this sequence ? == unique selected experts? + # and indices == how to sort tokens? + bin_ids, indices = ops.sort(selected_experts, self.sort_end_bit) + # bin_ids => [0, 0, 0, 2, 2, ...] => [num_tokens * top_k] + # indices => [14, 32, 33, ...] => [num_tokens * top_k] + + # Histogram the expert ids to identify the number of + # tokens routed to each expert. + tokens_per_expert = ops.histogram(selected_experts, self.num_experts) + # tokens_per_expert => [3, 0, 2, ...] => [num_experts] + + # Round the token counts up to the block size used in + # the matrix muliplications. Caculate the starting + # position of each bin. + + # List of size num_experts + padded_tokens_per_expert = round_up(tokens_per_expert, self.blocking) + # padded_tokens_per_expert => [128, O, 128, ...] + + # Cumulative selected experts per token + padded_bins = ops.inclusive_cumsum(padded_tokens_per_expert, 0) + padded_bins = promote_scalar(padded_bins) + # padded_bins => [128, 128, 256, ...] + + # Calculate the bin bounds for the sorted tokens. + bins = ops.inclusive_cumsum(tokens_per_expert, 0) + bins = promote_scalar(bins) + # bins => [3, 3, 5, ...] + + return indices, bin_ids, bins, padded_bins, tokens_per_expert + + def sparse_forward(self, x: torch.Tensor) -> torch.Tensor: + """ + x: (sequence_length, model_dim) + gate_logits: (sequence_length, n_experts) + """ + # optional reshape + input_shape = x.shape + x = x.view(-1, input_shape[-1]) + + # gate_logits: (sequence_length, n_experts) + gate_logits = self.gate(x) + selected_experts, weights = select_experts( + gate_logits, self.top_k, self.moe_normalize_expert_weights + ) + + ( + indices, + bin_ids, + bins, + padded_bins, + _, + ) = self.indices_and_padded_bins(selected_experts) + + # Permute tokens and pad to prepare expert computation + # (top_k * sequence_length + padding, model_dim) + x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins, self.top_k) + + # Create the sparse matrix topology + with torch.no_grad(): + topo = self.topology(x, padded_bins) + + # Perform the expert computation + # First Dense x Dense -> Sparse for w1 and v1, + # (top_k * sequence_length + padding, ffn_dim * n_experts) + x = stk.Matrix( + topo.size(), + self.act(stk.ops.sdd(x, self.w1.t(), topo).data) + * stk.ops.sdd(x, self.v1.t(), topo).data, + topo.row_indices, + topo.column_indices, + topo.offsets, + topo.column_indices_t, + topo.offsets_t, + topo.block_offsets_t, + ) + + # Then Sparse x Dense -> Dense for w2 + # (top_k * sequence_length + padding, model_dim) + x = stk.ops.dsd(x, self.w2) + + # Permute back and remove padding + # (sequence_length, model_dim) + x = ops.padded_scatter( + x, + indices, + bin_ids, + weights, + bins, + padded_bins, + self.top_k, + self.quantize_scatter_num_bits, + ).view(*input_shape) + + if self.process_group.size() > 1: + torch.distributed.all_reduce(x, group=self.process_group) + + return x.view(*input_shape) + + def dense_forward(self, x: torch.Tensor) -> torch.Tensor: + """ + x: (sequence_length, model_dim) + gate_logits: (sequence_length, n_experts) + """ + # optional reshape + input_shape = x.shape + x = x.view(-1, input_shape[-1]) + + # gate_logits: (sequence_length, n_experts) + gate_logits = self.gate(x) + # all_probs: (sequence_length, n_experts) and upcast for softmax + weights = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float) + + if self.top_k < self.num_experts: + _, not_selected_experts = torch.topk( + weights, + self.num_experts - self.top_k, + largest=False, + sorted=False, + dim=1, + ) + # Mask not selected experts + weights.scatter_(1, not_selected_experts, 0) + + # Re-normalize + if self.moe_normalize_expert_weights: + weights = weights / torch.norm( + weights, p=self.moe_normalize_expert_weights, dim=-1, keepdim=True + ) + weights = weights.to(x.dtype) + + # Expand to [num_experts, sequence_length, model_dim] + x = x.view(1, -1, input_shape[-1]).expand(self.num_experts, -1, input_shape[-1]) + + # Permute to [num_experts, model_dim, ffn_dim] + w1 = self.w1.view(self.num_experts, self.ffn_dim, self.hidden_dim).permute( + 0, 2, 1 + ) + v1 = self.v1.view(self.num_experts, self.ffn_dim, self.hidden_dim).permute( + 0, 2, 1 + ) + + inter = self.act(torch.bmm(x, w1)) * torch.bmm(x, v1) + + out = torch.bmm( + inter, self.w2.view(self.num_experts, self.ffn_dim, self.hidden_dim) + ) + # Mask not selected experts + out *= weights.t().view(self.num_experts, -1, 1) + + # Sum experts + out = out.sum(0) + + # Reduce sum + if self.process_group.size() > 1: + torch.distributed.all_reduce(out, group=self.process_group) + + return out + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if len(x) > 256 and HAS_MEGABLOCKS: + return self.sparse_forward(x) + # This is faster when there is not a lot of tokens + return self.dense_forward(x) + + +class DenseMoE(nn.Module): + def __init__(self, prefix, config: DbrxConfig, weights): + super().__init__() + + self.moe_normalize_expert_weights = ( + config.ffn_config.moe_normalize_expert_weights + ) + self.hidden_dim = config.d_model + self.ffn_dim = config.ffn_config.ffn_hidden_size // weights.process_group.size() + self.num_experts = config.ffn_config.moe_num_experts + self.top_k = config.ffn_config.moe_top_k + + act = config.ffn_config.ffn_act_fn["name"] + if "gelu" in act: + self.act = lambda x: torch.nn.functional.gelu( + x, + approximate=( + "tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none" + ), + ) + elif "silu" in act: + self.act = torch.nn.functional.silu + else: + self.act = ACT2FN[act] + + # gating + self.gate = FastLinear.load( + config, f"{prefix}.router.layer", weights, bias=False + ) + + self.w1 = _load_experts_quantized( + config, + prefix=f"{prefix}.experts.mlp.w1", + weights=weights, + cls=TensorParallelColumnLinear, + ) + self.w2 = _load_experts_quantized( + config, + prefix=f"{prefix}.experts.mlp.w2", + weights=weights, + cls=TensorParallelRowLinear, + ) + self.v1 = _load_experts_quantized( + config, + prefix=f"{prefix}.experts.mlp.v1", + weights=weights, + cls=TensorParallelColumnLinear, + ) + + self.process_group = weights.process_group + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + x: (sequence_length, model_dim) + gate_logits: (sequence_length, n_experts) + """ + # optional reshape + input_shape = x.shape + x = x.view(-1, input_shape[-1]) + + # gate_logits: (sequence_length, n_experts) + gate_logits = self.gate(x) + # all_probs: (sequence_length, n_experts) and upcast for softmax + weights = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float) + + if self.top_k < self.num_experts: + _, not_selected_experts = torch.topk( + weights, + self.num_experts - self.top_k, + largest=False, + sorted=False, + dim=1, + ) + # Mask not selected experts + weights.scatter_(1, not_selected_experts, 0) + + # Re-normalize + if self.moe_normalize_expert_weights: + weights = weights / torch.norm( + weights, p=self.moe_normalize_expert_weights, dim=-1, keepdim=True + ) + weights = weights.to(x.dtype) + + # Final output tensor + out = x.new_zeros(x.shape[0], self.hidden_dim) + for i in range(self.num_experts): + h = self.act(self.w1[i](x)) * self.v1[i](x) + h = self.w2[i](h, reduce=False) + # Add expert output to out with masking + out += h * weights[:, i].view(-1, 1) + + # Reduce sum + if self.process_group.size() > 1: + torch.distributed.all_reduce(out, group=self.process_group) + + return out + + +class DbrxLayer(nn.Module): + def __init__(self, layer_id, config, weights): + super().__init__() + prefix = f"transformer.blocks.{layer_id}" + + self.attn = DbrxNormAttentionNorm( + prefix=f"{prefix}.norm_attn_norm", config=config, weights=weights + ) + + moe_cls = BlockSparseMoE if config.quantize is None else DenseMoE + self.moe = moe_cls(f"{prefix}.ffn", config, weights) + + def forward( + self, + hidden_states, + residual, + cos, + sin, + cu_seqlen_prefill, + kv_cache, + block_tables, + slots, + input_lengths, + max_s, + ): + # Self Attention + attn_output, attn_res = self.attn( + hidden_states, + residual, + cos, + sin, + cu_seqlen_prefill, + kv_cache, + block_tables, + slots, + input_lengths, + max_s, + ) + + moe_output = self.moe(attn_output) + + return moe_output, attn_res + + +class DbrxModel(torch.nn.Module): + def __init__(self, config, weights): + super().__init__() + + self.embed_tokens = TensorParallelEmbedding( + prefix="transformer.wte", weights=weights + ) + + self.layers = nn.ModuleList( + [ + DbrxLayer( + layer_id, + config, + weights, + ) + for layer_id in range(config.n_layers) + ] + ) + self.norm = FastLayerNorm.load_no_bias( + prefix="transformer.norm_f", weights=weights, eps=1e-5 + ) + + self.head_size = self.layers[0].attn.self_attn.head_size + self.num_heads = self.layers[0].attn.self_attn.num_heads + self.num_key_value_heads = self.layers[0].attn.self_attn.num_key_value_heads + + def forward( + self, + input_ids: torch.Tensor, + position_ids: torch.Tensor, + cu_seqlen_prefill: Optional[torch.Tensor], + kv_cache: List[Tuple[torch.Tensor, torch.Tensor]], + block_tables: torch.Tensor, + slots: torch.Tensor, + input_lengths: torch.Tensor, + max_s: int, + ) -> torch.Tensor: + hidden_states = self.embed_tokens(input_ids) + + # Get rotary cos and sin for this forward + # Avoid to index in each layer + cos, sin = self.layers[0].attn.self_attn.rotary_emb.get_cos_sin( + position_ids, max_s, hidden_states.dtype + ) + + residual = None + for i, layer in enumerate(self.layers): + hidden_states, residual = layer( + hidden_states, + residual, + cos, + sin, + cu_seqlen_prefill, + kv_cache[i], + block_tables, + slots, + input_lengths, + max_s, + ) + + hidden_states, _ = self.norm(hidden_states, residual) + + return hidden_states + + +class FlashDbrxForCausalLM(torch.nn.Module): + def __init__(self, config, weights): + super().__init__() + + self.model = DbrxModel(config, weights) + self.lm_head = SpeculativeHead.load( + config, + prefix="lm_head", + weights=weights, + ) + + def forward( + self, + input_ids: torch.Tensor, + position_ids: torch.Tensor, + cu_seqlen_prefill: Optional[torch.Tensor], + kv_cache: List[Tuple[torch.Tensor, torch.Tensor]], + block_tables: torch.Tensor, + slots: torch.Tensor, + input_lengths: torch.Tensor, + max_s: int, + lm_head_indices: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + hidden_states = self.model( + input_ids, + position_ids, + cu_seqlen_prefill, + kv_cache, + block_tables, + slots, + input_lengths, + max_s, + ) + if lm_head_indices is not None: + hidden_states = hidden_states[lm_head_indices] + logits, speculative_logits = self.lm_head(hidden_states) + return logits, speculative_logits diff --git a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py index 17d4f708..d71a3f0c 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py @@ -552,6 +552,7 @@ class BlockSparseMoE(nn.Module): # Re-normalize weights = all_probs / all_probs.sum(dim=1, keepdim=True) + weights = weights.to(x.dtype) # Expand to [num_experts, sequence_length, model_dim] x = x.view(1, -1, input_shape[-1]).expand(self.num_experts, -1, input_shape[-1]) @@ -660,6 +661,7 @@ class DenseMoE(nn.Module): # Re-normalize weights = all_probs / all_probs.sum(dim=1, keepdim=True) + weights = weights.to(x.dtype) # Final output tensor out = x.new_zeros(x.shape[0], self.hidden_dim) diff --git a/server/text_generation_server/models/flash_dbrx.py b/server/text_generation_server/models/flash_dbrx.py new file mode 100644 index 00000000..367d3db0 --- /dev/null +++ b/server/text_generation_server/models/flash_dbrx.py @@ -0,0 +1,99 @@ +import torch +import torch.distributed + +from opentelemetry import trace +from typing import Optional +from transformers import AutoTokenizer +from transformers.models.gpt2 import GPT2TokenizerFast + +from text_generation_server.models import FlashCausalLM +from text_generation_server.models.custom_modeling.flash_dbrx_modeling import ( + FlashDbrxForCausalLM, + DbrxConfig, +) +from text_generation_server.utils import ( + initialize_torch_distributed, + weight_files, + Weights, +) + +tracer = trace.get_tracer(__name__) + + +class FlashDbrx(FlashCausalLM): + def __init__( + self, + model_id: str, + revision: Optional[str] = None, + quantize: Optional[str] = None, + use_medusa: Optional[str] = None, + dtype: Optional[torch.dtype] = None, + trust_remote_code: bool = False, + ): + self.process_group, rank, world_size = initialize_torch_distributed() + if torch.cuda.is_available(): + device = torch.device(f"cuda:{rank}") + dtype = torch.bfloat16 if dtype is None else dtype + else: + raise NotImplementedError("FlashDBRX is only available on GPU") + + try: + tokenizer = GPT2TokenizerFast.from_pretrained( + model_id, + revision=revision, + padding_side="left", + truncation_side="left", + trust_remote_code=trust_remote_code, + use_fast=True, + from_slow=False, + ) + except: + try: + tokenizer = AutoTokenizer.from_pretrained( + model_id, + revision=revision, + padding_side="left", + truncation_side="left", + trust_remote_code=trust_remote_code, + use_fast=True, + from_slow=False, + ) + except: + # FIXME: change back to model id once the tokenizer.json is merged + tokenizer = GPT2TokenizerFast.from_pretrained( + "Xenova/dbrx-instruct-tokenizer", + revision=revision, + padding_side="left", + truncation_side="left", + trust_remote_code=trust_remote_code, + use_fast=True, + from_slow=False, + ) + + config = DbrxConfig.from_pretrained( + model_id, revision=revision, trust_remote_code=trust_remote_code + ) + config.quantize = quantize + config.use_medusa = use_medusa + + torch.distributed.barrier(group=self.process_group) + + filenames = weight_files(model_id, revision=revision, extension=".safetensors") + weights = Weights(filenames, device, dtype, process_group=self.process_group) + if config.quantize in ["gptq", "awq"]: + weights._set_gptq_params(model_id, revision) + + model = FlashDbrxForCausalLM(config, weights) + + torch.distributed.barrier(group=self.process_group) + super(FlashDbrx, self).__init__( + model=model, + tokenizer=tokenizer, + num_layers=len(model.model.layers), + num_kv_heads=model.model.num_key_value_heads, + head_size=model.model.head_size, + dtype=dtype, + device=device, + rank=rank, + world_size=world_size, + ) From 4ee0a0c4010b6e000f176977648aa1749339e8cb Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Fri, 29 Mar 2024 19:17:24 +0100 Subject: [PATCH 02/74] v1.4.5 (#1686) --- Cargo.lock | 158 +++---- Cargo.toml | 2 +- benchmark/src/app.rs | 8 +- benchmark/src/table.rs | 4 +- docs/openapi.json | 401 +++++++++++++++++- .../test_flash_llama_grammar_no_tools.json | 2 +- .../test_flash_llama_grammar_tools.json | 2 +- .../test_flash_llama_grammar_tools_auto.json | 2 +- ...test_flash_llama_grammar_tools_choice.json | 2 +- ...test_flash_llama_grammar_tools_stream.json | 2 +- integration-tests/pyproject.toml | 2 +- launcher/src/main.rs | 6 +- router/src/server.rs | 8 +- server/pyproject.toml | 2 +- 14 files changed, 494 insertions(+), 107 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 2df5d320..8078d24f 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -96,9 +96,9 @@ checksum = "0952808a6c2afd1aa8947271f3a60f1a6763c7b912d210184c5149b5cf147247" [[package]] name = "arc-swap" -version = "1.7.0" +version = "1.7.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7b3d0060af21e8d11a926981cc00c6c1541aa91dd64b9f881985c3da1094425f" +checksum = "69f7f8c3906b62b754cd5326047894316021dcfe5a194c8ea52bdd94934a3457" [[package]] name = "async-rustls" @@ -130,25 +130,25 @@ checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] name = "async-trait" -version = "0.1.78" +version = "0.1.79" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "461abc97219de0eaaf81fe3ef974a540158f3d079c2ab200f891f1a2ef201e85" +checksum = "a507401cad91ec6a857ed5513a2073c82a9b9048762b885bb98655b306964681" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] name = "autocfg" -version = "1.1.0" +version = "1.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d468802bab17cbc0cc575e9b053f41e72aa36bfa6b7f55e3529ffa43161b97fa" +checksum = "f1fdabc7756949593fe60f30ec81974b613357de856987752631dea1e3394c80" [[package]] name = "average" @@ -242,9 +242,9 @@ dependencies = [ [[package]] name = "backtrace" -version = "0.3.70" +version = "0.3.71" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "95d8e92cac0961e91dbd517496b00f7e9b92363dbe6d42c3198268323798860c" +checksum = "26b05800d2e817c8b3b4b54abd461726265fa9789ae34330622f2db9ee696f9d" dependencies = [ "addr2line", "cc", @@ -323,9 +323,9 @@ checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b" [[package]] name = "bytes" -version = "1.5.0" +version = "1.6.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a2bd12c1caf447e69cd4528f47f94d203fd2582878ecb9e9465484c4148a8223" +checksum = "514de17de45fdb8dc022b1a7975556c53c86f9f0aa5f534b98977b171857c2c9" [[package]] name = "camino" @@ -385,9 +385,9 @@ checksum = "fd16c4719339c4530435d38e511904438d07cce7950afa3718a84ac36c10e89e" [[package]] name = "clap" -version = "4.5.3" +version = "4.5.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "949626d00e063efc93b6dca932419ceb5432f99769911c0b995f7e884c778813" +checksum = "90bc066a67923782aa8515dbaea16946c5bcc5addbd668bb80af688e53e548a0" dependencies = [ "clap_builder", "clap_derive", @@ -407,14 +407,14 @@ dependencies = [ [[package]] name = "clap_derive" -version = "4.5.3" +version = "4.5.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "90239a040c80f5e14809ca132ddc4176ab33d5e17e49691793296e3fcb34d72f" +checksum = "528131438037fd55894f62d6e9f068b8f45ac57ffa77517819645d10aed04f64" dependencies = [ "heck 0.5.0", "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -748,9 +748,9 @@ dependencies = [ [[package]] name = "fastrand" -version = "2.0.1" +version = "2.0.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "25cbce373ec4653f1a01a31e8a5e5ec0c622dc27ff9c4e6606eefef5cbbed4a5" +checksum = "658bd65b1cf4c852a3cc96f18a8ce7b5640f6b703f905c7d74532294c2a63984" [[package]] name = "fixedbitset" @@ -876,7 +876,7 @@ checksum = "87750cf4b7a4c0625b1529e4c543c2182106e4dedc60a2a6455e00d212c489ac" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -969,7 +969,7 @@ dependencies = [ "futures-sink", "futures-util", "http", - "indexmap 2.2.5", + "indexmap 2.2.6", "slab", "tokio", "tokio-util", @@ -1173,9 +1173,9 @@ dependencies = [ [[package]] name = "indexmap" -version = "2.2.5" +version = "2.2.6" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7b0b929d511467233429c45a44ac1dcaa21ba0f5ba11e4879e6ed28ddb4f9df4" +checksum = "168fb715dda47215e360912c096649d23d58bf392ac62f73919e831745e40f26" dependencies = [ "equivalent", "hashbrown 0.14.3", @@ -1197,9 +1197,9 @@ dependencies = [ [[package]] name = "indoc" -version = "2.0.4" +version = "2.0.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1e186cfbae8084e513daff4240b4797e342f988cecda4fb6c939150f96315fd8" +checksum = "b248f5224d1d606005e02c97f5aa4e88eeb230488bcc03bc9ca4d7991399f2b5" [[package]] name = "init-tracing-opentelemetry" @@ -1267,9 +1267,9 @@ dependencies = [ [[package]] name = "itoa" -version = "1.0.10" +version = "1.0.11" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b1a46d1a171d865aa5f83f92695765caa047a9b4cbae2cbf37dbd613a793fd4c" +checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b" [[package]] name = "js-sys" @@ -1409,9 +1409,9 @@ checksum = "0e7465ac9959cc2b1404e8e2367b43684a6d13790fe23056cc8c6c5a6b7bcb94" [[package]] name = "memchr" -version = "2.7.1" +version = "2.7.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "523dc4f511e55ab87b694dc30d0f820d60906ef06413f93d4d7a1385599cc149" +checksum = "6c8640c5d730cb13ebd907d8d04b52f55ac9a2eec55b440c8892f40d56c76c1d" [[package]] name = "metrics" @@ -1450,7 +1450,7 @@ checksum = "38b4faf00617defe497754acde3024865bc143d44a86799b24e191ecff91354f" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -1486,8 +1486,8 @@ dependencies = [ [[package]] name = "minijinja" -version = "1.0.15" -source = "git+https://github.com/mitsuhiko/minijinja.git?branch=main#045d6a0f7ee1ea9cb4aa7725bc8fb1c39ab1d90b" +version = "1.0.16" +source = "git+https://github.com/mitsuhiko/minijinja.git?branch=main#82d0160b5513844e5429db084f1cbdd3313ed482" dependencies = [ "serde", ] @@ -1537,7 +1537,7 @@ checksum = "f686d68a09079e63b1d2c64aa305095887ce50565f00a922ebfaeeee0d9ba6ce" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -1834,7 +1834,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -1845,9 +1845,9 @@ checksum = "ff011a302c396a5197692431fc1948019154afc178baf7d8e37367442a4601cf" [[package]] name = "openssl-sys" -version = "0.9.101" +version = "0.9.102" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dda2b0f344e78efc2facf7d195d098df0dd72151b26ab98da807afc26c198dff" +checksum = "c597637d56fbc83893a35eb0dd04b2b8e7a50c91e64e9493e398b5df4fb45fa2" dependencies = [ "cc", "libc", @@ -2030,7 +2030,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e1d3afd2628e69da2be385eb6f2fd57c8ac7977ceeff6dc166ff1657b0e386a9" dependencies = [ "fixedbitset", - "indexmap 2.2.5", + "indexmap 2.2.6", ] [[package]] @@ -2050,7 +2050,7 @@ checksum = "2f38a4412a78282e09a2cf38d195ea5420d15ba0602cb375210efbc877243965" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -2091,12 +2091,12 @@ checksum = "5b40af805b3121feab8a3c29f04d8ad262fa8e0561883e7653e024ae4479e6de" [[package]] name = "prettyplease" -version = "0.2.16" +version = "0.2.17" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a41cf62165e97c7f814d2221421dbb9afcbcdb0a88068e5ea206e19951c2cbb5" +checksum = "8d3928fb5db768cb86f891ff014f0144589297e3c6a1aba6ed7cecfdace270c7" dependencies = [ "proc-macro2", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -2169,7 +2169,7 @@ dependencies = [ "prost 0.12.3", "prost-types", "regex", - "syn 2.0.53", + "syn 2.0.55", "tempfile", "which", ] @@ -2197,7 +2197,7 @@ dependencies = [ "itertools 0.11.0", "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -2292,9 +2292,9 @@ dependencies = [ [[package]] name = "rayon" -version = "1.9.0" +version = "1.10.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e4963ed1bc86e4f3ee217022bd855b297cef07fb9eac5dfa1f788b220b49b3bd" +checksum = "b418a60154510ca1a002a752ca9714984e21e4241e804d32555251faf8b78ffa" dependencies = [ "either", "rayon-core", @@ -2343,14 +2343,14 @@ dependencies = [ [[package]] name = "regex" -version = "1.10.3" +version = "1.10.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b62dbe01f0b06f9d8dc7d49e05a0785f153b00b2c227856282f671e0318c9b15" +checksum = "c117dbdfde9c8308975b6a18d71f3f385c89461f7b3fb054288ecf2a2058ba4c" dependencies = [ "aho-corasick", "memchr", "regex-automata 0.4.6", - "regex-syntax 0.8.2", + "regex-syntax 0.8.3", ] [[package]] @@ -2370,7 +2370,7 @@ checksum = "86b83b8b9847f9bf95ef68afb0b8e6cdb80f498442f5179a29fad448fcc1eaea" dependencies = [ "aho-corasick", "memchr", - "regex-syntax 0.8.2", + "regex-syntax 0.8.3", ] [[package]] @@ -2387,9 +2387,9 @@ checksum = "dbb5fb1acd8a1a18b3dd5be62d25485eb770e05afb408a9627d14d451bae12da" [[package]] name = "regex-syntax" -version = "0.8.2" +version = "0.8.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c08c74e62047bb2de4ff487b251e4a92e24f48745648451635cec7d591162d9f" +checksum = "adad44e29e4c806119491a7f06f03de4d1af22c3a680dd47f1e6e179439d1f56" [[package]] name = "reqwest" @@ -2482,7 +2482,7 @@ dependencies = [ "quote", "rust-embed-utils", "shellexpand", - "syn 2.0.53", + "syn 2.0.55", "walkdir", ] @@ -2538,9 +2538,9 @@ dependencies = [ [[package]] name = "rustls" -version = "0.22.2" +version = "0.22.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e87c9956bd9807afa1f77e0f7594af32566e830e088a5576d27c5b6f30f49d41" +checksum = "99008d7ad0bbbea527ec27bddbc0e432c5b87d8175178cee68d2eec9c4a1813c" dependencies = [ "log", "ring 0.17.8", @@ -2561,9 +2561,9 @@ dependencies = [ [[package]] name = "rustls-pki-types" -version = "1.3.1" +version = "1.4.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5ede67b28608b4c60685c7d54122d4400d90f62b40caee7700e700380a390fa8" +checksum = "ecd36cc4259e3e4514335c4a138c6b43171a8d61d8f5c9348f9fc7529416f247" [[package]] name = "rustls-webpki" @@ -2671,14 +2671,14 @@ checksum = "7eb0b34b42edc17f6b7cac84a52a1c5f0e1bb2227e997ca9011ea3dd34e8610b" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] name = "serde_json" -version = "1.0.114" +version = "1.0.115" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c5f09b1bd632ef549eaa9f60a1f8de742bdbc698e6cee2095fc84dde5f549ae0" +checksum = "12dc5c46daa8e9fdf4f5e71b6cf9a53f2487da0e86e55808e2d35539666497dd" dependencies = [ "itoa", "ryu", @@ -2861,7 +2861,7 @@ dependencies = [ "proc-macro2", "quote", "rustversion", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -2883,9 +2883,9 @@ dependencies = [ [[package]] name = "syn" -version = "2.0.53" +version = "2.0.55" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7383cd0e49fff4b6b90ca5670bfd3e9d6a733b3f90c686605aa7eec8c4996032" +checksum = "002a1b3dbf967edfafc32655d0f377ab0bb7b994aa1d32c8cc7e9b8bf3ebb8f0" dependencies = [ "proc-macro2", "quote", @@ -2971,7 +2971,7 @@ dependencies = [ [[package]] name = "text-generation-benchmark" -version = "1.4.4" +version = "1.4.5" dependencies = [ "average", "clap", @@ -2992,7 +2992,7 @@ dependencies = [ [[package]] name = "text-generation-client" -version = "1.4.4" +version = "1.4.5" dependencies = [ "futures", "grpc-metadata", @@ -3008,7 +3008,7 @@ dependencies = [ [[package]] name = "text-generation-launcher" -version = "1.4.4" +version = "1.4.5" dependencies = [ "clap", "ctrlc", @@ -3024,7 +3024,7 @@ dependencies = [ [[package]] name = "text-generation-router" -version = "1.4.4" +version = "1.4.5" dependencies = [ "async-stream", "axum", @@ -3079,7 +3079,7 @@ checksum = "c61f3ba182994efc43764a46c018c347bc492c79f024e705f46567b418f6d4f7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -3198,7 +3198,7 @@ dependencies = [ "rayon", "rayon-cond", "regex", - "regex-syntax 0.8.2", + "regex-syntax 0.8.3", "serde", "serde_json", "spm_precompiled", @@ -3210,9 +3210,9 @@ dependencies = [ [[package]] name = "tokio" -version = "1.36.0" +version = "1.37.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "61285f6515fa018fb2d1e46eb21223fff441ee8db5d0f1435e8ab4f5cdb80931" +checksum = "1adbebffeca75fcfd058afa480fb6c0b81e165a0323f9c9d39c9697e37c46787" dependencies = [ "backtrace", "bytes", @@ -3245,7 +3245,7 @@ checksum = "5b8a1e28f2deaa14e508979454cb3a223b10b938b45af148bc0986de36f1923b" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -3360,7 +3360,7 @@ dependencies = [ "proc-macro2", "prost-build", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -3433,7 +3433,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -3620,7 +3620,7 @@ dependencies = [ "log", "native-tls", "once_cell", - "rustls 0.22.2", + "rustls 0.22.3", "rustls-pki-types", "rustls-webpki", "serde", @@ -3658,7 +3658,7 @@ version = "3.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d82b1bc5417102a73e8464c686eef947bdfb99fcdfc0a4f228e81afa9526470a" dependencies = [ - "indexmap 2.2.5", + "indexmap 2.2.6", "serde", "serde_json", "utoipa-gen", @@ -3674,7 +3674,7 @@ dependencies = [ "proc-macro2", "quote", "regex", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] @@ -3779,7 +3779,7 @@ dependencies = [ "once_cell", "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", "wasm-bindgen-shared", ] @@ -3813,7 +3813,7 @@ checksum = "e94f17b526d0a461a191c78ea52bbce64071ed5c04c9ffe424dcb38f74171bb7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", "wasm-bindgen-backend", "wasm-bindgen-shared", ] @@ -4140,7 +4140,7 @@ checksum = "9ce1b18ccd8e73a9321186f97e46f9f04b778851177567b1975109d26a08d2a6" dependencies = [ "proc-macro2", "quote", - "syn 2.0.53", + "syn 2.0.55", ] [[package]] diff --git a/Cargo.toml b/Cargo.toml index d76cbc68..77a30f55 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -9,7 +9,7 @@ members = [ resolver = "2" [workspace.package] -version = "1.4.4" +version = "1.4.5" edition = "2021" authors = ["Olivier Dehaene"] homepage = "https://github.com/huggingface/text-generation-inference" diff --git a/benchmark/src/app.rs b/benchmark/src/app.rs index b27c56b4..48ac976a 100644 --- a/benchmark/src/app.rs +++ b/benchmark/src/app.rs @@ -444,7 +444,7 @@ fn progress_gauge(title: &str, label: String, progress: f64, color: Color) -> Ga } /// Throughput paragraph -fn throughput_paragraph<'a>(throughput: &Vec, name: &'static str) -> Paragraph<'a> { +fn throughput_paragraph<'a>(throughput: &[f64], name: &'static str) -> Paragraph<'a> { // Throughput average/high/low texts let throughput_texts = statis_spans(throughput, "tokens/secs"); @@ -457,7 +457,7 @@ fn throughput_paragraph<'a>(throughput: &Vec, name: &'static str) -> Paragr } /// Latency paragraph -fn latency_paragraph<'a>(latency: &mut Vec, name: &'static str) -> Paragraph<'a> { +fn latency_paragraph<'a>(latency: &mut [f64], name: &'static str) -> Paragraph<'a> { // Latency average/high/low texts let mut latency_texts = statis_spans(latency, "ms"); @@ -483,7 +483,7 @@ fn latency_paragraph<'a>(latency: &mut Vec, name: &'static str) -> Paragrap } /// Average/High/Low spans -fn statis_spans<'a>(data: &Vec, unit: &'static str) -> Vec> { +fn statis_spans<'a>(data: &[f64], unit: &'static str) -> Vec> { vec![ Line::from(vec![Span::styled( format!( @@ -543,7 +543,7 @@ fn latency_histogram<'a>( /// Latency/Throughput chart fn latency_throughput_chart<'a>( - latency_throughput: &'a Vec<(f64, f64)>, + latency_throughput: &'a [(f64, f64)], batch_sizes: &'a [u32], zoom: bool, name: &'static str, diff --git a/benchmark/src/table.rs b/benchmark/src/table.rs index c4819ff3..e18d7310 100644 --- a/benchmark/src/table.rs +++ b/benchmark/src/table.rs @@ -151,7 +151,7 @@ fn add_throuhgputs( } } -fn avg_min_max(data: &Vec) -> (f64, f64, f64) { +fn avg_min_max(data: &[f64]) -> (f64, f64, f64) { let average = data.iter().sum::() / data.len() as f64; let min = data .iter() @@ -164,7 +164,7 @@ fn avg_min_max(data: &Vec) -> (f64, f64, f64) { (average, *min, *max) } -fn px(data: &Vec, p: u32) -> f64 { +fn px(data: &[f64], p: u32) -> f64 { let i = (f64::from(p) / 100.0 * data.len() as f64) as usize; *data.get(i).unwrap_or(&std::f64::NAN) } diff --git a/docs/openapi.json b/docs/openapi.json index 75965d98..fdf1c804 100644 --- a/docs/openapi.json +++ b/docs/openapi.json @@ -10,7 +10,7 @@ "name": "Apache 2.0", "url": "https://www.apache.org/licenses/LICENSE-2.0" }, - "version": "1.4.4" + "version": "1.4.5" }, "paths": { "/": { @@ -471,6 +471,90 @@ } } } + }, + "/v1/completions": { + "post": { + "tags": [ + "Text Generation Inference" + ], + "summary": "Generate tokens", + "description": "Generate tokens", + "operationId": "completions", + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/CompletionRequest" + } + } + }, + "required": true + }, + "responses": { + "200": { + "description": "Generated Text", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ChatCompletionChunk" + } + } + } + }, + "422": { + "description": "Input validation error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ErrorResponse" + }, + "example": { + "error": "Input validation error" + } + } + } + }, + "424": { + "description": "Generation Error", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ErrorResponse" + }, + "example": { + "error": "Request failed during generation" + } + } + } + }, + "429": { + "description": "Model is overloaded", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ErrorResponse" + }, + "example": { + "error": "Model is overloaded" + } + } + } + }, + "500": { + "description": "Incomplete generation", + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/ErrorResponse" + }, + "example": { + "error": "Incomplete generation" + } + } + } + } + } + } } }, "components": { @@ -669,17 +753,25 @@ "ChatCompletionDelta": { "type": "object", "required": [ - "role", - "content" + "role" ], "properties": { "content": { "type": "string", - "example": "What is Deep Learning?" + "example": "What is Deep Learning?", + "nullable": true }, "role": { "type": "string", "example": "user" + }, + "tool_calls": { + "allOf": [ + { + "$ref": "#/components/schemas/DeltaToolCall" + } + ], + "nullable": true } } }, @@ -739,7 +831,8 @@ "ChatRequest": { "type": "object", "required": [ - "model" + "model", + "messages" ], "properties": { "frequency_penalty": { @@ -777,11 +870,12 @@ "items": { "$ref": "#/components/schemas/Message" }, - "description": "A list of messages comprising the conversation so far." + "description": "A list of messages comprising the conversation so far.", + "example": "[{\"role\": \"user\", \"content\": \"What is Deep Learning?\"}]" }, "model": { "type": "string", - "description": "UNUSED\nID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.", + "description": "[UNUSED] ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.", "example": "mistralai/Mistral-7B-Instruct-v0.2" }, "n": { @@ -806,6 +900,15 @@ "nullable": true, "minimum": 0 }, + "stop": { + "type": "array", + "items": { + "type": "string" + }, + "description": "Up to 4 sequences where the API will stop generating further tokens.", + "example": "null", + "nullable": true + }, "stream": { "type": "boolean" }, @@ -816,6 +919,29 @@ "example": 1.0, "nullable": true }, + "tool_choice": { + "allOf": [ + { + "$ref": "#/components/schemas/ToolType" + } + ], + "nullable": true + }, + "tool_prompt": { + "type": "string", + "description": "A prompt to be appended before the tools", + "example": "\"Based on the conversation, please choose the most appropriate tool to use: \"", + "nullable": true + }, + "tools": { + "type": "array", + "items": { + "$ref": "#/components/schemas/Tool" + }, + "description": "A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of\nfunctions the model may generate JSON inputs for.", + "example": "null", + "nullable": true + }, "top_logprobs": { "type": "integer", "format": "int32", @@ -852,6 +978,164 @@ } } }, + "CompletionComplete": { + "type": "object", + "required": [ + "index", + "text", + "finish_reason" + ], + "properties": { + "finish_reason": { + "type": "string" + }, + "index": { + "type": "integer", + "format": "int32", + "minimum": 0 + }, + "logprobs": { + "type": "array", + "items": { + "type": "number", + "format": "float" + }, + "nullable": true + }, + "text": { + "type": "string" + } + } + }, + "CompletionCompleteChunk": { + "type": "object", + "required": [ + "id", + "object", + "created", + "choices", + "model", + "system_fingerprint" + ], + "properties": { + "choices": { + "type": "array", + "items": { + "$ref": "#/components/schemas/CompletionComplete" + } + }, + "created": { + "type": "integer", + "format": "int64", + "minimum": 0 + }, + "id": { + "type": "string" + }, + "model": { + "type": "string" + }, + "object": { + "type": "string" + }, + "system_fingerprint": { + "type": "string" + } + } + }, + "CompletionRequest": { + "type": "object", + "required": [ + "model", + "prompt" + ], + "properties": { + "frequency_penalty": { + "type": "number", + "format": "float", + "description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far,\ndecreasing the model's likelihood to repeat the same line verbatim.", + "example": "1.0", + "nullable": true + }, + "max_tokens": { + "type": "integer", + "format": "int32", + "description": "The maximum number of tokens that can be generated in the chat completion.", + "default": "32", + "nullable": true, + "minimum": 0 + }, + "model": { + "type": "string", + "description": "UNUSED\nID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.", + "example": "mistralai/Mistral-7B-Instruct-v0.2" + }, + "prompt": { + "type": "string", + "description": "The prompt to generate completions for.", + "example": "What is Deep Learning?" + }, + "repetition_penalty": { + "type": "number", + "format": "float", + "nullable": true + }, + "seed": { + "type": "integer", + "format": "int64", + "example": 42, + "nullable": true, + "minimum": 0 + }, + "stream": { + "type": "boolean" + }, + "suffix": { + "type": "string", + "description": "The text to append to the prompt. This is useful for completing sentences or generating a paragraph of text.\nplease see the completion_template field in the model's tokenizer_config.json file for completion template.", + "nullable": true + }, + "temperature": { + "type": "number", + "format": "float", + "description": "What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while\nlower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or `top_p` but not both.", + "example": 1.0, + "nullable": true + }, + "top_p": { + "type": "number", + "format": "float", + "description": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the\ntokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.", + "example": 0.95, + "nullable": true + } + } + }, + "DeltaToolCall": { + "type": "object", + "required": [ + "index", + "id", + "type", + "function" + ], + "properties": { + "function": { + "$ref": "#/components/schemas/Function" + }, + "id": { + "type": "string" + }, + "index": { + "type": "integer", + "format": "int32", + "minimum": 0 + }, + "type": { + "type": "string" + } + } + }, "Details": { "type": "object", "required": [ @@ -931,6 +1215,38 @@ ], "example": "Length" }, + "Function": { + "type": "object", + "required": [ + "arguments" + ], + "properties": { + "arguments": { + "type": "string" + }, + "name": { + "type": "string", + "nullable": true + } + } + }, + "FunctionDefinition": { + "type": "object", + "required": [ + "name", + "parameters" + ], + "properties": { + "description": { + "type": "string", + "nullable": true + }, + "name": { + "type": "string" + }, + "parameters": {} + } + }, "GenerateParameters": { "type": "object", "properties": { @@ -1261,13 +1577,13 @@ "Message": { "type": "object", "required": [ - "role", - "content" + "role" ], "properties": { "content": { "type": "string", - "example": "My name is David and I" + "example": "My name is David and I", + "nullable": true }, "name": { "type": "string", @@ -1277,6 +1593,13 @@ "role": { "type": "string", "example": "user" + }, + "tool_calls": { + "type": "array", + "items": { + "$ref": "#/components/schemas/ToolCall" + }, + "nullable": true } } }, @@ -1437,6 +1760,64 @@ "$ref": "#/components/schemas/SimpleToken" } }, + "Tool": { + "type": "object", + "required": [ + "type", + "function" + ], + "properties": { + "function": { + "$ref": "#/components/schemas/FunctionDefinition" + }, + "type": { + "type": "string", + "example": "function" + } + } + }, + "ToolCall": { + "type": "object", + "required": [ + "id", + "type", + "function" + ], + "properties": { + "function": { + "$ref": "#/components/schemas/FunctionDefinition" + }, + "id": { + "type": "integer", + "format": "int32", + "minimum": 0 + }, + "type": { + "type": "string" + } + } + }, + "ToolType": { + "oneOf": [ + { + "type": "object", + "required": [ + "FunctionName" + ], + "properties": { + "FunctionName": { + "type": "string" + } + } + }, + { + "type": "string", + "enum": [ + "OneOf" + ] + } + ] + }, "Usage": { "type": "object", "required": [ diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json index 467b8ce3..543be115 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json @@ -17,7 +17,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.4-native", + "system_fingerprint": "1.4.5-native", "usage": { "completion_tokens": 100, "prompt_tokens": 60, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json index 8bdb7465..728e90a4 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json @@ -31,7 +31,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.4-native", + "system_fingerprint": "1.4.5-native", "usage": { "completion_tokens": 29, "prompt_tokens": 316, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json index 5ba297b1..2e0efb86 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json @@ -31,7 +31,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.4-native", + "system_fingerprint": "1.4.5-native", "usage": { "completion_tokens": 29, "prompt_tokens": 316, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json index 522624bc..91854223 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json @@ -30,7 +30,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.4-native", + "system_fingerprint": "1.4.5-native", "usage": { "completion_tokens": 21, "prompt_tokens": 187, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json index c085100d..e0c7aed6 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json @@ -23,5 +23,5 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.4-native" + "system_fingerprint": "1.4.5-native" } diff --git a/integration-tests/pyproject.toml b/integration-tests/pyproject.toml index cab74c46..ad217072 100644 --- a/integration-tests/pyproject.toml +++ b/integration-tests/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "text-generation-integration-tests" -version = "1.4.4" +version = "1.4.5" description = "Text Generation Inference integration tests" authors = ["Nicolas Patry "] diff --git a/launcher/src/main.rs b/launcher/src/main.rs index d52e2669..990eade4 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -604,7 +604,7 @@ fn shard_manager( // We read stderr in another thread as it seems that lines() can block in some cases let (err_sender, err_receiver) = mpsc::channel(); thread::spawn(move || { - for line in shard_stderr_reader.lines().flatten() { + for line in shard_stderr_reader.lines().map_while(Result::ok) { err_sender.send(line).unwrap_or(()); } }); @@ -722,7 +722,7 @@ impl TryFrom<&String> for PythonLogMessage { } fn log_lines(lines: Lines) { - for line in lines.flatten() { + for line in lines.map_while(Result::ok) { match PythonLogMessage::try_from(&line) { Ok(log) => log.trace(), Err(_) => tracing::debug!("{line}"), @@ -874,7 +874,7 @@ fn download_convert_model(args: &Args, running: Arc) -> Result<(), L // We read stderr in another thread as it seems that lines() can block in some cases let (err_sender, err_receiver) = mpsc::channel(); thread::spawn(move || { - for line in download_stderr.lines().flatten() { + for line in download_stderr.lines().map_while(Result::ok) { err_sender.send(line).unwrap_or(()); } }); diff --git a/router/src/server.rs b/router/src/server.rs index b328a8e8..eaed3f1c 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -12,7 +12,7 @@ use crate::{ ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionComplete, ChatCompletionDelta, ChatCompletionLogprob, ChatCompletionLogprobs, ChatCompletionTopLogprob, ChatRequest, CompatGenerateRequest, Completion, CompletionComplete, CompletionCompleteChunk, - CompletionRequest, VertexRequest, VertexResponse, + CompletionRequest, DeltaToolCall, Function, Tool, VertexRequest, VertexResponse, }; use crate::{FunctionDefinition, FunctionRef, FunctionsMap, Properties, ToolCall, ToolType, Tools}; use axum::extract::Extension; @@ -1211,6 +1211,12 @@ pub async fn run( ErrorResponse, GrammarType, Usage, + DeltaToolCall, + ToolType, + Tool, + ToolCall, + Function, + FunctionDefinition, ) ), tags( diff --git a/server/pyproject.toml b/server/pyproject.toml index 241c632d..b5e9c3fd 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "text-generation-server" -version = "1.4.4" +version = "1.4.5" description = "Text Generation Inference Python gRPC Server" authors = ["Olivier Dehaene "] From 99874eae7490c4a6aa90fe55f183b58d88397c8f Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 4 Apr 2024 23:01:56 +0200 Subject: [PATCH 03/74] Add cuda graphs sizes and make it default. (#1703) # What does this PR do? ``` text-generation-launcher --model-id XXX # Uses cuda graphs by default text-generation-launcher --model-id XXX --cuda-graphs "1,2" #Restrict the number of cuda graphs which saves VRAM text-generation-launcher --model-id XXX --cuda-graphs "0" # Disabling it entirely ``` Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- docs/source/basic_tutorials/launcher.md | 9 ++--- integration-tests/conftest.py | 1 - launcher/src/main.rs | 34 ++++++++++++++----- .../models/flash_causal_lm.py | 8 ++--- .../text_generation_server/models/globals.py | 10 +++++- server/text_generation_server/models/mamba.py | 8 ++--- 6 files changed, 48 insertions(+), 22 deletions(-) diff --git a/docs/source/basic_tutorials/launcher.md b/docs/source/basic_tutorials/launcher.md index 36fa1241..86394ff7 100644 --- a/docs/source/basic_tutorials/launcher.md +++ b/docs/source/basic_tutorials/launcher.md @@ -206,12 +206,13 @@ Options: [env: MAX_BATCH_SIZE=] ``` -## ENABLE_CUDA_GRAPHS +## CUDA_GRAPHS ```shell - --enable-cuda-graphs - Enable experimental support for cuda graphs + --cuda-graphs + Specify the batch sizes to compute cuda graphs for. Use "0" to disable - [env: ENABLE_CUDA_GRAPHS=] + [env: CUDA_GRAPHS=] + [default: 1,2,4,8,16,32,64,96,128] ``` ## HOSTNAME diff --git a/integration-tests/conftest.py b/integration-tests/conftest.py index 32bf4e54..022b2298 100644 --- a/integration-tests/conftest.py +++ b/integration-tests/conftest.py @@ -383,7 +383,6 @@ def launcher(event_loop): env = { "LOG_LEVEL": "info,text_generation_router=debug", - "ENABLE_CUDA_GRAPHS": "true", } if not use_flash_attention: env["USE_FLASH_ATTENTION"] = "false" diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 990eade4..63676392 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -284,9 +284,15 @@ struct Args { #[clap(long, env)] max_batch_size: Option, - /// Enable experimental support for cuda graphs - #[clap(long, env)] - enable_cuda_graphs: bool, + /// Specify the batch sizes to compute cuda graphs for. + /// Use "0" to disable. + #[clap( + long, + env, + value_delimiter = ',', + default_value = "1,2,4,8,16,32,64,96,128" + )] + cuda_graphs: Vec, /// The IP address to listen on #[clap(default_value = "0.0.0.0", long, env)] @@ -416,7 +422,7 @@ fn shard_manager( disable_custom_kernels: bool, watermark_gamma: Option, watermark_delta: Option, - enable_cuda_graphs: bool, + cuda_graphs: Vec, cuda_memory_fraction: f32, rope_scaling: Option, rope_factor: Option, @@ -549,8 +555,16 @@ fn shard_manager( }; // Enable experimental support for cuda graphs - if enable_cuda_graphs { - envs.push(("ENABLE_CUDA_GRAPHS".into(), "True".into())) + if !cuda_graphs.is_empty() { + envs.push(( + "CUDA_GRAPHS".into(), + cuda_graphs + .into_iter() + .map(|c| c.to_string()) + .collect::>() + .join(",") + .into(), + )); } // If disable_custom_kernels is true, pass it to the shard as an env var @@ -941,7 +955,11 @@ fn spawn_shards( let disable_custom_kernels = args.disable_custom_kernels; let watermark_gamma = args.watermark_gamma; let watermark_delta = args.watermark_delta; - let enable_cuda_graphs = args.enable_cuda_graphs; + let cuda_graphs: Vec = args + .cuda_graphs + .iter() + .filter_map(|&c| if c > 0 { Some(c) } else { None }) + .collect(); let cuda_memory_fraction = args.cuda_memory_fraction; let rope_scaling = args.rope_scaling; let rope_factor = args.rope_factor; @@ -963,7 +981,7 @@ fn spawn_shards( disable_custom_kernels, watermark_gamma, watermark_delta, - enable_cuda_graphs, + cuda_graphs, cuda_memory_fraction, rope_scaling, rope_factor, diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index 20f93820..5c25f341 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -28,7 +28,7 @@ from text_generation_server.models.cache_manager import ( BLOCK_SIZE, ) from text_generation_server.pb import generate_pb2 -from text_generation_server.models.globals import MEM_POOL, ENABLE_CUDA_GRAPHS +from text_generation_server.models.globals import MEM_POOL, CUDA_GRAPHS from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser from text_generation_server.utils.dist import MEMORY_FRACTION @@ -798,11 +798,11 @@ class FlashCausalLM(Model): self.device, ) - if ENABLE_CUDA_GRAPHS: + if CUDA_GRAPHS: try: - logger.info("Experimental support for Cuda Graphs is enabled") + logger.info(f"Cuda Graphs are enabled for sizes {CUDA_GRAPHS}") # Warmup cuda graphs - for bs in [1, 2, 4] + [8 * i for i in range(1, 9)]: + for bs in CUDA_GRAPHS: if self.speculate is None or self.speculate + 1 <= bs: self.cuda_graph_warmup(bs, max_s, max_bt) except Exception: diff --git a/server/text_generation_server/models/globals.py b/server/text_generation_server/models/globals.py index 3b8a70bc..6f554049 100644 --- a/server/text_generation_server/models/globals.py +++ b/server/text_generation_server/models/globals.py @@ -3,4 +3,12 @@ import os MEM_POOL = torch.cuda.graph_pool_handle() # This is overridden by the cli -ENABLE_CUDA_GRAPHS = os.getenv("ENABLE_CUDA_GRAPHS", "false").lower() in {"1", "true"} +cuda_graphs = os.getenv("CUDA_GRAPHS") +if cuda_graphs is not None: + try: + cuda_graphs = [int(item) for item in cuda_graphs.split(",")] + except Exception as e: + raise RuntimeError( + f"Could not parse cuda graphs {cuda_graphs}, expected comma separated list for batch sizes to run on: {e}" + ) +CUDA_GRAPHS = cuda_graphs diff --git a/server/text_generation_server/models/mamba.py b/server/text_generation_server/models/mamba.py index 2500d454..07a81491 100644 --- a/server/text_generation_server/models/mamba.py +++ b/server/text_generation_server/models/mamba.py @@ -13,7 +13,7 @@ from text_generation_server.utils import ( weight_files, Weights, ) -from text_generation_server.models.globals import ENABLE_CUDA_GRAPHS, MEM_POOL +from text_generation_server.models.globals import CUDA_GRAPHS, MEM_POOL import time from text_generation_server.models.custom_modeling.mamba_modeling import ( MambaModel, @@ -465,12 +465,12 @@ class Mamba(Model): def warmup(self, batch) -> Optional[int]: # TODO: implement warmup for Mamba if needed - if ENABLE_CUDA_GRAPHS: + if CUDA_GRAPHS: if self.speculate is None or self.speculate == 0: try: - logger.info("Experimental support for Cuda Graphs is enabled") + logger.info(f"Cuda Graphs are enabled for sizes {CUDA_GRAPHS}") # Warmup cuda graphs - for bs in [1, 2, 4] + [8 * i for i in range(1, 9)]: + for bs in CUDA_GRAPHS: self.cuda_graph_warmup(bs) except Exception: logger.exception(f"Decode cuda graph warmup failed") From c7e570e59de7dc47c35acb1b14d5fe31b123b234 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 5 Apr 2024 13:32:53 +0200 Subject: [PATCH 04/74] Pickle conversion now requires `--trust-remote-code`. (#1704) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- docs/source/_toctree.yml | 2 ++ docs/source/basic_tutorials/safety.md | 31 +++++++++++++++++++++++++++ server/text_generation_server/cli.py | 7 ++++++ 3 files changed, 40 insertions(+) create mode 100644 docs/source/basic_tutorials/safety.md diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index ad4f29f6..1598c248 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -23,6 +23,8 @@ title: All TGI CLI options - local: basic_tutorials/non_core_models title: Non-core Model Serving + - local: basic_tutorials/safety + title: Safety title: Tutorials - sections: - local: conceptual/streaming diff --git a/docs/source/basic_tutorials/safety.md b/docs/source/basic_tutorials/safety.md new file mode 100644 index 00000000..0b865db4 --- /dev/null +++ b/docs/source/basic_tutorials/safety.md @@ -0,0 +1,31 @@ +# Model safety. + +[Pytorch uses pickle](https://pytorch.org/docs/master/generated/torch.load.html) by default meaning that for quite a long while +*Every* model using that format is potentially executing unintended code while purely loading the model. + +There is a big red warning on Python's page for pickle [link](https://docs.python.org/3/library/pickle.html) but for quite a while +this was ignored by the community. Now that AI/ML is getting used much more ubiquitously we need to switch away from this format. + +HuggingFace is leading the effort here by creating a new format which contains pure data ([safetensors](https://github.com/huggingface/safetensors)) +and moving slowly but surely all the libs to make use of it by default. +The move is intentionnally slow in order to make breaking changes as little impact as possible on users throughout. + + +# TGI 2.0 + +Since the release of TGI 2.0, we take the opportunity of this major version increase to break backward compatibility for these pytorch +models (since they are a huge security risk for anyone deploying them). + + +From now on, TGI will not convert automatically pickle files without having `--trust-remote-code` flag or `TRUST_REMOTE_CODE=true` in the environment variables. +This flag is already used for community defined inference code, and is therefore quite representative of the level of confidence you are giving the model providers. + + +If you want to use a model that uses pickle, but you still do not want to trust the authors entirely we recommend making a convertion on our space made for that. + +https://huggingface.co/spaces/safetensors/convert + +This space will create a PR on the original model, which you are use directly regardless of merge status from the original authors. Just use +``` +docker run .... --revision refs/pr/#ID # Or use REVISION=refs/pr/#ID in the environment +``` diff --git a/server/text_generation_server/cli.py b/server/text_generation_server/cli.py index a513f5e6..e8b126d9 100644 --- a/server/text_generation_server/cli.py +++ b/server/text_generation_server/cli.py @@ -249,6 +249,13 @@ def download_weights( local_pt_files = utils.download_weights(pt_filenames, model_id, revision) if auto_convert: + if not trust_remote_code: + logger.warning( + f"🚨🚨BREAKING CHANGE in 2.0🚨🚨: Safetensors conversion is disabled without `--trust-remote-code` because " + f"Pickle files are unsafe and can essentially contain remote code execution!" + f"Please check for more information here: https://huggingface.co/docs/text-generation-inference/basic_tutorials/safety", + ) + logger.warning( f"No safetensors weights found for model {model_id} at revision {revision}. " f"Converting PyTorch weights to safetensors." From 5062fda4ffba60fa0f22e69a31722fe6aaebabe3 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 5 Apr 2024 16:44:10 +0200 Subject: [PATCH 05/74] Push users to streaming in the readme. (#1698) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 60fe83cd..bffe1e8a 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingf And then you can make requests like ```bash -curl 127.0.0.1:8080/generate \ +curl 127.0.0.1:8080/generate_stream \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \ -H 'Content-Type: application/json' From f9958ee191057cadf0c6c1f41577eae86819812c Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 5 Apr 2024 16:44:19 +0200 Subject: [PATCH 06/74] Fixing cohere tokenizer. (#1697) --- server/text_generation_server/models/flash_cohere.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/server/text_generation_server/models/flash_cohere.py b/server/text_generation_server/models/flash_cohere.py index 33b053a6..181a93b1 100644 --- a/server/text_generation_server/models/flash_cohere.py +++ b/server/text_generation_server/models/flash_cohere.py @@ -3,7 +3,7 @@ import torch.distributed from opentelemetry import trace from typing import Optional -from transformers.models.llama import LlamaTokenizerFast +from transformers import AutoTokenizer from text_generation_server.models import FlashCausalLM from text_generation_server.models.custom_modeling.flash_cohere_modeling import ( @@ -36,7 +36,7 @@ class FlashCohere(FlashCausalLM): else: raise NotImplementedError("FlashCohere is only available on GPU") - tokenizer = LlamaTokenizerFast.from_pretrained( + tokenizer = AutoTokenizer.from_pretrained( model_id, revision=revision, padding_side="left", From 8dca3b04f8d6e07b10f60cb15b4c4f21d755a945 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 5 Apr 2024 19:23:57 +0200 Subject: [PATCH 07/74] Force weights_only (before fully breaking pickle files anyway). (#1710) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- server/text_generation_server/utils/convert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server/text_generation_server/utils/convert.py b/server/text_generation_server/utils/convert.py index 0b62f520..d9c3276b 100644 --- a/server/text_generation_server/utils/convert.py +++ b/server/text_generation_server/utils/convert.py @@ -68,7 +68,7 @@ def convert_file(pt_file: Path, sf_file: Path, discard_names: List[str]): Forcing us to check for potentially different keys during load when looking for specific tensors (making tensor sharing explicit). """ - loaded = torch.load(pt_file, map_location="cpu") + loaded = torch.load(pt_file, map_location="cpu", weights_only=True) if "state_dict" in loaded: loaded = loaded["state_dict"] to_removes = _remove_duplicate_names(loaded, discard_names=discard_names) From 53c2c3dbc7cb8a7d91d519a898fc8d74eae91e48 Mon Sep 17 00:00:00 2001 From: oOraph <13552058+oOraph@users.noreply.github.com> Date: Mon, 8 Apr 2024 08:52:10 +0200 Subject: [PATCH 08/74] Regenerate ld.so.cache (#1708) fixes https://github.com/huggingface/text-generation-inference/issues/1711 Signed-off-by: Raphael Glon Co-authored-by: Raphael Glon --- launcher/src/main.rs | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 63676392..aef09433 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -1209,6 +1209,16 @@ fn terminate(process_name: &str, mut process: Child, timeout: Duration) -> io::R } fn main() -> Result<(), LauncherError> { + match Command::new("ldconfig").spawn() { + Ok(_) => {} + Err(err) => { + tracing::warn!( + "Unable to refresh ldconfig cache. Skipping (useless in most cases). Details {:?}", + err + ) + } + } + // Pattern match configuration let args: Args = Args::parse(); From ff42d33e9944832a19171967d2edd6c292bdb2d6 Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Mon, 8 Apr 2024 15:06:16 +0200 Subject: [PATCH 09/74] Revert license to Apache 2.0 (#1714) Reverts huggingface/text-generation-inference#725 --------- Co-authored-by: Julien Chaumond --- LICENSE | 318 ++++++++++++++++++++++++++++++-------------------------- 1 file changed, 169 insertions(+), 149 deletions(-) diff --git a/LICENSE b/LICENSE index 19a34fcf..7d0e8034 100644 --- a/LICENSE +++ b/LICENSE @@ -1,181 +1,201 @@ -Hugging Face Optimized Inference License 1.0 (HFOILv1.0) + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION -This License Agreement governs the use of the Software and its Modifications. It is a -binding agreement between the Licensor and You. + 1. 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From 106d8ee81899e6461648e1efd56f5a873ce7294d Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Tue, 9 Apr 2024 10:27:57 +0200 Subject: [PATCH 10/74] Automatic quantization config. (#1719) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- server/text_generation_server/models/__init__.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index 06219e7c..ec167303 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -186,6 +186,14 @@ def get_model( raise RuntimeError( f"Could not determine model type for {model_id} revision {revision}" ) + quantization_config = config_dict.get("quantization_config", None) + if quantization_config is not None and quantize is None: + method = quantization_config.get("quant_method", None) + if method in {"gptq", "awq"}: + logger.info(f"Auto selecting quantization method {method}") + quantize = method + else: + logger.info(f"Unknown quantization method {method}") if model_type == "ssm": return Mamba( From 4634b00c2a6ec85978b0653781ef6b7397d80509 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Tue, 9 Apr 2024 21:32:00 +0200 Subject: [PATCH 11/74] Adding Llava-Next (Llava 1.6) with full support. (#1709) # What does this PR do? - Changed all models to extract `embed_tokens` in order to enable llava to separately call the embeddings and the core model layers. - Added VlmCausalLM to inherit from FlashMistral in order to be maximally supported. The only added logics sits on top and parses images into pixel values, preallocates input_ids space for the image embeddings, and passes them for the model. - Added Clip for the vision tower. - Didn't add flash for the vision tower since there's no padding anyway. - Added heuristic (potentially incomplete) to calculate number of features *before* calculating the clip patches (allows for easier logic reuse of the LLM under the hood). Still needs to be done: - [x] Implement the image parsing in the controller side, to avoid downloading n times per TP shard and also refusing requests too large early and avoid issues where the truncation actually truncates the image. - [ ] Make sure it works with quantization properly. - [x] Make sure it works with TP>1 Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- Cargo.lock | 546 +- docs/source/supported_models.md | 2 + integration-tests/conftest.py | 16 + .../test_flash_llava_next_all_params.json | 65 + .../test_flash_llava_next_load.json | 59178 ++++++++++++++++ .../test_flash_llava_next_simple.json | 73 + .../test_mt0_base_all_params.json | 51 +- integration-tests/models/test_idefics.py | 6 + integration-tests/models/test_llava_next.py | 84 + integration-tests/models/test_mt0_base.py | 2 +- router/Cargo.toml | 4 +- router/client/src/client.rs | 7 +- router/src/config.rs | 158 + router/src/lib.rs | 1 + router/src/main.rs | 27 +- router/src/server.rs | 6 +- router/src/validation.rs | 173 +- .../text_generation_server/models/__init__.py | 14 + .../models/custom_modeling/clip.py | 827 + .../custom_modeling/flash_llama_modeling.py | 46 +- .../custom_modeling/flash_mistral_modeling.py | 43 +- .../models/custom_modeling/llava_next.py | 302 + .../models/flash_causal_lm.py | 24 +- .../models/flash_llama.py | 3 +- .../models/flash_mistral.py | 81 +- .../models/idefics_causal_lm.py | 89 +- .../models/llava_next.py | 36 + .../models/vlm_causal_lm.py | 329 + server/text_generation_server/server.py | 21 +- 29 files changed, 62022 insertions(+), 192 deletions(-) create mode 100644 integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_all_params.json create mode 100644 integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_load.json create mode 100644 integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_simple.json create mode 100644 integration-tests/models/test_llava_next.py create mode 100644 router/src/config.rs create mode 100644 server/text_generation_server/models/custom_modeling/clip.py create mode 100644 server/text_generation_server/models/custom_modeling/llava_next.py create mode 100644 server/text_generation_server/models/llava_next.py create mode 100644 server/text_generation_server/models/vlm_causal_lm.py diff --git a/Cargo.lock b/Cargo.lock index 8078d24f..65b9f8b7 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -40,6 +40,12 @@ dependencies = [ "memchr", ] +[[package]] +name = "aligned-vec" +version = "0.5.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "4aa90d7ce82d4be67b64039a3d588d38dbcc6736577de4a847025ce5b0c468d1" + [[package]] name = "anstream" version = "0.6.13" @@ -94,12 +100,35 @@ version = "1.0.81" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "0952808a6c2afd1aa8947271f3a60f1a6763c7b912d210184c5149b5cf147247" +[[package]] +name = "arbitrary" +version = "1.3.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7d5a26814d8dcb93b0e5a0ff3c6d80a8843bafb21b39e8e18a6f05471870e110" + [[package]] name = "arc-swap" version = "1.7.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "69f7f8c3906b62b754cd5326047894316021dcfe5a194c8ea52bdd94934a3457" +[[package]] +name = "arg_enum_proc_macro" +version = "0.3.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0ae92a5119aa49cdbcf6b9f893fe4e1d98b04ccbf82ee0584ad948a44a734dea" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.55", +] + +[[package]] +name = "arrayvec" +version = "0.7.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "96d30a06541fbafbc7f82ed10c06164cfbd2c401138f6addd8404629c4b16711" + [[package]] name = "async-rustls" version = "0.3.0" @@ -150,6 +179,20 @@ version = "1.2.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f1fdabc7756949593fe60f30ec81974b613357de856987752631dea1e3394c80" +[[package]] +name = "av1-grain" +version = "0.2.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6678909d8c5d46a42abcf571271e15fdbc0a225e3646cf23762cd415046c78bf" +dependencies = [ + "anyhow", + "arrayvec", + "log", + "nom", + "num-rational", + "v_frame", +] + [[package]] name = "average" version = "0.14.2" @@ -161,6 +204,15 @@ dependencies = [ "num-traits", ] +[[package]] +name = "avif-serialize" +version = "0.8.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "876c75a42f6364451a033496a14c44bffe41f5f4a8236f697391f11024e596d2" +dependencies = [ + "arrayvec", +] + [[package]] name = "awaitdrop" version = "0.1.2" @@ -267,6 +319,12 @@ version = "0.21.7" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9d297deb1925b89f2ccc13d7635fa0714f12c87adce1c75356b39ca9b7178567" +[[package]] +name = "base64" +version = "0.22.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9475866fec1451be56a3c2400fd081ff546538961565ccb5b7142cbd22bc7a51" + [[package]] name = "bit-set" version = "0.5.3" @@ -282,6 +340,12 @@ version = "0.6.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "349f9b6a179ed607305526ca489b34ad0a41aed5f7980fa90eb03160b69598fb" +[[package]] +name = "bit_field" +version = "0.10.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dc827186963e592360843fb5ba4b973e145841266c1357f7180c43526f2e5b61" + [[package]] name = "bitflags" version = "1.3.2" @@ -294,6 +358,12 @@ version = "2.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "cf4b9d6a944f767f8e5e0db018570623c85f3d925ac718db4e06d0187adb21c1" +[[package]] +name = "bitstream-io" +version = "2.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "06c9989a51171e2e81038ab168b6ae22886fe9ded214430dbb4f41c28cf176da" + [[package]] name = "block-buffer" version = "0.10.4" @@ -303,6 +373,12 @@ dependencies = [ "generic-array", ] +[[package]] +name = "built" +version = "0.7.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "38d17f4d6e4dc36d1a02fbedc2753a096848e7c1b0772f7654eab8e2c927dd53" + [[package]] name = "bumpalo" version = "3.15.4" @@ -315,6 +391,12 @@ version = "0.6.7" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e1e5f035d16fc623ae5f74981db80a439803888314e3a555fd6f04acd51a3205" +[[package]] +name = "bytemuck" +version = "1.15.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5d6d68c57235a3a081186990eca2867354726650f42f7516ca50c28d6281fd15" + [[package]] name = "byteorder" version = "1.5.0" @@ -370,6 +452,20 @@ name = "cc" version = "1.0.90" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "8cd6604a82acf3039f1144f54b8eb34e91ffba622051189e71b781822d5ee1f5" +dependencies = [ + "jobserver", + "libc", +] + +[[package]] +name = "cfg-expr" +version = "0.15.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "fa50868b64a9a6fda9d593ce778849ea8715cd2a3d2cc17ffdb4a2f2f2f1961d" +dependencies = [ + "smallvec", + "target-lexicon", +] [[package]] name = "cfg-if" @@ -423,6 +519,12 @@ version = "0.7.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "98cc8fbded0c607b7ba9dd60cd98df59af97e84d24e49c8557331cfc26d301ce" +[[package]] +name = "color_quant" +version = "1.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3d7b894f5411737b7867f4827955924d7c254fc9f4d91a6aad6b097804b1018b" + [[package]] name = "colorchoice" version = "1.0.0" @@ -535,6 +637,12 @@ dependencies = [ "winapi", ] +[[package]] +name = "crunchy" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7a81dae078cea95a014a339291cec439d2f232ebe854a9d672b796c6afafa9b7" + [[package]] name = "crypto-common" version = "0.1.6" @@ -736,6 +844,22 @@ dependencies = [ "cc", ] +[[package]] +name = "exr" +version = "1.72.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "887d93f60543e9a9362ef8a21beedd0a833c5d9610e18c67abe15a5963dcb1a4" +dependencies = [ + "bit_field", + "flume", + "half", + "lebe", + "miniz_oxide", + "rayon-core", + "smallvec", + "zune-inflate", +] + [[package]] name = "fancy-regex" version = "0.11.0" @@ -752,6 +876,15 @@ version = "2.0.2" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "658bd65b1cf4c852a3cc96f18a8ce7b5640f6b703f905c7d74532294c2a63984" +[[package]] +name = "fdeflate" +version = "0.3.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "4f9bfee30e4dedf0ab8b422f03af778d9612b63f502710fc500a334ebe2de645" +dependencies = [ + "simd-adler32", +] + [[package]] name = "fixedbitset" version = "0.4.2" @@ -780,6 +913,15 @@ version = "1.0.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "28a80e3145d8ad11ba0995949bbcf48b9df2be62772b3d351ef017dff6ecb853" +[[package]] +name = "flume" +version = "0.11.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "55ac459de2512911e4b674ce33cf20befaba382d05b62b008afc1c8b57cbf181" +dependencies = [ + "spin 0.9.8", +] + [[package]] name = "fnv" version = "1.0.7" @@ -941,6 +1083,16 @@ dependencies = [ "wasm-bindgen", ] +[[package]] +name = "gif" +version = "0.13.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3fb2d69b19215e18bb912fa30f7ce15846e301408695e44e0ef719f1da9e19f2" +dependencies = [ + "color_quant", + "weezl", +] + [[package]] name = "gimli" version = "0.28.1" @@ -976,6 +1128,16 @@ dependencies = [ "tracing", ] +[[package]] +name = "half" +version = "2.4.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6dd08c532ae367adf81c312a4580bc67f1d0fe8bc9c460520283f4c0ff277888" +dependencies = [ + "cfg-if", + "crunchy", +] + [[package]] name = "hashbrown" version = "0.12.3" @@ -1161,6 +1323,45 @@ dependencies = [ "unicode-normalization", ] +[[package]] +name = "image" +version = "0.25.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "fd54d660e773627692c524beaad361aca785a4f9f5730ce91f42aabe5bce3d11" +dependencies = [ + "bytemuck", + "byteorder", + "color_quant", + "exr", + "gif", + "image-webp", + "num-traits", + "png", + "qoi", + "ravif", + "rayon", + "rgb", + "tiff", + "zune-core", + "zune-jpeg", +] + +[[package]] +name = "image-webp" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7a84a25dcae3ac487bc24ef280f9e20c79c9b1a3e5e32cbed3041d1c514aa87c" +dependencies = [ + "byteorder", + "thiserror", +] + +[[package]] +name = "imgref" +version = "1.10.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "44feda355f4159a7c757171a77de25daf6411e217b4cabd03bd6650690468126" + [[package]] name = "indexmap" version = "1.9.3" @@ -1223,6 +1424,17 @@ dependencies = [ "cfg-if", ] +[[package]] +name = "interpolate_name" +version = "0.2.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "c34819042dc3d3971c46c2190835914dfbe0c3c13f61449b2997f4e9722dfa60" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.55", +] + [[package]] name = "ipnet" version = "2.9.0" @@ -1271,6 +1483,21 @@ version = "1.0.11" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b" +[[package]] +name = "jobserver" +version = "0.1.28" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ab46a6e9526ddef3ae7f787c06f0f2600639ba80ea3eade3d8e670a2230f51d6" +dependencies = [ + "libc", +] + +[[package]] +name = "jpeg-decoder" +version = "0.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f5d4a7da358eff58addd2877a45865158f0d78c911d43a5784ceb7bbf52833b0" + [[package]] name = "js-sys" version = "0.3.69" @@ -1316,12 +1543,29 @@ version = "1.4.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e2abad23fbc42b3700f2f279844dc832adb2b2eb069b2df918f455c4e18cc646" +[[package]] +name = "lebe" +version = "0.5.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "03087c2bad5e1034e8cace5926dec053fb3790248370865f5117a7d0213354c8" + [[package]] name = "libc" version = "0.2.153" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9c198f91728a82281a64e1f4f9eeb25d82cb32a5de251c6bd1b5154d63a8e7bd" +[[package]] +name = "libfuzzer-sys" +version = "0.4.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a96cfd5557eb82f2b83fed4955246c988d331975a002961b07c81584d107e7f7" +dependencies = [ + "arbitrary", + "cc", + "once_cell", +] + [[package]] name = "libm" version = "0.2.8" @@ -1361,6 +1605,15 @@ version = "0.4.21" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "90ed8c1e510134f979dbc4f070f87d4313098b704861a105fe34231c70a3901c" +[[package]] +name = "loop9" +version = "0.1.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0fae87c125b03c1d2c0150c90365d7d6bcc53fb73a9acaef207d2d065860f062" +dependencies = [ + "imgref", +] + [[package]] name = "mach2" version = "0.4.2" @@ -1407,6 +1660,16 @@ version = "0.7.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "0e7465ac9959cc2b1404e8e2367b43684a6d13790fe23056cc8c6c5a6b7bcb94" +[[package]] +name = "maybe-rayon" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8ea1f30cedd69f0a2954655f7188c6a834246d2bcf1e315e2ac40c4b24dc9519" +dependencies = [ + "cfg-if", + "rayon", +] + [[package]] name = "memchr" version = "2.7.2" @@ -1486,8 +1749,8 @@ dependencies = [ [[package]] name = "minijinja" -version = "1.0.16" -source = "git+https://github.com/mitsuhiko/minijinja.git?branch=main#82d0160b5513844e5429db084f1cbdd3313ed482" +version = "1.0.12" +source = "git+https://github.com/mitsuhiko/minijinja.git?rev=5cd4efb#5cd4efb9e2639247df275fe6e22a5dbe0ce71b28" dependencies = [ "serde", ] @@ -1505,6 +1768,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "9d811f3e15f28568be3407c8e7fdb6514c1cda3cb30683f15b6a1a1dc4ea14a7" dependencies = [ "adler", + "simd-adler32", ] [[package]] @@ -1583,6 +1847,12 @@ dependencies = [ "tempfile", ] +[[package]] +name = "new_debug_unreachable" +version = "1.0.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "650eef8c711430f1a879fdd01d4745a7deea475becfb90269c06775983bbf086" + [[package]] name = "ngrok" version = "0.13.1" @@ -1642,6 +1912,12 @@ dependencies = [ "minimal-lexical", ] +[[package]] +name = "noop_proc_macro" +version = "0.3.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0676bb32a98c1a483ce53e500a81ad9c3d5b3f7c920c28c24e9cb0980d0b5bc8" + [[package]] name = "ntapi" version = "0.4.1" @@ -1707,6 +1983,17 @@ version = "0.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "51d515d32fb182ee37cda2ccdcb92950d6a3c2893aa280e540671c2cd0f3b1d9" +[[package]] +name = "num-derive" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ed3955f1a9c7c0c15e092f9c887db08b1fc683305fdf6eb6684f22555355e202" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.55", +] + [[package]] name = "num-integer" version = "0.1.46" @@ -2071,6 +2358,19 @@ version = "0.3.30" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d231b230927b5e4ad203db57bbcbee2802f6bce620b1e4a9024a07d94e2907ec" +[[package]] +name = "png" +version = "0.17.13" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "06e4b0d3d1312775e782c86c91a111aa1f910cbb65e1337f9975b5f9a554b5e1" +dependencies = [ + "bitflags 1.3.2", + "crc32fast", + "fdeflate", + "flate2", + "miniz_oxide", +] + [[package]] name = "portable-atomic" version = "1.6.0" @@ -2132,6 +2432,25 @@ dependencies = [ "unicode-ident", ] +[[package]] +name = "profiling" +version = "1.0.15" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "43d84d1d7a6ac92673717f9f6d1518374ef257669c24ebc5ac25d5033828be58" +dependencies = [ + "profiling-procmacros", +] + +[[package]] +name = "profiling-procmacros" +version = "1.0.15" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8021cf59c8ec9c432cfc2526ac6b8aa508ecaf29cd415f271b8406c1b851c3fd" +dependencies = [ + "quote", + "syn 2.0.55", +] + [[package]] name = "prost" version = "0.11.9" @@ -2209,6 +2528,15 @@ dependencies = [ "prost 0.12.3", ] +[[package]] +name = "qoi" +version = "0.4.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7f6d64c71eb498fe9eae14ce4ec935c555749aef511cca85b5568910d6e48001" +dependencies = [ + "bytemuck", +] + [[package]] name = "quanta" version = "0.11.1" @@ -2225,6 +2553,12 @@ dependencies = [ "winapi", ] +[[package]] +name = "quick-error" +version = "2.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a993555f31e5a609f617c12db6250dedcac1b0a85076912c436e6fc9b2c8e6a3" + [[package]] name = "quote" version = "1.0.35" @@ -2281,6 +2615,56 @@ dependencies = [ "unicode-width", ] +[[package]] +name = "rav1e" +version = "0.7.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "cd87ce80a7665b1cce111f8a16c1f3929f6547ce91ade6addf4ec86a8dda5ce9" +dependencies = [ + "arbitrary", + "arg_enum_proc_macro", + "arrayvec", + "av1-grain", + "bitstream-io", + "built", + "cfg-if", + "interpolate_name", + "itertools 0.12.1", + "libc", + "libfuzzer-sys", + "log", + "maybe-rayon", + "new_debug_unreachable", + "noop_proc_macro", + "num-derive", + "num-traits", + "once_cell", + "paste", + "profiling", + "rand", + "rand_chacha", + "simd_helpers", + "system-deps", + "thiserror", + "v_frame", + "wasm-bindgen", +] + +[[package]] +name = "ravif" +version = "0.11.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bc13288f5ab39e6d7c9d501759712e6969fcc9734220846fc9ed26cae2cc4234" +dependencies = [ + "avif-serialize", + "imgref", + "loop9", + "quick-error", + "rav1e", + "rayon", + "rgb", +] + [[package]] name = "raw-cpuid" version = "10.7.0" @@ -2431,6 +2815,15 @@ dependencies = [ "winreg", ] +[[package]] +name = "rgb" +version = "0.8.37" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "05aaa8004b64fd573fc9d002f4e632d51ad4f026c2b5ba95fcb6c2f32c2c47d8" +dependencies = [ + "bytemuck", +] + [[package]] name = "ring" version = "0.16.20" @@ -2695,6 +3088,15 @@ dependencies = [ "serde", ] +[[package]] +name = "serde_spanned" +version = "0.6.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "eb3622f419d1296904700073ea6cc23ad690adbd66f13ea683df73298736f0c1" +dependencies = [ + "serde", +] + [[package]] name = "serde_urlencoded" version = "0.7.1" @@ -2766,6 +3168,21 @@ dependencies = [ "libc", ] +[[package]] +name = "simd-adler32" +version = "0.3.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d66dc143e6b11c1eddc06d5c423cfc97062865baf299914ab64caa38182078fe" + +[[package]] +name = "simd_helpers" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "95890f873bec569a0362c235787f3aca6e1e887302ba4840839bcc6459c42da6" +dependencies = [ + "quote", +] + [[package]] name = "sketches-ddsketch" version = "0.2.2" @@ -2817,6 +3234,9 @@ name = "spin" version = "0.9.8" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67" +dependencies = [ + "lock_api", +] [[package]] name = "spm_precompiled" @@ -2933,6 +3353,19 @@ dependencies = [ "libc", ] +[[package]] +name = "system-deps" +version = "6.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a3e535eb8dded36d55ec13eddacd30dec501792ff23a0b1682c38601b8cf2349" +dependencies = [ + "cfg-expr", + "heck 0.5.0", + "pkg-config", + "toml", + "version-compare", +] + [[package]] name = "tabled" version = "0.14.0" @@ -2957,6 +3390,12 @@ dependencies = [ "syn 1.0.109", ] +[[package]] +name = "target-lexicon" +version = "0.12.14" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e1fc403891a21bcfb7c37834ba66a547a8f402146eba7265b5a6d88059c9ff2f" + [[package]] name = "tempfile" version = "3.10.1" @@ -3029,10 +3468,12 @@ dependencies = [ "async-stream", "axum", "axum-tracing-opentelemetry", + "base64 0.22.0", "clap", "futures", "futures-util", "hf-hub", + "image", "init-tracing-opentelemetry", "jsonschema", "metrics", @@ -3092,6 +3533,17 @@ dependencies = [ "once_cell", ] +[[package]] +name = "tiff" +version = "0.9.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ba1310fcea54c6a9a4fd1aad794ecc02c31682f6bfbecdf460bf19533eed1e3e" +dependencies = [ + "flate2", + "jpeg-decoder", + "weezl", +] + [[package]] name = "time" version = "0.3.34" @@ -3295,6 +3747,40 @@ dependencies = [ "tracing", ] +[[package]] +name = "toml" +version = "0.8.12" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e9dd1545e8208b4a5af1aa9bbd0b4cf7e9ea08fabc5d0a5c67fcaafa17433aa3" +dependencies = [ + "serde", + "serde_spanned", + "toml_datetime", + "toml_edit", +] + +[[package]] +name = "toml_datetime" +version = "0.6.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3550f4e9685620ac18a50ed434eb3aec30db8ba93b0287467bca5826ea25baf1" +dependencies = [ + "serde", +] + +[[package]] +name = "toml_edit" +version = "0.22.9" +source = 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"852e951cb7832cb45cb1169900d19760cfa39b82bc0ea9c0e5a14ae88411c98b" + [[package]] name = "version_check" version = "0.9.4" @@ -3853,6 +4356,12 @@ dependencies = [ "rustls-pki-types", ] +[[package]] +name = "weezl" +version = "0.1.8" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "53a85b86a771b1c87058196170769dd264f66c0782acf1ae6cc51bfd64b39082" + [[package]] name = "which" version = "4.4.2" @@ -4113,6 +4622,15 @@ version = "0.52.4" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "32b752e52a2da0ddfbdbcc6fceadfeede4c939ed16d13e648833a61dfb611ed8" +[[package]] +name = "winnow" +version = "0.6.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dffa400e67ed5a4dd237983829e66475f0a4a26938c4b04c21baede6262215b8" +dependencies = [ + "memchr", +] + [[package]] name = "winreg" version = "0.50.0" @@ -4160,3 +4678,27 @@ dependencies = [ "crossbeam-utils", "flate2", ] + +[[package]] +name = "zune-core" +version = "0.4.12" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3f423a2c17029964870cfaabb1f13dfab7d092a62a29a89264f4d36990ca414a" + +[[package]] +name = "zune-inflate" +version = "0.2.54" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "73ab332fe2f6680068f3582b16a24f90ad7096d5d39b974d1c0aff0125116f02" +dependencies = [ + "simd-adler32", +] + +[[package]] +name = "zune-jpeg" +version = "0.4.11" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ec866b44a2a1fd6133d363f073ca1b179f438f99e7e5bfb1e33f7181facfe448" +dependencies = [ + "zune-core", +] diff --git a/docs/source/supported_models.md b/docs/source/supported_models.md index 87e76df1..fa1f9f61 100644 --- a/docs/source/supported_models.md +++ b/docs/source/supported_models.md @@ -22,6 +22,8 @@ The following models are optimized and can be served with TGI, which uses custom - [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) - [Phi](https://huggingface.co/microsoft/phi-2) +- [Idefics](HuggingFaceM4/idefics-9b-instruct) (Multimodal) +- [Llava-next](llava-hf/llava-v1.6-mistral-7b-hf) (Multimodal) If the above list lacks the model you would like to serve, depending on the model's pipeline type, you can try to initialize and serve the model anyways to see how well it performs, but performance isn't guaranteed for non-optimized models: diff --git a/integration-tests/conftest.py b/integration-tests/conftest.py index 022b2298..e8ce0d84 100644 --- a/integration-tests/conftest.py +++ b/integration-tests/conftest.py @@ -277,6 +277,8 @@ def launcher(event_loop): disable_grammar_support: bool = False, dtype: Optional[str] = None, revision: Optional[str] = None, + max_input_length: Optional[int] = None, + max_total_tokens: Optional[int] = None, ): port = random.randint(8000, 10_000) master_port = random.randint(10_000, 20_000) @@ -314,6 +316,12 @@ def launcher(event_loop): args.append(revision) if trust_remote_code: args.append("--trust-remote-code") + if max_input_length: + args.append("--max-input-length") + args.append(str(max_input_length)) + if max_total_tokens: + args.append("--max-total-tokens") + args.append(str(max_total_tokens)) env["LOG_LEVEL"] = "info,text_generation_router=debug" @@ -347,6 +355,8 @@ def launcher(event_loop): disable_grammar_support: bool = False, dtype: Optional[str] = None, revision: Optional[str] = None, + max_input_length: Optional[int] = None, + max_total_tokens: Optional[int] = None, ): port = random.randint(8000, 10_000) @@ -367,6 +377,12 @@ def launcher(event_loop): args.append(revision) if trust_remote_code: args.append("--trust-remote-code") + if max_input_length: + args.append("--max-input-length") + args.append(str(max_input_length)) + if max_total_tokens: + args.append("--max-total-tokens") + args.append(str(max_total_tokens)) client = docker.from_env() diff --git a/integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_all_params.json b/integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_all_params.json new file mode 100644 index 00000000..e9d3e5ef --- /dev/null +++ b/integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_all_params.json @@ -0,0 +1,65 @@ +{ + "details": { + "best_of_sequences": null, + "finish_reason": "stop_sequence", + "generated_tokens": 6, + "prefill": [ + { + "id": 1, + "logprob": null, + "text": "" + }, + { + "id": 3735, + "logprob": -10.5, + "text": "Test" + }, + { + "id": 2159, + "logprob": -12.140625, + "text": "request" + } + ], + "seed": 0, + "tokens": [ + { + "id": 13, + "logprob": -1.0654297, + "special": false, + "text": "\n" + }, + { + "id": 1014, + "logprob": -2.7460938, + "special": false, + "text": "The" + }, + { + "id": 6032, + "logprob": -1.359375, + "special": false, + "text": " purpose" + }, + { + "id": 302, + "logprob": 0.0, + "special": 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28804, + "logprob": -0.43017578, + "text": "?" + } + ], + "seed": null, + "tokens": [ + { + "id": 13, + "logprob": -0.007446289, + "special": false, + "text": "\n" + }, + { + "id": 13, + "logprob": -0.20129395, + "special": false, + "text": "\n" + }, + { + "id": 16114, + "logprob": -1.2587891, + "special": false, + "text": "Once" + }, + { + "id": 3714, + "logprob": -0.20825195, + "special": false, + "text": " upon" + }, + { + "id": 264, + "logprob": -0.0017786026, + "special": false, + "text": " a" + }, + { + "id": 727, + "logprob": -0.011955261, + "special": false, + "text": " time" + }, + { + "id": 28725, + "logprob": -0.17297363, + "special": false, + "text": "," + }, + { + "id": 736, + "logprob": -0.91015625, + "special": false, + "text": " there" + }, + { + "id": 403, + "logprob": -0.05758667, + "special": false, + "text": " was" + }, + { + "id": 264, + "logprob": -0.009544373, + "special": false, + "text": " a" + } + ], + "top_tokens": null + }, + "generated_text": "\n\nOnce upon a time, there was a" + } +] diff --git a/integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_simple.json b/integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_simple.json new file mode 100644 index 00000000..f0f2ee9e --- /dev/null +++ b/integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_simple.json @@ -0,0 +1,73 @@ +{ + "details": { + "best_of_sequences": null, + "finish_reason": "length", + "generated_tokens": 10, + "prefill": [], + "seed": null, + "tokens": [ + { + "id": 13, + "logprob": -0.00756073, + "special": false, + "text": "\n" + }, + { + "id": 13, + "logprob": -0.20117188, + "special": false, + "text": "\n" + }, + { + "id": 16114, + "logprob": -1.2597656, + "special": false, + "text": "Once" + }, + { + "id": 3714, + "logprob": -0.20825195, + "special": false, + "text": " upon" + }, + { + "id": 264, + "logprob": -0.00178051, + "special": false, + "text": " a" + }, + { + "id": 727, + "logprob": -0.011955261, + "special": false, + "text": " time" + }, + { + "id": 28725, + "logprob": -0.17541504, + "special": false, + "text": "," + }, + { + "id": 736, + "logprob": -0.91308594, + "special": false, + "text": " there" + }, + { + "id": 403, + "logprob": -0.058410645, + "special": false, + "text": " was" + }, + { + "id": 264, + "logprob": -0.009689331, + "special": false, + "text": " a" + } + ], + "top_tokens": null + }, + "generated_text": "\n\nOnce upon a time, there was a" +} diff --git a/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json b/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json index 024823d0..5cacf3e9 100644 --- a/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json +++ b/integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_all_params.json @@ -1,8 +1,8 @@ { "details": { "best_of_sequences": null, - "finish_reason": "eos_token", - "generated_tokens": 9, + "finish_reason": "length", + "generated_tokens": 10, "prefill": [ { "id": 0, @@ -14,7 +14,7 @@ "tokens": [ { "id": 16017, - "logprob": -0.30908203, + "logprob": 0.0, "special": false, "text": " blue" }, @@ -26,39 +26,45 @@ }, { "id": 259, - "logprob": -0.28271484, + "logprob": -0.4716797, "special": false, "text": " " }, { - "id": 15484, - "logprob": -1.7929688, + "id": 261, + "logprob": -0.044677734, "special": false, - "text": "appear" + "text": "," }, { - "id": 345, - "logprob": -0.8935547, + "id": 35622, + "logprob": -0.79589844, "special": false, - "text": "ed" + "text": " cloud" }, { - "id": 281, + "id": 263, + "logprob": -1.2958984, + "special": false, + "text": "s" + }, + { + "id": 305, "logprob": 0.0, "special": false, - "text": " in" + "text": " and" }, { - "id": 287, + "id": 35622, + "logprob": -1.1630859, + "special": false, + "text": " cloud" + }, + { + "id": 263, "logprob": 0.0, "special": false, - "text": " the" - }, - { - "id": 20495, - "logprob": -0.32299805, - "special": false, - "text": " sky" + "text": "s" }, { "id": 1, @@ -66,7 +72,8 @@ "special": true, "text": "" } - ] + ], + "top_tokens": null }, - "generated_text": "Why is the sky blue?blue sky appeared in the sky" + "generated_text": "Why is the sky blue?blue sky, clouds and clouds" } diff --git a/integration-tests/models/test_idefics.py b/integration-tests/models/test_idefics.py index 882971f2..aeeaffa1 100644 --- a/integration-tests/models/test_idefics.py +++ b/integration-tests/models/test_idefics.py @@ -33,6 +33,9 @@ async def test_idefics(idefics, response_snapshot): ) assert response.details.generated_tokens == 10 + assert ( + response.generated_text == " \nAssistant: A rooster stands" + ), f"{repr(response.generated_text)}" assert response == response_snapshot @@ -48,6 +51,9 @@ async def test_idefics_load(idefics, generate_load, response_snapshot): generated_texts = [r.generated_text for r in responses] + assert ( + generated_texts[0] == " \nAssistant: A rooster stands" + ), f"{response.generated_text}" assert len(generated_texts) == 4 assert generated_texts, all( [text == generated_texts[0] for text in generated_texts] diff --git a/integration-tests/models/test_llava_next.py b/integration-tests/models/test_llava_next.py new file mode 100644 index 00000000..f5b290b1 --- /dev/null +++ b/integration-tests/models/test_llava_next.py @@ -0,0 +1,84 @@ +import pytest +import base64 + + +# TODO fix the server parsser to count inline image tokens correctly +def get_chicken(): + with open("integration-tests/images/chicken_on_money.png", "rb") as image_file: + encoded_string = base64.b64encode(image_file.read()) + return f"data:image/png;base64,{encoded_string.decode('utf-8')}" + + +@pytest.fixture(scope="module") +def flash_llava_next_handle(launcher): + with launcher( + "llava-hf/llava-v1.6-mistral-7b-hf", + num_shard=4, + max_input_length=4000, + max_total_tokens=4096, + ) as handle: + yield handle + + +@pytest.fixture(scope="module") +async def flash_llava_next(flash_llava_next_handle): + await flash_llava_next_handle.health(300) + return flash_llava_next_handle.client + + +@pytest.mark.asyncio +@pytest.mark.private +async def test_flash_llava_next_simple(flash_llava_next, response_snapshot): + chicken = get_chicken() + response = await flash_llava_next.generate( + f"User:![]({chicken})Can you tell me a very short story based on the image?", + max_new_tokens=10, + ) + assert ( + response.generated_text == "\n\nOnce upon a time, there was a" + ), f"{repr(response.generated_text)}" + assert response.details.generated_tokens == 10 + assert response == response_snapshot + + +@pytest.mark.asyncio +@pytest.mark.private +async def test_flash_llava_next_all_params(flash_llava_next, response_snapshot): + response = await flash_llava_next.generate( + "Test request", + max_new_tokens=10, + repetition_penalty=1.2, + return_full_text=True, + stop_sequences=["test"], + temperature=0.5, + top_p=0.9, + top_k=10, + truncate=5, + typical_p=0.9, + watermark=True, + decoder_input_details=True, + seed=0, + ) + + assert response.details.generated_tokens == 6 + assert response == response_snapshot + + +@pytest.mark.asyncio +@pytest.mark.private +async def test_flash_llava_next_load( + flash_llava_next, generate_load, response_snapshot +): + chicken = get_chicken() + responses = await generate_load( + flash_llava_next, + f"User:![]({chicken})Can you tell me a very short story based on the image?", + max_new_tokens=10, + n=4, + ) + generated_texts = [r.generated_text for r in responses] + assert generated_texts[0] == "\n\nOnce upon a time, there was a" + assert len(generated_texts) == 4 + assert all([r.generated_text == generated_texts[0] for r in responses]) + + assert responses == response_snapshot diff --git a/integration-tests/models/test_mt0_base.py b/integration-tests/models/test_mt0_base.py index 12f23e4c..c877056a 100644 --- a/integration-tests/models/test_mt0_base.py +++ b/integration-tests/models/test_mt0_base.py @@ -45,7 +45,7 @@ async def test_mt0_base_all_params(mt0_base, response_snapshot): seed=0, ) - assert response.details.generated_tokens == 9 + assert response.details.generated_tokens == 10 assert response == response_snapshot diff --git a/router/Cargo.toml b/router/Cargo.toml index 0c57a886..582bbdfb 100644 --- a/router/Cargo.toml +++ b/router/Cargo.toml @@ -44,10 +44,12 @@ utoipa = { version = "3.5.0", features = ["axum_extras"] } utoipa-swagger-ui = { version = "3.1.5", features = ["axum"] } ngrok = { version = "0.13.1", features = ["axum"], optional = true } init-tracing-opentelemetry = { version = "0.14.1", features = ["opentelemetry-otlp"] } -minijinja = { git = "https://github.com/mitsuhiko/minijinja.git", branch = "main", commit = "5cd4efb" } +minijinja = { git = "https://github.com/mitsuhiko/minijinja.git", rev = "5cd4efb" } futures-util = "0.3.30" regex = "1.10.3" once_cell = "1.19.0" +image = "0.25.1" +base64 = "0.22.0" [build-dependencies] vergen = { version = "8.2.5", features = ["build", "git", "gitcl"] } diff --git a/router/client/src/client.rs b/router/client/src/client.rs index f8658318..ae926139 100644 --- a/router/client/src/client.rs +++ b/router/client/src/client.rs @@ -112,10 +112,15 @@ impl Client { // Create requests while n_tokens < max_prefill_tokens { let truncate = min(max_input_length, max_prefill_tokens - n_tokens); + + let mut inputs = String::new(); + inputs.push_str("![](data:image/jpeg;base64,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"); + inputs.push_str(&"_test ".to_string().repeat(max_input_length as usize)); + requests.push(Request { id: 0, // We truncate the input on the server side to be sure that it has the correct size - inputs: "_test ".to_string().repeat(max_input_length as usize), + inputs, truncate, // Set sampling parameters to also take these ops into account in the max memory parameters: Some(NextTokenChooserParameters { diff --git a/router/src/config.rs b/router/src/config.rs new file mode 100644 index 00000000..9b5a2404 --- /dev/null +++ b/router/src/config.rs @@ -0,0 +1,158 @@ +use serde::{Deserialize, Serialize}; + +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(tag = "model_type")] +#[serde(rename_all = "snake_case")] +pub struct LlavaNext { + text_config: TextConfig, + vision_config: VisionConfig, + image_grid_pinpoints: Vec<(usize, usize)>, +} + +fn get_anyres_image_grid_shape( + height: usize, + width: usize, + grid_pinpoints: &[(usize, usize)], + patch_size: usize, +) -> (usize, usize) { + let (height, width) = select_best_resolution(height, width, grid_pinpoints); + (height / patch_size, width / patch_size) +} + +/// Selects the best resolution from a list of possible resolutions based on the original size. +/// This is done by calculating the effective and wasted resolution for each possible resolution. +/// The best fit resolution is the one that maximizes the effective resolution and minimizes the wasted resolution. +fn select_best_resolution( + original_height: usize, + original_width: usize, + possible_resolutions: &[(usize, usize)], +) -> (usize, usize) { + let mut best_fit = None; + let mut max_effective_resolution = 0; + let mut min_wasted_resolution = f32::NEG_INFINITY; + + for (height, width) in possible_resolutions { + let wscale = *width as f32 / original_width as f32; + let hscale = *height as f32 / original_height as f32; + // f32 partial ord. + let scale = if wscale > hscale { hscale } else { wscale }; + let downscaled_width = (*width as f32 * scale) as usize; + let downscaled_height = (*height as f32 * scale) as usize; + let effective_resolution = std::cmp::min( + downscaled_width * downscaled_height, + original_width * original_height, + ); + let wasted_resolution = (width * height) - effective_resolution; + + if effective_resolution > max_effective_resolution + || (effective_resolution == max_effective_resolution + && (wasted_resolution as f32) < min_wasted_resolution) + { + max_effective_resolution = effective_resolution; + min_wasted_resolution = wasted_resolution as f32; + best_fit = Some((*height, *width)); + } + } + + best_fit.unwrap_or((original_height, original_width)) +} + +impl LlavaNext { + pub fn get_number_of_features(&self, height: usize, width: usize) -> usize { + let image_size = self.vision_config.image_size; + let patch_size = self.vision_config.patch_size; + assert!(image_size % patch_size == 0); + let npatches = image_size / patch_size; + let (num_patch_height, num_patch_width) = + get_anyres_image_grid_shape(height, width, &self.image_grid_pinpoints, image_size); + // Ceil + let height_of_patch = (height * npatches + width - 1) / width; + let unpadded_features = npatches * height_of_patch * num_patch_height * num_patch_width; + // They are only added after width + let newline_features = height_of_patch * num_patch_width; + // The base patch covers the entire image + let base_features = npatches.pow(2); + unpadded_features + newline_features + base_features + } +} + +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(tag = "model_type")] +#[serde(rename_all = "snake_case")] +pub struct ClipVisionModel { + image_size: usize, + patch_size: usize, +} + +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(tag = "model_type")] +#[serde(rename_all = "snake_case")] +pub enum Config { + LlavaNext(LlavaNext), + ClipVisionModel(ClipVisionModel), + Mistral, + Idefics, + Ssm, + GptBigcode, + Santacoder, + Bloom, + Mpt, + GptNeox, + Phi, + #[serde(rename = "phi-msft")] + PhiMsft, + Llama, + Baichuan, + Gemma, + Cohere, + Drbx, + Falcon, + Mixtral, + Starcoder2, + Qwen2, + Opt, + T5, +} + +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(rename_all = "snake_case")] +pub struct TextConfig {} + +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(rename_all = "snake_case")] +pub struct VisionConfig { + image_size: usize, + patch_size: usize, +} + +#[cfg(test)] +mod test { + use super::*; + + #[test] + fn test_llava_next_features() { + let config = LlavaNext { + text_config: TextConfig {}, + vision_config: VisionConfig { + image_size: 336, + patch_size: 14, + }, + image_grid_pinpoints: vec![ + (336, 672), + (672, 336), + (672, 672), + (1008, 336), + (336, 1008), + ], + }; + + let slots = config.get_number_of_features(640, 640); + assert_eq!(slots, 2928); + let slots = config.get_number_of_features(480, 640); + assert_eq!(slots, 2340); + let slots = config.get_number_of_features(899, 1024); + assert_eq!(slots, 2732); + let slots = config.get_number_of_features(1024, 899); + assert_eq!(slots, 3320); + } +} diff --git a/router/src/lib.rs b/router/src/lib.rs index 5415a956..c787470b 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -1,3 +1,4 @@ +pub mod config; mod health; /// Text Generation Inference Webserver mod infer; diff --git a/router/src/main.rs b/router/src/main.rs index 6a736b12..aace2ff9 100644 --- a/router/src/main.rs +++ b/router/src/main.rs @@ -13,6 +13,7 @@ use std::io::BufReader; use std::net::{IpAddr, Ipv4Addr, SocketAddr}; use std::path::Path; use text_generation_client::{ClientError, ShardedClient}; +use text_generation_router::config::Config; use text_generation_router::{server, HubModelInfo, HubTokenizerConfig}; use thiserror::Error; use tokenizers::Tokenizer; @@ -191,15 +192,19 @@ async fn main() -> Result<(), RouterError> { }; // Load tokenizer and model info - let (tokenizer, model_info) = if local_model { + let (tokenizer, model_info, config) = if local_model { let tokenizer = Tokenizer::from_file(local_path.join("tokenizer.json")).ok(); let model_info = HubModelInfo { model_id: tokenizer_name.to_string(), sha: None, pipeline_tag: None, }; + let config: Option = std::fs::read_to_string(local_path.join("config.json")) + .ok() + .as_ref() + .and_then(|c| serde_json::from_str(c).ok()); - (tokenizer, model_info) + (tokenizer, model_info, config) } else if let Some(api) = api.clone() { let api_repo = api.repo(Repo::with_revision( tokenizer_name.to_string(), @@ -212,6 +217,19 @@ async fn main() -> Result<(), RouterError> { Err(_) => get_base_tokenizer(&api, &api_repo).await, }; + let config: Option = api_repo.get("config.json").await.ok().and_then(|filename| { + std::fs::read_to_string(filename) + .ok() + .as_ref() + .and_then(|c| { + let config: Result = serde_json::from_str(c); + if let Err(err) = &config { + tracing::warn!("Could not parse config {err:?}"); + } + config.ok() + }) + }); + let model_info = get_model_info(&api_repo).await.unwrap_or_else(|| { tracing::warn!("Could not retrieve model info from the Hugging Face hub."); HubModelInfo { @@ -221,7 +239,7 @@ async fn main() -> Result<(), RouterError> { } }); - (tokenizer, model_info) + (tokenizer, model_info, config) } else { // No API and no local model return Err(RouterError::ArgumentValidation( @@ -229,6 +247,8 @@ async fn main() -> Result<(), RouterError> { )); }; + tracing::info!("Using config {config:?}"); + // Load tokenizer config if found locally, or check if we can get it from the API if needed let tokenizer_config = if let Some(path) = tokenizer_config_path { tracing::info!("Using local tokenizer config from user specified path"); @@ -363,6 +383,7 @@ async fn main() -> Result<(), RouterError> { max_batch_size, sharded_client, tokenizer, + config, validation_workers, addr, cors_allow_origin, diff --git a/router/src/server.rs b/router/src/server.rs index eaed3f1c..b8f93514 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -1,3 +1,4 @@ +use crate::config::Config; /// HTTP Server logic use crate::health::Health; use crate::infer::{InferError, InferResponse, InferStreamResponse}; @@ -164,7 +165,8 @@ async fn generate( let start_time = Instant::now(); metrics::increment_counter!("tgi_request_count"); - tracing::debug!("Input: {}", req.inputs); + // Do not long ultra long inputs, like image payloads. + tracing::debug!("Input: {}", &req.inputs[..1000.min(req.inputs.len())]); let compute_characters = req.inputs.chars().count(); let mut add_prompt = None; @@ -1154,6 +1156,7 @@ pub async fn run( max_batch_size: Option, client: ShardedClient, tokenizer: Option, + config: Option, validation_workers: usize, addr: SocketAddr, allow_origin: Option, @@ -1236,6 +1239,7 @@ pub async fn run( let validation = Validation::new( validation_workers, tokenizer, + config, max_best_of, max_stop_sequences, max_top_n_tokens, diff --git a/router/src/validation.rs b/router/src/validation.rs index 2aa9775a..24bcf191 100644 --- a/router/src/validation.rs +++ b/router/src/validation.rs @@ -1,15 +1,19 @@ +use crate::config::Config; /// Payload validation logic use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput}; use crate::{GenerateParameters, GenerateRequest, GrammarType}; use jsonschema::{Draft, JSONSchema}; use rand::{thread_rng, Rng}; use serde_json::Value; +use std::io::Cursor; use text_generation_client::{ GrammarType as ProtoGrammarType, NextTokenChooserParameters, StoppingCriteriaParameters, }; use thiserror::Error; use tokenizers::tokenizer::Tokenizer; -use tokenizers::TruncationDirection; +// use tokenizers::TruncationDirection; +use base64::{engine::general_purpose::STANDARD, Engine}; +use image::{io::Reader as ImageReader, ImageFormat}; use tokio::sync::mpsc; use tokio::sync::oneshot; use tracing::{instrument, Span}; @@ -34,6 +38,7 @@ impl Validation { pub(crate) fn new( workers: usize, tokenizer: Option, + config: Option, max_best_of: usize, max_stop_sequences: usize, max_top_n_tokens: u32, @@ -50,12 +55,13 @@ impl Validation { // Create workers for _ in 0..workers { let tokenizer_clone = tokenizer.clone(); + let config_clone = config.clone(); let (tokenizer_sender, tokenizer_receiver) = mpsc::unbounded_channel(); senders.push(tokenizer_sender); // Spawn worker tokio::task::spawn_blocking(move || { - tokenizer_worker(tokenizer_clone, tokenizer_receiver) + tokenizer_worker(tokenizer_clone, config_clone, tokenizer_receiver) }); } @@ -408,48 +414,137 @@ async fn round_robin_task( } /// Start tokenization workers -fn tokenizer_worker(tokenizer: Tokenizer, mut receiver: mpsc::UnboundedReceiver) { +fn tokenizer_worker( + tokenizer: Tokenizer, + config: Option, + mut receiver: mpsc::UnboundedReceiver, +) { // Loop over requests - let is_multimodal = { - let vocab = tokenizer.get_vocab(true); - vocab.contains_key("") - }; while let Some(((inputs, truncate), response_tx, parent_span)) = receiver.blocking_recv() { parent_span.in_scope(|| { response_tx - .send(prepare_input(inputs, truncate, &tokenizer, is_multimodal)) + .send(prepare_input(inputs, truncate, &tokenizer, &config)) .unwrap_or(()) }) } } +fn format_from_mimetype(mimetype: &str) -> Option { + match mimetype { + "image/png" => Some(ImageFormat::Png), + "image/jpeg" => Some(ImageFormat::Jpeg), + "image/jpg" => Some(ImageFormat::Jpeg), + "image/gif" => Some(ImageFormat::Gif), + "image/webp" => Some(ImageFormat::WebP), + "image/tiff" => Some(ImageFormat::Tiff), + // "image/pnm"=>Some(ImageFormat::Pnm), + // "image/tga"=>Some(ImageFormat::Tga), + // "image/dds"=>Some(ImageFormat::Dds), + // "image/bmp"=>Some(ImageFormat::Bmp), + // "image/ico"=>Some(ImageFormat::Ico), + // "image/x-exr"=>Some(ImageFormat::OpenExr), + _ => None, + } +} +fn format_to_mimetype(format: ImageFormat) -> String { + match format { + ImageFormat::Png => "image/png", + ImageFormat::Jpeg => "image/jpeg", + ImageFormat::Gif => "image/gif", + ImageFormat::WebP => "image/webp", + ImageFormat::Tiff => "image/tiff", + _ => "application/octet-stream", + } + .to_string() +} + +fn fetch_image(input: &str) -> Result<(String, usize, usize), ValidationError> { + if input.starts_with("![](http://") || input.starts_with("![](https://") { + let url = &input["![](".len()..input.len() - 1]; + let data = reqwest::blocking::get(url)?.bytes()?; + + let format = image::guess_format(&data)?; + // TODO Remove this clone + let img = ImageReader::with_format(Cursor::new(data.clone()), format).decode()?; + let height: usize = img.height().try_into()?; + let width: usize = img.width().try_into()?; + let mimetype = format_to_mimetype(format); + let encoded = STANDARD.encode(data); + let data_uri = format!("![](data:{mimetype};base64,{encoded})"); + Ok((data_uri, height, width)) + } else if input.starts_with("![](data:") { + // Remove ![](....) + let content = &input["![](data:".len()..input.len() - 1]; + let tokens: Vec<_> = content.split(';').collect(); + if tokens.len() != 2 { + return Err(ValidationError::InvalidImageContent(content.to_string())); + } + let mimetype = tokens[0]; + let content = tokens[1]; + + if !content.starts_with("base64,") { + return Err(ValidationError::InvalidImageContent(content.to_string())); + } + + let data = STANDARD.decode(content["base64,".len()..].as_bytes())?; + let img = if let Some(format) = format_from_mimetype(mimetype) { + ImageReader::with_format(Cursor::new(data), format).decode()? + } else { + ImageReader::new(Cursor::new(data)) + .with_guessed_format() + .map_err(|_io_error| ValidationError::InvalidImageContent(content.to_string()))? + .decode()? + }; + + let height: usize = img.height().try_into()?; + let width: usize = img.width().try_into()?; + Ok((input.to_string(), height, width)) + } else { + Err(ValidationError::InvalidImageContent(input.to_string())) + } +} + /// Get input length and optionally truncate it fn prepare_input( mut inputs: String, - truncate: Option, + _truncate: Option, tokenizer: &Tokenizer, - is_multimodal: bool, + config: &Option, ) -> Result<(tokenizers::Encoding, String), ValidationError> { - let simplified_query = if is_multimodal { - static RE: Lazy = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap()); - RE.replace_all(&inputs, "").into() - } else { - inputs.clone() - }; - // Get the number of tokens in the input - let mut encoding = tokenizer - .encode(simplified_query, true) - .map_err(|err| ValidationError::Tokenizer(err.to_string()))?; - - // Optionally truncate - if let Some(truncate) = truncate { - if truncate < encoding.len() && !is_multimodal { - encoding.truncate(truncate, 0, TruncationDirection::Left); - inputs = tokenizer - .decode(encoding.get_ids(), false) - .map_err(|err| ValidationError::Tokenizer(err.to_string()))?; + static RE: Lazy = Lazy::new(|| Regex::new(r"!\[\]\([^\)]*\)").unwrap()); + let tokenizer_query = match config { + Some(Config::LlavaNext(config)) => { + let mut modified_inputs = String::with_capacity(inputs.len()); + let mut tokenizer_query = String::with_capacity(inputs.len()); + let mut start = 0; + for chunk in RE.find_iter(&inputs) { + let chunk_start = chunk.start(); + let chunk_end = chunk.end(); + if chunk_start != start { + modified_inputs.push_str(&inputs[start..chunk_start]); + tokenizer_query.push_str(&inputs[start..chunk_start]); + } + let (image_uri, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?; + let slots = config.get_number_of_features(height, width); + tokenizer_query.push_str(&"".repeat(slots)); + modified_inputs.push_str(&image_uri); + start = chunk_end; + } + if start != inputs.len() - 1 { + modified_inputs.push_str(&inputs[start..]); + tokenizer_query.push_str(&inputs[start..]); + } + inputs = modified_inputs; + tokenizer_query } - } + Some(Config::Idefics) => RE.replace_all(&inputs, "").into(), + _ => inputs.clone(), + }; + + // Get the number of tokens in the input + let encoding = tokenizer + .encode(tokenizer_query, true) + .map_err(|err| ValidationError::Tokenizer(err.to_string()))?; Ok((encoding, inputs)) } @@ -523,6 +618,16 @@ pub enum ValidationError { Grammar, #[error("grammar is not valid: {0}")] InvalidGrammar(String), + #[error("base64 encoding is invalid: {0}")] + InvalidBase64(#[from] base64::DecodeError), + #[error("invalid image: {0}")] + InvalidImage(#[from] image::ImageError), + #[error("invalid integer: {0}")] + InvalidInt(#[from] core::num::TryFromIntError), + #[error("invalid image content: {0}")] + InvalidImageContent(String), + #[error("Could not fetch image: {0}")] + FailedFetchImage(#[from] reqwest::Error), } #[cfg(test)] @@ -541,9 +646,11 @@ mod tests { let max_total_tokens = 6; let workers = 1; let disable_grammar_support = true; + let config = None; let validation = Validation::new( workers, tokenizer, + config, max_best_of, max_stop_sequence, max_top_n_tokens, @@ -572,9 +679,11 @@ mod tests { let max_total_tokens = 6; let disable_grammar_support = true; let workers = 1; + let config = None; let validation = Validation::new( workers, tokenizer, + config, max_best_of, max_stop_sequence, max_top_n_tokens, @@ -603,9 +712,11 @@ mod tests { let max_total_tokens = 6; let workers = 1; let disable_grammar_support = true; + let config = None; let validation = Validation::new( workers, tokenizer, + config, max_best_of, max_stop_sequence, max_top_n_tokens, @@ -639,9 +750,11 @@ mod tests { let max_total_tokens = 106; let workers = 1; let disable_grammar_support = true; + let config = None; let validation = Validation::new( workers, tokenizer, + config, max_best_of, max_stop_sequence, max_top_n_tokens, @@ -704,9 +817,11 @@ mod tests { let max_total_tokens = 106; let workers = 1; let disable_grammar_support = true; + let config = None; let validation = Validation::new( workers, tokenizer, + config, max_best_of, max_stop_sequences, max_top_n_tokens, diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index ec167303..00c8b9a8 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -67,6 +67,7 @@ try: FlashSantacoderSharded, ) from text_generation_server.models.idefics import IDEFICSSharded + from text_generation_server.models.llava_next import LlavaNext from text_generation_server.models.flash_mistral import FlashMistral from text_generation_server.models.flash_mixtral import FlashMixtral from text_generation_server.models.flash_phi import FlashPhi @@ -579,6 +580,19 @@ def get_model( else: raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics")) + if model_type == "llava_next": + if FLASH_ATTENTION: + return LlavaNext( + model_id, + revision, + quantize=quantize, + use_medusa=use_medusa, + dtype=dtype, + trust_remote_code=trust_remote_code, + ) + else: + raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("LlavaNext")) + if sharded: raise NotImplementedError("sharded is not supported for AutoModel") if quantize == "gptq": diff --git a/server/text_generation_server/models/custom_modeling/clip.py b/server/text_generation_server/models/custom_modeling/clip.py new file mode 100644 index 00000000..c4917733 --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/clip.py @@ -0,0 +1,827 @@ +from typing import Optional, Tuple, Union + +import torch +from torch import nn + +from transformers.activations import ACT2FN +from transformers.modeling_attn_mask_utils import ( + _create_4d_causal_attention_mask, + _prepare_4d_attention_mask, +) +from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, + ImageClassifierOutput, +) +from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig + +from text_generation_server.utils.layers import ( + TensorParallelEmbedding, + TensorParallelColumnLinear, + TensorParallelRowLinear, +) + + +class CLIPVisionEmbeddings(nn.Module): + def __init__(self, prefix, config: CLIPVisionConfig, weights): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + # TODO Should we TP this ? + self.class_embedding = weights.get_tensor(f"{prefix}.class_embedding") + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + bias=False, + ) + self.patch_embedding.weight = nn.Parameter( + weights.get_tensor(f"{prefix}.patch_embedding.weight"), requires_grad=False + ) + + self.num_patches = (self.image_size // self.patch_size) ** 2 + self.num_positions = self.num_patches + 1 + self.position_embedding = TensorParallelEmbedding( + prefix=f"{prefix}.position_embedding", weights=weights + ) + self.register_buffer( + "position_ids", + torch.arange(self.num_positions, device=weights.device).expand((1, -1)), + persistent=False, + ) + + def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: + batch_size = pixel_values.shape[0] + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding( + pixel_values.to(dtype=target_dtype) + ) # shape = [*, width, grid, grid] + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + + class_embeds = self.class_embedding.expand(batch_size, 1, -1) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + embeddings = embeddings + self.position_embedding(self.position_ids) + return embeddings + + +class CLIPTextEmbeddings(nn.Module): + def __init__(self, config: CLIPTextConfig): + super().__init__() + embed_dim = config.hidden_size + + self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) + self.position_embedding = nn.Embedding( + config.max_position_embeddings, embed_dim + ) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", + torch.arange(config.max_position_embeddings).expand((1, -1)), + persistent=False, + ) + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + ) -> torch.Tensor: + seq_length = ( + input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] + ) + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + if inputs_embeds is None: + inputs_embeds = self.token_embedding(input_ids) + + position_embeddings = self.position_embedding(position_ids) + embeddings = inputs_embeds + position_embeddings + + return embeddings + + +class CLIPAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, prefix, config, weights): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_size = self.embed_dim // self.num_heads + if self.head_size * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.num_heads = self.num_heads // weights.process_group.size() + self.embed_dim = self.embed_dim // weights.process_group.size() + self.scale = self.head_size**-0.5 + self.dropout = config.attention_dropout + + self.qkv = TensorParallelColumnLinear.load_multi( + config, + prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"], + dim=0, + weights=weights, + bias=True, + ) + self.out_proj = TensorParallelRowLinear.load( + config, + prefix=f"{prefix}.out_proj", + weights=weights, + bias=True, + ) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return ( + tensor.view(bsz, seq_len, self.num_heads, self.head_size) + .transpose(1, 2) + .contiguous() + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + causal_attention_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + + qkv = self.qkv(hidden_states) + query_states, key_states, value_states = qkv.split( + [ + self.head_size * self.num_heads, + ] + * 3, + dim=2, + ) + query_states = query_states * self.scale + key_states = self._shape(key_states, -1, bsz) + value_states = self._shape(value_states, -1, bsz) + + proj_shape = (bsz * self.num_heads, -1, self.head_size) + query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + # apply the causal_attention_mask first + if causal_attention_mask is not None: + if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" + f" {causal_attention_mask.size()}" + ) + attn_weights = ( + attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + + causal_attention_mask + ) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = ( + attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + + attention_mask + ) + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_probs = nn.functional.dropout( + attn_weights, p=self.dropout, training=self.training + ) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_size): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_size)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_size) + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, None + + +class CLIPMLP(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = TensorParallelColumnLinear.load( + prefix=f"{prefix}.fc1", config=config, weights=weights, bias=True + ) + self.fc2 = TensorParallelRowLinear.load( + prefix=f"{prefix}.fc2", config=config, weights=weights, bias=True + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class CLIPEncoderLayer(nn.Module): + def __init__(self, prefix, config: CLIPConfig, weights): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = CLIPAttention( + prefix=f"{prefix}.self_attn", config=config, weights=weights + ) + self.layer_norm1 = nn.LayerNorm.load( + prefix=f"{prefix}.layer_norm1", weights=weights, eps=config.layer_norm_eps + ) + self.mlp = CLIPMLP(prefix=f"{prefix}.mlp", config=config, weights=weights) + self.layer_norm2 = nn.LayerNorm.load( + prefix=f"{prefix}.layer_norm2", weights=weights, eps=config.layer_norm_eps + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + causal_attention_mask: torch.Tensor, + ): + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + `(config.encoder_attention_heads,)`. + """ + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + causal_attention_mask=causal_attention_mask, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class CLIPPreTrainedModel(nn.Module): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = CLIPConfig + base_model_prefix = "clip" + supports_gradient_checkpointing = True + + +CLIP_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`CLIPConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +CLIP_TEXT_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) +""" + +CLIP_VISION_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. +""" + +CLIP_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. +""" + + +class CLIPEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`CLIPEncoderLayer`]. + + Args: + config: CLIPConfig + """ + + def __init__(self, prefix, config: CLIPConfig, weights): + super().__init__() + self.config = config + self.layers = nn.ModuleList( + [ + CLIPEncoderLayer( + prefix=f"{prefix}.layers.{i}", config=config, weights=weights + ) + for i in range(config.num_hidden_layers) + ] + ) + + def forward( + self, + inputs_embeds, + attention_mask: Optional[torch.Tensor] = None, + causal_attention_mask: Optional[torch.Tensor] = None, + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Causal mask for the text model. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + """ + + hidden_states = inputs_embeds + for idx, encoder_layer in enumerate(self.layers): + hidden_states = encoder_layer( + hidden_states, + attention_mask, + causal_attention_mask, + ) + + return hidden_states + + +class CLIPTextTransformer(nn.Module): + def __init__(self, config: CLIPTextConfig): + super().__init__() + self.config = config + embed_dim = config.hidden_size + self.embeddings = CLIPTextEmbeddings(config) + self.encoder = CLIPEncoder( + prefix=f"{prefix}.encoder", config=config, weights=weights + ) + self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + # For `pooled_output` computation + self.eos_token_id = config.eos_token_id + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + ): + r""" + Returns: + + """ + if input_ids is None: + raise ValueError("You have to specify input_ids") + + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) + + # CLIP's text model uses causal mask, prepare it here. + # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324 + causal_attention_mask = _create_4d_causal_attention_mask( + input_shape, hidden_states.dtype, device=hidden_states.device + ) + # expand attention_mask + if attention_mask is not None: + # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] + attention_mask = _prepare_4d_attention_mask( + attention_mask, hidden_states.dtype + ) + + encoder_outputs = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + causal_attention_mask=causal_attention_mask, + ) + + last_hidden_state = encoder_outputs[0] + last_hidden_state = self.final_layer_norm(last_hidden_state) + + if self.eos_token_id == 2: + # The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here. + # A CLIP model with such `eos_token_id` in the config can't work correctly with extra new tokens added + # ------------------------------------------------------------ + # text_embeds.shape = [batch_size, sequence_length, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 + pooled_output = last_hidden_state[ + torch.arange( + last_hidden_state.shape[0], device=last_hidden_state.device + ), + input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax( + dim=-1 + ), + ] + else: + # The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible) + pooled_output = last_hidden_state[ + torch.arange( + last_hidden_state.shape[0], device=last_hidden_state.device + ), + # We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`) + ( + input_ids.to(dtype=torch.int, device=last_hidden_state.device) + == self.eos_token_id + ) + .int() + .argmax(dim=-1), + ] + + return last_hidden_state + + +class CLIPTextModel(CLIPPreTrainedModel): + config_class = CLIPTextConfig + + _no_split_modules = ["CLIPTextEmbeddings", "CLIPEncoderLayer"] + + def __init__(self, config: CLIPTextConfig): + super().__init__(config) + self.text_model = CLIPTextTransformer(config) + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + ): + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoTokenizer, CLIPTextModel + + >>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32") + >>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32") + + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled (EOS token) states + ```""" + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + return self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + ) + + +class CLIPVisionTransformer(nn.Module): + def __init__(self, prefix, config: CLIPVisionConfig, weights): + super().__init__() + self.config = config + embed_dim = config.hidden_size + + self.embeddings = CLIPVisionEmbeddings( + prefix=f"{prefix}.embeddings", config=config, weights=weights + ) + self.pre_layrnorm = nn.LayerNorm.load( + prefix=f"{prefix}.pre_layrnorm", weights=weights, eps=config.layer_norm_eps + ) + self.encoder = CLIPEncoder( + prefix=f"{prefix}.encoder", config=config, weights=weights + ) + # self.post_layernorm = nn.LayerNorm.load(prefix=f"{prefix}.post_layernorm", weights=weights, eps=config.layer_norm_eps) + + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + ): + r""" + Returns: + + """ + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.embeddings(pixel_values) + hidden_states = self.pre_layrnorm(hidden_states) + + encoder_outputs = self.encoder( + inputs_embeds=hidden_states, + ) + last_hidden_state = encoder_outputs + # pooled_output = last_hidden_state[:, 0, :] + # pooled_output = self.post_layernorm(pooled_output) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + # pooler_output=pooled_output, + # hidden_states=encoder_outputs, + ) + + +class CLIPVisionModel(CLIPPreTrainedModel): + config_class = CLIPVisionConfig + main_input_name = "pixel_values" + _no_split_modules = ["CLIPEncoderLayer"] + + def __init__(self, config: CLIPVisionConfig): + super().__init__(config) + self.vision_model = CLIPVisionTransformer(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_model.embeddings.patch_embedding + + def forward( + self, + pixel_values: Optional[torch.FloatTensor] = None, + ): + r""" + Returns: + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, CLIPVisionModel + + >>> model = CLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32") + >>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled CLS states + ```""" + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + return self.vision_model( + pixel_values=pixel_values, + ) + + +class CLIPModel(nn.Module): + def __init__(self, prefix, config: CLIPConfig, weights): + super().__init__() + text_config = config.text_config + vision_config = config.vision_config + + self.projection_dim = config.projection_dim + self.text_embed_dim = text_config.hidden_size + self.vision_embed_dim = vision_config.hidden_size + + self.text_model = CLIPTextTransformer(text_config) + self.vision_model = CLIPVisionTransformer(vision_config) + + self.visual_projection = nn.Linear( + self.vision_embed_dim, self.projection_dim, bias=False + ) + self.text_projection = nn.Linear( + self.text_embed_dim, self.projection_dim, bias=False + ) + self.logit_scale = nn.Parameter( + torch.tensor(self.config.logit_scale_init_value) + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_text_features( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + ) -> torch.FloatTensor: + r""" + Returns: + text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by + applying the projection layer to the pooled output of [`CLIPTextModel`]. + + Examples: + + ```python + >>> from transformers import AutoTokenizer, CLIPModel + + >>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + >>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32") + + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") + >>> text_features = model.get_text_features(**inputs) + ```""" + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + ) + + pooled_output = text_outputs[1] + text_features = self.text_projection(pooled_output) + + return text_features + + def get_image_features( + self, + pixel_values: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + r""" + Returns: + image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by + applying the projection layer to the pooled output of [`CLIPVisionModel`]. + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, CLIPModel + + >>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + >>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> image_features = model.get_image_features(**inputs) + ```""" + # Use CLIP model's config for some fields (if specified) instead of those of vision & text components. + vision_outputs = self.vision_model( + pixel_values=pixel_values, + ) + + pooled_output = vision_outputs[1] # pooled_output + image_features = self.visual_projection(pooled_output) + + return image_features + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + pixel_values: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + ): + r""" + Returns: + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, CLIPModel + + >>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") + >>> processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = processor( + ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True + ... ) + + >>> outputs = model(**inputs) + >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score + >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities + ```""" + # Use CLIP model's config for some fields (if specified) instead of those of vision & text components. + vision_outputs = self.vision_model( + pixel_values=pixel_values, + return_dict=return_dict, + ) + + text_outputs = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + return_dict=return_dict, + ) + + image_embeds = vision_outputs[1] + image_embeds = self.visual_projection(image_embeds) + + text_embeds = text_outputs[1] + text_embeds = self.text_projection(text_embeds) + + # normalized features + image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) + text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) + + # cosine similarity as logits + logit_scale = self.logit_scale.exp() + logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale + logits_per_image = logits_per_text.t() + + return logits_per_image, logits_per_text diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index 3a269fc0..4cf0fcf2 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -281,9 +281,8 @@ class LlamaMLP(nn.Module): class FlashLlamaLayer(nn.Module): - def __init__(self, layer_id, config, weights): + def __init__(self, prefix, config, weights): super().__init__() - prefix = f"model.layers.{layer_id}" self.self_attn = FlashLlamaAttention( prefix=f"{prefix}.self_attn", config=config, weights=weights ) @@ -337,27 +336,30 @@ class FlashLlamaLayer(nn.Module): class FlashLlamaModel(torch.nn.Module): - def __init__(self, config, weights): + def __init__(self, prefix, config, weights): super().__init__() process_group = weights.process_group self.tp_rank = process_group.rank() self.tp_world_size = process_group.size() - self.embed_tokens = TensorParallelEmbedding( - prefix="model.embed_tokens", weights=weights - ) self.layers = nn.ModuleList( [ FlashLlamaLayer( - layer_id, - config, - weights, + prefix=( + f"model.layers.{layer_id}" + if not prefix + else f"{prefix}.model.layers.{layer_id}" + ), + config=config, + weights=weights, ) for layer_id in range(config.num_hidden_layers) ] ) self.norm = FastRMSNorm.load( - prefix="model.norm", weights=weights, eps=config.rms_norm_eps + prefix="model.norm" if not prefix else f"{prefix}.model.norm", + weights=weights, + eps=config.rms_norm_eps, ) self.gradient_checkpointing = False @@ -368,7 +370,7 @@ class FlashLlamaModel(torch.nn.Module): def forward( self, - input_ids: torch.Tensor, + inputs_embeds: torch.Tensor, position_ids: torch.Tensor, cu_seqlen_prefill: Optional[torch.Tensor], kv_cache: List[Tuple[torch.Tensor, torch.Tensor]], @@ -376,8 +378,10 @@ class FlashLlamaModel(torch.nn.Module): slots: torch.Tensor, input_lengths: torch.Tensor, max_s: int, + true_max_s: int, + prefill_cache_indices: Optional[torch.Tensor], ) -> torch.Tensor: - hidden_states = self.embed_tokens(input_ids) + hidden_states = inputs_embeds # Get rotary cos and sin for this forward # Avoid to index in each layer @@ -406,13 +410,19 @@ class FlashLlamaModel(torch.nn.Module): class FlashLlamaForCausalLM(torch.nn.Module): - def __init__(self, config, weights): + def __init__(self, prefix, config, weights): super().__init__() - self.model = FlashLlamaModel(config, weights) + self.embed_tokens = TensorParallelEmbedding( + prefix=( + "model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens" + ), + weights=weights, + ) + self.model = FlashLlamaModel(prefix, config, weights) self.lm_head = SpeculativeHead.load( config, - prefix="lm_head", + prefix="lm_head" if not prefix else f"{prefix}.lm_head", weights=weights, ) @@ -426,10 +436,12 @@ class FlashLlamaForCausalLM(torch.nn.Module): slots: torch.Tensor, input_lengths: torch.Tensor, max_s: int, + prefill_cache_indices: Optional[torch.Tensor] = None, lm_head_indices: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + inputs_embeds = self.embed_tokens(input_ids) hidden_states = self.model( - input_ids, + inputs_embeds, position_ids, cu_seqlen_prefill, kv_cache, @@ -437,6 +449,8 @@ class FlashLlamaForCausalLM(torch.nn.Module): slots, input_lengths, max_s, + true_max_s=max_s, + prefill_cache_indices=prefill_cache_indices, ) if lm_head_indices is not None: hidden_states = hidden_states[lm_head_indices] diff --git a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py index ed9306e0..ffaa0c32 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py @@ -285,9 +285,8 @@ class MistralMLP(nn.Module): class MistralLayer(nn.Module): - def __init__(self, layer_id, config, weights): + def __init__(self, prefix, config, weights): super().__init__() - prefix = f"model.layers.{layer_id}" self.self_attn = MistralAttention( prefix=f"{prefix}.self_attn", config=config, weights=weights ) @@ -343,27 +342,24 @@ class MistralLayer(nn.Module): class MistralModel(torch.nn.Module): - def __init__(self, config, weights): + def __init__(self, prefix, config, weights): super().__init__() process_group = weights.process_group self.tp_rank = process_group.rank() self.tp_world_size = process_group.size() - self.embed_tokens = TensorParallelEmbedding( - prefix="model.embed_tokens", weights=weights - ) self.layers = nn.ModuleList( [ MistralLayer( - layer_id, - config, - weights, + prefix=f"{prefix}.layers.{layer_id}", + config=config, + weights=weights, ) for layer_id in range(config.num_hidden_layers) ] ) self.norm = FastRMSNorm.load( - prefix="model.norm", weights=weights, eps=config.rms_norm_eps + prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps ) self.gradient_checkpointing = False @@ -374,7 +370,7 @@ class MistralModel(torch.nn.Module): def forward( self, - input_ids: torch.Tensor, + inputs_embeds: torch.Tensor, position_ids: torch.Tensor, cu_seqlen_prefill: Optional[torch.Tensor], kv_cache: List[Tuple[torch.Tensor, torch.Tensor]], @@ -384,9 +380,8 @@ class MistralModel(torch.nn.Module): max_s: int, true_max_s: int, prefill_cache_indices: Optional[torch.Tensor], - ) -> torch.Tensor: - hidden_states = self.embed_tokens(input_ids) - + ): + hidden_states = inputs_embeds # Get rotary cos and sin for this forward # Avoid to index in each layer cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin( @@ -410,18 +405,27 @@ class MistralModel(torch.nn.Module): ) hidden_states, _ = self.norm(hidden_states, residual) - return hidden_states class FlashMistralForCausalLM(torch.nn.Module): - def __init__(self, config, weights): + def __init__(self, prefix, config, weights): super().__init__() - self.model = MistralModel(config, weights) + self.embed_tokens = TensorParallelEmbedding( + prefix=( + "model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens" + ), + weights=weights, + ) + self.model = MistralModel( + prefix="model" if not prefix else f"{prefix}.model", + config=config, + weights=weights, + ) self.lm_head = SpeculativeHead.load( config, - prefix="lm_head", + prefix="lm_head" if not prefix else f"{prefix}.lm_head", weights=weights, ) self.max_past = config.sliding_window @@ -453,8 +457,9 @@ class FlashMistralForCausalLM(torch.nn.Module): # kernel requires the true values input_lengths = torch.clamp(input_lengths, max=self.max_past_tensor) + inputs_embeds = self.embed_tokens(input_ids) hidden_states = self.model( - input_ids, + inputs_embeds, position_ids, cu_seqlen_prefill, kv_cache, diff --git a/server/text_generation_server/models/custom_modeling/llava_next.py b/server/text_generation_server/models/custom_modeling/llava_next.py new file mode 100644 index 00000000..ed21a52b --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/llava_next.py @@ -0,0 +1,302 @@ +# coding=utf-8 +# Copyright 2024 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Llava-NeXT model.""" + +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn + +from transformers.activations import ACT2FN +from transformers.image_processing_utils import select_best_resolution + +from text_generation_server.utils.layers import ( + TensorParallelColumnLinear, + TensorParallelRowLinear, +) + + +def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size): + """ + Calculate the shape of the image patch grid after the preprocessing for images of any resolution. + + Args: + image_size (`tuple`): + The size of the input image in the format (width, height). + grid_pinpoints (`List`): + A list containing possible resolutions. Each item in the list should be a tuple or list + of the form `(height, width)`. + patch_size (`int`): + The size of each image patch. + + Returns: + tuple: The shape of the image patch grid in the format (width, height). + """ + if not isinstance(grid_pinpoints, list): + raise ValueError("grid_pinpoints should be a list of tuples or lists") + + height, width = select_best_resolution(image_size, grid_pinpoints) + return height // patch_size, width // patch_size + + +def unpad_image(tensor, original_size): + """ + Unpads a PyTorch tensor of a padded and resized image. + + Args: + tensor (`torch.Tensor`): + The image tensor, assumed to be of shape (num_channels, height, width). + original_size (`tuple`): + The original size of the image (height, width). + + Returns: + `torch.Tensor`: The unpadded image tensor. + """ + original_height, original_width = original_size + current_height, current_width = tensor.shape[1:] + + original_aspect_ratio = original_width / original_height + current_aspect_ratio = current_width / current_height + + if original_aspect_ratio > current_aspect_ratio: + scale_factor = current_width / original_width + new_height = int(original_height * scale_factor) + padding = (current_height - new_height) // 2 + unpadded_tensor = tensor[:, padding : current_height - padding, :] + else: + scale_factor = current_height / original_height + new_width = int(original_width * scale_factor) + padding = (current_width - new_width) // 2 + unpadded_tensor = tensor[:, :, padding : current_width - padding] + + return unpadded_tensor + + +# Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->LlavaNext +class LlavaNextMultiModalProjector(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + + self.linear_1 = TensorParallelColumnLinear.load( + prefix=f"{prefix}.linear_1", config=config, weights=weights, bias=True + ) + self.act = ACT2FN[config.projector_hidden_act] + self.linear_2 = TensorParallelRowLinear.load( + prefix=f"{prefix}.linear_2", config=config, weights=weights, bias=True + ) + + def forward(self, image_features): + hidden_states = self.linear_1(image_features) + hidden_states = self.act(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +def load_vision_model(prefix, config, weights): + if config.model_type == "clip_vision_model": + from text_generation_server.models.custom_modeling.clip import ( + CLIPVisionTransformer, + ) + + return CLIPVisionTransformer( + prefix=f"{prefix}.vision_model", config=config, weights=weights + ) + else: + raise RuntimeError(f"Unsupported model type {config.model_type}") + + +def load_text_model(prefix, config, weights): + if config.model_type == "llama": + from text_generation_server.models.custom_modeling.flash_llama_modeling import ( + FlashLlamaForCausalLM, + ) + + return FlashLlamaForCausalLM(prefix, config, weights) + elif config.model_type == "mistral": + from text_generation_server.models.custom_modeling.flash_mistral_modeling import ( + FlashMistralForCausalLM, + ) + + return FlashMistralForCausalLM(prefix, config, weights) + else: + raise RuntimeError(f"Unsupported model type {config.model_type}") + + +class LlavaNextForConditionalGeneration(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + config.vision_config.quantize = config.quantize + vision_config = config.vision_config + # Instead of selecting in hidden_states[-2]. + # Instead compute only the n -2 + 1 layers and don't pool + if config.vision_feature_layer < 0: + vision_config.num_hidden_layers += config.vision_feature_layer + 1 + else: + vision_config.num_hidden_layers = config.vision_feature_layer + 1 + self.vision_tower = load_vision_model( + prefix="vision_tower" if not prefix else f"{prefix}.vision_tower", + config=config.vision_config, + weights=weights, + ) + + self.multi_modal_projector = LlavaNextMultiModalProjector( + prefix="multi_modal_projector", config=config, weights=weights + ) + + self.image_newline = weights.get_tensor("image_newline") + + self.vocab_size = config.text_config.vocab_size + self.config = config + config.text_config.quantize = config.quantize + config.text_config.use_medusa = config.use_medusa + self.language_model = load_text_model( + prefix="language_model" if not prefix else f"{prefix}.language_model", + config=config.text_config, + weights=weights, + ) + self.pad_token_id = ( + config.pad_token_id if config.pad_token_id is not None else -1 + ) + + def _merge_input_ids_with_image_features( + self, + input_ids: torch.Tensor, + inputs_embeds: torch.Tensor, + image_features: torch.Tensor, + ): + """In place merges in vision_embeddings with inputs_embeds.""" + mask = input_ids == self.config.image_token_index + # Let's pray we have enabled enough slots ! + inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1]) + return inputs_embeds + + def forward( + self, + input_ids: torch.Tensor, + position_ids: torch.Tensor, + cu_seqlen_prefill: Optional[torch.Tensor], + kv_cache: List[Tuple[torch.Tensor, torch.Tensor]], + block_tables: torch.Tensor, + slots: torch.Tensor, + input_lengths: torch.Tensor, + max_s: int, + prefill_cache_indices: Optional[torch.Tensor], + lm_head_indices: Optional[torch.Tensor] = None, + pixel_values: torch.FloatTensor = None, + image_sizes: Optional[torch.LongTensor] = None, + ): + inputs_embeds = self.language_model.embed_tokens(input_ids) + if pixel_values is not None and len(pixel_values) > 0: + # num_special_image_tokens = (input_ids == self.config.image_token_index).sum() + # assert num_special_image_tokens == len(pixel_values), f"Received {num_special_image_tokens} for {len(pixel_values)} images, this is invalid" + # 1. Extract the input embeddings + + # 2. Merge text and images + num_images, num_patches, channels, height, width = pixel_values.shape + pixel_values = pixel_values.view( + num_images * num_patches, channels, height, width + ) + image_features = self.vision_tower(pixel_values) + + # selected_image_feature = image_features.hidden_states[self.config.vision_feature_layer] + # Already done within the clip model + selected_image_feature = image_features.last_hidden_state + + if self.config.vision_feature_select_strategy == "default": + selected_image_feature = selected_image_feature[:, 1:] + elif self.config.vision_feature_select_strategy == "full": + selected_image_feature = selected_image_feature + else: + raise RuntimeError( + f"Strategy `{self.config.vision_feature_select_strategy}` is not supported/valid." + ) + + image_features = self.multi_modal_projector(selected_image_feature) + + # split up image_features for each of the individual images + # hence we get a list of image_features, each of shape (5, num_patches, hidden_size) + # if we assume each image has 5 image features (base image + 4 patches) + split_sizes = [num_patches] * num_images + image_features = torch.split(image_features, split_sizes, dim=0) + + # NOTE we only support multimodal_patch_merge_type == "spatial_unpad" + height = width = ( + self.config.vision_config.image_size + // self.config.vision_config.patch_size + ) + + new_image_features = [] + for image_idx, image_feature in enumerate(image_features): + if image_feature.shape[0] > 1: + base_image_feature = image_feature[0] + image_feature = image_feature[1:] + + if height * width != base_image_feature.shape[0]: + raise ValueError( + "The number of patches is not consistent with the image size." + ) + num_patch_height, num_patch_width = get_anyres_image_grid_shape( + image_sizes[image_idx], + self.config.image_grid_pinpoints, + self.config.vision_config.image_size, + ) + image_feature = image_feature.view( + num_patch_height, num_patch_width, height, width, -1 + ) + image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous() + image_feature = image_feature.flatten(1, 2).flatten(2, 3) + image_feature = unpad_image(image_feature, image_sizes[image_idx]) + image_feature = torch.cat( + ( + image_feature, + self.image_newline[:, None, None].expand( + *image_feature.shape[:-1], 1 + ), + ), + dim=-1, + ) + image_feature = image_feature.flatten(1, 2).transpose(0, 1) + image_feature = torch.cat( + (base_image_feature, image_feature), dim=0 + ) + else: + image_feature = image_feature[0] + image_feature = torch.cat( + (image_feature, self.image_newline[None]), dim=0 + ) + new_image_features.append(image_feature) + image_features = torch.stack(new_image_features, dim=0) + + inputs_embeds = self._merge_input_ids_with_image_features( + input_ids, inputs_embeds, image_features + ) + + hidden_states = self.language_model.model( + inputs_embeds=inputs_embeds, + position_ids=position_ids, + cu_seqlen_prefill=cu_seqlen_prefill, + kv_cache=kv_cache, + block_tables=block_tables, + slots=slots, + input_lengths=input_lengths, + max_s=max_s, + true_max_s=max_s, + prefill_cache_indices=None, + ) + if lm_head_indices is not None: + hidden_states = hidden_states[lm_head_indices] + logits, speculative_logits = self.language_model.lm_head(hidden_states) + return logits, speculative_logits diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index 5c25f341..be513511 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -106,6 +106,19 @@ class FlashCausalLMBatch(Batch): max_tokens=self.blocks * BLOCK_SIZE, ) + @classmethod + def batch_tokenized_inputs(cls, requests, tokenizer): + batch_inputs = [] + max_truncation = 0 + for r in requests: + batch_inputs.append(r.inputs) + max_truncation = max(max_truncation, r.truncate) + + batch_tokenized_inputs = tokenizer( + batch_inputs, truncation=True, max_length=max_truncation + )["input_ids"] + return batch_tokenized_inputs + @classmethod def from_pb( cls, @@ -114,16 +127,7 @@ class FlashCausalLMBatch(Batch): dtype: torch.dtype, device: torch.device, ) -> "FlashCausalLMBatch": - batch_inputs = [] - max_truncation = 0 - for r in pb.requests: - batch_inputs.append(r.inputs) - max_truncation = max(max_truncation, r.truncate) - - batch_tokenized_inputs = tokenizer( - batch_inputs, truncation=True, max_length=max_truncation - )["input_ids"] - + batch_tokenized_inputs = cls.batch_tokenized_inputs(pb.requests, tokenizer) position_ids = [] speculative_ids = [] cu_seqlen_prefill = [0] diff --git a/server/text_generation_server/models/flash_llama.py b/server/text_generation_server/models/flash_llama.py index a2ac759a..56768942 100644 --- a/server/text_generation_server/models/flash_llama.py +++ b/server/text_generation_server/models/flash_llama.py @@ -67,7 +67,8 @@ class FlashLlama(FlashCausalLM): if config.quantize in ["gptq", "awq"]: weights._set_gptq_params(model_id, revision) - model = FlashLlamaForCausalLM(config, weights) + prefix = "" + model = FlashLlamaForCausalLM(prefix, config, weights) torch.distributed.barrier(group=self.process_group) super(FlashLlama, self).__init__( model=model, diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py index 2e1055b2..575dbba0 100644 --- a/server/text_generation_server/models/flash_mistral.py +++ b/server/text_generation_server/models/flash_mistral.py @@ -6,8 +6,7 @@ import numpy as np from dataclasses import dataclass from opentelemetry import trace -from transformers import PreTrainedTokenizerBase, AutoTokenizer -from transformers.models.llama import LlamaTokenizerFast +from transformers import PreTrainedTokenizerBase, AutoTokenizer, AutoConfig from typing import Optional, Tuple, Type from text_generation_server.pb import generate_pb2 @@ -65,19 +64,21 @@ class FlashMistralBatch(FlashCausalLMBatch): tokenizer: PreTrainedTokenizerBase, dtype: torch.dtype, device: torch.device, + ) -> "FlashCausalLMBatch": + batch_tokenized_inputs = cls.batch_tokenized_inputs(pb.requests, tokenizer) + return cls.from_tokenized(pb, tokenizer, batch_tokenized_inputs, dtype, device) + + @classmethod + def from_tokenized( + cls, + pb: generate_pb2.Batch, + tokenizer: PreTrainedTokenizerBase, + batch_tokenized_inputs, + dtype: torch.dtype, + device: torch.device, ) -> "FlashCausalLMBatch": sliding_window, sliding_window_blocks = get_sliding_windows() - batch_inputs = [] - max_truncation = 0 - for r in pb.requests: - batch_inputs.append(r.inputs) - max_truncation = max(max_truncation, r.truncate) - - batch_tokenized_inputs = tokenizer( - batch_inputs, truncation=True, max_length=max_truncation - )["input_ids"] - position_ids = [] cu_seqlen_prefill = [0] needed_blocks_slots = [] @@ -301,14 +302,15 @@ class FlashMistralBatch(FlashCausalLMBatch): class BaseFlashMistral(FlashCausalLM): def __init__( self, - config_cls, model_cls, model_id: str, + config_cls=AutoConfig, revision: Optional[str] = None, quantize: Optional[str] = None, use_medusa: Optional[str] = None, dtype: Optional[torch.dtype] = None, trust_remote_code: bool = False, + tokenizer_class=AutoTokenizer, ): self.process_group, rank, world_size = initialize_torch_distributed() if torch.cuda.is_available(): @@ -317,22 +319,13 @@ class BaseFlashMistral(FlashCausalLM): else: raise NotImplementedError("FlashMistral is only available on GPU") - try: - tokenizer = LlamaTokenizerFast.from_pretrained( - model_id, - revision=revision, - padding_side="left", - truncation_side="left", - trust_remote_code=trust_remote_code, - ) - except Exception: - tokenizer = AutoTokenizer.from_pretrained( - model_id, - revision=revision, - padding_side="left", - truncation_side="left", - trust_remote_code=trust_remote_code, - ) + tokenizer = tokenizer_class.from_pretrained( + model_id, + revision=revision, + padding_side="left", + truncation_side="left", + trust_remote_code=trust_remote_code, + ) config = config_cls.from_pretrained( model_id, revision=revision, trust_remote_code=trust_remote_code @@ -341,10 +334,12 @@ class BaseFlashMistral(FlashCausalLM): config.use_medusa = use_medusa # Set context windows - if config.sliding_window is not None: + if getattr(config, "sliding_window", None) is not None: set_sliding_window( config.sliding_window, math.ceil(config.sliding_window / BLOCK_SIZE) ) + else: + config.sliding_window = None torch.distributed.barrier(group=self.process_group) @@ -353,17 +348,19 @@ class BaseFlashMistral(FlashCausalLM): if config.quantize in ["gptq", "awq"]: weights._set_gptq_params(model_id, revision) - model = model_cls(config, weights) + prefix = "" + model = model_cls(prefix, config, weights) self.cuda_graphs = {} torch.distributed.barrier(group=self.process_group) - super(BaseFlashMistral, self).__init__( + num_layers, num_kv_heads, head_size = self.get_layer_config(model) + super().__init__( model=model, tokenizer=tokenizer, - num_layers=len(model.model.layers), - num_kv_heads=model.model.num_key_value_heads, - head_size=model.model.head_size, + num_layers=num_layers, + num_kv_heads=num_kv_heads, + head_size=head_size, dtype=dtype, device=device, rank=rank, @@ -371,6 +368,16 @@ class BaseFlashMistral(FlashCausalLM): sliding_window=config.sliding_window, ) + def get_layer_config(self, model) -> Tuple[int, int, int]: + return ( + len(model.model.layers), + model.model.num_key_value_heads, + model.model.head_size, + ) + + def max_past(self) -> int: + return self.model.max_past + @property def batch_type(self) -> Type[FlashMistralBatch]: return FlashMistralBatch @@ -485,11 +492,11 @@ class BaseFlashMistral(FlashCausalLM): max_s = batch.max_seqlen lm_head_indices = batch.prefill_head_indices - if cu_seqlen_prefill is None and self.model.max_past is not None: + if cu_seqlen_prefill is None and self.max_past() is not None: # In decode, not prefill, we're actually overwriting the KV-cache # in a circular buffer mode. # This makes sure the max_s for the decode pass is correct. - max_s = min(self.model.max_past, max_s) + max_s = min(self.max_past(), max_s) bs = input_ids.shape[0] padded_bs = bs diff --git a/server/text_generation_server/models/idefics_causal_lm.py b/server/text_generation_server/models/idefics_causal_lm.py index c96e8152..e78a9655 100644 --- a/server/text_generation_server/models/idefics_causal_lm.py +++ b/server/text_generation_server/models/idefics_causal_lm.py @@ -1,4 +1,5 @@ import torch +import torch import time from dataclasses import dataclass @@ -20,29 +21,13 @@ from text_generation_server.models.types import ( ) from text_generation_server.pb import generate_pb2 from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling +from text_generation_server.models.vlm_causal_lm import split import re IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)") -def split(string): - parts = [] - cursor = 0 - for pattern in IMAGES.finditer(string): - start = pattern.start() - if start != cursor: - parts.append(string[cursor:start]) - - parts.append(pattern.group(1)) - cursor = pattern.end() - - if cursor != len(string): - parts.append(string[cursor:]) - - return parts - - tracer = trace.get_tracer(__name__) @@ -93,10 +78,21 @@ class IdeficsCausalLMBatch(Batch): @classmethod def from_pb( + cls, + pb: generate_pb2.Batch, + tokenizer: PreTrainedTokenizerBase, + dtype: torch.dtype, + device: torch.device, + ) -> "IdeficsCausalLMBatch": + raise NotImplementedError + + @classmethod + def from_pb_processor( cls, pb: generate_pb2.Batch, tokenizer: PreTrainedTokenizerBase, processor: ProcessorMixin, # Hack + config, dtype: torch.dtype, device: torch.device, ) -> "IdeficsCausalLMBatch": @@ -127,10 +123,14 @@ class IdeficsCausalLMBatch(Batch): padding_right_offset, stopping_criteria.max_new_tokens ) + # TODO Check impact on idefics prompts = [] for inp in inputs: # Each input is encoded into a list, where each element of this input list is either a string or a URL - prompts.append(split(inp)) + prompt = [] + for chunk in split(inp): + prompt.append(chunk["content"]) + prompts.append(prompt) # The processor replaces the call to tokenizer, and # a/ takes care of fetching images from the URL @@ -141,7 +141,8 @@ class IdeficsCausalLMBatch(Batch): padding=True, truncation=True, max_length=max_truncation, - add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token + # TODO Check impact on idefics + # add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token ).to(device) for _ in pb.requests: input_len = tokenized_inputs["input_ids"].shape[1] @@ -156,7 +157,7 @@ class IdeficsCausalLMBatch(Batch): max_input_length = input_lengths.max() input_ids = tokenized_inputs["input_ids"] - pixel_values = tokenized_inputs["pixel_values"] + pixel_values = tokenized_inputs.get("pixel_values", None) image_hidden_states = None # Allocate maximum attention_mask attention_mask = input_ids.new_zeros( @@ -165,16 +166,19 @@ class IdeficsCausalLMBatch(Batch): # Copy tokenizer attention_mask into fully allocated attention_mask attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"] # Do the same for image_attention_mask - image_attention_mask = input_ids.new_zeros( - ( - pb.size, - max_input_length + padding_right_offset, - tokenized_inputs["pixel_values"].size(1), + if pixel_values is None: + image_attention_mask = None + else: + image_attention_mask = input_ids.new_zeros( + ( + pb.size, + max_input_length + padding_right_offset, + pixel_values.size(1), + ) ) - ) - image_attention_mask[:, :max_input_length, :] = tokenized_inputs[ - "image_attention_mask" - ] + image_attention_mask[:, :max_input_length, :] = tokenized_inputs[ + "image_attention_mask" + ] position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1 position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1) @@ -677,19 +681,22 @@ class IdeficsCausalLM(Model): start = time.time_ns() # slice the attention mask to the correct shape attention_mask = batch.attention_mask[:, : -batch.padding_right_offset] - if batch.input_ids.size(1) == 1: - # THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images), - # but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension - # this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated - # token need to attend to the encoder hidden states (i.e. the vision encoder) - # Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic - image_attention_mask = batch.image_attention_mask[ - :, -(batch.padding_right_offset + 1) - ].unsqueeze(1) + if batch.image_attention_mask is None: + image_attention_mask = None else: - image_attention_mask = batch.image_attention_mask[ - :, : -batch.padding_right_offset - ] + if batch.input_ids.size(1) == 1: + # THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images), + # but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension + # this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated + # token need to attend to the encoder hidden states (i.e. the vision encoder) + # Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic + image_attention_mask = batch.image_attention_mask[ + :, -(batch.padding_right_offset + 1) + ].unsqueeze(1) + else: + image_attention_mask = batch.image_attention_mask[ + :, : -batch.padding_right_offset + ] logits, speculative_logits, past, image_hidden_states = self.forward( input_ids=batch.input_ids, diff --git a/server/text_generation_server/models/llava_next.py b/server/text_generation_server/models/llava_next.py new file mode 100644 index 00000000..0ae1b46d --- /dev/null +++ b/server/text_generation_server/models/llava_next.py @@ -0,0 +1,36 @@ +import torch + +from typing import Optional + +from transformers import ( + AutoProcessor, +) +from text_generation_server.models.custom_modeling.llava_next import ( + LlavaNextForConditionalGeneration, +) + +from text_generation_server.models.vlm_causal_lm import VlmCausalLM + + +class LlavaNext(VlmCausalLM): + def __init__( + self, + model_id: str, + revision: Optional[str] = None, + quantize: Optional[str] = None, + use_medusa: Optional[str] = None, + dtype: Optional[torch.dtype] = None, + trust_remote_code: bool = False, + ): + self.processor = AutoProcessor.from_pretrained( + model_id, revision=revision, trust_remote_code=trust_remote_code + ) + super().__init__( + model_cls=LlavaNextForConditionalGeneration, + model_id=model_id, + revision=revision, + quantize=quantize, + use_medusa=use_medusa, + dtype=dtype, + trust_remote_code=trust_remote_code, + ) diff --git a/server/text_generation_server/models/vlm_causal_lm.py b/server/text_generation_server/models/vlm_causal_lm.py new file mode 100644 index 00000000..16042fc9 --- /dev/null +++ b/server/text_generation_server/models/vlm_causal_lm.py @@ -0,0 +1,329 @@ +import re +import torch +import math +from PIL import Image +from io import BytesIO +import base64 + +from opentelemetry import trace +from typing import Optional, Tuple, List, Type, Dict + +from transformers import PreTrainedTokenizerBase +from transformers.image_processing_utils import select_best_resolution +from text_generation_server.pb import generate_pb2 +from text_generation_server.models.flash_mistral import ( + BaseFlashMistral, + FlashMistralBatch, +) +from text_generation_server.models.cache_manager import ( + get_cache_manager, +) + +tracer = trace.get_tracer(__name__) + +IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)") + + +def split(string) -> List[Dict[str, str]]: + parts = [] + cursor = 0 + for pattern in IMAGES.finditer(string): + start = pattern.start() + if start != cursor: + parts.append({"type": "text", "content": string[cursor:start]}) + + parts.append({"type": "image", "content": pattern.group(1)}) + cursor = pattern.end() + + if cursor != len(string): + parts.append({"type": "text", "content": string[cursor:]}) + + return parts + + +def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size): + """ + Calculate the shape of the image patch grid after the preprocessing for images of any resolution. + + Args: + image_size (`tuple`): + The size of the input image in the format (width, height). + grid_pinpoints (`List`): + A list containing possible resolutions. Each item in the list should be a tuple or list + of the form `(height, width)`. + patch_size (`int`): + The size of each image patch. + + Returns: + tuple: The shape of the image patch grid in the format (width, height). + """ + if not isinstance(grid_pinpoints, list): + raise ValueError("grid_pinpoints should be a list of tuples or lists") + + height, width = select_best_resolution(image_size, grid_pinpoints) + return height // patch_size, width // patch_size + + +def get_number_of_features(height: int, width: int, config) -> int: + # From config + # Hardcoded for CLIP for now + # image_grid_pinpoints = [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]] + image_grid_pinpoints = config.image_grid_pinpoints + image_size = config.vision_config.image_size + patch_size = config.vision_config.patch_size + + assert image_size % patch_size == 0 + + npatches = image_size // patch_size + + num_patch_height, num_patch_width = get_anyres_image_grid_shape( + [height, width], + image_grid_pinpoints, + image_size, + ) + + height_of_patch = math.ceil(height / width * npatches) + + unpadded_features = npatches * height_of_patch * num_patch_height * num_patch_width + # They are only added after width + newline_features = height_of_patch * num_patch_width + # The base patch covers the entire image + base_features = npatches**2 + return unpadded_features + newline_features + base_features + + +def load_data_uri(image_uri: str) -> Image.Image: + image_uri = image_uri.split(",")[-1] + content = base64.b64decode(image_uri) + image = Image.open(BytesIO(content)) + return image + + +# assert get_number_of_features(889, 1024) == 2634, f"{get_number_of_features(889, 1024)}" +# assert get_number_of_features(640, 640) == 2928 + + +class VlmCausalLMBatch(FlashMistralBatch): + pixel_values: Optional[List[torch.Tensor]] + image_sizes: Optional[List[Tuple[int, int]]] + + @classmethod + @tracer.start_as_current_span("concatenate") + def concatenate(cls, batches): + batch = super(VlmCausalLMBatch, cls).concatenate(batches) + batch.pixel_values = None + batch.image_sizes = None + return batch + + @tracer.start_as_current_span("filter") + def filter(self, request_ids: List[int]): + batch = super().filter(request_ids) + batch.pixel_values = None + batch.image_sizes = None + return batch + + @classmethod + def batch_tokenized_inputs(cls, requests, tokenizer, processor, config): + batch_inputs = [] + image_inputs = [] + max_truncation = 0 + for r in requests: + chunks = split(r.inputs) + full_text = "" + for chunk in chunks: + if chunk["type"] == "text": + full_text += chunk["content"] + elif chunk["type"] == "image": + image = chunk["content"] + # Should never receive URLs anymore, processing should be done + # On the rust layer. + # This avoid making n queries per TP + # if image.startswith("https://") or image.startswith("http://"): + # image = processor.image_processor.fetch_images(image) + if image.startswith("data:"): + image = load_data_uri(image) + else: + raise RuntimeError( + "Cannot process input image not starting with data:" + ) + image_input = processor.image_processor(image, return_tensors="pt") + height, width = image_input["image_sizes"][0] + num_features = get_number_of_features(height, width, config) + full_text += "" * num_features + image_inputs.append(image_input) + else: + raise RuntimeError(f"Invalid chunk type {chunk['type']}") + + batch_inputs.append(full_text) + max_truncation = max(max_truncation, r.truncate) + + batch_tokenized_inputs = tokenizer( + batch_inputs, truncation=True, max_length=max_truncation + )["input_ids"] + if image_inputs: + image_inputs = { + "pixel_values": torch.cat( + [img["pixel_values"] for img in image_inputs], dim=0 + ), + "image_sizes": torch.cat([img["image_sizes"] for img in image_inputs]), + } + else: + image_inputs = None + return batch_tokenized_inputs, image_inputs + + @classmethod + def from_pb_processor( + cls, + pb: generate_pb2.Batch, + tokenizer: PreTrainedTokenizerBase, + processor, + config, + dtype: torch.dtype, + device: torch.device, + ) -> "VlmCausalLMBatch": + batch_tokenized_inputs, image_inputs = cls.batch_tokenized_inputs( + pb.requests, tokenizer, processor, config + ) + batch = cls.from_tokenized(pb, tokenizer, batch_tokenized_inputs, dtype, device) + if image_inputs is not None: + batch.pixel_values = image_inputs["pixel_values"].to(device=device) + batch.image_sizes = image_inputs["image_sizes"].to(device=device) + else: + batch.pixel_values = None + batch.image_sizes = None + return batch + + +class VlmCausalLM(BaseFlashMistral): + @property + def batch_type(self) -> Type[VlmCausalLMBatch]: + return VlmCausalLMBatch + + def get_layer_config(self, model) -> Tuple[int, int, int]: + return ( + len(model.language_model.model.layers), + model.language_model.model.num_key_value_heads, + model.language_model.model.head_size, + ) + + def max_past(self) -> Optional[int]: + return getattr(self.model.language_model, "max_past", None) + + def forward( + self, batch: VlmCausalLMBatch + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + # Model Forward + if batch.speculative_ids is not None: + input_ids = batch.input_ids + position_ids = batch.position_ids + cu_seqlen_prefill = batch.cu_seqlen_prefill + kv_cache = get_cache_manager().kv_cache + block_tables = batch.block_tables_tensor + slots = batch.slots[batch.slot_indices] + input_lengths = batch.input_lengths_tensor + max_s = batch.max_seqlen + lm_head_indices = batch.prefill_head_indices + + speculative_ids = batch.speculative_ids + + B, speculative_length = speculative_ids.shape + new_length = speculative_length + 1 + new_input_ids = torch.cat( + [input_ids.unsqueeze(-1), speculative_ids], dim=1 + ).reshape(-1) + arange = torch.arange(new_length, device=position_ids.device).unsqueeze(0) + arange_int = arange.to(dtype=torch.int32) + new_position_ids = ( + position_ids.unsqueeze(-1).expand(B, new_length) + arange + ).view(-1) + slots = (slots.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1) + input_lengths = ( + input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int + ).view(-1) + + # Add Copy the block tables for all members + block_tables = ( + block_tables.unsqueeze(1) + .expand(B, new_length, -1) + .reshape(B * new_length, -1) + .contiguous() + ) + max_s = max_s + speculative_length + + input_ids = new_input_ids + position_ids = new_position_ids + else: + input_ids = batch.input_ids + position_ids = batch.position_ids + cu_seqlen_prefill = batch.cu_seqlen_prefill + kv_cache = get_cache_manager().kv_cache + block_tables = batch.block_tables_tensor + slots = batch.slots[batch.slot_indices] + input_lengths = batch.input_lengths_tensor + max_s = batch.max_seqlen + lm_head_indices = batch.prefill_head_indices + + if cu_seqlen_prefill is None and self.max_past() is not None: + # In decode, not prefill, we're actually overwriting the KV-cache + # in a circular buffer mode. + # This makes sure the max_s for the decode pass is correct. + max_s = min(self.max_past(), max_s) + + bs = input_ids.shape[0] + padded_bs = bs + if bs == 3: + padded_bs = 4 + elif 3 < bs <= 8: + padded_bs = 8 + elif bs > 8: + padded_bs = (bs + 7) // 8 * 8 + + # Try to find an associated cuda graph + cuda_graph = self.cuda_graphs.get(padded_bs, None) + + if cu_seqlen_prefill is not None or cuda_graph is None: + logits, speculative_logits = self.model.forward( + input_ids=input_ids, + position_ids=position_ids, + cu_seqlen_prefill=cu_seqlen_prefill, + kv_cache=kv_cache, + block_tables=block_tables, + slots=slots, + input_lengths=input_lengths, + max_s=max_s, + prefill_cache_indices=batch.prefill_cache_indices, + lm_head_indices=lm_head_indices, + pixel_values=batch.pixel_values, + image_sizes=batch.image_sizes, + ) + if batch.prefill_cache_indices is not None: + batch.prefill_cache_indices = None + if batch.pixel_values is not None: + batch.pixel_values = None + if batch.image_sizes is not None: + batch.image_sizes = None + return logits, speculative_logits + + # Copy inputs to the static inputs of the cuda graph + # Static inputs are potentially padded + cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids + cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids + cuda_graph["block_tables"][ + : block_tables.shape[0], : block_tables.shape[1] + ] = block_tables + cuda_graph["slots"].fill_(-1) + cuda_graph["slots"][: slots.shape[0]] = slots + cuda_graph["input_lengths"].zero_() + cuda_graph["input_lengths"][: input_lengths.shape[0]] = input_lengths + + # Replay the graph + cuda_graph["graph"].replay() + + # Slice output to the correct shape + speculative_logits = ( + cuda_graph["speculative_logits"][:bs] + if cuda_graph["speculative_logits"] is not None + else None + ) + logits = cuda_graph["logits"][:bs] + return logits, speculative_logits diff --git a/server/text_generation_server/server.py b/server/text_generation_server/server.py index d5adbd32..495c2c0c 100644 --- a/server/text_generation_server/server.py +++ b/server/text_generation_server/server.py @@ -13,6 +13,7 @@ from typing import List, Optional from text_generation_server.cache import Cache from text_generation_server.interceptor import ExceptionInterceptor from text_generation_server.models import Model, get_model +from text_generation_server.models.vlm_causal_lm import VlmCausalLMBatch from text_generation_server.pb import generate_pb2_grpc, generate_pb2 from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch @@ -78,13 +79,15 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer): except ImportError: pass - if ( - self.model.batch_type == IdeficsCausalLMBatch - ): # Hack, i would rather use kwargs in the `from_pb` call - batch = self.model.batch_type.from_pb( + if self.model.batch_type in { + IdeficsCausalLMBatch, + VlmCausalLMBatch, + }: # Hack, i would rather use kwargs in the `from_pb` call + batch = self.model.batch_type.from_pb_processor( request.batch, self.model.tokenizer, self.model.processor, + self.model.model.config, self.model.dtype, self.model.device, ) @@ -100,13 +103,15 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer): async def Prefill(self, request, context): start = time.time_ns() - if ( - self.model.batch_type == IdeficsCausalLMBatch - ): # Hack, i would rather use kwargs in the `from_pb` call - batch = self.model.batch_type.from_pb( + if self.model.batch_type in { + IdeficsCausalLMBatch, + VlmCausalLMBatch, + }: # Hack, i would rather use kwargs in the `from_pb` call + batch = self.model.batch_type.from_pb_processor( request.batch, self.model.tokenizer, self.model.processor, + self.model.model.config, self.model.dtype, self.model.device, ) From ad9d6288c843735df2c8a9f6c6289fed74268f38 Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Wed, 10 Apr 2024 17:20:25 +0200 Subject: [PATCH 12/74] fix: fix CohereForAI/c4ai-command-r-plus (#1707) @Narsil @drbh this will update flash attention v2 and vllm. You will need to re-install them. --- Dockerfile | 13 +- launcher/src/main.rs | 8 + router/src/infer.rs | 78 ++--- router/src/lib.rs | 27 +- server/Makefile | 3 - server/Makefile-flash-att-v2 | 2 +- server/Makefile-vllm | 4 +- .../models/cache_manager.py | 2 +- .../custom_modeling/flash_cohere_modeling.py | 188 ++++++++---- .../custom_modeling/flash_dbrx_modeling.py | 270 ++---------------- .../custom_modeling/flash_mixtral_modeling.py | 263 ++--------------- .../models/flash_causal_lm.py | 7 +- .../models/flash_cohere.py | 7 +- .../models/flash_mistral.py | 2 +- .../utils/flash_attn.py | 3 + server/text_generation_server/utils/layers.py | 9 +- .../utils/paged_attention.py | 15 +- 17 files changed, 281 insertions(+), 620 deletions(-) diff --git a/Dockerfile b/Dockerfile index e79372a3..0bc5f8d9 100644 --- a/Dockerfile +++ b/Dockerfile @@ -85,7 +85,7 @@ FROM pytorch-install as kernel-builder ARG MAX_JOBS=8 RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - ninja-build \ + ninja-build cmake \ && rm -rf /var/lib/apt/lists/* # Build Flash Attention CUDA kernels @@ -160,11 +160,6 @@ WORKDIR /usr/src COPY server/Makefile-selective-scan Makefile RUN make build-all -# Build megablocks -FROM kernel-builder as megablocks-builder - -RUN pip install git+https://github.com/OlivierDehaene/megablocks@181709df192de9a941fdf3a641cdc65a0462996e - # Text Generation Inference base image FROM nvidia/cuda:12.1.0-base-ubuntu22.04 as base @@ -186,8 +181,8 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins curl \ && rm -rf /var/lib/apt/lists/* -# Copy conda with PyTorch and Megablocks installed -COPY --from=megablocks-builder /opt/conda /opt/conda +# Copy conda with PyTorch installed +COPY --from=pytorch-install /opt/conda /opt/conda # Copy build artifacts from flash attention builder COPY --from=flash-att-builder /usr/src/flash-attention/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages @@ -215,7 +210,7 @@ COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-310 /opt/c COPY --from=mamba-builder /usr/src/mamba/build/lib.linux-x86_64-cpython-310/ /opt/conda/lib/python3.10/site-packages COPY --from=mamba-builder /usr/src/causal-conv1d/build/lib.linux-x86_64-cpython-310/ /opt/conda/lib/python3.10/site-packages -# Install flash-attention dependencies +# Install vllm/flash-attention dependencies RUN pip install einops --no-cache-dir # Install server diff --git a/launcher/src/main.rs b/launcher/src/main.rs index aef09433..3f810023 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -499,6 +499,9 @@ fn shard_manager( // Copy current process env let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect(); + // Remove LOG_LEVEL if present + envs.retain(|(name, _)| name != "LOG_LEVEL"); + // Torch Distributed Env vars envs.push(("RANK".into(), rank.to_string().into())); envs.push(("WORLD_SIZE".into(), world_size.to_string().into())); @@ -586,6 +589,7 @@ fn shard_manager( tracing::info!("Starting shard"); let mut p = match Command::new("text-generation-server") .args(shard_args) + .env_clear() .envs(envs) .stdout(Stdio::piped()) .stderr(Stdio::piped()) @@ -824,6 +828,9 @@ fn download_convert_model(args: &Args, running: Arc) -> Result<(), L // Copy current process env let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect(); + // Remove LOG_LEVEL if present + envs.retain(|(name, _)| name != "LOG_LEVEL"); + // Disable progress bar envs.push(("HF_HUB_DISABLE_PROGRESS_BARS".into(), "1".into())); @@ -858,6 +865,7 @@ fn download_convert_model(args: &Args, running: Arc) -> Result<(), L tracing::info!("Starting download process."); let mut download_process = match Command::new("text-generation-server") .args(download_args) + .env_clear() .envs(envs) .stdout(Stdio::piped()) .stderr(Stdio::piped()) diff --git a/router/src/infer.rs b/router/src/infer.rs index e5517511..075e76d8 100644 --- a/router/src/infer.rs +++ b/router/src/infer.rs @@ -1,8 +1,8 @@ /// Batching and inference logic use crate::validation::{Validation, ValidationError}; use crate::{ - ChatTemplateInputs, Entry, GenerateRequest, GenerateStreamResponse, HubTokenizerConfig, - Message, PrefillToken, Queue, Token, + ChatTemplateInputs, ChatTemplateVersions, Entry, GenerateRequest, GenerateStreamResponse, + HubTokenizerConfig, Message, PrefillToken, Queue, Token, }; use futures::future::try_join_all; use minijinja::{Environment, ErrorKind, Template}; @@ -86,7 +86,18 @@ impl Infer { let chat_template = tokenizer_config .chat_template - .map(|t| ChatTemplate::new(t, tokenizer_config.bos_token, tokenizer_config.eos_token)); + .and_then(|t| match t { + ChatTemplateVersions::Single(template) => Some(template), + ChatTemplateVersions::Multiple(templates) => templates + .into_iter() + .find(|t| t.name == "default") + .map(|t| t.template), + }) + .map(|t| { + // .strip() is not supported in minijinja + let t = t.replace(".strip()", " | trim"); + ChatTemplate::new(t, tokenizer_config.bos_token, tokenizer_config.eos_token) + }); // Inference limit with a semaphore let semaphore = Arc::new(Semaphore::new(max_concurrent_requests)); @@ -1099,7 +1110,7 @@ mod tests { ChatTemplateTestItem { name: "_base", chat_template: "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}", - input: ChatTemplateInputs{ + input: ChatTemplateInputs { messages: example_chat.clone(), add_generation_prompt: false, bos_token: Some(""), @@ -1110,7 +1121,7 @@ mod tests { ChatTemplateTestItem { name: "blenderbot", chat_template: "{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}", - input: ChatTemplateInputs{ + input: ChatTemplateInputs { messages: example_chat.clone(), add_generation_prompt: false, bos_token: Some(""), @@ -1121,7 +1132,7 @@ mod tests { ChatTemplateTestItem { name: "blenderbot_small", chat_template: "{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}", - input: ChatTemplateInputs{ + input: ChatTemplateInputs { messages: example_chat.clone(), add_generation_prompt: false, bos_token: Some(""), @@ -1132,7 +1143,7 @@ mod tests { ChatTemplateTestItem { name: "bloom", chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}", - input: ChatTemplateInputs{ + input: ChatTemplateInputs { messages: example_chat.clone(), add_generation_prompt: false, bos_token: Some(""), @@ -1143,7 +1154,7 @@ mod tests { ChatTemplateTestItem { name: "gpt_neox", chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}", - input: ChatTemplateInputs{ + input: ChatTemplateInputs { messages: example_chat.clone(), add_generation_prompt: false, bos_token: Some(""), @@ -1154,38 +1165,37 @@ mod tests { ChatTemplateTestItem { name: "gpt2", chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}", - input: ChatTemplateInputs{ - messages: example_chat.clone(), - add_generation_prompt: false, - bos_token: Some(""), - eos_token: Some("<|endoftext|>"), + input: ChatTemplateInputs { + messages: example_chat.clone(), + add_generation_prompt: false, + bos_token: Some(""), + eos_token: Some("<|endoftext|>"), }, - target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>" + target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>", }, ChatTemplateTestItem { name: "llama", // NOTE: the `.strip()` has been replaced with `| trim` in the following template chat_template: "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\\n' + system_message + '\\n<>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token +'[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<>\\n' + content | trim + '\\n<>\\n\\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + eos_token }}{% endif %}{% endfor %}", - input: ChatTemplateInputs{ - messages: example_chat_with_system.clone(), - add_generation_prompt: true, - bos_token: Some(""), - eos_token: Some(""), + input: ChatTemplateInputs { + messages: example_chat_with_system.clone(), + add_generation_prompt: true, + bos_token: Some(""), + eos_token: Some(""), }, - target: "[INST] <>\nYou are a friendly chatbot who always responds in the style of a pirate\n<>\n\nHello, how are you? [/INST] I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]" + target: "[INST] <>\nYou are a friendly chatbot who always responds in the style of a pirate\n<>\n\nHello, how are you? [/INST] I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]", }, ChatTemplateTestItem { name: "whisper", chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}", - input: ChatTemplateInputs{ - messages: example_chat.clone(), - add_generation_prompt: true, - bos_token: Some(""), - eos_token: Some("<|endoftext|>"), + input: ChatTemplateInputs { + messages: example_chat.clone(), + add_generation_prompt: true, + bos_token: Some(""), + eos_token: Some("<|endoftext|>"), }, - target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>" - } - + target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>", + }, ]; #[allow(unused_variables)] // name is unused @@ -1211,7 +1221,7 @@ mod tests { messages: example_chat_with_system.clone(), add_generation_prompt: false, bos_token: Some(""), - eos_token: Some("") + eos_token: Some(""), }, target: "<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate<|user|>\nHello, how are you?<|assistant|>\nI'm doing great. How can I help you today?<|user|>\nI'd like to show off how chat templating works!", }, @@ -1237,7 +1247,7 @@ mod tests { bos_token: Some(""), eos_token: Some(""), }, - target: "<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate<|user|>\nHow many helicopters can a human eat in one sitting?<|assistant|>" + target: "<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate<|user|>\nHow many helicopters can a human eat in one sitting?<|assistant|>", }, ChatTemplateTestItem { name: "HuggingFaceH4/zephyr-7b-gemma-v0.1", @@ -1259,7 +1269,7 @@ mod tests { bos_token: Some(""), eos_token: Some(""), }, - target: "[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]" + target: "[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]", }, ChatTemplateTestItem { name: "mistralai/Mixtral-8x7B-Instruct-v0.1", @@ -1276,7 +1286,7 @@ mod tests { name: "cognitivecomputations/dolphin-2.5-mixtral-8x7b", chat_template: "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}", input: ChatTemplateInputs { - messages: example_chat.clone(), + messages: example_chat.clone(), add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), @@ -1360,7 +1370,7 @@ mod tests { bos_token: Some(""), eos_token: Some(""), }, - target: "<|prompt|>Hello, how are you?<|answer|>I'm doing great. How can I help you today?<|prompt|>I'd like to show off how chat templating works!" + target: "<|prompt|>Hello, how are you?<|answer|>I'm doing great. How can I help you today?<|prompt|>I'd like to show off how chat templating works!", }, ChatTemplateTestItem { name: "internlm/internlm2-chat-7b", @@ -1443,7 +1453,7 @@ mod tests { eos_token: Some(""), }, target: "You are a friendly chatbot who always responds in the style of a pirateYou are a friendly chatbot who always responds in the style of a pirate### Instruction: Hello, how are you?### Response: I'm doing great. How can I help you today?### Instruction: I'd like to show off how chat templating works!", - } + }, ]; #[allow(unused_variables)] // name is unused diff --git a/router/src/lib.rs b/router/src/lib.rs index c787470b..2e412f1a 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -49,9 +49,22 @@ pub struct HubModelInfo { pub pipeline_tag: Option, } -#[derive(Clone, Deserialize, Default)] +#[derive(Debug, Clone, Deserialize, PartialEq)] +pub struct ChatTemplate { + name: String, + template: String, +} + +#[derive(Debug, Clone, Deserialize, PartialEq)] +#[serde(untagged)] +pub enum ChatTemplateVersions { + Single(String), + Multiple(Vec), +} + +#[derive(Debug, Clone, Deserialize, Default)] pub struct HubTokenizerConfig { - pub chat_template: Option, + pub chat_template: Option, pub completion_template: Option, #[serde(deserialize_with = "token_serde::deserialize")] pub bos_token: Option, @@ -978,7 +991,10 @@ mod tests { let config: HubTokenizerConfig = serde_json::from_str(json_content).unwrap(); // check that we successfully parsed the tokens - assert_eq!(config.chat_template, Some("test".to_string())); + assert_eq!( + config.chat_template, + Some(ChatTemplateVersions::Single("test".to_string())) + ); assert_eq!( config.bos_token, Some("<｜begin▁of▁sentence｜>".to_string()) @@ -1010,7 +1026,10 @@ mod tests { let config: HubTokenizerConfig = serde_json::from_str(json_content).unwrap(); // check that we successfully parsed the tokens - assert_eq!(config.chat_template, Some("test".to_string())); + assert_eq!( + config.chat_template, + Some(ChatTemplateVersions::Single("test".to_string())) + ); assert_eq!( config.bos_token, Some("<｜begin▁of▁sentence｜>".to_string()) diff --git a/server/Makefile b/server/Makefile index da5171b2..32d01709 100644 --- a/server/Makefile +++ b/server/Makefile @@ -17,9 +17,6 @@ gen-server: find text_generation_server/pb/ -type f -name "*.py" -print0 -exec sed -i -e 's/^\(import.*pb2\)/from . \1/g' {} \; touch text_generation_server/pb/__init__.py -install-megablocks: - pip install git+https://github.com/OlivierDehaene/megablocks@181709df192de9a941fdf3a641cdc65a0462996e - install: gen-server pip install pip --upgrade pip install -r requirements_cuda.txt diff --git a/server/Makefile-flash-att-v2 b/server/Makefile-flash-att-v2 index 71c6cabe..803b3d1f 100644 --- a/server/Makefile-flash-att-v2 +++ b/server/Makefile-flash-att-v2 @@ -1,4 +1,4 @@ -flash_att_v2_commit_cuda := 02ac572f3ffc4f402e4183aaa6824b45859d3ed3 +flash_att_v2_commit_cuda := 23e8fa5a263d1c7122bc46a86ef32030ee7130f9 flash_att_v2_commit_rocm := 8736558c287ff2ef28b24878e42828c595ac3e69 diff --git a/server/Makefile-vllm b/server/Makefile-vllm index c9c1d520..ada484a6 100644 --- a/server/Makefile-vllm +++ b/server/Makefile-vllm @@ -1,10 +1,10 @@ vllm-cuda: # Clone vllm pip install -U ninja packaging --no-cache-dir - git clone https://github.com/vllm-project/vllm.git vllm + git clone https://github.com/OlivierDehaene/vllm.git vllm build-vllm-cuda: vllm-cuda - cd vllm && git fetch && git checkout f8a1e39fae05ca610be8d5a78be9d40f5274e5fc + cd vllm && git fetch && git checkout 4bec8cee87f6bb8cebaec297029713cd2082e0b2 cd vllm && python setup.py build install-vllm-cuda: build-vllm-cuda diff --git a/server/text_generation_server/models/cache_manager.py b/server/text_generation_server/models/cache_manager.py index 2e6ae086..4be8b1b9 100644 --- a/server/text_generation_server/models/cache_manager.py +++ b/server/text_generation_server/models/cache_manager.py @@ -43,7 +43,7 @@ class CacheManager: ] self.free_block_mask = torch.ones(num_blocks, dtype=torch.int32, device="cpu") self.slots = torch.arange( - 0, num_blocks * self.block_size, dtype=torch.int32 + 0, num_blocks * self.block_size, dtype=torch.int64 ).view(num_blocks, self.block_size) def allocate( diff --git a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py index 985bbd8e..56d9a966 100644 --- a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py @@ -23,10 +23,10 @@ import torch.distributed from torch import nn from transformers.activations import ACT2FN -from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn +from text_generation_server.utils.import_utils import IS_ROCM_SYSTEM, IS_CUDA_SYSTEM from text_generation_server.utils.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, @@ -34,65 +34,106 @@ from text_generation_server.utils.layers import ( PositionRotaryEmbedding, SpeculativeHead, get_linear, - FastRMSNorm, + FastLayerNorm, ) +if IS_CUDA_SYSTEM: + import dropout_layer_norm +else: + dropout_layer_norm = None -class CohereConfig(PretrainedConfig): - def __init__( + +class CohereRotary(PositionRotaryEmbedding): + def forward( self, - vocab_size=256000, - hidden_size=8192, - intermediate_size=22528, - num_hidden_layers=40, - num_attention_heads=64, - num_key_value_heads=None, - hidden_act="silu", - max_position_embeddings=8192, - initializer_range=0.02, - layer_norm_eps=1e-5, - use_cache=True, - pad_token_id=0, - bos_token_id=5, - eos_token_id=255001, - pretraining_tp=1, - tie_word_embeddings=True, - rope_theta=10000.0, - attention_bias=False, - attention_dropout=0.0, - logit_scale=1.0, - **kwargs, + query: torch.Tensor, + key: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, ): - self.vocab_size = vocab_size - self.max_position_embeddings = max_position_embeddings - self.hidden_size = hidden_size - self.intermediate_size = intermediate_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads + # Such controlflows may add some overhead. + if IS_CUDA_SYSTEM: + import rotary_emb - # for backward compatibility - if num_key_value_heads is None: - num_key_value_heads = num_attention_heads + q1 = query[..., ::2] + q2 = query[..., 1::2] - self.num_key_value_heads = num_key_value_heads - self.hidden_act = hidden_act - self.initializer_range = initializer_range - self.layer_norm_eps = layer_norm_eps - self.pretraining_tp = pretraining_tp - self.use_cache = use_cache - self.rope_theta = rope_theta - self.attention_bias = attention_bias - self.attention_dropout = attention_dropout - self.logit_scale = logit_scale + rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False) - super().__init__( - pad_token_id=pad_token_id, - bos_token_id=bos_token_id, - eos_token_id=eos_token_id, - tie_word_embeddings=tie_word_embeddings, - **kwargs, + k1 = key[..., ::2] + k2 = key[..., 1::2] + + rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False) + elif IS_ROCM_SYSTEM: + from vllm import pos_encoding_ops + + # NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems. + # Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773 + + head_size = query.shape[-1] + + # Inplace operation, updating query and key. + pos_encoding_ops.rotary_embedding(query, key, head_size, cos, sin, False) + else: + raise ValueError( + "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction." + ) + + +class CohereLayerNorm(nn.Module): + def __init__(self, prefix, weights, eps): + super().__init__() + weight = weights.get_sharded(f"{prefix}.weight", dim=0) + self.weight = nn.Parameter(weight) + # Fake weights + self.ones = weight.new_ones(weight.shape[1]) + self.eps = eps + + def forward(self, hidden_states): + if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM: + hidden_states = hidden_states.reshape( + -1, self.weight.shape[0], self.weight.shape[1] + ) + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + mean = hidden_states.mean(-1, keepdim=True) + hidden_states_minus_mean = hidden_states - mean + variance = hidden_states_minus_mean.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states_minus_mean * torch.rsqrt(variance + self.eps) + hidden_states = self.weight.to(torch.float32) * hidden_states + hidden_states = hidden_states.view(-1, self.weight.shape[1]) + return hidden_states.to(input_dtype) + + ( + hidden_states, + *rest, + ) = dropout_layer_norm.dropout_add_ln_fwd( + hidden_states, + None, + self.ones, + None, + None, + None, + None, + None, + 0.0, + self.eps, + 1.0, + 0, + None, + False, + False, ) + # Required to apply one weight matrix per head + hidden_states = hidden_states.view( + -1, self.weight.shape[0], self.weight.shape[1] + ) + hidden_states = self.weight * hidden_states + hidden_states = hidden_states.view(-1, self.weight.shape[1]) + + return hidden_states + def load_attention(config, prefix, weights): if config.num_attention_heads != config.num_key_value_heads: @@ -154,7 +195,7 @@ class FlashCohereAttention(torch.nn.Module): self.hidden_size = config.hidden_size self.head_size = self.hidden_size // self.num_heads - self.rotary_emb = PositionRotaryEmbedding.static( + self.rotary_emb = CohereRotary.static( config=config, dim=self.head_size, base=config.rope_theta, @@ -175,6 +216,22 @@ class FlashCohereAttention(torch.nn.Module): self.query_key_value = load_attention(config, prefix, weights) + self.use_qk_norm = config.use_qk_norm + if self.use_qk_norm: + self.q_norm = CohereLayerNorm( + prefix=f"{prefix}.q_norm", + weights=weights, + eps=config.layer_norm_eps, + ) + self.k_norm = CohereLayerNorm( + prefix=f"{prefix}.k_norm", + weights=weights, + eps=config.layer_norm_eps, + ) + else: + self.q_norm = None + self.k_norm = None + self.o_proj = TensorParallelRowLinear.load( config, prefix=f"{prefix}.o_proj", @@ -199,21 +256,28 @@ class FlashCohereAttention(torch.nn.Module): max_s, ): qkv = self.query_key_value(hidden_states) - query, kv = qkv.split( + query, key, value = qkv.split( [ self.head_size * self.num_heads, - 2 * self.head_size * self.num_key_value_heads, + self.head_size * self.num_key_value_heads, + self.head_size * self.num_key_value_heads, ], dim=1, ) + + if self.use_qk_norm: + query = query.reshape(-1, self.head_size) + key = key.reshape(-1, self.head_size) + query = self.q_norm(query.contiguous()) + key = self.k_norm(key.contiguous()) + query = query.view(-1, self.num_heads, self.head_size) - kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size) + key = key.view(-1, self.num_key_value_heads, self.head_size) + value = value.view(-1, self.num_key_value_heads, self.head_size) - self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin) + self.rotary_emb(query, key, cos, sin) - paged_attention.reshape_and_cache( - kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots - ) + paged_attention.reshape_and_cache(key, value, kv_cache[0], kv_cache[1], slots) # output tensor attn_output = torch.empty_like(query) @@ -223,8 +287,8 @@ class FlashCohereAttention(torch.nn.Module): # flash attention flash_attn.attention( query, - torch.select(kv, dim=1, index=0), - torch.select(kv, dim=1, index=1), + key, + value, attn_output, cu_seqlen_prefill, max_s, @@ -298,7 +362,7 @@ class FlashCohereLayer(nn.Module): ) self.mlp = CohereMLP(prefix=f"{prefix}.mlp", config=config, weights=weights) - self.input_layernorm = FastRMSNorm.load( + self.input_layernorm = FastLayerNorm.load_no_bias( prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.layer_norm_eps, @@ -362,7 +426,7 @@ class FlashCohereModel(torch.nn.Module): for layer_id in range(config.num_hidden_layers) ] ) - self.norm = FastRMSNorm.load( + self.norm = FastLayerNorm.load_no_bias( prefix="model.norm", weights=weights, eps=config.layer_norm_eps ) diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py index dd0bcca5..d04ce39e 100644 --- a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py @@ -16,14 +16,13 @@ import torch import torch.distributed -import numpy as np - from torch import nn from transformers.activations import ACT2FN from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple, Any from loguru import logger +from vllm.model_executor.layers.fused_moe import fused_moe from text_generation_server.utils import paged_attention, flash_attn from text_generation_server.utils.layers import ( FastLinear, @@ -37,14 +36,6 @@ from text_generation_server.utils.layers import ( ) from text_generation_server.utils.log import log_once -HAS_MEGABLOCKS = True -try: - import stk - import megablocks.ops as ops -except ImportError: - logger.warning("Dbrx: megablocks is not installed") - HAS_MEGABLOCKS = False - class DbrxAttentionConfig(PretrainedConfig): def __init__( @@ -531,18 +522,6 @@ def round_up(x: torch.Tensor, value: int): class BlockSparseMoE(nn.Module): - """ - Built on the paper and library Megablocks as described in - https://arxiv.org/abs/2211.15841. This implementation is - strictly equivalent to standard MoE with full capacity (no - dropped tokens). It's faster since it formulates MoE operations - in terms of block-sparse operations to accomodate imbalanced - assignments of tokens to experts, whereas standard MoE either - (1) drop tokens at the cost of reduced performance or (2) set - capacity factor to number of experts and thus waste computation - and memory on padding. - """ - def __init__(self, prefix, config: DbrxConfig, weights): super().__init__() self.moe_normalize_expert_weights = ( @@ -572,241 +551,40 @@ class BlockSparseMoE(nn.Module): ) # merged expert weights, all of size (n_experts * ffn_dim, hidden_dim) - self.w1 = _load_experts(config, f"{prefix}.experts.mlp.w1", weights) - self.w2 = _load_experts(config, f"{prefix}.experts.mlp.w2", weights) - self.v1 = _load_experts(config, f"{prefix}.experts.mlp.v1", weights) - - self.offsets = None - self.offsets_block_rows = 0 + w1 = _load_experts(config, f"{prefix}.experts.mlp.w1", weights).view( + self.num_experts, self.ffn_dim, self.hidden_dim + ) + v1 = _load_experts(config, f"{prefix}.experts.mlp.v1", weights).view( + self.num_experts, self.ffn_dim, self.hidden_dim + ) + self.wv1 = torch.cat([w1, v1], dim=1) + self.w2 = ( + _load_experts(config, f"{prefix}.experts.mlp.w2", weights) + .view(self.num_experts, self.ffn_dim, self.hidden_dim) + .transpose(1, 2) + .contiguous() + ) self.process_group = weights.process_group - # Calculate the number of bits needed to represent the expert indices - # so that we can pass it to radix sort. - self.sort_end_bit = max(int(np.ceil(np.log2(self.num_experts))), 1) - self.blocking = 128 - self.quantize_scatter_num_bits = -1 - - def topology(self, x: torch.Tensor, padded_bins: torch.Tensor): - padded_tokens, _ = x.size() - assert padded_tokens % self.blocking == 0 - assert self.ffn_dim % self.blocking == 0 - - # Offsets for the sparse matrix. All rows have the - # same number of nonzero blocks dictated by the - # dimensionality of a single expert. - block_rows = padded_tokens // self.blocking - blocks_per_row = self.ffn_dim // self.blocking - if self.offsets is None or block_rows > self.offsets_block_rows: - self.offsets = torch.arange( - 0, - block_rows * blocks_per_row + 1, - blocks_per_row, - dtype=torch.int32, - device=x.device, - ) - self.offsets_block_rows = block_rows - offsets = self.offsets - else: - offsets = self.offsets[: block_rows + 1] - - # Indices for the sparse matrix. The indices for - # the intermediate matrix are dynamic depending - # on the mapping of tokens to experts. - column_indices = ops.topology( - padded_bins, self.blocking, block_rows, blocks_per_row - ) - - # For now, use meta init to save the device memory. - data = torch.empty( - column_indices.numel(), - self.blocking, - self.blocking, - dtype=x.dtype, - device="meta", - ) - shape = (padded_tokens, self.ffn_dim * self.num_experts) - row_indices = stk.ops.row_indices(shape, data, offsets, column_indices) - return stk.Matrix( - shape, - data, - row_indices, - column_indices, - offsets, - False, - False, - False, - ) - - def indices_and_padded_bins(self, selected_experts: torch.Tensor): - # Sort the expert ids to produce the scatter/gather - # indices for the permutation. - # selected_experts = selected_experts.int() - - # returns bin_ids == num of experts for this sequence ? == unique selected experts? - # and indices == how to sort tokens? - bin_ids, indices = ops.sort(selected_experts, self.sort_end_bit) - # bin_ids => [0, 0, 0, 2, 2, ...] => [num_tokens * top_k] - # indices => [14, 32, 33, ...] => [num_tokens * top_k] - - # Histogram the expert ids to identify the number of - # tokens routed to each expert. - tokens_per_expert = ops.histogram(selected_experts, self.num_experts) - # tokens_per_expert => [3, 0, 2, ...] => [num_experts] - - # Round the token counts up to the block size used in - # the matrix muliplications. Caculate the starting - # position of each bin. - - # List of size num_experts - padded_tokens_per_expert = round_up(tokens_per_expert, self.blocking) - # padded_tokens_per_expert => [128, O, 128, ...] - - # Cumulative selected experts per token - padded_bins = ops.inclusive_cumsum(padded_tokens_per_expert, 0) - padded_bins = promote_scalar(padded_bins) - # padded_bins => [128, 128, 256, ...] - - # Calculate the bin bounds for the sorted tokens. - bins = ops.inclusive_cumsum(tokens_per_expert, 0) - bins = promote_scalar(bins) - # bins => [3, 3, 5, ...] - - return indices, bin_ids, bins, padded_bins, tokens_per_expert - - def sparse_forward(self, x: torch.Tensor) -> torch.Tensor: - """ - x: (sequence_length, model_dim) - gate_logits: (sequence_length, n_experts) - """ - # optional reshape - input_shape = x.shape - x = x.view(-1, input_shape[-1]) - - # gate_logits: (sequence_length, n_experts) - gate_logits = self.gate(x) - selected_experts, weights = select_experts( - gate_logits, self.top_k, self.moe_normalize_expert_weights - ) - - ( - indices, - bin_ids, - bins, - padded_bins, - _, - ) = self.indices_and_padded_bins(selected_experts) - - # Permute tokens and pad to prepare expert computation - # (top_k * sequence_length + padding, model_dim) - x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins, self.top_k) - - # Create the sparse matrix topology - with torch.no_grad(): - topo = self.topology(x, padded_bins) - - # Perform the expert computation - # First Dense x Dense -> Sparse for w1 and v1, - # (top_k * sequence_length + padding, ffn_dim * n_experts) - x = stk.Matrix( - topo.size(), - self.act(stk.ops.sdd(x, self.w1.t(), topo).data) - * stk.ops.sdd(x, self.v1.t(), topo).data, - topo.row_indices, - topo.column_indices, - topo.offsets, - topo.column_indices_t, - topo.offsets_t, - topo.block_offsets_t, - ) - - # Then Sparse x Dense -> Dense for w2 - # (top_k * sequence_length + padding, model_dim) - x = stk.ops.dsd(x, self.w2) - - # Permute back and remove padding - # (sequence_length, model_dim) - x = ops.padded_scatter( + def forward(self, x: torch.Tensor) -> torch.Tensor: + # router_logits: (num_tokens, n_experts) + router_logits = self.gate(x) + out = fused_moe( x, - indices, - bin_ids, - weights, - bins, - padded_bins, + self.wv1, + self.w2, + router_logits, self.top_k, - self.quantize_scatter_num_bits, - ).view(*input_shape) - - if self.process_group.size() > 1: - torch.distributed.all_reduce(x, group=self.process_group) - - return x.view(*input_shape) - - def dense_forward(self, x: torch.Tensor) -> torch.Tensor: - """ - x: (sequence_length, model_dim) - gate_logits: (sequence_length, n_experts) - """ - # optional reshape - input_shape = x.shape - x = x.view(-1, input_shape[-1]) - - # gate_logits: (sequence_length, n_experts) - gate_logits = self.gate(x) - # all_probs: (sequence_length, n_experts) and upcast for softmax - weights = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float) - - if self.top_k < self.num_experts: - _, not_selected_experts = torch.topk( - weights, - self.num_experts - self.top_k, - largest=False, - sorted=False, - dim=1, - ) - # Mask not selected experts - weights.scatter_(1, not_selected_experts, 0) - - # Re-normalize - if self.moe_normalize_expert_weights: - weights = weights / torch.norm( - weights, p=self.moe_normalize_expert_weights, dim=-1, keepdim=True - ) - weights = weights.to(x.dtype) - - # Expand to [num_experts, sequence_length, model_dim] - x = x.view(1, -1, input_shape[-1]).expand(self.num_experts, -1, input_shape[-1]) - - # Permute to [num_experts, model_dim, ffn_dim] - w1 = self.w1.view(self.num_experts, self.ffn_dim, self.hidden_dim).permute( - 0, 2, 1 + renormalize=self.moe_normalize_expert_weights, + inplace=True, ) - v1 = self.v1.view(self.num_experts, self.ffn_dim, self.hidden_dim).permute( - 0, 2, 1 - ) - - inter = self.act(torch.bmm(x, w1)) * torch.bmm(x, v1) - - out = torch.bmm( - inter, self.w2.view(self.num_experts, self.ffn_dim, self.hidden_dim) - ) - # Mask not selected experts - out *= weights.t().view(self.num_experts, -1, 1) - - # Sum experts - out = out.sum(0) # Reduce sum if self.process_group.size() > 1: torch.distributed.all_reduce(out, group=self.process_group) - return out - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if len(x) > 256 and HAS_MEGABLOCKS: - return self.sparse_forward(x) - # This is faster when there is not a lot of tokens - return self.dense_forward(x) + return out.view(*x.shape) class DenseMoE(nn.Module): diff --git a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py index d71a3f0c..89eb8f43 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py @@ -24,6 +24,7 @@ import torch.distributed import numpy as np from torch import nn +from vllm.model_executor.layers.fused_moe import fused_moe from transformers.activations import ACT2FN from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple @@ -41,14 +42,6 @@ from text_generation_server.utils.layers import ( get_linear, ) -HAS_MEGABLOCKS = True -try: - import stk - import megablocks.ops as ops -except ImportError: - logger.warning("Mixtral: megablocks is not installed") - HAS_MEGABLOCKS = False - class MixtralConfig(PretrainedConfig): model_type = "mixtral" @@ -321,18 +314,6 @@ def round_up(x: torch.Tensor, value: int): class BlockSparseMoE(nn.Module): - """ - Built on the paper and library Megablocks as described in - https://arxiv.org/abs/2211.15841. This implementation is - strictly equivalent to standard MoE with full capacity (no - dropped tokens). It's faster since it formulates MoE operations - in terms of block-sparse operations to accomodate imbalanced - assignments of tokens to experts, whereas standard MoE either - (1) drop tokens at the cost of reduced performance or (2) set - capacity factor to number of experts and thus waste computation - and memory on padding. - """ - def __init__(self, prefix, config: MixtralConfig, weights): super().__init__() self.hidden_dim = config.hidden_size @@ -357,236 +338,40 @@ class BlockSparseMoE(nn.Module): self.gate = FastLinear.load(config, f"{prefix}.gate", weights, bias=False) # merged expert weights, all of size (n_experts * ffn_dim, hidden_dim) - self.w1 = _load_experts(config, f"{prefix}.experts", "w1", weights) - self.w2 = _load_experts(config, f"{prefix}.experts", "w2", weights) - self.w3 = _load_experts(config, f"{prefix}.experts", "w3", weights) - - self.offsets = None - self.offsets_block_rows = 0 + w1 = _load_experts(config, f"{prefix}.experts", "w1", weights).view( + self.num_experts, self.ffn_dim, self.hidden_dim + ) + w3 = _load_experts(config, f"{prefix}.experts", "w3", weights).view( + self.num_experts, self.ffn_dim, self.hidden_dim + ) + self.w13 = torch.cat([w1, w3], dim=1) + self.w2 = ( + _load_experts(config, f"{prefix}.experts", "w2", weights) + .view(self.num_experts, self.ffn_dim, self.hidden_dim) + .transpose(1, 2) + .contiguous() + ) self.process_group = weights.process_group - # Calculate the number of bits needed to represent the expert indices - # so that we can pass it to radix sort. - self.sort_end_bit = max(int(np.ceil(np.log2(self.num_experts))), 1) - self.blocking = 128 - self.quantize_scatter_num_bits = -1 - - def topology(self, x: torch.Tensor, padded_bins: torch.Tensor): - padded_tokens, _ = x.size() - assert padded_tokens % self.blocking == 0 - assert self.ffn_dim % self.blocking == 0 - - # Offsets for the sparse matrix. All rows have the - # same number of nonzero blocks dictated by the - # dimensionality of a single expert. - block_rows = padded_tokens // self.blocking - blocks_per_row = self.ffn_dim // self.blocking - if self.offsets is None or block_rows > self.offsets_block_rows: - self.offsets = torch.arange( - 0, - block_rows * blocks_per_row + 1, - blocks_per_row, - dtype=torch.int32, - device=x.device, - ) - self.offsets_block_rows = block_rows - offsets = self.offsets - else: - offsets = self.offsets[: block_rows + 1] - - # Indices for the sparse matrix. The indices for - # the intermediate matrix are dynamic depending - # on the mapping of tokens to experts. - column_indices = ops.topology( - padded_bins, self.blocking, block_rows, blocks_per_row - ) - - # For now, use meta init to save the device memory. - data = torch.empty( - column_indices.numel(), - self.blocking, - self.blocking, - dtype=x.dtype, - device="meta", - ) - shape = (padded_tokens, self.ffn_dim * self.num_experts) - row_indices = stk.ops.row_indices(shape, data, offsets, column_indices) - return stk.Matrix( - shape, - data, - row_indices, - column_indices, - offsets, - False, - False, - False, - ) - - def indices_and_padded_bins(self, selected_experts: torch.Tensor): - # Sort the expert ids to produce the scatter/gather - # indices for the permutation. - # selected_experts = selected_experts.int() - - # returns bin_ids == num of experts for this sequence ? == unique selected experts? - # and indices == how to sort tokens? - bin_ids, indices = ops.sort(selected_experts, self.sort_end_bit) - # bin_ids => [0, 0, 0, 2, 2, ...] => [num_tokens * top_k] - # indices => [14, 32, 33, ...] => [num_tokens * top_k] - - # Histogram the expert ids to identify the number of - # tokens routed to each expert. - tokens_per_expert = ops.histogram(selected_experts, self.num_experts) - # tokens_per_expert => [3, 0, 2, ...] => [num_experts] - - # Round the token counts up to the block size used in - # the matrix muliplications. Caculate the starting - # position of each bin. - - # List of size num_experts - padded_tokens_per_expert = round_up(tokens_per_expert, self.blocking) - # padded_tokens_per_expert => [128, O, 128, ...] - - # Cumulative selected experts per token - padded_bins = ops.inclusive_cumsum(padded_tokens_per_expert, 0) - padded_bins = promote_scalar(padded_bins) - # padded_bins => [128, 128, 256, ...] - - # Calculate the bin bounds for the sorted tokens. - bins = ops.inclusive_cumsum(tokens_per_expert, 0) - bins = promote_scalar(bins) - # bins => [3, 3, 5, ...] - - return indices, bin_ids, bins, padded_bins, tokens_per_expert - - def sparse_forward(self, x: torch.Tensor) -> torch.Tensor: - """ - x: (sequence_length, model_dim) - gate_logits: (sequence_length, n_experts) - """ - # optional reshape - input_shape = x.shape - x = x.view(-1, input_shape[-1]) - - # gate_logits: (sequence_length, n_experts) - gate_logits = self.gate(x) - selected_experts, weights = select_experts(gate_logits, self.top_k) - - ( - indices, - bin_ids, - bins, - padded_bins, - _, - ) = self.indices_and_padded_bins(selected_experts) - - # Permute tokens and pad to prepare expert computation - # (top_k * sequence_length + padding, model_dim) - x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins, self.top_k) - - # Create the sparse matrix topology - with torch.no_grad(): - topo = self.topology(x, padded_bins) - - # Perform the expert computation - # First Dense x Dense -> Sparse for w1 and w3, - # (top_k * sequence_length + padding, ffn_dim * n_experts) - x = stk.Matrix( - topo.size(), - self.act(stk.ops.sdd(x, self.w1.t(), topo).data) - * stk.ops.sdd(x, self.w3.t(), topo).data, - topo.row_indices, - topo.column_indices, - topo.offsets, - topo.column_indices_t, - topo.offsets_t, - topo.block_offsets_t, - ) - - # Then Sparse x Dense -> Dense for w2 - # (top_k * sequence_length + padding, model_dim) - x = stk.ops.dsd(x, self.w2) - - # Permute back and remove padding - # (sequence_length, model_dim) - x = ops.padded_scatter( + def forward(self, x: torch.Tensor) -> torch.Tensor: + # router_logits: (num_tokens, n_experts) + router_logits = self.gate(x) + out = fused_moe( x, - indices, - bin_ids, - weights, - bins, - padded_bins, + self.w13, + self.w2, + router_logits, self.top_k, - self.quantize_scatter_num_bits, - ).view(*input_shape) - - if self.process_group.size() > 1: - torch.distributed.all_reduce(x, group=self.process_group) - - return x.view(*input_shape) - - def dense_forward(self, x: torch.Tensor) -> torch.Tensor: - """ - x: (sequence_length, model_dim) - gate_logits: (sequence_length, n_experts) - """ - # optional reshape - input_shape = x.shape - x = x.view(-1, input_shape[-1]) - - # gate_logits: (sequence_length, n_experts) - gate_logits = self.gate(x) - # all_probs: (sequence_length, n_experts) and upcast for softmax - all_probs = torch.nn.functional.softmax(gate_logits, dim=1, dtype=torch.float) - - if self.top_k < self.num_experts: - _, not_selected_experts = torch.topk( - all_probs, - self.num_experts - self.top_k, - largest=False, - sorted=False, - dim=1, - ) - # Mask not selected experts - all_probs.scatter_(1, not_selected_experts, 0) - - # Re-normalize - weights = all_probs / all_probs.sum(dim=1, keepdim=True) - weights = weights.to(x.dtype) - - # Expand to [num_experts, sequence_length, model_dim] - x = x.view(1, -1, input_shape[-1]).expand(self.num_experts, -1, input_shape[-1]) - - # Permute to [num_experts, model_dim, ffn_dim] - w1 = self.w1.view(self.num_experts, self.ffn_dim, self.hidden_dim).permute( - 0, 2, 1 + renormalize=True, + inplace=True, ) - w3 = self.w3.view(self.num_experts, self.ffn_dim, self.hidden_dim).permute( - 0, 2, 1 - ) - - inter = self.act(torch.bmm(x, w1)) * torch.bmm(x, w3) - - out = torch.bmm( - inter, self.w2.view(self.num_experts, self.ffn_dim, self.hidden_dim) - ) - # Mask not selected experts - out *= weights.t().view(self.num_experts, -1, 1) - - # Sum experts - out = out.sum(0) # Reduce sum if self.process_group.size() > 1: torch.distributed.all_reduce(out, group=self.process_group) - return out - - def forward(self, x: torch.Tensor) -> torch.Tensor: - if len(x) > 256 and HAS_MEGABLOCKS: - return self.sparse_forward(x) - # This is faster when there is not a lot of tokens - return self.dense_forward(x) + return out.view(*x.shape) class DenseMoE(nn.Module): diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index be513511..2a9d3914 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -169,6 +169,11 @@ class FlashCausalLMBatch(Batch): requests_idx_mapping[r.id] = i tokenized_input = tokenized_input[-r.truncate :] + if ( + tokenized_input[0] == tokenizer.bos_token_id + and tokenized_input[1] == tokenizer.bos_token_id + ): + tokenized_input = tokenized_input[1:] input_length = len(tokenized_input) input_lengths.append(input_length) @@ -694,7 +699,7 @@ class FlashCausalLM(Model): def cuda_graph_warmup(self, bs: int, max_s: int, max_bt: int): input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device) position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device) - slots = torch.arange(bs, dtype=torch.int32, device=self.device) + slots = torch.arange(bs, dtype=torch.int64, device=self.device) input_lengths = torch.ones(bs, dtype=torch.int32, device=self.device) * max_s block_tables = ( torch.arange(max_bt, dtype=torch.int32, device=self.device) diff --git a/server/text_generation_server/models/flash_cohere.py b/server/text_generation_server/models/flash_cohere.py index 181a93b1..f85c7722 100644 --- a/server/text_generation_server/models/flash_cohere.py +++ b/server/text_generation_server/models/flash_cohere.py @@ -3,12 +3,11 @@ import torch.distributed from opentelemetry import trace from typing import Optional -from transformers import AutoTokenizer +from transformers import AutoTokenizer, AutoConfig from text_generation_server.models import FlashCausalLM from text_generation_server.models.custom_modeling.flash_cohere_modeling import ( FlashCohereForCausalLM, - CohereConfig, ) from text_generation_server.utils import ( initialize_torch_distributed, @@ -32,7 +31,7 @@ class FlashCohere(FlashCausalLM): self.process_group, rank, world_size = initialize_torch_distributed() if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") - dtype = torch.bfloat16 if dtype is None else dtype + dtype = torch.float16 if dtype is None else dtype else: raise NotImplementedError("FlashCohere is only available on GPU") @@ -46,7 +45,7 @@ class FlashCohere(FlashCausalLM): from_slow=False, ) - config = CohereConfig.from_pretrained( + config = AutoConfig.from_pretrained( model_id, revision=revision, trust_remote_code=trust_remote_code ) config.quantize = quantize diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py index 575dbba0..ace7ea8e 100644 --- a/server/text_generation_server/models/flash_mistral.py +++ b/server/text_generation_server/models/flash_mistral.py @@ -385,7 +385,7 @@ class BaseFlashMistral(FlashCausalLM): def cuda_graph_warmup(self, bs: int, max_s: int, max_bt: int): input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device) position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device) - slots = torch.arange(bs, dtype=torch.int32, device=self.device) + slots = torch.arange(bs, dtype=torch.int64, device=self.device) input_lengths = torch.ones(bs, dtype=torch.int32, device=self.device) * max_s block_tables = ( torch.arange(max_bt, dtype=torch.int32, device=self.device) diff --git a/server/text_generation_server/utils/flash_attn.py b/server/text_generation_server/utils/flash_attn.py index 48f8ef70..45090c64 100644 --- a/server/text_generation_server/utils/flash_attn.py +++ b/server/text_generation_server/utils/flash_attn.py @@ -88,6 +88,9 @@ def attention( out, cu_seqlens, cu_seqlens, + None, + None, + None, max_s, max_s, 0.0, diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index 209f1c8a..f29e55c5 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -19,7 +19,6 @@ from accelerate import init_empty_weights from text_generation_server.utils.gptq.quant_linear import QuantLinear from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM -from text_generation_server.utils.log import log_once HAS_AWQ = True try: @@ -35,12 +34,6 @@ except Exception: HAS_EXLLAMA = False CAN_EXLLAMA = major >= 8 or IS_ROCM_SYSTEM V2 = os.getenv("EXLLAMA_VERSION", "2") == "2" -# if V2 and int(os.getenv("WORLD_SIZE", "1")) > 1: -# V2 = False -# log_once( -# logger.warning, -# "Disabling exllama v2 and using v1 instead because there are issues when sharding", -# ) if os.getenv("DISABLE_EXLLAMA") == "True": HAS_EXLLAMA = False @@ -174,6 +167,8 @@ class EETQLinear(nn.Module): ) -> None: super().__init__() device = weight.device + if weight.dtype != torch.float16: + weight = weight.to(dtype=torch.float16) weight = torch.t(weight).contiguous().cpu() weight, scale = quant_weights(weight, torch.int8, False) diff --git a/server/text_generation_server/utils/paged_attention.py b/server/text_generation_server/utils/paged_attention.py index 4b12744c..18e605b0 100644 --- a/server/text_generation_server/utils/paged_attention.py +++ b/server/text_generation_server/utils/paged_attention.py @@ -1,8 +1,7 @@ import torch # vllm imports -from vllm import cache_ops -from vllm import attention_ops +from vllm._C import cache_ops, ops _PARTITION_SIZE = 512 @@ -14,7 +13,7 @@ def reshape_and_cache( value_cache: torch.Tensor, slots: torch.Tensor, ): - cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots) + cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0) def attention( @@ -54,9 +53,9 @@ def attention( # V1 to avoid the overhead of reduction. Also, if the number of # sequences or heads is large, we use V1 since there is enough work # to parallelize. - use_v1 = max_num_partitions == 1 or num_seqs * num_heads > 512 + use_v1 = max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512) if use_v1: - attention_ops.paged_attention_v1( + ops.paged_attention_v1( out, query, key_cache, @@ -68,6 +67,8 @@ def attention( block_size, max_s, None, + "auto", + 1.0, ) else: # Run PagedAttention V2. @@ -83,7 +84,7 @@ def attention( device=out.device, ) max_logits = torch.empty_like(exp_sums) - attention_ops.paged_attention_v2( + ops.paged_attention_v2( out, exp_sums, max_logits, @@ -98,4 +99,6 @@ def attention( block_size, max_s, None, + "auto", + 1.0, ) From 30620a9a44c3e1357d4f9e9a84b00fa1ede011cc Mon Sep 17 00:00:00 2001 From: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com> Date: Wed, 10 Apr 2024 18:38:08 +0200 Subject: [PATCH 13/74] hotfix: mixtral --- .../custom_modeling/flash_mixtral_modeling.py | 22 ++++++++++++------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py index 89eb8f43..be8cb965 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py @@ -464,9 +464,9 @@ class DenseMoE(nn.Module): class MixtralLayer(nn.Module): - def __init__(self, layer_id, config, weights): + def __init__(self, prefix, layer_id, config, weights): super().__init__() - prefix = f"model.layers.{layer_id}" + prefix = f"{prefix}.layers.{layer_id}" self.self_attn = MixtralAttention( prefix=f"{prefix}.self_attn", config=config, weights=weights @@ -525,16 +525,20 @@ class MixtralLayer(nn.Module): class MixtralModel(torch.nn.Module): - def __init__(self, config, weights): + def __init__(self, prefix, config, weights): super().__init__() self.embed_tokens = TensorParallelEmbedding( - prefix="model.embed_tokens", weights=weights + prefix=( + "model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens" + ), + weights=weights, ) self.layers = nn.ModuleList( [ MixtralLayer( + "model" if not prefix else f"{prefix}.model", layer_id, config, weights, @@ -543,7 +547,9 @@ class MixtralModel(torch.nn.Module): ] ) self.norm = FastRMSNorm.load( - prefix="model.norm", weights=weights, eps=config.rms_norm_eps + prefix="model.norm" if not prefix else f"{prefix}.model.norm", + weights=weights, + eps=config.rms_norm_eps, ) self.head_size = self.layers[0].self_attn.head_size @@ -593,13 +599,13 @@ class MixtralModel(torch.nn.Module): class FlashMixtralForCausalLM(torch.nn.Module): - def __init__(self, config, weights): + def __init__(self, prefix, config, weights): super().__init__() - self.model = MixtralModel(config, weights) + self.model = MixtralModel(prefix, config, weights) self.lm_head = SpeculativeHead.load( config, - prefix="lm_head", + prefix="lm_head" if not prefix else f"{prefix}.lm_head", weights=weights, ) self.max_past = config.sliding_window From 10d9083b2d5c47e73d9acb4b6cf64b9fcad6c934 Mon Sep 17 00:00:00 2001 From: abhishek thakur <1183441+abhishekkrthakur@users.noreply.github.com> Date: Thu, 11 Apr 2024 10:37:35 +0200 Subject: [PATCH 14/74] Update libraries (#1713) Co-authored-by: Nicolas Patry --- server/poetry.lock | 303 ++++++++++++++++++----------------- server/pyproject.toml | 6 +- server/requirements_cuda.txt | 10 +- server/requirements_rocm.txt | 10 +- 4 files changed, 165 insertions(+), 164 deletions(-) diff --git a/server/poetry.lock b/server/poetry.lock index 7d140390..f6768565 100644 --- a/server/poetry.lock +++ b/server/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "accelerate" -version = "0.28.0" +version = "0.29.1" description = "Accelerate" optional = true python-versions = ">=3.8.0" files = [ - {file = "accelerate-0.28.0-py3-none-any.whl", hash = "sha256:8ae25f8a8dc4cf12283842c469113836300545fb0dfa46fef331fb0a2ac8b421"}, - {file = "accelerate-0.28.0.tar.gz", hash = "sha256:32019a49f4b3a85cc179ac4e38e9e2971f1a997dee026be0512816499464c4d5"}, + {file = "accelerate-0.29.1-py3-none-any.whl", hash = "sha256:7eda0c8bc62bc59129103310f1272a0fb7b3ebc55fc8920cfe1c102db30aca58"}, + {file = "accelerate-0.29.1.tar.gz", hash = "sha256:d1d0e5a591177891812fd6d1bc843af191e1192c80e5180258f52fefcb653a9f"}, ] [package.dependencies] @@ -21,14 +21,14 @@ safetensors = ">=0.3.1" torch = ">=1.10.0" [package.extras] -dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "deepspeed (<0.13.0)", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.2.1,<0.3.0)", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "deepspeed", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.2.1,<0.3.0)", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"] quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.2.1,<0.3.0)"] rich = ["rich"] sagemaker = ["sagemaker"] -test-dev = ["bitsandbytes", "datasets", "deepspeed (<0.13.0)", "evaluate", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +test-dev = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"] test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"] test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"] -testing = ["bitsandbytes", "datasets", "deepspeed (<0.13.0)", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchpippy (>=0.2.0)", "tqdm", "transformers"] [[package]] name = "aiohttp" @@ -471,18 +471,18 @@ test = ["pytest (>=6)"] [[package]] name = "filelock" -version = "3.13.1" +version = "3.13.3" description = "A platform independent file lock." optional = false python-versions = ">=3.8" files = [ - {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"}, - {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"}, + {file = "filelock-3.13.3-py3-none-any.whl", hash = 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"sha256:7838034a12cca3168247f9d2d1dba6724c9de3ae0f73a108258c6b8fc5912601"}, + {file = "transformers-4.39.3.tar.gz", hash = "sha256:2586e5ff4150f122716fc40f5530e92871befc051848fbe82600969c535b762d"}, ] [package.dependencies] @@ -3111,13 +3112,13 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=5.2,<6.0)", "isort (>=5.0.6,<6. [[package]] name = "typing-extensions" -version = "4.10.0" +version = "4.11.0" description = "Backported and Experimental Type Hints for Python 3.8+" optional = false python-versions = ">=3.8" files = [ - {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"}, - {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"}, + {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"}, + {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"}, ] [[package]] @@ -3472,4 +3473,4 @@ torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "b19c9b3f63778f3fc862907c9377d9389b8d0ea8aa02f69bdba628f4829bc62d" +content-hash = "2bbb3d5acafe4bd1a616106ad3956058078a1f54c9a906a0065ee474f5af407d" diff --git a/server/pyproject.toml b/server/pyproject.toml index b5e9c3fd..58c54b94 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -15,7 +15,7 @@ grpcio-status = "^1.51.1" grpcio-reflection = "^1.51.1" grpc-interceptor = "^0.15.0" typer = "^0.6.1" -accelerate = { version = "^0.28.0", optional = true } +accelerate = { version = "^0.29.1", optional = true } bitsandbytes = { version = "^0.43.0", optional = true } safetensors = "^0.4" loguru = "^0.6.0" @@ -26,11 +26,11 @@ hf-transfer = "^0.1.2" sentencepiece = "^0.1.97" tokenizers = "^0.15.0" huggingface-hub = "^0.19.3" -transformers = "^4.38" +transformers = "^4.39" einops = "^0.6.1" texttable = { version = "^1.6.7", optional = true } datasets = { version = "^2.14.0", optional = true } -peft = { version = "^0.9", optional = true } +peft = { version = "^0.10", optional = true } torch = { version = "^2.1.1", optional = true } scipy = "^1.11.1" pillow = "^10.0.0" diff --git a/server/requirements_cuda.txt b/server/requirements_cuda.txt index 4b2fbc24..6ad5235a 100644 --- a/server/requirements_cuda.txt +++ b/server/requirements_cuda.txt @@ -5,7 +5,7 @@ click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13" einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13" -filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13" +filelock==3.13.3 ; python_version >= "3.9" and python_version < "3.13" fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13" googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13" grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" @@ -27,20 +27,20 @@ opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13 opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13" opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13" packaging==24.0 ; python_version >= "3.9" and python_version < "3.13" -pillow==10.2.0 ; python_version >= "3.9" and python_version < "3.13" +pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13" protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13" regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13" requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13" safetensors==0.4.2 ; python_version >= "3.9" and python_version < "3.13" -scipy==1.12.0 ; python_version >= "3.9" and python_version < "3.13" +scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13" sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13" setuptools==69.2.0 ; python_version >= "3.9" and python_version < "3.13" tokenizers==0.15.2 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.39.0 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.39.3 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" -typing-extensions==4.10.0 ; python_version >= "3.9" and python_version < "3.13" +typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13" win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32" wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13" diff --git a/server/requirements_rocm.txt b/server/requirements_rocm.txt index 4b2fbc24..6ad5235a 100644 --- a/server/requirements_rocm.txt +++ b/server/requirements_rocm.txt @@ -5,7 +5,7 @@ click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13" einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13" -filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13" +filelock==3.13.3 ; python_version >= "3.9" and python_version < "3.13" fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13" googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13" grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" @@ -27,20 +27,20 @@ opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13 opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13" opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13" packaging==24.0 ; python_version >= "3.9" and python_version < "3.13" -pillow==10.2.0 ; python_version >= "3.9" and python_version < "3.13" +pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13" protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13" regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13" requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13" safetensors==0.4.2 ; python_version >= "3.9" and python_version < "3.13" -scipy==1.12.0 ; python_version >= "3.9" and python_version < "3.13" +scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13" sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13" setuptools==69.2.0 ; python_version >= "3.9" and python_version < "3.13" tokenizers==0.15.2 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.39.0 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.39.3 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" -typing-extensions==4.10.0 ; python_version >= "3.9" and python_version < "3.13" +typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13" win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32" wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13" From b83aab9bb390c85d6675f00883c29a5aa205a1ca Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 11 Apr 2024 12:48:39 +0000 Subject: [PATCH 15/74] Easier defaults for models stemmed from configs. --- Cargo.lock | 2 + docs/source/basic_tutorials/launcher.md | 21 ++-- launcher/Cargo.toml | 4 +- launcher/src/main.rs | 154 +++++++++++++++++++----- router/src/main.rs | 16 +-- 5 files changed, 153 insertions(+), 44 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 65b9f8b7..8c1784bf 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -3452,7 +3452,9 @@ dependencies = [ "clap", "ctrlc", "float_eq", + "hf-hub", "nix", + "once_cell", "reqwest", "serde", "serde_json", diff --git a/docs/source/basic_tutorials/launcher.md b/docs/source/basic_tutorials/launcher.md index 86394ff7..4b876e64 100644 --- a/docs/source/basic_tutorials/launcher.md +++ b/docs/source/basic_tutorials/launcher.md @@ -60,9 +60,9 @@ Options: [env: QUANTIZE=] Possible values: - - awq: 4 bit quantization. Requires a specific AWQ quantized model: https://hf.co/models?search=awq. Should replace GPTQ models wherever possible because of the better latency - - eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from https://github.com/NetEase-FuXi/EETQ.git - - gptq: 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq. text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels + - awq: 4 bit quantization. Requires a specific AWQ quantized model: . Should replace GPTQ models wherever possible because of the better latency + - eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from + - gptq: 4 bit quantization. Requires a specific GTPQ quantized model: . text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels - bitsandbytes: Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16 - bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16 - bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model @@ -128,23 +128,29 @@ Options: [env: MAX_TOP_N_TOKENS=] [default: 5] +``` +## MAX_INPUT_TOKENS +```shell + --max-input-tokens + This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle. Default to min(max_position_embeddings - 1, 13383) + + [env: MAX_INPUT_TOKENS=] + ``` ## MAX_INPUT_LENGTH ```shell --max-input-length - This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle + Legacy version of [`Args::max_input_tokens`] [env: MAX_INPUT_LENGTH=] - [default: 1024] ``` ## MAX_TOTAL_TOKENS ```shell --max-total-tokens - This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be + This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be. Default to min(max_position_embeddings - 1, 16384) [env: MAX_TOTAL_TOKENS=] - [default: 2048] ``` ## WAITING_SERVED_RATIO @@ -164,7 +170,6 @@ Options: Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent [env: MAX_BATCH_PREFILL_TOKENS=] - [default: 4096] ``` ## MAX_BATCH_TOTAL_TOKENS diff --git a/launcher/Cargo.toml b/launcher/Cargo.toml index 7c598c37..6b6fd58e 100644 --- a/launcher/Cargo.toml +++ b/launcher/Cargo.toml @@ -9,8 +9,10 @@ homepage.workspace = true [dependencies] clap = { version = "4.4.5", features = ["derive", "env"] } ctrlc = { version = "3.4.1", features = ["termination"] } +hf-hub = "0.3.2" nix = { version = "0.28.0", features = ["signal"] } -serde = { version = "1.0.188", features = ["derive"] } +once_cell = "1.19.0" +serde = { version = "1.0.188", features = ["derive"] } serde_json = "1.0.107" tracing = "0.1.37" tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] } diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 3f810023..2db55ddc 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -22,14 +22,14 @@ mod env_runtime; #[derive(Clone, Copy, Debug, ValueEnum)] enum Quantization { /// 4 bit quantization. Requires a specific AWQ quantized model: - /// https://hf.co/models?search=awq. + /// . /// Should replace GPTQ models wherever possible because of the better latency Awq, /// 8 bit quantization, doesn't require specific model. /// Should be a drop-in replacement to bitsandbytes with much better performance. - /// Kernels are from https://github.com/NetEase-FuXi/EETQ.git + /// Kernels are from Eetq, - /// 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq. + /// 4 bit quantization. Requires a specific GTPQ quantized model: . /// text-generation-inference will use exllama (faster) kernels wherever possible, and use /// triton kernel (wider support) when it's not. /// AWQ has faster kernels. @@ -206,8 +206,13 @@ struct Args { /// for users. The larger this value, the longer prompt users can send which /// can impact the overall memory required to handle the load. /// Please note that some models have a finite range of sequence they can handle. - #[clap(default_value = "1024", long, env)] - max_input_length: usize, + /// Default to min(max_position_embeddings - 1, 13383) + #[clap(long, env)] + max_input_tokens: Option, + + /// Legacy version of [`Args::max_input_tokens`]. + #[clap(long, env)] + max_input_length: Option, /// This is the most important value to set as it defines the "memory budget" /// of running clients requests. @@ -217,8 +222,9 @@ struct Args { /// `1511` max_new_tokens. /// The larger this value, the larger amount each request will be in your RAM /// and the less effective batching can be. - #[clap(default_value = "2048", long, env)] - max_total_tokens: usize, + /// Default to min(max_position_embeddings - 1, 16384) + #[clap(long, env)] + max_total_tokens: Option, /// This represents the ratio of waiting queries vs running queries where /// you want to start considering pausing the running queries to include the waiting @@ -236,8 +242,8 @@ struct Args { /// Limits the number of tokens for the prefill operation. /// Since this operation take the most memory and is compute bound, it is interesting /// to limit the number of requests that can be sent. - #[clap(default_value = "4096", long, env)] - max_batch_prefill_tokens: u32, + #[clap(long, env)] + max_batch_prefill_tokens: Option, /// **IMPORTANT** This is one critical control to allow maximum usage /// of the available hardware. @@ -1045,6 +1051,9 @@ fn compute_type(num_shard: usize) -> Option { fn spawn_webserver( num_shard: usize, args: Args, + max_input_tokens: usize, + max_total_tokens: usize, + max_batch_prefill_tokens: u32, shutdown: Arc, shutdown_receiver: &mpsc::Receiver<()>, ) -> Result { @@ -1060,12 +1069,12 @@ fn spawn_webserver( args.max_stop_sequences.to_string(), "--max-top-n-tokens".to_string(), args.max_top_n_tokens.to_string(), - "--max-input-length".to_string(), - args.max_input_length.to_string(), + "--max-input-tokens".to_string(), + max_input_tokens.to_string(), "--max-total-tokens".to_string(), - args.max_total_tokens.to_string(), + max_total_tokens.to_string(), "--max-batch-prefill-tokens".to_string(), - args.max_batch_prefill_tokens.to_string(), + max_batch_prefill_tokens.to_string(), "--waiting-served-ratio".to_string(), args.waiting_served_ratio.to_string(), "--max-waiting-tokens".to_string(), @@ -1253,16 +1262,99 @@ fn main() -> Result<(), LauncherError> { tracing::info!("{:?}", args); + use hf_hub::{api::sync::Api, Repo, RepoType}; + + #[derive(Deserialize)] + struct Config { + max_position_embeddings: usize, + } + + let config: Config = { + let model_id = args.model_id.clone(); + let mut path = std::path::Path::new(&args.model_id).to_path_buf(); + let filename = if !path.exists() { + // Assume it's a hub id + let api = Api::new().unwrap(); + let repo = if let Some(ref revision) = args.revision { + api.repo(Repo::with_revision( + model_id, + RepoType::Model, + revision.to_string(), + )) + } else { + api.model(model_id) + }; + repo.get("config.json").unwrap() + } else { + path.push("config.json"); + path + }; + + let content = std::fs::read_to_string(filename).unwrap(); + let config: Config = serde_json::from_str(&content).unwrap(); + + let max_default = 2usize.pow(14); + + let max_position_embeddings = if config.max_position_embeddings > max_default { + let max = config.max_position_embeddings; + tracing::info!("Model supports up to {max} but tgi will now set its default to {max_default} instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens={} --max-total-tokens={max} --max-input-tokens={}`.", max - 1, max - 1); + max_default + } else { + config.max_position_embeddings + }; + + Config { + max_position_embeddings, + } + }; + + let max_input_tokens = { + match (args.max_input_tokens, args.max_input_length) { + (Some(max_input_tokens), Some(max_input_length)) => { + return Err(LauncherError::ArgumentValidation( + format!("Both `max_input_tokens` ({max_input_tokens}) and `max_input_length` ({max_input_length}) are set. Please define only `max_input_tokens` as `max_input_length is deprecated for naming consistency.", + ))); + } + (Some(max_input_tokens), None) | (None, Some(max_input_tokens)) => max_input_tokens, + (None, None) => { + let value = config.max_position_embeddings - 1; + tracing::info!("Default `max_input_tokens` to {value}"); + value + } + } + }; + let max_total_tokens = { + match args.max_total_tokens { + Some(max_total_tokens) => max_total_tokens, + None => { + let value = config.max_position_embeddings; + tracing::info!("Default `max_total_tokens` to {value}"); + value + } + } + }; + let max_batch_prefill_tokens = { + // TODO get config. + match args.max_batch_prefill_tokens { + Some(max_batch_prefill_tokens) => max_batch_prefill_tokens, + None => { + let value = config.max_position_embeddings as u32 - 1; + tracing::info!("Default `max_batch_prefill_tokens` to {value}"); + value + } + } + }; + // Validate args - if args.max_input_length >= args.max_total_tokens { + if max_input_tokens >= max_total_tokens { return Err(LauncherError::ArgumentValidation( - "`max_input_length` must be < `max_total_tokens`".to_string(), + "`max_input_tokens must be < `max_total_tokens`".to_string(), )); } - if args.max_input_length as u32 > args.max_batch_prefill_tokens { + if max_input_tokens as u32 > max_batch_prefill_tokens { return Err(LauncherError::ArgumentValidation(format!( - "`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {} and {}", - args.max_batch_prefill_tokens, args.max_input_length + "`max_batch_prefill_tokens` must be >= `max_input_tokens`. Given: {} and {}", + max_batch_prefill_tokens, max_input_tokens ))); } @@ -1284,16 +1376,16 @@ fn main() -> Result<(), LauncherError> { } if let Some(ref max_batch_total_tokens) = args.max_batch_total_tokens { - if args.max_batch_prefill_tokens > *max_batch_total_tokens { + if max_batch_prefill_tokens > *max_batch_total_tokens { return Err(LauncherError::ArgumentValidation(format!( "`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}", - args.max_batch_prefill_tokens, max_batch_total_tokens + max_batch_prefill_tokens, max_batch_total_tokens ))); } - if args.max_total_tokens as u32 > *max_batch_total_tokens { + if max_total_tokens as u32 > *max_batch_total_tokens { return Err(LauncherError::ArgumentValidation(format!( "`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}", - args.max_total_tokens, max_batch_total_tokens + max_total_tokens, max_batch_total_tokens ))); } } @@ -1354,11 +1446,19 @@ fn main() -> Result<(), LauncherError> { return Ok(()); } - let mut webserver = spawn_webserver(num_shard, args, shutdown.clone(), &shutdown_receiver) - .map_err(|err| { - shutdown_shards(shutdown.clone(), &shutdown_receiver); - err - })?; + let mut webserver = spawn_webserver( + num_shard, + args, + max_input_tokens, + max_total_tokens, + max_batch_prefill_tokens, + shutdown.clone(), + &shutdown_receiver, + ) + .map_err(|err| { + shutdown_shards(shutdown.clone(), &shutdown_receiver); + err + })?; // Default exit code let mut exit_code = Ok(()); diff --git a/router/src/main.rs b/router/src/main.rs index aace2ff9..16a031ae 100644 --- a/router/src/main.rs +++ b/router/src/main.rs @@ -35,7 +35,7 @@ struct Args { #[clap(default_value = "5", long, env)] max_top_n_tokens: u32, #[clap(default_value = "1024", long, env)] - max_input_length: usize, + max_input_tokens: usize, #[clap(default_value = "2048", long, env)] max_total_tokens: usize, #[clap(default_value = "1.2", long, env)] @@ -90,7 +90,7 @@ async fn main() -> Result<(), RouterError> { max_best_of, max_stop_sequences, max_top_n_tokens, - max_input_length, + max_input_tokens, max_total_tokens, waiting_served_ratio, max_batch_prefill_tokens, @@ -118,13 +118,13 @@ async fn main() -> Result<(), RouterError> { init_logging(otlp_endpoint, json_output); // Validate args - if max_input_length >= max_total_tokens { + if max_input_tokens >= max_total_tokens { return Err(RouterError::ArgumentValidation( - "`max_input_length` must be < `max_total_tokens`".to_string(), + "`max_input_tokens` must be < `max_total_tokens`".to_string(), )); } - if max_input_length as u32 > max_batch_prefill_tokens { - return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {max_batch_prefill_tokens} and {max_input_length}"))); + if max_input_tokens as u32 > max_batch_prefill_tokens { + return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_tokens`. Given: {max_batch_prefill_tokens} and {max_input_tokens}"))); } if validation_workers == 0 { @@ -311,7 +311,7 @@ async fn main() -> Result<(), RouterError> { tracing::info!("Warming up model"); let max_supported_batch_total_tokens = match sharded_client .warmup( - max_input_length as u32, + max_input_tokens as u32, max_batch_prefill_tokens, max_total_tokens as u32, max_batch_size, @@ -374,7 +374,7 @@ async fn main() -> Result<(), RouterError> { max_best_of, max_stop_sequences, max_top_n_tokens, - max_input_length, + max_input_tokens, max_total_tokens, waiting_served_ratio, max_batch_prefill_tokens, From 842f6658e286f21f0212ae36ad2114125d81d46a Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 11 Apr 2024 12:51:57 +0000 Subject: [PATCH 16/74] Revert "Easier defaults for models stemmed from configs." This reverts commit b83aab9bb390c85d6675f00883c29a5aa205a1ca. --- Cargo.lock | 2 - docs/source/basic_tutorials/launcher.md | 21 ++-- launcher/Cargo.toml | 4 +- launcher/src/main.rs | 154 +++++------------------- router/src/main.rs | 16 +-- 5 files changed, 44 insertions(+), 153 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 8c1784bf..65b9f8b7 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -3452,9 +3452,7 @@ dependencies = [ "clap", "ctrlc", "float_eq", - "hf-hub", "nix", - "once_cell", "reqwest", "serde", "serde_json", diff --git a/docs/source/basic_tutorials/launcher.md b/docs/source/basic_tutorials/launcher.md index 4b876e64..86394ff7 100644 --- a/docs/source/basic_tutorials/launcher.md +++ b/docs/source/basic_tutorials/launcher.md @@ -60,9 +60,9 @@ Options: [env: QUANTIZE=] Possible values: - - awq: 4 bit quantization. Requires a specific AWQ quantized model: . Should replace GPTQ models wherever possible because of the better latency - - eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from - - gptq: 4 bit quantization. Requires a specific GTPQ quantized model: . text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels + - awq: 4 bit quantization. Requires a specific AWQ quantized model: https://hf.co/models?search=awq. Should replace GPTQ models wherever possible because of the better latency + - eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from https://github.com/NetEase-FuXi/EETQ.git + - gptq: 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq. text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels - bitsandbytes: Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16 - bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16 - bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model @@ -128,29 +128,23 @@ Options: [env: MAX_TOP_N_TOKENS=] [default: 5] -``` -## MAX_INPUT_TOKENS -```shell - --max-input-tokens - This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle. Default to min(max_position_embeddings - 1, 13383) - - [env: MAX_INPUT_TOKENS=] - ``` ## MAX_INPUT_LENGTH ```shell --max-input-length - Legacy version of [`Args::max_input_tokens`] + This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle [env: MAX_INPUT_LENGTH=] + [default: 1024] ``` ## MAX_TOTAL_TOKENS ```shell --max-total-tokens - This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be. Default to min(max_position_embeddings - 1, 16384) + This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be [env: MAX_TOTAL_TOKENS=] + [default: 2048] ``` ## WAITING_SERVED_RATIO @@ -170,6 +164,7 @@ Options: Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent [env: MAX_BATCH_PREFILL_TOKENS=] + [default: 4096] ``` ## MAX_BATCH_TOTAL_TOKENS diff --git a/launcher/Cargo.toml b/launcher/Cargo.toml index 6b6fd58e..7c598c37 100644 --- a/launcher/Cargo.toml +++ b/launcher/Cargo.toml @@ -9,10 +9,8 @@ homepage.workspace = true [dependencies] clap = { version = "4.4.5", features = ["derive", "env"] } ctrlc = { version = "3.4.1", features = ["termination"] } -hf-hub = "0.3.2" nix = { version = "0.28.0", features = ["signal"] } -once_cell = "1.19.0" -serde = { version = "1.0.188", features = ["derive"] } +serde = { version = "1.0.188", features = ["derive"] } serde_json = "1.0.107" tracing = "0.1.37" tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] } diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 2db55ddc..3f810023 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -22,14 +22,14 @@ mod env_runtime; #[derive(Clone, Copy, Debug, ValueEnum)] enum Quantization { /// 4 bit quantization. Requires a specific AWQ quantized model: - /// . + /// https://hf.co/models?search=awq. /// Should replace GPTQ models wherever possible because of the better latency Awq, /// 8 bit quantization, doesn't require specific model. /// Should be a drop-in replacement to bitsandbytes with much better performance. - /// Kernels are from + /// Kernels are from https://github.com/NetEase-FuXi/EETQ.git Eetq, - /// 4 bit quantization. Requires a specific GTPQ quantized model: . + /// 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq. /// text-generation-inference will use exllama (faster) kernels wherever possible, and use /// triton kernel (wider support) when it's not. /// AWQ has faster kernels. @@ -206,13 +206,8 @@ struct Args { /// for users. The larger this value, the longer prompt users can send which /// can impact the overall memory required to handle the load. /// Please note that some models have a finite range of sequence they can handle. - /// Default to min(max_position_embeddings - 1, 13383) - #[clap(long, env)] - max_input_tokens: Option, - - /// Legacy version of [`Args::max_input_tokens`]. - #[clap(long, env)] - max_input_length: Option, + #[clap(default_value = "1024", long, env)] + max_input_length: usize, /// This is the most important value to set as it defines the "memory budget" /// of running clients requests. @@ -222,9 +217,8 @@ struct Args { /// `1511` max_new_tokens. /// The larger this value, the larger amount each request will be in your RAM /// and the less effective batching can be. - /// Default to min(max_position_embeddings - 1, 16384) - #[clap(long, env)] - max_total_tokens: Option, + #[clap(default_value = "2048", long, env)] + max_total_tokens: usize, /// This represents the ratio of waiting queries vs running queries where /// you want to start considering pausing the running queries to include the waiting @@ -242,8 +236,8 @@ struct Args { /// Limits the number of tokens for the prefill operation. /// Since this operation take the most memory and is compute bound, it is interesting /// to limit the number of requests that can be sent. - #[clap(long, env)] - max_batch_prefill_tokens: Option, + #[clap(default_value = "4096", long, env)] + max_batch_prefill_tokens: u32, /// **IMPORTANT** This is one critical control to allow maximum usage /// of the available hardware. @@ -1051,9 +1045,6 @@ fn compute_type(num_shard: usize) -> Option { fn spawn_webserver( num_shard: usize, args: Args, - max_input_tokens: usize, - max_total_tokens: usize, - max_batch_prefill_tokens: u32, shutdown: Arc, shutdown_receiver: &mpsc::Receiver<()>, ) -> Result { @@ -1069,12 +1060,12 @@ fn spawn_webserver( args.max_stop_sequences.to_string(), "--max-top-n-tokens".to_string(), args.max_top_n_tokens.to_string(), - "--max-input-tokens".to_string(), - max_input_tokens.to_string(), + "--max-input-length".to_string(), + args.max_input_length.to_string(), "--max-total-tokens".to_string(), - max_total_tokens.to_string(), + args.max_total_tokens.to_string(), "--max-batch-prefill-tokens".to_string(), - max_batch_prefill_tokens.to_string(), + args.max_batch_prefill_tokens.to_string(), "--waiting-served-ratio".to_string(), args.waiting_served_ratio.to_string(), "--max-waiting-tokens".to_string(), @@ -1262,99 +1253,16 @@ fn main() -> Result<(), LauncherError> { tracing::info!("{:?}", args); - use hf_hub::{api::sync::Api, Repo, RepoType}; - - #[derive(Deserialize)] - struct Config { - max_position_embeddings: usize, - } - - let config: Config = { - let model_id = args.model_id.clone(); - let mut path = std::path::Path::new(&args.model_id).to_path_buf(); - let filename = if !path.exists() { - // Assume it's a hub id - let api = Api::new().unwrap(); - let repo = if let Some(ref revision) = args.revision { - api.repo(Repo::with_revision( - model_id, - RepoType::Model, - revision.to_string(), - )) - } else { - api.model(model_id) - }; - repo.get("config.json").unwrap() - } else { - path.push("config.json"); - path - }; - - let content = std::fs::read_to_string(filename).unwrap(); - let config: Config = serde_json::from_str(&content).unwrap(); - - let max_default = 2usize.pow(14); - - let max_position_embeddings = if config.max_position_embeddings > max_default { - let max = config.max_position_embeddings; - tracing::info!("Model supports up to {max} but tgi will now set its default to {max_default} instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens={} --max-total-tokens={max} --max-input-tokens={}`.", max - 1, max - 1); - max_default - } else { - config.max_position_embeddings - }; - - Config { - max_position_embeddings, - } - }; - - let max_input_tokens = { - match (args.max_input_tokens, args.max_input_length) { - (Some(max_input_tokens), Some(max_input_length)) => { - return Err(LauncherError::ArgumentValidation( - format!("Both `max_input_tokens` ({max_input_tokens}) and `max_input_length` ({max_input_length}) are set. Please define only `max_input_tokens` as `max_input_length is deprecated for naming consistency.", - ))); - } - (Some(max_input_tokens), None) | (None, Some(max_input_tokens)) => max_input_tokens, - (None, None) => { - let value = config.max_position_embeddings - 1; - tracing::info!("Default `max_input_tokens` to {value}"); - value - } - } - }; - let max_total_tokens = { - match args.max_total_tokens { - Some(max_total_tokens) => max_total_tokens, - None => { - let value = config.max_position_embeddings; - tracing::info!("Default `max_total_tokens` to {value}"); - value - } - } - }; - let max_batch_prefill_tokens = { - // TODO get config. - match args.max_batch_prefill_tokens { - Some(max_batch_prefill_tokens) => max_batch_prefill_tokens, - None => { - let value = config.max_position_embeddings as u32 - 1; - tracing::info!("Default `max_batch_prefill_tokens` to {value}"); - value - } - } - }; - // Validate args - if max_input_tokens >= max_total_tokens { + if args.max_input_length >= args.max_total_tokens { return Err(LauncherError::ArgumentValidation( - "`max_input_tokens must be < `max_total_tokens`".to_string(), + "`max_input_length` must be < `max_total_tokens`".to_string(), )); } - if max_input_tokens as u32 > max_batch_prefill_tokens { + if args.max_input_length as u32 > args.max_batch_prefill_tokens { return Err(LauncherError::ArgumentValidation(format!( - "`max_batch_prefill_tokens` must be >= `max_input_tokens`. Given: {} and {}", - max_batch_prefill_tokens, max_input_tokens + "`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {} and {}", + args.max_batch_prefill_tokens, args.max_input_length ))); } @@ -1376,16 +1284,16 @@ fn main() -> Result<(), LauncherError> { } if let Some(ref max_batch_total_tokens) = args.max_batch_total_tokens { - if max_batch_prefill_tokens > *max_batch_total_tokens { + if args.max_batch_prefill_tokens > *max_batch_total_tokens { return Err(LauncherError::ArgumentValidation(format!( "`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}", - max_batch_prefill_tokens, max_batch_total_tokens + args.max_batch_prefill_tokens, max_batch_total_tokens ))); } - if max_total_tokens as u32 > *max_batch_total_tokens { + if args.max_total_tokens as u32 > *max_batch_total_tokens { return Err(LauncherError::ArgumentValidation(format!( "`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}", - max_total_tokens, max_batch_total_tokens + args.max_total_tokens, max_batch_total_tokens ))); } } @@ -1446,19 +1354,11 @@ fn main() -> Result<(), LauncherError> { return Ok(()); } - let mut webserver = spawn_webserver( - num_shard, - args, - max_input_tokens, - max_total_tokens, - max_batch_prefill_tokens, - shutdown.clone(), - &shutdown_receiver, - ) - .map_err(|err| { - shutdown_shards(shutdown.clone(), &shutdown_receiver); - err - })?; + let mut webserver = spawn_webserver(num_shard, args, shutdown.clone(), &shutdown_receiver) + .map_err(|err| { + shutdown_shards(shutdown.clone(), &shutdown_receiver); + err + })?; // Default exit code let mut exit_code = Ok(()); diff --git a/router/src/main.rs b/router/src/main.rs index 16a031ae..aace2ff9 100644 --- a/router/src/main.rs +++ b/router/src/main.rs @@ -35,7 +35,7 @@ struct Args { #[clap(default_value = "5", long, env)] max_top_n_tokens: u32, #[clap(default_value = "1024", long, env)] - max_input_tokens: usize, + max_input_length: usize, #[clap(default_value = "2048", long, env)] max_total_tokens: usize, #[clap(default_value = "1.2", long, env)] @@ -90,7 +90,7 @@ async fn main() -> Result<(), RouterError> { max_best_of, max_stop_sequences, max_top_n_tokens, - max_input_tokens, + max_input_length, max_total_tokens, waiting_served_ratio, max_batch_prefill_tokens, @@ -118,13 +118,13 @@ async fn main() -> Result<(), RouterError> { init_logging(otlp_endpoint, json_output); // Validate args - if max_input_tokens >= max_total_tokens { + if max_input_length >= max_total_tokens { return Err(RouterError::ArgumentValidation( - "`max_input_tokens` must be < `max_total_tokens`".to_string(), + "`max_input_length` must be < `max_total_tokens`".to_string(), )); } - if max_input_tokens as u32 > max_batch_prefill_tokens { - return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_tokens`. Given: {max_batch_prefill_tokens} and {max_input_tokens}"))); + if max_input_length as u32 > max_batch_prefill_tokens { + return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {max_batch_prefill_tokens} and {max_input_length}"))); } if validation_workers == 0 { @@ -311,7 +311,7 @@ async fn main() -> Result<(), RouterError> { tracing::info!("Warming up model"); let max_supported_batch_total_tokens = match sharded_client .warmup( - max_input_tokens as u32, + max_input_length as u32, max_batch_prefill_tokens, max_total_tokens as u32, max_batch_size, @@ -374,7 +374,7 @@ async fn main() -> Result<(), RouterError> { max_best_of, max_stop_sequences, max_top_n_tokens, - max_input_tokens, + max_input_length, max_total_tokens, waiting_served_ratio, max_batch_prefill_tokens, From c2fd35d875155d858a60542edabb9df59587e1f8 Mon Sep 17 00:00:00 2001 From: oOraph <13552058+oOraph@users.noreply.github.com> Date: Thu, 11 Apr 2024 19:31:48 +0200 Subject: [PATCH 17/74] Dev/mask ldconfig output v2 (#1716) wrap text-generation-launcher in docker image mask ldconfig failures to user (no need in most cases anyway) --------- Signed-off-by: Raphael Glon Co-authored-by: Raphael Glon --- Dockerfile | 4 +++- launcher/src/main.rs | 10 ---------- tgi-entrypoint.sh | 5 +++++ 3 files changed, 8 insertions(+), 11 deletions(-) create mode 100755 tgi-entrypoint.sh diff --git a/Dockerfile b/Dockerfile index 0bc5f8d9..c2ae4c85 100644 --- a/Dockerfile +++ b/Dockerfile @@ -245,5 +245,7 @@ ENTRYPOINT ["./entrypoint.sh"] # Final image FROM base -ENTRYPOINT ["text-generation-launcher"] +COPY ./tgi-entrypoint.sh /tgi-entrypoint.sh + +ENTRYPOINT ["/tgi-entrypoint.sh"] CMD ["--json-output"] diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 3f810023..836b0381 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -1217,16 +1217,6 @@ fn terminate(process_name: &str, mut process: Child, timeout: Duration) -> io::R } fn main() -> Result<(), LauncherError> { - match Command::new("ldconfig").spawn() { - Ok(_) => {} - Err(err) => { - tracing::warn!( - "Unable to refresh ldconfig cache. Skipping (useless in most cases). Details {:?}", - err - ) - } - } - // Pattern match configuration let args: Args = Args::parse(); diff --git a/tgi-entrypoint.sh b/tgi-entrypoint.sh new file mode 100755 index 00000000..ea94dcd9 --- /dev/null +++ b/tgi-entrypoint.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +ldconfig 2>/dev/null || echo 'unable to refresh ld cache, not a big deal in most cases' + +text-generation-launcher $@ From 408dbc485c112e023a97fc959d4cb3721cf89fed Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 12 Apr 2024 08:13:30 +0200 Subject: [PATCH 18/74] Fp8 Support (#1726) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --------- Co-authored-by: Dong Shin --- docs/source/basic_tutorials/launcher.md | 1 + launcher/src/main.rs | 8 ++++ server/text_generation_server/cli.py | 1 + server/text_generation_server/utils/layers.py | 44 +++++++++++++++++++ 4 files changed, 54 insertions(+) diff --git a/docs/source/basic_tutorials/launcher.md b/docs/source/basic_tutorials/launcher.md index 86394ff7..40ee55d7 100644 --- a/docs/source/basic_tutorials/launcher.md +++ b/docs/source/basic_tutorials/launcher.md @@ -66,6 +66,7 @@ Options: - bitsandbytes: Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16 - bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16 - bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model + - fp8: [FP8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) (e4m3) works on H100 and above This dtype has native ops should be the fastest if available. This is currently not the fastest because of local unpacking + padding to satisfy matrix multiplication limitations ``` ## SPECULATE diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 836b0381..cf876fbd 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -47,6 +47,11 @@ enum Quantization { /// Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better /// perplexity performance for you model BitsandbytesFP4, + /// [FP8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) (e4m3) works on H100 and above + /// This dtype has native ops should be the fastest if available. + /// This is currently not the fastest because of local unpacking + padding to satisfy matrix + /// multiplication limitations. + Fp8, } impl std::fmt::Display for Quantization { @@ -73,6 +78,9 @@ impl std::fmt::Display for Quantization { Quantization::Eetq => { write!(f, "eetq") } + Quantization::Fp8 => { + write!(f, "fp8") + } } } } diff --git a/server/text_generation_server/cli.py b/server/text_generation_server/cli.py index e8b126d9..bb0963d4 100644 --- a/server/text_generation_server/cli.py +++ b/server/text_generation_server/cli.py @@ -19,6 +19,7 @@ class Quantization(str, Enum): gptq = "gptq" awq = "awq" eetq = "eetq" + fp8 = "fp8" class Dtype(str, Enum): diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index f29e55c5..2b95bc74 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -182,6 +182,48 @@ class EETQLinear(nn.Module): return output +def fp8_quantize(weight, qdtype=torch.float8_e4m3fn): + device = weight.device + # weight, scale = quant_weights(weight, torch.int8, False) + finfo = torch.finfo(qdtype) + # Calculate the scale as dtype max divided by absmax + scale = finfo.max / weight.abs().max().clamp(min=1e-12) + # scale and clamp the tensor to bring it to + # the representative range of float8 data type + # (as default cast is unsaturated) + qweight = (weight * scale).clamp(min=finfo.min, max=finfo.max) + # Return both float8 data and the inverse scale (as float), + # as both required as inputs to torch._scaled_mm + qweight = qweight.to(qdtype) + scale = scale.float().reciprocal() + return qweight, scale + + +class Fp8Linear(nn.Module): + def __init__( + self, + weight, + bias, + ) -> None: + super().__init__() + self.dtype = weight.dtype + self.qweight, self.scale = fp8_quantize(weight) + + self.bias = bias if bias is not None else None + + def forward(self, input: torch.Tensor) -> torch.Tensor: + qinput, scale = fp8_quantize(input) + output, _ = torch._scaled_mm( + qinput, + self.qweight.t(), + out_dtype=self.dtype, + scale_a=scale, + scale_b=self.scale, + bias=self.bias, + ) + return output + + class Linear8bitLt(nn.Module): def __init__( self, @@ -293,6 +335,8 @@ def get_linear(weight, bias, quantize): raise ImportError( "Please install EETQ from https://github.com/NetEase-FuXi/EETQ" ) + elif quantize == "fp8": + linear = Fp8Linear(weight, bias) elif quantize == "bitsandbytes": warn_deprecate_bnb() linear = Linear8bitLt( From 6c2c44b84c829d2c375e4c647e3fa1ebb5905503 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 12 Apr 2024 08:15:28 +0200 Subject: [PATCH 19/74] Upgrade EETQ (Fixes the cuda graphs). (#1729) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- server/Makefile-eetq | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server/Makefile-eetq b/server/Makefile-eetq index 8c060987..726e47b5 100644 --- a/server/Makefile-eetq +++ b/server/Makefile-eetq @@ -1,4 +1,4 @@ -eetq_commit := 71adb5e191bb8290069a580abff0355d7b2dd5c9 +eetq_commit := 1657b1504faa359e2ce0ac02999439d7ac8c74c0 eetq: # Clone eetq From c2c98725f868027bc66366e3cf2cc13dd4ba90b3 Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Fri, 12 Apr 2024 10:59:04 +0200 Subject: [PATCH 20/74] fix(router): fix a possible deadlock in next_batch (#1731) --- router/src/queue.rs | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/router/src/queue.rs b/router/src/queue.rs index 52ea16ca..20f25b09 100644 --- a/router/src/queue.rs +++ b/router/src/queue.rs @@ -200,6 +200,10 @@ impl State { } } + // Pad prefill_token_budget to be a multiple of block size + let prefill_token_budget = + ((prefill_token_budget + self.block_size - 1) / self.block_size) * self.block_size; + // Create span for this batch to add context to inference calls let next_batch_span = info_span!(parent: None, "batch", batch_size = tracing::field::Empty); next_batch_span.follows_from(&Span::current()); From 9d8f21cace66e8593bc559174de0eed3ecdab6a2 Mon Sep 17 00:00:00 2001 From: Christof Weickhardt Date: Fri, 12 Apr 2024 12:34:13 +0200 Subject: [PATCH 21/74] chore(cargo-toml): apply lto fat and codegen-units of one (#1651) # What does this PR do? I have suggested similar changes over at https://github.com/huggingface/text-embeddings-inference/pull/201. Here being my additional question, why `debug` is enabled during release building? (hence I didn't add the flag to script things) Applying the following optimizations: - `lto` (link time optimizations) over all code (including dependencies) - Using a single `codegen-unit` to apply optimizations within 1 code unit at build time ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @OlivierDehaene OR @Narsil --- Cargo.toml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Cargo.toml b/Cargo.toml index 77a30f55..a9e7f1c8 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -17,5 +17,7 @@ homepage = "https://github.com/huggingface/text-generation-inference" [profile.release] debug = 1 incremental = true -lto = "off" +lto = "fat" +opt-level = 3 +codegen-units = 1 panic = "abort" From 1b2670c823e5232a4e81c16314ac6d4dc8e59cff Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 12 Apr 2024 14:20:31 +0200 Subject: [PATCH 22/74] Improve the defaults for the launcher (#1727) # What does this PR do? - Renamed `max_input_length` into `max_input_tokens` for consistency (backward compatible change, will yell if both are set.) - Will now use the config for `max_input_tokens` `max_total_token` and `max_batch_total_tokens`. - Capping the values to 16k in order to save VRAM on behalf of users (overriddable by simply setting the values). Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- Cargo.lock | 2 + docs/source/basic_tutorials/launcher.md | 26 ++- integration-tests/models/test_t5_sharded.py | 2 +- launcher/Cargo.toml | 4 +- launcher/src/main.rs | 217 ++++++++++++++++---- router/src/main.rs | 19 +- router/src/queue.rs | 6 + router/src/validation.rs | 18 +- 8 files changed, 226 insertions(+), 68 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 65b9f8b7..8c1784bf 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -3452,7 +3452,9 @@ dependencies = [ "clap", "ctrlc", "float_eq", + "hf-hub", "nix", + "once_cell", "reqwest", "serde", "serde_json", diff --git a/docs/source/basic_tutorials/launcher.md b/docs/source/basic_tutorials/launcher.md index 40ee55d7..d9b272db 100644 --- a/docs/source/basic_tutorials/launcher.md +++ b/docs/source/basic_tutorials/launcher.md @@ -60,9 +60,9 @@ Options: [env: QUANTIZE=] Possible values: - - awq: 4 bit quantization. Requires a specific AWQ quantized model: https://hf.co/models?search=awq. Should replace GPTQ models wherever possible because of the better latency - - eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from https://github.com/NetEase-FuXi/EETQ.git - - gptq: 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq. text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels + - awq: 4 bit quantization. Requires a specific AWQ quantized model: . Should replace GPTQ models wherever possible because of the better latency + - eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from + - gptq: 4 bit quantization. Requires a specific GTPQ quantized model: . text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels - bitsandbytes: Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16 - bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16 - bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model @@ -129,23 +129,29 @@ Options: [env: MAX_TOP_N_TOKENS=] [default: 5] +``` +## MAX_INPUT_TOKENS +```shell + --max-input-tokens + This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle. Default to min(max_position_embeddings - 1, 4095) + + [env: MAX_INPUT_TOKENS=] + ``` ## MAX_INPUT_LENGTH ```shell --max-input-length - This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle + Legacy version of [`Args::max_input_tokens`] [env: MAX_INPUT_LENGTH=] - [default: 1024] ``` ## MAX_TOTAL_TOKENS ```shell --max-total-tokens - This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be + This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be. Default to min(max_position_embeddings, 4096) [env: MAX_TOTAL_TOKENS=] - [default: 2048] ``` ## WAITING_SERVED_RATIO @@ -162,10 +168,9 @@ Options: ## MAX_BATCH_PREFILL_TOKENS ```shell --max-batch-prefill-tokens - Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent + Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent. Default to `max_input_tokens + 50` to give a bit of room [env: MAX_BATCH_PREFILL_TOKENS=] - [default: 4096] ``` ## MAX_BATCH_TOTAL_TOKENS @@ -210,10 +215,9 @@ Options: ## CUDA_GRAPHS ```shell --cuda-graphs - Specify the batch sizes to compute cuda graphs for. Use "0" to disable + Specify the batch sizes to compute cuda graphs for. Use "0" to disable. Default = "1,2,4,8,16,32" [env: CUDA_GRAPHS=] - [default: 1,2,4,8,16,32,64,96,128] ``` ## HOSTNAME diff --git a/integration-tests/models/test_t5_sharded.py b/integration-tests/models/test_t5_sharded.py index 7c288b23..4b4cfd98 100644 --- a/integration-tests/models/test_t5_sharded.py +++ b/integration-tests/models/test_t5_sharded.py @@ -3,7 +3,7 @@ import pytest @pytest.fixture(scope="module") def t5_sharded_handle(launcher): - with launcher("google/flan-t5-xxl", num_shard=2) as handle: + with launcher("google/flan-t5-xxl", num_shard=4) as handle: yield handle diff --git a/launcher/Cargo.toml b/launcher/Cargo.toml index 7c598c37..6b6fd58e 100644 --- a/launcher/Cargo.toml +++ b/launcher/Cargo.toml @@ -9,8 +9,10 @@ homepage.workspace = true [dependencies] clap = { version = "4.4.5", features = ["derive", "env"] } ctrlc = { version = "3.4.1", features = ["termination"] } +hf-hub = "0.3.2" nix = { version = "0.28.0", features = ["signal"] } -serde = { version = "1.0.188", features = ["derive"] } +once_cell = "1.19.0" +serde = { version = "1.0.188", features = ["derive"] } serde_json = "1.0.107" tracing = "0.1.37" tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] } diff --git a/launcher/src/main.rs b/launcher/src/main.rs index cf876fbd..be2426ee 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -1,4 +1,5 @@ use clap::{Parser, ValueEnum}; +use hf_hub::{api::sync::Api, Repo, RepoType}; use nix::sys::signal::{self, Signal}; use nix::unistd::Pid; use serde::Deserialize; @@ -19,17 +20,23 @@ use tracing_subscriber::EnvFilter; mod env_runtime; +#[derive(Deserialize)] +struct Config { + max_position_embeddings: Option, + max_seq_len: Option, +} + #[derive(Clone, Copy, Debug, ValueEnum)] enum Quantization { /// 4 bit quantization. Requires a specific AWQ quantized model: - /// https://hf.co/models?search=awq. + /// . /// Should replace GPTQ models wherever possible because of the better latency Awq, /// 8 bit quantization, doesn't require specific model. /// Should be a drop-in replacement to bitsandbytes with much better performance. - /// Kernels are from https://github.com/NetEase-FuXi/EETQ.git + /// Kernels are from Eetq, - /// 4 bit quantization. Requires a specific GTPQ quantized model: https://hf.co/models?search=gptq. + /// 4 bit quantization. Requires a specific GTPQ quantized model: . /// text-generation-inference will use exllama (faster) kernels wherever possible, and use /// triton kernel (wider support) when it's not. /// AWQ has faster kernels. @@ -214,8 +221,13 @@ struct Args { /// for users. The larger this value, the longer prompt users can send which /// can impact the overall memory required to handle the load. /// Please note that some models have a finite range of sequence they can handle. - #[clap(default_value = "1024", long, env)] - max_input_length: usize, + /// Default to min(max_position_embeddings - 1, 4095) + #[clap(long, env)] + max_input_tokens: Option, + + /// Legacy version of [`Args::max_input_tokens`]. + #[clap(long, env)] + max_input_length: Option, /// This is the most important value to set as it defines the "memory budget" /// of running clients requests. @@ -225,8 +237,9 @@ struct Args { /// `1511` max_new_tokens. /// The larger this value, the larger amount each request will be in your RAM /// and the less effective batching can be. - #[clap(default_value = "2048", long, env)] - max_total_tokens: usize, + /// Default to min(max_position_embeddings, 4096) + #[clap(long, env)] + max_total_tokens: Option, /// This represents the ratio of waiting queries vs running queries where /// you want to start considering pausing the running queries to include the waiting @@ -244,8 +257,9 @@ struct Args { /// Limits the number of tokens for the prefill operation. /// Since this operation take the most memory and is compute bound, it is interesting /// to limit the number of requests that can be sent. - #[clap(default_value = "4096", long, env)] - max_batch_prefill_tokens: u32, + /// Default to `max_input_tokens + 50` to give a bit of room. + #[clap(long, env)] + max_batch_prefill_tokens: Option, /// **IMPORTANT** This is one critical control to allow maximum usage /// of the available hardware. @@ -294,13 +308,9 @@ struct Args { /// Specify the batch sizes to compute cuda graphs for. /// Use "0" to disable. - #[clap( - long, - env, - value_delimiter = ',', - default_value = "1,2,4,8,16,32,64,96,128" - )] - cuda_graphs: Vec, + /// Default = "1,2,4,8,16,32" + #[clap(long, env, value_delimiter = ',')] + cuda_graphs: Option>, /// The IP address to listen on #[clap(default_value = "0.0.0.0", long, env)] @@ -808,6 +818,14 @@ enum LauncherError { WebserverCannotStart, } +impl core::fmt::Display for LauncherError { + fn fmt(&self, f: &mut core::fmt::Formatter) -> core::fmt::Result { + write!(f, "{self:?}") + } +} + +impl std::error::Error for LauncherError {} + fn download_convert_model(args: &Args, running: Arc) -> Result<(), LauncherError> { // Enter download tracing span let _span = tracing::span!(tracing::Level::INFO, "download").entered(); @@ -944,6 +962,7 @@ fn download_convert_model(args: &Args, running: Arc) -> Result<(), L fn spawn_shards( num_shard: usize, args: &Args, + cuda_graphs: Vec, shutdown: Arc, shutdown_receiver: &mpsc::Receiver<()>, shutdown_sender: mpsc::Sender<()>, @@ -971,11 +990,7 @@ fn spawn_shards( let disable_custom_kernels = args.disable_custom_kernels; let watermark_gamma = args.watermark_gamma; let watermark_delta = args.watermark_delta; - let cuda_graphs: Vec = args - .cuda_graphs - .iter() - .filter_map(|&c| if c > 0 { Some(c) } else { None }) - .collect(); + let cuda_graphs_clone = cuda_graphs.clone(); let cuda_memory_fraction = args.cuda_memory_fraction; let rope_scaling = args.rope_scaling; let rope_factor = args.rope_factor; @@ -997,7 +1012,7 @@ fn spawn_shards( disable_custom_kernels, watermark_gamma, watermark_delta, - cuda_graphs, + cuda_graphs_clone, cuda_memory_fraction, rope_scaling, rope_factor, @@ -1053,6 +1068,9 @@ fn compute_type(num_shard: usize) -> Option { fn spawn_webserver( num_shard: usize, args: Args, + max_input_tokens: usize, + max_total_tokens: usize, + max_batch_prefill_tokens: u32, shutdown: Arc, shutdown_receiver: &mpsc::Receiver<()>, ) -> Result { @@ -1068,12 +1086,12 @@ fn spawn_webserver( args.max_stop_sequences.to_string(), "--max-top-n-tokens".to_string(), args.max_top_n_tokens.to_string(), - "--max-input-length".to_string(), - args.max_input_length.to_string(), + "--max-input-tokens".to_string(), + max_input_tokens.to_string(), "--max-total-tokens".to_string(), - args.max_total_tokens.to_string(), + max_total_tokens.to_string(), "--max-batch-prefill-tokens".to_string(), - args.max_batch_prefill_tokens.to_string(), + max_batch_prefill_tokens.to_string(), "--waiting-served-ratio".to_string(), args.waiting_served_ratio.to_string(), "--max-waiting-tokens".to_string(), @@ -1251,19 +1269,129 @@ fn main() -> Result<(), LauncherError> { tracing::info!("{:?}", args); + let get_max_position_embeddings = || -> Result> { + let model_id = args.model_id.clone(); + let mut path = std::path::Path::new(&args.model_id).to_path_buf(); + let filename = if !path.exists() { + // Assume it's a hub id + let api = Api::new()?; + let repo = if let Some(ref revision) = args.revision { + api.repo(Repo::with_revision( + model_id, + RepoType::Model, + revision.to_string(), + )) + } else { + api.model(model_id) + }; + repo.get("config.json")? + } else { + path.push("config.json"); + path + }; + + let content = std::fs::read_to_string(filename)?; + let config: Config = serde_json::from_str(&content)?; + + // Quantization usually means you're even more RAM constrained. + let max_default = 4096; + + let max_position_embeddings = match (config.max_position_embeddings, config.max_seq_len) { + (Some(max_position_embeddings), _) | (None, Some(max_position_embeddings)) => { + if max_position_embeddings > max_default { + let max = max_position_embeddings; + tracing::info!("Model supports up to {max} but tgi will now set its default to {max_default} instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens={} --max-total-tokens={max} --max-input-tokens={}`.", max + 50, max - 1); + max_default + } else { + max_position_embeddings + } + } + _ => { + return Err(Box::new(LauncherError::ArgumentValidation( + "no max defined".to_string(), + ))); + } + }; + Ok(max_position_embeddings) + }; + let max_position_embeddings: usize = get_max_position_embeddings().unwrap_or(4096); + + let max_input_tokens = { + match (args.max_input_tokens, args.max_input_length) { + (Some(max_input_tokens), Some(max_input_length)) => { + return Err(LauncherError::ArgumentValidation( + format!("Both `max_input_tokens` ({max_input_tokens}) and `max_input_length` ({max_input_length}) are set. Please define only `max_input_tokens` as `max_input_length is deprecated for naming consistency.", + ))); + } + (Some(max_input_tokens), None) | (None, Some(max_input_tokens)) => max_input_tokens, + (None, None) => { + let value = max_position_embeddings - 1; + tracing::info!("Default `max_input_tokens` to {value}"); + value + } + } + }; + let max_total_tokens = { + match args.max_total_tokens { + Some(max_total_tokens) => max_total_tokens, + None => { + let value = max_position_embeddings; + tracing::info!("Default `max_total_tokens` to {value}"); + value + } + } + }; + let max_batch_prefill_tokens = { + match args.max_batch_prefill_tokens { + Some(max_batch_prefill_tokens) => max_batch_prefill_tokens, + None => { + let value: u32 = if let Some(max_batch_size) = args.max_batch_size { + max_batch_size * max_input_tokens + } else { + // Adding some edge in order to account for potential block_size alignement + // issue. + max_input_tokens + 50 + } as u32; + tracing::info!("Default `max_batch_prefill_tokens` to {value}"); + value + } + } + }; + // Validate args - if args.max_input_length >= args.max_total_tokens { + if max_input_tokens >= max_total_tokens { return Err(LauncherError::ArgumentValidation( - "`max_input_length` must be < `max_total_tokens`".to_string(), + "`max_input_tokens must be < `max_total_tokens`".to_string(), )); } - if args.max_input_length as u32 > args.max_batch_prefill_tokens { + if max_input_tokens as u32 > max_batch_prefill_tokens { return Err(LauncherError::ArgumentValidation(format!( - "`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {} and {}", - args.max_batch_prefill_tokens, args.max_input_length + "`max_batch_prefill_tokens` must be >= `max_input_tokens`. Given: {} and {}", + max_batch_prefill_tokens, max_input_tokens ))); } + let cuda_graphs = match (&args.cuda_graphs, &args.quantize) { + (Some(cuda_graphs), Some(_q)) => cuda_graphs.clone(), + #[allow(deprecated)] + ( + None, + Some( + Quantization::Bitsandbytes + | Quantization::BitsandbytesNF4 + | Quantization::BitsandbytesFP4, + ), + ) => { + tracing::info!("Bitsandbytes doesn't work with cuda graphs, deactivating them"); + vec![] + } + _ => { + let cuda_graphs = vec![1, 2, 4, 8, 16, 32]; + tracing::info!("Using default cuda graphs {cuda_graphs:?}"); + cuda_graphs + } + }; + if args.validation_workers == 0 { return Err(LauncherError::ArgumentValidation( "`validation_workers` must be > 0".to_string(), @@ -1282,16 +1410,16 @@ fn main() -> Result<(), LauncherError> { } if let Some(ref max_batch_total_tokens) = args.max_batch_total_tokens { - if args.max_batch_prefill_tokens > *max_batch_total_tokens { + if max_batch_prefill_tokens > *max_batch_total_tokens { return Err(LauncherError::ArgumentValidation(format!( "`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}", - args.max_batch_prefill_tokens, max_batch_total_tokens + max_batch_prefill_tokens, max_batch_total_tokens ))); } - if args.max_total_tokens as u32 > *max_batch_total_tokens { + if max_total_tokens as u32 > *max_batch_total_tokens { return Err(LauncherError::ArgumentValidation(format!( "`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {} and {}", - args.max_total_tokens, max_batch_total_tokens + max_total_tokens, max_batch_total_tokens ))); } } @@ -1338,6 +1466,7 @@ fn main() -> Result<(), LauncherError> { spawn_shards( num_shard, &args, + cuda_graphs, shutdown.clone(), &shutdown_receiver, shutdown_sender, @@ -1352,11 +1481,19 @@ fn main() -> Result<(), LauncherError> { return Ok(()); } - let mut webserver = spawn_webserver(num_shard, args, shutdown.clone(), &shutdown_receiver) - .map_err(|err| { - shutdown_shards(shutdown.clone(), &shutdown_receiver); - err - })?; + let mut webserver = spawn_webserver( + num_shard, + args, + max_input_tokens, + max_total_tokens, + max_batch_prefill_tokens, + shutdown.clone(), + &shutdown_receiver, + ) + .map_err(|err| { + shutdown_shards(shutdown.clone(), &shutdown_receiver); + err + })?; // Default exit code let mut exit_code = Ok(()); diff --git a/router/src/main.rs b/router/src/main.rs index aace2ff9..f3a6c46f 100644 --- a/router/src/main.rs +++ b/router/src/main.rs @@ -35,7 +35,7 @@ struct Args { #[clap(default_value = "5", long, env)] max_top_n_tokens: u32, #[clap(default_value = "1024", long, env)] - max_input_length: usize, + max_input_tokens: usize, #[clap(default_value = "2048", long, env)] max_total_tokens: usize, #[clap(default_value = "1.2", long, env)] @@ -90,7 +90,7 @@ async fn main() -> Result<(), RouterError> { max_best_of, max_stop_sequences, max_top_n_tokens, - max_input_length, + max_input_tokens, max_total_tokens, waiting_served_ratio, max_batch_prefill_tokens, @@ -118,13 +118,13 @@ async fn main() -> Result<(), RouterError> { init_logging(otlp_endpoint, json_output); // Validate args - if max_input_length >= max_total_tokens { + if max_input_tokens >= max_total_tokens { return Err(RouterError::ArgumentValidation( - "`max_input_length` must be < `max_total_tokens`".to_string(), + "`max_input_tokens` must be < `max_total_tokens`".to_string(), )); } - if max_input_length as u32 > max_batch_prefill_tokens { - return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {max_batch_prefill_tokens} and {max_input_length}"))); + if max_input_tokens as u32 > max_batch_prefill_tokens { + return Err(RouterError::ArgumentValidation(format!("`max_batch_prefill_tokens` must be >= `max_input_tokens`. Given: {max_batch_prefill_tokens} and {max_input_tokens}"))); } if validation_workers == 0 { @@ -311,7 +311,7 @@ async fn main() -> Result<(), RouterError> { tracing::info!("Warming up model"); let max_supported_batch_total_tokens = match sharded_client .warmup( - max_input_length as u32, + max_input_tokens as u32, max_batch_prefill_tokens, max_total_tokens as u32, max_batch_size, @@ -374,7 +374,7 @@ async fn main() -> Result<(), RouterError> { max_best_of, max_stop_sequences, max_top_n_tokens, - max_input_length, + max_input_tokens, max_total_tokens, waiting_served_ratio, max_batch_prefill_tokens, @@ -402,12 +402,15 @@ async fn main() -> Result<(), RouterError> { /// - otlp_endpoint is an optional URL to an Open Telemetry collector /// - LOG_LEVEL may be TRACE, DEBUG, INFO, WARN or ERROR (default to INFO) /// - LOG_FORMAT may be TEXT or JSON (default to TEXT) +/// - LOG_COLORIZE may be "false" or "true" (default to "true" or ansi supported platforms) fn init_logging(otlp_endpoint: Option, json_output: bool) { let mut layers = Vec::new(); // STDOUT/STDERR layer + let ansi = std::env::var("LOG_COLORIZE") != Ok("1".to_string()); let fmt_layer = tracing_subscriber::fmt::layer() .with_file(true) + .with_ansi(ansi) .with_line_number(true); let fmt_layer = match json_output { diff --git a/router/src/queue.rs b/router/src/queue.rs index 20f25b09..a32673dd 100644 --- a/router/src/queue.rs +++ b/router/src/queue.rs @@ -190,12 +190,14 @@ impl State { token_budget: u32, ) -> Option { if self.entries.is_empty() { + tracing::debug!("No queue"); return None; } // Check if we have enough entries if let Some(min_size) = min_size { if self.entries.len() < min_size { + tracing::debug!("Not enough entries"); return None; } } @@ -222,6 +224,7 @@ impl State { // was dropped by the client) if entry.response_tx.is_closed() { metrics::increment_counter!("tgi_request_failure", "err" => "dropped"); + tracing::debug!("Dropping entry"); continue; } @@ -258,10 +261,12 @@ impl State { { // Entry is over budget // Add it back to the front + tracing::debug!("Over budget: prefill_tokens={prefill_tokens} > {prefill_token_budget} || {prefill_tokens} + {decode_tokens} + {} > {token_budget}", self.speculate); self.entries.push_front((id, entry)); break; } + tracing::debug!("Accepting entry"); // Create a new span to link the batch back to this entry let entry_batch_span = info_span!(parent: &entry.span, "infer"); // Add relationships @@ -292,6 +297,7 @@ impl State { // Empty batch if batch_requests.is_empty() { + tracing::debug!("Filterered out all entries"); return None; } diff --git a/router/src/validation.rs b/router/src/validation.rs index 24bcf191..2029c7e0 100644 --- a/router/src/validation.rs +++ b/router/src/validation.rs @@ -161,14 +161,17 @@ impl Validation { } else { return Err(ValidationError::UnsetMaxNewTokens); }; - let input_length = truncate.unwrap_or(self.max_input_length); + let mut input_length = truncate.unwrap_or(self.max_input_length); + // We don't have a tokenizer, therefore we have no idea how long is the query, let + // them through and hope for the best. // Validate MaxNewTokens if (input_length as u32 + max_new_tokens) > self.max_total_tokens as u32 { - return Err(ValidationError::MaxNewTokens( - self.max_total_tokens - self.max_input_length, - max_new_tokens, - )); + input_length = input_length.saturating_sub(max_new_tokens as usize); + // return Err(ValidationError::MaxNewTokens( + // self.max_total_tokens - self.max_input_length, + // max_new_tokens, + // )); } Ok((inputs, input_length, max_new_tokens)) @@ -664,8 +667,9 @@ mod tests { .validate_input("Hello".to_string(), None, Some(max_new_tokens)) .await { - Err(ValidationError::MaxNewTokens(1, 10)) => (), - _ => panic!("Unexpected not max new tokens"), + // Err(ValidationError::MaxNewTokens(1, 10)) => (), + Ok((_s, 0, 10)) => (), + r => panic!("Unexpected not max new tokens: {r:?}"), } } From eefea5ee3184179b2f440238e403d26e34a17491 Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Fri, 12 Apr 2024 16:24:45 +0200 Subject: [PATCH 23/74] feat: medusa v2 (#1734) --- .../text_generation_server/models/__init__.py | 2 +- .../models/flash_causal_lm.py | 24 +-- server/text_generation_server/utils/layers.py | 165 +++++++++++++++--- 3 files changed, 146 insertions(+), 45 deletions(-) diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index 00c8b9a8..06792b0d 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -145,7 +145,7 @@ def get_model( if speculate is not None: if speculate > speculate_medusa: raise RuntimeError( - "Speculate is set to `{speculate}` but this medusa models only has `{speculate_medusa}` heads, please make them match" + f"Speculate is set to `{speculate}` but this medusa models only has `{speculate_medusa}` heads, please make them match" ) else: set_speculate(speculate) diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index 2a9d3914..2c440083 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -814,7 +814,7 @@ class FlashCausalLM(Model): for bs in CUDA_GRAPHS: if self.speculate is None or self.speculate + 1 <= bs: self.cuda_graph_warmup(bs, max_s, max_bt) - except Exception: + except torch.cuda.OutOfMemoryError: logger.exception(f"Decode cuda graph warmup failed") return int(num_blocks * BLOCK_SIZE) @@ -874,22 +874,14 @@ class FlashCausalLM(Model): lm_head_indices = batch.prefill_head_indices bs = input_ids.shape[0] - padded_bs = bs - if bs == 3: - padded_bs = 4 - elif 3 < bs <= 8: - padded_bs = 8 - elif bs > 8: - padded_bs = (bs + 7) // 8 * 8 + sorted_padded_bs = sorted([k for k in self.cuda_graphs.keys() if k >= bs]) + if sorted_padded_bs: + # Get associated cuda graph + cuda_graph = self.cuda_graphs[sorted_padded_bs[0]] + else: + cuda_graph = None - # Try to find an associated cuda graph - cuda_graph = self.cuda_graphs.get(padded_bs, None) - - if ( - cu_seqlen_prefill is not None - or cuda_graph is None - or batch.speculative_ids is not None - ): + if cu_seqlen_prefill is not None or cuda_graph is None: return self.model.forward( input_ids=input_ids, position_ids=position_ids, diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index 2b95bc74..2c12984b 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -432,12 +432,12 @@ class ResBlock(torch.nn.Module): class MedusaModel(torch.nn.Module): - def __init__(self, config, weights): + def __init__(self, config, medusa_config, weights): super().__init__() self.heads = torch.nn.ModuleList( [ - MedusaHead(config, prefix=f"{i}", weights=weights) - for i in range(config["medusa_num_heads"]) + MedusaHead(config, medusa_config, prefix=f"{i}", weights=weights) + for i in range(medusa_config["medusa_num_heads"]) ] ) @@ -447,12 +447,12 @@ class MedusaModel(torch.nn.Module): class MedusaHead(torch.nn.Module): - def __init__(self, config, prefix, weights): + def __init__(self, config, medusa_config, prefix, weights): super().__init__() self.blocks = torch.nn.ModuleList( [ ResBlock(config, prefix=f"{prefix}.{i}", weights=weights) - for i in range(config["medusa_num_layers"]) + for i in range(medusa_config["medusa_num_layers"]) ] ) n = len(self.blocks) @@ -467,7 +467,7 @@ class MedusaHead(torch.nn.Module): return x -class SpeculativeHead(nn.Module): +class MedusaHeadV1(nn.Module): def __init__(self, lm_head, medusa): super().__init__() self.lm_head = lm_head @@ -475,38 +475,147 @@ class SpeculativeHead(nn.Module): @staticmethod def load(config, prefix: str, weights): + from pathlib import Path + from safetensors import safe_open + import json + + use_medusa = config.use_medusa + + medusa_config = str(Path(use_medusa) / "config.json") + filename = str(Path(use_medusa) / "medusa_lm_head.safetensors") + + with open(medusa_config, "r") as f: + medusa_config = json.load(f) + routing = weights.routing + with safe_open(filename, framework="pytorch") as f: + for k in f.keys(): + if k in routing and routing[k] != filename: + raise RuntimeError( + f"Key {k} was found in multiple files: {filename} and {routing[k]}" + ) + routing[k] = filename + + medusa = MedusaModel(config, medusa_config, weights) lm_head = TensorParallelHead.load(config, prefix, weights) + return MedusaHeadV1(lm_head, medusa) + + def forward( + self, input: torch.Tensor + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + logits = self.lm_head(input) + speculative_logits = self.medusa(input) + return logits, speculative_logits + + +class MedusaHeadV2(nn.Module): + def __init__(self, config, prefix, weights): + super().__init__() + from pathlib import Path + from safetensors import safe_open + import json + + use_medusa = config.use_medusa + + medusa_config = str(Path(use_medusa) / "config.json") + filename = str(Path(use_medusa) / "medusa_lm_head.safetensors") + + with open(medusa_config, "r") as f: + medusa_config = json.load(f) + routing = weights.routing + with safe_open(filename, framework="pytorch") as f: + for k in f.keys(): + if k in routing and routing[k] != filename: + raise RuntimeError( + f"Key {k} was found in multiple files: {filename} and {routing[k]}" + ) + routing[k] = filename + + self.n_medusa_heads = medusa_config["medusa_num_heads"] + + assert medusa_config["medusa_num_layers"] == 1 + self.linear = TensorParallelColumnLinear.load_multi( + config, + prefixes=[f"{i}.0.linear" for i in range(self.n_medusa_heads)], + dim=0, + weights=weights, + bias=True, + ) + self.process_group = weights.process_group + self.world_size = self.process_group.size() + self.rank = self.process_group.rank() + + self.act = torch.nn.SiLU() + + self.lm_head = TensorParallelHead.load(config, prefix, weights) + + def forward(self, x): + size = x.shape[-1] + block_size = (size + self.world_size - 1) // self.world_size + start = self.rank * block_size + stop = (self.rank + 1) * block_size + + x_block = x[:, start:stop] + + # Compute all medusa heads at the same time, then reshape and move the n_medusa_heads dim to dim 1 + medusa_res = self.act(self.linear(x)).reshape( + *x_block.shape[:-1], self.n_medusa_heads, x_block.shape[-1] + ) + + # Apply all residual medusa heads + output = x[:, start:stop].unsqueeze(-2) + medusa_res + + # Gather medusa heads + world_output = [ + torch.empty_like(output) for _ in range(self.process_group.size()) + ] + torch.distributed.all_gather(world_output, output, group=self.process_group) + world_output = torch.cat(world_output, dim=-1) + + # Stack x and medusa residual x + stacked_x = torch.cat([x.unsqueeze(-2), world_output], dim=-2) + + # Compute lm head on x + medusa residual x + logits = self.lm_head(stacked_x) + + # Finally, split logits from speculative logits + logits, speculative_logits = torch.split( + logits, [1, self.n_medusa_heads], dim=-2 + ) + # Squeeze added dimension + logits = logits.squeeze(-2) + + return logits, speculative_logits + + +class SpeculativeHead(nn.Module): + def __init__(self, lm_head, medusa): + super().__init__() + self.head = lm_head + self.medusa = medusa + + @staticmethod + def load(config, prefix: str, weights): use_medusa = config.use_medusa if use_medusa: - from pathlib import Path - from safetensors import safe_open - import json - - medusa_config = str(Path(use_medusa) / "config.json") - filename = str(Path(use_medusa) / "medusa_lm_head.safetensors") - - with open(medusa_config, "r") as f: - config = json.load(f) - routing = weights.routing - with safe_open(filename, framework="pytorch") as f: - for k in f.keys(): - if k in routing: - raise RuntimeError( - f"Key {k} was found in multiple files: {filename} and {routing[k]}" - ) - weights.routing[k] = filename - - medusa = MedusaModel(config, weights) + lm_head = None + try: + medusa = MedusaHeadV1.load(config, prefix, weights) + except: + medusa = MedusaHeadV2(config, prefix, weights) else: + lm_head = TensorParallelHead.load(config, prefix, weights) medusa = None return SpeculativeHead(lm_head, medusa) def forward( self, input: torch.Tensor ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - logits = self.lm_head(input) - speculative_logits = self.medusa(input) if self.medusa is not None else None - return logits, speculative_logits + if self.medusa is not None: + return self.medusa(input) + + assert self.head is not None + logits = self.head(input) + return logits, None class TensorParallelHead(SuperLayer): From 275caa04b1ce69e3ea513e9bc7629e410be77b3e Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Fri, 12 Apr 2024 23:51:07 +0900 Subject: [PATCH 24/74] Fix typo in guidance.md (#1735) # What does this PR do? compliation -> compilation ## Before submitting - [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- docs/source/conceptual/guidance.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conceptual/guidance.md b/docs/source/conceptual/guidance.md index e3e80f22..0a3bbd60 100644 --- a/docs/source/conceptual/guidance.md +++ b/docs/source/conceptual/guidance.md @@ -74,7 +74,7 @@ curl localhost:3000/generate \ A grammar can be defined using Pydantic models, JSON schema, or regular expressions. The AI will then generate a response that conforms to the specified grammar. -> Note: A grammar must compile to a intermediate representation to constrain the output. Grammar compliation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster. +> Note: A grammar must compile to a intermediate representation to constrain the output. Grammar compilation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster. ### Constrain with Pydantic From c38a7d7ddd9c612e368adec1ef94583be602fc7e Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Fri, 12 Apr 2024 18:38:34 +0200 Subject: [PATCH 25/74] v2.0.0 (#1736) --- Cargo.lock | 213 ++++++++---------- Cargo.toml | 2 +- README.md | 6 +- docs/openapi.json | 2 +- .../test_flash_llama_grammar_no_tools.json | 2 +- .../test_flash_llama_grammar_tools.json | 2 +- .../test_flash_llama_grammar_tools_auto.json | 2 +- ...test_flash_llama_grammar_tools_choice.json | 2 +- ...test_flash_llama_grammar_tools_stream.json | 2 +- integration-tests/pyproject.toml | 2 +- server/pyproject.toml | 2 +- server/text_generation_server/interceptor.py | 4 + .../models/cache_manager.py | 7 +- server/text_generation_server/utils/layers.py | 9 + 14 files changed, 124 insertions(+), 133 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 8c1784bf..fd6baab5 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -96,9 +96,9 @@ dependencies = [ [[package]] name = "anyhow" -version = "1.0.81" +version = "1.0.82" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0952808a6c2afd1aa8947271f3a60f1a6763c7b912d210184c5149b5cf147247" +checksum = "f538837af36e6f6a9be0faa67f9a314f8119e4e4b5867c6ab40ed60360142519" [[package]] name = "arbitrary" @@ -120,7 +120,7 @@ checksum = "0ae92a5119aa49cdbcf6b9f893fe4e1d98b04ccbf82ee0584ad948a44a734dea" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -159,18 +159,18 @@ checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] name = "async-trait" -version = "0.1.79" +version = "0.1.80" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a507401cad91ec6a857ed5513a2073c82a9b9048762b885bb98655b306964681" +checksum = "c6fa2087f2753a7da8cc1c0dbfcf89579dd57458e36769de5ac750b4671737ca" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -375,15 +375,15 @@ dependencies = [ [[package]] name = "built" -version = "0.7.1" +version = "0.7.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "38d17f4d6e4dc36d1a02fbedc2753a096848e7c1b0772f7654eab8e2c927dd53" +checksum = "41bfbdb21256b87a8b5e80fab81a8eed158178e812fd7ba451907518b2742f16" [[package]] name = "bumpalo" -version = "3.15.4" +version = "3.16.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7ff69b9dd49fd426c69a0db9fc04dd934cdb6645ff000864d98f7e2af8830eaa" +checksum = "79296716171880943b8470b5f8d03aa55eb2e645a4874bdbb28adb49162e012c" [[package]] name = "bytecount" @@ -449,9 +449,9 @@ checksum = "df8670b8c7b9dae1793364eafadf7239c40d669904660c5960d74cfd80b46a53" [[package]] name = "cc" -version = "1.0.90" +version = "1.0.92" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8cd6604a82acf3039f1144f54b8eb34e91ffba622051189e71b781822d5ee1f5" +checksum = "2678b2e3449475e95b0aa6f9b506a28e61b3dc8996592b983695e8ebb58a8b41" dependencies = [ "jobserver", "libc", @@ -459,9 +459,9 @@ dependencies = [ [[package]] name = "cfg-expr" -version = "0.15.7" +version = "0.15.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fa50868b64a9a6fda9d593ce778849ea8715cd2a3d2cc17ffdb4a2f2f2f1961d" +checksum = "d067ad48b8650848b989a59a86c6c36a995d02d2bf778d45c3c5d57bc2718f02" dependencies = [ "smallvec", "target-lexicon", @@ -498,7 +498,7 @@ dependencies = [ "anstream", "anstyle", "clap_lex", - "strsim 0.11.0", + "strsim 0.11.1", ] [[package]] @@ -510,7 +510,7 @@ dependencies = [ "heck 0.5.0", "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -812,9 +812,9 @@ checksum = "a357d28ed41a50f9c765dbfe56cbc04a64e53e5fc58ba79fbc34c10ef3df831f" [[package]] name = "encoding_rs" -version = "0.8.33" +version = "0.8.34" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7268b386296a025e474d5140678f75d6de9493ae55a5d709eeb9dd08149945e1" +checksum = "b45de904aa0b010bce2ab45264d0631681847fa7b6f2eaa7dab7619943bc4f59" dependencies = [ "cfg-if", ] @@ -1018,7 +1018,7 @@ checksum = "87750cf4b7a4c0625b1529e4c543c2182106e4dedc60a2a6455e00d212c489ac" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -1072,9 +1072,9 @@ dependencies = [ [[package]] name = "getrandom" -version = "0.2.12" +version = "0.2.14" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "190092ea657667030ac6a35e305e62fc4dd69fd98ac98631e5d3a2b1575a12b5" +checksum = "94b22e06ecb0110981051723910cbf0b5f5e09a2062dd7663334ee79a9d1286c" dependencies = [ "cfg-if", "js-sys", @@ -1111,9 +1111,9 @@ dependencies = [ [[package]] name = "h2" -version = "0.3.25" +version = "0.3.26" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4fbd2820c5e49886948654ab546d0688ff24530286bdcf8fca3cefb16d4618eb" +checksum = "81fe527a889e1532da5c525686d96d4c2e74cdd345badf8dfef9f6b39dd5f5e8" dependencies = [ "bytes", "fnv", @@ -1198,15 +1198,6 @@ dependencies = [ "ureq", ] -[[package]] -name = "home" -version = "0.5.9" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e3d1354bf6b7235cb4a0576c2619fd4ed18183f689b12b006a0ee7329eeff9a5" -dependencies = [ - "windows-sys 0.52.0", -] - [[package]] name = "hostname" version = "0.3.1" @@ -1432,7 +1423,7 @@ checksum = "c34819042dc3d3971c46c2190835914dfbe0c3c13f61449b2997f4e9722dfa60" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -1485,9 +1476,9 @@ checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b" [[package]] name = "jobserver" -version = "0.1.28" +version = "0.1.29" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ab46a6e9526ddef3ae7f787c06f0f2600639ba80ea3eade3d8e670a2230f51d6" +checksum = "f08474e32172238f2827bd160c67871cdb2801430f65c3979184dc362e3ca118" dependencies = [ "libc", ] @@ -1574,13 +1565,12 @@ checksum = "4ec2a862134d2a7d32d7983ddcdd1c4923530833c9f2ea1a44fc5fa473989058" [[package]] name = "libredox" -version = "0.0.1" +version = "0.1.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "85c833ca1e66078851dba29046874e38f08b2c883700aa29a03ddd3b23814ee8" +checksum = "c0ff37bd590ca25063e35af745c343cb7a0271906fb7b37e4813e8f79f00268d" dependencies = [ "bitflags 2.5.0", "libc", - "redox_syscall", ] [[package]] @@ -1713,7 +1703,7 @@ checksum = "38b4faf00617defe497754acde3024865bc143d44a86799b24e191ecff91354f" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -1801,14 +1791,14 @@ checksum = "f686d68a09079e63b1d2c64aa305095887ce50565f00a922ebfaeeee0d9ba6ce" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] name = "multimap" -version = "0.8.3" +version = "0.10.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e5ce46fe64a9d73be07dcbe690a38ce1b293be448fd8ce1e6c1b8062c9f72c6a" +checksum = "defc4c55412d89136f966bbb339008b474350e5e6e78d2714439c386b3137a03" [[package]] name = "muxado" @@ -1991,7 +1981,7 @@ checksum = "ed3955f1a9c7c0c15e092f9c887db08b1fc683305fdf6eb6684f22555355e202" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -2121,7 +2111,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -2337,14 +2327,14 @@ checksum = "2f38a4412a78282e09a2cf38d195ea5420d15ba0602cb375210efbc877243965" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] name = "pin-project-lite" -version = "0.2.13" +version = "0.2.14" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8afb450f006bf6385ca15ef45d71d2288452bc3683ce2e2cacc0d18e4be60b58" +checksum = "bda66fc9667c18cb2758a2ac84d1167245054bcf85d5d1aaa6923f45801bdd02" [[package]] name = "pin-utils" @@ -2396,7 +2386,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "8d3928fb5db768cb86f891ff014f0144589297e3c6a1aba6ed7cecfdace270c7" dependencies = [ "proc-macro2", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -2448,7 +2438,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "8021cf59c8ec9c432cfc2526ac6b8aa508ecaf29cd415f271b8406c1b851c3fd" dependencies = [ "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -2463,34 +2453,33 @@ dependencies = [ [[package]] name = "prost" -version = "0.12.3" +version = "0.12.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "146c289cda302b98a28d40c8b3b90498d6e526dd24ac2ecea73e4e491685b94a" +checksum = "d0f5d036824e4761737860779c906171497f6d55681139d8312388f8fe398922" dependencies = [ "bytes", - "prost-derive 0.12.3", + "prost-derive 0.12.4", ] [[package]] name = "prost-build" -version = "0.12.3" +version = "0.12.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c55e02e35260070b6f716a2423c2ff1c3bb1642ddca6f99e1f26d06268a0e2d2" +checksum = "80b776a1b2dc779f5ee0641f8ade0125bc1298dd41a9a0c16d8bd57b42d222b1" dependencies = [ "bytes", - "heck 0.4.1", - "itertools 0.11.0", + "heck 0.5.0", + "itertools 0.12.1", "log", "multimap", "once_cell", "petgraph", "prettyplease", - "prost 0.12.3", + "prost 0.12.4", "prost-types", "regex", - "syn 2.0.55", + "syn 2.0.58", "tempfile", - "which", ] [[package]] @@ -2508,24 +2497,24 @@ dependencies = [ [[package]] name = "prost-derive" -version = "0.12.3" +version = "0.12.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "efb6c9a1dd1def8e2124d17e83a20af56f1570d6c2d2bd9e266ccb768df3840e" +checksum = "19de2de2a00075bf566bee3bd4db014b11587e84184d3f7a791bc17f1a8e9e48" dependencies = [ "anyhow", - "itertools 0.11.0", + "itertools 0.12.1", "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] name = "prost-types" -version = "0.12.3" +version = "0.12.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "193898f59edcf43c26227dcd4c8427f00d99d61e95dcde58dabd49fa291d470e" +checksum = "3235c33eb02c1f1e212abdbe34c78b264b038fb58ca612664343271e36e55ffe" dependencies = [ - "prost 0.12.3", + "prost 0.12.4", ] [[package]] @@ -2561,9 +2550,9 @@ checksum = "a993555f31e5a609f617c12db6250dedcac1b0a85076912c436e6fc9b2c8e6a3" [[package]] name = "quote" -version = "1.0.35" +version = "1.0.36" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "291ec9ab5efd934aaf503a6466c5d5251535d108ee747472c3977cc5acc868ef" +checksum = "0fa76aaf39101c457836aec0ce2316dbdc3ab723cdda1c6bd4e6ad4208acaca7" dependencies = [ "proc-macro2", ] @@ -2716,9 +2705,9 @@ dependencies = [ [[package]] name = "redox_users" -version = "0.4.4" +version = "0.4.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a18479200779601e498ada4e8c1e1f50e3ee19deb0259c25825a98b5603b2cb4" +checksum = "bd283d9651eeda4b2a83a43c1c91b266c40fd76ecd39a50a8c630ae69dc72891" dependencies = [ "getrandom", "libredox", @@ -2875,7 +2864,7 @@ dependencies = [ "quote", "rust-embed-utils", "shellexpand", - "syn 2.0.55", + "syn 2.0.58", "walkdir", ] @@ -2971,9 +2960,9 @@ dependencies = [ [[package]] name = "rustversion" -version = "1.0.14" +version = "1.0.15" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7ffc183a10b4478d04cbbbfc96d0873219d962dd5accaff2ffbd4ceb7df837f4" +checksum = "80af6f9131f277a45a3fba6ce8e2258037bb0477a67e610d3c1fe046ab31de47" [[package]] name = "ryu" @@ -3017,9 +3006,9 @@ dependencies = [ [[package]] name = "security-framework" -version = "2.9.2" +version = "2.10.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "05b64fb303737d99b81884b2c63433e9ae28abebe5eb5045dcdd175dc2ecf4de" +checksum = "770452e37cad93e0a50d5abc3990d2bc351c36d0328f86cefec2f2fb206eaef6" dependencies = [ "bitflags 1.3.2", "core-foundation", @@ -3030,9 +3019,9 @@ dependencies = [ [[package]] name = "security-framework-sys" -version = "2.9.1" +version = "2.10.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e932934257d3b408ed8f30db49d85ea163bfe74961f017f405b025af298f0c7a" +checksum = "41f3cc463c0ef97e11c3461a9d3787412d30e8e7eb907c79180c4a57bf7c04ef" dependencies = [ "core-foundation-sys", "libc", @@ -3064,7 +3053,7 @@ checksum = "7eb0b34b42edc17f6b7cac84a52a1c5f0e1bb2227e997ca9011ea3dd34e8610b" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -3258,9 +3247,9 @@ checksum = "73473c0e59e6d5812c5dfe2a064a6444949f089e20eec9a2e5506596494e4623" [[package]] name = "strsim" -version = "0.11.0" +version = "0.11.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5ee073c9e4cd00e28217186dbe12796d692868f432bf2e97ee73bed0c56dfa01" +checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f" [[package]] name = "strum" @@ -3281,7 +3270,7 @@ dependencies = [ "proc-macro2", "quote", "rustversion", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -3303,9 +3292,9 @@ dependencies = [ [[package]] name = "syn" -version = "2.0.55" +version = "2.0.58" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "002a1b3dbf967edfafc32655d0f377ab0bb7b994aa1d32c8cc7e9b8bf3ebb8f0" +checksum = "44cfb93f38070beee36b3fef7d4f5a16f27751d94b187b666a5cc5e9b0d30687" dependencies = [ "proc-macro2", "quote", @@ -3320,9 +3309,9 @@ checksum = "2047c6ded9c721764247e62cd3b03c09ffc529b2ba5b10ec482ae507a4a70160" [[package]] name = "sysinfo" -version = "0.30.7" +version = "0.30.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0c385888ef380a852a16209afc8cfad22795dd8873d69c9a14d2e2088f118d18" +checksum = "26d7c217777061d5a2d652aea771fb9ba98b6dade657204b08c4b9604d11555b" dependencies = [ "cfg-if", "core-foundation-sys", @@ -3410,7 +3399,7 @@ dependencies = [ [[package]] name = "text-generation-benchmark" -version = "1.4.5" +version = "2.0.0" dependencies = [ "average", "clap", @@ -3431,11 +3420,11 @@ dependencies = [ [[package]] name = "text-generation-client" -version = "1.4.5" +version = "2.0.0" dependencies = [ "futures", "grpc-metadata", - "prost 0.12.3", + "prost 0.12.4", "prost-build", "thiserror", "tokio", @@ -3447,7 +3436,7 @@ dependencies = [ [[package]] name = "text-generation-launcher" -version = "1.4.5" +version = "2.0.0" dependencies = [ "clap", "ctrlc", @@ -3465,7 +3454,7 @@ dependencies = [ [[package]] name = "text-generation-router" -version = "1.4.5" +version = "2.0.0" dependencies = [ "async-stream", "axum", @@ -3522,7 +3511,7 @@ checksum = "c61f3ba182994efc43764a46c018c347bc492c79f024e705f46567b418f6d4f7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -3548,9 +3537,9 @@ dependencies = [ [[package]] name = "time" -version = "0.3.34" +version = "0.3.36" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c8248b6521bb14bc45b4067159b9b6ad792e2d6d754d6c41fb50e29fefe38749" +checksum = "5dfd88e563464686c916c7e46e623e520ddc6d79fa6641390f2e3fa86e83e885" dependencies = [ "deranged", "itoa", @@ -3571,9 +3560,9 @@ checksum = "ef927ca75afb808a4d64dd374f00a2adf8d0fcff8e7b184af886c3c87ec4a3f3" [[package]] name = "time-macros" -version = "0.2.17" +version = "0.2.18" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7ba3a3ef41e6672a2f0f001392bb5dcd3ff0a9992d618ca761a11c3121547774" +checksum = "3f252a68540fde3a3877aeea552b832b40ab9a69e318efd078774a01ddee1ccf" dependencies = [ "num-conv", "time-core", @@ -3699,7 +3688,7 @@ checksum = "5b8a1e28f2deaa14e508979454cb3a223b10b938b45af148bc0986de36f1923b" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -3829,7 +3818,7 @@ dependencies = [ "hyper-timeout", "percent-encoding", "pin-project", - "prost 0.12.3", + "prost 0.12.4", "tokio", "tokio-stream", "tower", @@ -3848,7 +3837,7 @@ dependencies = [ "proc-macro2", "prost-build", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -3921,7 +3910,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -4162,7 +4151,7 @@ dependencies = [ "proc-macro2", "quote", "regex", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] @@ -4284,7 +4273,7 @@ dependencies = [ "once_cell", "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", "wasm-bindgen-shared", ] @@ -4318,7 +4307,7 @@ checksum = "e94f17b526d0a461a191c78ea52bbce64071ed5c04c9ffe424dcb38f74171bb7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", "wasm-bindgen-backend", "wasm-bindgen-shared", ] @@ -4364,18 +4353,6 @@ version = "0.1.8" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "53a85b86a771b1c87058196170769dd264f66c0782acf1ae6cc51bfd64b39082" -[[package]] -name = "which" -version = "4.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "87ba24419a2078cd2b0f2ede2691b6c66d8e47836da3b6db8265ebad47afbfc7" -dependencies = [ - "either", - "home", - "once_cell", - "rustix", -] - [[package]] name = "winapi" version = "0.3.9" @@ -4626,9 +4603,9 @@ checksum = "32b752e52a2da0ddfbdbcc6fceadfeede4c939ed16d13e648833a61dfb611ed8" [[package]] name = "winnow" -version = "0.6.5" +version = "0.6.6" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dffa400e67ed5a4dd237983829e66475f0a4a26938c4b04c21baede6262215b8" +checksum = "f0c976aaaa0e1f90dbb21e9587cdaf1d9679a1cde8875c0d6bd83ab96a208352" dependencies = [ "memchr", ] @@ -4660,7 +4637,7 @@ checksum = "9ce1b18ccd8e73a9321186f97e46f9f04b778851177567b1975109d26a08d2a6" dependencies = [ "proc-macro2", "quote", - "syn 2.0.55", + "syn 2.0.58", ] [[package]] diff --git a/Cargo.toml b/Cargo.toml index a9e7f1c8..ecb4878f 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -9,7 +9,7 @@ members = [ resolver = "2" [workspace.package] -version = "1.4.5" +version = "2.0.0" edition = "2021" authors = ["Olivier Dehaene"] homepage = "https://github.com/huggingface/text-generation-inference" diff --git a/README.md b/README.md index bffe1e8a..ad66e328 100644 --- a/README.md +++ b/README.md @@ -76,7 +76,7 @@ For a detailed starting guide, please see the [Quick Tour](https://huggingface.c model=HuggingFaceH4/zephyr-7b-beta volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run -docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model +docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model ``` And then you can make requests like @@ -90,7 +90,7 @@ curl 127.0.0.1:8080/generate_stream \ **Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar. -**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4-rocm --model-id $model` instead of the command above. +**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0-rocm --model-id $model` instead of the command above. To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli): ``` @@ -120,7 +120,7 @@ model=meta-llama/Llama-2-7b-chat-hf volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run token= -docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model +docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $model ``` ### A note on Shared Memory (shm) diff --git a/docs/openapi.json b/docs/openapi.json index fdf1c804..34b030f2 100644 --- a/docs/openapi.json +++ b/docs/openapi.json @@ -10,7 +10,7 @@ "name": "Apache 2.0", "url": "https://www.apache.org/licenses/LICENSE-2.0" }, - "version": "1.4.5" + "version": "2.0.0" }, "paths": { "/": { diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json index 543be115..153a508d 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json @@ -17,7 +17,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.5-native", + "system_fingerprint": "2.0.0-native", "usage": { "completion_tokens": 100, "prompt_tokens": 60, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json index 728e90a4..56920b3e 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json @@ -31,7 +31,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.5-native", + "system_fingerprint": "2.0.0-native", "usage": { "completion_tokens": 29, "prompt_tokens": 316, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json index 2e0efb86..fe679362 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json @@ -31,7 +31,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.5-native", + "system_fingerprint": "2.0.0-native", "usage": { "completion_tokens": 29, "prompt_tokens": 316, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json index 91854223..e48a1e7d 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json @@ -30,7 +30,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.5-native", + "system_fingerprint": "2.0.0-native", "usage": { "completion_tokens": 21, "prompt_tokens": 187, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json index e0c7aed6..cfebc05f 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json @@ -23,5 +23,5 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "1.4.5-native" + "system_fingerprint": "2.0.0-native" } diff --git a/integration-tests/pyproject.toml b/integration-tests/pyproject.toml index ad217072..6923ff23 100644 --- a/integration-tests/pyproject.toml +++ b/integration-tests/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "text-generation-integration-tests" -version = "1.4.5" +version = "2.0.0" description = "Text Generation Inference integration tests" authors = ["Nicolas Patry "] diff --git a/server/pyproject.toml b/server/pyproject.toml index 58c54b94..e7c576a8 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "text-generation-server" -version = "1.4.5" +version = "2.0.0" description = "Text Generation Inference Python gRPC Server" authors = ["Olivier Dehaene "] diff --git a/server/text_generation_server/interceptor.py b/server/text_generation_server/interceptor.py index 725105f3..57df1725 100644 --- a/server/text_generation_server/interceptor.py +++ b/server/text_generation_server/interceptor.py @@ -23,6 +23,10 @@ class ExceptionInterceptor(AsyncServerInterceptor): method_name = method_name.split("/")[-1] logger.exception(f"Method {method_name} encountered an error.") + # Runtime Error cannot be recovered from + if isinstance(err, RuntimeError): + exit(1) + if torch.cuda.is_available(): torch.cuda.empty_cache() diff --git a/server/text_generation_server/models/cache_manager.py b/server/text_generation_server/models/cache_manager.py index 4be8b1b9..85e1b19b 100644 --- a/server/text_generation_server/models/cache_manager.py +++ b/server/text_generation_server/models/cache_manager.py @@ -55,9 +55,10 @@ class CacheManager: ): # Get free blocks indices by finding values in mask that are not set to 0 free_block_indices = self.free_block_mask.nonzero() - assert ( - len(free_block_indices) >= blocks - ), f"Out of available cache blocks: asked {blocks}, only {len(free_block_indices)} free blocks" + if blocks > len(free_block_indices): + raise RuntimeError( + f"Out of available cache blocks: asked {blocks}, only {len(free_block_indices)} free blocks" + ) # Slice by the number of required blocks block_indices = free_block_indices[:blocks] diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index 2c12984b..9cf5c80f 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -503,6 +503,10 @@ class MedusaHeadV1(nn.Module): self, input: torch.Tensor ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: logits = self.lm_head(input) + # If we have too many tokens, we skip speculative logits + if input.shape[0] > 128: + return logits, None + speculative_logits = self.medusa(input) return logits, speculative_logits @@ -549,6 +553,11 @@ class MedusaHeadV2(nn.Module): self.lm_head = TensorParallelHead.load(config, prefix, weights) def forward(self, x): + # If we have too many tokens, we skip speculative logits + if x.shape[0] > 128: + logits = self.lm_head(x) + return logits, None + size = x.shape[-1] block_size = (size + self.world_size - 1) // self.world_size start = self.rank * block_size From 88702d876383f7200eccf67e28ba00500dc804bb Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Mon, 15 Apr 2024 18:47:36 +0200 Subject: [PATCH 26/74] Fixing CI. (#1748) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- .github/workflows/autodocs.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/workflows/autodocs.yml b/.github/workflows/autodocs.yml index 7c5c6eca..c378e177 100644 --- a/.github/workflows/autodocs.yml +++ b/.github/workflows/autodocs.yml @@ -13,7 +13,10 @@ jobs: - name: Install Launcher id: install-launcher - run: cargo install --git https://github.com/${{ github.repository }} --branch ${{ github.head_ref }} text-generation-launcher + env: + REF: ${{ github.head_ref }} + REPO: ${{ github.repository }} + run: cargo install --git "https://github.com/$REPO" --branch "$REF" text-generation-launcher - name: Check launcher Docs are up-to-date run: | From 7276d4349510bdf3198d6b255a140fd7c1dc7216 Mon Sep 17 00:00:00 2001 From: drbh Date: Tue, 16 Apr 2024 09:02:46 -0400 Subject: [PATCH 27/74] feat: improve tools to include name and add tests (#1693) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This PR makes tool calling aware of the name of the function selected. Fixes: https://github.com/huggingface/text-generation-inference/issues/1657 Thank you @puppetm4st3r for the helpful snippets, large parts of this PR are simply refactors of the code shared 🙏 **opening draft PR because small tweaks are needed before merging --- .../test_flash_llama_simple.json} | 2 +- .../test_flash_llama_grammar_tools.json | 19 +- .../test_flash_llama_grammar_tools_auto.json | 19 +- ...test_flash_llama_grammar_tools_choice.json | 16 +- ...rammar_tools_insufficient_information.json | 38 ++++ ...test_flash_llama_grammar_tools_stream.json | 2 +- integration-tests/models/test_chat_llama.py | 42 ++++ integration-tests/models/test_tools_llama.py | 113 ++++++----- router/src/infer.rs | 191 +++++++++++++++++- router/src/lib.rs | 21 +- router/src/server.rs | 141 ++++++------- 11 files changed, 429 insertions(+), 175 deletions(-) rename integration-tests/models/__snapshots__/{test_tools_llama/test_flash_llama_grammar_no_tools.json => test_chat_llama/test_flash_llama_simple.json} (97%) create mode 100644 integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json create mode 100644 integration-tests/models/test_chat_llama.py diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json b/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json similarity index 97% rename from integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json rename to integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json index 153a508d..0ff874f1 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_no_tools.json +++ b/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json @@ -13,7 +13,7 @@ "usage": null } ], - "created": 1710795556, + "created": 1712874856, "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json index 56920b3e..45f8ca99 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json @@ -11,13 +11,12 @@ "tool_calls": [ { "function": { - "description": null, - "name": "tools", - "parameters": { + "arguments": { "format": "celsius", - "location": "New York, NY", - "num_days": 14 - } + "location": "Brooklyn" + }, + "description": null, + "name": "get_current_weather" }, "id": 0, "type": "function" @@ -27,14 +26,14 @@ "usage": null } ], - "created": 1710795556, + "created": 1712782670, "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", "system_fingerprint": "2.0.0-native", "usage": { - "completion_tokens": 29, - "prompt_tokens": 316, - "total_tokens": 345 + "completion_tokens": 37, + "prompt_tokens": 524, + "total_tokens": 561 } } diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json index fe679362..e0ed0947 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json @@ -11,13 +11,12 @@ "tool_calls": [ { "function": { - "description": null, - "name": "tools", - "parameters": { + "arguments": { "format": "celsius", - "location": "New York, NY", - "num_days": 14 - } + "location": "Brooklyn" + }, + "description": null, + "name": "get_current_weather" }, "id": 0, "type": "function" @@ -27,14 +26,14 @@ "usage": null } ], - "created": 1710795557, + "created": 1712787937, "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", "system_fingerprint": "2.0.0-native", "usage": { - "completion_tokens": 29, - "prompt_tokens": 316, - "total_tokens": 345 + "completion_tokens": 37, + "prompt_tokens": 524, + "total_tokens": 561 } } diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json index e48a1e7d..b70c2d6f 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json @@ -11,12 +11,12 @@ "tool_calls": [ { "function": { - "description": null, - "name": "tools", - "parameters": { + "arguments": { "format": "celsius", "location": "New York, NY" - } + }, + "description": null, + "name": "get_current_weather" }, "id": 0, "type": "function" @@ -26,14 +26,14 @@ "usage": null } ], - "created": 1710795557, + "created": 1712852394, "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", "system_fingerprint": "2.0.0-native", "usage": { - "completion_tokens": 21, - "prompt_tokens": 187, - "total_tokens": 208 + "completion_tokens": 48, + "prompt_tokens": 320, + "total_tokens": 368 } } diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json new file mode 100644 index 00000000..0cd3c67f --- /dev/null +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_insufficient_information.json @@ -0,0 +1,38 @@ +{ + "choices": [ + { + "finish_reason": "eos_token", + "index": 0, + "logprobs": null, + "message": { + "content": null, + "name": null, + "role": "assistant", + "tool_calls": [ + { + "function": { + "arguments": { + "error": "Cannot get current weather forecast from specified location and temperature unit. Please try again with different options." + }, + "description": null, + "name": "notify_error" + }, + "id": 0, + "type": "function" + } + ] + }, + "usage": null + } + ], + "created": 1712852597, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "1.4.5-native", + "usage": { + "completion_tokens": 39, + "prompt_tokens": 496, + "total_tokens": 535 + } +} diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json index cfebc05f..6787b39b 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json @@ -19,7 +19,7 @@ "logprobs": null } ], - "created": 1710795499, + "created": 1712788218, "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", diff --git a/integration-tests/models/test_chat_llama.py b/integration-tests/models/test_chat_llama.py new file mode 100644 index 00000000..11419a0e --- /dev/null +++ b/integration-tests/models/test_chat_llama.py @@ -0,0 +1,42 @@ +import pytest +import json + +from text_generation.types import GrammarType + + +@pytest.fixture(scope="module") +def flash_llama_chat_handle(launcher): + with launcher( + "TinyLlama/TinyLlama-1.1B-Chat-v1.0", num_shard=2, disable_grammar_support=False + ) as handle: + yield handle + + +@pytest.fixture(scope="module") +async def flash_llama_chat(flash_llama_chat_handle): + await flash_llama_chat_handle.health(300) + return flash_llama_chat_handle.client + + +@pytest.mark.private +async def test_flash_llama_simple(flash_llama_chat, response_snapshot): + response = await flash_llama_chat.chat( + max_tokens=100, + seed=1, + messages=[ + { + "role": "system", + "content": "Youre a helpful assistant! Answer the users question best you can.", + }, + { + "role": "user", + "content": "What is the weather like in Brooklyn, New York?", + }, + ], + ) + + assert ( + response.choices[0].message.content + == "As of today, there is a Update available for the Brooklyn, New York, area. According to the latest forecast, it's warm with high temperatures throughout the day. It's forecasted at 75°F for today and 77°F for tomorrow. However, in autumn, the weather typically changes drastically, becoming cooler and wetter. You can find the current weather forecast for the area through your local weather service. Additionally" + ) + assert response == response_snapshot diff --git a/integration-tests/models/test_tools_llama.py b/integration-tests/models/test_tools_llama.py index d0ae331f..0af3f66a 100644 --- a/integration-tests/models/test_tools_llama.py +++ b/integration-tests/models/test_tools_llama.py @@ -71,34 +71,7 @@ tools = [ ] -@pytest.mark.asyncio -@pytest.mark.private -async def test_flash_llama_grammar_no_tools( - flash_llama_grammar_tools, response_snapshot -): - response = await flash_llama_grammar_tools.chat( - max_tokens=100, - seed=1, - messages=[ - { - "role": "system", - "content": "Youre a helpful assistant! Answer the users question best you can.", - }, - { - "role": "user", - "content": "What is the weather like in Brooklyn, New York?", - }, - ], - ) - - assert ( - response.choices[0].message.content - == "As of today, there is a Update available for the Brooklyn, New York, area. According to the latest forecast, it's warm with high temperatures throughout the day. It's forecasted at 75°F for today and 77°F for tomorrow. However, in autumn, the weather typically changes drastically, becoming cooler and wetter. You can find the current weather forecast for the area through your local weather service. Additionally" - ) - assert response == response_snapshot - - -@pytest.mark.skip +@pytest.mark.skip(reason="Takes too long to run") @pytest.mark.asyncio @pytest.mark.private async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_snapshot): @@ -121,23 +94,19 @@ async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_sna assert response.choices[0].message.content == None assert response.choices[0].message.tool_calls == [ { - "function": { - "description": None, - "name": "tools", - "parameters": { - "format": "celsius", - "location": "New York, NY", - "num_days": 14, - }, - }, "id": 0, "type": "function", + "function": { + "description": None, + "name": "get_current_weather", + "arguments": {"format": "celsius", "location": "New York, NY"}, + }, } ] assert response == response_snapshot -@pytest.mark.skip +@pytest.mark.skip(reason="Takes too long to run") @pytest.mark.asyncio @pytest.mark.private async def test_flash_llama_grammar_tools_auto( @@ -163,23 +132,20 @@ async def test_flash_llama_grammar_tools_auto( assert response.choices[0].message.content == None assert response.choices[0].message.tool_calls == [ { - "function": { - "description": None, - "name": "tools", - "parameters": { - "format": "celsius", - "location": "New York, NY", - "num_days": 14, - }, - }, "id": 0, "type": "function", + "function": { + "description": None, + "name": "get_current_weather", + "arguments": {"format": "celsius", "location": "New York, NY"}, + }, } ] + assert response == response_snapshot -@pytest.mark.skip +@pytest.mark.skip(reason="Takes too long to run") @pytest.mark.asyncio @pytest.mark.private async def test_flash_llama_grammar_tools_choice( @@ -209,15 +175,16 @@ async def test_flash_llama_grammar_tools_choice( "type": "function", "function": { "description": None, - "name": "tools", - "parameters": {"format": "celsius", "location": "New York, NY"}, + "name": "get_current_weather", + "arguments": {"format": "celsius", "location": "New York, NY"}, }, } ] + assert response == response_snapshot -@pytest.mark.skip +@pytest.mark.skip(reason="Takes too long to run") @pytest.mark.asyncio @pytest.mark.private async def test_flash_llama_grammar_tools_stream( @@ -246,5 +213,47 @@ async def test_flash_llama_grammar_tools_stream( async for response in responses: count += 1 - assert count == 20 + assert count == 38 assert response == response_snapshot + + +@pytest.mark.skip(reason="Takes too long to run") +@pytest.mark.asyncio +@pytest.mark.private +async def test_flash_llama_grammar_tools_insufficient_information( + flash_llama_grammar_tools, response_snapshot +): + responses = await flash_llama_grammar_tools.chat( + max_tokens=100, + seed=8, + tools=tools, + tool_choice="auto", + messages=[ + { + "role": "system", + "content": "ONLY RESPOND IF THE USER ASKS A WEATHER RELATED QUESTION", + }, + { + "role": "user", + "content": "Tell me a story about 3 sea creatures", + }, + ], + stream=False, + ) + + assert responses.choices[0].message.content == None + assert responses.choices[0].message.tool_calls == [ + { + "function": { + "arguments": { + "error": "Cannot get current weather forecast from specified location and temperature unit. Please try again with different options." + }, + "description": None, + "name": "notify_error", + }, + "id": 0, + "type": "function", + } + ] + + assert responses == response_snapshot diff --git a/router/src/infer.rs b/router/src/infer.rs index 075e76d8..da0db072 100644 --- a/router/src/infer.rs +++ b/router/src/infer.rs @@ -4,9 +4,12 @@ use crate::{ ChatTemplateInputs, ChatTemplateVersions, Entry, GenerateRequest, GenerateStreamResponse, HubTokenizerConfig, Message, PrefillToken, Queue, Token, }; +use crate::{FunctionRef, FunctionsMap, GrammarType, Properties, Tool, ToolType, Tools}; use futures::future::try_join_all; use minijinja::{Environment, ErrorKind, Template}; use nohash_hasher::IntMap; +use serde_json::{json, Map, Value}; +use std::collections::HashMap; use std::sync::{ atomic::{AtomicBool, Ordering}, Arc, @@ -185,11 +188,15 @@ impl Infer { /// Apply the chat template to the chat request #[instrument(skip_all)] - pub(crate) fn apply_chat_template(&self, messages: Vec) -> Result { + pub(crate) fn apply_chat_template( + &self, + messages: Vec, + grammar_with_prompt: Option<(GrammarType, String)>, + ) -> Result { self.chat_template .as_ref() .ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))? - .apply(messages) + .apply(messages, grammar_with_prompt) .map_err(|e| { metrics::increment_counter!("tgi_request_failure", "err" => "template"); tracing::error!("{e}"); @@ -322,6 +329,7 @@ struct ChatTemplate { template: Template<'static, 'static>, bos_token: Option, eos_token: Option, + use_default_tool_template: bool, } impl ChatTemplate { @@ -329,6 +337,10 @@ impl ChatTemplate { let mut env = Box::new(Environment::new()); let template_str = template.into_boxed_str(); env.add_function("raise_exception", raise_exception); + + // check if contains the tools variable within the template + let use_default_tool_template = + !template_str.as_ref().replace(' ', "").contains("{{tools}}"); // leaking env and template_str as read-only, static resources for performance. let template = Box::leak(env) .template_from_str(Box::leak(template_str)) @@ -338,21 +350,159 @@ impl ChatTemplate { template, bos_token, eos_token, + use_default_tool_template, } } - fn apply(&self, messages: Vec) -> Result { + fn apply( + &self, + mut messages: Vec, + grammar_with_prompt: Option<(GrammarType, String)>, + ) -> Result { + if self.use_default_tool_template { + if let Some(last_message) = messages.last_mut() { + if let Some((GrammarType::Json(tools), tool_prompt)) = grammar_with_prompt { + last_message.content = Some(format!( + "{}\n---\n{}\n{}", + last_message.content.as_deref().unwrap_or_default(), + tool_prompt, + tools + )); + } + } + } + self.template .render(ChatTemplateInputs { messages, bos_token: self.bos_token.as_deref(), eos_token: self.eos_token.as_deref(), add_generation_prompt: true, + tools: None, + tools_prompt: None, }) .map_err(InferError::TemplateError) } } +pub struct ToolGrammar {} + +impl ToolGrammar { + pub fn apply( + tools: Option>, + tool_choice: Option, + ) -> Result, InferError> { + if let Some((req_tools, tool_choice)) = tools.zip(tool_choice) { + // let tool_prompt = tool_prompt.unwrap_or_default(); + let tools_to_use = match tool_choice { + ToolType::FunctionName(name) => { + vec![req_tools + .iter() + .find(|tool| tool.function.name == *name) + .unwrap_or_else(|| panic!("Tool with name {} not found", name)) + .clone()] + } + ToolType::OneOf => req_tools.to_owned(), + }; + + // adds the error notification function for LLM feedback if required + let mut text_response_properties = Map::new(); + text_response_properties.insert( + "error".to_string(), + serde_json::json!({ + "type": "string", + "description": "The error or issue to notify" + }), + ); + text_response_properties.insert( + "_name".to_string(), + serde_json::json!({ + "type": "string", + "const": "notify_error" + }), + ); + + let functions: HashMap = tools_to_use + .iter() + .map(|tool| { + let func = tool.function.clone(); + + // Clone the existing parameters, which are expected to be a JSON object + let mut params = if let Value::Object(params) = &func.arguments { + params.clone() + } else { + Map::new() + }; + + // Insert the function's description at the top level, outside of properties + params.insert( + "description".to_string(), + Value::String(func.description.clone().unwrap_or_default()), + ); + + // Ensure 'properties' exists and is an object + let properties = params + .entry("properties".to_string()) + .or_insert_with(|| json!({})) + .as_object_mut() + .unwrap(); + + // Insert the constant for the function name inside 'properties' + properties.insert( + "_name".to_string(), + json!({ + "type": "string", + "const": func.name.clone(), + // "description": "The name of the function" + }), + ); + + // Check if 'required' exists, and it is an array. If not, create an empty array. + let required = params + .entry("required".to_string()) + .or_insert_with(|| json!([])) + .as_array_mut() + .unwrap(); + + // Add 'name' to the 'required' array if it is not already present + if !required.iter().any(|r| r == "_name") { + required.push(json!("_name")); + } + + (func.name, Value::Object(params)) + }) + .chain([( + "notify_error".to_string(), + serde_json::json!({ + "properties": text_response_properties, + "required": ["error", "_name"], + "type": "object" + }), + )]) + .collect(); + + let tools = Tools { + functions_map: FunctionsMap { functions }, + properties: Properties { + function: tools_to_use + .iter() + .map(|tool| FunctionRef { + ref_path: format!("#/$functions/{}", tool.function.name.clone()), + }) + .chain(std::iter::once(FunctionRef { + ref_path: "#/$functions/notify_error".to_string(), + })) + .collect(), + }, + }; + + return Ok(Some(tools)); + } + // Err(InferError::ToolError("No tools provided".to_string())) + Ok(None) + } +} + /// Batching logic /// Will be launched in a background Tokio task /// @@ -768,6 +918,8 @@ pub enum InferError { IncompleteGeneration, #[error("Template error: {0}")] TemplateError(#[from] minijinja::Error), + #[error("Tool error: {0}")] + ToolError(String), } impl InferError { @@ -778,6 +930,7 @@ impl InferError { InferError::ValidationError(_) => "validation", InferError::IncompleteGeneration => "incomplete_generation", InferError::TemplateError(_) => "template_error", + InferError::ToolError(_) => "tool_error", } } } @@ -849,6 +1002,7 @@ mod tests { bos_token: Some("[BOS]"), eos_token: Some("[EOS]"), add_generation_prompt: true, + ..Default::default() }; let result = tmpl.unwrap().render(chat_template_inputs).unwrap(); @@ -924,6 +1078,7 @@ mod tests { bos_token: Some("[BOS]"), eos_token: Some("[EOS]"), add_generation_prompt: true, + ..Default::default() }; let result = tmpl.unwrap().render(chat_template_inputs); //.err().unwrap(); @@ -998,6 +1153,7 @@ mod tests { bos_token: Some("[BOS]"), eos_token: Some("[EOS]"), add_generation_prompt: true, + ..Default::default() }; let result = tmpl.unwrap().render(chat_template_inputs).unwrap(); @@ -1056,6 +1212,7 @@ mod tests { bos_token: Some("[BOS]"), eos_token: Some("[EOS]"), add_generation_prompt: true, + ..Default::default() }; let result = tmpl.unwrap().render(chat_template_inputs).unwrap(); @@ -1115,6 +1272,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "<|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!<|im_end|>\n", }, @@ -1126,6 +1284,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: " Hello, how are you? I'm doing great. How can I help you today? I'd like to show off how chat templating works!", }, @@ -1137,6 +1296,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: " Hello, how are you? I'm doing great. How can I help you today? I'd like to show off how chat templating works!", }, @@ -1148,6 +1308,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "Hello, how are you?I'm doing great. How can I help you today?I'd like to show off how chat templating works!", }, @@ -1159,6 +1320,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some("<|endoftext|>"), + ..Default::default() }, target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>", }, @@ -1170,6 +1332,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some("<|endoftext|>"), + ..Default::default() }, target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>", }, @@ -1182,6 +1345,7 @@ mod tests { add_generation_prompt: true, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "[INST] <>\nYou are a friendly chatbot who always responds in the style of a pirate\n<>\n\nHello, how are you? [/INST] I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]", }, @@ -1193,6 +1357,7 @@ mod tests { add_generation_prompt: true, bos_token: Some(""), eos_token: Some("<|endoftext|>"), + ..Default::default() }, target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>", }, @@ -1222,6 +1387,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate<|user|>\nHello, how are you?<|assistant|>\nI'm doing great. How can I help you today?<|user|>\nI'd like to show off how chat templating works!", }, @@ -1246,6 +1412,7 @@ mod tests { add_generation_prompt: true, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate<|user|>\nHow many helicopters can a human eat in one sitting?<|assistant|>", }, @@ -1257,6 +1424,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "<|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!<|im_end|>\n", }, @@ -1268,6 +1436,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]", }, @@ -1279,6 +1448,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?[INST] I'd like to show off how chat templating works! [/INST]", }, @@ -1290,6 +1460,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "<|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!<|im_end|>\n", }, @@ -1302,6 +1473,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "GPT4 Correct User: Hello, how are you?<|end_of_turn|>GPT4 Correct Assistant: I'm doing great. How can I help you today?<|end_of_turn|>GPT4 Correct User: I'd like to show off how chat templating works!<|end_of_turn|>", }, @@ -1313,6 +1485,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "Hello, how are you?I'm doing great. How can I help you today?I'd like to show off how chat templating works!", }, @@ -1325,6 +1498,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "Source: user\n\n Hello, how are you? Source: assistant\n\n I'm doing great. How can I help you today? Source: user\n\n I'd like to show off how chat templating works! Source: assistant\nDestination: user\n\n ", }, @@ -1336,6 +1510,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "### User:\nHello, how are you?### Assistant:\nI'm doing great. How can I help you today?### User:\nI'd like to show off how chat templating works!", }, @@ -1347,6 +1522,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!", }, @@ -1358,6 +1534,7 @@ mod tests { add_generation_prompt: false, bos_token: Some("<｜begin▁of▁sentence｜>"), eos_token: Some("<｜end▁of▁sentence｜>"), + ..Default::default() }, target: "<｜begin▁of▁sentence｜>User: Hello, how are you?\n\nAssistant: I'm doing great. How can I help you today?<｜end▁of▁sentence｜>User: I'd like to show off how chat templating works!\n\n", }, @@ -1369,6 +1546,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "<|prompt|>Hello, how are you?<|answer|>I'm doing great. How can I help you today?<|prompt|>I'd like to show off how chat templating works!", }, @@ -1380,6 +1558,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "<|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!<|im_end|>\n", }, @@ -1391,6 +1570,7 @@ mod tests { add_generation_prompt: false, bos_token: Some("<｜begin▁of▁sentence｜>"), eos_token: Some("<|EOT|>"), + ..Default::default() }, target: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.\n### Instruction:\nHello, how are you?\n### Response:\nI'm doing great. How can I help you today?\n<|EOT|>\n### Instruction:\nI'd like to show off how chat templating works!\n### Response:\n", }, @@ -1403,6 +1583,7 @@ mod tests { add_generation_prompt: false, bos_token: Some("<|endoftext|>"), eos_token: Some("<|endoftext|>"), + ..Default::default() }, target: "[INST] Hello, how are you? [RESP] I'm doing great. How can I help you today?<|endoftext|>[INST] I'd like to show off how chat templating works!", }, @@ -1414,6 +1595,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "Hello, how are you? [/INST] I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]", }, @@ -1425,6 +1607,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "Below is an instruction that describes a task. Write a response that appropriately completes the request.### Instruction:Hello, how are you?### Response:I'm doing great. How can I help you today?### Instruction:I'd like to show off how chat templating works!", }, @@ -1436,6 +1619,7 @@ mod tests { add_generation_prompt: false, bos_token: Some("<｜begin▁of▁sentence｜>"), eos_token: Some(""), + ..Default::default() }, target: "<｜begin▁of▁sentence｜>You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n### Instruction:\nHello, how are you?\n### Response:\nI'm doing great. How can I help you today?\n<|EOT|>\n### Instruction:\nI'd like to show off how chat templating works!\n", }, @@ -1451,6 +1635,7 @@ mod tests { add_generation_prompt: false, bos_token: Some(""), eos_token: Some(""), + ..Default::default() }, target: "You are a friendly chatbot who always responds in the style of a pirateYou are a friendly chatbot who always responds in the style of a pirate### Instruction: Hello, how are you?### Response: I'm doing great. How can I help you today?### Instruction: I'd like to show off how chat templating works!", }, diff --git a/router/src/lib.rs b/router/src/lib.rs index 2e412f1a..ddb28848 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -79,7 +79,7 @@ impl HubTokenizerConfig { } } -#[derive(Clone, Debug, Deserialize, ToSchema)] +#[derive(Clone, Debug, Deserialize, ToSchema, Serialize)] #[serde(tag = "type", content = "value")] pub(crate) enum GrammarType { /// A string that represents a [JSON Schema](https://json-schema.org/). @@ -669,7 +669,7 @@ pub(crate) struct ChatRequest { #[serde(default = "default_tool_prompt")] #[schema( nullable = true, - example = "\"Based on the conversation, please choose the most appropriate tool to use: \"" + example = "\"You will be presented with a JSON schema representing a set of tools.\nIf the user request lacks of sufficient information to make a precise tool selection: Do not invent any tool's properties, instead notify with an error message.\n\nJSON Schema:\n\"" )] pub tool_prompt: Option, @@ -682,7 +682,7 @@ pub(crate) struct ChatRequest { fn default_tool_prompt() -> Option { Some( - "\nBased on the conversation, please choose the most appropriate tool to use: ".to_string(), + "\nYou will be presented with a JSON schema representing a set of tools.\nIf the user request lacks of sufficient information to make a precise tool selection: Do not invent any tool's properties, instead notify with an error message.\n\nJSON Schema:\n".to_string(), ) } #[derive(Clone, Deserialize, ToSchema, Serialize)] @@ -727,26 +727,26 @@ mod deserialize_tool_choice { } } -#[derive(Debug, Deserialize, Serialize, ToSchema)] +#[derive(Debug, Deserialize, Serialize, ToSchema, PartialEq)] pub struct Tools { #[serde(flatten)] functions_map: FunctionsMap, properties: Properties, } -#[derive(Debug, Serialize, Deserialize)] +#[derive(Debug, Serialize, Deserialize, PartialEq)] struct FunctionsMap { #[serde(rename = "$functions")] functions: std::collections::HashMap, } -#[derive(Debug, Serialize, Deserialize)] +#[derive(Debug, Serialize, Deserialize, PartialEq)] struct FunctionRef { #[serde(rename = "$ref")] ref_path: String, } -#[derive(Debug, Serialize, Deserialize)] +#[derive(Debug, Serialize, Deserialize, PartialEq)] struct Properties { #[serde(serialize_with = "serialize_function")] function: Vec, @@ -767,7 +767,8 @@ pub(crate) struct FunctionDefinition { #[serde(default)] pub description: Option, pub name: String, - pub parameters: serde_json::Value, + #[serde(alias = "parameters")] + pub arguments: serde_json::Value, } #[derive(Clone, Debug, Deserialize, Serialize, ToSchema)] @@ -779,12 +780,14 @@ pub(crate) struct Tool { pub function: FunctionDefinition, } -#[derive(Clone, Serialize, Deserialize)] +#[derive(Clone, Serialize, Deserialize, Default)] pub(crate) struct ChatTemplateInputs<'a> { messages: Vec, bos_token: Option<&'a str>, eos_token: Option<&'a str>, add_generation_prompt: bool, + tools: Option<&'a str>, + tools_prompt: Option<&'a str>, } #[derive(Clone, Deserialize, Serialize, ToSchema, Default, Debug)] diff --git a/router/src/server.rs b/router/src/server.rs index b8f93514..c1648f9e 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -1,7 +1,7 @@ use crate::config::Config; /// HTTP Server logic use crate::health::Health; -use crate::infer::{InferError, InferResponse, InferStreamResponse}; +use crate::infer::{InferError, InferResponse, InferStreamResponse, ToolGrammar}; use crate::validation::ValidationError; use crate::{ BestOfSequence, Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest, @@ -15,7 +15,7 @@ use crate::{ ChatRequest, CompatGenerateRequest, Completion, CompletionComplete, CompletionCompleteChunk, CompletionRequest, DeltaToolCall, Function, Tool, VertexRequest, VertexResponse, }; -use crate::{FunctionDefinition, FunctionRef, FunctionsMap, Properties, ToolCall, ToolType, Tools}; +use crate::{FunctionDefinition, ToolCall, ToolType}; use axum::extract::Extension; use axum::http::{HeaderMap, Method, StatusCode}; use axum::response::sse::{Event, KeepAlive, Sse}; @@ -29,7 +29,6 @@ use futures::Stream; use futures::TryStreamExt; use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle}; use serde_json::Value; -use std::collections::HashMap; use std::convert::Infallible; use std::net::SocketAddr; use std::sync::atomic::AtomicBool; @@ -757,19 +756,29 @@ async fn chat_completions( ) -> Result)> { metrics::increment_counter!("tgi_request_count"); - let stream = req.stream; - let max_new_tokens = req.max_tokens.or(Some(100)); - let repetition_penalty = req - .presence_penalty - // rescale repetition_penalty from (-2.0, 2.0) to (0.0, 4.0) - .map(|x| x + 2.0); - let logprobs = req.logprobs.unwrap_or(false); - let seed = req.seed; - let stop = req.stop.unwrap_or_default(); + let ChatRequest { + logprobs, + max_tokens, + messages, + presence_penalty, + seed, + stop, + stream, + tools, + tool_choice, + tool_prompt, + .. + } = req; - // apply chat template to flatten the request into a single input - let mut inputs = match infer.apply_chat_template(req.messages) { - Ok(inputs) => inputs, + let repetition_penalty = presence_penalty.map(|x| x + 2.0); + let max_new_tokens = max_tokens.or(Some(100)); + let logprobs = logprobs.unwrap_or(false); + let tool_prompt = tool_prompt.unwrap_or_default(); + let stop = stop.unwrap_or_default(); + + // extract tool grammar if present + let tool_grammar = match ToolGrammar::apply(tools, tool_choice) { + Ok(grammar) => grammar, Err(err) => { metrics::increment_counter!("tgi_request_failure", "err" => "validation"); tracing::error!("{err}"); @@ -783,60 +792,28 @@ async fn chat_completions( } }; - let tool_grammar = if let Some((req_tools, tool_choice)) = req.tools.zip(req.tool_choice) { - let tool_prompt = req.tool_prompt.unwrap_or_default(); - let tools_to_use = match tool_choice { - ToolType::FunctionName(name) => { - vec![req_tools - .iter() - .find(|tool| tool.function.name == *name) - .ok_or_else(|| { - ( - StatusCode::UNPROCESSABLE_ENTITY, - Json(ErrorResponse { - error: "Tool choice not found in tool names".to_string(), - error_type: "Tool not found".to_string(), - }), - ) - })? - .clone()] - } - ToolType::OneOf => req_tools.to_owned(), - }; + let grammar_with_prompt = tool_grammar + .as_ref() + .map(|t| (GrammarType::Json(serde_json::json!(t)), tool_prompt)); - let functions: HashMap = tools_to_use - .iter() - .map(|tool| { - let func = tool.function.clone(); - (func.name, func.parameters) - }) - .collect(); + let typed_grammar = grammar_with_prompt + .as_ref() + .map(|(grammar, _)| grammar.clone()); - let tools = Tools { - functions_map: FunctionsMap { functions }, - properties: Properties { - function: tools_to_use - .iter() - .map(|tool| FunctionRef { - ref_path: format!("#/$functions/{}", tool.function.name.clone()), - }) - .collect(), - }, - }; - - let tools_str = serde_json::to_string(&tools).map_err(|e| { - ( + // apply chat template to flatten the request into a single input + let inputs = match infer.apply_chat_template(messages, grammar_with_prompt) { + Ok(inputs) => inputs, + Err(err) => { + metrics::increment_counter!("tgi_request_failure", "err" => "validation"); + tracing::error!("{err}"); + return Err(( StatusCode::UNPROCESSABLE_ENTITY, Json(ErrorResponse { - error: e.to_string(), - error_type: "Input validation error".to_string(), + error: err.to_string(), + error_type: err.error_type().to_string(), }), - ) - })?; - inputs = format!("{inputs}{tool_prompt}{tools_str}"); - Some(GrammarType::Json(serde_json::json!(tools))) - } else { - None + )); + } }; // build the request passing some parameters @@ -860,7 +837,7 @@ async fn chat_completions( decoder_input_details: !stream, seed, top_n_tokens: req.top_logprobs, - grammar: tool_grammar.clone(), + grammar: typed_grammar, }, }; @@ -943,27 +920,28 @@ async fn chat_completions( }), ) })?; - let tool_calls = vec![ToolCall { id: 0, r#type: "function".to_string(), function: FunctionDefinition { description: None, - name: "tools".to_string(), - parameters: gen_text_value.get("function").map_or_else( - || { - serde_json::from_str(&generation.generated_text).map_err(|e| { - ( - StatusCode::UNPROCESSABLE_ENTITY, - Json(ErrorResponse { - error: e.to_string(), - error_type: "Input validation error".to_string(), - }), - ) - }) - }, - |f| Ok(f.clone()), - )?, + name: gen_text_value + .get("function") + .and_then(|f| f.get("_name")) + .and_then(|name| name.as_str()) + .unwrap_or("default_function_name") + .to_string(), + // Serialize the JSON object obtained from "function" to an escaped JSON string + arguments: gen_text_value + .get("function") + .map(|f| { + let mut f_cloned = f.clone(); + if let Value::Object(ref mut props) = f_cloned { + props.remove("_name"); + } + f_cloned + }) + .unwrap_or_default(), }, }]; (Some(tool_calls), None) @@ -1539,6 +1517,7 @@ impl From for (StatusCode, Json) { InferError::ValidationError(_) => StatusCode::UNPROCESSABLE_ENTITY, InferError::IncompleteGeneration => StatusCode::INTERNAL_SERVER_ERROR, InferError::TemplateError(_) => StatusCode::UNPROCESSABLE_ENTITY, + InferError::ToolError(_) => StatusCode::UNPROCESSABLE_ENTITY, }; ( From 00f365353ea5cf29438ba1d51baadaab79ae4674 Mon Sep 17 00:00:00 2001 From: Lucain Date: Tue, 16 Apr 2024 19:26:32 +0200 Subject: [PATCH 28/74] Update response type for `/v1/chat/completions` and `/v1/completions` (#1747) `/v1/chat/completions` and `/v1/completions` have different output types depending on the `stream` parameter. This PR aims at fixing the inconsistency in the auto-generated [openapi.json](https://huggingface.github.io/text-generation-inference/openapi.json) specs. cc @OlivierDehaene @drbh I reused what had been done for the `/` endpoint but haven't tested anything myself. Could you confirm this is the correct way of handling things? Also, should I update the openapi.json file manually? If yes, how can I do it? --- router/src/server.rs | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/router/src/server.rs b/router/src/server.rs index c1648f9e..3f033a9d 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -548,7 +548,11 @@ async fn generate_stream_internal( path = "/v1/completions", request_body = CompletionRequest, responses( - (status = 200, description = "Generated Text", body = ChatCompletionChunk), + (status = 200, description = "Generated Chat Completion", + content( + ("application/json" = Completion), + ("text/event-stream" = CompletionCompleteChunk), + )), (status = 424, description = "Generation Error", body = ErrorResponse, example = json ! ({"error": "Request failed during generation"})), (status = 429, description = "Model is overloaded", body = ErrorResponse, @@ -652,7 +656,7 @@ async fn completions( }) .map_or_else( |e| { - println!("Failed to serialize ChatCompletionChunk: {:?}", e); + println!("Failed to serialize CompletionCompleteChunk: {:?}", e); Event::default() }, |data| data, @@ -725,7 +729,11 @@ async fn completions( path = "/v1/chat/completions", request_body = ChatRequest, responses( - (status = 200, description = "Generated Text", body = ChatCompletionChunk), + (status = 200, description = "Generated Chat Completion", + content( + ("application/json" = ChatCompletion), + ("text/event-stream" = ChatCompletionChunk), + )), (status = 424, description = "Generation Error", body = ErrorResponse, example = json ! ({"error": "Request failed during generation"})), (status = 429, description = "Model is overloaded", body = ErrorResponse, From e4d31a40db0721a9b1bc9c9121f0a712d87bce1f Mon Sep 17 00:00:00 2001 From: drbh Date: Tue, 16 Apr 2024 16:56:47 -0400 Subject: [PATCH 29/74] fix: bump clients test base url to llama (#1751) This PR bumps the client tests from `google/flan-t5-xxl` to `meta-llama/Llama-2-7b-chat-hf` to resolve issues when calling the endpoint and `google/flan-t5-xxl` is not available run with ```bash make python-client-tests clients/python/tests/test_client.py .............. [ 43%] clients/python/tests/test_errors.py .......... [ 75%] clients/python/tests/test_inference_api.py ...... [ 93%] clients/python/tests/test_types.py .. [100%] ``` **note `google/flan-t5-xxl` function is currently unused but still included in the `conftest.py` --- clients/python/tests/conftest.py | 10 +++++ clients/python/tests/test_client.py | 67 +++++++++++++++-------------- 2 files changed, 45 insertions(+), 32 deletions(-) diff --git a/clients/python/tests/conftest.py b/clients/python/tests/conftest.py index 48734f0d..17bb73b5 100644 --- a/clients/python/tests/conftest.py +++ b/clients/python/tests/conftest.py @@ -9,6 +9,11 @@ def flan_t5_xxl(): return "google/flan-t5-xxl" +@pytest.fixture +def llama_7b(): + return "meta-llama/Llama-2-7b-chat-hf" + + @pytest.fixture def fake_model(): return "fake/model" @@ -34,6 +39,11 @@ def flan_t5_xxl_url(base_url, flan_t5_xxl): return f"{base_url}/{flan_t5_xxl}" +@pytest.fixture +def llama_7b_url(base_url, llama_7b): + return f"{base_url}/{llama_7b}" + + @pytest.fixture def fake_url(base_url, fake_model): return f"{base_url}/{fake_model}" diff --git a/clients/python/tests/test_client.py b/clients/python/tests/test_client.py index 1e25e1b1..8aed865b 100644 --- a/clients/python/tests/test_client.py +++ b/clients/python/tests/test_client.py @@ -5,24 +5,24 @@ from text_generation.errors import NotFoundError, ValidationError from text_generation.types import FinishReason, InputToken -def test_generate(flan_t5_xxl_url, hf_headers): - client = Client(flan_t5_xxl_url, hf_headers) +def test_generate(llama_7b_url, hf_headers): + client = Client(llama_7b_url, hf_headers) response = client.generate("test", max_new_tokens=1, decoder_input_details=True) - assert response.generated_text == "" + assert response.generated_text == "_" assert response.details.finish_reason == FinishReason.Length assert response.details.generated_tokens == 1 assert response.details.seed is None - assert len(response.details.prefill) == 1 - assert response.details.prefill[0] == InputToken(id=0, text="", logprob=None) + assert len(response.details.prefill) == 2 + assert response.details.prefill[0] == InputToken(id=1, text="", logprob=None) assert len(response.details.tokens) == 1 - assert response.details.tokens[0].id == 3 - assert response.details.tokens[0].text == " " + assert response.details.tokens[0].id == 29918 + assert response.details.tokens[0].text == "_" assert not response.details.tokens[0].special -def test_generate_best_of(flan_t5_xxl_url, hf_headers): - client = Client(flan_t5_xxl_url, hf_headers) +def test_generate_best_of(llama_7b_url, hf_headers): + client = Client(llama_7b_url, hf_headers) response = client.generate( "test", max_new_tokens=1, best_of=2, do_sample=True, decoder_input_details=True ) @@ -39,14 +39,14 @@ def test_generate_not_found(fake_url, hf_headers): client.generate("test") -def test_generate_validation_error(flan_t5_xxl_url, hf_headers): - client = Client(flan_t5_xxl_url, hf_headers) +def test_generate_validation_error(llama_7b_url, hf_headers): + client = Client(llama_7b_url, hf_headers) with pytest.raises(ValidationError): client.generate("test", max_new_tokens=10_000) -def test_generate_stream(flan_t5_xxl_url, hf_headers): - client = Client(flan_t5_xxl_url, hf_headers) +def test_generate_stream(llama_7b_url, hf_headers): + client = Client(llama_7b_url, hf_headers) responses = [ response for response in client.generate_stream("test", max_new_tokens=1) ] @@ -54,7 +54,7 @@ def test_generate_stream(flan_t5_xxl_url, hf_headers): assert len(responses) == 1 response = responses[0] - assert response.generated_text == "" + assert response.generated_text == "_" assert response.details.finish_reason == FinishReason.Length assert response.details.generated_tokens == 1 assert response.details.seed is None @@ -66,34 +66,37 @@ def test_generate_stream_not_found(fake_url, hf_headers): list(client.generate_stream("test")) -def test_generate_stream_validation_error(flan_t5_xxl_url, hf_headers): - client = Client(flan_t5_xxl_url, hf_headers) +def test_generate_stream_validation_error(llama_7b_url, hf_headers): + client = Client(llama_7b_url, hf_headers) with pytest.raises(ValidationError): list(client.generate_stream("test", max_new_tokens=10_000)) @pytest.mark.asyncio -async def test_generate_async(flan_t5_xxl_url, hf_headers): - client = AsyncClient(flan_t5_xxl_url, hf_headers) +async def test_generate_async(llama_7b_url, hf_headers): + client = AsyncClient(llama_7b_url, hf_headers) response = await client.generate( "test", max_new_tokens=1, decoder_input_details=True ) - assert response.generated_text == "" + assert response.generated_text == "_" assert response.details.finish_reason == FinishReason.Length assert response.details.generated_tokens == 1 assert response.details.seed is None - assert len(response.details.prefill) == 1 - assert response.details.prefill[0] == InputToken(id=0, text="", logprob=None) + assert len(response.details.prefill) == 2 + assert response.details.prefill[0] == InputToken(id=1, text="", logprob=None) + assert response.details.prefill[1] == InputToken( + id=1243, text="test", logprob=-10.96875 + ) assert len(response.details.tokens) == 1 - assert response.details.tokens[0].id == 3 - assert response.details.tokens[0].text == " " + assert response.details.tokens[0].id == 29918 + assert response.details.tokens[0].text == "_" assert not response.details.tokens[0].special @pytest.mark.asyncio -async def test_generate_async_best_of(flan_t5_xxl_url, hf_headers): - client = AsyncClient(flan_t5_xxl_url, hf_headers) +async def test_generate_async_best_of(llama_7b_url, hf_headers): + client = AsyncClient(llama_7b_url, hf_headers) response = await client.generate( "test", max_new_tokens=1, best_of=2, do_sample=True, decoder_input_details=True ) @@ -112,15 +115,15 @@ async def test_generate_async_not_found(fake_url, hf_headers): @pytest.mark.asyncio -async def test_generate_async_validation_error(flan_t5_xxl_url, hf_headers): - client = AsyncClient(flan_t5_xxl_url, hf_headers) +async def test_generate_async_validation_error(llama_7b_url, hf_headers): + client = AsyncClient(llama_7b_url, hf_headers) with pytest.raises(ValidationError): await client.generate("test", max_new_tokens=10_000) @pytest.mark.asyncio -async def test_generate_stream_async(flan_t5_xxl_url, hf_headers): - client = AsyncClient(flan_t5_xxl_url, hf_headers) +async def test_generate_stream_async(llama_7b_url, hf_headers): + client = AsyncClient(llama_7b_url, hf_headers) responses = [ response async for response in client.generate_stream("test", max_new_tokens=1) ] @@ -128,7 +131,7 @@ async def test_generate_stream_async(flan_t5_xxl_url, hf_headers): assert len(responses) == 1 response = responses[0] - assert response.generated_text == "" + assert response.generated_text == "_" assert response.details.finish_reason == FinishReason.Length assert response.details.generated_tokens == 1 assert response.details.seed is None @@ -143,8 +146,8 @@ async def test_generate_stream_async_not_found(fake_url, hf_headers): @pytest.mark.asyncio -async def test_generate_stream_async_validation_error(flan_t5_xxl_url, hf_headers): - client = AsyncClient(flan_t5_xxl_url, hf_headers) +async def test_generate_stream_async_validation_error(llama_7b_url, hf_headers): + client = AsyncClient(llama_7b_url, hf_headers) with pytest.raises(ValidationError): async for _ in client.generate_stream("test", max_new_tokens=10_000): pass From 06c3d4b1eccbfa9134082d45bc69afa6c43a3e2f Mon Sep 17 00:00:00 2001 From: drbh Date: Wed, 17 Apr 2024 04:41:12 -0400 Subject: [PATCH 30/74] feat: accept list as prompt and use first string (#1702) This PR allows the `CompletionRequest.prompt` to be sent as a string or array of strings. When an array is sent the first value will be used if it's a string; otherwise the according error will be thrown Fixes: https://github.com/huggingface/text-generation-inference/issues/1690 Similar to: https://github.com/vllm-project/vllm/pull/323/files --- clients/python/text_generation/types.py | 21 + docs/source/basic_tutorials/launcher.md | 9 + integration-tests/conftest.py | 27 +- ...t_flash_llama_completion_many_prompts.json | 38 ++ ..._llama_completion_many_prompts_stream.json | 602 ++++++++++++++++++ ..._flash_llama_completion_single_prompt.json | 20 + .../models/test_completion_prompts.py | 109 ++++ launcher/src/main.rs | 6 + router/src/lib.rs | 33 +- router/src/main.rs | 4 + router/src/server.rs | 422 +++++++++--- 11 files changed, 1186 insertions(+), 105 deletions(-) create mode 100644 integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json create mode 100644 integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json create mode 100644 integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_single_prompt.json create mode 100644 integration-tests/models/test_completion_prompts.py diff --git a/clients/python/text_generation/types.py b/clients/python/text_generation/types.py index deb987c5..cfa2a9ed 100644 --- a/clients/python/text_generation/types.py +++ b/clients/python/text_generation/types.py @@ -59,6 +59,17 @@ class ChatCompletionComplete(BaseModel): usage: Optional[Any] = None +class CompletionComplete(BaseModel): + # Index of the chat completion + index: int + # Message associated with the chat completion + text: str + # Log probabilities for the chat completion + logprobs: Optional[Any] + # Reason for completion + finish_reason: str + + class Function(BaseModel): name: Optional[str] arguments: str @@ -104,6 +115,16 @@ class ChatComplete(BaseModel): usage: Any +class Completion(BaseModel): + # Completion details + id: str + object: str + created: int + model: str + system_fingerprint: str + choices: List[CompletionComplete] + + class ChatRequest(BaseModel): # Model identifier model: str diff --git a/docs/source/basic_tutorials/launcher.md b/docs/source/basic_tutorials/launcher.md index d9b272db..de7c995d 100644 --- a/docs/source/basic_tutorials/launcher.md +++ b/docs/source/basic_tutorials/launcher.md @@ -398,6 +398,15 @@ Options: -e, --env Display a lot of information about your runtime environment +``` +## MAX_CLIENT_BATCH_SIZE +```shell + --max-client-batch-size + Control the maximum number of inputs that a client can send in a single request + + [env: MAX_CLIENT_BATCH_SIZE=] + [default: 4] + ``` ## HELP ```shell diff --git a/integration-tests/conftest.py b/integration-tests/conftest.py index e8ce0d84..cf0f498d 100644 --- a/integration-tests/conftest.py +++ b/integration-tests/conftest.py @@ -9,6 +9,7 @@ import json import math import time import random +import re from docker.errors import NotFound from typing import Optional, List, Dict @@ -26,6 +27,7 @@ from text_generation.types import ( ChatComplete, ChatCompletionChunk, ChatCompletionComplete, + Completion, ) DOCKER_IMAGE = os.getenv("DOCKER_IMAGE", None) @@ -69,17 +71,22 @@ class ResponseComparator(JSONSnapshotExtension): data = json.loads(data) if isinstance(data, Dict) and "choices" in data: choices = data["choices"] - if ( - isinstance(choices, List) - and len(choices) >= 1 - and "delta" in choices[0] - ): - return ChatCompletionChunk(**data) + if isinstance(choices, List) and len(choices) >= 1: + if "delta" in choices[0]: + return ChatCompletionChunk(**data) + if "text" in choices[0]: + return Completion(**data) return ChatComplete(**data) if isinstance(data, Dict): return Response(**data) if isinstance(data, List): + if ( + len(data) > 0 + and "object" in data[0] + and data[0]["object"] == "text_completion" + ): + return [Completion(**d) for d in data] return [Response(**d) for d in data] raise NotImplementedError @@ -161,6 +168,9 @@ class ResponseComparator(JSONSnapshotExtension): ) ) + def eq_completion(response: Completion, other: Completion) -> bool: + return response.choices[0].text == other.choices[0].text + def eq_chat_complete(response: ChatComplete, other: ChatComplete) -> bool: return ( response.choices[0].message.content == other.choices[0].message.content @@ -184,6 +194,11 @@ class ResponseComparator(JSONSnapshotExtension): if not isinstance(snapshot_data, List): snapshot_data = [snapshot_data] + if isinstance(serialized_data[0], Completion): + return len(snapshot_data) == len(serialized_data) and all( + [eq_completion(r, o) for r, o in zip(serialized_data, snapshot_data)] + ) + if isinstance(serialized_data[0], ChatComplete): return len(snapshot_data) == len(serialized_data) and all( [eq_chat_complete(r, o) for r, o in zip(serialized_data, snapshot_data)] diff --git a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json new file mode 100644 index 00000000..3fe0a482 --- /dev/null +++ b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json @@ -0,0 +1,38 @@ +{ + "choices": [ + { + "finish_reason": "eos_token", + "index": 1, + "logprobs": null, + "text": " PR for more information?" + }, + { + "finish_reason": "length", + "index": 0, + "logprobs": null, + "text": "le Business Incubator is providing a workspace" + }, + { + "finish_reason": "length", + "index": 2, + "logprobs": null, + "text": " severely flawed and often has a substandard" + }, + { + "finish_reason": "length", + "index": 3, + "logprobs": null, + "text": "hd20220811-" + } + ], + "created": 1713284455, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native", + "usage": { + "completion_tokens": 36, + "prompt_tokens": 8, + "total_tokens": 44 + } +} diff --git a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json new file mode 100644 index 00000000..702d48f4 --- /dev/null +++ b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json @@ -0,0 +1,602 @@ +[ + { + "choices": [ + { + "finish_reason": "", + "index": 0, + "logprobs": null, + "text": "\n" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 1, + "logprobs": null, + "text": "\n" + } + ], + "created": 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"text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 0, + "logprobs": null, + "text": " Room" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 1, + "logprobs": null, + "text": "s" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 2, + "logprobs": null, + "text": " the" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 3, + "logprobs": null, + "text": " tired" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 0, + "logprobs": null, + "text": ":" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 1, + "logprobs": null, + "text": "'" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 2, + "logprobs": null, + "text": " capital" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 3, + "logprobs": null, + "text": " of" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 0, + "logprobs": null, + "text": " She" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 1, + "logprobs": null, + "text": " scale" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 2, + "logprobs": null, + "text": " of" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + }, + { + "choices": [ + { + "finish_reason": "", + "index": 3, + "logprobs": null, + "text": " being" + } + ], + "created": 1713284431, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native" + } +] diff --git a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_single_prompt.json b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_single_prompt.json new file mode 100644 index 00000000..8b001a09 --- /dev/null +++ b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_single_prompt.json @@ -0,0 +1,20 @@ +{ + "choices": [ + { + "finish_reason": "length", + "index": 0, + "logprobs": null, + "text": " PR for flake8" + } + ], + "created": 1713284454, + "id": "", + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "object": "text_completion", + "system_fingerprint": "2.0.0-native", + "usage": { + "completion_tokens": 5, + "prompt_tokens": 6, + "total_tokens": 11 + } +} diff --git a/integration-tests/models/test_completion_prompts.py b/integration-tests/models/test_completion_prompts.py new file mode 100644 index 00000000..cafa8ea6 --- /dev/null +++ b/integration-tests/models/test_completion_prompts.py @@ -0,0 +1,109 @@ +import pytest +import requests +import json +from aiohttp import ClientSession + +from text_generation.types import ( + Completion, +) + + +@pytest.fixture(scope="module") +def flash_llama_completion_handle(launcher): + with launcher( + "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + ) as handle: + yield handle + + +@pytest.fixture(scope="module") +async def flash_llama_completion(flash_llama_completion_handle): + await flash_llama_completion_handle.health(300) + return flash_llama_completion_handle.client + + +# NOTE: since `v1/completions` is a deprecated inferface/endpoint we do not provide a convience +# method for it. Instead, we use the `requests` library to make the HTTP request directly. + + +def test_flash_llama_completion_single_prompt( + flash_llama_completion, response_snapshot +): + response = requests.post( + f"{flash_llama_completion.base_url}/v1/completions", + json={ + "model": "tgi", + "prompt": "Say this is a test", + "max_tokens": 5, + "seed": 0, + }, + headers=flash_llama_completion.headers, + stream=False, + ) + response = response.json() + assert len(response["choices"]) == 1 + + assert response == response_snapshot + + +def test_flash_llama_completion_many_prompts(flash_llama_completion, response_snapshot): + response = requests.post( + f"{flash_llama_completion.base_url}/v1/completions", + json={ + "model": "tgi", + "prompt": ["Say", "this", "is", "a"], + "max_tokens": 10, + "seed": 0, + }, + headers=flash_llama_completion.headers, + stream=False, + ) + response = response.json() + assert len(response["choices"]) == 4 + + all_indexes = [choice["index"] for choice in response["choices"]] + all_indexes.sort() + assert all_indexes == [0, 1, 2, 3] + + assert response == response_snapshot + + +async def test_flash_llama_completion_many_prompts_stream( + flash_llama_completion, response_snapshot +): + request = { + "model": "tgi", + "prompt": [ + "What color is the sky?", + "Is water wet?", + "What is the capital of France?", + "def mai", + ], + "max_tokens": 10, + "seed": 0, + "stream": True, + } + + url = f"{flash_llama_completion.base_url}/v1/completions" + + chunks = [] + async with ClientSession(headers=flash_llama_completion.headers) as session: + async with session.post(url, json=request) as response: + # iterate over the stream + async for chunk in response.content.iter_any(): + # remove "data:" + chunk = chunk.decode().split("\n\n") + # remove "data:" if present + chunk = [c.replace("data:", "") for c in chunk] + # remove empty strings + chunk = [c for c in chunk if c] + # parse json + chunk = [json.loads(c) for c in chunk] + + for c in chunk: + chunks.append(Completion(**c)) + assert "choices" in c + assert 0 <= c["choices"][0]["index"] <= 4 + + assert response.status == 200 + assert chunks == response_snapshot diff --git a/launcher/src/main.rs b/launcher/src/main.rs index be2426ee..d904f91b 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -414,6 +414,10 @@ struct Args { /// Display a lot of information about your runtime environment #[clap(long, short, action)] env: bool, + + /// Control the maximum number of inputs that a client can send in a single request + #[clap(default_value = "4", long, env)] + max_client_batch_size: usize, } #[derive(Debug)] @@ -1078,6 +1082,8 @@ fn spawn_webserver( // Start webserver tracing::info!("Starting Webserver"); let mut router_args = vec![ + "--max-client-batch-size".to_string(), + args.max_client_batch_size.to_string(), "--max-concurrent-requests".to_string(), args.max_concurrent_requests.to_string(), "--max-best-of".to_string(), diff --git a/router/src/lib.rs b/router/src/lib.rs index ddb28848..2395e3e2 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -155,6 +155,8 @@ pub struct Info { pub max_batch_size: Option, #[schema(example = "2")] pub validation_workers: usize, + #[schema(example = "32")] + pub max_client_batch_size: usize, /// Router Info #[schema(example = "0.5.0")] pub version: &'static str, @@ -280,6 +282,34 @@ fn default_parameters() -> GenerateParameters { } } +mod prompt_serde { + use serde::{self, Deserialize, Deserializer}; + use serde_json::Value; + + pub fn deserialize<'de, D>(deserializer: D) -> Result, D::Error> + where + D: Deserializer<'de>, + { + let value = Value::deserialize(deserializer)?; + match value { + Value::String(s) => Ok(vec![s]), + Value::Array(arr) if arr.is_empty() => Err(serde::de::Error::custom( + "Empty array detected. Do not use an empty array for the prompt.", + )), + Value::Array(arr) => arr + .iter() + .map(|v| match v { + Value::String(s) => Ok(s.to_owned()), + _ => Err(serde::de::Error::custom("Expected a string")), + }) + .collect(), + _ => Err(serde::de::Error::custom( + "Expected a string or an array of strings", + )), + } + } +} + #[derive(Clone, Deserialize, Serialize, ToSchema, Debug)] pub struct CompletionRequest { /// UNUSED @@ -289,7 +319,8 @@ pub struct CompletionRequest { /// The prompt to generate completions for. #[schema(example = "What is Deep Learning?")] - pub prompt: String, + #[serde(deserialize_with = "prompt_serde::deserialize")] + pub prompt: Vec, /// The maximum number of tokens that can be generated in the chat completion. #[serde(default)] diff --git a/router/src/main.rs b/router/src/main.rs index f3a6c46f..b77117a1 100644 --- a/router/src/main.rs +++ b/router/src/main.rs @@ -78,6 +78,8 @@ struct Args { messages_api_enabled: bool, #[clap(long, env, default_value_t = false)] disable_grammar_support: bool, + #[clap(default_value = "4", long, env)] + max_client_batch_size: usize, } #[tokio::main] @@ -112,6 +114,7 @@ async fn main() -> Result<(), RouterError> { ngrok_edge, messages_api_enabled, disable_grammar_support, + max_client_batch_size, } = args; // Launch Tokio runtime @@ -393,6 +396,7 @@ async fn main() -> Result<(), RouterError> { tokenizer_config, messages_api_enabled, disable_grammar_support, + max_client_batch_size, ) .await?; Ok(()) diff --git a/router/src/server.rs b/router/src/server.rs index 3f033a9d..302a4753 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -16,6 +16,7 @@ use crate::{ CompletionRequest, DeltaToolCall, Function, Tool, VertexRequest, VertexResponse, }; use crate::{FunctionDefinition, ToolCall, ToolType}; +use async_stream::__private::AsyncStream; use axum::extract::Extension; use axum::http::{HeaderMap, Method, StatusCode}; use axum::response::sse::{Event, KeepAlive, Sse}; @@ -23,8 +24,8 @@ use axum::response::{IntoResponse, Response}; use axum::routing::{get, post}; use axum::{http, Json, Router}; use axum_tracing_opentelemetry::middleware::OtelAxumLayer; -use futures::stream::FuturesUnordered; use futures::stream::StreamExt; +use futures::stream::{FuturesOrdered, FuturesUnordered}; use futures::Stream; use futures::TryStreamExt; use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle}; @@ -35,7 +36,9 @@ use std::sync::atomic::AtomicBool; use std::sync::Arc; use text_generation_client::{ShardInfo, ShardedClient}; use tokenizers::Tokenizer; +use tokio::select; use tokio::signal; +use tokio::sync::oneshot; use tokio::time::Instant; use tower_http::cors::{AllowOrigin, CorsLayer}; use tracing::{info_span, instrument, Instrument}; @@ -161,6 +164,15 @@ async fn generate( Json(req): Json, ) -> Result<(HeaderMap, Json), (StatusCode, Json)> { let span = tracing::Span::current(); + generate_internal(infer, ComputeType(compute_type), Json(req), span).await +} + +async fn generate_internal( + infer: Extension, + ComputeType(compute_type): ComputeType, + Json(req): Json, + span: tracing::Span, +) -> Result<(HeaderMap, Json), (StatusCode, Json)> { let start_time = Instant::now(); metrics::increment_counter!("tgi_request_count"); @@ -359,12 +371,13 @@ async fn generate_stream( HeaderMap, Sse>>, ) { + let span = tracing::Span::current(); let on_message_callback = |stream_token: StreamResponse| { let event = Event::default(); event.json_data(stream_token).unwrap() }; let (headers, response_stream) = - generate_stream_internal(infer, compute_type, Json(req), on_message_callback).await; + generate_stream_internal(infer, compute_type, Json(req), on_message_callback, span).await; let sse = Sse::new(response_stream).keep_alive(KeepAlive::default()); (headers, sse) } @@ -374,8 +387,8 @@ async fn generate_stream_internal( ComputeType(compute_type): ComputeType, Json(req): Json, on_message_callback: impl Fn(StreamResponse) -> Event, + span: tracing::Span, ) -> (HeaderMap, impl Stream>) { - let span = tracing::Span::current(); let start_time = Instant::now(); metrics::increment_counter!("tgi_request_count"); @@ -581,6 +594,7 @@ async fn completions( Extension(info): Extension, Json(req): Json, ) -> Result)> { + let span = tracing::Span::current(); metrics::increment_counter!("tgi_request_count"); let stream = req.stream; @@ -600,100 +614,299 @@ async fn completions( )); } - // build the request passing some parameters - let generate_request = GenerateRequest { - inputs: req.prompt.to_string(), - parameters: GenerateParameters { - best_of: None, - temperature: req.temperature, - repetition_penalty: req.repetition_penalty, - frequency_penalty: req.frequency_penalty, - top_k: None, - top_p: req.top_p, - typical_p: None, - do_sample: true, - max_new_tokens, - return_full_text: None, - stop: Vec::new(), - truncate: None, - watermark: false, - details: true, - decoder_input_details: !stream, - seed, - top_n_tokens: None, - grammar: None, - }, - }; + if req.prompt.len() > info.max_client_batch_size { + metrics::increment_counter!("tgi_request_failure", "err" => "validation"); + return Err(( + StatusCode::UNPROCESSABLE_ENTITY, + Json(ErrorResponse { + error: format!( + "Number of prompts exceeds the maximum allowed batch size of {}", + info.max_client_batch_size + ), + error_type: "batch size exceeded".to_string(), + }), + )); + } + + let generate_requests: Vec = req + .prompt + .iter() + .map(|prompt| GenerateRequest { + inputs: prompt.to_string(), + parameters: GenerateParameters { + best_of: None, + temperature: req.temperature, + repetition_penalty: req.repetition_penalty, + frequency_penalty: req.frequency_penalty, + top_k: None, + top_p: req.top_p, + typical_p: None, + do_sample: true, + max_new_tokens, + return_full_text: None, + stop: Vec::new(), + truncate: None, + watermark: false, + details: true, + decoder_input_details: !stream, + seed, + top_n_tokens: None, + grammar: None, + }, + }) + .collect(); + + let mut x_compute_type = None; + let mut x_compute_characters = 0u32; + let mut x_accel_buffering = None; if stream { - let on_message_callback = move |stream_token: StreamResponse| { - let event = Event::default(); + let mut response_streams = FuturesOrdered::new(); + for (index, generate_request) in generate_requests.into_iter().enumerate() { + let model_id = info.model_id.clone(); + let system_fingerprint = + format!("{}-{}", info.version, info.docker_label.unwrap_or("native")); + let infer_clone = infer.clone(); + let compute_type_clone = compute_type.clone(); + let span_clone = span.clone(); - let current_time = std::time::SystemTime::now() - .duration_since(std::time::UNIX_EPOCH) - .unwrap_or_else(|_| std::time::Duration::from_secs(0)) - .as_secs(); + // Create a future for each generate_stream_internal call. + let generate_future = async move { + let on_message_callback = move |stream_token: StreamResponse| { + let event = Event::default(); - event - .json_data(CompletionCompleteChunk { - id: "".to_string(), - object: "text_completion".to_string(), - created: current_time, + let current_time = std::time::SystemTime::now() + .duration_since(std::time::UNIX_EPOCH) + .unwrap_or_else(|_| std::time::Duration::from_secs(0)) + .as_secs(); - choices: vec![CompletionComplete { - finish_reason: "".to_string(), - index: 0, - logprobs: None, - text: stream_token.token.text, - }], + event + .json_data(CompletionCompleteChunk { + id: "".to_string(), + object: "text_completion".to_string(), + created: current_time, - model: info.model_id.clone(), - system_fingerprint: format!( - "{}-{}", - info.version, - info.docker_label.unwrap_or("native") - ), - }) - .map_or_else( - |e| { - println!("Failed to serialize CompletionCompleteChunk: {:?}", e); - Event::default() - }, - |data| data, + choices: vec![CompletionComplete { + finish_reason: "".to_string(), + index: index as u32, + logprobs: None, + text: stream_token.token.text, + }], + + model: model_id.clone(), + system_fingerprint: system_fingerprint.clone(), + }) + .map_or_else(|_e| Event::default(), |data| data) + }; + + let (header_tx, header_rx) = oneshot::channel(); + let (sse_tx, sse_rx) = tokio::sync::mpsc::unbounded_channel(); + + tokio::spawn(async move { + let (header_map, sse) = generate_stream_internal( + infer_clone.clone(), + compute_type_clone.clone(), + Json(generate_request), + on_message_callback, + span_clone.clone(), + ) + .await; + + // send and dont wait for response + let _ = header_tx.send(header_map); + + // pin an emit messages to the sse_tx + let mut sse = Box::pin(sse); + while let Some(event) = sse.next().await { + if sse_tx.send(event).is_err() { + tracing::error!("Failed to send event. Receiver dropped."); + break; + } + } + }); + + (header_rx, sse_rx) + }; + response_streams.push_back(generate_future); + } + + let mut all_rxs = vec![]; + + while let Some((header_rx, sse_rx)) = response_streams.next().await { + all_rxs.push(sse_rx); + + // get the headers from the first response of each stream + let headers = header_rx.await.map_err(|e| { + tracing::error!("Failed to get headers: {:?}", e); + ( + StatusCode::INTERNAL_SERVER_ERROR, + Json(ErrorResponse { + error: "Failed to get headers".to_string(), + error_type: "headers".to_string(), + }), ) + })?; + if x_compute_type.is_none() { + x_compute_type = headers + .get("x-compute-type") + .and_then(|v| v.to_str().ok()) + .map(|v| v.to_string()); + + x_accel_buffering = headers + .get("x-accel-buffering") + .and_then(|v| v.to_str().ok()) + .map(|v| v.to_string()); + } + x_compute_characters += headers + .get("x-compute-characters") + .and_then(|v| v.to_str().ok()) + .and_then(|v| v.parse().ok()) + .unwrap_or(0); + } + + let mut headers = HeaderMap::new(); + if let Some(x_compute_type) = x_compute_type { + headers.insert("x-compute-type", x_compute_type.parse().unwrap()); + } + headers.insert("x-compute-characters", x_compute_characters.into()); + if let Some(x_accel_buffering) = x_accel_buffering { + headers.insert("x-accel-buffering", x_accel_buffering.parse().unwrap()); + } + + // now sink the sse streams into a single stream and remove the ones that are done + let stream: AsyncStream, _> = async_stream::stream! { + loop { + let mut i = 0; + while i < all_rxs.len() { + let rx = &mut all_rxs[i]; + select! { + Some(event) = rx.recv() => { + yield event; + } + else => { + all_rxs.remove(i); + continue; // skip the increment to handle the next element at the same index + } + } + i += 1; // only increment when no element was removed + } + + if all_rxs.is_empty() { + break; + } + } }; - let (headers, response_stream) = generate_stream_internal( - infer, - compute_type, - Json(generate_request), - on_message_callback, - ) - .await; - - let sse = Sse::new(response_stream).keep_alive(KeepAlive::default()); + let sse = Sse::new(stream).keep_alive(KeepAlive::default()); Ok((headers, sse).into_response()) } else { - let (headers, Json(generation)) = generate( - Extension(infer), - Extension(compute_type), - Json(generate_request), - ) - .await?; - let current_time = std::time::SystemTime::now() .duration_since(std::time::UNIX_EPOCH) .unwrap_or_else(|_| std::time::Duration::from_secs(0)) .as_secs(); - let details = generation.details.ok_or(( - // this should never happen but handle if details are missing unexpectedly - StatusCode::INTERNAL_SERVER_ERROR, - Json(ErrorResponse { - error: "No details in generation".to_string(), - error_type: "no details".to_string(), - }), - ))?; + let responses = FuturesUnordered::new(); + for (index, generate_request) in generate_requests.into_iter().enumerate() { + let infer_clone = infer.clone(); + let compute_type_clone = compute_type.clone(); + let span_clone = span.clone(); + let response_future = async move { + let result = generate_internal( + Extension(infer_clone), + compute_type_clone, + Json(generate_request), + span_clone, + ) + .await; + result.map(|(headers, generation)| (index, headers, generation)) + }; + responses.push(response_future); + } + let generate_responses = responses.try_collect::>().await?; + + let mut prompt_tokens = 0u32; + let mut completion_tokens = 0u32; + let mut total_tokens = 0u32; + + let mut x_compute_time = 0u32; + let mut x_total_time = 0u32; + let mut x_validation_time = 0u32; + let mut x_queue_time = 0u32; + let mut x_inference_time = 0u32; + let mut x_time_per_token = 0u32; + let mut x_prompt_tokens = 0u32; + let mut x_generated_tokens = 0u32; + + let choices = generate_responses + .into_iter() + .map(|(index, headers, Json(generation))| { + let details = generation.details.ok_or(( + // this should never happen but handle if details are missing unexpectedly + StatusCode::INTERNAL_SERVER_ERROR, + Json(ErrorResponse { + error: "No details in generation".to_string(), + error_type: "no details".to_string(), + }), + ))?; + + if x_compute_type.is_none() { + x_compute_type = headers + .get("x-compute-type") + .and_then(|v| v.to_str().ok()) + .map(|v| v.to_string()); + } + + // accumulate headers and usage from each response + x_compute_time += headers + .get("x-compute-time") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + x_compute_characters += headers + .get("x-compute-characters") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + x_total_time += headers + .get("x-total-time") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + x_validation_time += headers + .get("x-validation-time") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + x_queue_time += headers + .get("x-queue-time") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + x_inference_time += headers + .get("x-inference-time") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + x_time_per_token += headers + .get("x-time-per-token") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + x_prompt_tokens += headers + .get("x-prompt-tokens") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + x_generated_tokens += headers + .get("x-generated-tokens") + .and_then(|v| v.to_str().ok()?.parse().ok()) + .unwrap_or(0); + + prompt_tokens += details.prefill.len() as u32; + completion_tokens += details.generated_tokens; + total_tokens += details.prefill.len() as u32 + details.generated_tokens; + + Ok(CompletionComplete { + finish_reason: details.finish_reason.to_string(), + index: index as u32, + logprobs: None, + text: generation.generated_text, + }) + }) + .collect::, _>>() + .map_err(|(status, Json(err))| (status, Json(err)))?; let response = Completion { id: "".to_string(), @@ -705,19 +918,30 @@ async fn completions( info.version, info.docker_label.unwrap_or("native") ), - choices: vec![CompletionComplete { - finish_reason: details.finish_reason.to_string(), - index: 0, - logprobs: None, - text: generation.generated_text, - }], + choices, usage: Usage { - prompt_tokens: details.prefill.len() as u32, - completion_tokens: details.generated_tokens, - total_tokens: details.prefill.len() as u32 + details.generated_tokens, + prompt_tokens, + completion_tokens, + total_tokens, }, }; + // headers similar to `generate` but aggregated + let mut headers = HeaderMap::new(); + if let Some(x_compute_type) = x_compute_type { + headers.insert("x-compute-type", x_compute_type.parse().unwrap()); + } + headers.insert("x-compute-characters", x_compute_characters.into()); + headers.insert("x-total-time", x_total_time.into()); + headers.insert("x-validation-time", x_validation_time.into()); + headers.insert("x-queue-time", x_queue_time.into()); + headers.insert("x-inference-time", x_inference_time.into()); + headers.insert("x-time-per-token", x_time_per_token.into()); + headers.insert("x-prompt-tokens", x_prompt_tokens.into()); + headers.insert("x-generated-tokens", x_generated_tokens.into()); + if let Some(x_accel_buffering) = x_accel_buffering { + headers.insert("x-accel-buffering", x_accel_buffering.parse().unwrap()); + } Ok((headers, Json(response)).into_response()) } } @@ -762,6 +986,7 @@ async fn chat_completions( Extension(info): Extension, Json(req): Json, ) -> Result)> { + let span = tracing::Span::current(); metrics::increment_counter!("tgi_request_count"); let ChatRequest { @@ -899,17 +1124,14 @@ async fn chat_completions( compute_type, Json(generate_request), on_message_callback, + span, ) .await; let sse = Sse::new(response_stream).keep_alive(KeepAlive::default()); Ok((headers, sse).into_response()) } else { - let (headers, Json(generation)) = generate( - Extension(infer), - Extension(compute_type), - Json(generate_request), - ) - .await?; + let (headers, Json(generation)) = + generate_internal(Extension(infer), compute_type, Json(generate_request), span).await?; let current_time = std::time::SystemTime::now() .duration_since(std::time::UNIX_EPOCH) @@ -1006,6 +1228,7 @@ async fn vertex_compatibility( Extension(compute_type): Extension, Json(req): Json, ) -> Result)> { + let span = tracing::Span::current(); metrics::increment_counter!("tgi_request_count"); // check that theres at least one instance @@ -1037,10 +1260,11 @@ async fn vertex_compatibility( }; async { - generate( + generate_internal( Extension(infer.clone()), - Extension(compute_type.clone()), + compute_type.clone(), Json(generate_request), + span.clone(), ) .await .map(|(_, Json(generation))| generation.generated_text) @@ -1152,6 +1376,7 @@ pub async fn run( tokenizer_config: HubTokenizerConfig, messages_api_enabled: bool, grammar_support: bool, + max_client_batch_size: usize, ) -> Result<(), axum::BoxError> { // OpenAPI documentation #[derive(OpenApi)] @@ -1326,6 +1551,7 @@ pub async fn run( max_waiting_tokens, max_batch_size, validation_workers, + max_client_batch_size, version: env!("CARGO_PKG_VERSION"), sha: option_env!("VERGEN_GIT_SHA"), docker_label: option_env!("DOCKER_LABEL"), From f9ee2c41b9664ad299f593ecdb500eba29373d63 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 18 Apr 2024 17:17:40 +0200 Subject: [PATCH 31/74] Upgrading all versions. (#1759) --- Cargo.lock | 240 ++-- Cargo.toml | 4 + benchmark/Cargo.toml | 4 +- docs/openapi.json | 38 +- router/Cargo.toml | 4 +- router/src/lib.rs | 3 +- server/poetry.lock | 1081 ++++++++--------- server/pyproject.toml | 4 +- server/requirements_cuda.txt | 14 +- server/requirements_rocm.txt | 14 +- .../utils/paged_attention.py | 129 +- 11 files changed, 781 insertions(+), 754 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index fd6baab5..6088d7b1 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -120,7 +120,7 @@ checksum = "0ae92a5119aa49cdbcf6b9f893fe4e1d98b04ccbf82ee0584ad948a44a734dea" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -159,7 +159,7 @@ checksum = "16e62a023e7c117e27523144c5d2459f4397fcc3cab0085af8e2224f643a0193" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -170,7 +170,7 @@ checksum = "c6fa2087f2753a7da8cc1c0dbfcf89579dd57458e36769de5ac750b4671737ca" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -449,9 +449,9 @@ checksum = "df8670b8c7b9dae1793364eafadf7239c40d669904660c5960d74cfd80b46a53" [[package]] name = "cc" -version = "1.0.92" +version = "1.0.94" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2678b2e3449475e95b0aa6f9b506a28e61b3dc8996592b983695e8ebb58a8b41" +checksum = "17f6e324229dc011159fcc089755d1e2e216a90d43a7dea6853ca740b84f35e7" dependencies = [ "jobserver", "libc", @@ -510,7 +510,7 @@ dependencies = [ "heck 0.5.0", "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -665,9 +665,9 @@ dependencies = [ [[package]] name = "darling" -version = "0.14.4" +version = "0.20.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7b750cb3417fd1b327431a470f388520309479ab0bf5e323505daf0290cd3850" +checksum = "54e36fcd13ed84ffdfda6f5be89b31287cbb80c439841fe69e04841435464391" dependencies = [ "darling_core", "darling_macro", @@ -675,27 +675,27 @@ dependencies = [ [[package]] name = "darling_core" -version = "0.14.4" +version = "0.20.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "109c1ca6e6b7f82cc233a97004ea8ed7ca123a9af07a8230878fcfda9b158bf0" +checksum = "9c2cf1c23a687a1feeb728783b993c4e1ad83d99f351801977dd809b48d0a70f" dependencies = [ "fnv", "ident_case", "proc-macro2", "quote", "strsim 0.10.0", - "syn 1.0.109", + "syn 2.0.60", ] [[package]] name = "darling_macro" -version = "0.14.4" +version = "0.20.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a4aab4dbc9f7611d8b55048a3a16d2d010c2c8334e46304b40ac1cc14bf3b48e" +checksum = "a668eda54683121533a393014d8692171709ff57a7d61f187b6e782719f8933f" dependencies = [ "darling_core", "quote", - "syn 1.0.109", + "syn 2.0.60", ] [[package]] @@ -709,33 +709,33 @@ dependencies = [ [[package]] name = "derive_builder" -version = "0.12.0" +version = "0.20.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8d67778784b508018359cbc8696edb3db78160bab2c2a28ba7f56ef6932997f8" +checksum = "0350b5cb0331628a5916d6c5c0b72e97393b8b6b03b47a9284f4e7f5a405ffd7" dependencies = [ "derive_builder_macro", ] [[package]] name = "derive_builder_core" -version = "0.12.0" +version = "0.20.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c11bdc11a0c47bc7d37d582b5285da6849c96681023680b906673c5707af7b0f" +checksum = "d48cda787f839151732d396ac69e3473923d54312c070ee21e9effcaa8ca0b1d" dependencies = [ "darling", "proc-macro2", "quote", - "syn 1.0.109", + "syn 2.0.60", ] [[package]] name = "derive_builder_macro" -version = "0.12.0" +version = "0.20.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ebcda35c7a396850a55ffeac740804b40ffec779b98fffbb1738f4033f0ee79e" +checksum = "206868b8242f27cecce124c19fd88157fbd0dd334df2587f36417bafbc85097b" dependencies = [ "derive_builder_core", - "syn 1.0.109", + "syn 2.0.60", ] [[package]] @@ -800,9 +800,9 @@ dependencies = [ [[package]] name = "either" -version = "1.10.0" +version = "1.11.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "11157ac094ffbdde99aa67b23417ebdd801842852b500e395a45a9c0aac03e4a" +checksum = "a47c1c47d2f5964e29c61246e81db715514cd532db6b5116a25ea3c03d6780a2" [[package]] name = "encode_unicode" @@ -1018,7 +1018,7 @@ checksum = "87750cf4b7a4c0625b1529e4c543c2182106e4dedc60a2a6455e00d212c489ac" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -1423,7 +1423,7 @@ checksum = "c34819042dc3d3971c46c2190835914dfbe0c3c13f61449b2997f4e9722dfa60" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -1476,9 +1476,9 @@ checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b" [[package]] name = "jobserver" -version = "0.1.29" +version = "0.1.30" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f08474e32172238f2827bd160c67871cdb2801430f65c3979184dc362e3ca118" +checksum = "685a7d121ee3f65ae4fddd72b25a04bb36b6af81bc0828f7d5434c0fe60fa3a2" dependencies = [ "libc", ] @@ -1703,7 +1703,7 @@ checksum = "38b4faf00617defe497754acde3024865bc143d44a86799b24e191ecff91354f" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -1775,9 +1775,9 @@ dependencies = [ [[package]] name = "monostate" -version = "0.1.11" +version = "0.1.12" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "878c2a1f1c70e5724fa28f101ca787b6a7e8ad5c5e4ae4ca3b0fa4a419fa9075" +checksum = "a20fffcd8ca4c69d31e036a71abc400147b41f90895df4edcb36497a1f8af8bf" dependencies = [ "monostate-impl", "serde", @@ -1785,13 +1785,13 @@ dependencies = [ [[package]] name = "monostate-impl" -version = "0.1.11" +version = "0.1.12" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f686d68a09079e63b1d2c64aa305095887ce50565f00a922ebfaeeee0d9ba6ce" +checksum = "bf307cbbbd777a9c10cec88ddafee572b3484caad5cce0c9236523c3803105a6" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -1929,9 +1929,9 @@ dependencies = [ [[package]] name = "num" -version = "0.4.1" +version = "0.4.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b05180d69e3da0e530ba2a1dae5110317e49e3b7f3d41be227dc5f92e49ee7af" +checksum = "3135b08af27d103b0a51f2ae0f8632117b7b185ccf931445affa8df530576a41" dependencies = [ "num-bigint", "num-complex", @@ -1981,7 +1981,7 @@ checksum = "ed3955f1a9c7c0c15e092f9c887db08b1fc683305fdf6eb6684f22555355e202" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -2111,7 +2111,7 @@ checksum = "a948666b637a0f465e8564c73e89d4dde00d72d4d473cc972f390fc3dcee7d9c" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -2327,7 +2327,7 @@ checksum = "2f38a4412a78282e09a2cf38d195ea5420d15ba0602cb375210efbc877243965" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -2381,12 +2381,12 @@ checksum = "5b40af805b3121feab8a3c29f04d8ad262fa8e0561883e7653e024ae4479e6de" [[package]] name = "prettyplease" -version = "0.2.17" +version = "0.2.19" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8d3928fb5db768cb86f891ff014f0144589297e3c6a1aba6ed7cecfdace270c7" +checksum = "5ac2cf0f2e4f42b49f5ffd07dae8d746508ef7526c13940e5f524012ae6c6550" dependencies = [ "proc-macro2", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -2415,9 +2415,9 @@ dependencies = [ [[package]] name = "proc-macro2" -version = "1.0.79" +version = "1.0.81" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e835ff2298f5721608eb1a980ecaee1aef2c132bf95ecc026a11b7bf3c01c02e" +checksum = "3d1597b0c024618f09a9c3b8655b7e430397a36d23fdafec26d6965e9eec3eba" dependencies = [ "unicode-ident", ] @@ -2438,7 +2438,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "8021cf59c8ec9c432cfc2526ac6b8aa508ecaf29cd415f271b8406c1b851c3fd" dependencies = [ "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -2478,7 +2478,7 @@ dependencies = [ "prost 0.12.4", "prost-types", "regex", - "syn 2.0.58", + "syn 2.0.60", "tempfile", ] @@ -2505,7 +2505,7 @@ dependencies = [ "itertools 0.12.1", "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -2752,12 +2752,6 @@ version = "0.6.29" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "f162c6dd7b008981e4d40210aca20b4bd0f9b60ca9271061b07f78537722f2e1" -[[package]] -name = "regex-syntax" -version = "0.7.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dbb5fb1acd8a1a18b3dd5be62d25485eb770e05afb408a9627d14d451bae12da" - [[package]] name = "regex-syntax" version = "0.8.3" @@ -2864,7 +2858,7 @@ dependencies = [ "quote", "rust-embed-utils", "shellexpand", - "syn 2.0.58", + "syn 2.0.60", "walkdir", ] @@ -3038,29 +3032,29 @@ dependencies = [ [[package]] name = "serde" -version = "1.0.197" +version = "1.0.198" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3fb1c873e1b9b056a4dc4c0c198b24c3ffa059243875552b2bd0933b1aee4ce2" +checksum = "9846a40c979031340571da2545a4e5b7c4163bdae79b301d5f86d03979451fcc" dependencies = [ "serde_derive", ] [[package]] name = "serde_derive" -version = "1.0.197" +version = "1.0.198" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7eb0b34b42edc17f6b7cac84a52a1c5f0e1bb2227e997ca9011ea3dd34e8610b" +checksum = "e88edab869b01783ba905e7d0153f9fc1a6505a96e4ad3018011eedb838566d9" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] name = "serde_json" -version = "1.0.115" +version = "1.0.116" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "12dc5c46daa8e9fdf4f5e71b6cf9a53f2487da0e86e55808e2d35539666497dd" +checksum = "3e17db7126d17feb94eb3fad46bf1a96b034e8aacbc2e775fe81505f8b0b2813" dependencies = [ "itoa", "ryu", @@ -3270,7 +3264,7 @@ dependencies = [ "proc-macro2", "quote", "rustversion", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -3292,9 +3286,9 @@ dependencies = [ [[package]] name = "syn" -version = "2.0.58" +version = "2.0.60" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "44cfb93f38070beee36b3fef7d4f5a16f27751d94b187b666a5cc5e9b0d30687" +checksum = "909518bc7b1c9b779f1bbf07f2929d35af9f0f37e47c6e9ef7f9dddc1e1821f3" dependencies = [ "proc-macro2", "quote", @@ -3412,7 +3406,7 @@ dependencies = [ "tabled", "text-generation-client", "thiserror", - "tokenizers 0.14.1", + "tokenizers", "tokio", "tracing", "tracing-subscriber", @@ -3482,7 +3476,7 @@ dependencies = [ "serde_json", "text-generation-client", "thiserror", - "tokenizers 0.15.2", + "tokenizers", "tokio", "tokio-stream", "tower-http", @@ -3511,7 +3505,7 @@ checksum = "c61f3ba182994efc43764a46c018c347bc492c79f024e705f46567b418f6d4f7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -3585,46 +3579,11 @@ checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20" [[package]] name = "tokenizers" -version = "0.14.1" +version = "0.19.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d9be88c795d8b9f9c4002b3a8f26a6d0876103a6f523b32ea3bac52d8560c17c" +checksum = "e500fad1dd3af3d626327e6a3fe5050e664a6eaa4708b8ca92f1794aaf73e6fd" dependencies = [ "aho-corasick", - "clap", - "derive_builder", - "esaxx-rs", - "getrandom", - "hf-hub", - "indicatif", - "itertools 0.11.0", - "lazy_static", - "log", - "macro_rules_attribute", - "monostate", - "onig", - "paste", - "rand", - "rayon", - "rayon-cond", - "regex", - "regex-syntax 0.7.5", - "serde", - "serde_json", - "spm_precompiled", - "thiserror", - "unicode-normalization-alignments", - "unicode-segmentation", - "unicode_categories", -] - -[[package]] -name = "tokenizers" -version = "0.15.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3dd47962b0ba36e7fd33518fbf1754d136fd1474000162bbf2a8b5fcb2d3654d" -dependencies = [ - "aho-corasick", - "clap", "derive_builder", "esaxx-rs", "getrandom", @@ -3688,7 +3647,7 @@ checksum = "5b8a1e28f2deaa14e508979454cb3a223b10b938b45af148bc0986de36f1923b" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -3837,7 +3796,7 @@ dependencies = [ "proc-macro2", "prost-build", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -3910,7 +3869,7 @@ checksum = "34704c8d6ebcbc939824180af020566b01a7c01f80641264eba0999f6c2b6be7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -4151,7 +4110,7 @@ dependencies = [ "proc-macro2", "quote", "regex", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] @@ -4273,7 +4232,7 @@ dependencies = [ "once_cell", "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", "wasm-bindgen-shared", ] @@ -4307,7 +4266,7 @@ checksum = "e94f17b526d0a461a191c78ea52bbce64071ed5c04c9ffe424dcb38f74171bb7" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", "wasm-bindgen-backend", "wasm-bindgen-shared", ] @@ -4391,7 +4350,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "e48a53791691ab099e5e2ad123536d0fff50652600abaf43bbf952894110d0be" dependencies = [ "windows-core", - "windows-targets 0.52.4", + "windows-targets 0.52.5", ] [[package]] @@ -4400,7 +4359,7 @@ version = "0.52.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "33ab640c8d7e35bf8ba19b884ba838ceb4fba93a4e8c65a9059d08afcfc683d9" dependencies = [ - "windows-targets 0.52.4", + "windows-targets 0.52.5", ] [[package]] @@ -4427,7 +4386,7 @@ version = "0.52.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "282be5f36a8ce781fad8c8ae18fa3f9beff57ec1b52cb3de0789201425d9a33d" dependencies = [ - "windows-targets 0.52.4", + "windows-targets 0.52.5", ] [[package]] @@ -4462,17 +4421,18 @@ dependencies = [ [[package]] name = "windows-targets" -version = "0.52.4" +version = "0.52.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7dd37b7e5ab9018759f893a1952c9420d060016fc19a472b4bb20d1bdd694d1b" +checksum = "6f0713a46559409d202e70e28227288446bf7841d3211583a4b53e3f6d96e7eb" dependencies = [ - "windows_aarch64_gnullvm 0.52.4", - "windows_aarch64_msvc 0.52.4", - "windows_i686_gnu 0.52.4", - "windows_i686_msvc 0.52.4", - "windows_x86_64_gnu 0.52.4", - "windows_x86_64_gnullvm 0.52.4", - "windows_x86_64_msvc 0.52.4", + "windows_aarch64_gnullvm 0.52.5", + "windows_aarch64_msvc 0.52.5", + "windows_i686_gnu 0.52.5", + "windows_i686_gnullvm", + "windows_i686_msvc 0.52.5", + "windows_x86_64_gnu 0.52.5", + "windows_x86_64_gnullvm 0.52.5", + "windows_x86_64_msvc 0.52.5", ] [[package]] @@ -4489,9 +4449,9 @@ checksum = "2b38e32f0abccf9987a4e3079dfb67dcd799fb61361e53e2882c3cbaf0d905d8" [[package]] name = "windows_aarch64_gnullvm" -version = "0.52.4" +version = "0.52.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bcf46cf4c365c6f2d1cc93ce535f2c8b244591df96ceee75d8e83deb70a9cac9" +checksum = "7088eed71e8b8dda258ecc8bac5fb1153c5cffaf2578fc8ff5d61e23578d3263" [[package]] name = "windows_aarch64_msvc" @@ -4507,9 +4467,9 @@ checksum = "dc35310971f3b2dbbf3f0690a219f40e2d9afcf64f9ab7cc1be722937c26b4bc" [[package]] name = "windows_aarch64_msvc" -version = "0.52.4" +version = "0.52.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "da9f259dd3bcf6990b55bffd094c4f7235817ba4ceebde8e6d11cd0c5633b675" +checksum = "9985fd1504e250c615ca5f281c3f7a6da76213ebd5ccc9561496568a2752afb6" [[package]] name = "windows_i686_gnu" @@ -4525,9 +4485,15 @@ checksum = "a75915e7def60c94dcef72200b9a8e58e5091744960da64ec734a6c6e9b3743e" [[package]] name = "windows_i686_gnu" -version = "0.52.4" +version = "0.52.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b474d8268f99e0995f25b9f095bc7434632601028cf86590aea5c8a5cb7801d3" +checksum = "88ba073cf16d5372720ec942a8ccbf61626074c6d4dd2e745299726ce8b89670" + +[[package]] +name = "windows_i686_gnullvm" +version = "0.52.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "87f4261229030a858f36b459e748ae97545d6f1ec60e5e0d6a3d32e0dc232ee9" [[package]] name = "windows_i686_msvc" @@ -4543,9 +4509,9 @@ checksum = "8f55c233f70c4b27f66c523580f78f1004e8b5a8b659e05a4eb49d4166cca406" [[package]] name = "windows_i686_msvc" -version = "0.52.4" +version = "0.52.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1515e9a29e5bed743cb4415a9ecf5dfca648ce85ee42e15873c3cd8610ff8e02" +checksum = "db3c2bf3d13d5b658be73463284eaf12830ac9a26a90c717b7f771dfe97487bf" [[package]] name = "windows_x86_64_gnu" @@ -4561,9 +4527,9 @@ checksum = "53d40abd2583d23e4718fddf1ebec84dbff8381c07cae67ff7768bbf19c6718e" [[package]] name = "windows_x86_64_gnu" -version = "0.52.4" +version = "0.52.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5eee091590e89cc02ad514ffe3ead9eb6b660aedca2183455434b93546371a03" +checksum = "4e4246f76bdeff09eb48875a0fd3e2af6aada79d409d33011886d3e1581517d9" [[package]] name = "windows_x86_64_gnullvm" @@ -4579,9 +4545,9 @@ checksum = "0b7b52767868a23d5bab768e390dc5f5c55825b6d30b86c844ff2dc7414044cc" [[package]] name = "windows_x86_64_gnullvm" -version = "0.52.4" +version = "0.52.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "77ca79f2451b49fa9e2af39f0747fe999fcda4f5e241b2898624dca97a1f2177" +checksum = "852298e482cd67c356ddd9570386e2862b5673c85bd5f88df9ab6802b334c596" [[package]] name = "windows_x86_64_msvc" @@ -4597,9 +4563,9 @@ checksum = "ed94fce61571a4006852b7389a063ab983c02eb1bb37b47f8272ce92d06d9538" [[package]] name = "windows_x86_64_msvc" -version = "0.52.4" +version = "0.52.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "32b752e52a2da0ddfbdbcc6fceadfeede4c939ed16d13e648833a61dfb611ed8" +checksum = "bec47e5bfd1bff0eeaf6d8b485cc1074891a197ab4225d504cb7a1ab88b02bf0" [[package]] name = "winnow" @@ -4637,7 +4603,7 @@ checksum = "9ce1b18ccd8e73a9321186f97e46f9f04b778851177567b1975109d26a08d2a6" dependencies = [ "proc-macro2", "quote", - "syn 2.0.58", + "syn 2.0.60", ] [[package]] diff --git a/Cargo.toml b/Cargo.toml index ecb4878f..fffe4ef7 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -14,6 +14,10 @@ edition = "2021" authors = ["Olivier Dehaene"] homepage = "https://github.com/huggingface/text-generation-inference" +[workspace.dependencies] +tokenizers = { version = "0.19.1", features = ["http"] } +hf-hub = { version = "0.3.1", features = ["tokio"] } + [profile.release] debug = 1 incremental = true diff --git a/benchmark/Cargo.toml b/benchmark/Cargo.toml index 40738c4d..756460e0 100644 --- a/benchmark/Cargo.toml +++ b/benchmark/Cargo.toml @@ -23,9 +23,9 @@ serde_json = "1.0" tabled = "0.14.0" text-generation-client = { path = "../router/client" } thiserror = "1.0.48" -tokenizers = { version = "0.14.0", features = ["http"] } +tokenizers = { workspace = true } tokio = { version = "1.32.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync", "macros"] } tui = {package = "ratatui", version = "0.23", default-features = false, features = ["crossterm"]} tracing = "0.1.37" tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] } -hf-hub = "0.3.1" +hf-hub = { workspace = true } diff --git a/docs/openapi.json b/docs/openapi.json index 34b030f2..3093e7e2 100644 --- a/docs/openapi.json +++ b/docs/openapi.json @@ -408,9 +408,14 @@ }, "responses": { "200": { - "description": "Generated Text", + "description": "Generated Chat Completion", "content": { "application/json": { + "schema": { + "$ref": "#/components/schemas/ChatCompletion" + } + }, + "text/event-stream": { "schema": { "$ref": "#/components/schemas/ChatCompletionChunk" } @@ -492,11 +497,16 @@ }, "responses": { "200": { - "description": "Generated Text", + "description": "Generated Chat Completion", "content": { "application/json": { "schema": { - "$ref": "#/components/schemas/ChatCompletionChunk" + "$ref": "#/components/schemas/Completion" + } + }, + "text/event-stream": { + "schema": { + "$ref": "#/components/schemas/CompletionCompleteChunk" } } } @@ -930,7 +940,7 @@ "tool_prompt": { "type": "string", "description": "A prompt to be appended before the tools", - "example": "\"Based on the conversation, please choose the most appropriate tool to use: \"", + "example": "\"You will be presented with a JSON schema representing a set of tools.\nIf the user request lacks of sufficient information to make a precise tool selection: Do not invent any tool's properties, instead notify with an error message.\n\nJSON Schema:\n\"", "nullable": true }, "tools": { @@ -1071,7 +1081,10 @@ "example": "mistralai/Mistral-7B-Instruct-v0.2" }, "prompt": { - "type": "string", + "type": "array", + "items": { + "type": "string" + }, "description": "The prompt to generate completions for.", "example": "What is Deep Learning?" }, @@ -1234,17 +1247,17 @@ "type": "object", "required": [ "name", - "parameters" + "arguments" ], "properties": { + "arguments": {}, "description": { "type": "string", "nullable": true }, "name": { "type": "string" - }, - "parameters": {} + } } }, "GenerateParameters": { @@ -1260,7 +1273,7 @@ }, "decoder_input_details": { "type": "boolean", - "default": "true" + "default": "false" }, "details": { "type": "boolean", @@ -1285,6 +1298,7 @@ "$ref": "#/components/schemas/GrammarType" } ], + "default": "null", "nullable": true }, "max_new_tokens": { @@ -1478,6 +1492,7 @@ "max_batch_total_tokens", "max_waiting_tokens", "validation_workers", + "max_client_batch_size", "version" ], "properties": { @@ -1503,6 +1518,11 @@ "example": "2", "minimum": 0 }, + "max_client_batch_size": { + "type": "integer", + "example": "32", + "minimum": 0 + }, "max_concurrent_requests": { "type": "integer", "description": "Router Parameters", diff --git a/router/Cargo.toml b/router/Cargo.toml index 582bbdfb..d164183e 100644 --- a/router/Cargo.toml +++ b/router/Cargo.toml @@ -21,7 +21,7 @@ axum-tracing-opentelemetry = "0.14.1" text-generation-client = { path = "client" } clap = { version = "4.4.5", features = ["derive", "env"] } futures = "0.3.28" -hf-hub = { version = "0.3.0", features = ["tokio"] } +hf-hub = { workspace = true } jsonschema = { version = "0.17.1", features = ["draft202012"] } metrics = "0.21.1" metrics-exporter-prometheus = { version = "0.12.1", features = [] } @@ -33,7 +33,7 @@ reqwest = { version = "0.11.20", features = [] } serde = "1.0.188" serde_json = "1.0.107" thiserror = "1.0.48" -tokenizers = { version = "0.15.1", features = ["http"] } +tokenizers = { workspace = true} tokio = { version = "1.32.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] } tokio-stream = "0.1.14" tower-http = { version = "0.4.4", features = ["cors"] } diff --git a/router/src/lib.rs b/router/src/lib.rs index 2395e3e2..53982c36 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -238,7 +238,7 @@ pub(crate) struct GenerateParameters { #[schema(default = "true")] pub details: bool, #[serde(default)] - #[schema(default = "true")] + #[schema(default = "false")] pub decoder_input_details: bool, #[serde(default)] #[schema( @@ -252,6 +252,7 @@ pub(crate) struct GenerateParameters { #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 5)] pub top_n_tokens: Option, #[serde(default)] + #[schema(nullable = true, default = "null", example = "null")] pub grammar: Option, } diff --git a/server/poetry.lock b/server/poetry.lock index f6768565..aea9c4c2 100644 --- a/server/poetry.lock +++ b/server/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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"protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] -docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"] +dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] +dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"] +dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] +docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"] docs-specific = ["hf-doc-builder"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] @@ -3054,16 +3033,16 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"] +testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] timm = ["timm"] -tokenizers = ["tokenizers (>=0.14,<0.19)"] +tokenizers = ["tokenizers (>=0.19,<0.20)"] torch = ["accelerate (>=0.21.0)", "torch"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] video = ["av (==9.2.0)", "decord (==0.6.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -3473,4 +3452,4 @@ torch = ["torch"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "2bbb3d5acafe4bd1a616106ad3956058078a1f54c9a906a0065ee474f5af407d" +content-hash = "438514f5cf9978fbd39b910596634e6ec4a6191d10026dafbb026808eb63c70b" diff --git a/server/pyproject.toml b/server/pyproject.toml index e7c576a8..ccfccdb2 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -24,9 +24,9 @@ opentelemetry-exporter-otlp = "^1.15.0" opentelemetry-instrumentation-grpc = "^0.36b0" hf-transfer = "^0.1.2" sentencepiece = "^0.1.97" -tokenizers = "^0.15.0" +tokenizers = "^0.19.1" huggingface-hub = "^0.19.3" -transformers = "^4.39" +transformers = "^4.40" einops = "^0.6.1" texttable = { version = "^1.6.7", optional = true } datasets = { version = "^2.14.0", optional = true } diff --git a/server/requirements_cuda.txt b/server/requirements_cuda.txt index 6ad5235a..5cd03130 100644 --- a/server/requirements_cuda.txt +++ b/server/requirements_cuda.txt @@ -5,7 +5,7 @@ click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13" einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13" -filelock==3.13.3 ; python_version >= "3.9" and python_version < "3.13" +filelock==3.13.4 ; python_version >= "3.9" and python_version < "3.13" fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13" googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13" grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" @@ -14,7 +14,7 @@ grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13" grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13" hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13" huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13" -idna==3.6 ; python_version >= "3.9" and python_version < "3.13" +idna==3.7 ; python_version >= "3.9" and python_version < "3.13" loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13" numpy==1.26.4 ; python_version >= "3.9" and python_version < "3.13" opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13" @@ -30,15 +30,15 @@ packaging==24.0 ; python_version >= "3.9" and python_version < "3.13" pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13" protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13" -regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13" +regex==2024.4.16 ; python_version >= "3.9" and python_version < "3.13" requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13" -safetensors==0.4.2 ; python_version >= "3.9" and python_version < "3.13" +safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13" scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13" sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13" -setuptools==69.2.0 ; python_version >= "3.9" and python_version < "3.13" -tokenizers==0.15.2 ; python_version >= "3.9" and python_version < "3.13" +setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13" +tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.39.3 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.40.0 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13" diff --git a/server/requirements_rocm.txt b/server/requirements_rocm.txt index 6ad5235a..5cd03130 100644 --- a/server/requirements_rocm.txt +++ b/server/requirements_rocm.txt @@ -5,7 +5,7 @@ click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13" einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13" -filelock==3.13.3 ; python_version >= "3.9" and python_version < "3.13" +filelock==3.13.4 ; python_version >= "3.9" and python_version < "3.13" fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13" googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13" grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" @@ -14,7 +14,7 @@ grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13" grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13" hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13" huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13" -idna==3.6 ; python_version >= "3.9" and python_version < "3.13" +idna==3.7 ; python_version >= "3.9" and python_version < "3.13" loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13" numpy==1.26.4 ; python_version >= "3.9" and python_version < "3.13" opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13" @@ -30,15 +30,15 @@ packaging==24.0 ; python_version >= "3.9" and python_version < "3.13" pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13" protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13" -regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13" +regex==2024.4.16 ; python_version >= "3.9" and python_version < "3.13" requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13" -safetensors==0.4.2 ; python_version >= "3.9" and python_version < "3.13" +safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13" scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13" sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13" -setuptools==69.2.0 ; python_version >= "3.9" and python_version < "3.13" -tokenizers==0.15.2 ; python_version >= "3.9" and python_version < "3.13" +setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13" +tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.39.3 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.40.0 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13" diff --git a/server/text_generation_server/utils/paged_attention.py b/server/text_generation_server/utils/paged_attention.py index 18e605b0..487a3a72 100644 --- a/server/text_generation_server/utils/paged_attention.py +++ b/server/text_generation_server/utils/paged_attention.py @@ -1,7 +1,6 @@ import torch -# vllm imports -from vllm._C import cache_ops, ops +from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM _PARTITION_SIZE = 512 @@ -13,7 +12,18 @@ def reshape_and_cache( value_cache: torch.Tensor, slots: torch.Tensor, ): - cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0) + if IS_CUDA_SYSTEM: + from vllm._C import cache_ops + + cache_ops.reshape_and_cache( + key, value, key_cache, value_cache, slots, "auto", 1.0 + ) + elif IS_ROCM_SYSTEM: + from vllm import cache_ops + + cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots) + else: + raise ValueError("vllm is not supported on your system") def attention( @@ -55,21 +65,43 @@ def attention( # to parallelize. use_v1 = max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512) if use_v1: - ops.paged_attention_v1( - out, - query, - key_cache, - value_cache, - kv_head_mapping, - softmax_scale, - block_tables, - input_lengths, - block_size, - max_s, - None, - "auto", - 1.0, - ) + if IS_CUDA_SYSTEM: + from vllm._C import ops + + ops.paged_attention_v1( + out, + query, + key_cache, + value_cache, + kv_head_mapping, + softmax_scale, + block_tables, + input_lengths, + block_size, + max_s, + None, + "auto", + 1.0, + ) + elif IS_ROCM_SYSTEM: + from vllm import attention_ops + + attention_ops.paged_attention_v1( + out, + query, + key_cache, + value_cache, + kv_head_mapping, + softmax_scale, + block_tables, + input_lengths, + block_size, + max_s, + None, + ) + else: + raise ValueError("vllm is not supported on your system") + else: # Run PagedAttention V2. assert _PARTITION_SIZE % block_size == 0 @@ -84,21 +116,46 @@ def attention( device=out.device, ) max_logits = torch.empty_like(exp_sums) - ops.paged_attention_v2( - out, - exp_sums, - max_logits, - tmp_output, - query, - key_cache, - value_cache, - kv_head_mapping, - softmax_scale, - block_tables, - input_lengths, - block_size, - max_s, - None, - "auto", - 1.0, - ) + + if IS_CUDA_SYSTEM: + from vllm._C import ops + + ops.paged_attention_v2( + out, + exp_sums, + max_logits, + tmp_output, + query, + key_cache, + value_cache, + kv_head_mapping, + softmax_scale, + block_tables, + input_lengths, + block_size, + max_s, + None, + "auto", + 1.0, + ) + elif IS_ROCM_SYSTEM: + from vllm import attention_ops + + attention_ops.paged_attention_v2( + out, + exp_sums, + max_logits, + tmp_output, + query, + key_cache, + value_cache, + kv_head_mapping, + softmax_scale, + block_tables, + input_lengths, + block_size, + max_s, + None, + ) + else: + raise ValueError("vllm is not supported on your system") From 2d0a7173d4891e7cd5f9b77f8e0987b82a339e51 Mon Sep 17 00:00:00 2001 From: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com> Date: Thu, 18 Apr 2024 17:20:36 +0200 Subject: [PATCH 32/74] v2.0.1 --- Cargo.lock | 8 +- Cargo.toml | 2 +- docs/openapi.json | 2 +- .../test_flash_llama_simple.json | 2 +- ...t_flash_llama_completion_many_prompts.json | 2 +- ..._llama_completion_many_prompts_stream.json | 80 +++++++++---------- ..._flash_llama_completion_single_prompt.json | 2 +- .../test_flash_llama_grammar_tools.json | 2 +- .../test_flash_llama_grammar_tools_auto.json | 2 +- ...test_flash_llama_grammar_tools_choice.json | 2 +- ...test_flash_llama_grammar_tools_stream.json | 2 +- integration-tests/pyproject.toml | 2 +- server/pyproject.toml | 2 +- 13 files changed, 55 insertions(+), 55 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index 6088d7b1..de54c536 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -3393,7 +3393,7 @@ dependencies = [ [[package]] name = "text-generation-benchmark" -version = "2.0.0" +version = "2.0.1" dependencies = [ "average", "clap", @@ -3414,7 +3414,7 @@ dependencies = [ [[package]] name = "text-generation-client" -version = "2.0.0" +version = "2.0.1" dependencies = [ "futures", "grpc-metadata", @@ -3430,7 +3430,7 @@ dependencies = [ [[package]] name = "text-generation-launcher" -version = "2.0.0" +version = "2.0.1" dependencies = [ "clap", "ctrlc", @@ -3448,7 +3448,7 @@ dependencies = [ [[package]] name = "text-generation-router" -version = "2.0.0" +version = "2.0.1" dependencies = [ "async-stream", "axum", diff --git a/Cargo.toml b/Cargo.toml index fffe4ef7..593fd950 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -9,7 +9,7 @@ members = [ resolver = "2" [workspace.package] -version = "2.0.0" +version = "2.0.1" edition = "2021" authors = ["Olivier Dehaene"] homepage = "https://github.com/huggingface/text-generation-inference" diff --git a/docs/openapi.json b/docs/openapi.json index 3093e7e2..2a387c2f 100644 --- a/docs/openapi.json +++ b/docs/openapi.json @@ -10,7 +10,7 @@ "name": "Apache 2.0", "url": "https://www.apache.org/licenses/LICENSE-2.0" }, - "version": "2.0.0" + "version": "2.0.1" }, "paths": { "/": { diff --git a/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json b/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json index 0ff874f1..4cb548d2 100644 --- a/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json +++ b/integration-tests/models/__snapshots__/test_chat_llama/test_flash_llama_simple.json @@ -17,7 +17,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native", + "system_fingerprint": "2.0.1-native", "usage": { "completion_tokens": 100, "prompt_tokens": 60, diff --git a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json index 3fe0a482..99c33cf7 100644 --- a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json +++ b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts.json @@ -29,7 +29,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native", + "system_fingerprint": "2.0.1-native", "usage": { "completion_tokens": 36, "prompt_tokens": 8, diff --git a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json index 702d48f4..d87071cf 100644 --- a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json +++ b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_many_prompts_stream.json @@ -12,7 +12,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -27,7 +27,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -42,7 +42,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -57,7 +57,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -72,7 +72,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -87,7 +87,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -102,7 +102,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -117,7 +117,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -132,7 +132,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -147,7 +147,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -162,7 +162,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -177,7 +177,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -192,7 +192,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -207,7 +207,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -222,7 +222,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -237,7 +237,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -252,7 +252,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -267,7 +267,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -282,7 +282,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -297,7 +297,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -312,7 +312,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -327,7 +327,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -342,7 +342,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -357,7 +357,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -372,7 +372,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -387,7 +387,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -402,7 +402,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -417,7 +417,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -432,7 +432,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -447,7 +447,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -462,7 +462,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -477,7 +477,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -492,7 +492,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -507,7 +507,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -522,7 +522,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -537,7 +537,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -552,7 +552,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -567,7 +567,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -582,7 +582,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" }, { "choices": [ @@ -597,6 +597,6 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" } ] diff --git a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_single_prompt.json b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_single_prompt.json index 8b001a09..5aed4935 100644 --- a/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_single_prompt.json +++ b/integration-tests/models/__snapshots__/test_completion_prompts/test_flash_llama_completion_single_prompt.json @@ -11,7 +11,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native", + "system_fingerprint": "2.0.1-native", "usage": { "completion_tokens": 5, "prompt_tokens": 6, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json index 45f8ca99..a4c34a10 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools.json @@ -30,7 +30,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native", + "system_fingerprint": "2.0.1-native", "usage": { "completion_tokens": 37, "prompt_tokens": 524, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json index e0ed0947..04bcdc4e 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_auto.json @@ -30,7 +30,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native", + "system_fingerprint": "2.0.1-native", "usage": { "completion_tokens": 37, "prompt_tokens": 524, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json index b70c2d6f..603c90af 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_choice.json @@ -30,7 +30,7 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native", + "system_fingerprint": "2.0.1-native", "usage": { "completion_tokens": 48, "prompt_tokens": 320, diff --git a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json index 6787b39b..f72a5d38 100644 --- a/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json +++ b/integration-tests/models/__snapshots__/test_tools_llama/test_flash_llama_grammar_tools_stream.json @@ -23,5 +23,5 @@ "id": "", "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "object": "text_completion", - "system_fingerprint": "2.0.0-native" + "system_fingerprint": "2.0.1-native" } diff --git a/integration-tests/pyproject.toml b/integration-tests/pyproject.toml index 6923ff23..88e9761a 100644 --- a/integration-tests/pyproject.toml +++ b/integration-tests/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "text-generation-integration-tests" -version = "2.0.0" +version = "2.0.1" description = "Text Generation Inference integration tests" authors = ["Nicolas Patry "] diff --git a/server/pyproject.toml b/server/pyproject.toml index ccfccdb2..23dc5eee 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "text-generation-server" -version = "2.0.0" +version = "2.0.1" description = "Text Generation Inference Python gRPC Server" authors = ["Olivier Dehaene "] From 26b3916612bb85067b8328d988138f67453a89e1 Mon Sep 17 00:00:00 2001 From: fxmarty <9808326+fxmarty@users.noreply.github.com> Date: Mon, 22 Apr 2024 16:09:19 +0200 Subject: [PATCH 33/74] Make `--cuda-graphs` work as expected (bis) (#1768) This was ignored up to now, even with `--cuda-graphs 0`. With this fix, `--cuda-graphs` is obeyed to. --- launcher/src/main.rs | 1 + server/text_generation_server/models/flash_causal_lm.py | 2 ++ server/text_generation_server/models/globals.py | 5 ++++- server/text_generation_server/models/mamba.py | 2 ++ 4 files changed, 9 insertions(+), 1 deletion(-) diff --git a/launcher/src/main.rs b/launcher/src/main.rs index d904f91b..40e7364f 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -1379,6 +1379,7 @@ fn main() -> Result<(), LauncherError> { let cuda_graphs = match (&args.cuda_graphs, &args.quantize) { (Some(cuda_graphs), Some(_q)) => cuda_graphs.clone(), + (Some(cuda_graphs), None) => cuda_graphs.clone(), #[allow(deprecated)] ( None, diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index 2c440083..1189ccdd 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -816,6 +816,8 @@ class FlashCausalLM(Model): self.cuda_graph_warmup(bs, max_s, max_bt) except torch.cuda.OutOfMemoryError: logger.exception(f"Decode cuda graph warmup failed") + else: + logger.info(f"Cuda Graphs are disabled (CUDA_GRAPHS={CUDA_GRAPHS}).") return int(num_blocks * BLOCK_SIZE) diff --git a/server/text_generation_server/models/globals.py b/server/text_generation_server/models/globals.py index 6f554049..91b4225a 100644 --- a/server/text_generation_server/models/globals.py +++ b/server/text_generation_server/models/globals.py @@ -4,11 +4,14 @@ import os MEM_POOL = torch.cuda.graph_pool_handle() # This is overridden by the cli cuda_graphs = os.getenv("CUDA_GRAPHS") -if cuda_graphs is not None: +if cuda_graphs is not None and cuda_graphs != "0": try: cuda_graphs = [int(item) for item in cuda_graphs.split(",")] except Exception as e: raise RuntimeError( f"Could not parse cuda graphs {cuda_graphs}, expected comma separated list for batch sizes to run on: {e}" ) +else: + cuda_graphs = None + CUDA_GRAPHS = cuda_graphs diff --git a/server/text_generation_server/models/mamba.py b/server/text_generation_server/models/mamba.py index 07a81491..0884317e 100644 --- a/server/text_generation_server/models/mamba.py +++ b/server/text_generation_server/models/mamba.py @@ -474,6 +474,8 @@ class Mamba(Model): self.cuda_graph_warmup(bs) except Exception: logger.exception(f"Decode cuda graph warmup failed") + else: + logger.info(f"Cuda Graphs are disabled (CUDA_GRAPHS={CUDA_GRAPHS}).") return None From ed72e9212620d4de10fbe476f0b7af2ab94e4cd7 Mon Sep 17 00:00:00 2001 From: Moritz Laurer <41862082+MoritzLaurer@users.noreply.github.com> Date: Mon, 22 Apr 2024 18:15:48 +0200 Subject: [PATCH 34/74] fix typos in docs and add small clarifications (#1790) # What does this PR do? Fix some small typos in the docs; add minor clarifications; add guidance to features on landing page ## Before submitting - [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? @OlivierDehaene --- docs/source/conceptual/guidance.md | 10 +++++----- docs/source/conceptual/speculation.md | 2 +- docs/source/conceptual/streaming.md | 4 ++-- docs/source/index.md | 2 +- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/source/conceptual/guidance.md b/docs/source/conceptual/guidance.md index 0a3bbd60..a9ff61d8 100644 --- a/docs/source/conceptual/guidance.md +++ b/docs/source/conceptual/guidance.md @@ -1,6 +1,6 @@ # Guidance -Text Generation Inference (TGI) now supports [JSON and regex grammars](#grammar-and-constraints) and [tools and functions](#tools-and-functions) to help developer guide LLM responses to fit their needs. +Text Generation Inference (TGI) now supports [JSON and regex grammars](#grammar-and-constraints) and [tools and functions](#tools-and-functions) to help developers guide LLM responses to fit their needs. These feature are available starting from version `1.4.3`. They are accessible via the [text_generation](https://pypi.org/project/text-generation/) library and is compatible with OpenAI's client libraries. The following guide will walk you through the new features and how to use them! @@ -16,7 +16,7 @@ If you're not up to date, grab the latest version and let's get started! - [The Grammar Parameter](#the-grammar-parameter): Shape your AI's responses with precision. - [Constrain with Pydantic](#constrain-with-pydantic): Define a grammar using Pydantic models. -- [JSON Schema Integration](#json-schema-integration): Fine grain control over your requests via JSON schema. +- [JSON Schema Integration](#json-schema-integration): Fine-grained control over your requests via JSON schema. - [Using the client](#using-the-client): Use TGI's client libraries to shape the AI's responses. ### Tools and Functions @@ -72,9 +72,9 @@ curl localhost:3000/generate \ ``` -A grammar can be defined using Pydantic models, JSON schema, or regular expressions. The AI will then generate a response that conforms to the specified grammar. +A grammar can be defined using Pydantic models, JSON schemas, or regular expressions. The AI will then generate a response that conforms to the specified grammar. -> Note: A grammar must compile to a intermediate representation to constrain the output. Grammar compilation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster. +> Note: A grammar must compile to an intermediate representation to constrain the output. Grammar compilation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster. ### Constrain with Pydantic @@ -151,7 +151,7 @@ json_schema = { } data = { - "inputs": "[INST]convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park [/INST]", + "inputs": "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park", "parameters": { "max_new_tokens": 200, "repetition_penalty": 1.3, diff --git a/docs/source/conceptual/speculation.md b/docs/source/conceptual/speculation.md index 071b7b68..f306c48a 100644 --- a/docs/source/conceptual/speculation.md +++ b/docs/source/conceptual/speculation.md @@ -36,7 +36,7 @@ In order to use medusa models in TGI, simply point to a medusa enabled model, an If you don't have a medusa model, or don't have the resource to fine-tune, you can try to use `n-gram`. -Ngram works by trying to find in the previous sequence existing tokens that match, and use those as speculation. +N-gram works by trying to find matching tokens in the previous sequence, and use those as speculation for generating new tokens. For example, if the tokens "np.mean" appear multiple times in the sequence, the model can speculate that the next continuation of the tokens "np." is probably also "mean". This is an extremely simple method, which works best for code, or highly repetitive text. This might not be beneficial, if the speculation misses too much. diff --git a/docs/source/conceptual/streaming.md b/docs/source/conceptual/streaming.md index 505a0d9e..71ec9b25 100644 --- a/docs/source/conceptual/streaming.md +++ b/docs/source/conceptual/streaming.md @@ -15,7 +15,7 @@ Token streaming is the mode in which the server returns the tokens one by one as /> -With token streaming, the server can start returning the tokens one by one before having to generate the whole response. Users can have a sense of the generation's quality earlier than the end of the generation. This has different positive effects: +With token streaming, the server can start returning the tokens one by one before having to generate the whole response. Users can have a sense of the generation's quality before the end of the generation. This has different positive effects: * Users can get results orders of magnitude earlier for extremely long queries. * Seeing something in progress allows users to stop the generation if it's not going in the direction they expect. @@ -116,7 +116,7 @@ curl -N 127.0.0.1:8080/generate_stream \ First, we need to install the `@huggingface/inference` library. `npm install @huggingface/inference` -If you're using the free Inference API, you can use `HfInference`. If you're using inference endpoints, you can use `HfInferenceEndpoint`. Let's +If you're using the free Inference API, you can use `HfInference`. If you're using inference endpoints, you can use `HfInferenceEndpoint`. We can create a `HfInferenceEndpoint` providing our endpoint URL and credential. diff --git a/docs/source/index.md b/docs/source/index.md index 8bf45dce..309442b1 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -18,8 +18,8 @@ Text Generation Inference implements many optimizations and features, such as: - Logits warper (temperature scaling, top-p, top-k, repetition penalty) - Stop sequences - Log probabilities -- Custom Prompt Generation: Easily generate text by providing custom prompts to guide the model's output. - Fine-tuning Support: Utilize fine-tuned models for specific tasks to achieve higher accuracy and performance. +- [Guidance](../conceptual/guidance): Enable function calling and tool-use by forcing the model to generate structured outputs based on your own predefined output schemas. Text Generation Inference is used in production by multiple projects, such as: From 455cada527152a7bd2e17385b705873fdb015e51 Mon Sep 17 00:00:00 2001 From: Lucain Date: Tue, 23 Apr 2024 16:22:12 +0200 Subject: [PATCH 35/74] Add attribute descriptions for `GenerateParameters` (#1798) Once https://github.com/huggingface/huggingface.js/pull/629 gets merged, we will rely on TGI's specs to generate jsonschema types for `text_generation` and `chat_completion`. This PR adds some documentation for `GenerationParameters`'s properties so that they get documented in the downstream tools (TGI docs but also `huggingface.js`/`huggingface_hub` inference clients). I mostly took inspiration from [the python client](https://github.com/huggingface/text-generation-inference/blob/main/clients/python/text_generation/types.py) for the descriptions. --- router/src/lib.rs | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) diff --git a/router/src/lib.rs b/router/src/lib.rs index 53982c36..9e847fe2 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -168,9 +168,12 @@ pub struct Info { #[derive(Clone, Debug, Deserialize, ToSchema, Default)] pub(crate) struct GenerateParameters { + /// Generate best_of sequences and return the one if the highest token logprobs. #[serde(default)] #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 1)] pub best_of: Option, + + /// The value used to module the logits distribution. #[serde(default)] #[schema( exclusive_minimum = 0.0, @@ -179,6 +182,9 @@ pub(crate) struct GenerateParameters { example = 0.5 )] pub temperature: Option, + + /// The parameter for repetition penalty. 1.0 means no penalty. + /// See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details. #[serde(default)] #[schema( exclusive_minimum = 0.0, @@ -187,6 +193,10 @@ pub(crate) struct GenerateParameters { example = 1.03 )] pub repetition_penalty: Option, + + /// The parameter for frequency penalty. 1.0 means no penalty + /// Penalize new tokens based on their existing frequency in the text so far, + /// decreasing the model's likelihood to repeat the same line verbatim. #[serde(default)] #[schema( exclusive_minimum = -2.0, @@ -195,9 +205,13 @@ pub(crate) struct GenerateParameters { example = 0.1 )] pub frequency_penalty: Option, + + /// The number of highest probability vocabulary tokens to keep for top-k-filtering. #[serde(default)] #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 10)] pub top_k: Option, + + /// Top-p value for nucleus sampling. #[serde(default)] #[schema( exclusive_minimum = 0.0, @@ -207,6 +221,9 @@ pub(crate) struct GenerateParameters { example = 0.95 )] pub top_p: Option, + + /// Typical Decoding mass + /// See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information. #[serde(default)] #[schema( exclusive_minimum = 0.0, @@ -216,30 +233,48 @@ pub(crate) struct GenerateParameters { example = 0.95 )] pub typical_p: Option, + + /// Activate logits sampling. #[serde(default)] #[schema(default = "false", example = true)] pub do_sample: bool, + + /// Maximum number of tokens to generate. #[serde(default = "default_max_new_tokens")] #[schema(nullable = true, default = "100", example = "20")] pub max_new_tokens: Option, + + /// Whether to prepend the prompt to the generated text #[serde(default)] #[schema(nullable = true, default = "null", example = false)] pub return_full_text: Option, + + /// Stop generating tokens if a member of `stop` is generated. #[serde(default)] #[schema(inline, max_items = 4, example = json ! (["photographer"]))] pub stop: Vec, + + /// Truncate inputs tokens to the given size. #[serde(default)] #[schema(nullable = true, default = "null", example = "null")] pub truncate: Option, + + /// Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226). #[serde(default)] #[schema(default = "false", example = true)] pub watermark: bool, + + /// Whether to return generation details. #[serde(default)] #[schema(default = "true")] pub details: bool, + + /// Whether to return decoder input token logprobs and ids. #[serde(default)] #[schema(default = "false")] pub decoder_input_details: bool, + + /// Random sampling seed. #[serde(default)] #[schema( exclusive_minimum = 0, @@ -248,9 +283,13 @@ pub(crate) struct GenerateParameters { example = "null" )] pub seed: Option, + + /// The number of highest probability vocabulary tokens to keep for top-n-filtering. #[serde(default)] #[schema(exclusive_minimum = 0, nullable = true, default = "null", example = 5)] pub top_n_tokens: Option, + + /// Grammar constraints for the generation. #[serde(default)] #[schema(nullable = true, default = "null", example = "null")] pub grammar: Option, From 9be1db3101a9c02ecbffb56f4f97ac49d5441dbc Mon Sep 17 00:00:00 2001 From: drbh Date: Tue, 23 Apr 2024 10:26:54 -0400 Subject: [PATCH 36/74] feat: allow null eos and bos tokens in config (#1791) This PR resolves an issue loading in tokenizer_configs where the eos or bos token is null as in: [Qwen/Qwen1.5-72B-Chat](https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/tokenizer_config.json) resolves: https://github.com/huggingface/text-generation-inference/issues/1545 and related to https://github.com/QwenLM/Qwen1.5/issues/162 --- router/src/lib.rs | 1 + router/src/main.rs | 5 ++++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/router/src/lib.rs b/router/src/lib.rs index 9e847fe2..5e207a03 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -116,6 +116,7 @@ mod token_serde { )) } } + Value::Null => Ok(None), _ => Err(de::Error::custom("invalid token format")), } } diff --git a/router/src/main.rs b/router/src/main.rs index b77117a1..c7e3f90b 100644 --- a/router/src/main.rs +++ b/router/src/main.rs @@ -254,7 +254,10 @@ async fn main() -> Result<(), RouterError> { // Load tokenizer config if found locally, or check if we can get it from the API if needed let tokenizer_config = if let Some(path) = tokenizer_config_path { - tracing::info!("Using local tokenizer config from user specified path"); + tracing::info!( + "Using local tokenizer config from user specified path {}", + path + ); HubTokenizerConfig::from_file(&std::path::PathBuf::from(path)) } else if local_model { tracing::info!("Using local tokenizer config"); From 986b4044d17067c8ff851df800f22fa9fb2ace45 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Tue, 23 Apr 2024 18:40:05 +0200 Subject: [PATCH 37/74] Phi3 support (#1797) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- .../text_generation_server/models/__init__.py | 2 +- .../custom_modeling/flash_llama_modeling.py | 29 ++++++++++++++----- server/text_generation_server/utils/layers.py | 13 +++++++++ .../text_generation_server/utils/weights.py | 19 ++++++++---- 4 files changed, 49 insertions(+), 14 deletions(-) diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index 06792b0d..e4e8717d 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -327,7 +327,7 @@ def get_model( trust_remote_code=trust_remote_code, ) - elif model_type == "llama" or model_type == "baichuan": + elif model_type == "llama" or model_type == "baichuan" or model_type == "phi3": if FLASH_ATTENTION: return FlashLlama( model_id, diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index 4cf0fcf2..6d796ac3 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -101,6 +101,13 @@ def load_attention(config, prefix, weights): weights=weights, bias=False, ) + elif config.model_type == "phi3": + return TensorParallelColumnLinear.load_qkv( + config, + prefix=f"{prefix}.qkv_proj", + weights=weights, + bias=False, + ) else: return TensorParallelColumnLinear.load_multi( config, @@ -257,13 +264,21 @@ class LlamaMLP(nn.Module): ) ) # Fuse gate and up proj - self.gate_up_proj = TensorParallelColumnLinear.load_multi( - config, - prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"], - weights=weights, - dim=0, - bias=False, - ) + if config.model_type == "phi3": + self.gate_up_proj = TensorParallelColumnLinear.load_gate_up( + config, + prefix=f"{prefix}.gate_up_proj", + weights=weights, + bias=False, + ) + else: + self.gate_up_proj = TensorParallelColumnLinear.load_multi( + config, + prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"], + weights=weights, + dim=0, + bias=False, + ) self.down_proj = TensorParallelRowLinear.load( config, prefix=f"{prefix}.down_proj", diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index 9cf5c80f..69bd5e88 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -696,6 +696,19 @@ class TensorParallelHead(SuperLayer): class TensorParallelColumnLinear(SuperLayer): + @classmethod + def load_gate_up(cls, config, prefix: str, weights, bias: bool): + """Specific method when the QKV was joined after the fact""" + weight = weights.get_weights_col_packed_gate_up( + prefix, quantize=config.quantize + ) + if bias: + raise NotImplementedError("packed_gate_up only implemented without bias") + else: + bias = None + linear = get_linear(weight, bias, config.quantize) + return cls(linear) + @classmethod def load_qkv(cls, config, prefix: str, weights, bias: bool): """Specific method when the QKV was joined after the fact""" diff --git a/server/text_generation_server/utils/weights.py b/server/text_generation_server/utils/weights.py index d0614346..da7aed1a 100644 --- a/server/text_generation_server/utils/weights.py +++ b/server/text_generation_server/utils/weights.py @@ -141,6 +141,12 @@ class Weights: return weight def get_weights_col_packed_qkv(self, prefix: str, quantize: str): + return self.get_weights_col_packed(prefix, quantize, 3) + + def get_weights_col_packed_gate_up(self, prefix: str, quantize: str): + return self.get_weights_col_packed(prefix, quantize, 2) + + def get_weights_col_packed(self, prefix: str, quantize: str, blocks: int): """ Highly specific when the underlying tensor is a simple cat of Q,K,V instead of being already alternating Q,K,V within the main tensor @@ -181,8 +187,8 @@ class Weights: else: slice_ = self._get_slice(f"{prefix}.weight") total_size = slice_.get_shape()[0] - assert total_size % 3 == 0, "Prepacked qkv is not divisible by 3" - single_size = total_size // 3 + assert total_size % blocks == 0, f"Prepacked is not divisible by {blocks}" + single_size = total_size // blocks world_size = self.process_group.size() rank = self.process_group.rank() @@ -192,10 +198,11 @@ class Weights: block_size = single_size // world_size start = rank * block_size stop = (rank + 1) * block_size - q = slice_[start:stop] - k = slice_[start + single_size : stop + single_size] - v = slice_[start + 2 * single_size : stop + 2 * single_size] - weight = torch.cat([q, k, v], dim=0) + tensors = [] + for i in range(blocks): + tensor = slice_[start + i * single_size : stop + i * single_size] + tensors.append(tensor) + weight = torch.cat(tensors, dim=0) weight = weight.to(device=self.device) weight = weight.to(dtype=self.dtype) return weight From bfddfa5955dd6558814d313e4364ddf534848632 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Tue, 23 Apr 2024 23:04:44 +0200 Subject: [PATCH 38/74] Idefics2. (#1756) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- docs/source/conceptual/speculation.md | 3 +- integration-tests/conftest.py | 8 + .../test_flash_idefics2_next_all_params.json | 89 + .../test_flash_idefics2_next_load.json | 7018 +++ .../test_flash_idefics2_next_simple.json | 73 + .../test_flash_llava_next_load.json | 39794 ++++++++-------- integration-tests/models/test_idefics2.py | 81 + router/client/src/client.rs | 6 +- router/src/config.rs | 53 +- router/src/validation.rs | 52 +- .../text_generation_server/models/__init__.py | 13 + .../custom_modeling/flash_mistral_modeling.py | 16 +- .../models/custom_modeling/idefics2.py | 829 + .../models/custom_modeling/llava_next.py | 43 +- .../models/custom_modeling/vlm.py | 28 + .../models/flash_mistral.py | 39 +- .../text_generation_server/models/idefics2.py | 51 + .../models/llava_next.py | 12 +- .../models/vlm_causal_lm.py | 116 +- 19 files changed, 28332 insertions(+), 19992 deletions(-) create mode 100644 integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_all_params.json create mode 100644 integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_load.json create mode 100644 integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_simple.json create mode 100644 integration-tests/models/test_idefics2.py create mode 100644 server/text_generation_server/models/custom_modeling/idefics2.py create mode 100644 server/text_generation_server/models/custom_modeling/vlm.py create mode 100644 server/text_generation_server/models/idefics2.py diff --git a/docs/source/conceptual/speculation.md b/docs/source/conceptual/speculation.md index f306c48a..79b1c82e 100644 --- a/docs/source/conceptual/speculation.md +++ b/docs/source/conceptual/speculation.md @@ -1,5 +1,6 @@ ## Speculation + Speculative decoding, assisted generation, Medusa, and others are a few different names for the same idea. The idea is to generate tokens *before* the large model actually runs, and only *check* if those tokens where valid. @@ -36,7 +37,7 @@ In order to use medusa models in TGI, simply point to a medusa enabled model, an If you don't have a medusa model, or don't have the resource to fine-tune, you can try to use `n-gram`. -N-gram works by trying to find matching tokens in the previous sequence, and use those as speculation for generating new tokens. For example, if the tokens "np.mean" appear multiple times in the sequence, the model can speculate that the next continuation of the tokens "np." is probably also "mean". +N-gram works by trying to find matching tokens in the previous sequence, and use those as speculation for generating new tokens. For example, if the tokens "np.mean" appear multiple times in the sequence, the model can speculate that the next continuation of the tokens "np." is probably also "mean". This is an extremely simple method, which works best for code, or highly repetitive text. This might not be beneficial, if the speculation misses too much. diff --git a/integration-tests/conftest.py b/integration-tests/conftest.py index cf0f498d..ae3f977b 100644 --- a/integration-tests/conftest.py +++ b/integration-tests/conftest.py @@ -293,6 +293,7 @@ def launcher(event_loop): dtype: Optional[str] = None, revision: Optional[str] = None, max_input_length: Optional[int] = None, + max_batch_prefill_tokens: Optional[int] = None, max_total_tokens: Optional[int] = None, ): port = random.randint(8000, 10_000) @@ -334,6 +335,9 @@ def launcher(event_loop): if max_input_length: args.append("--max-input-length") args.append(str(max_input_length)) + if max_batch_prefill_tokens: + args.append("--max-batch-prefill-tokens") + args.append(str(max_batch_prefill_tokens)) if max_total_tokens: args.append("--max-total-tokens") args.append(str(max_total_tokens)) @@ -371,6 +375,7 @@ def launcher(event_loop): dtype: Optional[str] = None, revision: Optional[str] = None, max_input_length: Optional[int] = None, + max_batch_prefill_tokens: Optional[int] = None, max_total_tokens: Optional[int] = None, ): port = random.randint(8000, 10_000) @@ -395,6 +400,9 @@ def launcher(event_loop): if max_input_length: args.append("--max-input-length") args.append(str(max_input_length)) + if max_batch_prefill_tokens: + args.append("--max-batch-prefill-tokens") + args.append(str(max_batch_prefill_tokens)) if max_total_tokens: args.append("--max-total-tokens") args.append(str(max_total_tokens)) diff --git a/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_all_params.json b/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_all_params.json new file mode 100644 index 00000000..45601505 --- /dev/null +++ b/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_all_params.json @@ -0,0 +1,89 @@ +{ + "details": { + "best_of_sequences": null, + "finish_reason": "length", + "generated_tokens": 10, + "prefill": [ + { + "id": 1, + "logprob": null, + "text": "" + }, + { + "id": 3735, + "logprob": -8.5625, + "text": "Test" + }, + { + "id": 2159, + "logprob": -10.78125, + "text": "request" + } + ], + "seed": 0, + "tokens": [ + { + "id": 288, + "logprob": -0.2854004, + "special": false, + "text": "ing" + }, + { + "id": 264, + "logprob": -0.37573242, + "special": false, + "text": " a" + }, + { + "id": 633, + "logprob": -0.09301758, + "special": false, + "text": " new" + }, + { + "id": 4480, + "logprob": -0.3322754, + "special": false, + "text": " feature" + }, + { + "id": 297, + "logprob": -0.8510742, + "special": false, + "text": " in" + }, + { + "id": 272, + "logprob": -0.13464355, + "special": false, + "text": " the" + }, + { + "id": 2039, + "logprob": 0.0, + "special": false, + "text": " game" + }, + { + "id": 28723, + "logprob": -0.89990234, + "special": false, + "text": "." + }, + { + "id": 13, + "logprob": 0.0, + "special": false, + "text": "\n" + }, + { + "id": 13, + "logprob": 0.0, + "special": false, + "text": "\n" + } + ], + "top_tokens": null + }, + "generated_text": "Test requesting a new feature in the game.\n\n" +} diff --git a/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_load.json b/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_load.json new file mode 100644 index 00000000..4bc90896 --- /dev/null +++ b/integration-tests/models/__snapshots__/test_idefics2/test_flash_idefics2_next_load.json @@ -0,0 +1,7018 @@ +[ + { + "details": { + "best_of_sequences": null, + "finish_reason": "length", + "generated_tokens": 10, + "prefill": [ + { + "id": 1, + "logprob": null, + "text": "" + }, + { + "id": 1247, + "logprob": -5.2421875, + "text": "User" + }, + { + "id": 28747, + "logprob": -6.9570312, + "text": ":" + }, + { + "id": 32000, + "logprob": -16.234375, + "text": "" + }, + { + "id": 32001, + "logprob": -18.96875, + "text": "" + }, + { + "id": 32001, + "logprob": -18.1875, + "text": "" + }, + { + "id": 32001, + 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-10.9765625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.0234375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.96875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.1484375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.703125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.71875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8984375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2890625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.640625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7109375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.75, + "text": "" + }, + { + "id": 32000, + "logprob": -10.640625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.6875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5078125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2265625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9609375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4921875, "text": "" }, { @@ -565,21 +565,21 @@ "logprob": -11.5546875, "text": "" }, - { - "id": 32000, - "logprob": -11.2578125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8984375, - "text": "" - }, { "id": 32000, "logprob": -11.2265625, "text": "" }, + { + "id": 32000, + "logprob": -11.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.21875, + "text": "" + }, { "id": 32000, "logprob": -9.984375, @@ -592,27 +592,17 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -11.1015625, + "logprob": -11.09375, "text": "" }, { "id": 32000, - "logprob": -14.0625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.90625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.96875, + "logprob": -14.046875, "text": "" }, { @@ -622,22 +612,7 @@ }, { "id": 32000, - "logprob": -11.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -15.15625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.9765625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.890625, + "logprob": -10.9609375, "text": "" }, { @@ -647,7 +622,32 @@ }, { "id": 32000, - "logprob": -10.9765625, + "logprob": -11.8125, + "text": "" + }, + { + "id": 32000, + "logprob": -15.140625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9609375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9609375, "text": "" }, { @@ -657,27 +657,27 @@ }, { "id": 32000, - "logprob": -13.234375, + "logprob": -13.5234375, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.328125, "text": "" }, { "id": 32000, - "logprob": -11.390625, + "logprob": -11.375, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3515625, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.578125, "text": "" }, { @@ -687,22 +687,22 @@ }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.640625, "text": "" }, { "id": 32000, - "logprob": -11.203125, + "logprob": -11.1953125, "text": "" }, { "id": 32000, - "logprob": -12.015625, + "logprob": -11.9921875, "text": "" }, { "id": 32000, - "logprob": -10.9296875, + "logprob": -10.921875, "text": "" }, { @@ -712,7 +712,7 @@ }, { "id": 32000, - "logprob": -15.3828125, + "logprob": -15.390625, "text": "" }, { @@ -722,12 +722,12 @@ }, { "id": 32000, - "logprob": -12.7578125, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -12.421875, "text": "" }, { @@ -737,12 +737,12 @@ }, { "id": 32000, - "logprob": -11.3203125, + "logprob": -11.2890625, "text": "" }, { "id": 32000, - "logprob": -13.3515625, + "logprob": -13.34375, "text": "" }, { @@ -757,37 +757,37 @@ }, { "id": 32000, - "logprob": 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-12.2421875, + "logprob": -11.25, "text": "" }, { "id": 32000, - "logprob": -10.3515625, + "logprob": -12.4375, "text": "" }, { "id": 32000, - "logprob": -12.3828125, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -11.859375, + "logprob": -11.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.359375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.765625, "text": "" }, { @@ -862,7 +862,7 @@ }, { "id": 32000, - "logprob": -11.25, + "logprob": -11.2421875, "text": "" }, { @@ -872,12 +872,12 @@ }, { "id": 32000, - "logprob": -10.5703125, + "logprob": -10.5625, "text": "" }, { "id": 32000, - "logprob": -10.7109375, + "logprob": -10.7421875, "text": "" }, { @@ -892,27 +892,12 @@ }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -10.9921875, "text": "" }, { "id": 32000, - "logprob": -10.625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.234375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.265625, + "logprob": -10.6171875, "text": "" }, { @@ -922,7 +907,22 @@ }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -10.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.265625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.25, + "text": "" + }, + { + "id": 32000, + "logprob": -12.859375, "text": "" }, { @@ -942,22 +942,22 @@ }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.4296875, "text": "" }, { "id": 32000, - "logprob": -13.0859375, + "logprob": -13.2421875, "text": "" }, { "id": 32000, - "logprob": -11.2890625, + "logprob": -11.3046875, "text": "" }, { "id": 32000, - "logprob": -10.3125, + "logprob": -10.2890625, "text": "" }, { @@ -967,117 +967,37 @@ }, { "id": 32000, - "logprob": -11.1953125, + "logprob": -10.9140625, "text": "" }, { "id": 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}, - { - "id": 32000, - "logprob": -13.421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5546875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.1953125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.515625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.90625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.6015625, + "logprob": -10.359375, "text": "" }, { @@ -1085,6 +1005,86 @@ "logprob": -10.6171875, "text": "" }, + { + "id": 32000, + "logprob": -10.8125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8671875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.1796875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8984375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7265625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.59375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.421875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.4375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.203125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4453125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8984375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.59375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.609375, + "text": "" + }, { "id": 32000, "logprob": -10.90625, @@ -1092,7 +1092,7 @@ }, { "id": 32000, - "logprob": -11.25, + "logprob": -11.2578125, "text": "" }, { @@ -1102,12 +1102,12 @@ }, { "id": 32000, - "logprob": -10.984375, + "logprob": -10.9921875, "text": "" }, { "id": 32000, - "logprob": -12.09375, + "logprob": -12.0390625, "text": "" }, { @@ -1122,62 +1122,62 @@ }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -10.765625, + "logprob": -10.7578125, "text": "" }, { "id": 32000, - "logprob": -13.0, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -11.4453125, + "logprob": -11.4375, "text": "" }, { "id": 32000, - "logprob": -11.390625, + "logprob": -11.3828125, "text": "" }, { "id": 32000, - "logprob": -10.9375, + "logprob": -10.9296875, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -13.5703125, + "logprob": -13.546875, "text": "" }, { "id": 32000, - "logprob": -13.9140625, + "logprob": -13.9921875, "text": "" }, { "id": 32000, - "logprob": -14.390625, + "logprob": -14.375, "text": "" }, { "id": 32000, - "logprob": -11.390625, + "logprob": -11.359375, "text": "" }, { "id": 32000, - "logprob": -13.3203125, + "logprob": -13.328125, "text": "" }, { @@ -1192,7 +1192,7 @@ }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.9609375, "text": "" }, { @@ -1207,37 +1207,37 @@ }, { "id": 32000, - "logprob": -11.3984375, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -11.1484375, + "logprob": -11.1640625, "text": "" }, { "id": 32000, - "logprob": -10.8671875, + "logprob": -10.859375, "text": "" }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.1328125, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.75, "text": "" }, { "id": 32000, - "logprob": -10.890625, + "logprob": -10.875, "text": "" }, { "id": 32000, - "logprob": -15.15625, + "logprob": -14.171875, "text": "" }, { @@ -1247,22 +1247,22 @@ }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.4140625, "text": "" }, { "id": 32000, - "logprob": -11.40625, + "logprob": -11.3984375, "text": "" }, { "id": 32000, - "logprob": -14.265625, + "logprob": -14.2734375, "text": "" }, { "id": 32000, - "logprob": -13.5859375, + "logprob": -13.6171875, "text": "" }, { @@ -1272,12 +1272,12 @@ }, { "id": 32000, - "logprob": -11.890625, + "logprob": -11.8671875, "text": "" }, { "id": 32000, - "logprob": -12.84375, + "logprob": -12.8359375, "text": "" }, { @@ -1287,32 +1287,32 @@ }, { "id": 32000, - "logprob": -13.3125, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -11.1484375, + "logprob": -11.171875, "text": "" }, { "id": 32000, - "logprob": -13.078125, + "logprob": -13.0703125, "text": "" }, { "id": 32000, - "logprob": -12.4453125, + "logprob": -12.4375, "text": "" }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -11.234375, + "logprob": -11.1875, "text": "" }, { @@ -1322,7 +1322,7 @@ }, { "id": 32000, - "logprob": -12.6796875, + "logprob": -12.6640625, "text": "" }, { @@ -1332,42 +1332,17 @@ }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.1328125, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -12.1875, "text": "" }, { "id": 32000, - "logprob": -11.9375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4765625, - "text": "" - }, - { - "id": 32000, - "logprob": 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"logprob": -11.2421875, "text": "" }, { "id": 32000, - "logprob": -11.7109375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.21875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5546875, + "logprob": -11.6640625, "text": "" }, { @@ -1430,6 +1420,16 @@ "logprob": -12.2109375, "text": "" }, + { + "id": 32000, + "logprob": -13.5546875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.21875, + "text": "" + }, { "id": 32000, "logprob": -11.7109375, @@ -1437,7 +1437,7 @@ }, { "id": 32000, - "logprob": -15.0546875, + "logprob": -15.0234375, "text": "" }, { @@ -1447,37 +1447,37 @@ }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.859375, "text": "" }, { "id": 32000, - "logprob": -13.1171875, + "logprob": -13.125, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -12.5546875, + "logprob": -12.5390625, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.4921875, "text": "" }, { @@ -1492,22 +1492,22 @@ }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.6484375, "text": "" }, { "id": 32000, - "logprob": -11.8203125, + "logprob": -11.8125, "text": "" }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.90625, "text": "" }, { @@ -1517,7 +1517,7 @@ }, { "id": 32000, - "logprob": -13.03125, + "logprob": -13.046875, "text": "" }, { @@ -1527,7 +1527,7 @@ }, { "id": 32000, - "logprob": -13.15625, + "logprob": -13.140625, "text": "" }, { @@ -1537,27 +1537,27 @@ }, { "id": 32000, - "logprob": -13.8359375, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -13.765625, + "logprob": -13.78125, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -13.3671875, + "logprob": -13.359375, "text": "" }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -13.2421875, "text": "" }, { @@ -1567,12 +1567,12 @@ }, { "id": 32000, - "logprob": -11.859375, + "logprob": -11.875, "text": "" }, { "id": 32000, - "logprob": -13.59375, + "logprob": -13.6015625, "text": "" }, { @@ -1587,27 +1587,27 @@ }, { "id": 32000, - "logprob": -11.578125, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -13.6015625, + "logprob": -13.59375, "text": "" }, { "id": 32000, - "logprob": -12.421875, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -14.6328125, + "logprob": -14.5703125, "text": "" }, { "id": 32000, - "logprob": -11.7890625, + "logprob": -11.78125, "text": "" }, { @@ -1622,17 +1622,17 @@ }, { "id": 32000, - "logprob": -11.640625, + "logprob": -11.6328125, "text": "" }, { "id": 32000, - "logprob": -14.4375, + "logprob": -14.4296875, "text": "" }, { "id": 32000, - "logprob": -13.5390625, + "logprob": -13.53125, "text": "" }, { @@ -1642,17 +1642,17 @@ }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -11.578125, + "logprob": -11.609375, "text": "" }, { "id": 32000, - "logprob": -13.1640625, + "logprob": -13.171875, "text": "" }, { @@ -1667,42 +1667,42 @@ }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.1484375, "text": "" }, { "id": 32000, - "logprob": -12.6796875, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -13.7265625, + "logprob": -13.7109375, "text": "" }, { "id": 32000, - "logprob": -12.453125, + "logprob": -12.4296875, "text": "" }, { "id": 32000, - "logprob": -12.265625, + "logprob": -12.296875, "text": "" }, { "id": 32000, - "logprob": -13.203125, + "logprob": -13.1796875, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.2421875, "text": "" }, { "id": 32000, - "logprob": -14.84375, + "logprob": -14.828125, "text": "" }, { @@ -1712,7 +1712,7 @@ }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.3359375, "text": "" }, { @@ -1727,7 +1727,7 @@ }, { "id": 32000, - "logprob": -15.78125, + "logprob": -15.765625, "text": "" }, { @@ -1737,32 +1737,32 @@ }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.203125, "text": "" }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.875, "text": "" }, { "id": 32000, - "logprob": -13.28125, + "logprob": -13.2578125, "text": "" }, { "id": 32000, - "logprob": -13.5859375, + "logprob": -13.5546875, "text": "" }, { "id": 32000, - "logprob": -13.171875, + "logprob": -13.1640625, "text": "" }, { "id": 32000, - "logprob": -14.28125, + "logprob": -14.2734375, "text": "" }, { @@ -1772,32 +1772,32 @@ }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -12.28125, + "logprob": -12.2890625, "text": "" }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.7734375, "text": "" }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.6953125, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -12.0, "text": "" }, { "id": 32000, - "logprob": -12.2421875, + "logprob": -12.234375, "text": "" }, { @@ -1812,22 +1812,22 @@ }, { "id": 32000, - "logprob": -15.78125, + "logprob": -15.796875, "text": "" }, { "id": 32000, - "logprob": -12.734375, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -10.9765625, "text": "" }, { @@ -1837,7 +1837,7 @@ }, { "id": 32000, - "logprob": -14.7109375, + "logprob": -14.6953125, "text": "" }, { @@ -1847,17 +1847,17 @@ }, { "id": 32000, - "logprob": -13.3046875, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -12.6171875, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.2265625, "text": "" }, { @@ -1867,17 +1867,17 @@ }, { "id": 32000, - "logprob": -14.171875, + "logprob": -14.1875, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -12.1953125, "text": "" }, { @@ -1887,12 +1887,12 @@ }, { "id": 32000, - "logprob": -12.890625, + "logprob": -12.8828125, "text": "" }, { "id": 32000, - "logprob": -12.2890625, + "logprob": -12.296875, "text": "" }, { @@ -1902,12 +1902,12 @@ }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -15.2109375, + "logprob": -15.1875, "text": "" }, { @@ -1917,37 +1917,7 @@ }, { "id": 32000, - "logprob": -14.4296875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8828125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.84375, + "logprob": -14.421875, "text": "" }, { @@ -1957,72 +1927,102 @@ }, { "id": 32000, - "logprob": -13.0390625, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -13.9609375, + "logprob": -11.875, "text": "" }, { "id": 32000, - "logprob": -10.984375, + "logprob": -13.7265625, "text": "" }, { "id": 32000, - "logprob": -12.5, + "logprob": -13.5859375, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -11.859375, "text": "" }, { "id": 32000, - "logprob": -12.1796875, + "logprob": -12.28125, "text": "" }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -13.1015625, "text": "" }, { "id": 32000, - "logprob": -11.4296875, + "logprob": -13.9453125, "text": "" }, { "id": 32000, - "logprob": -13.453125, + "logprob": -10.9765625, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -12.5078125, "text": "" }, { "id": 32000, - "logprob": -13.21875, + "logprob": -12.734375, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -12.203125, "text": "" }, { "id": 32000, - "logprob": -15.34375, + "logprob": -12.59375, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -11.3984375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.5, + "text": "" + }, + { + "id": 32000, + "logprob": -11.765625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2265625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.7578125, + "text": "" + }, + { + "id": 32000, + "logprob": -15.3515625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.1875, "text": "" }, { @@ -2032,37 +2032,37 @@ }, { "id": 32000, - "logprob": -13.3515625, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -11.8984375, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -12.5703125, + "logprob": -12.5625, "text": "" }, { "id": 32000, - "logprob": -13.515625, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -10.8359375, + "logprob": -10.875, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.6328125, "text": "" }, { @@ -2072,7 +2072,7 @@ }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.8359375, "text": "" }, { @@ -2092,7 +2092,7 @@ }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9765625, "text": "" }, { @@ -2102,12 +2102,12 @@ }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1796875, "text": "" }, { @@ -2117,17 +2117,17 @@ }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -13.0390625, + "logprob": -12.984375, "text": "" }, { @@ -2147,27 +2147,27 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.40625, + "logprob": -11.421875, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -12.125, "text": "" }, { @@ -2177,17 +2177,22 @@ }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.40625, "text": "" }, { "id": 32000, - "logprob": -13.4765625, + "logprob": -13.484375, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.15625, "text": "" }, { @@ -2197,57 +2202,52 @@ }, { "id": 32000, - "logprob": -13.1640625, + "logprob": -12.78125, "text": "" }, { "id": 32000, - "logprob": -12.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -12.3984375, "text": "" }, { "id": 32000, - "logprob": -12.375, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -12.28125, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -14.6171875, + "logprob": -12.9296875, "text": "" }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.3125, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -11.4140625, "text": "" }, { "id": 32000, - "logprob": -11.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.390625, + "logprob": -11.3984375, "text": "" }, { @@ -2257,27 +2257,27 @@ }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.6171875, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -13.8671875, + "logprob": -13.875, "text": "" }, { "id": 32000, - "logprob": -12.09375, + "logprob": -12.109375, "text": "" }, { @@ -2287,27 +2287,27 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.4921875, "text": "" }, { "id": 32000, - "logprob": 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{ @@ -2372,32 +2372,32 @@ }, { "id": 32000, - "logprob": -16.125, + "logprob": -16.140625, "text": "" }, { "id": 32000, - "logprob": -15.453125, + "logprob": -15.4921875, "text": "" }, { "id": 32000, - "logprob": -13.9453125, + "logprob": -13.9296875, "text": "" }, { "id": 32000, - "logprob": -11.6328125, + "logprob": -11.609375, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -15.953125, + "logprob": -15.984375, "text": "" }, { @@ -2407,27 +2407,27 @@ }, { "id": 32000, - "logprob": -12.875, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -12.5, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -14.1796875, + "logprob": -14.1875, "text": "" }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.3515625, "text": "" }, { @@ -2442,37 +2442,37 @@ }, { "id": 32000, - "logprob": -14.859375, + "logprob": -14.8984375, "text": "" }, { "id": 32000, - "logprob": -12.4453125, + "logprob": -12.421875, "text": "" }, { "id": 32000, - "logprob": -14.2421875, + "logprob": -14.2734375, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -12.734375, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -12.96875, "text": "" }, { @@ -2487,7 +2487,7 @@ }, { "id": 32000, - "logprob": -11.9609375, + "logprob": -11.96875, "text": "" }, { @@ -2497,7 +2497,7 @@ }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5390625, "text": "" }, { @@ -2507,32 +2507,32 @@ }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.53125, "text": "" }, { "id": 32000, - "logprob": -11.109375, + "logprob": -11.1171875, "text": "" }, { "id": 32000, - "logprob": -11.6328125, + "logprob": -11.5859375, "text": "" }, { 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"" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.3984375, "text": "" }, { @@ -2602,7 +2602,7 @@ }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -11.5546875, "text": "" }, { @@ -2617,12 +2617,12 @@ }, { "id": 32000, - "logprob": -14.53125, + "logprob": -14.5234375, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.625, "text": "" }, { @@ -2632,12 +2632,12 @@ }, { "id": 32000, - "logprob": -13.6796875, + "logprob": -13.6015625, "text": "" }, { "id": 32000, - "logprob": -13.1953125, + "logprob": -13.28125, "text": "" }, { @@ -2652,22 +2652,22 @@ }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.46875, "text": "" }, { @@ -2677,27 +2677,27 @@ }, { "id": 32000, - "logprob": -11.859375, + "logprob": -11.8828125, "text": "" }, { "id": 32000, - "logprob": -13.0859375, + "logprob": -13.1015625, "text": "" }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -12.2421875, + "logprob": -12.234375, "text": "" }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5234375, "text": "" }, { @@ -2707,42 +2707,42 @@ }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -12.4609375, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -15.5546875, + "logprob": -15.5390625, "text": "" }, { "id": 32000, - "logprob": -14.265625, + "logprob": -14.2578125, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -13.9140625, + "logprob": -13.9296875, "text": "" }, { "id": 32000, - "logprob": -13.203125, + "logprob": -13.1875, "text": "" }, { "id": 32000, - "logprob": -14.8203125, + "logprob": -14.796875, "text": "" }, { @@ -2757,7 +2757,7 @@ }, { "id": 32000, - "logprob": -13.1171875, + "logprob": -13.109375, "text": "" }, { @@ -2767,12 +2767,12 @@ }, { "id": 32000, - "logprob": -10.671875, + "logprob": -10.6796875, "text": "" }, { "id": 32000, - "logprob": -14.6875, + "logprob": -14.6640625, "text": "" }, { @@ -2787,27 +2787,27 @@ }, { "id": 32000, - "logprob": -11.2265625, + "logprob": -11.2421875, "text": "" }, { "id": 32000, - "logprob": -14.4921875, + "logprob": -14.484375, "text": "" }, { "id": 32000, - "logprob": -12.46875, + "logprob": -12.53125, "text": "" }, { "id": 32000, - "logprob": -16.234375, + "logprob": -16.21875, "text": "" }, { "id": 32000, - "logprob": -14.515625, + "logprob": -14.625, "text": "" }, { @@ -2817,12 +2817,12 @@ }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.28125, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.578125, "text": "" }, { @@ -2832,12 +2832,42 @@ }, { "id": 32000, - "logprob": -13.015625, + "logprob": -12.984375, "text": "" }, { "id": 32000, - "logprob": -11.2578125, + "logprob": -11.265625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.1484375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.8671875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.3828125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0703125, "text": "" }, { @@ -2847,62 +2877,32 @@ }, { "id": 32000, - "logprob": -13.78125, + "logprob": -11.9921875, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -15.15625, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -10.84375, "text": "" }, { "id": 32000, - "logprob": -11.34375, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -15.46875, "text": "" }, { "id": 32000, - "logprob": -12.15625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0, - "text": "" - }, - { - "id": 32000, - "logprob": -15.1484375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.203125, - "text": "" - }, - { - "id": 32000, - "logprob": -15.4375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.1328125, + "logprob": -11.1484375, "text": "" }, { @@ -2912,12 +2912,12 @@ }, { "id": 32000, - "logprob": -11.859375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -11.015625, "text": "" }, { @@ -2927,82 +2927,22 @@ }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -12.4453125, + "logprob": -12.3984375, "text": "" }, { "id": 32000, - "logprob": -16.578125, + "logprob": -16.375, "text": "" }, { "id": 32000, - "logprob": -13.5859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.203125, - "text": "" - }, - { - "id": 32000, - 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}, - { - "id": 32000, - "logprob": -14.2109375, + "logprob": -12.3828125, "text": "" }, { @@ -3217,12 +3132,42 @@ }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -12.25, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -11.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -15.9453125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3203125, "text": "" }, { @@ -3232,12 +3177,12 @@ }, { "id": 32000, - "logprob": -12.203125, + "logprob": -11.359375, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.3359375, "text": "" }, { @@ -3247,17 +3192,32 @@ }, { "id": 32000, - "logprob": -12.1796875, + "logprob": -12.359375, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": 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-12.6015625, + "logprob": -12.59375, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.1171875, "text": "" }, { @@ -3367,27 +3367,27 @@ }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -12.0234375, + "logprob": -12.03125, "text": "" }, { "id": 32000, - "logprob": -12.75, + "logprob": -12.765625, "text": "" }, { "id": 32000, - "logprob": -14.4921875, + "logprob": -14.5078125, "text": "" }, { "id": 32000, - "logprob": -13.2421875, + "logprob": -13.2265625, "text": "" }, { @@ -3407,32 +3407,32 @@ }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.4765625, "text": "" }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -12.5078125, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -13.21875, + "logprob": -13.2265625, "text": "" }, { "id": 32000, - "logprob": -13.578125, + "logprob": -13.6171875, "text": "" }, { @@ -3447,7 +3447,7 @@ }, { "id": 32000, - "logprob": -13.1171875, + "logprob": -13.125, "text": "" }, { @@ -3457,37 +3457,37 @@ }, { "id": 32000, - "logprob": -14.609375, + "logprob": -14.7890625, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -12.8984375, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -12.6171875, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.609375, "text": "" }, { @@ -3497,107 +3497,27 @@ }, { "id": 32000, - "logprob": -12.734375, + "logprob": -12.7421875, "text": "" }, { "id": 32000, - "logprob": -12.9609375, + "logprob": -12.984375, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": 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"text": "" - }, - { - "id": 32000, - "logprob": -13.9140625, + "logprob": -13.9609375, "text": "" }, { @@ -3672,107 +3577,107 @@ }, { "id": 32000, - "logprob": -10.484375, + "logprob": -16.140625, "text": "" }, { "id": 32000, - "logprob": -13.1796875, + "logprob": -13.4609375, "text": "" }, { "id": 32000, - "logprob": -13.5703125, + "logprob": -13.7265625, "text": "" }, { "id": 32000, - "logprob": -13.3828125, + "logprob": -13.609375, "text": "" }, { "id": 32000, - "logprob": -14.03125, + "logprob": -13.234375, "text": "" }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.2578125, "text": "" }, { "id": 32000, - "logprob": -14.1875, + "logprob": -13.4921875, "text": "" }, { "id": 32000, - "logprob": -16.890625, + "logprob": -13.8359375, "text": "" }, { "id": 32000, - "logprob": -14.0546875, + "logprob": -12.6171875, "text": "" }, { "id": 32000, - "logprob": -14.1171875, + "logprob": -14.2890625, "text": "" }, { "id": 32000, - "logprob": -13.859375, + "logprob": -14.4765625, "text": "" }, { "id": 32000, - "logprob": -13.34375, + "logprob": -13.15625, "text": "" }, { "id": 32000, - "logprob": -13.0234375, + "logprob": -17.046875, "text": "" }, { "id": 32000, - "logprob": -13.390625, + "logprob": -14.046875, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -14.265625, "text": "" }, { "id": 32000, - "logprob": -14.78125, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -12.5625, "text": "" }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -13.9375, "text": "" }, { "id": 32000, - "logprob": -11.453125, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -13.1015625, + "logprob": -10.46875, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -13.1640625, "text": "" }, { @@ -3782,17 +3687,112 @@ }, { "id": 32000, - "logprob": -12.765625, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -14.828125, + "logprob": -14.140625, "text": "" }, { "id": 32000, - "logprob": -13.1015625, + "logprob": -13.5, + "text": "" + }, + { + "id": 32000, + "logprob": -14.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -16.90625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0546875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.1328125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.8125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.5234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4140625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.46875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.09375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7578125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0859375, "text": "" }, { @@ -3802,7 +3802,7 @@ }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.0625, "text": "" }, { @@ -3817,7 +3817,7 @@ }, { "id": 32000, - "logprob": -14.7109375, + "logprob": -14.6953125, "text": "" }, { @@ -3827,62 +3827,62 @@ }, { "id": 32000, - "logprob": -16.3125, + "logprob": -16.328125, "text": "" }, { "id": 32000, - "logprob": -15.0, + "logprob": -14.9921875, "text": "" }, { "id": 32000, - "logprob": -13.8046875, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -15.3671875, + "logprob": -15.40625, "text": "" }, { "id": 32000, - "logprob": -13.4921875, + "logprob": -13.4296875, "text": "" }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5859375, "text": "" }, { "id": 32000, - "logprob": 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"logprob": -13.546875, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.0546875, "text": "" }, { "id": 32000, - "logprob": -13.5625, + "logprob": -13.65625, "text": "" }, { @@ -3957,7 +3957,7 @@ }, { "id": 32000, - "logprob": -13.3125, + "logprob": -13.328125, "text": "" }, { @@ -3967,47 +3967,17 @@ }, { "id": 32000, - "logprob": -13.6328125, + "logprob": -13.640625, "text": "" }, { "id": 32000, - "logprob": -15.125, + "logprob": -15.15625, "text": "" }, { "id": 32000, - "logprob": -13.234375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.2265625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.03125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.328125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.6484375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.984375, + "logprob": -13.2421875, "text": "" }, { @@ -4015,6 +3985,36 @@ "logprob": -13.25, "text": "" }, + { + "id": 32000, + "logprob": -12.046875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.546875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.6484375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0078125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.234375, + "text": "" + }, { "id": 32000, "logprob": -11.625, @@ -4022,12 +4022,12 @@ }, { "id": 32000, - "logprob": -12.84375, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -12.5390625, + "logprob": -12.578125, "text": "" }, { @@ -4037,7 +4037,7 @@ }, { "id": 32000, - "logprob": -13.96875, + "logprob": -13.9453125, "text": "" }, { @@ -4052,7 +4052,7 @@ }, { "id": 32000, - "logprob": -13.8359375, + "logprob": -13.828125, "text": "" }, { @@ -4062,7 +4062,7 @@ }, { "id": 32000, - "logprob": -14.5859375, + "logprob": -14.578125, "text": "" }, { @@ -4077,27 +4077,27 @@ }, { "id": 32000, - "logprob": -14.53125, + "logprob": -14.5234375, "text": "" }, { "id": 32000, - "logprob": -14.8984375, + "logprob": -14.9140625, "text": "" }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -13.4609375, + "logprob": -13.4453125, "text": "" }, { "id": 32000, - "logprob": -14.7734375, + "logprob": -14.78125, "text": "" }, { @@ -4112,17 +4112,17 @@ }, { "id": 32000, - "logprob": -13.5859375, + "logprob": -13.5390625, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0859375, "text": "" }, { "id": 32000, - "logprob": -14.484375, + "logprob": -14.4765625, "text": "" }, { @@ -4132,7 +4132,7 @@ }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.2265625, "text": "" }, { @@ -4142,7 +4142,22 @@ }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6796875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.65625, "text": "" }, { @@ -4152,22 +4167,7 @@ }, { "id": 32000, - "logprob": -12.78125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.640625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.671875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.6171875, + "logprob": -13.65625, "text": "" }, { @@ -4177,22 +4177,22 @@ }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -12.578125, + "logprob": -12.6171875, "text": "" }, { "id": 32000, - "logprob": -12.7578125, + "logprob": -12.7890625, "text": "" }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.25, "text": "" }, { @@ -4202,27 +4202,27 @@ }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.78125, "text": "" }, { "id": 32000, - "logprob": -13.0, + "logprob": -13.0234375, "text": "" }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -15.078125, + "logprob": -15.0703125, "text": "" }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9375, "text": "" }, { @@ -4232,7 +4232,7 @@ }, { "id": 32000, - "logprob": -10.5, + "logprob": -10.5078125, "text": "" }, { @@ -4247,17 +4247,17 @@ }, { "id": 32000, - "logprob": -11.0546875, + "logprob": -11.0625, "text": "" }, { "id": 32000, - "logprob": -11.296875, + "logprob": -11.3046875, "text": "" }, { "id": 32000, - "logprob": -11.046875, + "logprob": -11.0390625, "text": "" }, { @@ -4272,12 +4272,22 @@ }, { "id": 32000, - "logprob": -12.5390625, + "logprob": -12.53125, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.203125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.015625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.8359375, "text": "" }, { @@ -4285,16 +4295,6 @@ "logprob": -12.0234375, "text": "" }, - { - "id": 32000, - "logprob": -13.8515625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, { "id": 32000, "logprob": -14.1796875, @@ -4302,22 +4302,22 @@ }, { "id": 32000, - "logprob": -14.0859375, + "logprob": -14.078125, "text": "" }, { "id": 32000, - "logprob": -14.0703125, + "logprob": -14.0546875, "text": "" }, { "id": 32000, - "logprob": -14.046875, + "logprob": -14.03125, "text": "" }, { "id": 32000, - "logprob": -13.0, + "logprob": -13.0625, "text": "" }, { @@ -4327,17 +4327,17 @@ }, { "id": 32000, - "logprob": -14.4609375, + "logprob": -14.46875, "text": "" }, { "id": 32000, - "logprob": -14.6875, + "logprob": -14.703125, "text": "" }, { "id": 32000, - "logprob": -14.3046875, + "logprob": -14.296875, "text": "" }, { @@ -4352,37 +4352,37 @@ }, { "id": 32000, - "logprob": -13.203125, + "logprob": -13.2265625, "text": "" }, { "id": 32000, - "logprob": -13.1015625, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.9375, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -16.15625, + "logprob": 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"text": "" }, { "id": 32000, - "logprob": -14.453125, + "logprob": -14.4609375, "text": "" }, { "id": 32000, - "logprob": -13.3359375, + "logprob": -13.328125, "text": "" }, { "id": 32000, - "logprob": -12.71875, + "logprob": -12.6484375, "text": "" }, { @@ -4477,42 +4477,42 @@ }, { "id": 32000, - "logprob": -11.640625, + "logprob": -11.6484375, "text": "" }, { "id": 32000, - "logprob": -10.8515625, + "logprob": -10.84375, "text": "" }, { "id": 32000, - "logprob": -10.90625, + "logprob": -10.9140625, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -11.2734375, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -10.8515625, + "logprob": -10.84375, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.046875, "text": "" }, { @@ -4522,12 +4522,12 @@ }, { "id": 32000, - "logprob": -12.703125, + "logprob": -12.7421875, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.09375, "text": "" }, { @@ -4537,17 +4537,17 @@ }, { "id": 32000, - "logprob": -13.1015625, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.84375, "text": "" }, { @@ -4557,12 +4557,12 @@ }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -13.6171875, + "logprob": -13.625, "text": "" }, { @@ -4572,17 +4572,17 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -12.9921875, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -14.046875, + "logprob": -14.0703125, "text": "" }, { @@ -4592,22 +4592,22 @@ }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.7890625, "text": "" }, { "id": 32000, - "logprob": -13.1953125, + "logprob": -13.2421875, "text": "" }, { "id": 32000, - "logprob": -13.4609375, + "logprob": -13.484375, "text": "" }, { "id": 32000, - "logprob": -13.71875, + "logprob": -13.7109375, "text": "" }, { @@ -4617,7 +4617,7 @@ }, { "id": 32000, - "logprob": -15.8125, + "logprob": -15.796875, "text": "" }, { @@ -4627,17 +4627,17 @@ }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -12.0, + "logprob": -12.015625, "text": "" }, { @@ -4657,27 +4657,22 @@ }, { "id": 32000, - "logprob": -13.8359375, + "logprob": -13.8203125, "text": "" }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.7578125, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -11.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.625, + "logprob": -11.4765625, "text": "" }, { @@ -4687,7 +4682,12 @@ }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.640625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5859375, "text": "" }, { @@ -4697,22 +4697,22 @@ }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.8515625, "text": "" }, { "id": 32000, - "logprob": -13.25, + "logprob": -13.0078125, "text": "" }, { "id": 32000, - "logprob": -15.2578125, + "logprob": -15.3828125, "text": "" }, { "id": 32000, - "logprob": -11.1875, + "logprob": -11.203125, "text": "" }, { @@ -4732,7 +4732,7 @@ }, { "id": 32000, - "logprob": -13.4375, + "logprob": -13.4453125, "text": "" }, { @@ -4742,7 +4742,7 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5078125, "text": "" }, { @@ -4752,12 +4752,12 @@ }, { "id": 32000, - "logprob": -11.1171875, + "logprob": -11.15625, "text": "" }, { "id": 32000, - "logprob": -11.8203125, + "logprob": -11.8359375, "text": "" }, { @@ -4767,52 +4767,52 @@ }, { "id": 32000, - "logprob": -11.15625, + "logprob": -11.171875, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -12.46875, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -13.3828125, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.2734375, "text": "" }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.046875, "text": "" }, { "id": 32000, - "logprob": -13.09375, + "logprob": -13.0859375, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1640625, "text": "" }, { @@ -4822,12 +4822,17 @@ }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.7265625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.34375, "text": "" }, { @@ -4837,27 +4842,22 @@ }, { "id": 32000, - "logprob": 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"" }, { "id": 32000, - "logprob": -11.046875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.203125, + "logprob": -13.0390625, "text": "" }, { @@ -5207,17 +5007,217 @@ }, { "id": 32000, - "logprob": -11.1484375, + "logprob": -14.0390625, "text": "" }, { "id": 32000, - "logprob": -10.796875, + "logprob": -13.828125, "text": "" }, { "id": 32000, - "logprob": -10.953125, + "logprob": -12.359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.953125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0859375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.78125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7109375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.4765625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.25, + "text": "" + }, + { + "id": 32000, + "logprob": -13.6875, + "text": "" + }, + { + "id": 32000, + 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"id": 32000, + "logprob": -12.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2421875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0859375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.421875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0859375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.25, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3359375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.46875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.265625, + "text": "" + }, { "id": 32000, "logprob": -12.21875, @@ -6922,22 +6922,22 @@ }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.1875, "text": "" }, { "id": 32000, - "logprob": -12.0234375, + "logprob": -12.03125, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -11.9765625, "text": "" }, { "id": 32000, - "logprob": -11.2578125, + "logprob": -11.2421875, "text": "" }, { @@ -6972,17 +6972,17 @@ }, { "id": 32000, - "logprob": -11.40625, + "logprob": -11.453125, "text": "" }, { "id": 32000, - "logprob": -12.8984375, + "logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -14.1640625, + "logprob": -14.15625, "text": "" }, { @@ -6992,12 +6992,12 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -12.234375, + "logprob": -12.2421875, "text": "" }, { @@ -7012,7 +7012,7 @@ }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.8671875, "text": "" }, { @@ -7022,62 +7022,62 @@ }, { "id": 32000, - "logprob": -15.796875, + "logprob": -15.765625, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.609375, "text": "" }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.34375, "text": "" }, { "id": 32000, - "logprob": -13.6640625, + "logprob": -13.671875, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.9140625, "text": "" }, { "id": 32000, - "logprob": -13.75, + "logprob": -13.7265625, "text": "" }, { "id": 32000, - "logprob": -13.515625, + "logprob": -13.53125, "text": "" }, { "id": 32000, - "logprob": -13.2421875, + "logprob": -13.25, "text": "" }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.6171875, "text": "" }, { "id": 32000, - "logprob": -13.796875, + "logprob": -13.8046875, "text": "" }, { "id": 32000, - "logprob": -14.390625, + "logprob": -14.375, "text": "" }, { "id": 32000, - "logprob": -12.15625, + "logprob": -12.171875, "text": "" }, { @@ -7092,17 +7092,17 @@ }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.5625, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.640625, "text": "" }, { @@ -7112,17 +7112,17 @@ }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.59375, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -12.3515625, + "logprob": -12.359375, "text": "" }, { @@ -7132,7 +7132,7 @@ }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0234375, "text": "" }, { @@ -7147,17 +7147,17 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -11.265625, + "logprob": -11.2578125, "text": "" }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.328125, "text": "" }, { @@ -7167,12 +7167,17 @@ }, { "id": 32000, - "logprob": -12.0, + "logprob": -11.9921875, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.3046875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4140625, "text": "" }, { @@ -7182,57 +7187,22 @@ }, { "id": 32000, - "logprob": -11.3984375, + "logprob": -11.0390625, "text": "" }, { "id": 32000, - "logprob": -11.046875, + "logprob": -12.1953125, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -13.6640625, "text": "" }, { "id": 32000, - "logprob": -13.328125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.5859375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5703125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.65625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2578125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.703125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9140625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3515625, + "logprob": -12.6640625, "text": "" }, { @@ -7242,102 +7212,17 @@ }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.65625, "text": "" }, { "id": 32000, - "logprob": -11.296875, + "logprob": -12.25, "text": "" }, { "id": 32000, - "logprob": -12.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.703125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8671875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3671875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1640625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.71875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4140625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2421875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.09375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.5703125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.0078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.7890625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.6953125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.59375, + "logprob": -11.7109375, "text": "" }, { @@ -7347,27 +7232,47 @@ }, { "id": 32000, - "logprob": -12.75, + "logprob": -11.34375, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -14.34375, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.296875, "text": "" }, { "id": 32000, - "logprob": -13.578125, + "logprob": -12.3671875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7265625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3515625, "text": "" }, { @@ -7377,12 +7282,107 @@ }, { "id": 32000, - "logprob": -11.0703125, + "logprob": -13.1875, "text": "" }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -13.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.09375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5703125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8671875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.3359375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.90625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.296875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4296875, "text": "" }, { @@ -7402,7 +7402,7 @@ }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.5546875, "text": "" }, { @@ -7412,37 +7412,37 @@ }, { "id": 32000, - "logprob": -12.125, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.5, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.640625, "text": "" }, { "id": 32000, - "logprob": -11.875, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.2890625, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.6484375, "text": "" }, { "id": 32000, - "logprob": -12.9140625, + "logprob": -12.8984375, "text": "" }, { @@ -7462,42 +7462,42 @@ }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.53125, "text": "" }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.3515625, "text": "" }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.875, "text": "" }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.890625, "text": "" }, { "id": 32000, - "logprob": -11.046875, + "logprob": -11.03125, "text": "" }, { "id": 32000, - "logprob": -13.3515625, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -11.9921875, + "logprob": -11.96875, "text": "" }, { @@ -7507,47 +7507,47 @@ }, { "id": 32000, - "logprob": -12.40625, + "logprob": -12.390625, "text": "" }, { "id": 32000, - "logprob": -14.390625, + "logprob": -14.2890625, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -15.1484375, + "logprob": -15.1796875, "text": "" }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.7421875, "text": "" }, { "id": 32000, - "logprob": -14.7734375, + "logprob": -14.6484375, "text": "" }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.546875, "text": "" }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.5703125, "text": "" }, { @@ -7567,37 +7567,37 @@ }, { "id": 32000, - "logprob": -12.6171875, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.9609375, "text": "" }, { "id": 32000, - "logprob": -12.953125, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.453125, "text": "" }, { "id": 32000, - "logprob": -13.9609375, + "logprob": -13.8046875, "text": "" }, { @@ -7607,7 +7607,7 @@ }, { "id": 32000, - "logprob": -13.6171875, + "logprob": -13.7890625, "text": "" }, { @@ -7622,7 +7622,7 @@ }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.09375, "text": "" }, { @@ -7632,22 +7632,22 @@ }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -13.109375, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -13.9921875, + "logprob": -14.015625, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.6328125, "text": "" }, { @@ -7657,17 +7657,17 @@ }, { "id": 32000, - "logprob": -11.3203125, + "logprob": -11.328125, "text": "" }, { "id": 32000, - "logprob": -11.609375, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5859375, "text": "" }, { @@ -7677,27 +7677,27 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.9765625, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4609375, "text": "" }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -12.1328125, "text": "" }, { "id": 32000, - "logprob": -12.0703125, + "logprob": -12.0625, "text": "" }, { @@ -7712,12 +7712,12 @@ }, { "id": 32000, - "logprob": -12.2109375, + "logprob": -12.21875, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7421875, "text": "" }, { @@ -7732,42 +7732,42 @@ }, { "id": 32000, - "logprob": -13.15625, + "logprob": -13.203125, "text": "" }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -14.8515625, + "logprob": -14.6640625, "text": "" }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.1640625, "text": "" }, { "id": 32000, - "logprob": -15.1796875, + "logprob": -14.859375, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -11.53125, "text": "" }, { "id": 32000, - "logprob": -13.0078125, + "logprob": -12.9921875, "text": "" }, { @@ -7777,52 +7777,52 @@ }, { "id": 32000, - "logprob": -12.421875, + "logprob": -12.4375, "text": "" }, { "id": 32000, - "logprob": -13.7578125, + "logprob": -13.78125, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.265625, "text": "" }, { "id": 32000, - "logprob": -13.7421875, + "logprob": -13.6015625, "text": "" }, { "id": 32000, - "logprob": -14.140625, + "logprob": -14.1015625, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.703125, "text": "" }, { "id": 32000, - "logprob": -11.7578125, + "logprob": -11.7265625, "text": "" }, { @@ -7832,27 +7832,27 @@ }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.75, "text": "" }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.875, "text": "" }, { "id": 32000, - "logprob": -11.8203125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -11.25, + "logprob": -11.34375, "text": "" }, { @@ -7867,37 +7867,37 @@ }, { "id": 32000, - "logprob": -12.9921875, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -15.15625, + "logprob": -15.1875, "text": "" }, { "id": 32000, - "logprob": -11.9609375, + "logprob": -12.0, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.5390625, + "logprob": -13.515625, "text": "" }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.5703125, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.703125, "text": "" }, { @@ -7907,22 +7907,22 @@ }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.4921875, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.6171875, "text": "" }, { "id": 32000, - "logprob": -14.578125, + "logprob": -14.5546875, "text": "" }, { "id": 32000, - "logprob": -13.3359375, + "logprob": -13.390625, "text": "" }, { @@ -7942,12 +7942,12 @@ }, { "id": 32000, - "logprob": -11.609375, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -13.25, + "logprob": -13.2421875, "text": "" }, { @@ -7962,32 +7962,32 @@ }, { "id": 32000, - "logprob": -11.3671875, + "logprob": -11.3984375, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -11.1953125, + "logprob": -11.1875, "text": "" }, { "id": 32000, - "logprob": -13.390625, + "logprob": -13.359375, "text": "" }, { @@ -7997,27 +7997,27 @@ }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9140625, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -13.578125, + "logprob": -13.5625, "text": "" }, { "id": 32000, - "logprob": 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"logprob": -12.7890625, "text": "" }, { "id": 32000, - "logprob": -12.3203125, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -11.3515625, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -11.9921875, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -10.7578125, "text": "" }, { "id": 32000, - "logprob": -11.875, + "logprob": -11.9609375, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.09375, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -11.609375, + "logprob": -11.7890625, "text": "" }, { "id": 32000, - "logprob": -12.640625, + "logprob": -11.5234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.234375, "text": "" }, { @@ -8612,12 +8427,72 @@ }, { "id": 32000, - "logprob": -12.953125, + "logprob": -11.2109375, "text": "" }, { "id": 32000, - "logprob": -10.40625, + "logprob": -11.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.65625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.1953125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0, + "text": "" + }, + { + "id": 32000, + "logprob": -15.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.8046875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.0234375, "text": "" }, { @@ -8627,12 +8502,137 @@ }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -11.3828125, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -14.0078125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.7109375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.0234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.46875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.9765625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.3046875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.546875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2734375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.328125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3515625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.984375, + "text": "" + }, + { + "id": 32000, + "logprob": 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-11.8046875, "text": "" }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4609375, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -11.4765625, "text": "" }, { @@ -8852,7 +8852,7 @@ }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.953125, "text": "" }, { @@ -8862,37 +8862,37 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -13.2265625, + "logprob": -13.28125, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -10.84375, + "logprob": -10.8203125, "text": "" }, { "id": 32000, - "logprob": -10.6796875, + "logprob": -10.6953125, "text": "" }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.875, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9375, "text": "" }, { @@ -8902,22 +8902,22 @@ }, { "id": 32000, - 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"id": 32000, - "logprob": -12.3203125, + "logprob": -12.3359375, "text": "" }, { @@ -9067,32 +9067,32 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -14.6796875, + "logprob": -14.6875, "text": "" }, { "id": 32000, - "logprob": -11.0546875, + "logprob": -11.0625, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.8125, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9296875, "text": "" }, { @@ -9107,12 +9107,12 @@ }, { "id": 32000, - "logprob": -12.4375, + "logprob": -12.46875, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.0703125, "text": "" }, { @@ -9137,17 +9137,17 @@ }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6640625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.9765625, "text": "" }, { "id": 32000, - "logprob": -10.84375, + 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32000, - "logprob": -13.046875, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -13.484375, + "logprob": -13.3359375, "text": "" }, { "id": 32000, - "logprob": -14.1328125, + "logprob": -14.015625, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.8671875, "text": "" }, { "id": 32000, - "logprob": -13.484375, + "logprob": -13.4453125, "text": "" }, { "id": 32000, - "logprob": -13.5625, + "logprob": -13.5, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.71875, "text": "" }, { @@ -9297,27 +9297,27 @@ }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.703125, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -11.2265625, + "logprob": -11.21875, "text": "" }, { @@ -9327,7 +9327,7 @@ }, { "id": 32000, - "logprob": -12.2890625, + "logprob": -12.296875, "text": "" }, { @@ -9342,7 +9342,7 @@ }, { "id": 32000, - "logprob": -15.671875, + "logprob": -15.6953125, "text": "" }, { @@ -9352,7 +9352,7 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.5859375, "text": "" }, { @@ -9367,12 +9367,12 @@ }, { "id": 32000, - "logprob": -11.296875, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -12.3203125, + "logprob": -12.328125, "text": "" }, { @@ -9382,17 +9382,17 @@ }, { "id": 32000, - "logprob": -10.9375, + "logprob": -10.9453125, "text": "" }, { "id": 32000, - "logprob": -10.9375, + "logprob": -10.921875, "text": "" }, { "id": 32000, - "logprob": -10.859375, + "logprob": -10.8515625, "text": "" }, { @@ -9417,7 +9417,7 @@ }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.90625, "text": "" }, { @@ -9427,122 +9427,22 @@ }, { "id": 32000, - "logprob": -11.65625, + "logprob": 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- "logprob": -11.421875, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -11.4453125, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -11.9609375, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -12.9609375, + "logprob": -11.671875, "text": "" }, { @@ -9677,127 +9617,17 @@ }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -12.28125, "text": "" }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -11.109375, "text": "" }, { "id": 32000, - "logprob": -11.125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1484375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.859375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.921875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0234375, - "text": "" - }, - { - "id": 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+ { + "id": 32000, + "logprob": -11.421875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.453125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.96875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9765625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5859375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4140625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.1171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5, + "text": "" + }, + { + "id": 32000, + "logprob": -12.171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.4453125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.75, + "text": "" + }, + { + "id": 32000, + "logprob": -14.859375, + "text": "" + }, { "id": 32000, "logprob": -12.9453125, @@ -9892,17 +9727,182 @@ }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -12.234375, "text": 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"logprob": -11.6875, + "logprob": -13.0703125, "text": "" }, { "id": 32000, - "logprob": -12.71875, + "logprob": -11.6640625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.703125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7265625, "text": "" }, { @@ -11227,72 +11227,72 @@ }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8515625, "text": "" }, { "id": 32000, - "logprob": -13.6171875, + "logprob": -13.625, "text": "" }, { "id": 32000, - "logprob": -11.5, + "logprob": -11.5078125, "text": "" }, { "id": 32000, - "logprob": -11.890625, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -13.2265625, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -12.5390625, + "logprob": -12.5078125, "text": "" }, { "id": 32000, - "logprob": -15.1171875, + "logprob": -15.09375, "text": "" }, { "id": 32000, - "logprob": -15.9765625, + "logprob": -15.8515625, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.734375, "text": "" }, { "id": 32000, - "logprob": -12.2734375, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.28125, "text": "" }, { "id": 32000, - "logprob": -11.875, + "logprob": -11.859375, "text": "" }, { @@ -11302,17 +11302,17 @@ }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.5703125, "text": "" }, { "id": 32000, - "logprob": -10.609375, + "logprob": -10.59375, "text": "" }, { "id": 32000, - "logprob": -10.8828125, + "logprob": -10.890625, "text": "" }, { @@ -11322,17 +11322,17 @@ }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.53125, "text": "" }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.7109375, "text": "" }, { @@ -11342,12 +11342,12 @@ }, { "id": 32000, - "logprob": 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"logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -13.4140625, + "logprob": -13.4296875, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -13.453125, + "logprob": -13.46875, "text": "" }, { @@ -11462,22 +11462,22 @@ }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.0703125, "text": "" }, { "id": 32000, - "logprob": -11.2421875, + "logprob": -11.234375, "text": "" }, { "id": 32000, - "logprob": -15.375, + "logprob": -15.34375, "text": "" }, { "id": 32000, - "logprob": -13.5, + "logprob": -13.4921875, "text": "" }, { @@ -11487,27 +11487,27 @@ }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.53125, "text": "" }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.6953125, "text": "" }, { "id": 32000, - "logprob": -13.234375, + "logprob": -13.203125, "text": "" }, { "id": 32000, - "logprob": -17.171875, + "logprob": -17.078125, "text": "" }, { "id": 32000, - "logprob": -15.03125, + "logprob": -15.25, "text": "" }, { @@ -11527,7 +11527,7 @@ }, { "id": 32000, - "logprob": -16.09375, + "logprob": -16.0625, "text": "" }, { @@ -11537,27 +11537,27 @@ }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -14.640625, + "logprob": -14.5859375, "text": "" }, { "id": 32000, - "logprob": -11.8984375, + "logprob": -11.890625, "text": "" }, { "id": 32000, - "logprob": -10.9296875, + "logprob": -10.9375, "text": "" }, { "id": 32000, - "logprob": -13.28125, + "logprob": -13.3203125, "text": "" }, { @@ -11567,82 +11567,12 @@ }, { "id": 32000, - "logprob": -13.0078125, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -12.875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0, - "text": "" - }, - { - "id": 32000, - "logprob": -13.8125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.765625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4140625, - "text": "" - }, - { - "id": 32000, - "logprob": -14.34375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4765625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.8515625, - "text": "" - }, - { - "id": 32000, - "logprob": -14.1171875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.9140625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3203125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.65625, + "logprob": -12.9375, "text": "" }, { @@ -11652,17 +11582,87 @@ }, { "id": 32000, - "logprob": -11.078125, + "logprob": -13.796875, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -11.7578125, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -12.4140625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.328125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4765625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.6796875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.65625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0078125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.0859375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5625, "text": "" }, { @@ -11672,12 +11672,7 @@ }, { "id": 32000, - "logprob": -12.6640625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1171875, + "logprob": -12.6875, "text": "" }, { @@ -11685,6 +11680,11 @@ "logprob": -12.125, "text": "" }, + { + "id": 32000, + "logprob": -12.1171875, + "text": "" + }, { "id": 32000, "logprob": -16.109375, @@ -11692,32 +11692,12 @@ }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -13.421875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.34375, - "text": "" - }, - { - "id": 32000, - "logprob": -15.5078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.890625, - "text": "" - }, - { - "id": 32000, - "logprob": -14.828125, + "logprob": -13.40625, "text": "" }, { @@ -11725,6 +11705,26 @@ "logprob": -13.375, "text": "" }, + { + "id": 32000, + "logprob": -15.4921875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.921875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.3671875, + "text": "" + }, { "id": 32000, "logprob": -12.0546875, @@ -11732,7 +11732,7 @@ }, { "id": 32000, - "logprob": -15.1875, + "logprob": -15.21875, "text": "" }, { @@ -11742,37 +11742,37 @@ }, { "id": 32000, - "logprob": -10.5390625, + "logprob": -10.5234375, "text": "" }, { "id": 32000, - "logprob": -14.921875, + "logprob": -14.8984375, "text": "" }, { "id": 32000, - "logprob": -13.8203125, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -11.6875, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -17.484375, + "logprob": -17.4375, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -10.6484375, + "logprob": -10.640625, "text": "" }, { @@ -11787,17 +11787,17 @@ }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -14.2109375, + "logprob": -14.2421875, "text": "" }, { "id": 32000, - "logprob": -13.71875, + "logprob": -13.7265625, "text": "" }, { @@ -11807,57 +11807,57 @@ }, { "id": 32000, - "logprob": -10.5859375, + "logprob": -10.59375, "text": "" }, { "id": 32000, - "logprob": -12.8984375, + "logprob": -12.84375, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -14.7109375, + "logprob": -14.7421875, "text": "" }, { "id": 32000, - "logprob": -11.9921875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -14.75, + "logprob": -14.71875, "text": "" }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -13.015625, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.796875, "text": "" }, { "id": 32000, - "logprob": -11.015625, + "logprob": -11.0078125, "text": "" }, { "id": 32000, - "logprob": -16.390625, + "logprob": -16.46875, "text": "" }, { @@ -11867,12 +11867,12 @@ }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.6171875, "text": "" }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.8515625, "text": "" }, { @@ -11887,17 +11887,17 @@ }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -13.3671875, + "logprob": -13.359375, "text": "" }, { "id": 32000, - "logprob": -13.9609375, + "logprob": -13.96875, "text": "" }, { @@ -11912,12 +11912,12 @@ }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { @@ -11927,7 +11927,7 @@ }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.625, "text": "" }, { @@ -11935,16 +11935,6 @@ "logprob": -11.8046875, "text": "" }, - { - "id": 32000, - "logprob": -12.125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.09375, - "text": "" - }, { "id": 32000, "logprob": -12.1328125, @@ -11952,7 +11942,17 @@ }, { "id": 32000, - "logprob": -17.203125, + "logprob": -14.0546875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.125, + "text": "" + }, + { + "id": 32000, + "logprob": -17.21875, "text": "" }, { @@ -11962,7 +11962,7 @@ }, { "id": 32000, - "logprob": -13.3515625, + "logprob": -13.3671875, "text": "" }, { @@ -11972,17 +11972,17 @@ }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9609375, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.7265625, "text": "" }, { @@ -11992,7 +11992,7 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { @@ -12002,17 +12002,17 @@ }, { "id": 32000, - "logprob": -10.859375, + "logprob": -10.8515625, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.828125, "text": "" }, { "id": 32000, - "logprob": -11.6484375, + "logprob": -11.640625, "text": "" }, { @@ -12022,7 +12022,7 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0234375, "text": "" }, { @@ -12032,27 +12032,27 @@ }, { "id": 32000, - "logprob": -14.7734375, + "logprob": -14.7578125, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -11.5, "text": "" }, { "id": 32000, - "logprob": -11.265625, + "logprob": -11.2578125, "text": "" }, { "id": 32000, - "logprob": -10.734375, + "logprob": -10.7265625, "text": "" }, { "id": 32000, - "logprob": -10.8671875, + "logprob": -10.875, "text": "" }, { @@ -12062,27 +12062,27 @@ }, { "id": 32000, - "logprob": -12.953125, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -13.1015625, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -12.0234375, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -17.03125, + "logprob": -17.015625, "text": "" }, { "id": 32000, - "logprob": -12.2109375, + "logprob": -12.234375, "text": "" }, { @@ -12092,12 +12092,12 @@ }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.0390625, "text": "" }, { @@ -12107,22 +12107,22 @@ }, { "id": 32000, - "logprob": -15.2421875, + "logprob": -15.265625, "text": "" }, { "id": 32000, - "logprob": -17.0, + "logprob": -16.984375, "text": "" }, { "id": 32000, - "logprob": -11.046875, + "logprob": -11.0546875, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -11.453125, "text": "" }, { @@ -12132,32 +12132,32 @@ }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.140625, "text": "" }, { "id": 32000, - "logprob": -10.671875, + "logprob": -10.6796875, "text": "" }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -17.96875, + "logprob": -17.984375, "text": "" }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -12.421875, "text": "" }, { "id": 32000, - "logprob": -14.6875, + "logprob": -14.703125, "text": "" }, { @@ -12167,12 +12167,17 @@ }, { "id": 32000, - "logprob": -14.4140625, + "logprob": -14.3828125, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.890625, "text": "" }, { @@ -12182,22 +12187,17 @@ }, { "id": 32000, - "logprob": -12.828125, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -11.4765625, + "logprob": -13.5625, "text": "" }, { "id": 32000, - "logprob": -13.5546875, - "text": "" - }, - { - "id": 32000, - "logprob": -16.296875, + "logprob": -16.25, "text": "" }, { @@ -12207,12 +12207,12 @@ }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -14.3671875, + "logprob": -14.375, "text": "" }, { @@ -12222,17 +12222,17 @@ }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.671875, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -12.2734375, + "logprob": -12.28125, "text": "" }, { @@ -12242,17 +12242,17 @@ }, { "id": 32000, - "logprob": -17.546875, + "logprob": -17.5625, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -15.0703125, + "logprob": -15.0625, "text": "" }, { @@ -12267,7 +12267,7 @@ }, { "id": 32000, - "logprob": -13.3515625, + "logprob": -13.328125, "text": "" }, { @@ -12277,17 +12277,17 @@ }, { "id": 32000, - "logprob": -11.3125, + "logprob": -11.3046875, "text": "" }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.3515625, "text": "" }, { "id": 32000, - "logprob": -12.4375, + "logprob": -12.4609375, "text": "" }, { @@ -12307,27 +12307,27 @@ }, { "id": 32000, - "logprob": -14.640625, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.890625, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -13.1875, + "logprob": -13.15625, "text": "" }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.4296875, "text": "" }, { @@ -12337,22 +12337,22 @@ }, { "id": 32000, - "logprob": -12.46875, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -12.546875, + "logprob": -12.5390625, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.09375, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9609375, "text": "" }, { @@ -12362,12 +12362,12 @@ }, { "id": 32000, - "logprob": -11.3984375, + "logprob": -11.40625, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8125, "text": "" }, { @@ -12377,12 +12377,12 @@ }, { "id": 32000, - "logprob": -13.078125, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.6328125, "text": "" }, { @@ -12392,7 +12392,7 @@ }, { "id": 32000, - "logprob": -11.125, + "logprob": -11.1328125, "text": "" }, { @@ -12402,22 +12402,22 @@ }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -14.2421875, + "logprob": -14.265625, "text": "" }, { "id": 32000, - "logprob": -12.234375, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -16.65625, + "logprob": -16.640625, "text": "" }, { @@ -12427,32 +12427,32 @@ }, { "id": 32000, - "logprob": -12.703125, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.96875, "text": "" }, { "id": 32000, - "logprob": -14.296875, + "logprob": -14.3046875, "text": "" }, { @@ -12462,27 +12462,27 @@ }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8046875, "text": "" }, { "id": 32000, - "logprob": -14.5625, + "logprob": -14.5390625, "text": "" }, { "id": 32000, - "logprob": -19.46875, + "logprob": -19.4375, "text": "" }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.6796875, "text": "" }, { @@ -12492,12 +12492,12 @@ }, { "id": 32000, - "logprob": -11.4453125, + "logprob": -11.453125, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.2109375, "text": "" }, { @@ -12512,27 +12512,27 @@ }, { "id": 32000, - "logprob": -17.640625, + "logprob": -17.625, "text": "" }, { "id": 32000, - "logprob": -17.671875, + "logprob": -17.75, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -13.578125, + "logprob": -13.5546875, "text": "" }, { @@ -12542,17 +12542,17 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -13.4765625, + "logprob": -13.4921875, "text": "" }, { @@ -12562,47 +12562,47 @@ }, { "id": 32000, - "logprob": -12.28125, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -12.953125, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.3046875, "text": "" }, { "id": 32000, - "logprob": -14.3671875, + "logprob": -14.3359375, "text": "" }, { "id": 32000, - "logprob": -14.5, + "logprob": -14.5078125, "text": "" }, { "id": 32000, - "logprob": -11.578125, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -14.953125, + "logprob": -14.9375, "text": "" }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0390625, "text": "" }, { @@ -12617,12 +12617,22 @@ }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.890625, + "logprob": -11.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.90625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3359375, "text": "" }, { @@ -12632,27 +12642,17 @@ }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -11.8984375, + "logprob": -15.0546875, "text": "" }, { "id": 32000, - "logprob": -11.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -15.0625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1953125, + "logprob": -12.203125, "text": "" }, { @@ -12662,22 +12662,22 @@ }, { "id": 32000, - "logprob": -16.875, + "logprob": -16.84375, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.9296875, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.96875, "text": "" }, { @@ -12697,47 +12697,47 @@ }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -14.5, + "logprob": -14.515625, "text": "" }, { "id": 32000, - "logprob": -10.09375, + "logprob": -10.109375, "text": "" }, { "id": 32000, - "logprob": -12.0234375, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -11.5625, "text": "" }, { "id": 32000, - "logprob": -14.890625, + "logprob": -14.8125, "text": "" }, { "id": 32000, - "logprob": -11.8125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -12.6796875, + "logprob": -12.6484375, "text": "" }, { @@ -12747,12 +12747,12 @@ }, { "id": 32000, - "logprob": -12.15625, + "logprob": -12.1484375, "text": "" }, { "id": 32000, - "logprob": -14.890625, + "logprob": -14.8671875, "text": "" }, { @@ -12762,7 +12762,7 @@ }, { "id": 32000, - "logprob": -10.7578125, + "logprob": -10.75, "text": "" }, { @@ -12772,32 +12772,32 @@ }, { "id": 32000, - "logprob": -13.2734375, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -11.140625, + "logprob": -11.1484375, "text": "" }, { "id": 32000, - "logprob": -16.90625, + "logprob": -16.890625, "text": "" }, { "id": 32000, - "logprob": -12.8828125, + "logprob": 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-13.203125, "text": "" }, { @@ -12877,17 +12877,17 @@ }, { "id": 32000, - "logprob": -11.0703125, + "logprob": -11.078125, "text": "" }, { "id": 32000, - "logprob": -11.578125, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.6015625, "text": "" }, { @@ -12897,37 +12897,37 @@ }, { "id": 32000, - "logprob": -12.703125, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -13.453125, + "logprob": -13.46875, "text": "" }, { "id": 32000, - "logprob": -12.40625, + "logprob": -12.4140625, "text": "" }, { "id": 32000, - "logprob": -12.5546875, + "logprob": -12.5703125, "text": "" }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.03125, "text": "" }, { "id": 32000, - "logprob": -16.53125, + "logprob": -16.46875, "text": "" }, { "id": 32000, - "logprob": -13.1640625, + "logprob": -13.140625, "text": "" }, { @@ -12937,12 +12937,12 @@ }, { "id": 32000, - "logprob": -14.2421875, + "logprob": -14.109375, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.84375, "text": "" }, { @@ -12957,47 +12957,47 @@ }, { "id": 32000, - "logprob": -11.9296875, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -12.90625, + "logprob": -12.8984375, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.625, "text": "" }, { "id": 32000, - "logprob": -14.125, + "logprob": -14.1484375, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -14.6953125, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.625, "text": "" }, { @@ -13012,12 +13012,12 @@ }, { "id": 32000, - "logprob": -14.890625, + "logprob": -14.6484375, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -13.546875, "text": "" }, { @@ -13032,7 +13032,7 @@ }, { "id": 32000, - "logprob": -15.234375, + "logprob": -15.2265625, "text": "" }, { @@ -13042,7 +13042,7 @@ }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9375, "text": "" }, { @@ -13052,17 +13052,17 @@ }, { "id": 32000, - "logprob": -12.9375, + "logprob": -12.921875, "text": "" }, { "id": 32000, - "logprob": -13.453125, + "logprob": -13.46875, "text": "" }, { "id": 32000, - "logprob": -15.5078125, + "logprob": -15.5, "text": "" }, { @@ -13072,27 +13072,27 @@ }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.7734375, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1640625, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.90625, "text": "" }, { @@ -13102,17 +13102,17 @@ }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.75, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.28125, "text": "" }, { "id": 32000, - "logprob": -16.4375, + "logprob": -16.390625, "text": "" }, { @@ -13122,47 +13122,47 @@ }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -13.890625, + "logprob": -13.8984375, "text": "" }, { "id": 32000, - "logprob": -14.984375, + "logprob": -14.9921875, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -16.015625, + "logprob": -16.046875, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -13.578125, + "logprob": -13.6484375, "text": "" }, { "id": 32000, - "logprob": -15.328125, + "logprob": -15.3203125, "text": "" }, { @@ -13172,7 +13172,7 @@ }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.7109375, 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"logprob": -14.859375, "text": "" }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -15.1875, + "logprob": -15.203125, "text": "" }, { "id": 32000, - "logprob": -11.8203125, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.3671875, + "logprob": -11.4140625, "text": "" }, { "id": 32000, - "logprob": -11.28125, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -17.640625, + "logprob": -16.8125, "text": "" }, { "id": 32000, - "logprob": -13.109375, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -14.7890625, + "logprob": -14.8671875, "text": "" }, { "id": 32000, - "logprob": -12.125, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -11.3671875, + "logprob": -11.375, "text": "" }, { "id": 32000, - "logprob": -11.6796875, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.359375, "text": "" }, { @@ -13337,22 +13337,22 @@ }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.765625, "text": "" }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -14.6796875, + "logprob": -14.71875, "text": "" }, { "id": 32000, - "logprob": -12.5625, + "logprob": -12.6015625, "text": "" }, { @@ -13362,12 +13362,12 @@ }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.703125, "text": "" }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.40625, "text": "" }, { @@ -13382,17 +13382,17 @@ }, { "id": 32000, - "logprob": -12.953125, + "logprob": -12.921875, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.5234375, "text": "" }, { @@ -13402,7 +13402,7 @@ }, { "id": 32000, - "logprob": -11.6796875, + "logprob": -11.6875, "text": "" }, { @@ -13412,157 +13412,17 @@ }, { "id": 32000, - "logprob": -12.40625, + "logprob": -12.4140625, "text": "" }, { "id": 32000, - "logprob": -18.734375, + "logprob": -18.703125, "text": "" }, { "id": 32000, - "logprob": -15.65625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0546875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.7109375, - "text": "" - }, - { - "id": 32000, - "logprob": -15.078125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.0703125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.5625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.359375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.609375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.078125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1171875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.609375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.296875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.1484375, - 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-13.703125, + "text": "" + }, + { + "id": 32000, + "logprob": -15.0859375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0703125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.5234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3671875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.640625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.1015625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.3515625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.25, + "text": "" + }, + { + "id": 32000, + "logprob": -14.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.7265625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.90625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.578125, + "text": "" + }, + { + "id": 32000, + "logprob": 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-13.5234375, + "logprob": -13.546875, "text": "" }, { @@ -13607,22 +13607,22 @@ }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -12.828125, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -15.0078125, + "logprob": -14.9453125, "text": "" }, { "id": 32000, - "logprob": -16.578125, + "logprob": -16.5625, "text": "" }, { @@ -13632,27 +13632,27 @@ }, { "id": 32000, - "logprob": -14.7265625, + "logprob": -14.6953125, "text": "" }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -12.7890625, + "logprob": -12.75, "text": "" }, { @@ -13662,12 +13662,12 @@ }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.796875, "text": "" }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -12.4765625, "text": "" }, { @@ -13677,17 +13677,17 @@ }, { "id": 32000, - "logprob": -15.328125, + "logprob": -15.484375, "text": "" }, { "id": 32000, - "logprob": -14.640625, + "logprob": -14.6328125, "text": "" }, { "id": 32000, - "logprob": -13.609375, + "logprob": -13.6015625, "text": "" }, { @@ -13697,22 +13697,22 @@ }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -13.3046875, + "logprob": -13.3125, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.96875, "text": "" }, { "id": 32000, - "logprob": -13.5, + "logprob": -13.484375, "text": "" }, { @@ -13722,7 +13722,7 @@ }, { "id": 32000, - "logprob": -12.4375, + "logprob": -12.4453125, "text": "" }, { @@ -13732,37 +13732,37 @@ }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -16.546875, + "logprob": -16.53125, "text": "" }, { "id": 32000, - "logprob": -14.1171875, + "logprob": -14.1484375, 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{ "id": 32000, - "logprob": -15.2421875, + "logprob": -15.3046875, "text": "" }, { "id": 32000, - "logprob": -12.2109375, + "logprob": -12.1875, "text": "" }, { "id": 32000, - "logprob": -12.734375, + "logprob": -12.7578125, "text": "" }, { "id": 32000, - "logprob": -15.140625, + "logprob": -15.125, "text": "" }, { "id": 32000, - "logprob": -14.9765625, + "logprob": -14.9140625, "text": "" }, { "id": 32000, - "logprob": -15.5234375, + "logprob": -15.546875, "text": "" }, { "id": 32000, - "logprob": -16.03125, + "logprob": -16.015625, "text": "" }, { "id": 32000, - "logprob": -14.125, + "logprob": -14.109375, "text": "" }, { "id": 32000, - "logprob": -13.265625, + "logprob": -13.234375, "text": "" }, { "id": 32000, - "logprob": -13.1328125, + "logprob": -13.15625, "text": "" }, { @@ -13892,17 +13892,17 @@ }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.7734375, "text": "" }, { "id": 32000, - "logprob": -14.34375, + "logprob": -14.3515625, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7421875, "text": "" }, { @@ -13912,12 +13912,12 @@ }, { "id": 32000, - "logprob": -15.3046875, + "logprob": -15.296875, "text": "" }, { "id": 32000, - "logprob": -13.640625, + "logprob": -13.6015625, "text": "" }, { @@ -13927,7 +13927,7 @@ }, { "id": 32000, - "logprob": -13.4921875, + "logprob": -13.5, "text": "" }, { @@ -13942,52 +13942,52 @@ }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.1328125, "text": "" }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -12.5703125, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -13.3515625, + "logprob": -13.3359375, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -10.9375, + "logprob": -10.953125, "text": "" }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.8046875, + "logprob": -12.796875, "text": "" }, { "id": 32000, - "logprob": -13.4765625, + "logprob": -13.4296875, "text": "" }, { "id": 32000, - "logprob": -11.578125, + "logprob": -11.5625, "text": "" }, { @@ -13997,22 +13997,22 @@ }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.4375, "text": "" }, { "id": 32000, - "logprob": -19.671875, + "logprob": -19.65625, "text": "" }, { "id": 32000, - "logprob": -12.53125, + "logprob": -12.5, "text": "" }, { @@ -14022,32 +14022,32 @@ }, { "id": 32000, - "logprob": -13.875, + "logprob": -13.859375, "text": "" }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.34375, "text": "" }, { "id": 32000, - "logprob": -12.8828125, + "logprob": -12.828125, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -13.734375, + "logprob": -13.71875, "text": "" }, { @@ -14057,27 +14057,27 @@ }, { "id": 32000, - "logprob": -11.609375, + "logprob": -11.6171875, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -13.84375, + "logprob": -13.8984375, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -16.203125, + "logprob": -16.234375, "text": "" }, { @@ -14092,12 +14092,12 @@ }, { "id": 32000, - "logprob": -15.1953125, + "logprob": -15.2109375, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -11.5546875, "text": "" }, { @@ -14107,17 +14107,17 @@ }, { "id": 32000, - "logprob": -15.296875, + "logprob": -15.28125, "text": "" }, { "id": 32000, - "logprob": -14.078125, + "logprob": -14.046875, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -12.96875, "text": "" }, { @@ -14127,7 +14127,7 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3828125, "text": "" }, { @@ -14137,47 +14137,47 @@ }, { "id": 32000, - "logprob": -14.2421875, + "logprob": -14.2265625, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -12.5703125, + "logprob": -12.6171875, "text": "" }, { "id": 32000, - "logprob": -13.5546875, + "logprob": -13.5390625, "text": "" }, { "id": 32000, - "logprob": -11.2578125, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -14.9609375, + "logprob": -15.0703125, "text": "" }, { "id": 32000, - "logprob": -14.34375, + "logprob": -14.375, "text": "" }, { "id": 32000, - "logprob": -11.140625, + "logprob": -11.1484375, "text": "" }, { @@ -14192,32 +14192,32 @@ }, { "id": 32000, - "logprob": -15.8203125, + "logprob": -15.796875, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.921875, + "logprob": -14.0390625, "text": "" }, { "id": 32000, - "logprob": -11.4453125, + "logprob": -11.453125, "text": "" }, { "id": 32000, - "logprob": -14.2734375, + "logprob": -14.2890625, "text": "" }, { "id": 32000, - "logprob": -11.640625, + "logprob": -11.671875, "text": "" }, { @@ -14227,22 +14227,22 @@ }, { "id": 32000, - "logprob": -16.3125, + "logprob": -16.375, "text": "" }, { "id": 32000, - "logprob": -13.859375, + "logprob": -13.8828125, "text": "" }, { "id": 32000, - "logprob": -14.671875, + "logprob": -14.6171875, "text": "" }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.625, "text": "" }, { @@ -14272,67 +14272,37 @@ }, { "id": 32000, - "logprob": -11.3515625, + "logprob": -11.34375, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.0, + "logprob": -10.984375, "text": "" }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -13.2109375, "text": "" }, { "id": 32000, - "logprob": -12.2890625, + "logprob": -12.265625, "text": "" }, { "id": 32000, - "logprob": -12.2421875, + "logprob": -12.234375, "text": "" }, { "id": 32000, - "logprob": -11.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0703125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.4609375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.9375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.6328125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.296875, + "logprob": -11.9765625, "text": "" }, { @@ -14342,7 +14312,37 @@ }, { "id": 32000, - "logprob": -11.78125, + "logprob": -12.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4609375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.953125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.6484375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.296875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.7734375, "text": "" }, { @@ -14352,22 +14352,22 @@ }, { "id": 32000, - "logprob": -12.2734375, + "logprob": -12.265625, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -15.265625, + "logprob": -15.1953125, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7421875, "text": "" }, { @@ -14382,12 +14382,12 @@ }, { "id": 32000, - "logprob": -15.5859375, + "logprob": -15.59375, "text": "" }, { "id": 32000, - "logprob": -11.6796875, + "logprob": -11.671875, "text": "" }, { @@ -14397,27 +14397,27 @@ }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.90625, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.609375, "text": "" }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.671875, "text": "" }, { "id": 32000, - "logprob": -15.3515625, + "logprob": -15.34375, "text": "" }, { "id": 32000, - "logprob": -15.21875, + "logprob": -15.2265625, "text": "" }, { @@ -14427,7 +14427,7 @@ }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.6875, "text": "" }, { @@ -14437,37 +14437,37 @@ }, { "id": 32000, - "logprob": -11.7890625, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -12.03125, "text": "" }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.875, "text": "" }, { "id": 32000, - "logprob": -16.90625, + "logprob": -16.515625, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -12.46875, + "logprob": -12.4609375, "text": "" }, { "id": 32000, - "logprob": -13.375, + "logprob": -13.3984375, "text": "" }, { @@ -14477,22 +14477,22 @@ }, { "id": 32000, - "logprob": -11.890625, + "logprob": -11.8828125, "text": "" }, { "id": 32000, - "logprob": -11.4453125, + "logprob": -11.4375, "text": "" }, { "id": 32000, - "logprob": -12.71875, + "logprob": -12.734375, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.828125, "text": "" }, { @@ -14502,52 +14502,52 @@ }, { "id": 32000, - "logprob": -14.7734375, + "logprob": -14.734375, "text": "" }, { "id": 32000, - "logprob": -14.375, + "logprob": -14.3984375, "text": "" }, { "id": 32000, - "logprob": -12.0, + "logprob": -12.0078125, "text": "" }, { "id": 32000, - "logprob": -13.5703125, + "logprob": -13.578125, "text": "" }, { "id": 32000, - "logprob": -13.71875, + "logprob": -13.578125, "text": "" }, { "id": 32000, - "logprob": -11.3125, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -13.40625, + "logprob": -13.421875, "text": "" }, { "id": 32000, - "logprob": -12.90625, + "logprob": -12.9140625, "text": "" }, { "id": 32000, - "logprob": -14.25, + "logprob": -14.34375, "text": "" }, { @@ -14557,12 +14557,12 @@ }, { "id": 32000, - "logprob": -13.8359375, + "logprob": -13.890625, "text": "" }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.3203125, "text": "" }, { @@ -14572,7 +14572,7 @@ }, { "id": 32000, - "logprob": -11.9609375, + "logprob": -11.9765625, "text": "" }, { @@ -14587,52 +14587,52 @@ }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0390625, "text": "" }, { "id": 32000, - "logprob": -14.3984375, + "logprob": -14.40625, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -13.875, + "logprob": -13.8203125, "text": "" }, { "id": 32000, - "logprob": -13.4609375, + "logprob": -13.5078125, "text": "" }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -13.40625, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -16.96875, + "logprob": -17.3125, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.5234375, "text": "" }, { "id": 32000, - "logprob": -17.640625, + "logprob": -17.625, "text": "" }, { @@ -14647,17 +14647,17 @@ }, { "id": 32000, - "logprob": -15.953125, + "logprob": -15.9140625, "text": "" }, { "id": 32000, - "logprob": -16.625, + "logprob": -16.65625, "text": "" }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -12.5, "text": "" }, { @@ -14667,22 +14667,22 @@ }, { "id": 368, - "logprob": -0.19799805, + "logprob": -0.19726562, "text": "you" }, { "id": 1912, - "logprob": -1.5029297, + "logprob": -1.4990234, "text": "tell" }, { "id": 528, - "logprob": -0.30932617, + "logprob": -0.31152344, "text": "me" }, { "id": 264, - "logprob": -2.6328125, + "logprob": -2.6367188, "text": "a" }, { @@ -14692,22 +14692,22 @@ }, { "id": 2485, - "logprob": -0.99853516, + "logprob": -0.9941406, "text": "short" }, { "id": 2838, - "logprob": -0.4609375, + "logprob": -0.46118164, "text": "story" }, { "id": 2818, - "logprob": -3.3144531, + "logprob": -3.3183594, "text": "based" }, { "id": 356, - "logprob": -0.0289917, + "logprob": -0.029129028, "text": "on" }, { @@ -14717,7 +14717,7 @@ }, { "id": 3469, - "logprob": -0.28955078, + "logprob": -0.29052734, "text": "image" }, { @@ -14730,25 +14730,25 @@ "tokens": [ { "id": 13, - "logprob": -0.0075035095, + "logprob": -0.0076828003, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.20129395, + "logprob": -0.20092773, "special": false, "text": "\n" }, { "id": 16114, - "logprob": -1.2607422, + "logprob": -1.2587891, "special": false, "text": "Once" }, { "id": 3714, - "logprob": -0.20825195, + "logprob": -0.20861816, "special": false, "text": " upon" }, @@ -14760,31 +14760,31 @@ }, { "id": 727, - "logprob": -0.011932373, + "logprob": -0.011909485, "special": false, "text": " time" }, { "id": 28725, - "logprob": -0.17297363, + "logprob": -0.17529297, "special": false, "text": "," }, { "id": 736, - "logprob": -0.91015625, + "logprob": -0.9082031, "special": false, "text": " there" }, { "id": 403, - "logprob": -0.05758667, + "logprob": -0.057525635, "special": false, "text": " was" }, { "id": 264, - "logprob": -0.00969696, + "logprob": -0.009651184, "special": false, "text": " a" } @@ -14826,7 +14826,7 @@ }, { "id": 32000, - "logprob": -15.9140625, + "logprob": -15.7109375, "text": "" }, { @@ -14836,37 +14836,37 @@ }, { "id": 32000, - "logprob": -10.03125, + "logprob": -10.0234375, "text": "" }, { "id": 32000, - "logprob": -10.140625, + "logprob": -10.1328125, "text": "" }, { "id": 32000, - "logprob": -10.296875, + "logprob": -10.421875, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -16.0625, + "logprob": -16.59375, "text": "" }, { "id": 32000, - "logprob": -13.8515625, + "logprob": -13.828125, "text": "" }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.390625, "text": "" }, { @@ -14876,7 +14876,7 @@ }, { "id": 32000, - "logprob": -10.171875, + "logprob": -10.1640625, "text": "" }, { @@ -14886,22 +14886,22 @@ }, { "id": 32000, - "logprob": -10.359375, + "logprob": -10.3984375, "text": "" }, { "id": 32000, - "logprob": -14.953125, + "logprob": -15.015625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.0859375, "text": "" }, { "id": 32000, - "logprob": -13.3046875, + "logprob": -13.2734375, "text": "" }, { @@ -14911,67 +14911,37 @@ }, { "id": 32000, - "logprob": -11.0546875, + "logprob": -11.0390625, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9140625, "text": "" }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.5234375, "text": "" }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.4765625, "text": "" }, { "id": 32000, - "logprob": -14.2890625, + "logprob": -14.2421875, "text": "" }, { "id": 32000, - "logprob": -13.625, + "logprob": -13.6171875, "text": "" }, { "id": 32000, - "logprob": -11.21875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -17.03125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.6484375, - "text": "" - }, - { - "id": 32000, - "logprob": -18.265625, + "logprob": -11.359375, "text": "" }, { @@ -14981,17 +14951,47 @@ }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -17.34375, "text": "" }, { "id": 32000, - "logprob": -9.8671875, + "logprob": -10.3984375, "text": "" }, { "id": 32000, - "logprob": -10.765625, + "logprob": -10.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.640625, + "text": "" + }, + { + "id": 32000, + "logprob": -18.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -9.875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7734375, "text": "" }, { @@ -15001,12 +15001,12 @@ }, { "id": 32000, - "logprob": -10.984375, + "logprob": -10.96875, "text": "" }, { "id": 32000, - "logprob": -10.6171875, + "logprob": -10.609375, "text": "" }, { @@ -15016,17 +15016,17 @@ }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.5078125, "text": "" }, { "id": 32000, - "logprob": -11.296875, + "logprob": -11.4453125, "text": "" }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.59375, "text": "" }, { @@ -15041,27 +15041,27 @@ }, { "id": 32000, - "logprob": -10.6796875, + "logprob": -10.640625, "text": "" }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.9765625, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -12.390625, + "logprob": -12.3671875, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -12.0234375, "text": "" }, { @@ -15076,137 +15076,12 @@ }, { "id": 32000, - "logprob": -11.21875, + "logprob": -11.0625, "text": "" }, { "id": 32000, - "logprob": -10.390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.1875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0234375, - "text": "" - }, - { - "id": 32000, - "logprob": -18.3125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.9921875, - "text": "" - }, - { - "id": 32000, - "logprob": -15.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.171875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.4140625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.078125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.5625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.25, - "text": "" - }, - { - "id": 32000, - "logprob": -10.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2890625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.71875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.671875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.7890625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.15625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.515625, - "text": "" - }, - { - "id": 32000, - "logprob": -15.5546875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.234375, + "logprob": -10.3828125, "text": "" }, { @@ -15214,6 +15089,131 @@ "logprob": -11.171875, "text": "" }, + { + "id": 32000, + "logprob": -11.0234375, + "text": "" + }, + { + "id": 32000, + "logprob": -18.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9921875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.7109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.78125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.2421875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.2265625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.140625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.015625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.5234375, + "text": "" + }, + { + "id": 32000, + "logprob": -15.4453125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.09375, + "text": "" + }, { "id": 32000, "logprob": -14.6796875, @@ -15221,7 +15221,7 @@ }, { "id": 32000, - "logprob": -10.375, + "logprob": -10.3671875, "text": "" }, { @@ -15236,17 +15236,22 @@ }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -12.484375, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.09375, "text": "" }, { "id": 32000, - "logprob": -11.0859375, + "logprob": -11.1015625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.96875, "text": "" }, { @@ -15256,22 +15261,17 @@ }, { "id": 32000, - "logprob": -11.109375, + "logprob": -11.0234375, "text": "" }, { "id": 32000, - "logprob": -11.0390625, + "logprob": -10.96875, "text": "" }, { "id": 32000, - "logprob": -10.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.171875, + "logprob": -11.1484375, "text": "" }, { @@ -15281,7 +15281,7 @@ }, { "id": 32000, - "logprob": -10.7109375, + "logprob": -10.71875, "text": "" }, { @@ -15291,27 +15291,27 @@ }, { "id": 32000, - "logprob": -10.9140625, + "logprob": -10.8984375, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.2890625, "text": "" }, { "id": 32000, - "logprob": -11.09375, + "logprob": -10.984375, "text": "" }, { "id": 32000, - "logprob": -10.6328125, + "logprob": -10.640625, "text": "" }, { "id": 32000, - "logprob": -10.6953125, + "logprob": -10.7109375, "text": "" }, { @@ -15326,202 +15326,17 @@ }, { "id": 32000, - "logprob": -10.6953125, + "logprob": -10.6875, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -11.53125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2109375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4921875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.25, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8984375, - 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32000, + "logprob": -10.4609375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.9765625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4609375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.09375, "text": "" }, { @@ -15596,22 +15596,22 @@ }, { "id": 32000, - "logprob": -12.828125, + "logprob": -12.84375, "text": "" }, { "id": 32000, - "logprob": -11.2421875, + "logprob": -11.25, "text": "" }, { "id": 32000, - "logprob": -12.3984375, + "logprob": -12.4375, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.671875, "text": "" }, { @@ -15626,37 +15626,12 @@ }, { "id": 32000, - "logprob": -12.2421875, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -10.375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.859375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0234375, - "text": "" - }, - { - 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"" + }, + { + "id": 32000, + "logprob": -10.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.1328125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.75, + "text": "" + }, + { + "id": 32000, + "logprob": -10.875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4140625, "text": "" }, { @@ -16056,57 +16056,57 @@ }, { "id": 32000, - "logprob": -13.609375, + "logprob": -13.6171875, "text": "" }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.484375, "text": "" }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.8671875, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -14.9140625, + "logprob": -14.921875, "text": "" }, { "id": 32000, - "logprob": -13.3125, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -11.1484375, + "logprob": 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{ + "id": 32000, + "logprob": -11.546875, "text": "" }, { @@ -16206,7 +16206,17 @@ }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.6640625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.5546875, "text": "" }, { @@ -16216,32 +16226,22 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -15.0234375, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -12.5703125, "text": "" }, { "id": 32000, - "logprob": -15.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.5546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8671875, + "logprob": -11.859375, "text": "" }, { @@ -16251,27 +16251,27 @@ }, { "id": 32000, - "logprob": -12.3515625, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.5390625, "text": "" }, { "id": 32000, - "logprob": 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-11.84375, "text": "" }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.359375, "text": "" }, { @@ -16361,12 +16361,12 @@ }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.875, "text": "" }, { "id": 32000, - "logprob": -13.5859375, + "logprob": -13.6015625, "text": "" }, { @@ -16381,12 +16381,12 @@ }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -13.6015625, + "logprob": -13.59375, "text": "" }, { @@ -16396,7 +16396,7 @@ }, { "id": 32000, - "logprob": -14.5859375, + "logprob": -14.5703125, "text": "" }, { @@ -16406,12 +16406,12 @@ }, { "id": 32000, - "logprob": -13.40625, + "logprob": -13.4140625, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -11.7890625, "text": "" }, { @@ -16421,12 +16421,12 @@ }, { "id": 32000, - "logprob": -14.421875, + "logprob": -14.4296875, "text": "" }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.53125, "text": "" }, { @@ -16436,117 +16436,12 @@ }, { "id": 32000, - "logprob": -12.71875, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -11.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1484375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.09375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8828125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.140625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.65625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.6953125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.421875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2890625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1015625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -16.359375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.34375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.75, - "text": 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+ "logprob": -13.1796875, "text": "" }, { "id": 32000, - "logprob": -12.703125, + "logprob": -12.2421875, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -14.828125, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -16.359375, "text": "" }, { "id": 32000, - "logprob": -16.203125, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.75, "text": "" }, { "id": 32000, - "logprob": -15.78125, + "logprob": -11.7890625, "text": "" }, { "id": 32000, - "logprob": -12.734375, + "logprob": -15.765625, + "text": "" + }, + { + "id": 32000, + "logprob": -15.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.203125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.5546875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": 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-15.3359375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.2109375, + "logprob": -13.1875, "text": "" }, { @@ -16826,22 +16826,22 @@ }, { "id": 32000, - "logprob": -13.3359375, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -11.875, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -12.5390625, + "logprob": -12.5625, "text": "" }, { "id": 32000, - "logprob": -13.4140625, + "logprob": -13.3203125, "text": "" }, { @@ -16851,27 +16851,27 @@ }, { "id": 32000, - "logprob": -10.7421875, + "logprob": -10.875, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.6328125, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.15625, "text": "" }, { "id": 32000, - "logprob": -11.8046875, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -11.9921875, "text": "" }, { @@ -16881,32 +16881,32 @@ }, { "id": 32000, - "logprob": -13.375, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9765625, "text": "" }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -13.359375, + "logprob": -13.3359375, "text": "" }, { @@ -16916,12 +16916,12 @@ }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.984375, "text": "" }, { @@ -16931,7 +16931,7 @@ }, { "id": 32000, - "logprob": -11.421875, + "logprob": -11.4140625, "text": "" }, { @@ -16941,17 +16941,17 @@ }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.421875, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { @@ -16966,12 +16966,12 @@ }, { "id": 32000, - "logprob": -13.96875, + "logprob": -14.0, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.40625, "text": "" }, { @@ -16981,27 +16981,27 @@ }, { "id": 32000, - "logprob": -12.8046875, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -13.1796875, + "logprob": -13.15625, "text": "" }, { "id": 32000, - "logprob": -13.171875, + "logprob": -13.1640625, "text": "" }, { "id": 32000, - "logprob": -12.7734375, + "logprob": -12.78125, "text": "" }, { "id": 32000, - "logprob": -11.7578125, + "logprob": -11.765625, "text": "" }, { @@ -17011,37 +17011,37 @@ }, { "id": 32000, - "logprob": -12.2578125, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -14.6328125, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9296875, "text": "" }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3125, "text": "" }, { "id": 32000, - "logprob": -11.421875, + "logprob": -11.4140625, "text": "" }, { "id": 32000, - "logprob": -11.390625, + "logprob": -11.3984375, "text": "" }, { @@ -17051,27 +17051,42 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -13.8671875, + "logprob": -13.875, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.1171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4921875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2890625, "text": "" }, { @@ -17081,27 +17096,12 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -13.59375, "text": "" }, { "id": 32000, - "logprob": -11.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5703125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.796875, + "logprob": -11.8046875, "text": "" }, { @@ -17111,7 +17111,12 @@ }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.1015625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2265625, "text": "" }, { @@ -17121,12 +17126,7 @@ }, { "id": 32000, - "logprob": -13.1875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4765625, + "logprob": -12.4609375, "text": "" }, { @@ -17136,7 +17136,12 @@ }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.671875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.671875, "text": "" }, { @@ -17146,22 +17151,17 @@ }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -15.390625, "text": "" }, { "id": 32000, - "logprob": -15.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9453125, + "logprob": -11.953125, "text": "" }, { @@ -17171,27 +17171,27 @@ }, { "id": 32000, - "logprob": -15.46875, + "logprob": -15.4921875, "text": "" }, { "id": 32000, - "logprob": -13.9375, + "logprob": -13.9296875, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.609375, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -15.9375, + "logprob": -15.984375, "text": "" }, { @@ -17201,22 +17201,22 @@ }, { "id": 32000, - "logprob": -12.875, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -14.1640625, + "logprob": -14.1875, "text": "" }, { @@ -17226,42 +17226,42 @@ }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -13.078125, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + 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"logprob": -13.03125, "text": "" }, { "id": 32000, - "logprob": -16.15625, + "logprob": -16.140625, "text": "" }, { "id": 32000, - "logprob": -13.4375, + "logprob": -13.4609375, "text": "" }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.7265625, "text": "" }, { @@ -18391,12 +18391,12 @@ }, { "id": 32000, - "logprob": -13.25, + "logprob": -13.234375, "text": "" }, { "id": 32000, - "logprob": -13.2421875, + "logprob": -13.2578125, "text": "" }, { @@ -18406,12 +18406,12 @@ }, { "id": 32000, - "logprob": -13.8203125, + "logprob": -13.8359375, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.6171875, "text": "" }, { @@ -18421,12 +18421,12 @@ }, { "id": 32000, - "logprob": -14.4609375, + "logprob": -14.4765625, "text": "" }, { "id": 32000, - "logprob": -13.140625, + "logprob": -13.15625, "text": "" }, { @@ -18436,17 +18436,17 @@ }, { "id": 32000, - "logprob": -14.1015625, + "logprob": -14.046875, "text": "" }, { "id": 32000, - "logprob": -14.2578125, + 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"" }, { "id": 32000, - "logprob": -14.5, + "logprob": -12.7578125, "text": "" }, { "id": 32000, - "logprob": -18.125, + "logprob": -14.8359375, "text": "" }, { "id": 32000, - "logprob": -14.6875, + "logprob": -13.0859375, "text": "" }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.5078125, + "text": "" + }, + { + "id": 32000, + "logprob": -18.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.71875, "text": "" }, { @@ -18626,37 +18626,37 @@ }, { "id": 32000, - "logprob": -14.9765625, + "logprob": -14.9921875, "text": "" }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -15.34375, + "logprob": -15.40625, "text": "" }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.4296875, "text": "" }, { "id": 32000, - "logprob": 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+ "logprob": -13.0546875, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -13.65625, "text": "" }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.6953125, "text": "" }, { @@ -18746,12 +18746,12 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5390625, "text": "" }, { "id": 32000, - "logprob": -13.3203125, + "logprob": -13.328125, "text": "" }, { @@ -18761,17 +18761,12 @@ }, { "id": 32000, - "logprob": -13.6328125, + "logprob": -13.640625, "text": "" }, { "id": 32000, - "logprob": -15.109375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.234375, + "logprob": -15.15625, "text": "" }, { @@ -18781,32 +18776,37 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -13.25, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -11.3125, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -10.6328125, + "logprob": -11.3203125, "text": "" }, { "id": 32000, - 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-18926,7 +18926,7 @@ }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.2265625, "text": "" }, { @@ -18941,32 +18941,32 @@ }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.796875, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -13.6328125, + "logprob": -13.65625, "text": "" }, { "id": 32000, - "logprob": -12.8828125, + "logprob": -12.90625, "text": "" }, { @@ -18976,7 +18976,22 @@ }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.25, + "text": "" + }, + { + "id": 32000, + "logprob": -12.03125, "text": "" }, { @@ -18986,27 +19001,12 @@ }, { "id": 32000, - "logprob": -14.2421875, + "logprob": -13.0234375, "text": "" }, { "id": 32000, - "logprob": -12.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7578125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0, - "text": "" - }, - { - "id": 32000, - "logprob": -12.859375, + "logprob": -12.8671875, "text": "" }, { @@ -19016,17 +19016,17 @@ }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -10.5, + "logprob": -10.5078125, "text": "" }, { @@ -19036,12 +19036,12 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.375, "text": "" }, { "id": 32000, - "logprob": -11.0546875, + "logprob": -11.0625, "text": "" }, { @@ -19056,7 +19056,7 @@ }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.3515625, "text": "" }, { @@ -19066,12 +19066,12 @@ }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.53125, "text": "" }, { "id": 32000, - "logprob": -12.2109375, + "logprob": 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"logprob": -13.109375, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -11.9375, "text": "" }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.3203125, "text": "" }, { @@ -19171,17 +19171,17 @@ }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.734375, "text": "" }, { "id": 32000, - "logprob": -13.3828125, + "logprob": -13.3984375, "text": "" }, { @@ -19191,47 +19191,47 @@ }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.15625, "text": "" }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.8984375, "text": "" }, { "id": 32000, - "logprob": -13.671875, + "logprob": -13.6953125, "text": "" }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -13.3125, + "logprob": -13.3203125, "text": "" }, { "id": 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- }, - { - "id": 32000, - "logprob": -12.6875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.078125, + "logprob": -10.84375, "text": "" }, { @@ -19331,22 +19311,12 @@ }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -12.6640625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6484375, + "logprob": -12.7421875, "text": "" }, { @@ -19356,52 +19326,82 @@ }, { "id": 32000, - "logprob": -13.609375, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.8359375, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -12.9921875, + "logprob": -12.84375, "text": "" }, { "id": 32000, - "logprob": -14.046875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -13.9765625, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": 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"text": "" }, { @@ -20281,7 +20281,7 @@ }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.84375, "text": "" }, { @@ -20291,42 +20291,42 @@ }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.6953125, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.6796875, "text": "" }, { "id": 32000, - "logprob": -12.546875, + "logprob": -12.5546875, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.0859375, "text": "" }, { "id": 32000, - "logprob": -12.0234375, + "logprob": -12.0078125, "text": "" }, { "id": 32000, - "logprob": -12.2734375, + "logprob": -12.28125, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.234375, "text": "" }, { @@ -20336,42 +20336,32 @@ }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -13.9375, + "logprob": -13.953125, "text": "" }, { "id": 32000, - 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"id": 32000, + "logprob": -11.734375, "text": "" }, { @@ -22246,12 +22251,7 @@ }, { "id": 32000, - "logprob": -11.7265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3984375, + "logprob": -11.40625, "text": "" }, { @@ -22271,32 +22271,32 @@ }, { "id": 32000, - "logprob": -12.875, + "logprob": -12.890625, "text": "" }, { "id": 32000, - "logprob": -11.1171875, + "logprob": -11.03125, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -11.96875, "text": "" }, { "id": 32000, - "logprob": -14.6640625, + "logprob": -14.671875, "text": "" }, { @@ -22306,7 +22306,7 @@ }, { "id": 32000, - "logprob": -14.3828125, + "logprob": -14.2890625, "text": "" }, { @@ -22321,22 +22321,42 @@ }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.7421875, "text": "" }, { "id": 32000, - "logprob": -14.6953125, + "logprob": -14.6484375, "text": "" }, { "id": 32000, - "logprob": -13.515625, + "logprob": -13.546875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5703125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.96875, "text": "" }, { @@ -22346,67 +22366,47 @@ }, { "id": 32000, - "logprob": -13.4140625, + "logprob": -11.9609375, "text": "" }, { "id": 32000, - "logprob": -14.375, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -12.453125, "text": "" }, { "id": 32000, - "logprob": -12.9453125, + "logprob": -13.8046875, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -11.890625, + "logprob": -13.7890625, "text": "" }, { "id": 32000, - "logprob": -12.5, - "text": "" - }, - { - "id": 32000, - "logprob": -14.046875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.046875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.71875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.421875, + "logprob": -14.3828125, "text": "" }, { @@ -22426,22 +22426,22 @@ }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -13.1015625, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -13.9921875, + "logprob": -14.015625, "text": "" }, { "id": 32000, - "logprob": -11.640625, + "logprob": -11.6328125, "text": "" }, { @@ -22451,7 +22451,7 @@ }, { "id": 32000, - "logprob": -11.3203125, + "logprob": -11.328125, "text": "" }, { @@ -22461,7 +22461,7 @@ }, { "id": 32000, - "logprob": -13.578125, + "logprob": -13.5859375, "text": "" }, { @@ -22471,7 +22471,7 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0390625, "text": "" }, { @@ -22481,7 +22481,7 @@ }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4609375, "text": "" }, { @@ -22501,12 +22501,12 @@ }, { "id": 32000, - "logprob": -11.296875, + "logprob": -11.3046875, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -12.21875, "text": "" }, { @@ -22516,7 +22516,7 @@ }, { "id": 32000, - "logprob": -10.90625, + "logprob": -10.9140625, "text": "" }, { @@ -22526,37 +22526,37 @@ }, { "id": 32000, - "logprob": -13.140625, + "logprob": -13.203125, "text": "" }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -15.140625, + "logprob": -14.6640625, "text": "" }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.1640625, "text": "" }, { "id": 32000, - "logprob": -14.2734375, + "logprob": -14.859375, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -11.53125, "text": "" }, { @@ -22566,62 +22566,62 @@ }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.296875, "text": "" }, { "id": 32000, - "logprob": -12.40625, + "logprob": -12.4375, "text": "" }, { "id": 32000, - "logprob": -13.8125, + "logprob": -13.78125, "text": "" }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.265625, "text": "" }, { "id": 32000, - "logprob": -13.71875, + "logprob": -13.6015625, "text": "" }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.1015625, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.703125, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { @@ -22631,22 +22631,22 @@ }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.875, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -11.21875, + "logprob": -11.34375, "text": "" }, { @@ -22661,37 +22661,37 @@ }, { "id": 32000, - "logprob": -12.9921875, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -15.15625, + "logprob": -15.1875, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -12.0, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.5546875, + "logprob": -13.515625, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -11.5703125, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.703125, "text": "" }, { @@ -22711,27 +22711,27 @@ }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5546875, "text": "" }, { "id": 32000, - "logprob": -13.421875, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -11.1875, + "logprob": -11.1953125, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.7578125, "text": "" }, { @@ -22741,7 +22741,7 @@ }, { "id": 32000, - "logprob": -13.2734375, + "logprob": -13.2421875, "text": "" }, { @@ -22751,62 +22751,62 @@ }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.3984375, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -11.1796875, + "logprob": -11.1875, "text": "" }, { "id": 32000, - "logprob": -13.375, + "logprob": -13.359375, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -12.9453125, + "logprob": -12.9140625, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -13.59375, + "logprob": -13.5625, "text": "" }, { @@ -22814,21 +22814,6 @@ "logprob": -13.3203125, "text": "" }, - { - "id": 32000, - "logprob": -12.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1328125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6796875, - "text": "" - }, { "id": 32000, "logprob": -12.8046875, @@ -22836,12 +22821,17 @@ }, { "id": 32000, - "logprob": -12.78125, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -11.6484375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.828125, "text": "" }, { @@ -22849,6 +22839,16 @@ "logprob": -12.78125, "text": "" }, + { + "id": 32000, + "logprob": -12.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7578125, + "text": "" + }, { "id": 32000, "logprob": -11.484375, @@ -22856,37 +22856,37 @@ }, { "id": 32000, - "logprob": -12.75, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -11.6328125, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -11.3046875, + "logprob": -11.3125, "text": "" }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.265625, "text": "" }, { @@ -22921,7 +22921,7 @@ }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.046875, "text": "" }, { @@ -22931,7 +22931,7 @@ }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.7109375, "text": "" }, { @@ -22941,27 +22941,27 @@ }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3671875, "text": "" }, { "id": 32000, - "logprob": -14.3046875, + "logprob": -14.3125, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7578125, "text": "" }, { @@ -22971,142 +22971,12 @@ }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.3203125, "text": "" }, { "id": 32000, - "logprob": -14.203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.25, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.078125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": 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"logprob": -11.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5703125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5546875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.65625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.46875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6484375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.7734375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.078125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.15625, "text": "" }, { @@ -23131,12 +23131,12 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.9296875, + "logprob": -11.9375, "text": "" }, { @@ -23146,17 +23146,17 @@ }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.7890625, "text": "" }, { "id": 32000, - "logprob": -13.0234375, + "logprob": -13.0390625, "text": "" }, { @@ -23166,12 +23166,12 @@ }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -10.75, + "logprob": -10.7578125, "text": "" }, { @@ -23181,12 +23181,12 @@ }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -11.2578125, + "logprob": -11.265625, "text": "" }, { @@ -23196,27 +23196,27 @@ }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.7890625, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -11.25, + "logprob": -11.234375, "text": "" }, { "id": 32000, - "logprob": -12.453125, + "logprob": -12.46875, "text": "" }, { @@ -23226,7 +23226,7 @@ }, { "id": 32000, - "logprob": -11.890625, + "logprob": -11.8515625, "text": "" }, { @@ -23241,17 +23241,17 @@ }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.7890625, "text": "" }, { "id": 32000, - "logprob": -12.15625, + "logprob": -12.1953125, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.28125, "text": "" }, { @@ -23261,7 +23261,7 @@ }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.34375, "text": "" }, { @@ -23271,27 +23271,27 @@ }, { "id": 32000, - "logprob": -13.0078125, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -15.25, + "logprob": -15.28125, "text": "" }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0234375, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.109375, "text": "" }, { @@ -23306,32 +23306,32 @@ }, { "id": 32000, - "logprob": -14.7265625, + "logprob": -14.7109375, "text": "" }, { "id": 32000, - "logprob": -10.015625, + "logprob": -10.0234375, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.46875, "text": "" }, { "id": 32000, - "logprob": -13.9609375, + "logprob": -13.9765625, "text": "" }, { "id": 32000, - "logprob": -14.2734375, + "logprob": -14.3046875, "text": "" }, { "id": 32000, - "logprob": -14.9140625, + "logprob": -14.890625, "text": "" }, { @@ -23346,12 +23346,12 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.328125, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.3515625, "text": "" }, { @@ -23361,47 +23361,47 @@ }, { "id": 32000, - "logprob": -12.421875, + "logprob": -12.4296875, "text": "" }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.890625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5625, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.6171875, "text": "" }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.4609375, + "logprob": -12.4765625, "text": "" }, { @@ -23416,17 +23416,17 @@ }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.125, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8125, "text": "" }, { @@ -23441,52 +23441,52 @@ }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.375, + "logprob": -12.390625, "text": "" }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.3515625, "text": "" }, { "id": 32000, - "logprob": -12.015625, + "logprob": -12.0078125, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.25, "text": "" }, { @@ -23496,17 +23496,17 @@ }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -12.4765625, + "logprob": -12.4921875, "text": "" }, { @@ -23516,72 +23516,12 @@ }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -14.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1328125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.890625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.90625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -15.0, - "text": "" - }, - { - "id": 32000, - "logprob": -12.328125, + "logprob": -15.0625, "text": "" }, { @@ -23591,62 +23531,72 @@ }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -14.4453125, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -11.1015625, + "logprob": -14.828125, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -13.5546875, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -14.96875, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -13.9296875, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -10.5859375, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -15.0078125, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -12.3359375, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -12.09375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.578125, "text": "" }, { @@ -23656,37 +23606,87 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -11.1171875, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -11.8046875, "text": "" }, { "id": 32000, - "logprob": -10.8359375, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -10.7109375, + "logprob": -11.4609375, "text": "" }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -11.4609375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8203125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9375, "text": "" }, { @@ -23701,27 +23701,27 @@ }, { "id": 32000, - "logprob": -11.171875, + "logprob": -11.1796875, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.9609375, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.6328125, "text": "" }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.4453125, "text": "" }, { @@ -23736,17 +23736,47 @@ }, { "id": 32000, - "logprob": -11.1171875, + "logprob": -11.09375, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -15.4375, + "logprob": -15.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.484375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, "text": "" }, { @@ -23754,36 +23784,6 @@ "logprob": -11.8515625, "text": "" }, - { - "id": 32000, - "logprob": -11.4765625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0, - "text": "" - }, - { - "id": 32000, - "logprob": -12.21875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.84375, - "text": "" - }, { "id": 32000, "logprob": -11.0625, @@ -23791,27 +23791,27 @@ }, { "id": 32000, - "logprob": -13.8828125, + "logprob": -13.953125, "text": "" }, { "id": 32000, - "logprob": -13.5390625, + "logprob": -13.5625, "text": "" }, { "id": 32000, - "logprob": -13.203125, + "logprob": -13.1796875, "text": "" }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5859375, "text": "" }, { "id": 32000, - "logprob": -13.984375, + "logprob": -14.0234375, "text": "" }, { @@ -23821,22 +23821,22 @@ }, { "id": 32000, - "logprob": -14.1328125, + "logprob": -14.1484375, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.234375, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.3515625, "text": "" }, { @@ -23846,7 +23846,7 @@ }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.90625, "text": "" }, { @@ -23856,7 +23856,7 @@ }, { "id": 32000, - "logprob": -11.578125, + "logprob": -11.5703125, "text": "" }, { @@ -23866,7 +23866,7 @@ }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.6875, "text": "" }, { @@ -23876,12 +23876,12 @@ }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.8125, "text": "" }, { @@ -23901,12 +23901,12 @@ }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.46875, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.0703125, "text": "" }, { @@ -23936,7 +23936,7 @@ }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.9765625, "text": "" }, { @@ -23946,32 +23946,32 @@ }, { "id": 32000, - "logprob": -10.8828125, + "logprob": -10.890625, "text": "" }, { "id": 32000, - "logprob": -11.015625, + "logprob": -11.0078125, "text": "" }, { "id": 32000, - "logprob": -10.6015625, + "logprob": -10.609375, "text": "" }, { "id": 32000, - "logprob": -10.7890625, + "logprob": -10.796875, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.59375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { @@ -23981,12 +23981,12 @@ }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.5390625, "text": "" }, { @@ -23996,12 +23996,12 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -10.8515625, + "logprob": -10.9375, "text": "" }, { @@ -24011,117 +24011,52 @@ }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9375, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -12.0, "text": "" }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.8125, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.3671875, "text": "" }, { "id": 32000, - "logprob": -9.609375, + "logprob": -9.625, "text": "" }, { "id": 32000, - "logprob": -10.859375, + "logprob": -10.8515625, "text": "" }, { "id": 32000, - "logprob": -13.171875, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -13.015625, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.3359375, "text": "" }, { "id": 32000, - "logprob": -14.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5390625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.671875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.90625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.15625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.703125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8515625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2890625, + "logprob": -14.015625, "text": "" }, { @@ -24131,12 +24066,22 @@ }, { "id": 32000, - "logprob": -12.875, + "logprob": -13.4453125, "text": "" }, { "id": 32000, - "logprob": -15.6875, + "logprob": -13.5, + "text": "" + }, + { + "id": 32000, + "logprob": -12.71875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.90625, "text": "" }, { @@ -24146,12 +24091,67 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.75, + "text": "" + }, + { + "id": 32000, + "logprob": -12.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.296875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8671875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.140625, "text": "" }, { @@ -24161,12 +24161,12 @@ }, { "id": 32000, - "logprob": -11.2421875, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -12.3203125, + "logprob": -12.328125, "text": "" }, { @@ -24181,12 +24181,12 @@ }, { "id": 32000, - "logprob": -10.9296875, + "logprob": -10.921875, "text": "" }, { "id": 32000, - "logprob": -10.84375, + "logprob": -10.8515625, "text": "" }, { @@ -24196,47 +24196,47 @@ }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.40625, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -10.8125, + "logprob": -10.8046875, "text": "" }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.6484375, "text": "" }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.5078125, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -12.5546875, + "logprob": -12.5703125, "text": "" }, { @@ -24251,7 +24251,7 @@ }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.734375, "text": "" }, { @@ -24266,47 +24266,47 @@ }, { "id": 32000, - "logprob": -12.3515625, + "logprob": -12.34375, "text": "" }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -14.203125, + "logprob": -14.1796875, "text": "" }, { "id": 32000, - "logprob": -14.828125, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -18.140625, + "logprob": -18.046875, "text": "" }, { "id": 32000, - "logprob": -15.59375, + "logprob": -12.34375, "text": "" }, { "id": 32000, - "logprob": -14.515625, + "logprob": -14.4453125, "text": "" }, { "id": 32000, - "logprob": -10.6015625, + "logprob": -10.625, "text": "" }, { "id": 32000, - "logprob": 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32000, - "logprob": -15.171875, + "logprob": -15.2421875, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1171875, "text": "" }, { @@ -24401,7 +24401,7 @@ }, { "id": 32000, - "logprob": -11.6875, + "logprob": -11.671875, "text": "" }, { @@ -24421,12 +24421,12 @@ }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.734375, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.609375, "text": "" }, { @@ -24436,12 +24436,12 @@ }, { "id": 32000, - "logprob": -11.484375, + "logprob": -11.5078125, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2109375, "text": "" }, { @@ -24456,12 +24456,12 @@ }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.96875, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9765625, "text": "" }, { @@ -24481,87 +24481,87 @@ }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.1171875, 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32000, - "logprob": -14.140625, + "logprob": -14.1171875, "text": "" }, { @@ -24636,47 +24636,12 @@ }, { "id": 32000, - "logprob": -12.484375, + "logprob": -12.5, "text": "" }, { "id": 32000, - "logprob": -10.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3203125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.359375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2578125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1953125, + "logprob": -10.6015625, "text": "" }, { @@ -24686,207 +24651,7 @@ }, { "id": 32000, - "logprob": -11.9140625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6484375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.109375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.140625, - "text": "" - }, - { - "id": 32000, - 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"logprob": -13.46875, + "logprob": -13.515625, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -11.4921875, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.8203125, "text": "" }, { @@ -24986,12 +24986,12 @@ }, { "id": 32000, - "logprob": -13.9921875, + "logprob": -14.0078125, "text": "" }, { "id": 32000, - "logprob": -15.171875, + "logprob": -15.21875, "text": "" }, { @@ -25001,27 +25001,27 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -12.2890625, + "logprob": -12.296875, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.515625, "text": "" }, { @@ -25031,42 +25031,42 @@ }, { "id": 32000, - "logprob": -12.578125, + "logprob": -12.640625, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.4609375, "text": "" }, { "id": 32000, - "logprob": -14.796875, + "logprob": -14.84375, "text": "" }, { "id": 32000, - "logprob": -13.9140625, + "logprob": -13.953125, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -11.6171875, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.765625, "text": "" }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.5390625, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4453125, "text": "" }, { @@ -25076,22 +25076,22 @@ }, { "id": 32000, - "logprob": -11.984375, + "logprob": -11.96875, "text": "" }, { "id": 32000, - "logprob": -12.5703125, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -15.8984375, + "logprob": -15.890625, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.953125, "text": "" }, { @@ -25106,17 +25106,17 @@ }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.1875, "text": "" }, { "id": 32000, - "logprob": -12.1796875, + "logprob": -12.171875, "text": "" }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -12.125, "text": "" }, { @@ -25126,27 +25126,27 @@ }, { "id": 32000, - "logprob": -13.125, + "logprob": -13.140625, "text": "" }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -12.1484375, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -13.9609375, + "logprob": -13.9453125, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.2890625, "text": "" }, { @@ -25156,7 +25156,7 @@ }, { "id": 32000, - "logprob": -10.7421875, + "logprob": -10.734375, "text": "" }, { @@ -25166,32 +25166,32 @@ }, { "id": 32000, - "logprob": -12.6796875, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -12.4765625, + "logprob": 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"logprob": -13.0859375, + "logprob": -13.0703125, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.03125, "text": "" }, { @@ -25456,7 +25456,7 @@ }, { "id": 32000, - "logprob": -10.5859375, + "logprob": -10.59375, "text": "" }, { @@ -25466,22 +25466,22 @@ }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.453125, "text": "" }, { "id": 32000, - "logprob": -11.203125, + "logprob": -11.2109375, "text": "" }, { "id": 32000, - "logprob": -13.0390625, + "logprob": -13.0859375, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1796875, "text": "" }, { @@ -25491,52 +25491,57 @@ }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.984375, "text": "" }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.6328125, "text": "" }, { "id": 32000, - "logprob": -10.171875, + "logprob": -10.109375, "text": "" }, { "id": 32000, - "logprob": -11.859375, + "logprob": -11.8671875, "text": "" }, { "id": 32000, - "logprob": -13.09375, + "logprob": 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+25681,37 @@ }, { "id": 32000, - "logprob": -13.5546875, + "logprob": -13.5234375, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -11.9296875, + "logprob": -11.9375, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -12.171875, "text": "" }, { "id": 32000, - "logprob": -10.7421875, + "logprob": -10.734375, "text": "" }, { "id": 32000, - "logprob": -10.8046875, + "logprob": -10.8203125, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.390625, "text": "" }, { @@ -25721,7 +25721,7 @@ }, { "id": 32000, - "logprob": -15.8203125, + "logprob": -15.84375, "text": "" }, { @@ -25731,22 +25731,22 @@ }, { "id": 32000, - "logprob": -12.265625, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -13.1875, + "logprob": -13.1796875, "text": "" }, { "id": 32000, - "logprob": -16.796875, + "logprob": -16.8125, "text": "" }, { "id": 32000, - "logprob": -15.7109375, + "logprob": 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"logprob": -11.1015625, + "logprob": -11.0859375, "text": "" }, { "id": 32000, - "logprob": -11.65625, + "logprob": -11.671875, "text": "" }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.2109375, "text": "" }, { @@ -25926,7 +25926,7 @@ }, { "id": 32000, - "logprob": -12.234375, + "logprob": -12.2578125, "text": "" }, { @@ -25936,12 +25936,12 @@ }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -13.8125, + "logprob": -13.8203125, "text": "" }, { @@ -25956,42 +25956,42 @@ }, { "id": 32000, - "logprob": -10.953125, + "logprob": -10.984375, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -10.8046875, + "logprob": -10.8203125, "text": "" }, { "id": 32000, - "logprob": -12.6171875, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -13.125, + "logprob": 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- "text": "" - }, - { - "id": 32000, - "logprob": -12.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7265625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2578125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.2578125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.84375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6953125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.640625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3359375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0703125, + "logprob": -13.21875, "text": "" }, { @@ -26126,17 +26056,87 @@ }, { "id": 32000, - "logprob": -12.6875, + "logprob": -15.09375, "text": "" }, { "id": 32000, - "logprob": -10.75, + "logprob": -15.8515625, "text": "" }, { "id": 32000, - "logprob": -11.40625, + "logprob": -12.8203125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.703125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5703125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.59375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.53125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7109375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4140625, "text": "" }, { @@ -26156,12 +26156,12 @@ }, { "id": 32000, - "logprob": -13.171875, + "logprob": -13.140625, "text": "" }, { "id": 32000, - "logprob": 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"logprob": -13.2109375, + "logprob": -13.203125, "text": "" }, { "id": 32000, - "logprob": -17.15625, + "logprob": -17.078125, "text": "" }, { "id": 32000, - "logprob": -15.078125, + "logprob": -15.25, "text": "" }, { @@ -26311,32 +26311,32 @@ }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -16.109375, + "logprob": -16.0625, "text": "" }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -14.6796875, + "logprob": -14.5859375, "text": "" }, { @@ -26351,37 +26351,37 @@ }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -13.015625, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -12.8984375, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -12.0, + "logprob": -11.9921875, "text": "" }, { "id": 32000, - "logprob": -13.84375, + "logprob": -13.796875, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.7578125, "text": "" }, { @@ -26391,67 +26391,67 @@ }, { "id": 32000, - "logprob": -14.265625, + "logprob": -14.328125, "text": "" }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -13.8515625, + "logprob": -13.84375, "text": "" }, { "id": 32000, - "logprob": -13.9921875, + "logprob": -14.2109375, "text": "" }, { "id": 32000, - "logprob": -12.375, + "logprob": -12.390625, "text": "" }, { "id": 32000, - "logprob": -13.9296875, + "logprob": -13.953125, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -14.71875, + "logprob": -14.6796875, "text": "" }, { "id": 32000, - 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32000, - "logprob": -11.921875, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.7421875, "text": "" }, { "id": 32000, - "logprob": -11.9921875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -14.734375, + "logprob": -14.71875, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -11.578125, "text": "" }, { @@ -26641,7 +26641,7 @@ }, { "id": 32000, - "logprob": -12.7578125, + "logprob": -12.796875, "text": "" }, { @@ -26651,12 +26651,12 @@ }, { "id": 32000, - "logprob": -16.390625, + "logprob": -16.46875, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9296875, "text": "" }, { @@ -26666,7 +26666,7 @@ }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.8515625, "text": "" }, { @@ -26676,22 +26676,22 @@ }, { "id": 32000, - "logprob": -16.515625, + "logprob": -16.53125, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -13.3671875, + "logprob": -13.359375, "text": "" }, { "id": 32000, - "logprob": -14.0, + "logprob": -13.96875, "text": "" }, { @@ -26701,22 +26701,22 @@ }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.5078125, "text": "" }, { @@ -26736,77 +26736,77 @@ }, { "id": 32000, - "logprob": -14.0390625, + "logprob": -14.0546875, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -17.1875, + "logprob": -17.21875, "text": "" }, { "id": 32000, - "logprob": -13.171875, + "logprob": -13.1640625, "text": "" }, { "id": 32000, - "logprob": -13.359375, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -14.6015625, + "logprob": -14.6171875, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9609375, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -15.0234375, + "logprob": -15.03125, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -15.921875, + "logprob": -15.890625, "text": "" }, { "id": 32000, - "logprob": -10.859375, + "logprob": -10.8515625, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.828125, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.640625, "text": "" }, { @@ -26816,27 +26816,27 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.7578125, "text": "" }, { "id": 32000, - "logprob": -14.7421875, + "logprob": -14.7578125, "text": "" }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.5, "text": "" }, { "id": 32000, - "logprob": -11.265625, + "logprob": -11.2578125, "text": "" }, { @@ -26856,7 +26856,37 @@ }, { "id": 32000, - "logprob": -12.953125, + "logprob": -12.9453125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.1171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -17.015625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9140625, "text": "" }, { @@ -26864,36 +26894,6 @@ "logprob": -13.0390625, "text": "" }, - { - "id": 32000, - "logprob": -12.0234375, - "text": "" - }, - { - "id": 32000, - "logprob": -17.03125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6171875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1328125, - "text": "" - }, { "id": 32000, "logprob": -11.84375, @@ -26901,12 +26901,12 @@ }, { "id": 32000, - "logprob": -15.25, + "logprob": -15.265625, "text": "" }, { "id": 32000, - "logprob": -16.96875, + "logprob": -16.984375, "text": "" }, { @@ -26916,17 +26916,17 @@ }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -11.453125, "text": "" }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.8046875, "text": "" }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.140625, "text": "" }, { @@ -26936,77 +26936,77 @@ }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -17.953125, + "logprob": -17.984375, "text": "" }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -12.421875, "text": "" }, { "id": 32000, - "logprob": -14.6796875, + "logprob": -14.703125, "text": "" }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.8046875, "text": "" }, { "id": 32000, - 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"text": "" - }, - { - "id": 32000, - "logprob": -12.2265625, - "text": "" - }, - { - "id": 32000, - "logprob": -17.5625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.640625, - "text": "" - }, - { - "id": 32000, - "logprob": -15.1015625, - "text": "" - }, - { - "id": 32000, - "logprob": -16.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3203125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3046875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.34375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1328125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.7734375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1328125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.421875, + "logprob": -12.28125, "text": "" }, { @@ -27131,47 +27036,7 @@ }, { "id": 32000, - "logprob": -12.484375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.578125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1015625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0703125, + "logprob": -17.5625, "text": "" }, { @@ -27181,7 +27046,142 @@ }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -15.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -16.75, + "text": "" + }, + { + "id": 32000, + "logprob": -11.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.328125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3046875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3515625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4609375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.546875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.765625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4765625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.09375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9609375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.96875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6328125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.609375, "text": "" }, { @@ -27191,17 +27191,17 @@ }, { "id": 32000, - "logprob": -15.6171875, + "logprob": -15.640625, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -14.25, + "logprob": -14.265625, "text": "" }, { @@ -27216,17 +27216,17 @@ }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.828125, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { @@ -27236,7 +27236,7 @@ }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3203125, "text": "" }, { @@ -27246,37 +27246,37 @@ }, { "id": 32000, - "logprob": -14.2265625, + "logprob": -14.3046875, "text": "" }, { "id": 32000, - "logprob": -14.890625, + "logprob": -14.90625, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8046875, "text": "" }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5390625, "text": "" }, { "id": 32000, - "logprob": -19.421875, + "logprob": -19.4375, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.6796875, "text": "" }, { @@ -27291,7 +27291,7 @@ }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2109375, "text": "" }, { @@ -27301,27 +27301,27 @@ }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.328125, "text": "" }, { "id": 32000, - "logprob": -17.65625, + "logprob": -17.625, "text": "" }, { "id": 32000, - "logprob": -17.703125, + "logprob": -17.75, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -12.015625, + "logprob": -12.0234375, "text": "" }, { @@ -27331,7 +27331,7 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.5859375, "text": "" }, { @@ -27341,37 +27341,37 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -13.5, + "logprob": -13.4921875, "text": "" }, { "id": 32000, - "logprob": -10.5625, + "logprob": -10.5703125, "text": "" }, { "id": 32000, - "logprob": -12.28125, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -13.2734375, + "logprob": -13.3046875, "text": "" }, { "id": 32000, - "logprob": -14.34375, + "logprob": -14.3359375, "text": "" }, { @@ -27386,17 +27386,17 @@ }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -14.859375, + "logprob": -14.9375, "text": "" }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0390625, "text": "" }, { @@ -27421,27 +27421,27 @@ }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": -11.8984375, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -15.0703125, + "logprob": -15.0546875, "text": "" }, { @@ -27456,17 +27456,17 @@ }, { "id": 32000, - "logprob": -16.859375, + "logprob": -16.84375, "text": "" }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -14.8515625, + "logprob": -14.9296875, "text": "" }, { @@ -27476,12 +27476,12 @@ }, { "id": 32000, - "logprob": -12.7578125, + "logprob": -12.765625, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.984375, "text": "" }, { @@ -27491,12 +27491,12 @@ }, { "id": 32000, - "logprob": -12.6796875, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -14.4921875, + "logprob": -14.515625, "text": "" }, { @@ -27506,17 +27506,17 @@ }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.5625, "text": "" }, { "id": 32000, - "logprob": -14.859375, + "logprob": -14.8125, "text": "" }, { @@ -27526,12 +27526,12 @@ }, { "id": 32000, - "logprob": -12.4375, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6484375, "text": "" }, { @@ -27541,12 +27541,12 @@ }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1484375, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.8671875, "text": "" }, { @@ -27561,27 +27561,27 @@ }, { "id": 32000, - "logprob": -12.4453125, + "logprob": -12.453125, "text": "" }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.1484375, "text": "" }, { "id": 32000, - "logprob": -16.9375, + "logprob": -16.890625, "text": "" }, { "id": 32000, - "logprob": -12.890625, + "logprob": -12.84375, "text": "" }, { @@ -27591,12 +27591,12 @@ }, { "id": 32000, - "logprob": -14.6484375, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -10.9296875, + "logprob": -10.9375, "text": "" }, { @@ -27606,7 +27606,7 @@ }, { "id": 32000, - "logprob": -17.15625, + "logprob": -17.6875, "text": "" }, { @@ -27621,17 +27621,17 @@ }, { "id": 32000, - "logprob": -11.28125, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -11.2109375, + "logprob": -11.203125, "text": "" }, { @@ -27646,17 +27646,17 @@ }, { "id": 32000, - "logprob": -14.3046875, + "logprob": -14.3125, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -13.1875, + "logprob": -13.203125, "text": "" }, { @@ -27666,22 +27666,22 @@ }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.8515625, "text": "" }, { "id": 32000, - "logprob": -11.0703125, + "logprob": -11.078125, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -12.578125, + "logprob": -12.6015625, "text": "" }, { @@ -27691,12 +27691,12 @@ }, { "id": 32000, - "logprob": -12.71875, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -13.4453125, + "logprob": -13.46875, "text": "" }, { @@ -27706,22 +27706,22 @@ }, { "id": 32000, - "logprob": -12.53125, + "logprob": -12.5703125, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.03125, "text": "" }, { "id": 32000, - "logprob": -16.53125, + "logprob": -16.46875, "text": "" }, { "id": 32000, - "logprob": -13.1484375, + "logprob": -13.140625, "text": "" }, { @@ -27731,17 +27731,17 @@ }, { "id": 32000, - "logprob": -14.171875, + "logprob": -14.109375, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -14.3359375, + "logprob": -14.34375, "text": "" }, { @@ -27751,47 +27751,47 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -12.890625, + "logprob": -12.8984375, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -11.609375, + "logprob": -11.625, "text": "" }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.1484375, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -14.71875, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.625, "text": "" }, { @@ -27806,12 +27806,12 @@ }, { "id": 32000, - "logprob": -15.0, + "logprob": -14.6484375, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -13.546875, "text": "" }, { @@ -27821,7 +27821,7 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { @@ -27836,7 +27836,7 @@ }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9375, "text": "" }, { @@ -27846,12 +27846,12 @@ }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.921875, "text": "" }, { "id": 32000, - "logprob": -13.4453125, + "logprob": -13.46875, "text": "" }, { @@ -27861,17 +27861,17 @@ }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -12.421875, "text": "" }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.7734375, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1640625, "text": "" }, { @@ -27881,22 +27881,22 @@ }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.75, "text": "" }, { @@ -27906,7 +27906,7 @@ }, { "id": 32000, - "logprob": -16.40625, + "logprob": -16.390625, "text": "" }, { @@ -27916,17 +27916,17 @@ }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -13.921875, + "logprob": -13.8984375, "text": "" }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.9921875, "text": "" }, { @@ -27936,27 +27936,27 @@ }, { "id": 32000, - "logprob": -12.0703125, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -16.03125, + "logprob": -16.046875, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.6484375, "text": "" }, { "id": 32000, - "logprob": -15.3125, + "logprob": -15.3203125, "text": "" }, { @@ -27966,17 +27966,17 @@ }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -12.390625, + "logprob": -12.3984375, "text": "" }, { "id": 32000, - "logprob": -14.484375, + "logprob": -15.4765625, "text": "" }, { @@ -27986,37 +27986,12 @@ }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5859375, "text": "" }, { "id": 32000, - "logprob": -11.6328125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1171875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -15.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -17.640625, + "logprob": -11.5859375, "text": "" }, { @@ -28024,6 +27999,31 @@ "logprob": -12.1015625, "text": "" }, + { + "id": 32000, + "logprob": -12.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -17.65625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.09375, + "text": "" + }, { "id": 32000, "logprob": -11.84375, @@ -28031,17 +28031,17 @@ }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5546875, "text": "" }, { "id": 32000, - "logprob": -13.578125, + "logprob": -13.59375, "text": "" }, { @@ -28051,17 +28051,17 @@ }, { "id": 32000, - "logprob": -16.703125, + "logprob": -16.6875, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.03125, "text": "" }, { "id": 32000, - "logprob": -14.90625, + "logprob": -14.859375, "text": "" }, { @@ -28071,42 +28071,42 @@ }, { "id": 32000, - "logprob": -15.21875, + "logprob": -15.203125, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.4140625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -17.0, + "logprob": -16.8125, "text": "" }, { "id": 32000, - "logprob": -13.046875, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -14.734375, + "logprob": -14.8671875, "text": "" }, { "id": 32000, - "logprob": -12.125, + "logprob": -12.1171875, "text": "" }, { @@ -28121,7 +28121,7 @@ }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.359375, "text": "" }, { @@ -28131,7 +28131,7 @@ }, { "id": 32000, - "logprob": -13.75, + "logprob": -13.765625, "text": "" }, { @@ -28146,22 +28146,22 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.6015625, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.703125, "text": "" }, { "id": 32000, - "logprob": -11.390625, + "logprob": -11.40625, "text": "" }, { @@ -28171,17 +28171,17 @@ }, { "id": 32000, - "logprob": -11.8125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.9375, + "logprob": -12.921875, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6875, "text": "" }, { @@ -28191,47 +28191,47 @@ }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9609375, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -12.40625, + "logprob": -12.4140625, "text": "" }, { "id": 32000, - "logprob": -18.71875, + "logprob": -18.703125, "text": "" }, { "id": 32000, - "logprob": -15.59375, + "logprob": -15.6484375, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -13.6875, + "logprob": -13.703125, "text": "" }, { "id": 32000, - "logprob": -15.0703125, + "logprob": -15.0859375, "text": "" }, { @@ -28241,12 +28241,12 @@ }, { "id": 32000, - "logprob": -14.53125, + "logprob": -14.5234375, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3671875, "text": "" }, { @@ -28256,7 +28256,7 @@ }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.640625, "text": "" }, { @@ -28266,37 +28266,37 @@ }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.1015625, "text": "" }, { "id": 32000, - "logprob": -13.1640625, + "logprob": -13.3515625, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -11.2890625, + "logprob": -11.25, "text": "" }, { "id": 32000, - "logprob": -14.125, + "logprob": -14.15625, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -13.9140625, + "logprob": -13.90625, "text": "" }, { @@ -28311,22 +28311,22 @@ }, { "id": 32000, - "logprob": -10.53125, + "logprob": -10.5625, "text": "" }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -12.171875, "text": "" }, { "id": 32000, - "logprob": -12.234375, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9296875, "text": "" }, { @@ -28336,7 +28336,7 @@ }, { "id": 32000, - "logprob": -12.8828125, + "logprob": -12.875, "text": "" }, { @@ -28351,17 +28351,17 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.109375, "text": "" }, { @@ -28371,22 +28371,22 @@ }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.5078125, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9140625, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.2421875, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { @@ -28396,87 +28396,87 @@ }, { "id": 32000, - "logprob": -16.6875, + "logprob": -16.671875, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -15.0234375, + "logprob": -14.9453125, "text": "" }, { "id": 32000, - "logprob": -16.578125, + "logprob": -16.5625, "text": "" }, { "id": 32000, - "logprob": -15.1875, + "logprob": -15.203125, "text": "" }, { "id": 32000, - "logprob": -14.7109375, + "logprob": -14.6953125, "text": "" }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -12.2109375, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.34375, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -13.8046875, + "logprob": -13.796875, "text": "" }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.1640625, "text": "" }, { "id": 32000, - "logprob": -15.3125, + "logprob": -15.484375, "text": "" }, { "id": 32000, - "logprob": -14.6171875, + "logprob": -14.6328125, "text": "" }, { @@ -28486,17 +28486,17 @@ }, { "id": 32000, - "logprob": -13.7265625, + "logprob": -13.75, "text": "" }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.3125, "text": "" }, { @@ -28506,62 +28506,62 @@ }, { "id": 32000, - "logprob": -13.4765625, + "logprob": -13.484375, "text": "" }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.2734375, "text": "" }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -11.3984375, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -16.5, + "logprob": -16.53125, "text": "" }, { "id": 32000, - "logprob": -14.1171875, + "logprob": -14.1484375, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.2265625, "text": "" }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.8125, "text": "" }, { "id": 32000, - "logprob": -15.078125, + "logprob": -15.03125, "text": "" }, { "id": 32000, - "logprob": -15.4609375, + "logprob": -15.484375, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.6015625, "text": "" }, { @@ -28576,7 +28576,7 @@ }, { "id": 32000, - "logprob": -11.15625, + "logprob": -11.1640625, "text": "" }, { @@ -28586,82 +28586,82 @@ }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.5625, "text": "" }, { "id": 32000, - "logprob": -12.765625, + "logprob": -12.7421875, "text": "" }, { "id": 32000, - "logprob": -18.015625, + "logprob": -18.0, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.625, "text": "" }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.921875, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -15.1015625, + "logprob": -15.3046875, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1875, "text": "" }, { "id": 32000, - "logprob": -12.75, + "logprob": -12.7578125, "text": "" }, { "id": 32000, - "logprob": -15.1640625, + "logprob": -15.125, "text": "" }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.9140625, "text": "" }, { "id": 32000, - "logprob": -15.4453125, + "logprob": -15.546875, "text": "" }, { "id": 32000, - "logprob": -16.03125, + "logprob": -16.015625, "text": "" }, { "id": 32000, - "logprob": -14.125, + "logprob": -14.109375, "text": "" }, { @@ -28671,12 +28671,12 @@ }, { "id": 32000, - "logprob": -13.1484375, + "logprob": -13.15625, "text": "" }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.734375, "text": "" }, { @@ -28696,7 +28696,7 @@ }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.7421875, "text": "" }, { @@ -28706,12 +28706,12 @@ }, { "id": 32000, - "logprob": -15.3046875, + "logprob": -15.296875, "text": "" }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.6015625, "text": "" }, { @@ -28721,12 +28721,12 @@ }, { "id": 32000, - "logprob": -13.4921875, + "logprob": -13.5, "text": "" }, { "id": 32000, - "logprob": -10.8984375, + "logprob": -10.890625, "text": "" }, { @@ -28736,22 +28736,22 @@ }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.1328125, "text": "" }, { "id": 32000, - "logprob": -13.1015625, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -12.6171875, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -13.359375, + "logprob": -13.3359375, "text": "" }, { @@ -28766,127 +28766,17 @@ }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.796875, "text": "" }, { "id": 32000, - "logprob": -13.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5859375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.5234375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.4375, - "text": "" - }, - { - "id": 32000, - "logprob": -19.6875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.5234375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6171875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.359375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8671875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.046875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.7109375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.75, - "text": "" - }, - { - "id": 32000, - "logprob": -12.296875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.609375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.7109375, - "text": "" - }, - { - "id": 32000, - "logprob": -16.1875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.671875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5, - "text": "" - }, - { - "id": 32000, - "logprob": -15.265625, + "logprob": -13.4296875, "text": "" }, { @@ -28896,32 +28786,142 @@ }, { "id": 32000, - "logprob": -14.59375, + "logprob": -11.0859375, "text": "" }, { "id": 32000, - "logprob": -15.34375, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -14.0703125, + 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"text": "" + }, + { + "id": 32000, + "logprob": -11.703125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.4921875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5546875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.5703125, + "text": "" + }, + { + "id": 32000, + "logprob": -15.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.046875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.96875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3828125, "text": "" }, { @@ -28931,17 +28931,17 @@ }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.2265625, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.6171875, "text": "" }, { @@ -28956,17 +28956,17 @@ }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -14.96875, + "logprob": -15.0703125, "text": "" }, { "id": 32000, - "logprob": -14.34375, + "logprob": -14.375, "text": "" }, { @@ -28976,7 +28976,7 @@ }, { "id": 32000, - "logprob": -13.203125, + "logprob": -13.1796875, "text": "" }, { @@ -28986,17 +28986,17 @@ }, { "id": 32000, - "logprob": -15.828125, + "logprob": -15.796875, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.640625, + "logprob": -14.0390625, "text": "" }, { @@ -29006,12 +29006,12 @@ }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.2890625, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.671875, "text": "" }, { @@ -29021,22 +29021,22 @@ }, { "id": 32000, - "logprob": -16.34375, + "logprob": -16.375, "text": "" }, { "id": 32000, - "logprob": -13.8671875, + "logprob": -13.8828125, "text": "" }, { "id": 32000, - "logprob": -14.6484375, + "logprob": -14.6171875, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.625, "text": "" }, { @@ -29056,72 +29056,72 @@ }, { "id": 32000, - "logprob": -11.6796875, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5625, "text": "" }, { "id": 32000, - "logprob": -11.3515625, + "logprob": -11.34375, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -10.9765625, + "logprob": -10.984375, "text": "" }, { "id": 32000, - "logprob": -13.21875, + "logprob": -13.2109375, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.265625, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.234375, "text": "" }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.9765625, "text": "" }, { "id": 32000, - "logprob": -12.90625, + "logprob": -12.9296875, "text": "" }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -11.421875, + "logprob": -11.4609375, "text": "" }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.953125, "text": "" }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.6484375, "text": "" }, { @@ -29131,17 +29131,17 @@ }, { "id": 32000, - "logprob": -12.9140625, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -11.7890625, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -12.5625, + "logprob": -12.5703125, "text": "" }, { @@ -29156,17 +29156,17 @@ }, { "id": 32000, - "logprob": -15.28125, + "logprob": -15.1953125, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.09375, "text": "" }, { @@ -29176,47 +29176,47 @@ }, { "id": 32000, - "logprob": -15.5625, + "logprob": -15.59375, "text": "" }, { "id": 32000, - "logprob": -11.6875, + "logprob": -11.671875, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3515625, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.90625, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.609375, "text": "" }, { "id": 32000, - "logprob": -14.6640625, + "logprob": -14.671875, "text": "" }, { "id": 32000, - "logprob": -15.3515625, + "logprob": -15.34375, "text": "" }, { "id": 32000, - "logprob": -15.203125, + "logprob": -15.2265625, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.78125, "text": "" }, { @@ -29226,122 +29226,17 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -11.7890625, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -12.0078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8515625, - "text": "" - }, - { - "id": 32000, - "logprob": -16.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.890625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.84375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.3515625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.015625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5703125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.34375, + "logprob": -12.03125, "text": "" }, { @@ -29351,27 +29246,132 @@ }, { "id": 32000, - "logprob": -13.8203125, + "logprob": -16.515625, "text": "" }, { "id": 32000, - "logprob": -13.34375, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -13.1328125, + "logprob": -12.4609375, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -13.3984375, "text": "" }, { "id": 32000, - "logprob": -15.1484375, + "logprob": -13.171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2734375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.3984375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0078125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3359375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.421875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9765625, + "text": "" + }, + { + "id": 32000, + "logprob": -15.140625, "text": "" }, { @@ -29386,12 +29386,12 @@ }, { "id": 32000, - "logprob": -14.421875, + "logprob": -14.40625, "text": "" }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.046875, "text": "" }, { @@ -29401,52 +29401,52 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5078125, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -13.3828125, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -17.28125, + "logprob": -17.3125, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.5234375, "text": "" }, { "id": 32000, - "logprob": -17.59375, + "logprob": -17.625, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -15.9375, + "logprob": -15.9140625, "text": "" }, { "id": 32000, - "logprob": -16.625, + "logprob": -16.65625, "text": "" }, { @@ -29456,27 +29456,27 @@ }, { "id": 2418, - "logprob": -19.078125, + "logprob": -19.0625, "text": "Can" }, { "id": 368, - "logprob": -0.19665527, + "logprob": -0.19726562, "text": "you" }, { "id": 1912, - "logprob": -1.5009766, + "logprob": -1.4990234, "text": "tell" }, { "id": 528, - "logprob": -0.31054688, + "logprob": -0.31152344, "text": "me" }, { "id": 264, - "logprob": -2.6269531, + "logprob": -2.6367188, "text": "a" }, { @@ -29486,12 +29486,12 @@ }, { "id": 2485, - "logprob": -0.99365234, + "logprob": -0.9941406, "text": "short" }, { "id": 2838, - "logprob": -0.45996094, + "logprob": -0.46118164, "text": "story" }, { @@ -29501,22 +29501,22 @@ }, { "id": 356, - "logprob": -0.029006958, + "logprob": -0.029129028, "text": "on" }, { "id": 272, - "logprob": -0.9897461, + "logprob": -0.9902344, "text": "the" }, { "id": 3469, - "logprob": -0.29125977, + "logprob": -0.29052734, "text": "image" }, { "id": 28804, - "logprob": -0.43017578, + "logprob": -0.43188477, "text": "?" } ], @@ -29524,13 +29524,13 @@ "tokens": [ { "id": 13, - "logprob": -0.007446289, + "logprob": -0.0076828003, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.20129395, + "logprob": -0.19958496, "special": false, "text": "\n" }, @@ -29542,43 +29542,43 @@ }, { "id": 3714, - "logprob": -0.20825195, + "logprob": -0.20861816, "special": false, "text": " upon" }, { "id": 264, - "logprob": -0.0017786026, + "logprob": -0.0017719269, "special": false, "text": " a" }, { "id": 727, - "logprob": -0.011955261, + "logprob": -0.011749268, "special": false, "text": " time" }, { "id": 28725, - "logprob": -0.17297363, + "logprob": -0.17529297, "special": false, "text": "," }, { "id": 736, - "logprob": -0.91015625, + "logprob": -0.9086914, "special": false, "text": " there" }, { "id": 403, - "logprob": -0.05758667, + "logprob": -0.056732178, "special": false, "text": " was" }, { "id": 264, - "logprob": -0.009544373, + "logprob": -0.00970459, "special": false, "text": " a" } @@ -29620,7 +29620,7 @@ }, { "id": 32000, - "logprob": -15.9140625, + "logprob": -15.7109375, "text": "" }, { @@ -29630,37 +29630,37 @@ }, { "id": 32000, - "logprob": -10.03125, + "logprob": -10.0234375, "text": "" }, { "id": 32000, - "logprob": -10.140625, + "logprob": -10.1328125, "text": "" }, { "id": 32000, - "logprob": -10.296875, + "logprob": -10.421875, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -16.0625, + "logprob": -16.59375, "text": "" }, { "id": 32000, - "logprob": -13.8515625, + "logprob": -13.828125, "text": "" }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.390625, "text": "" }, { @@ -29670,7 +29670,7 @@ }, { "id": 32000, - "logprob": -10.171875, + "logprob": -10.1640625, "text": "" }, { @@ -29680,22 +29680,22 @@ }, { "id": 32000, - "logprob": -10.359375, + "logprob": -10.3984375, "text": "" }, { "id": 32000, - "logprob": -14.953125, + "logprob": -15.015625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.0859375, "text": "" }, { "id": 32000, - "logprob": -13.3046875, + "logprob": -13.2734375, "text": "" }, { @@ -29705,67 +29705,37 @@ }, { "id": 32000, - "logprob": -11.0546875, + "logprob": -11.0390625, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9140625, "text": "" }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.5234375, "text": "" }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.4765625, "text": "" }, { "id": 32000, - "logprob": -14.2890625, + "logprob": -14.2421875, "text": "" }, { "id": 32000, - "logprob": -13.625, + "logprob": -13.6171875, "text": "" }, { "id": 32000, - "logprob": -11.21875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -17.03125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.6484375, - "text": "" - }, - { - "id": 32000, - "logprob": -18.265625, + "logprob": -11.359375, "text": "" }, { @@ -29775,17 +29745,47 @@ }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -17.34375, "text": "" }, { "id": 32000, - "logprob": -9.8671875, + "logprob": -10.3984375, "text": "" }, { "id": 32000, - "logprob": -10.765625, + "logprob": -10.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.640625, + "text": "" + }, + { + "id": 32000, + "logprob": -18.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -9.875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7734375, "text": "" }, { @@ -29795,12 +29795,12 @@ }, { "id": 32000, - "logprob": -10.984375, + "logprob": -10.96875, "text": "" }, { "id": 32000, - "logprob": -10.6171875, + "logprob": -10.609375, "text": "" }, { @@ -29810,17 +29810,17 @@ }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.5078125, "text": "" }, { "id": 32000, - "logprob": -11.296875, + "logprob": -11.4453125, "text": "" }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.59375, "text": "" }, { @@ -29835,27 +29835,27 @@ }, { "id": 32000, - "logprob": -10.6796875, + "logprob": -10.640625, "text": "" }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.9765625, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -12.390625, + "logprob": -12.3671875, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -12.0234375, "text": "" }, { @@ -29870,137 +29870,12 @@ }, { "id": 32000, - "logprob": -11.21875, + "logprob": -11.0625, "text": "" }, { "id": 32000, - "logprob": -10.390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.1875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0234375, - "text": "" - }, - { - "id": 32000, - "logprob": -18.3125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.9921875, - "text": "" - }, - { - "id": 32000, - "logprob": -15.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.171875, - 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"logprob": -14.09375, "text": "" }, { @@ -30390,22 +30390,22 @@ }, { "id": 32000, - "logprob": -12.828125, + "logprob": -12.84375, "text": "" }, { "id": 32000, - "logprob": -11.2421875, + "logprob": -11.25, "text": "" }, { "id": 32000, - "logprob": -12.3984375, + "logprob": -12.4375, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.671875, "text": "" }, { @@ -30420,37 +30420,12 @@ }, { "id": 32000, - "logprob": -12.2421875, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -10.375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.859375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0234375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2734375, + "logprob": -10.359375, "text": "" }, { @@ -30460,12 +30435,37 @@ }, { "id": 32000, - "logprob": -10.5703125, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -10.71875, + "logprob": -10.875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2421875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7421875, "text": "" }, { @@ -30475,22 +30475,22 @@ }, { "id": 32000, - "logprob": -11.046875, + "logprob": -11.0390625, "text": "" }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -10.9921875, "text": "" }, { "id": 32000, - "logprob": -10.609375, + "logprob": -10.6171875, "text": "" }, { "id": 32000, - "logprob": -12.265625, + "logprob": -12.2421875, "text": "" }, { @@ -30500,7 +30500,7 @@ }, { "id": 32000, - "logprob": -12.2578125, + "logprob": -12.265625, "text": "" }, { @@ -30510,7 +30510,7 @@ }, { "id": 32000, - "logprob": -12.8359375, + "logprob": -12.859375, "text": "" }, { @@ -30520,12 +30520,12 @@ }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.2109375, "text": "" }, { @@ -30535,17 +30535,17 @@ }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.2421875, "text": "" }, { "id": 32000, - "logprob": -11.265625, + "logprob": -11.3046875, "text": "" }, { "id": 32000, - "logprob": -10.3046875, + "logprob": -10.2890625, "text": "" }, { @@ -30555,7 +30555,12 @@ }, { "id": 32000, - "logprob": -11.0234375, + "logprob": -10.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2421875, "text": "" }, { @@ -30565,27 +30570,22 @@ }, { "id": 32000, - "logprob": -11.21875, + "logprob": -11.515625, "text": "" }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.1328125, "text": "" }, { "id": 32000, - "logprob": -11.3515625, + "logprob": -10.5546875, "text": "" }, { "id": 32000, - "logprob": -10.5703125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.3828125, + "logprob": -10.359375, "text": "" }, { @@ -30595,57 +30595,37 @@ }, { "id": 32000, - "logprob": -10.828125, + "logprob": -10.8125, "text": "" }, { "id": 32000, - "logprob": -10.859375, + "logprob": -10.8671875, "text": "" }, { "id": 32000, - "logprob": -11.2734375, + "logprob": -11.1796875, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3125, "text": "" }, { "id": 32000, - "logprob": -11.5859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.53125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.203125, + "logprob": -11.59375, "text": "" }, { @@ -30655,22 +30635,27 @@ }, { "id": 32000, - "logprob": -12.5078125, + "logprob": -13.4375, "text": "" }, { "id": 32000, - "logprob": -10.90625, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -10.6015625, + "logprob": -14.203125, "text": "" }, { "id": 32000, - "logprob": -10.6171875, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4453125, "text": "" }, { @@ -30680,22 +30665,12 @@ }, { "id": 32000, - "logprob": -11.25, + "logprob": -10.59375, "text": "" }, { "id": 32000, - "logprob": -10.9296875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.9765625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.078125, + "logprob": -10.609375, "text": "" }, { @@ -30703,6 +30678,31 @@ "logprob": -10.90625, "text": "" }, + { + "id": 32000, + "logprob": -11.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.921875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9921875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.890625, + "text": "" + }, { "id": 32000, "logprob": -10.8671875, @@ -30720,7 +30720,7 @@ }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9921875, "text": "" }, { @@ -30730,7 +30730,7 @@ }, { "id": 32000, - "logprob": -11.421875, + "logprob": -11.3828125, "text": "" }, { @@ -30740,82 +30740,32 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -13.5703125, + "logprob": -13.546875, "text": "" }, { "id": 32000, - "logprob": -13.9296875, + "logprob": -13.9921875, "text": "" }, { "id": 32000, - "logprob": -14.390625, + "logprob": -14.375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.359375, "text": "" }, { "id": 32000, - "logprob": -13.296875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8828125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6953125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.453125, - "text": "" - }, - { - "id": 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"logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -11.2265625, + "logprob": -11.1875, "text": "" }, { @@ -30910,67 +30910,17 @@ }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -13.1875, + "logprob": -13.1953125, "text": "" }, { "id": 32000, - "logprob": -12.078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.1796875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.671875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.7421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8203125, + "logprob": -12.1328125, 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"" + }, + { + "id": 32000, + "logprob": -13.5546875, "text": "" }, { @@ -31010,32 +31020,22 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -15.0234375, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -12.5703125, "text": "" }, { "id": 32000, - "logprob": -15.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.5546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8671875, + "logprob": -11.859375, "text": "" }, { @@ -31045,27 +31045,27 @@ }, { "id": 32000, - "logprob": -12.3515625, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.5390625, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.4921875, "text": "" }, { @@ -31075,12 +31075,12 @@ }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3125, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.8984375, "text": "" }, { @@ -31095,12 +31095,12 @@ }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -14.0234375, + "logprob": -14.015625, "text": "" }, { @@ -31115,32 +31115,32 @@ }, { "id": 32000, - "logprob": -13.1484375, + "logprob": -13.140625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.9765625, "text": "" }, { "id": 32000, - "logprob": -13.8359375, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -13.7734375, + "logprob": -13.78125, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.359375, "text": "" }, { @@ -31155,12 +31155,12 @@ }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.875, "text": "" }, { "id": 32000, - "logprob": -13.5859375, + "logprob": -13.6015625, "text": "" }, { @@ -31175,12 +31175,12 @@ }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -13.6015625, + "logprob": -13.59375, "text": "" }, { @@ -31190,7 +31190,7 @@ }, { "id": 32000, - "logprob": -14.5859375, + "logprob": -14.5703125, "text": "" }, { @@ -31200,12 +31200,12 @@ }, { "id": 32000, - "logprob": -13.40625, + "logprob": -13.4140625, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -11.7890625, "text": "" }, { @@ -31215,12 +31215,12 @@ }, { "id": 32000, - "logprob": -14.421875, + "logprob": -14.4296875, "text": "" }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.53125, "text": "" }, { @@ -31230,117 +31230,12 @@ }, { "id": 32000, - "logprob": -12.71875, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -11.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1484375, - "text": "" - }, - { - "id": 32000, - "logprob": 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"id": 32000, + "logprob": -13.7734375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0, + "text": "" + }, + { + "id": 32000, + "logprob": -12.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -16.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -15.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7265625, "text": "" }, { @@ -31415,12 +31415,12 @@ }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -10.9765625, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.71875, "text": "" }, { @@ -31435,7 +31435,7 @@ }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.3203125, "text": "" }, { @@ -31445,17 +31445,17 @@ }, { "id": 32000, - "logprob": -14.21875, + "logprob": -14.2265625, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": 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"id": 32000, - "logprob": -11.875, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -12.5390625, + "logprob": -12.5625, "text": "" }, { "id": 32000, - "logprob": -13.4140625, + "logprob": -13.3203125, "text": "" }, { @@ -31645,27 +31645,27 @@ }, { "id": 32000, - "logprob": -10.7421875, + "logprob": -10.875, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.6328125, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.15625, "text": "" }, { "id": 32000, - "logprob": -11.8046875, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -11.9921875, "text": "" }, { @@ -31675,32 +31675,32 @@ }, { "id": 32000, - "logprob": -13.375, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9765625, "text": "" }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -13.359375, + "logprob": -13.3359375, "text": "" }, { @@ -31710,12 +31710,12 @@ }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.984375, "text": "" }, { @@ -31725,7 +31725,7 @@ }, { "id": 32000, - "logprob": -11.421875, + "logprob": -11.4140625, "text": "" }, { @@ -31735,17 +31735,17 @@ }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.421875, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { @@ -31760,12 +31760,12 @@ }, { "id": 32000, - "logprob": -13.96875, + "logprob": -14.0, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.40625, "text": "" }, { @@ -31775,27 +31775,27 @@ }, { "id": 32000, - "logprob": -12.8046875, + "logprob": 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"logprob": -13.1015625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2265625, "text": "" }, { @@ -31915,12 +31920,7 @@ }, { "id": 32000, - "logprob": -13.1875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4765625, + "logprob": -12.4609375, "text": "" }, { @@ -31930,7 +31930,12 @@ }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.671875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.671875, "text": "" }, { @@ -31940,22 +31945,17 @@ }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -15.390625, "text": "" }, { "id": 32000, - "logprob": -15.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9453125, + "logprob": -11.953125, "text": "" }, { @@ -31965,27 +31965,27 @@ }, { "id": 32000, - "logprob": -15.46875, + "logprob": -15.4921875, "text": "" }, { "id": 32000, - "logprob": -13.9375, + "logprob": -13.9296875, "text": "" }, { "id": 32000, - "logprob": 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{ "id": 32000, - "logprob": -13.53125, + "logprob": -13.6015625, "text": "" }, { "id": 32000, - "logprob": -13.1953125, + "logprob": -13.28125, "text": "" }, { "id": 32000, - "logprob": -12.765625, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -12.0703125, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -12.703125, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -11.5, + "logprob": -11.46875, "text": "" }, { "id": 32000, - "logprob": -15.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.09375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0703125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5390625, - "text": "" - }, - { - "id": 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"logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -11.3515625, + "logprob": -11.34375, "text": "" }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0703125, "text": "" }, { @@ -32475,62 +32475,62 @@ }, { "id": 32000, - "logprob": -10.8203125, + "logprob": -10.84375, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -15.4140625, + "logprob": -15.46875, "text": "" }, { "id": 32000, - "logprob": -11.1015625, + "logprob": -11.1484375, "text": "" }, { "id": 32000, - "logprob": -10.5078125, + "logprob": -10.515625, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -11.015625, "text": "" }, { "id": 32000, - "logprob": -11.2890625, + "logprob": -11.28125, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -12.453125, + "logprob": 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"logprob": -11.65625, "text": "" }, { @@ -32600,12 +32600,12 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -14.90625, + "logprob": -14.8359375, "text": "" }, { @@ -32615,7 +32615,7 @@ }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.8984375, "text": "" }, { @@ -32635,82 +32635,82 @@ }, { "id": 32000, - "logprob": -12.0703125, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -15.578125, + "logprob": -15.5390625, "text": "" }, { "id": 32000, - "logprob": -15.0546875, + "logprob": -15.2578125, "text": "" }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.1171875, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2421875, "text": "" }, { "id": 32000, - "logprob": -12.6640625, + "logprob": -12.5, "text": "" }, { "id": 32000, - "logprob": -12.6640625, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -12.421875, + "logprob": -12.4140625, "text": "" }, { "id": 32000, - "logprob": -10.9765625, + "logprob": -11.0, "text": "" }, { "id": 32000, - "logprob": -11.28125, + "logprob": -11.296875, "text": "" }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.28125, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -13.1015625, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -13.6640625, + "logprob": -13.671875, "text": "" }, { "id": 32000, - "logprob": -12.578125, + "logprob": -12.5859375, "text": "" }, { "id": 32000, - "logprob": -12.40625, + "logprob": -12.3828125, "text": "" }, { @@ -32725,27 +32725,27 @@ }, { "id": 32000, - "logprob": -11.8984375, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -13.1796875, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -15.9375, + "logprob": -15.9453125, "text": "" }, { "id": 32000, - "logprob": -13.1640625, + "logprob": -13.125, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.9375, "text": "" }, { @@ -32755,62 +32755,7 @@ }, { "id": 32000, - "logprob": -11.3125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5, - "text": "" - }, - { - "id": 32000, - "logprob": -11.359375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3515625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.390625, - "text": "" - }, - { - "id": 32000, - "logprob": -9.6953125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.7578125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.328125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9765625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1953125, + "logprob": -11.3203125, "text": "" }, { @@ -32820,12 +32765,12 @@ }, { "id": 32000, - "logprob": -12.1875, + "logprob": -11.359375, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.3359375, "text": "" }, { @@ -32835,17 +32780,32 @@ }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -12.359375, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -9.6953125, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -11.765625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9609375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0859375, "text": "" }, { @@ -32855,27 +32815,67 @@ }, { "id": 32000, - "logprob": -12.0234375, + "logprob": -11.4921875, "text": "" }, { "id": 32000, - "logprob": -12.6640625, + "logprob": -12.1953125, "text": "" }, { "id": 32000, - "logprob": -13.078125, + "logprob": -11.9765625, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.84375, + "text": "" + }, + { + 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32000, + "logprob": -13.2265625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.765625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8984375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.90625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.671875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6328125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.609375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7421875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.671875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.046875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.265625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.015625, "text": "" }, { @@ -33125,22 +33125,22 @@ }, { "id": 32000, - "logprob": -14.984375, + "logprob": -15.0234375, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.9453125, "text": "" }, { @@ -33155,27 +33155,27 @@ }, { "id": 32000, - "logprob": -13.9375, + "logprob": -13.9609375, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -13.03125, "text": "" }, { "id": 32000, - "logprob": -16.15625, + "logprob": -16.140625, "text": "" }, { "id": 32000, - "logprob": -13.4375, + "logprob": -13.4609375, "text": "" }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.7265625, "text": "" }, { @@ -33185,12 +33185,12 @@ }, { "id": 32000, - "logprob": -13.25, + "logprob": -13.234375, "text": "" }, { "id": 32000, - "logprob": -13.2421875, + "logprob": -13.2578125, "text": "" }, { @@ -33200,12 +33200,12 @@ }, { "id": 32000, - "logprob": -13.8203125, + "logprob": -13.8359375, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.6171875, "text": "" }, { @@ -33215,12 +33215,12 @@ }, { "id": 32000, - "logprob": -14.4609375, + "logprob": -14.4765625, "text": "" }, { "id": 32000, - "logprob": -13.140625, + "logprob": -13.15625, "text": "" }, { @@ -33230,17 +33230,17 @@ }, { "id": 32000, - "logprob": -14.1015625, + "logprob": -14.046875, "text": "" }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.265625, "text": "" }, { "id": 32000, - "logprob": -12.53125, + "logprob": -12.6328125, "text": "" }, { @@ -33255,42 +33255,42 @@ }, { "id": 32000, - "logprob": -13.03125, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -10.3984375, + "logprob": -10.46875, "text": "" }, { "id": 32000, - "logprob": -13.1484375, + "logprob": -13.1640625, "text": "" }, { "id": 32000, - "logprob": -13.5625, + "logprob": -13.59375, "text": "" }, { "id": 32000, - "logprob": -13.4140625, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -14.09375, + "logprob": -14.140625, "text": "" }, { "id": 32000, - "logprob": -13.5390625, + "logprob": -13.5, "text": "" }, { "id": 32000, - "logprob": -14.1640625, + "logprob": -14.15625, "text": "" }, { @@ -33300,32 +33300,32 @@ }, { "id": 32000, - "logprob": -14.0625, + "logprob": -14.0546875, "text": "" }, { "id": 32000, - "logprob": -14.1484375, + "logprob": -14.1328125, "text": "" }, { "id": 32000, - "logprob": -13.859375, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -13.3359375, + "logprob": -13.34375, "text": "" }, { "id": 32000, - "logprob": -13.0078125, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.40625, "text": "" }, { @@ -33335,7 +33335,7 @@ }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.8125, "text": "" }, { @@ -33350,32 +33350,7 @@ }, { "id": 32000, - "logprob": -11.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.328125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7734375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.859375, + "logprob": -11.46875, "text": "" }, { @@ -33385,32 +33360,57 @@ }, { "id": 32000, - "logprob": -14.171875, + "logprob": -12.34375, "text": "" }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.6171875, "text": "" }, { "id": 32000, - "logprob": -14.5, + "logprob": -12.7578125, "text": "" }, { "id": 32000, - "logprob": -18.125, + "logprob": -14.8359375, "text": "" }, { "id": 32000, - "logprob": -14.6875, + "logprob": -13.0859375, "text": "" }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.5078125, + "text": "" + }, + { + "id": 32000, + "logprob": -18.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.71875, "text": "" }, { @@ -33420,37 +33420,37 @@ }, { "id": 32000, - "logprob": -14.9765625, + "logprob": -14.9921875, "text": "" }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -15.34375, + "logprob": -15.40625, "text": "" }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.4296875, "text": "" }, { "id": 32000, - "logprob": -14.5390625, + "logprob": -14.5859375, "text": "" }, { "id": 32000, - "logprob": -15.265625, + "logprob": -15.2890625, "text": "" }, { "id": 32000, - "logprob": -13.625, + "logprob": -13.609375, "text": "" }, { @@ -33460,7 +33460,7 @@ }, { "id": 32000, - "logprob": -15.828125, + "logprob": -15.8359375, "text": "" }, { @@ -33475,12 +33475,12 @@ }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.328125, "text": "" }, { "id": 32000, - "logprob": -13.7734375, + "logprob": -13.78125, "text": "" }, { @@ -33490,27 +33490,27 @@ }, { "id": 32000, - "logprob": -13.484375, + "logprob": -13.4921875, "text": "" }, { "id": 32000, - "logprob": -13.40625, + "logprob": -13.4296875, "text": "" }, { "id": 32000, - "logprob": -13.265625, + "logprob": -13.28125, "text": "" }, { "id": 32000, - "logprob": -13.0234375, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -13.7578125, + "logprob": -13.75, "text": "" }, { @@ -33520,17 +33520,17 @@ }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.0546875, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -13.65625, "text": "" }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.6953125, "text": "" }, { @@ -33540,12 +33540,12 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5390625, "text": "" }, { "id": 32000, - "logprob": -13.3203125, + "logprob": -13.328125, "text": "" }, { @@ -33555,17 +33555,12 @@ }, { "id": 32000, - "logprob": -13.6328125, + "logprob": -13.640625, "text": "" }, { "id": 32000, - "logprob": -15.109375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.234375, + "logprob": -15.15625, "text": "" }, { @@ -33575,32 +33570,37 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -13.25, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -11.3125, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -10.6328125, + "logprob": -11.3203125, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -10.6484375, "text": "" }, { "id": 32000, - "logprob": -13.21875, + "logprob": -13.0078125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.234375, "text": "" }, { @@ -33610,77 +33610,52 @@ }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -12.5625, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.7890625, + "logprob": -12.8125, "text": "" }, { "id": 32000, - "logprob": -13.9140625, + "logprob": -13.9453125, "text": "" }, { "id": 32000, - "logprob": -13.1640625, + "logprob": -13.1796875, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9765625, "text": "" }, { "id": 32000, - "logprob": -13.8125, + "logprob": -13.828125, "text": "" }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.578125, "text": "" }, { "id": 32000, - "logprob": -14.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -14.7265625, - "text": "" - }, - { - "id": 32000, - "logprob": -14.5234375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.890625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.046875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.453125, + "logprob": -14.625, "text": "" }, { @@ -33690,22 +33665,47 @@ }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.5234375, "text": "" }, { "id": 32000, - "logprob": -13.9765625, + "logprob": -14.9140625, "text": "" }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -13.4453125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.78125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.9453125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0, + "text": "" + }, + { + "id": 32000, + "logprob": -13.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0859375, "text": "" }, { @@ -33720,7 +33720,7 @@ }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.2265625, "text": "" }, { @@ -33735,32 +33735,32 @@ }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.796875, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -13.6328125, + "logprob": -13.65625, "text": "" }, { "id": 32000, - "logprob": -12.8828125, + "logprob": -12.90625, "text": "" }, { @@ -33770,7 +33770,22 @@ }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.25, + "text": "" + }, + { + "id": 32000, + "logprob": -12.03125, "text": "" }, { @@ -33780,27 +33795,12 @@ }, { "id": 32000, - "logprob": -14.2421875, + "logprob": -13.0234375, "text": "" }, { "id": 32000, - "logprob": -12.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7578125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0, - "text": "" - }, - { - "id": 32000, - "logprob": -12.859375, + "logprob": -12.8671875, "text": "" }, { @@ -33810,17 +33810,17 @@ }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -10.5, + "logprob": -10.5078125, "text": "" }, { @@ -33830,12 +33830,12 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.375, "text": "" }, { "id": 32000, - "logprob": -11.0546875, + "logprob": -11.0625, "text": "" }, { @@ -33850,7 +33850,7 @@ }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.3515625, "text": "" }, { @@ -33860,12 +33860,12 @@ }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.53125, "text": "" }, { "id": 32000, - "logprob": -12.2109375, + "logprob": -12.203125, "text": "" }, { @@ -33875,7 +33875,7 @@ }, { "id": 32000, - "logprob": -13.828125, + "logprob": -13.8359375, "text": "" }, { @@ -33885,12 +33885,17 @@ }, { "id": 32000, - "logprob": -14.1953125, + "logprob": -14.1796875, "text": "" }, { "id": 32000, - "logprob": -14.0703125, + "logprob": -14.078125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0546875, "text": "" }, { @@ -33900,12 +33905,7 @@ }, { "id": 32000, - "logprob": -14.046875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0234375, + "logprob": -13.0625, "text": "" }, { @@ -33920,42 +33920,42 @@ }, { "id": 32000, - "logprob": -14.6875, + "logprob": -14.703125, "text": "" }, { "id": 32000, - "logprob": -14.3125, + "logprob": -14.296875, "text": "" }, { "id": 32000, - "logprob": -14.875, + "logprob": -14.8828125, "text": "" }, { "id": 32000, - "logprob": -14.1640625, + "logprob": -14.1796875, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.2265625, "text": "" }, { "id": 32000, - "logprob": -13.109375, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -11.9375, "text": "" }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.3203125, "text": "" }, { @@ -33965,17 +33965,17 @@ }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.734375, "text": "" }, { "id": 32000, - "logprob": -13.3828125, + "logprob": -13.3984375, "text": "" }, { @@ -33985,47 +33985,47 @@ }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.15625, "text": "" }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.8984375, "text": "" }, { "id": 32000, - "logprob": -13.671875, + "logprob": -13.6953125, "text": "" }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -13.3125, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -12.6640625, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.3515625, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -11.9921875, "text": "" }, { @@ -34035,7 +34035,7 @@ }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.03125, "text": "" }, { @@ -34055,7 +34055,7 @@ }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.6484375, "text": "" }, { @@ -34065,17 +34065,17 @@ }, { "id": 32000, - "logprob": -11.65625, + "logprob": -11.6484375, "text": "" }, { "id": 32000, - "logprob": -10.8515625, + "logprob": -10.84375, "text": "" }, { "id": 32000, - "logprob": -10.90625, + "logprob": -10.9140625, "text": "" }, { @@ -34090,32 +34090,12 @@ }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -10.8515625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.09375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.078125, + "logprob": -10.84375, "text": "" }, { @@ -34125,22 +34105,12 @@ }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -12.6640625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6484375, + "logprob": -12.7421875, "text": "" }, { @@ -34150,52 +34120,82 @@ }, { "id": 32000, - "logprob": -13.609375, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.8359375, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -12.9921875, + "logprob": -12.84375, "text": "" }, { "id": 32000, - "logprob": -14.046875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -13.9765625, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -13.796875, + "logprob": -13.625, "text": "" }, { "id": 32000, - "logprob": -13.203125, + "logprob": -12.8515625, "text": "" }, { "id": 32000, - "logprob": -13.46875, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -13.7265625, + "logprob": -13.0, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0703125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2421875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.484375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7109375, "text": "" }, { @@ -34205,7 +34205,7 @@ }, { "id": 32000, - "logprob": -15.8046875, + "logprob": -15.796875, "text": "" }, { @@ -34215,27 +34215,27 @@ }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -11.3203125, + "logprob": -11.328125, "text": "" }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.4296875, "text": "" }, { @@ -34250,17 +34250,17 @@ }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.7578125, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4765625, "text": "" }, { @@ -34270,37 +34270,37 @@ }, { "id": 32000, - "logprob": -11.6328125, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -15.234375, + "logprob": -15.2578125, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.8515625, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.0078125, "text": "" }, { "id": 32000, - "logprob": -15.2421875, + "logprob": -15.3828125, "text": "" }, { "id": 32000, - "logprob": -11.1953125, + "logprob": -11.203125, "text": "" }, { @@ -34315,12 +34315,12 @@ }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -13.4296875, + "logprob": -13.4453125, "text": "" }, { @@ -34330,12 +34330,12 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5078125, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -12.015625, "text": "" }, { @@ -34345,37 +34345,37 @@ }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.359375, "text": "" }, { "id": 32000, - "logprob": -11.140625, + "logprob": -11.171875, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -12.0234375, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -13.3671875, + "logprob": -13.375, "text": "" }, { @@ -34390,12 +34390,12 @@ }, { "id": 32000, - "logprob": -13.0390625, + "logprob": -13.046875, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.0859375, "text": "" }, { @@ -34405,7 +34405,7 @@ }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.4765625, "text": "" }, { @@ -34415,7 +34415,7 @@ }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.7265625, "text": "" }, { @@ -34425,122 +34425,122 @@ }, { "id": 32000, - "logprob": -14.3125, + "logprob": -14.3359375, "text": "" }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -14.28125, + "logprob": -14.296875, "text": "" }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.875, "text": "" }, { "id": 32000, - "logprob": -13.796875, + "logprob": -13.8046875, "text": "" }, { "id": 32000, - "logprob": -15.203125, + "logprob": -15.2109375, "text": "" }, { "id": 32000, - "logprob": -13.3046875, + "logprob": -13.2890625, "text": "" }, { "id": 32000, - "logprob": -12.3984375, + "logprob": -12.421875, "text": "" }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.78125, "text": "" }, { "id": 32000, - "logprob": -13.0390625, + "logprob": -13.03125, "text": "" }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -14.1015625, + "logprob": -13.78125, "text": "" }, { "id": 32000, - "logprob": -13.96875, + "logprob": -13.90625, "text": "" }, { "id": 32000, - "logprob": -12.5625, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -12.390625, + "logprob": -12.3515625, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.484375, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.65625, "text": "" }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.671875, "text": "" }, { "id": 32000, - "logprob": -14.640625, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.3203125, "text": "" }, { @@ -34550,7 +34550,12 @@ }, { "id": 32000, - "logprob": -11.8046875, + "logprob": -11.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5, "text": "" }, { @@ -34560,12 +34565,7 @@ }, { "id": 32000, - "logprob": -12.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2734375, + "logprob": -11.265625, "text": "" }, { @@ -34575,7 +34575,7 @@ }, { "id": 32000, - "logprob": -13.1328125, + "logprob": -13.15625, "text": "" }, { @@ -34585,7 +34585,7 @@ }, { "id": 32000, - "logprob": -12.984375, + "logprob": -13.0390625, "text": "" }, { @@ -34595,7 +34595,7 @@ }, { "id": 32000, - "logprob": -13.9765625, + "logprob": -14.0390625, "text": "" }, { @@ -34605,12 +34605,12 @@ }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.359375, "text": "" }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.953125, "text": "" }, { 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"" + }, + { + "id": 32000, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.515625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8671875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.8671875, "text": "" }, { @@ -34740,42 +34740,42 @@ }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.3671875, "text": "" }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.1640625, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -12.46875, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -12.0, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.4140625, "text": "" }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.828125, "text": "" }, { @@ -34790,17 +34790,17 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -11.140625, + "logprob": -11.1328125, "text": "" }, { "id": 32000, - "logprob": -10.796875, + "logprob": -10.7890625, "text": "" }, { @@ -34810,7 +34810,7 @@ }, { "id": 32000, - "logprob": -10.8984375, + "logprob": -10.890625, "text": "" }, { @@ -34825,12 +34825,7 @@ }, { "id": 32000, - "logprob": -12.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.1484375, + "logprob": -12.078125, "text": "" }, { @@ -34840,37 +34835,42 @@ }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.15625, "text": "" }, { "id": 32000, - "logprob": -12.8046875, + "logprob": -11.4296875, "text": "" }, { "id": 32000, - "logprob": -13.078125, + "logprob": -12.765625, "text": "" }, { "id": 32000, - "logprob": -14.03125, + "logprob": -13.09375, "text": "" }, { "id": 32000, - "logprob": -12.953125, + "logprob": -14.046875, "text": "" }, { "id": 32000, - "logprob": -12.3203125, + "logprob": -12.96875, "text": "" }, { "id": 32000, - "logprob": -12.84375, + "logprob": -12.328125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8359375, "text": "" }, { @@ -34880,12 +34880,12 @@ }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -13.4765625, + "logprob": -13.46875, "text": "" }, { @@ -34900,7 +34900,7 @@ }, { "id": 32000, - "logprob": -13.3515625, + "logprob": -13.40625, "text": "" }, { @@ -34910,7 +34910,7 @@ }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.265625, "text": "" }, { @@ -34920,7 +34920,7 @@ }, { "id": 32000, - "logprob": -13.6875, + "logprob": -13.65625, "text": "" }, { @@ -34930,17 +34930,17 @@ }, { "id": 32000, - "logprob": -14.03125, + "logprob": -14.09375, "text": "" }, { "id": 32000, - "logprob": -12.9375, + "logprob": -12.890625, "text": "" }, { "id": 32000, - "logprob": -15.65625, + "logprob": -15.640625, "text": "" }, { @@ -34950,37 +34950,42 @@ }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.3828125, "text": "" }, { "id": 32000, - "logprob": -12.703125, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -14.515625, + "logprob": -14.5234375, "text": "" }, { "id": 32000, - "logprob": -12.765625, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -12.28125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -13.5703125, + "logprob": -13.609375, "text": "" }, { "id": 32000, - "logprob": -13.140625, + "logprob": -13.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.203125, "text": "" }, { @@ -34990,22 +34995,17 @@ }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -11.59375, "text": "" }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -11.65625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1484375, + "logprob": -12.1796875, 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"id": 32000, - "logprob": -11.046875, + "logprob": -11.0390625, "text": "" }, { @@ -36785,27 +36785,27 @@ }, { "id": 32000, - "logprob": -13.421875, + "logprob": -13.6640625, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -11.6484375, + "logprob": -11.65625, "text": "" }, { "id": 32000, - "logprob": -12.265625, + "logprob": -12.25, "text": "" }, { @@ -36820,7 +36820,7 @@ }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.34375, "text": "" }, { @@ -36830,32 +36830,12 @@ }, { "id": 32000, - "logprob": -11.9921875, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -11.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.359375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.5625, - "text": "" - }, - { - "id": 32000, - "logprob": 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+36985,12 @@ }, { "id": 32000, - "logprob": -11.2890625, + "logprob": -11.28125, "text": "" }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.5546875, "text": "" }, { @@ -37000,37 +37000,42 @@ }, { "id": 32000, - "logprob": -12.125, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -11.453125, + "logprob": -11.5, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.640625, "text": "" }, { "id": 32000, - "logprob": -11.875, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.2890625, "text": "" }, { "id": 32000, - "logprob": -11.65625, + "logprob": -11.6484375, "text": "" }, { "id": 32000, - "logprob": -12.9140625, + "logprob": -12.8984375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.734375, "text": "" }, { @@ -37040,12 +37045,7 @@ }, { "id": 32000, - "logprob": -11.7265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3984375, + "logprob": -11.40625, "text": "" }, { @@ -37065,32 +37065,32 @@ }, { "id": 32000, - "logprob": -12.875, + "logprob": -12.890625, "text": "" }, { "id": 32000, - "logprob": -11.1171875, + "logprob": -11.03125, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -11.96875, "text": "" }, { "id": 32000, - "logprob": -14.6640625, + "logprob": -14.671875, "text": "" }, { @@ -37100,7 +37100,7 @@ }, { "id": 32000, - "logprob": -14.3828125, + "logprob": -14.2890625, "text": "" }, { @@ -37115,22 +37115,42 @@ }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.7421875, "text": "" }, { "id": 32000, - "logprob": -14.6953125, + "logprob": -14.6484375, "text": "" }, { "id": 32000, - "logprob": -13.515625, + "logprob": -13.546875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5703125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.96875, "text": "" }, { @@ -37140,67 +37160,47 @@ }, { "id": 32000, - "logprob": -13.4140625, + "logprob": -11.9609375, "text": "" }, { "id": 32000, - "logprob": -14.375, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -12.453125, "text": "" }, { "id": 32000, - "logprob": -12.9453125, + "logprob": -13.8046875, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -11.890625, + "logprob": -13.7890625, "text": "" }, { "id": 32000, - "logprob": -12.5, - "text": "" - }, - { - "id": 32000, - "logprob": -14.046875, - "text": "" - }, - { - "id": 32000, - 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}, { "id": 32000, - "logprob": -11.296875, + "logprob": -11.3046875, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -12.21875, "text": "" }, { @@ -37310,7 +37310,7 @@ }, { "id": 32000, - "logprob": -10.90625, + "logprob": -10.9140625, "text": "" }, { @@ -37320,37 +37320,37 @@ }, { "id": 32000, - "logprob": -13.140625, + "logprob": -13.203125, "text": "" }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -15.140625, + "logprob": -14.6640625, "text": "" }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.1640625, "text": "" }, { "id": 32000, - "logprob": -14.2734375, + "logprob": -14.859375, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -11.53125, "text": "" }, { @@ -37360,62 +37360,62 @@ }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.296875, "text": "" }, { "id": 32000, - "logprob": 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-11.84375, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -11.21875, + "logprob": -11.34375, "text": "" }, { @@ -37455,37 +37455,37 @@ }, { "id": 32000, - "logprob": -12.9921875, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -15.15625, + "logprob": -15.1875, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -12.0, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.5546875, + "logprob": -13.515625, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -11.5703125, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.703125, "text": "" }, { @@ -37505,27 +37505,27 @@ }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5546875, "text": "" }, { "id": 32000, - "logprob": -13.421875, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -11.1875, + "logprob": -11.1953125, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.7578125, "text": "" }, { @@ -37535,7 +37535,7 @@ }, { "id": 32000, - "logprob": -13.2734375, + "logprob": -13.2421875, "text": "" }, { @@ -37545,62 +37545,62 @@ }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.3984375, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -11.1796875, + "logprob": -11.1875, "text": "" }, { "id": 32000, - "logprob": -13.375, + "logprob": -13.359375, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -12.9453125, + "logprob": -12.9140625, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -13.59375, + "logprob": -13.5625, "text": "" }, { @@ -37608,21 +37608,6 @@ "logprob": -13.3203125, "text": "" }, - { - "id": 32000, - "logprob": -12.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1328125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6796875, - "text": "" - }, { "id": 32000, "logprob": -12.8046875, @@ -37630,12 +37615,17 @@ }, { "id": 32000, - "logprob": -12.78125, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -11.6484375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.828125, "text": "" }, { @@ -37643,6 +37633,16 @@ "logprob": -12.78125, "text": "" }, + { + "id": 32000, + "logprob": -12.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7578125, + "text": "" + }, { "id": 32000, "logprob": -11.484375, @@ -37650,37 +37650,37 @@ }, { "id": 32000, - "logprob": -12.75, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -11.6328125, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -11.3046875, + "logprob": -11.3125, "text": "" }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.265625, "text": "" }, { @@ -37715,7 +37715,7 @@ }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.046875, "text": "" }, { @@ -37725,7 +37725,7 @@ }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.7109375, "text": "" }, { @@ -37735,27 +37735,27 @@ }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": 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- "logprob": -13.0703125, + "logprob": -14.1640625, "text": "" }, { @@ -37910,7 +37780,137 @@ }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -11.3125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0703125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7265625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.046875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.3828125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5703125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5546875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.65625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.46875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6484375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.7734375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.078125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.15625, "text": "" }, { @@ -37925,12 +37925,12 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.9296875, + "logprob": -11.9375, "text": "" }, { @@ -37940,17 +37940,17 @@ }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.7890625, "text": "" }, { "id": 32000, - "logprob": -13.0234375, + "logprob": -13.0390625, "text": "" }, { @@ -37960,12 +37960,12 @@ }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -10.75, + "logprob": -10.7578125, "text": "" }, { @@ -37975,12 +37975,12 @@ }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -11.2578125, + "logprob": -11.265625, "text": "" }, { @@ -37990,27 +37990,27 @@ }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.7890625, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -11.25, + "logprob": -11.234375, "text": "" }, { "id": 32000, - "logprob": -12.453125, + "logprob": -12.46875, "text": "" }, { @@ -38020,7 +38020,7 @@ }, { "id": 32000, - "logprob": -11.890625, + "logprob": -11.8515625, "text": "" }, { @@ -38035,17 +38035,17 @@ }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.7890625, "text": "" }, { "id": 32000, - "logprob": -12.15625, + "logprob": -12.1953125, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.28125, "text": "" }, { @@ -38055,7 +38055,7 @@ }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.34375, "text": "" }, { @@ -38065,27 +38065,27 @@ }, { "id": 32000, - "logprob": -13.0078125, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -15.25, + "logprob": -15.28125, "text": "" }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0234375, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.109375, "text": "" }, { @@ -38100,32 +38100,32 @@ }, { "id": 32000, - "logprob": -14.7265625, + "logprob": -14.7109375, "text": "" }, { "id": 32000, - "logprob": -10.015625, + "logprob": -10.0234375, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.46875, "text": "" }, { "id": 32000, - "logprob": -13.9609375, + "logprob": -13.9765625, "text": "" }, { "id": 32000, - "logprob": -14.2734375, + "logprob": -14.3046875, "text": "" }, { "id": 32000, - "logprob": -14.9140625, + "logprob": -14.890625, "text": "" }, { @@ -38140,12 +38140,12 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.328125, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.3515625, "text": "" }, { @@ -38155,47 +38155,47 @@ }, { "id": 32000, - "logprob": -12.421875, + "logprob": -12.4296875, "text": "" }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.890625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5625, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.6171875, "text": "" }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.4609375, + "logprob": -12.4765625, "text": "" }, { @@ -38210,17 +38210,17 @@ }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.125, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8125, "text": "" }, { @@ -38235,52 +38235,52 @@ }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.375, + "logprob": -12.390625, "text": "" }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.3515625, "text": "" }, { "id": 32000, - "logprob": -12.015625, + "logprob": -12.0078125, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.25, "text": "" }, { @@ -38290,17 +38290,17 @@ }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -12.4765625, + "logprob": -12.4921875, "text": "" }, { @@ -38310,72 +38310,12 @@ }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -14.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1328125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.890625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.90625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -15.0, - "text": "" - }, - { - "id": 32000, - "logprob": -12.328125, + "logprob": -15.0625, "text": "" }, { @@ -38385,62 +38325,72 @@ }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -14.4453125, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -11.1015625, + "logprob": -14.828125, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -13.5546875, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -14.96875, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -13.9296875, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -10.5859375, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -15.0078125, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -12.3359375, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -12.09375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.578125, "text": "" }, { @@ -38450,37 +38400,87 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -11.1171875, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -11.8046875, "text": "" }, { "id": 32000, - "logprob": -10.8359375, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -10.7109375, + "logprob": -11.4609375, "text": "" }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -11.4609375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8203125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9375, "text": "" }, { @@ -38495,27 +38495,27 @@ }, { "id": 32000, - "logprob": -11.171875, + "logprob": -11.1796875, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.9609375, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.6328125, "text": "" }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.4453125, "text": "" }, { @@ -38530,17 +38530,47 @@ }, { "id": 32000, - "logprob": -11.1171875, + "logprob": -11.09375, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -15.4375, + "logprob": -15.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.484375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, "text": "" }, { @@ -38548,36 +38578,6 @@ "logprob": -11.8515625, "text": "" }, - { - "id": 32000, - "logprob": -11.4765625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0, - "text": "" - }, - { - "id": 32000, - "logprob": -12.21875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.84375, - "text": "" - }, { "id": 32000, "logprob": -11.0625, @@ -38585,27 +38585,27 @@ }, { "id": 32000, - "logprob": -13.8828125, + "logprob": -13.953125, "text": "" }, { "id": 32000, - "logprob": -13.5390625, + "logprob": -13.5625, "text": "" }, { "id": 32000, - "logprob": -13.203125, + "logprob": -13.1796875, "text": "" }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5859375, "text": "" }, { "id": 32000, - "logprob": -13.984375, + "logprob": -14.0234375, "text": "" }, { @@ -38615,22 +38615,22 @@ }, { "id": 32000, - "logprob": -14.1328125, + "logprob": -14.1484375, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.234375, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.3515625, "text": "" }, { @@ -38640,7 +38640,7 @@ }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.90625, "text": "" }, { @@ -38650,7 +38650,7 @@ }, { "id": 32000, - "logprob": -11.578125, + "logprob": -11.5703125, "text": "" }, { @@ -38660,7 +38660,7 @@ }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.6875, "text": "" }, { @@ -38670,12 +38670,12 @@ }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.8125, "text": "" }, { @@ -38695,12 +38695,12 @@ }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.46875, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.0703125, "text": "" }, { @@ -38730,7 +38730,7 @@ }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.9765625, "text": "" }, { @@ -38740,32 +38740,32 @@ }, { "id": 32000, - "logprob": -10.8828125, + "logprob": -10.890625, "text": "" }, { "id": 32000, - "logprob": -11.015625, + "logprob": -11.0078125, "text": "" }, { "id": 32000, - "logprob": -10.6015625, + "logprob": -10.609375, "text": "" }, { "id": 32000, - "logprob": -10.7890625, + "logprob": -10.796875, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.59375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { @@ -38775,12 +38775,12 @@ }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.5390625, "text": "" }, { @@ -38790,12 +38790,12 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -10.8515625, + "logprob": -10.9375, "text": "" }, { @@ -38805,117 +38805,52 @@ }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9375, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -12.0, "text": "" }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.8125, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.3671875, "text": "" }, { "id": 32000, - "logprob": -9.609375, + "logprob": -9.625, "text": "" }, { "id": 32000, - "logprob": -10.859375, + "logprob": -10.8515625, "text": "" }, { "id": 32000, - "logprob": -13.171875, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -13.015625, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.3359375, "text": "" }, { "id": 32000, - "logprob": -14.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5390625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.671875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.90625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.15625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6015625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.703125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8515625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2890625, + "logprob": -14.015625, "text": "" }, { @@ -38925,12 +38860,22 @@ }, { "id": 32000, - "logprob": -12.875, + "logprob": -13.4453125, "text": "" }, { "id": 32000, - "logprob": -15.6875, + "logprob": -13.5, + "text": "" + }, + { + "id": 32000, + "logprob": -12.71875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.90625, "text": "" }, { @@ -38940,12 +38885,67 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.75, + "text": "" + }, + { + "id": 32000, + "logprob": -12.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.296875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8671875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.140625, "text": "" }, { @@ -38955,12 +38955,12 @@ }, { "id": 32000, - "logprob": -11.2421875, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -12.3203125, + "logprob": -12.328125, "text": "" }, { @@ -38975,12 +38975,12 @@ }, { "id": 32000, - "logprob": -10.9296875, + "logprob": -10.921875, "text": "" }, { "id": 32000, - "logprob": -10.84375, + "logprob": -10.8515625, "text": "" }, { @@ -38990,47 +38990,47 @@ }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.40625, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -10.8125, + "logprob": -10.8046875, "text": "" }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.6484375, "text": "" }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.5078125, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -12.5546875, + "logprob": -12.5703125, "text": "" }, { @@ -39045,7 +39045,7 @@ }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.734375, "text": "" }, { @@ -39060,47 +39060,47 @@ }, { "id": 32000, - "logprob": -12.3515625, + "logprob": -12.34375, "text": "" }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -14.203125, + "logprob": -14.1796875, "text": "" }, { "id": 32000, - "logprob": -14.828125, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -18.140625, + "logprob": -18.046875, "text": "" }, { "id": 32000, - "logprob": -15.59375, + "logprob": -12.34375, "text": "" }, { "id": 32000, - "logprob": -14.515625, + "logprob": -14.4453125, "text": "" }, { "id": 32000, - "logprob": -10.6015625, + "logprob": -10.625, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.0546875, "text": "" }, { @@ -39110,47 +39110,47 @@ }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -13.140625, + "logprob": -13.2109375, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.3203125, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9296875, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -10.9453125, + "logprob": -10.9609375, "text": "" }, { "id": 32000, - "logprob": -11.7890625, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.828125, "text": "" }, { @@ -39165,27 +39165,27 @@ }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -11.53125, "text": "" }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -15.171875, + "logprob": -15.2421875, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1171875, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1171875, "text": "" }, { @@ -39195,7 +39195,7 @@ }, { "id": 32000, - "logprob": -11.6875, + "logprob": -11.671875, "text": "" }, { @@ -39215,12 +39215,12 @@ }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.734375, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.609375, "text": "" }, { @@ -39230,12 +39230,12 @@ }, { "id": 32000, - "logprob": -11.484375, + "logprob": -11.5078125, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2109375, "text": "" }, { @@ -39250,12 +39250,12 @@ }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.96875, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9765625, "text": "" }, { @@ -39275,87 +39275,87 @@ }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.1171875, "text": "" }, { "id": 32000, - "logprob": -11.5078125, + "logprob": -11.5, "text": "" }, { "id": 32000, - "logprob": -12.15625, + "logprob": -12.171875, "text": "" }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.875, 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"logprob": -12.6953125, "text": "" }, { @@ -39365,12 +39365,7 @@ }, { "id": 32000, - "logprob": -12.609375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.96875, + "logprob": -12.734375, "text": "" }, { @@ -39380,12 +39375,17 @@ }, { "id": 32000, - "logprob": -11.4296875, + "logprob": -14.0234375, "text": "" }, { "id": 32000, - "logprob": -12.5078125, + "logprob": -11.46875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.578125, "text": "" }, { @@ -39400,17 +39400,17 @@ }, { "id": 32000, - "logprob": -11.2109375, + "logprob": -11.1640625, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -16.53125, + "logprob": -16.5, "text": "" }, { @@ -39420,7 +39420,7 @@ }, { "id": 32000, - "logprob": -14.140625, + "logprob": -14.1171875, "text": "" }, { @@ -39430,47 +39430,12 @@ }, { "id": 32000, - "logprob": -12.484375, + "logprob": -12.5, "text": "" }, { "id": 32000, - "logprob": -10.59375, - "text": "" - }, - { 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32000, + "logprob": -12.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.59375, + "text": "" + }, + { + "id": 32000, + "logprob": -15.171875, "text": "" }, { @@ -39705,12 +39705,12 @@ }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.3671875, "text": "" }, { "id": 32000, - "logprob": -11.609375, + "logprob": -11.6171875, "text": "" }, { @@ -39735,22 +39735,22 @@ }, { "id": 32000, - "logprob": -11.3046875, + "logprob": -11.3125, "text": "" }, { "id": 32000, - "logprob": -11.171875, + "logprob": -11.1171875, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -13.0234375, + "logprob": -13.0859375, "text": "" }, { @@ -39760,17 +39760,17 @@ }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.515625, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -11.4921875, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.8203125, "text": "" }, { @@ -39780,12 +39780,12 @@ }, { "id": 32000, - "logprob": -13.9921875, + "logprob": -14.0078125, "text": "" }, { "id": 32000, - "logprob": -15.171875, + "logprob": -15.21875, "text": "" }, { @@ -39795,27 +39795,27 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -12.2890625, + "logprob": -12.296875, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.515625, "text": "" }, { @@ -39825,42 +39825,42 @@ }, { "id": 32000, - "logprob": -12.578125, + "logprob": -12.640625, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.4609375, "text": "" }, { "id": 32000, - "logprob": -14.796875, + "logprob": -14.84375, "text": "" }, { "id": 32000, - "logprob": -13.9140625, + "logprob": -13.953125, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + 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{ "id": 32000, - "logprob": -12.1640625, + "logprob": -12.1484375, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -13.9609375, + "logprob": -13.9453125, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.2890625, "text": "" }, { @@ -39950,7 +39950,7 @@ }, { "id": 32000, - "logprob": -10.7421875, + "logprob": -10.734375, "text": "" }, { @@ -39960,32 +39960,32 @@ }, { "id": 32000, - "logprob": -12.6796875, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -12.4765625, + "logprob": -12.5, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.140625, "text": "" }, { @@ -40000,12 +40000,12 @@ }, { "id": 32000, - "logprob": 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"logprob": -12.6640625, + "logprob": -12.671875, "text": "" }, { @@ -40080,7 +40080,7 @@ }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.9921875, "text": "" }, { @@ -40090,7 +40090,7 @@ }, { "id": 32000, - "logprob": -14.953125, + "logprob": -14.9453125, "text": "" }, { @@ -40100,12 +40100,12 @@ }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.265625, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.671875, "text": "" }, { @@ -40130,32 +40130,32 @@ }, { "id": 32000, - "logprob": -13.21875, + "logprob": -13.2109375, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3359375, "text": "" }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.15625, "text": "" }, { "id": 32000, - "logprob": -12.265625, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -13.203125, + "logprob": -13.171875, "text": "" }, { "id": 32000, - "logprob": -11.640625, + "logprob": -11.6328125, "text": "" }, { @@ -40165,17 +40165,17 @@ }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.3515625, "text": "" }, { @@ -40195,17 +40195,17 @@ }, { "id": 32000, - "logprob": -12.3984375, + "logprob": -12.40625, "text": "" }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -16.609375, + "logprob": -16.59375, "text": "" }, { @@ -40215,12 +40215,12 @@ }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -12.53125, + "logprob": -12.5390625, "text": "" }, { @@ -40235,12 +40235,12 @@ }, { "id": 32000, - "logprob": -13.0859375, + "logprob": -13.0703125, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.03125, "text": "" }, { @@ -40250,7 +40250,7 @@ }, { "id": 32000, - "logprob": -10.5859375, + "logprob": -10.59375, 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+40720,7 @@ }, { "id": 32000, - "logprob": -12.234375, + "logprob": -12.2578125, "text": "" }, { @@ -40730,12 +40730,12 @@ }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -13.8125, + "logprob": -13.8203125, "text": "" }, { @@ -40750,42 +40750,42 @@ }, { "id": 32000, - "logprob": -10.953125, + "logprob": -10.984375, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -10.8046875, + "logprob": -10.8203125, "text": "" }, { "id": 32000, - "logprob": -12.6171875, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -13.125, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -14.3125, + "logprob": -14.25, "text": "" }, { "id": 32000, - "logprob": -13.046875, + "logprob": -13.0703125, "text": "" }, { @@ -40800,32 +40800,32 @@ }, { "id": 32000, - "logprob": 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-41105,32 +41105,32 @@ }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -16.109375, + "logprob": -16.0625, "text": "" }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -14.6796875, + "logprob": -14.5859375, "text": "" }, { @@ -41145,37 +41145,37 @@ }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -13.015625, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -12.8984375, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -12.0, + "logprob": -11.9921875, "text": "" }, { "id": 32000, - "logprob": -13.84375, + "logprob": -13.796875, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.7578125, "text": "" }, { @@ -41185,67 +41185,67 @@ }, { "id": 32000, - "logprob": -14.265625, + "logprob": -14.328125, "text": "" }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -13.8515625, + "logprob": -13.84375, "text": "" }, { "id": 32000, - "logprob": -13.9921875, + "logprob": -14.2109375, "text": "" }, { "id": 32000, - "logprob": -12.375, + "logprob": -12.390625, "text": "" }, { "id": 32000, - "logprob": -13.9296875, + "logprob": -13.953125, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -14.71875, + "logprob": -14.6796875, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -10.6484375, + "logprob": -10.65625, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -12.0078125, "text": "" }, { "id": 32000, - "logprob": -11.0625, + "logprob": -11.0859375, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.5234375, "text": "" }, { @@ -41260,12 +41260,12 @@ }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.125, "text": "" }, { @@ -41280,7 +41280,7 @@ }, { "id": 32000, - "logprob": -11.9921875, + "logprob": -11.984375, "text": "" }, { @@ -41290,17 +41290,17 @@ }, { "id": 32000, - "logprob": -13.71875, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -15.4375, + "logprob": -15.4921875, "text": "" }, { "id": 32000, - "logprob": -12.7890625, + "logprob": -12.921875, "text": "" }, { @@ -41310,17 +41310,17 @@ }, { "id": 32000, - "logprob": -13.359375, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -12.0703125, + "logprob": -12.0546875, "text": "" }, { "id": 32000, - "logprob": -15.203125, + "logprob": -15.21875, "text": "" }, { @@ -41335,12 +41335,12 @@ }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.8984375, "text": "" }, { "id": 32000, - "logprob": -13.8359375, + "logprob": -13.8515625, "text": "" }, { @@ -41350,7 +41350,7 @@ }, { "id": 32000, - "logprob": -17.453125, + "logprob": -17.4375, "text": "" }, { @@ -41365,7 +41365,7 @@ }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.8125, "text": "" }, { @@ -41380,12 +41380,12 @@ }, { "id": 32000, - "logprob": -14.203125, + "logprob": -14.2421875, "text": "" }, { "id": 32000, - "logprob": -13.6953125, + "logprob": -13.7265625, "text": "" }, { @@ -41395,37 +41395,37 @@ }, { "id": 32000, - "logprob": -10.5859375, + "logprob": -10.59375, "text": "" }, { "id": 32000, - "logprob": -12.875, + "logprob": -12.84375, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9140625, "text": "" }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.7421875, "text": "" }, { "id": 32000, - "logprob": -11.9921875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -14.734375, + "logprob": -14.71875, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -11.578125, "text": "" }, { @@ -41435,7 +41435,7 @@ }, { "id": 32000, - "logprob": -12.7578125, + "logprob": -12.796875, "text": "" }, { @@ -41445,12 +41445,12 @@ }, { "id": 32000, - "logprob": -16.390625, + "logprob": -16.46875, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9296875, "text": "" }, { @@ -41460,7 +41460,7 @@ }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.8515625, "text": "" }, { @@ -41470,22 +41470,22 @@ }, { "id": 32000, - "logprob": -16.515625, + "logprob": -16.53125, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -13.3671875, + "logprob": -13.359375, "text": "" }, { "id": 32000, - "logprob": -14.0, + "logprob": -13.96875, "text": "" }, { @@ -41495,22 +41495,22 @@ }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.5078125, "text": "" }, { @@ -41530,77 +41530,77 @@ }, { "id": 32000, - "logprob": -14.0390625, + "logprob": -14.0546875, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.125, "text": "" }, { "id": 32000, - "logprob": -17.1875, + "logprob": -17.21875, "text": "" }, { "id": 32000, - "logprob": -13.171875, + "logprob": -13.1640625, "text": "" }, { "id": 32000, - "logprob": -13.359375, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -14.6015625, + "logprob": -14.6171875, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9609375, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -15.0234375, + "logprob": -15.03125, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -15.921875, + "logprob": -15.890625, "text": "" }, { "id": 32000, - "logprob": -10.859375, + "logprob": -10.8515625, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.828125, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.640625, "text": "" }, { @@ -41610,27 +41610,27 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.7578125, "text": "" }, { "id": 32000, - "logprob": -14.7421875, + "logprob": -14.7578125, "text": "" }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.5, "text": "" }, { "id": 32000, - "logprob": -11.265625, + "logprob": -11.2578125, "text": "" }, { @@ -41650,7 +41650,37 @@ }, { "id": 32000, - "logprob": -12.953125, + "logprob": -12.9453125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.1171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -17.015625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9140625, "text": "" }, { @@ -41658,36 +41688,6 @@ "logprob": -13.0390625, "text": "" }, - { - "id": 32000, - "logprob": -12.0234375, - "text": "" - }, - { - "id": 32000, - "logprob": -17.03125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6171875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1328125, - "text": "" - }, { "id": 32000, "logprob": -11.84375, @@ -41695,12 +41695,12 @@ }, { "id": 32000, - "logprob": -15.25, + "logprob": -15.265625, "text": "" }, { "id": 32000, - "logprob": 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32000, - "logprob": -12.84375, + "logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -11.4765625, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -13.5390625, + "logprob": -13.5625, "text": "" }, { "id": 32000, - "logprob": -16.1875, + "logprob": -16.25, "text": "" }, { "id": 32000, - "logprob": -16.796875, + "logprob": -16.8125, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -14.359375, + "logprob": -14.375, "text": "" }, { @@ -41810,112 +41810,17 @@ }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.671875, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -12.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2265625, - "text": "" - }, - { - "id": 32000, - "logprob": -17.5625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.640625, - "text": "" - }, - { - "id": 32000, - "logprob": 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+41830,7 @@ }, { "id": 32000, - "logprob": -12.484375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.578125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1015625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0703125, + "logprob": -17.5625, "text": "" }, { @@ -41975,7 +41840,142 @@ }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -15.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -16.75, + "text": "" + }, + { + "id": 32000, + "logprob": -11.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.328125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3046875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3515625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4609375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.546875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.765625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4765625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.09375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9609375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.96875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6328125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.609375, "text": "" }, { @@ -41985,17 +41985,17 @@ }, { "id": 32000, - "logprob": -15.6171875, + "logprob": -15.640625, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -14.25, + "logprob": -14.265625, "text": "" }, { @@ -42010,17 +42010,17 @@ }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.828125, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { @@ -42030,7 +42030,7 @@ }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3203125, "text": "" }, { @@ -42040,37 +42040,37 @@ }, { "id": 32000, - "logprob": -14.2265625, + "logprob": -14.3046875, "text": "" }, { "id": 32000, - "logprob": -14.890625, + "logprob": -14.90625, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8046875, "text": "" }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5390625, "text": "" }, { "id": 32000, - "logprob": -19.421875, + "logprob": -19.4375, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.6796875, "text": "" }, { @@ -42085,7 +42085,7 @@ }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2109375, "text": "" }, { @@ -42095,27 +42095,27 @@ }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.328125, "text": "" }, { "id": 32000, - "logprob": -17.65625, + "logprob": -17.625, "text": "" }, { "id": 32000, - "logprob": -17.703125, + "logprob": -17.75, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -12.015625, + "logprob": -12.0234375, "text": "" }, { @@ -42125,7 +42125,7 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.5859375, "text": "" }, { @@ -42135,37 +42135,37 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -13.5, + "logprob": -13.4921875, "text": "" }, { "id": 32000, - "logprob": -10.5625, + "logprob": -10.5703125, "text": "" }, { "id": 32000, - "logprob": -12.28125, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -13.2734375, + "logprob": -13.3046875, "text": "" }, { "id": 32000, - "logprob": -14.34375, + "logprob": -14.3359375, "text": "" }, { @@ -42180,17 +42180,17 @@ }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -14.859375, + "logprob": -14.9375, "text": "" }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0390625, "text": "" }, { @@ -42215,27 +42215,27 @@ }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": -11.8984375, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -15.0703125, + "logprob": -15.0546875, "text": "" }, { @@ -42250,17 +42250,17 @@ }, { "id": 32000, - "logprob": -16.859375, + "logprob": -16.84375, "text": "" }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -14.8515625, + "logprob": -14.9296875, "text": "" }, { @@ -42270,12 +42270,12 @@ }, { "id": 32000, - "logprob": -12.7578125, + "logprob": -12.765625, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.984375, "text": "" }, { @@ -42285,12 +42285,12 @@ }, { "id": 32000, - "logprob": -12.6796875, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -14.4921875, + "logprob": -14.515625, "text": "" }, { @@ -42300,17 +42300,17 @@ }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.5625, "text": "" }, { "id": 32000, - "logprob": -14.859375, + "logprob": -14.8125, "text": "" }, { @@ -42320,12 +42320,12 @@ }, { "id": 32000, - "logprob": -12.4375, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6484375, "text": "" }, { @@ -42335,12 +42335,12 @@ }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1484375, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.8671875, "text": "" }, { @@ -42355,27 +42355,27 @@ }, { "id": 32000, - "logprob": -12.4453125, + "logprob": -12.453125, "text": "" }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.1484375, "text": "" }, { "id": 32000, - "logprob": -16.9375, + "logprob": -16.890625, "text": "" }, { "id": 32000, - "logprob": -12.890625, + "logprob": -12.84375, "text": "" }, { @@ -42385,12 +42385,12 @@ }, { "id": 32000, - "logprob": -14.6484375, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -10.9296875, + "logprob": -10.9375, "text": "" }, { @@ -42400,7 +42400,7 @@ }, { "id": 32000, - "logprob": -17.15625, + "logprob": -17.6875, "text": "" }, { @@ -42415,17 +42415,17 @@ }, { "id": 32000, - "logprob": -11.28125, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -11.2109375, + "logprob": -11.203125, "text": "" }, { @@ -42440,17 +42440,17 @@ }, { "id": 32000, - "logprob": -14.3046875, + "logprob": -14.3125, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -13.1875, + "logprob": -13.203125, "text": "" }, { @@ -42460,22 +42460,22 @@ }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.8515625, "text": "" }, { "id": 32000, - "logprob": -11.0703125, + "logprob": -11.078125, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -12.578125, + "logprob": -12.6015625, "text": "" }, { @@ -42485,12 +42485,12 @@ }, { "id": 32000, - "logprob": -12.71875, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -13.4453125, + "logprob": -13.46875, "text": "" }, { @@ -42500,22 +42500,22 @@ }, { "id": 32000, - "logprob": -12.53125, + "logprob": -12.5703125, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.03125, "text": "" }, { "id": 32000, - "logprob": -16.53125, + "logprob": -16.46875, "text": "" }, { "id": 32000, - "logprob": -13.1484375, + "logprob": -13.140625, "text": "" }, { @@ -42525,17 +42525,17 @@ }, { "id": 32000, - "logprob": -14.171875, + "logprob": -14.109375, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -14.3359375, + "logprob": -14.34375, "text": "" }, { @@ -42545,47 +42545,47 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -12.890625, + "logprob": -12.8984375, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -11.609375, + "logprob": -11.625, "text": "" }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.1484375, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -14.71875, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.625, "text": "" }, { @@ -42600,12 +42600,12 @@ }, { "id": 32000, - "logprob": -15.0, + "logprob": -14.6484375, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -13.546875, "text": "" }, { @@ -42615,7 +42615,7 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { @@ -42630,7 +42630,7 @@ }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9375, "text": "" }, { @@ -42640,12 +42640,12 @@ }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.921875, "text": "" }, { "id": 32000, - "logprob": -13.4453125, + "logprob": -13.46875, "text": "" }, { @@ -42655,17 +42655,17 @@ }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -12.421875, "text": "" }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.7734375, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1640625, "text": "" }, { @@ -42675,22 +42675,22 @@ }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.75, "text": "" }, { @@ -42700,7 +42700,7 @@ }, { "id": 32000, - "logprob": -16.40625, + "logprob": -16.390625, "text": "" }, { @@ -42710,17 +42710,17 @@ }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -13.921875, + "logprob": -13.8984375, "text": "" }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.9921875, "text": "" }, { @@ -42730,27 +42730,27 @@ }, { "id": 32000, - "logprob": -12.0703125, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -16.03125, + "logprob": -16.046875, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.6484375, "text": "" }, { "id": 32000, - "logprob": -15.3125, + "logprob": -15.3203125, "text": "" }, { @@ -42760,17 +42760,17 @@ }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -12.390625, + "logprob": -12.3984375, "text": "" }, { "id": 32000, - "logprob": -14.484375, + "logprob": -15.4765625, "text": "" }, { @@ -42780,37 +42780,12 @@ }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5859375, "text": "" }, { "id": 32000, - "logprob": -11.6328125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1171875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -15.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -17.640625, + "logprob": -11.5859375, "text": "" }, { @@ -42818,6 +42793,31 @@ "logprob": -12.1015625, "text": "" }, + { + "id": 32000, + "logprob": -12.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -17.65625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.09375, + "text": "" + }, { "id": 32000, "logprob": -11.84375, @@ -42825,17 +42825,17 @@ }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5546875, "text": "" }, { "id": 32000, - "logprob": -13.578125, + "logprob": -13.59375, "text": "" }, { @@ -42845,17 +42845,17 @@ }, { "id": 32000, - "logprob": -16.703125, + "logprob": -16.6875, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.03125, "text": "" }, { "id": 32000, - "logprob": -14.90625, + "logprob": -14.859375, "text": "" }, { @@ -42865,42 +42865,42 @@ }, { "id": 32000, - "logprob": -15.21875, + "logprob": -15.203125, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.4140625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -17.0, + "logprob": -16.8125, "text": "" }, { "id": 32000, - "logprob": -13.046875, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -14.734375, + "logprob": -14.8671875, "text": "" }, { "id": 32000, - "logprob": -12.125, + "logprob": -12.1171875, "text": "" }, { @@ -42915,7 +42915,7 @@ }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.359375, "text": "" }, { @@ -42925,7 +42925,7 @@ }, { "id": 32000, - "logprob": -13.75, + "logprob": -13.765625, "text": "" }, { @@ -42940,22 +42940,22 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.6015625, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.703125, "text": "" }, { "id": 32000, - "logprob": -11.390625, + "logprob": -11.40625, "text": "" }, { @@ -42965,17 +42965,17 @@ }, { "id": 32000, - "logprob": -11.8125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.9375, + "logprob": -12.921875, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6875, "text": "" }, { @@ -42985,47 +42985,47 @@ }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9609375, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -12.40625, + "logprob": -12.4140625, "text": "" }, { "id": 32000, - "logprob": -18.71875, + "logprob": -18.703125, "text": "" }, { "id": 32000, - "logprob": -15.59375, + "logprob": -15.6484375, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -13.6875, + "logprob": -13.703125, "text": "" }, { "id": 32000, - "logprob": -15.0703125, + "logprob": -15.0859375, "text": "" }, { @@ -43035,12 +43035,12 @@ }, { "id": 32000, - "logprob": -14.53125, + "logprob": -14.5234375, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3671875, "text": "" }, { @@ -43050,7 +43050,7 @@ }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.640625, "text": "" }, { @@ -43060,37 +43060,37 @@ }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.1015625, "text": "" }, { "id": 32000, - "logprob": -13.1640625, + "logprob": -13.3515625, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -11.2890625, + "logprob": -11.25, "text": "" }, { "id": 32000, - "logprob": -14.125, + "logprob": -14.15625, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -13.9140625, + "logprob": -13.90625, "text": "" }, { @@ -43105,22 +43105,22 @@ }, { "id": 32000, - "logprob": -10.53125, + "logprob": -10.5625, "text": "" }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -12.171875, "text": "" }, { "id": 32000, - "logprob": -12.234375, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9296875, "text": "" }, { @@ -43130,7 +43130,7 @@ }, { "id": 32000, - "logprob": -12.8828125, + "logprob": -12.875, "text": "" }, { @@ -43145,17 +43145,17 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.109375, "text": "" }, { @@ -43165,22 +43165,22 @@ }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.5078125, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9140625, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.2421875, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { @@ -43190,87 +43190,87 @@ }, { "id": 32000, - "logprob": -16.6875, + "logprob": -16.671875, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -15.0234375, + "logprob": -14.9453125, "text": "" }, { "id": 32000, - "logprob": -16.578125, + "logprob": -16.5625, "text": "" }, { "id": 32000, - "logprob": -15.1875, + "logprob": -15.203125, "text": "" }, { "id": 32000, - "logprob": -14.7109375, + "logprob": -14.6953125, "text": "" }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -12.2109375, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.34375, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -13.8046875, + "logprob": -13.796875, "text": "" }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.1640625, "text": "" }, { "id": 32000, - "logprob": -15.3125, + "logprob": -15.484375, "text": "" }, { "id": 32000, - "logprob": -14.6171875, + "logprob": -14.6328125, "text": "" }, { @@ -43280,17 +43280,17 @@ }, { "id": 32000, - "logprob": -13.7265625, + "logprob": -13.75, "text": "" }, { "id": 32000, - "logprob": -12.6953125, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.3125, "text": "" }, { @@ -43300,62 +43300,62 @@ }, { "id": 32000, - "logprob": -13.4765625, + "logprob": -13.484375, "text": "" }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.2734375, "text": "" }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -11.3984375, + "logprob": -11.390625, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -16.5, + "logprob": -16.53125, "text": "" }, { "id": 32000, - "logprob": -14.1171875, + "logprob": -14.1484375, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.2265625, "text": "" }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.8125, "text": "" }, { "id": 32000, - "logprob": -15.078125, + "logprob": -15.03125, "text": "" }, { "id": 32000, - "logprob": -15.4609375, + "logprob": -15.484375, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.6015625, "text": "" }, { @@ -43370,7 +43370,7 @@ }, { "id": 32000, - "logprob": -11.15625, + "logprob": -11.1640625, "text": "" }, { @@ -43380,82 +43380,82 @@ }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.5625, "text": "" }, { "id": 32000, - "logprob": -12.765625, + "logprob": -12.7421875, "text": "" }, { "id": 32000, - "logprob": -18.015625, + "logprob": -18.0, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.625, 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"logprob": -11.609375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.828125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.71875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.953125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.8984375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6875, + "text": "" + }, + { + "id": 32000, + "logprob": -16.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.703125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.4921875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.2109375, + "text": "" + }, + { + "id": 32000, + 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"logprob": -13.203125, + "logprob": -13.1796875, "text": "" }, { @@ -43780,17 +43780,17 @@ }, { "id": 32000, - "logprob": -15.828125, + "logprob": -15.796875, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.640625, + "logprob": -14.0390625, "text": "" }, { @@ -43800,12 +43800,12 @@ }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.2890625, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.671875, "text": "" }, { @@ -43815,22 +43815,22 @@ }, { "id": 32000, - "logprob": -16.34375, + "logprob": -16.375, "text": "" }, { "id": 32000, - "logprob": -13.8671875, + "logprob": -13.8828125, "text": "" }, { "id": 32000, - "logprob": -14.6484375, + "logprob": -14.6171875, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.625, "text": "" }, { @@ -43850,72 +43850,72 @@ }, { "id": 32000, - "logprob": -11.6796875, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": 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-43925,17 +43925,17 @@ }, { "id": 32000, - "logprob": -12.9140625, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -11.7890625, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -12.5625, + "logprob": -12.5703125, "text": "" }, { @@ -43950,17 +43950,17 @@ }, { "id": 32000, - "logprob": -15.28125, + "logprob": -15.1953125, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.09375, "text": "" }, { @@ -43970,47 +43970,47 @@ }, { "id": 32000, - "logprob": -15.5625, + "logprob": -15.59375, "text": "" }, { "id": 32000, - "logprob": -11.6875, + "logprob": -11.671875, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3515625, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.90625, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.609375, "text": "" }, { "id": 32000, - "logprob": -14.6640625, + "logprob": -14.671875, "text": "" }, { "id": 32000, - "logprob": -15.3515625, + "logprob": -15.34375, "text": "" }, { "id": 32000, - "logprob": -15.203125, + "logprob": -15.2265625, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.78125, "text": "" }, { @@ -44020,122 +44020,17 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -11.7890625, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -12.0078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8515625, - "text": "" - }, - { - "id": 32000, - "logprob": -16.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.890625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.84375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.3515625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.015625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5703125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.34375, + "logprob": -12.03125, "text": "" }, { @@ -44145,27 +44040,132 @@ }, { "id": 32000, - "logprob": -13.8203125, + "logprob": -16.515625, "text": "" }, { "id": 32000, - "logprob": -13.34375, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -13.1328125, + "logprob": -12.4609375, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -13.3984375, "text": "" }, { "id": 32000, - "logprob": -15.1484375, + "logprob": -13.171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2734375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.3984375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0078125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3359375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.421875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9765625, + "text": "" + }, + { + "id": 32000, + "logprob": -15.140625, "text": "" }, { @@ -44180,12 +44180,12 @@ }, { "id": 32000, - "logprob": -14.421875, + "logprob": -14.40625, "text": "" }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.046875, "text": "" }, { @@ -44195,52 +44195,52 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5078125, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -13.3828125, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -17.28125, + "logprob": -17.3125, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.5234375, "text": "" }, { "id": 32000, - "logprob": -17.59375, + "logprob": -17.625, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -15.9375, + "logprob": -15.9140625, "text": "" }, { "id": 32000, - "logprob": -16.625, + "logprob": -16.65625, "text": "" }, { @@ -44250,27 +44250,27 @@ }, { "id": 2418, - "logprob": -19.078125, + "logprob": -19.0625, "text": "Can" }, { "id": 368, - "logprob": -0.19665527, + "logprob": -0.19726562, "text": "you" }, { "id": 1912, - "logprob": -1.5009766, + "logprob": -1.4990234, "text": "tell" }, { "id": 528, - "logprob": -0.31054688, + "logprob": -0.31152344, "text": "me" }, { "id": 264, - "logprob": -2.6269531, + "logprob": -2.6367188, "text": "a" }, { @@ -44280,12 +44280,12 @@ }, { "id": 2485, - "logprob": -0.99365234, + "logprob": -0.9941406, "text": "short" }, { "id": 2838, - "logprob": -0.45996094, + "logprob": -0.46118164, "text": "story" }, { @@ -44295,22 +44295,22 @@ }, { "id": 356, - "logprob": -0.029006958, + "logprob": -0.029129028, "text": "on" }, { "id": 272, - "logprob": -0.9897461, + "logprob": -0.9902344, "text": "the" }, { "id": 3469, - "logprob": -0.29125977, + "logprob": -0.29052734, "text": "image" }, { "id": 28804, - "logprob": -0.43017578, + "logprob": -0.43188477, "text": "?" } ], @@ -44318,13 +44318,13 @@ "tokens": [ { "id": 13, - "logprob": -0.007446289, + "logprob": -0.0076828003, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.20129395, + "logprob": -0.20092773, "special": false, "text": "\n" }, @@ -44336,43 +44336,43 @@ }, { "id": 3714, - "logprob": -0.20825195, + "logprob": -0.20861816, "special": false, "text": " upon" }, { "id": 264, - "logprob": -0.0017786026, + "logprob": -0.0017719269, "special": false, "text": " a" }, { "id": 727, - "logprob": -0.011955261, + "logprob": -0.011909485, "special": false, "text": " time" }, { "id": 28725, - "logprob": -0.17297363, + "logprob": -0.17529297, "special": false, "text": "," }, { "id": 736, - "logprob": -0.91015625, + "logprob": -0.9082031, "special": false, "text": " there" }, { "id": 403, - "logprob": -0.05758667, + "logprob": -0.057525635, "special": false, "text": " was" }, { "id": 264, - "logprob": -0.009544373, + "logprob": -0.009651184, "special": false, "text": " a" } @@ -44414,7 +44414,7 @@ }, { "id": 32000, - "logprob": -15.9140625, + "logprob": -15.7109375, "text": "" }, { @@ -44424,37 +44424,37 @@ }, { "id": 32000, - "logprob": -10.03125, + "logprob": -10.0234375, "text": "" }, { "id": 32000, - "logprob": -10.140625, + "logprob": -10.1328125, "text": "" }, { "id": 32000, - "logprob": -10.296875, + "logprob": -10.421875, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -16.0625, + "logprob": -16.59375, "text": "" }, { "id": 32000, - "logprob": -13.8515625, + "logprob": -13.828125, "text": "" }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.390625, "text": "" }, { @@ -44464,7 +44464,7 @@ }, { "id": 32000, - "logprob": -10.171875, + "logprob": -10.1640625, "text": "" }, { @@ -44474,22 +44474,22 @@ }, { "id": 32000, - "logprob": -10.359375, + "logprob": -10.3984375, "text": "" }, { "id": 32000, - "logprob": -14.953125, + "logprob": -15.015625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.0859375, "text": "" }, { "id": 32000, - "logprob": -13.3046875, + "logprob": -13.2734375, "text": "" }, { @@ -44499,67 +44499,37 @@ }, { "id": 32000, - "logprob": -11.0546875, + "logprob": -11.0390625, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9140625, "text": "" }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.5234375, "text": "" }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.4765625, "text": "" }, { "id": 32000, - "logprob": -14.2890625, + "logprob": -14.2421875, "text": "" }, { "id": 32000, - "logprob": -13.625, + "logprob": -13.6171875, "text": "" }, { "id": 32000, - "logprob": -11.21875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -17.03125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.6484375, - "text": "" - }, - { - "id": 32000, - "logprob": -18.265625, + "logprob": -11.359375, "text": "" }, { @@ -44569,17 +44539,47 @@ }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -17.34375, "text": "" }, { "id": 32000, - "logprob": -9.8671875, + "logprob": -10.3984375, "text": "" }, { "id": 32000, - "logprob": -10.765625, + "logprob": -10.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.640625, + "text": "" + }, + { + "id": 32000, + "logprob": -18.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -9.875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7734375, "text": "" }, { @@ -44589,12 +44589,12 @@ }, { "id": 32000, - "logprob": -10.984375, + "logprob": -10.96875, "text": "" }, { "id": 32000, - "logprob": -10.6171875, + "logprob": -10.609375, "text": "" }, { @@ -44604,17 +44604,17 @@ }, { "id": 32000, - "logprob": -10.515625, + "logprob": -10.5078125, "text": "" }, { "id": 32000, - "logprob": -11.296875, + "logprob": -11.4453125, "text": "" }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.59375, "text": "" }, { @@ -44629,27 +44629,27 @@ }, { "id": 32000, - "logprob": -10.6796875, + "logprob": -10.640625, "text": "" }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.9765625, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -12.390625, + "logprob": -12.3671875, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -12.0234375, "text": "" }, { @@ -44664,137 +44664,12 @@ }, { "id": 32000, - "logprob": -11.21875, + "logprob": -11.0625, "text": "" }, { "id": 32000, - "logprob": -10.390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.1875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0234375, - "text": "" - }, - { - "id": 32000, - "logprob": -18.3125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.9921875, - "text": "" - }, - { - "id": 32000, - "logprob": -15.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.171875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.4140625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.078125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.5625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.25, - "text": "" - }, - { - "id": 32000, - "logprob": -10.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2890625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.71875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.671875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.7890625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.15625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.515625, - "text": "" - }, - { - "id": 32000, - "logprob": -15.5546875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.234375, + "logprob": -10.3828125, "text": "" }, { @@ -44802,6 +44677,131 @@ "logprob": -11.171875, "text": "" }, + { + "id": 32000, + "logprob": -11.0234375, + "text": "" + }, + { + "id": 32000, + "logprob": -18.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9921875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.7109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.40625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.78125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.2421875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.2265625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.140625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.015625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.5234375, + "text": "" + }, + { + "id": 32000, + "logprob": -15.4453125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.09375, + "text": "" + }, { "id": 32000, "logprob": -14.6796875, @@ -44809,7 +44809,7 @@ }, { "id": 32000, - "logprob": -10.375, + "logprob": -10.3671875, "text": "" }, { @@ -44824,17 +44824,22 @@ }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -12.484375, "text": "" }, { "id": 32000, - "logprob": -12.109375, + "logprob": -12.09375, "text": "" }, { "id": 32000, - "logprob": -11.0859375, + "logprob": -11.1015625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.96875, "text": "" }, { @@ -44844,22 +44849,17 @@ }, { "id": 32000, - "logprob": -11.109375, + "logprob": -11.0234375, "text": "" }, { "id": 32000, - "logprob": -11.0390625, + "logprob": -10.96875, "text": "" }, { "id": 32000, - "logprob": -10.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.171875, + "logprob": -11.1484375, "text": "" }, { @@ -44869,7 +44869,7 @@ }, { "id": 32000, - "logprob": -10.7109375, + "logprob": -10.71875, "text": "" }, { @@ -44879,27 +44879,27 @@ }, { "id": 32000, - "logprob": -10.9140625, + "logprob": -10.8984375, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.2890625, "text": "" }, { "id": 32000, - "logprob": -11.09375, + "logprob": -10.984375, "text": "" }, { "id": 32000, - "logprob": -10.6328125, + "logprob": -10.640625, "text": "" }, { "id": 32000, - "logprob": -10.6953125, + "logprob": -10.7109375, "text": "" }, { @@ -44914,202 +44914,17 @@ }, { "id": 32000, - "logprob": -10.6953125, + "logprob": -10.6875, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -11.53125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2109375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4921875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.25, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2265625, - "text": "" - }, - { - "id": 32000, - "logprob": -9.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.9921875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.09375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.890625, - "text": "" - }, - { - "id": 32000, - 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+ "logprob": -12.4375, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.671875, "text": "" }, { @@ -45214,37 +45214,12 @@ }, { "id": 32000, - "logprob": -12.2421875, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -10.375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.859375, - "text": "" - }, - { - "id": 32000, - "logprob": -10.875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0234375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2734375, + "logprob": -10.359375, "text": "" }, { @@ -45254,12 +45229,37 @@ }, { "id": 32000, - "logprob": -10.5703125, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -10.71875, + "logprob": -10.875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2421875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.375, + "text": "" + }, + { + "id": 32000, + "logprob": -10.5625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.7421875, "text": "" }, { @@ -45269,22 +45269,22 @@ }, { "id": 32000, - "logprob": -11.046875, + "logprob": -11.0390625, "text": "" }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -10.9921875, "text": "" }, { "id": 32000, - "logprob": -10.609375, + "logprob": -10.6171875, "text": "" }, { "id": 32000, - "logprob": -12.265625, + "logprob": -12.2421875, "text": "" }, { @@ -45294,7 +45294,7 @@ }, { "id": 32000, - "logprob": -12.2578125, + "logprob": -12.265625, "text": "" }, { @@ -45304,7 +45304,7 @@ }, { "id": 32000, - "logprob": -12.8359375, + "logprob": -12.859375, "text": "" }, { @@ -45314,12 +45314,12 @@ }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.2109375, "text": "" }, { @@ -45329,17 +45329,17 @@ }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.2421875, "text": "" }, { "id": 32000, - "logprob": -11.265625, + "logprob": -11.3046875, "text": "" }, { "id": 32000, - "logprob": -10.3046875, + "logprob": -10.2890625, "text": "" }, { @@ -45349,7 +45349,12 @@ }, { "id": 32000, - "logprob": -11.0234375, + "logprob": -10.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2421875, "text": "" }, { @@ -45359,27 +45364,22 @@ }, { "id": 32000, - "logprob": -11.21875, + "logprob": -11.515625, "text": "" }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.1328125, "text": "" }, { "id": 32000, - "logprob": -11.3515625, + "logprob": -10.5546875, "text": "" }, { "id": 32000, - "logprob": -10.5703125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.3828125, + "logprob": -10.359375, "text": "" }, { @@ -45389,57 +45389,37 @@ }, { "id": 32000, - "logprob": -10.828125, + "logprob": -10.8125, "text": "" }, { "id": 32000, - "logprob": -10.859375, + "logprob": -10.8671875, "text": "" }, { "id": 32000, - "logprob": -11.2734375, + "logprob": -11.1796875, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3125, "text": "" }, { "id": 32000, - "logprob": -11.5859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.53125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.203125, + "logprob": -11.59375, "text": "" }, { @@ -45449,22 +45429,27 @@ }, { "id": 32000, - "logprob": -12.5078125, + "logprob": -13.4375, "text": "" }, { "id": 32000, - "logprob": -10.90625, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -10.6015625, + "logprob": -14.203125, "text": "" }, { "id": 32000, - "logprob": -10.6171875, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4453125, "text": "" }, { @@ -45474,22 +45459,12 @@ }, { "id": 32000, - "logprob": -11.25, + "logprob": -10.59375, "text": "" }, { "id": 32000, - "logprob": -10.9296875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.9765625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.078125, + "logprob": -10.609375, "text": "" }, { @@ -45497,6 +45472,31 @@ "logprob": -10.90625, "text": "" }, + { + "id": 32000, + "logprob": -11.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.921875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9921875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.890625, + "text": "" + }, { "id": 32000, "logprob": -10.8671875, @@ -45514,7 +45514,7 @@ }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9921875, "text": "" }, { @@ -45524,7 +45524,7 @@ }, { "id": 32000, - "logprob": -11.421875, + "logprob": -11.3828125, "text": "" }, { @@ -45534,82 +45534,32 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -13.5703125, + "logprob": -13.546875, "text": "" }, { "id": 32000, - "logprob": -13.9296875, + "logprob": -13.9921875, "text": "" }, { "id": 32000, - "logprob": -14.390625, + "logprob": -14.375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.359375, "text": "" }, { "id": 32000, - "logprob": -13.296875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8828125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.6953125, - "text": "" - }, - { - "id": 32000, - "logprob": -10.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -10.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.15625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.8671875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.109375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.78125, + "logprob": -13.328125, "text": "" }, { @@ -45619,17 +45569,67 @@ }, { "id": 32000, - "logprob": -13.8828125, + "logprob": -12.7109375, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -10.9609375, "text": "" }, { "id": 32000, - "logprob": -12.40625, + "logprob": -10.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4453125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -10.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.1328125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.75, + "text": "" + }, + { + "id": 32000, + "logprob": -10.875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.84375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4140625, "text": "" }, { @@ -45644,57 +45644,57 @@ }, { "id": 32000, - "logprob": -13.609375, + "logprob": -13.6171875, "text": "" }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.484375, "text": "" }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.8671875, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -14.9140625, + "logprob": -14.921875, "text": "" }, { "id": 32000, - "logprob": -13.3125, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -11.1484375, + "logprob": -11.171875, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.0703125, "text": "" }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -12.4375, "text": "" }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -11.2265625, + "logprob": -11.1875, "text": "" }, { @@ -45704,67 +45704,17 @@ }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.6640625, "text": "" }, { "id": 32000, - "logprob": -13.1875, + "logprob": -13.1953125, "text": "" }, { "id": 32000, - "logprob": -12.078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.1796875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.671875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.7421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8203125, + "logprob": -12.1328125, "text": "" }, { @@ -45774,12 +45724,62 @@ }, { "id": 32000, - "logprob": -12.4921875, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -11.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.203125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.1796875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9609375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.703125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.75, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.1953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5078125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.546875, "text": "" }, { @@ -45794,7 +45794,17 @@ }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.6640625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.5546875, "text": "" }, { @@ -45804,32 +45814,22 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -15.0234375, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -12.5703125, "text": "" }, { "id": 32000, - "logprob": -15.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.5546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8671875, + "logprob": -11.859375, "text": "" }, { @@ -45839,27 +45839,27 @@ }, { "id": 32000, - "logprob": -12.3515625, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.5390625, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9453125, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.4921875, "text": "" }, { @@ -45869,12 +45869,12 @@ }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3125, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.8984375, "text": "" }, { @@ -45889,12 +45889,12 @@ }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -14.0234375, + "logprob": -14.015625, "text": "" }, { @@ -45909,32 +45909,32 @@ }, { "id": 32000, - "logprob": -13.1484375, + "logprob": -13.140625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.9765625, "text": "" }, { "id": 32000, - "logprob": -13.8359375, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -13.7734375, + "logprob": -13.78125, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.359375, "text": "" }, { @@ -45949,12 +45949,12 @@ }, { "id": 32000, - "logprob": -11.9140625, + "logprob": -11.875, "text": "" }, { "id": 32000, - "logprob": -13.5859375, + "logprob": -13.6015625, "text": "" }, { @@ -45969,12 +45969,12 @@ }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -13.6015625, + "logprob": -13.59375, "text": "" }, { @@ -45984,7 +45984,7 @@ }, { "id": 32000, - "logprob": -14.5859375, + "logprob": -14.5703125, "text": "" }, { @@ -45994,12 +45994,12 @@ }, { "id": 32000, - "logprob": -13.40625, + "logprob": -13.4140625, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -11.7890625, "text": "" }, { @@ -46009,12 +46009,12 @@ }, { "id": 32000, - "logprob": -14.421875, + "logprob": -14.4296875, "text": "" }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.53125, "text": "" }, { @@ -46024,117 +46024,12 @@ }, { "id": 32000, - "logprob": -12.71875, + "logprob": -12.7265625, "text": "" }, { "id": 32000, - "logprob": -11.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1484375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.09375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8828125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.140625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.65625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.6953125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.421875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2890625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1015625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.8359375, - "text": "" - }, - { - "id": 32000, - "logprob": -16.359375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.34375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.75, - "text": "" - }, - { - "id": 32000, - "logprob": -11.7734375, - "text": "" - }, - { - "id": 32000, - "logprob": -15.765625, - "text": "" - }, - { - "id": 32000, - "logprob": -15.1171875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.234375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8671875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.265625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5234375, + "logprob": -11.609375, "text": "" }, { @@ -46144,17 +46039,32 @@ }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -14.5859375, + "logprob": -12.8828125, "text": "" }, { "id": 32000, - "logprob": -13.234375, + "logprob": -12.1484375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.671875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4296875, "text": "" }, { @@ -46164,42 +46074,132 @@ }, { "id": 32000, - "logprob": -13.8046875, + "logprob": -13.1796875, "text": "" }, { "id": 32000, - "logprob": -12.703125, + "logprob": -12.2421875, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -14.828125, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -16.359375, "text": "" }, { "id": 32000, - "logprob": -16.203125, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.75, "text": "" }, { "id": 32000, - "logprob": -15.78125, + "logprob": -11.7890625, "text": "" }, { "id": 32000, - "logprob": -12.734375, + "logprob": -15.765625, + "text": "" + }, + { + "id": 32000, + "logprob": -15.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.203125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.5546875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.2734375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.5859375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2890625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7734375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0, + "text": "" + }, + { + "id": 32000, + "logprob": -12.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -16.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -15.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7265625, "text": "" }, { @@ -46209,12 +46209,12 @@ }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -10.9765625, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.71875, "text": "" }, { @@ -46229,7 +46229,7 @@ }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.3203125, "text": "" }, { @@ -46239,17 +46239,17 @@ }, { "id": 32000, - "logprob": -14.21875, + "logprob": -14.2265625, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -14.1640625, + "logprob": -14.1875, "text": "" }, { @@ -46274,22 +46274,22 @@ }, { "id": 32000, - "logprob": -12.265625, + "logprob": -12.296875, "text": "" }, { "id": 32000, - "logprob": -12.203125, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -12.09375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -15.203125, + "logprob": -15.1875, "text": "" }, { @@ -46299,12 +46299,12 @@ }, { "id": 32000, - "logprob": -14.4140625, + "logprob": -14.421875, "text": "" }, { "id": 32000, - "logprob": -12.265625, + "logprob": -12.2890625, "text": "" }, { @@ -46324,27 +46324,27 @@ }, { "id": 32000, - "logprob": -13.5390625, + "logprob": -13.5859375, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.859375, "text": "" }, { "id": 32000, - "logprob": -12.2734375, + "logprob": -12.28125, "text": "" }, { "id": 32000, - "logprob": -13.015625, + "logprob": -13.1015625, "text": "" }, { "id": 32000, - "logprob": -13.9296875, + "logprob": -13.9453125, "text": "" }, { @@ -46359,27 +46359,37 @@ }, { "id": 32000, - "logprob": -12.75, + "logprob": -12.734375, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -12.203125, "text": "" }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.59375, "text": "" }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.3984375, "text": "" }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.5, + "text": "" + }, + { + "id": 32000, + "logprob": -11.765625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2265625, "text": "" }, { @@ -46389,22 +46399,12 @@ }, { "id": 32000, - "logprob": -13.203125, + "logprob": -15.3515625, "text": "" }, { "id": 32000, - "logprob": -11.7578125, - "text": "" - }, - { - "id": 32000, - "logprob": -15.3359375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.2109375, + "logprob": -13.1875, "text": "" }, { @@ -46414,22 +46414,22 @@ }, { "id": 32000, - "logprob": -13.3359375, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -11.875, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -12.5390625, + "logprob": -12.5625, "text": "" }, { "id": 32000, - "logprob": -13.4140625, + "logprob": -13.3203125, "text": "" }, { @@ -46439,27 +46439,27 @@ }, { "id": 32000, - "logprob": -10.7421875, + "logprob": -10.875, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.6328125, "text": "" }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.15625, "text": "" }, { "id": 32000, - "logprob": -11.8046875, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -11.9921875, "text": "" }, { @@ -46469,32 +46469,32 @@ }, { "id": 32000, - "logprob": -13.375, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9765625, "text": "" }, { "id": 32000, - "logprob": -12.5234375, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -13.359375, + "logprob": -13.3359375, "text": "" }, { @@ -46504,12 +46504,12 @@ }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.984375, "text": "" }, { @@ -46519,7 +46519,7 @@ }, { "id": 32000, - "logprob": -11.421875, + "logprob": -11.4140625, "text": "" }, { @@ -46529,17 +46529,17 @@ }, { "id": 32000, - "logprob": -11.953125, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.421875, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { @@ -46554,12 +46554,12 @@ }, { "id": 32000, - "logprob": -13.96875, + "logprob": -14.0, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.40625, "text": "" }, { @@ -46569,27 +46569,27 @@ }, { "id": 32000, - "logprob": -12.8046875, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -13.1796875, + "logprob": -13.15625, "text": "" }, { "id": 32000, - "logprob": -13.171875, + "logprob": -13.1640625, "text": "" }, { "id": 32000, - "logprob": -12.7734375, + "logprob": -12.78125, "text": "" }, { "id": 32000, - "logprob": -11.7578125, + "logprob": -11.765625, "text": "" }, { @@ -46599,37 +46599,37 @@ }, { "id": 32000, - "logprob": -12.2578125, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -14.6328125, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9296875, "text": "" }, { "id": 32000, - "logprob": -12.640625, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3125, "text": "" }, { "id": 32000, - "logprob": -11.421875, + "logprob": -11.4140625, "text": "" }, { "id": 32000, - "logprob": -11.390625, + "logprob": -11.3984375, "text": "" }, { @@ -46639,27 +46639,42 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.6328125, "text": "" }, { "id": 32000, - "logprob": -13.8671875, + "logprob": -13.875, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.1171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4921875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2890625, "text": "" }, { @@ -46669,27 +46684,12 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -13.59375, "text": "" }, { "id": 32000, - "logprob": -11.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.5703125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.796875, + "logprob": -11.8046875, "text": "" }, { @@ -46699,7 +46699,12 @@ }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.1015625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2265625, "text": "" }, { @@ -46709,12 +46714,7 @@ }, { "id": 32000, - "logprob": -13.1875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4765625, + "logprob": -12.4609375, "text": "" }, { @@ -46724,7 +46724,12 @@ }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.671875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.671875, "text": "" }, { @@ -46734,22 +46739,17 @@ }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -15.390625, "text": "" }, { "id": 32000, - "logprob": -15.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.9453125, + "logprob": -11.953125, "text": "" }, { @@ -46759,27 +46759,27 @@ }, { "id": 32000, - "logprob": -15.46875, + "logprob": -15.4921875, "text": "" }, { "id": 32000, - "logprob": -13.9375, + "logprob": -13.9296875, "text": "" }, { "id": 32000, - "logprob": -11.6171875, + "logprob": -11.609375, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -15.9375, + "logprob": -15.984375, "text": "" }, { @@ -46789,22 +46789,22 @@ }, { "id": 32000, - "logprob": -12.875, + "logprob": -12.8671875, "text": "" }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.4921875, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -14.1640625, + "logprob": -14.1875, "text": "" }, { @@ -46814,42 +46814,42 @@ }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -13.078125, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.8984375, "text": "" }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.421875, "text": "" }, { "id": 32000, - "logprob": -14.2421875, + "logprob": -14.2734375, "text": "" }, { "id": 32000, - "logprob": -12.125, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -12.2109375, "text": "" }, { @@ -46884,12 +46884,12 @@ }, { "id": 32000, - "logprob": -14.0546875, + "logprob": -14.0234375, "text": "" }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.53125, "text": "" }, { @@ -46899,17 +46899,17 @@ }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.0546875, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.328125, "text": "" }, { @@ -46924,22 +46924,22 @@ }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.078125, "text": "" }, { "id": 32000, - "logprob": -12.28125, + "logprob": -12.4296875, "text": "" }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.828125, "text": "" }, { "id": 32000, - "logprob": -12.7890625, + "logprob": -12.8046875, "text": "" }, { @@ -46949,7 +46949,7 @@ }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.59375, "text": "" }, { @@ -46959,32 +46959,32 @@ }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.921875, "text": "" }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.7734375, + "logprob": -12.78125, "text": "" }, { "id": 32000, - "logprob": -11.4140625, + "logprob": -11.3984375, "text": "" }, { "id": 32000, - "logprob": -14.0703125, + "logprob": -14.0546875, "text": "" }, { "id": 32000, - "logprob": -11.5625, + "logprob": -11.5546875, "text": "" }, { @@ -46994,142 +46994,67 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5234375, "text": "" }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -14.6015625, + "logprob": -14.609375, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -13.6015625, "text": "" }, { "id": 32000, - "logprob": -13.1953125, + "logprob": -13.28125, "text": "" }, { "id": 32000, - "logprob": -12.765625, + "logprob": -12.7734375, "text": "" }, { "id": 32000, - "logprob": -12.0703125, + "logprob": 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- { - "id": 32000, - "logprob": -12.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.9140625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1796875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.78125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.59375, + "logprob": -15.609375, "text": "" }, { @@ -47139,27 +47064,102 @@ }, { "id": 32000, - "logprob": -13.1328125, + "logprob": -13.1015625, "text": "" }, { "id": 32000, - "logprob": -12.421875, + "logprob": -12.0625, "text": "" }, { "id": 32000, - "logprob": -10.6640625, + "logprob": -12.234375, "text": "" }, { "id": 32000, - "logprob": -14.6875, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -15.765625, + "logprob": -12.1171875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4921875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0703125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.9296875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.1875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.59375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8671875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.109375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.6796875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.6640625, + "text": "" + }, + { + "id": 32000, + "logprob": -15.7890625, "text": "" }, { @@ -47169,52 +47169,52 @@ }, { "id": 32000, - "logprob": -11.234375, + "logprob": -11.2421875, "text": "" }, { "id": 32000, - "logprob": -14.5078125, + "logprob": -14.484375, "text": "" }, { "id": 32000, - "logprob": -12.5078125, + "logprob": -12.53125, "text": "" }, { "id": 32000, - "logprob": 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"text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -15.4140625, + "logprob": -15.46875, "text": "" }, { "id": 32000, - "logprob": -11.1015625, + "logprob": -11.1484375, "text": "" }, { "id": 32000, - "logprob": -10.5078125, + "logprob": -10.515625, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -11.0078125, + "logprob": -11.015625, "text": "" }, { "id": 32000, - "logprob": -11.2890625, + "logprob": -11.28125, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -12.453125, + "logprob": -12.3984375, "text": "" }, { "id": 32000, - "logprob": -16.53125, + "logprob": -16.375, "text": "" }, { "id": 32000, - "logprob": -13.6484375, + "logprob": -13.5625, "text": "" }, { @@ -47334,7 +47334,7 @@ }, { "id": 32000, - "logprob": -11.203125, + "logprob": -11.2109375, "text": "" }, { @@ -47344,47 +47344,47 @@ }, { "id": 32000, - "logprob": -12.1640625, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6640625, "text": "" }, { "id": 32000, - "logprob": -11.890625, + "logprob": -11.8828125, "text": "" }, { "id": 32000, - "logprob": -10.1953125, + "logprob": -9.9375, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.2734375, "text": "" }, { "id": 32000, - "logprob": -12.2109375, + "logprob": -12.203125, "text": "" }, { "id": 32000, - "logprob": -14.25, + "logprob": -14.2890625, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1953125, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.65625, "text": "" }, { @@ -47394,12 +47394,12 @@ }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -14.90625, + "logprob": -14.8359375, "text": "" }, { @@ -47409,7 +47409,7 @@ }, { "id": 32000, - "logprob": -11.921875, + 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-47919,22 +47919,22 @@ }, { "id": 32000, - "logprob": -14.984375, + "logprob": -15.0234375, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.9453125, "text": "" }, { @@ -47949,27 +47949,27 @@ }, { "id": 32000, - "logprob": -13.9375, + "logprob": -13.9609375, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -13.03125, "text": "" }, { "id": 32000, - "logprob": -16.15625, + "logprob": -16.140625, "text": "" }, { "id": 32000, - "logprob": -13.4375, + "logprob": -13.4609375, "text": "" }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.7265625, "text": "" }, { @@ -47979,12 +47979,12 @@ }, { "id": 32000, - "logprob": -13.25, + "logprob": -13.234375, "text": "" }, { "id": 32000, - "logprob": -13.2421875, + "logprob": -13.2578125, "text": "" }, { @@ -47994,12 +47994,12 @@ }, { "id": 32000, - "logprob": -13.8203125, + "logprob": -13.8359375, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.6171875, "text": "" }, { @@ -48009,12 +48009,12 @@ }, { "id": 32000, - "logprob": -14.4609375, + "logprob": -14.4765625, "text": "" }, { "id": 32000, - "logprob": -13.140625, + "logprob": -13.15625, "text": "" }, { @@ -48024,17 +48024,17 @@ }, { "id": 32000, - "logprob": -14.1015625, + "logprob": -14.046875, "text": "" }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.265625, "text": "" }, { "id": 32000, - "logprob": -12.53125, + "logprob": -12.6328125, "text": "" }, { @@ -48049,42 +48049,42 @@ }, { "id": 32000, - "logprob": -13.03125, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -10.3984375, + "logprob": -10.46875, "text": "" }, { "id": 32000, - "logprob": -13.1484375, + "logprob": -13.1640625, "text": "" }, { "id": 32000, - "logprob": -13.5625, + "logprob": -13.59375, "text": "" }, { "id": 32000, - "logprob": -13.4140625, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -14.09375, + "logprob": -14.140625, "text": "" }, { "id": 32000, - "logprob": -13.5390625, + "logprob": -13.5, "text": "" }, { "id": 32000, - "logprob": -14.1640625, + "logprob": -14.15625, "text": "" }, { @@ -48094,32 +48094,32 @@ }, { "id": 32000, - "logprob": -14.0625, + "logprob": -14.0546875, "text": "" }, { "id": 32000, - "logprob": -14.1484375, + "logprob": -14.1328125, "text": "" }, { "id": 32000, - "logprob": -13.859375, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -13.3359375, + "logprob": -13.34375, "text": "" }, { "id": 32000, - "logprob": -13.0078125, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.40625, "text": "" }, { @@ -48129,7 +48129,7 @@ }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.8125, "text": "" }, { @@ -48144,32 +48144,7 @@ }, { "id": 32000, - "logprob": -11.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.328125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7734375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.859375, + "logprob": -11.46875, "text": "" }, { @@ -48179,32 +48154,57 @@ }, { "id": 32000, - "logprob": -14.171875, + "logprob": -12.34375, "text": "" }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.6171875, "text": "" }, { "id": 32000, - "logprob": -14.5, + "logprob": -12.7578125, "text": "" }, { "id": 32000, - "logprob": -18.125, + "logprob": -14.8359375, "text": "" }, { "id": 32000, - "logprob": -14.6875, + "logprob": -13.0859375, "text": "" }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.1640625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.5078125, + "text": "" + }, + { + "id": 32000, + "logprob": -18.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.71875, "text": "" }, { @@ -48214,37 +48214,37 @@ }, { "id": 32000, - "logprob": -14.9765625, + "logprob": -14.9921875, "text": "" }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.8515625, "text": "" }, { "id": 32000, - "logprob": -15.34375, + "logprob": -15.40625, "text": "" }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.4296875, "text": "" }, { "id": 32000, - "logprob": -14.5390625, + "logprob": -14.5859375, "text": "" }, { "id": 32000, - "logprob": -15.265625, + "logprob": -15.2890625, "text": "" }, { "id": 32000, - "logprob": -13.625, + "logprob": -13.609375, "text": "" }, { @@ -48254,7 +48254,7 @@ }, { "id": 32000, - "logprob": -15.828125, + "logprob": -15.8359375, "text": "" }, { @@ -48269,12 +48269,12 @@ }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.328125, "text": "" }, { "id": 32000, - "logprob": -13.7734375, + "logprob": -13.78125, "text": "" }, { @@ -48284,27 +48284,27 @@ }, { "id": 32000, - "logprob": -13.484375, + "logprob": -13.4921875, "text": "" }, { "id": 32000, - "logprob": -13.40625, + "logprob": -13.4296875, "text": "" }, { "id": 32000, - "logprob": -13.265625, + "logprob": -13.28125, "text": "" }, { "id": 32000, - "logprob": -13.0234375, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -13.7578125, + "logprob": -13.75, "text": "" }, { @@ -48314,17 +48314,17 @@ }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.0546875, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -13.65625, "text": "" }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.6953125, "text": "" }, { @@ -48334,12 +48334,12 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5390625, "text": "" }, { "id": 32000, - "logprob": -13.3203125, + "logprob": -13.328125, "text": "" }, { @@ -48349,17 +48349,12 @@ }, { "id": 32000, - "logprob": -13.6328125, + "logprob": -13.640625, "text": "" }, { 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-14.9140625, "text": "" }, { "id": 32000, - "logprob": -13.5078125, + "logprob": -13.0390625, "text": "" }, { "id": 32000, - "logprob": -12.1875, + "logprob": -13.4453125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.78125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.9453125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0, + "text": "" + }, + { + "id": 32000, + "logprob": -13.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0859375, "text": "" }, { @@ -48514,7 +48514,7 @@ }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.2265625, "text": "" }, { @@ -48529,32 +48529,32 @@ }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.796875, "text": "" }, { "id": 32000, - "logprob": -12.625, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -13.6328125, + "logprob": -13.65625, "text": "" }, { "id": 32000, - "logprob": -12.8828125, + "logprob": -12.90625, "text": "" }, { @@ -48564,7 +48564,22 @@ }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.25, + "text": "" + }, + { + "id": 32000, + "logprob": -12.03125, "text": "" }, { @@ -48574,27 +48589,12 @@ }, { "id": 32000, - "logprob": -14.2421875, + "logprob": -13.0234375, "text": "" }, { "id": 32000, - "logprob": -12.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7578125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0, - "text": "" - }, - { - "id": 32000, - "logprob": -12.859375, + "logprob": -12.8671875, "text": "" }, { @@ -48604,17 +48604,17 @@ }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -10.5, + "logprob": -10.5078125, "text": "" }, { @@ -48624,12 +48624,12 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.375, "text": "" }, { "id": 32000, - "logprob": -11.0546875, + "logprob": -11.0625, "text": "" }, { @@ -48644,7 +48644,7 @@ }, { "id": 32000, - "logprob": -13.328125, + "logprob": -13.3515625, "text": "" }, { @@ -48654,12 +48654,12 @@ }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.53125, "text": "" }, { "id": 32000, - "logprob": -12.2109375, + "logprob": -12.203125, "text": "" }, { @@ -48669,7 +48669,7 @@ }, { "id": 32000, - "logprob": -13.828125, + "logprob": -13.8359375, "text": "" }, { @@ -48679,12 +48679,17 @@ }, { "id": 32000, - "logprob": -14.1953125, + "logprob": -14.1796875, "text": "" }, { "id": 32000, - "logprob": -14.0703125, + "logprob": -14.078125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0546875, "text": "" }, { @@ -48694,12 +48699,7 @@ }, { "id": 32000, - "logprob": -14.046875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0234375, + "logprob": -13.0625, "text": "" }, { @@ -48714,42 +48714,42 @@ }, { "id": 32000, - "logprob": -14.6875, + "logprob": -14.703125, "text": "" }, { "id": 32000, - "logprob": -14.3125, + "logprob": -14.296875, "text": "" }, { "id": 32000, - "logprob": -14.875, + "logprob": -14.8828125, "text": "" }, { "id": 32000, - "logprob": -14.1640625, + "logprob": -14.1796875, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.2265625, "text": "" }, { "id": 32000, - "logprob": -13.109375, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -11.9375, "text": "" }, { "id": 32000, - "logprob": -13.2890625, + "logprob": -13.3203125, "text": "" }, { @@ -48759,17 +48759,17 @@ }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.734375, "text": "" }, { "id": 32000, - "logprob": -13.3828125, + "logprob": -13.3984375, "text": "" }, { @@ -48779,47 +48779,47 @@ }, { "id": 32000, - "logprob": -12.140625, + "logprob": -12.15625, "text": "" }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.8984375, "text": "" }, { "id": 32000, - "logprob": -13.671875, + "logprob": -13.6953125, "text": "" }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -13.3125, + "logprob": -13.3203125, "text": "" }, { "id": 32000, - "logprob": -12.6640625, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.3515625, "text": "" }, { "id": 32000, - "logprob": -11.984375, + "logprob": -11.9921875, "text": "" }, { @@ -48829,7 +48829,7 @@ }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.03125, "text": "" }, { @@ -48849,7 +48849,7 @@ }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.6484375, "text": "" }, { @@ -48859,17 +48859,17 @@ }, { "id": 32000, 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}, { "id": 32000, - "logprob": -13.609375, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -12.8359375, + "logprob": -13.109375, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -12.9921875, + "logprob": -12.84375, "text": "" }, { "id": 32000, - "logprob": -14.046875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -13.9765625, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -13.796875, + "logprob": -13.625, "text": "" }, { "id": 32000, - "logprob": -13.203125, + "logprob": -12.8515625, "text": "" }, { "id": 32000, - "logprob": -13.46875, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -13.7265625, + "logprob": -13.0, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0703125, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7890625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2421875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.484375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7109375, "text": "" }, { @@ -48999,7 +48999,7 @@ }, { "id": 32000, - "logprob": -15.8046875, + "logprob": -15.796875, "text": "" }, { @@ -49009,27 +49009,27 @@ }, { "id": 32000, - "logprob": -12.3671875, + "logprob": -12.375, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -11.3203125, + "logprob": -11.328125, "text": "" }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.4296875, "text": "" }, { @@ -49044,17 +49044,17 @@ }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.7578125, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4765625, "text": "" }, { @@ -49064,37 +49064,37 @@ }, { "id": 32000, - "logprob": -11.6328125, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -15.234375, + "logprob": -15.2578125, "text": "" }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.8515625, "text": "" }, { "id": 32000, - "logprob": -13.2109375, + "logprob": -13.0078125, "text": "" }, { "id": 32000, - "logprob": -15.2421875, + "logprob": -15.3828125, "text": "" }, { "id": 32000, - "logprob": -11.1953125, + "logprob": -11.203125, "text": "" }, { @@ -49109,12 +49109,12 @@ }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -13.4296875, + "logprob": -13.4453125, "text": "" }, { @@ -49124,12 +49124,12 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5078125, "text": "" }, { "id": 32000, - "logprob": -12.0078125, + "logprob": -12.015625, "text": "" }, { @@ -49139,37 +49139,37 @@ }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.359375, "text": "" }, { "id": 32000, - "logprob": -11.140625, + "logprob": -11.171875, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -12.0234375, + "logprob": -12.015625, "text": "" }, { "id": 32000, - "logprob": -13.3671875, + "logprob": -13.375, "text": "" }, { @@ -49184,12 +49184,12 @@ }, { "id": 32000, - "logprob": -13.0390625, + "logprob": -13.046875, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.0859375, "text": "" }, { @@ -49199,7 +49199,7 @@ }, { "id": 32000, - "logprob": -13.46875, + "logprob": -13.4765625, "text": "" }, { @@ -49209,7 +49209,7 @@ }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.7265625, "text": "" }, { @@ -49219,122 +49219,122 @@ }, { "id": 32000, - "logprob": 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"id": 32000, - "logprob": -12.5625, + "logprob": -12.625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -12.390625, + "logprob": -12.3515625, "text": "" }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.6796875, "text": "" }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.484375, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.65625, "text": "" }, { "id": 32000, - "logprob": -11.7265625, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -11.6640625, + "logprob": -11.671875, "text": "" }, { "id": 32000, - "logprob": -14.640625, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.3203125, "text": "" }, { @@ -49344,7 +49344,12 @@ }, { "id": 32000, - "logprob": -11.8046875, + "logprob": -11.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5, "text": "" }, { @@ -49354,12 +49359,7 @@ }, { "id": 32000, - "logprob": -12.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.2734375, + "logprob": -11.265625, "text": "" }, { @@ -49369,7 +49369,7 @@ }, { "id": 32000, - "logprob": -13.1328125, + "logprob": -13.15625, "text": "" }, { @@ -49379,7 +49379,7 @@ }, { "id": 32000, - "logprob": -12.984375, + "logprob": -13.0390625, "text": "" }, { @@ -49389,7 +49389,7 @@ }, { "id": 32000, - "logprob": -13.9765625, + "logprob": -14.0390625, "text": "" }, { @@ -49399,12 +49399,12 @@ }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.359375, "text": "" }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.953125, "text": "" }, { @@ -49414,97 +49414,27 @@ }, { "id": 32000, - "logprob": -12.8359375, + "logprob": -12.8515625, "text": "" }, { "id": 32000, - "logprob": -13.0546875, + "logprob": -13.0859375, "text": "" }, { "id": 32000, - "logprob": -11.7578125, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.7109375, "text": "" }, { "id": 32000, - "logprob": -14.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.2421875, - "text": "" - }, - { - "id": 32000, - "logprob": -13.6875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.34375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.2421875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.0078125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.6171875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.859375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -15.4375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.109375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.40625, - "text": "" - }, - { - "id": 32000, - "logprob": -14.7890625, + "logprob": -14.4765625, "text": "" }, { @@ -49514,7 +49444,37 @@ }, { "id": 32000, - "logprob": -11.859375, + "logprob": -13.6875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.90625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.25, + "text": "" + }, + { + "id": 32000, + "logprob": -14.2890625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.0234375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.640625, "text": "" }, { @@ -49524,7 +49484,47 @@ }, { "id": 32000, - "logprob": -13.8203125, + "logprob": -13.453125, + "text": "" + }, + { + "id": 32000, + "logprob": -15.4375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.1171875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.4296875, + "text": "" + }, + { + "id": 32000, + "logprob": -14.515625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.21875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8671875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.8671875, "text": "" }, { @@ -49534,42 +49534,42 @@ }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.3671875, "text": "" }, { "id": 32000, - "logprob": -12.1484375, + "logprob": -12.1640625, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -12.46875, + "logprob": -12.4765625, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -12.0, + "logprob": -12.0234375, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.4140625, "text": "" }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.828125, "text": "" }, { @@ -49584,17 +49584,17 @@ }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -11.140625, + "logprob": -11.1328125, "text": "" }, { "id": 32000, - "logprob": -10.796875, + 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"logprob": -11.453125, + "logprob": -11.5, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.640625, "text": "" }, { "id": 32000, - "logprob": -11.875, + "logprob": -11.8984375, "text": "" }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.2890625, "text": "" }, { "id": 32000, - "logprob": -11.65625, + "logprob": -11.6484375, "text": "" }, { "id": 32000, - "logprob": -12.9140625, + "logprob": -12.8984375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.734375, "text": "" }, { @@ -51834,12 +51839,7 @@ }, { "id": 32000, - "logprob": -11.7265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3984375, + "logprob": -11.40625, "text": "" }, { @@ -51859,32 +51859,32 @@ }, { "id": 32000, - "logprob": -12.875, + "logprob": -12.890625, "text": "" }, { "id": 32000, - "logprob": -11.1171875, + "logprob": -11.03125, "text": "" }, { "id": 32000, - "logprob": -13.3984375, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -12.6484375, + 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"logprob": -15.15625, + "logprob": -15.1875, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -12.0, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.5546875, + "logprob": -13.515625, "text": "" }, { "id": 32000, - "logprob": -11.5546875, + "logprob": -11.5703125, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.703125, "text": "" }, { @@ -52299,27 +52299,27 @@ }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5546875, "text": "" }, { "id": 32000, - "logprob": -13.421875, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -11.1875, + "logprob": -11.1953125, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.7578125, "text": "" }, { @@ -52329,7 +52329,7 @@ }, { "id": 32000, - "logprob": -13.2734375, + "logprob": -13.2421875, "text": "" }, { @@ -52339,62 +52339,62 @@ }, { "id": 32000, - "logprob": -11.34375, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.3984375, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -11.1796875, + "logprob": -11.1875, "text": "" }, { "id": 32000, - "logprob": -13.375, + "logprob": -13.359375, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -12.6875, + "logprob": -12.6796875, "text": "" }, { "id": 32000, - "logprob": -12.9453125, + "logprob": -12.9140625, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -12.3203125, "text": "" }, { "id": 32000, - "logprob": -13.59375, + "logprob": -13.5625, "text": "" }, { @@ -52402,21 +52402,6 @@ "logprob": -13.3203125, "text": "" }, - { - "id": 32000, - "logprob": -12.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1328125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6796875, - "text": "" - }, { "id": 32000, "logprob": -12.8046875, @@ -52424,12 +52409,17 @@ }, { "id": 32000, - "logprob": -12.78125, + "logprob": -13.1171875, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -11.6484375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.828125, "text": "" }, { @@ -52437,6 +52427,16 @@ "logprob": -12.78125, "text": "" }, + { + "id": 32000, + "logprob": -12.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.7578125, + "text": "" + }, { "id": 32000, "logprob": -11.484375, @@ -52444,37 +52444,37 @@ }, { "id": 32000, - "logprob": -12.75, + "logprob": -11.5390625, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.6015625, "text": "" }, { "id": 32000, - "logprob": -11.6328125, + "logprob": -11.640625, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -12.6015625, + "logprob": -12.65625, "text": "" }, { "id": 32000, - "logprob": -11.3046875, + "logprob": -11.3125, "text": "" }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.265625, "text": "" }, { @@ -52509,7 +52509,7 @@ }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.046875, "text": "" }, { @@ -52519,7 +52519,7 @@ }, { "id": 32000, - "logprob": -11.71875, + "logprob": -11.7109375, "text": "" }, { @@ -52529,27 +52529,27 @@ }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7734375, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3671875, "text": "" }, { "id": 32000, - "logprob": -14.3046875, + "logprob": -14.3125, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.7578125, "text": "" }, { @@ -52559,142 +52559,12 @@ }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.3203125, "text": "" }, { "id": 32000, - "logprob": -14.203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.25, - "text": "" - }, - { - "id": 32000, - "logprob": -11.3125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.078125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0703125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.0703125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.640625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.53125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.859375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5546875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6640625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4765625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.65625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.4765625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.9609375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8828125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.6171875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.5390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.859375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0703125, + "logprob": -14.1640625, "text": "" }, { @@ -52704,7 +52574,137 @@ }, { "id": 32000, - "logprob": -12.1171875, + "logprob": -11.3125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0703125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.2578125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.7265625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.046875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.6171875, + "text": "" + }, + { + "id": 32000, + "logprob": -13.3828125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8515625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.15625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5703125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5546875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.65625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.46875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6484375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.7734375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.0859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.5390625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.078125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.15625, "text": "" }, { @@ -52719,12 +52719,12 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.9296875, + "logprob": -11.9375, "text": "" }, { @@ -52734,17 +52734,17 @@ }, { "id": 32000, - "logprob": -11.5234375, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.7890625, "text": "" }, { "id": 32000, - "logprob": -13.0234375, + "logprob": -13.0390625, "text": "" }, { @@ -52754,12 +52754,12 @@ }, { "id": 32000, - "logprob": -12.5859375, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -10.75, + "logprob": -10.7578125, "text": "" }, { @@ -52769,12 +52769,12 @@ }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.484375, "text": "" }, { "id": 32000, - "logprob": -11.2578125, + "logprob": -11.265625, "text": "" }, { @@ -52784,27 +52784,27 @@ }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.7890625, "text": "" }, { "id": 32000, - "logprob": -11.53125, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -11.25, + "logprob": -11.234375, "text": "" }, { "id": 32000, - "logprob": -12.453125, + "logprob": -12.46875, "text": "" }, { @@ -52814,7 +52814,7 @@ }, { "id": 32000, - "logprob": -11.890625, + "logprob": -11.8515625, "text": "" }, { @@ -52829,17 +52829,17 @@ }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.7890625, "text": "" }, { "id": 32000, - "logprob": -12.15625, + "logprob": -12.1953125, "text": "" }, { "id": 32000, - "logprob": -13.296875, + "logprob": -13.28125, "text": "" }, { @@ -52849,7 +52849,7 @@ }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.34375, "text": "" }, { @@ -52859,27 +52859,27 @@ }, { "id": 32000, - "logprob": -13.0078125, + "logprob": -13.0, "text": "" }, { "id": 32000, - "logprob": -15.25, + "logprob": -15.28125, "text": "" }, { "id": 32000, - "logprob": -14.78125, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0234375, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.109375, "text": "" }, { @@ -52894,32 +52894,32 @@ }, { "id": 32000, - "logprob": -14.7265625, + "logprob": -14.7109375, "text": "" }, { "id": 32000, - "logprob": -10.015625, + "logprob": -10.0234375, "text": "" }, { "id": 32000, - "logprob": -11.8515625, + "logprob": -11.46875, "text": "" }, { "id": 32000, - "logprob": -13.9609375, + "logprob": -13.9765625, "text": "" }, { "id": 32000, - "logprob": -14.2734375, + "logprob": -14.3046875, "text": "" }, { "id": 32000, - "logprob": -14.9140625, + "logprob": -14.890625, "text": "" }, { @@ -52934,12 +52934,12 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.328125, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.3515625, "text": "" }, { @@ -52949,47 +52949,47 @@ }, { "id": 32000, - "logprob": -12.421875, + "logprob": -12.4296875, "text": "" }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.890625, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.7421875, "text": "" }, { "id": 32000, - "logprob": -11.546875, + "logprob": -11.5625, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.1796875, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -11.6171875, "text": "" }, { "id": 32000, - "logprob": -12.7265625, + "logprob": -12.578125, "text": "" }, { "id": 32000, - "logprob": -12.4609375, + "logprob": -12.4765625, "text": "" }, { @@ -53004,17 +53004,17 @@ }, { "id": 32000, - "logprob": -13.0703125, + "logprob": -13.125, "text": "" }, { "id": 32000, - "logprob": -12.65625, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8125, "text": "" }, { @@ -53029,52 +53029,52 @@ }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.859375, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.375, + "logprob": -12.390625, "text": "" }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.3515625, "text": "" }, { "id": 32000, - "logprob": -12.015625, + "logprob": -12.0078125, "text": "" }, { "id": 32000, - "logprob": -11.75, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -12.2265625, + "logprob": -12.25, "text": "" }, { @@ -53084,17 +53084,17 @@ }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -12.4765625, + "logprob": -12.4921875, "text": "" }, { @@ -53104,72 +53104,12 @@ }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.1015625, "text": "" }, { "id": 32000, - "logprob": -14.96875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.0859375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": -14.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1328125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.890625, - "text": "" - }, - { - "id": 32000, - "logprob": -13.46875, - "text": "" - }, - { - "id": 32000, - "logprob": -14.984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.90625, - "text": "" - }, - { - "id": 32000, - "logprob": -10.59375, - "text": "" - }, - { - "id": 32000, - "logprob": -15.0, - "text": "" - }, - { - "id": 32000, - "logprob": -12.328125, + "logprob": -15.0625, "text": "" }, { @@ -53179,62 +53119,72 @@ }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.2578125, "text": "" }, { "id": 32000, - "logprob": -11.5703125, + "logprob": -14.4453125, "text": "" }, { "id": 32000, - "logprob": -12.796875, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -11.78125, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -11.1015625, + "logprob": -14.828125, "text": "" }, { "id": 32000, - "logprob": -11.5859375, + "logprob": -13.5546875, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -14.96875, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -13.9296875, "text": "" }, { "id": 32000, - "logprob": -11.484375, + "logprob": -10.5859375, "text": "" }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -15.0078125, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -12.3359375, "text": "" }, { "id": 32000, - "logprob": -12.9765625, + "logprob": -12.09375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.6015625, + "text": "" + }, + { + "id": 32000, + "logprob": -11.578125, "text": "" }, { @@ -53244,37 +53194,87 @@ }, { "id": 32000, - "logprob": -12.03125, + "logprob": -11.78125, "text": "" }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -11.1171875, "text": "" }, { "id": 32000, - "logprob": -12.328125, + "logprob": -11.8046875, "text": "" }, { "id": 32000, - "logprob": -10.8359375, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -10.7109375, + "logprob": -11.4609375, "text": "" }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -11.4609375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.953125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.859375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0390625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.28125, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.8203125, + "text": "" + }, + { + "id": 32000, + "logprob": -10.6953125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.9375, "text": "" }, { @@ -53289,27 +53289,27 @@ }, { "id": 32000, - "logprob": -11.171875, + "logprob": -11.1796875, "text": "" }, { "id": 32000, - "logprob": -12.296875, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.9609375, "text": "" }, { "id": 32000, - "logprob": -11.625, + "logprob": -11.6328125, "text": "" }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.4453125, "text": "" }, { @@ -53324,17 +53324,47 @@ }, { "id": 32000, - "logprob": -11.1171875, + "logprob": -11.09375, "text": "" }, { "id": 32000, - "logprob": -11.5390625, + "logprob": -11.546875, "text": "" }, { "id": 32000, - "logprob": -15.4375, + "logprob": -15.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.875, + "text": "" + }, + { + "id": 32000, + "logprob": -11.484375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -10.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.234375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.8359375, "text": "" }, { @@ -53342,36 +53372,6 @@ "logprob": -11.8515625, "text": "" }, - { - "id": 32000, - "logprob": -11.4765625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8046875, - "text": "" - }, - { - "id": 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"logprob": -12.3515625, "text": "" }, { @@ -53434,7 +53434,7 @@ }, { "id": 32000, - "logprob": -11.8671875, + "logprob": -11.90625, "text": "" }, { @@ -53444,7 +53444,7 @@ }, { "id": 32000, - "logprob": -11.578125, + "logprob": -11.5703125, "text": "" }, { @@ -53454,7 +53454,7 @@ }, { "id": 32000, - "logprob": -14.703125, + "logprob": -14.6875, "text": "" }, { @@ -53464,12 +53464,12 @@ }, { "id": 32000, - "logprob": -11.4921875, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -12.78125, + "logprob": -12.8125, "text": "" }, { @@ -53489,12 +53489,12 @@ }, { "id": 32000, - "logprob": -12.4296875, + "logprob": -12.46875, "text": "" }, { "id": 32000, - "logprob": -12.0859375, + "logprob": -12.0703125, "text": "" }, { @@ -53524,7 +53524,7 @@ }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.9765625, "text": "" }, { @@ -53534,32 +53534,32 @@ }, { "id": 32000, - "logprob": -10.8828125, + "logprob": -10.890625, "text": "" }, { "id": 32000, - "logprob": -11.015625, + 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"text": "" - }, - { - "id": 32000, - "logprob": -10.65625, + "logprob": -10.6640625, "text": "" }, { @@ -55149,7 +55149,7 @@ }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9140625, "text": "" }, { @@ -55179,22 +55179,22 @@ }, { "id": 32000, - "logprob": -10.796875, + "logprob": -10.8046875, "text": "" }, { "id": 32000, - "logprob": -12.359375, + "logprob": -12.34375, "text": "" }, { "id": 32000, - "logprob": -11.4375, + "logprob": -11.4296875, "text": "" }, { "id": 32000, - "logprob": -14.28125, + "logprob": -14.265625, "text": "" }, { @@ -55204,17 +55204,17 @@ }, { "id": 32000, - "logprob": -10.96875, + "logprob": -10.9765625, "text": "" }, { "id": 32000, - "logprob": -12.0546875, + "logprob": -12.046875, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.6171875, "text": "" }, { @@ -55224,12 +55224,12 @@ }, { "id": 32000, - "logprob": -12.4453125, + "logprob": -12.4296875, "text": "" }, { "id": 32000, - "logprob": -11.875, + "logprob": -11.890625, 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"text": "" }, { "id": 32000, - "logprob": -12.9375, + "logprob": -12.953125, "text": "" }, { @@ -55394,12 +55394,12 @@ }, { "id": 32000, - "logprob": -12.703125, + "logprob": -13.03125, "text": "" }, { "id": 32000, - "logprob": -17.265625, + "logprob": -17.34375, "text": "" }, { @@ -55409,32 +55409,32 @@ }, { "id": 32000, - "logprob": -12.4765625, + "logprob": -12.46875, "text": "" }, { "id": 32000, - "logprob": -12.3515625, + "logprob": -12.328125, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -13.5546875, + "logprob": -13.3828125, "text": "" }, { "id": 32000, - "logprob": -14.15625, + "logprob": -14.1953125, "text": "" }, { "id": 32000, - "logprob": -12.9609375, + "logprob": -12.984375, "text": "" }, { @@ -55449,12 +55449,12 @@ }, { "id": 32000, - "logprob": -11.1171875, + "logprob": -11.125, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9375, "text": "" }, { @@ -55464,7 +55464,7 @@ }, { "id": 32000, - "logprob": -11.2421875, + "logprob": -11.25, "text": "" }, { @@ -55474,37 +55474,37 @@ }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.8046875, "text": "" }, { "id": 32000, - "logprob": -11.2421875, + "logprob": -11.25, "text": "" }, { "id": 32000, - "logprob": -11.8203125, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -14.2890625, + "logprob": -14.3046875, "text": "" }, { "id": 32000, - "logprob": -11.1015625, + "logprob": -11.0859375, "text": "" }, { "id": 32000, - "logprob": -11.65625, + "logprob": -11.671875, "text": "" }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.2109375, "text": "" }, { @@ -55514,7 +55514,7 @@ }, { "id": 32000, - "logprob": -12.234375, + "logprob": -12.2578125, "text": "" }, { @@ -55524,12 +55524,12 @@ }, { "id": 32000, - "logprob": -11.8359375, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -13.8125, + "logprob": -13.8203125, "text": "" }, { @@ -55544,42 +55544,42 @@ }, { "id": 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-56074,7 +56074,7 @@ }, { "id": 32000, - "logprob": -11.9921875, + "logprob": -11.984375, "text": "" }, { @@ -56084,17 +56084,17 @@ }, { "id": 32000, - "logprob": -13.71875, + "logprob": -13.375, "text": "" }, { "id": 32000, - "logprob": -15.4375, + "logprob": -15.4921875, "text": "" }, { "id": 32000, - "logprob": -12.7890625, + "logprob": -12.921875, "text": "" }, { @@ -56104,17 +56104,17 @@ }, { "id": 32000, - "logprob": -13.359375, + "logprob": -13.3671875, "text": "" }, { "id": 32000, - "logprob": -12.0703125, + "logprob": -12.0546875, "text": "" }, { "id": 32000, - "logprob": -15.203125, + "logprob": -15.21875, "text": "" }, { @@ -56129,12 +56129,12 @@ }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.8984375, "text": "" }, { "id": 32000, - "logprob": -13.8359375, + "logprob": -13.8515625, "text": "" }, { @@ -56144,7 +56144,7 @@ }, { "id": 32000, - "logprob": -17.453125, + "logprob": -17.4375, "text": "" }, { @@ -56159,7 +56159,7 @@ }, { "id": 32000, - "logprob": 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-16.390625, + "logprob": -16.46875, "text": "" }, { "id": 32000, - "logprob": -11.921875, + "logprob": -11.9296875, "text": "" }, { @@ -56254,7 +56254,7 @@ }, { "id": 32000, - "logprob": -12.859375, + "logprob": -12.8515625, "text": "" }, { @@ -56264,22 +56264,22 @@ }, { "id": 32000, - "logprob": -16.515625, + "logprob": -16.53125, "text": "" }, { "id": 32000, - "logprob": -12.1328125, + "logprob": -12.140625, "text": "" }, { "id": 32000, - "logprob": -13.3671875, + "logprob": -13.359375, "text": "" }, { "id": 32000, - "logprob": -14.0, + "logprob": -13.96875, "text": "" }, { @@ -56289,22 +56289,22 @@ }, { "id": 32000, - "logprob": -11.6015625, + "logprob": -11.578125, "text": "" }, { "id": 32000, - "logprob": -12.8515625, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.5078125, "text": "" }, { @@ -56324,77 +56324,77 @@ }, { "id": 32000, - "logprob": 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"" + }, + { + "id": 32000, + "logprob": -11.9140625, "text": "" }, { @@ -56452,36 +56482,6 @@ "logprob": -13.0390625, "text": "" }, - { - "id": 32000, - "logprob": -12.0234375, - "text": "" - }, - { - "id": 32000, - "logprob": -17.03125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2421875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.6171875, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8984375, - "text": "" - }, - { - "id": 32000, - "logprob": -13.1328125, - "text": "" - }, { "id": 32000, "logprob": -11.84375, @@ -56489,12 +56489,12 @@ }, { "id": 32000, - "logprob": -15.25, + "logprob": -15.265625, "text": "" }, { "id": 32000, - "logprob": -16.96875, + "logprob": -16.984375, "text": "" }, { @@ -56504,17 +56504,17 @@ }, { "id": 32000, - "logprob": -11.4609375, + "logprob": -11.453125, "text": "" }, { "id": 32000, - "logprob": -11.796875, + "logprob": -11.8046875, "text": "" }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.140625, "text": "" }, { 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"logprob": -16.796875, + "logprob": -16.8125, "text": "" }, { "id": 32000, - "logprob": -11.90625, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -14.359375, + "logprob": -14.375, "text": "" }, { @@ -56604,112 +56604,17 @@ }, { "id": 32000, - "logprob": -13.703125, + "logprob": -13.671875, "text": "" }, { "id": 32000, - "logprob": -12.984375, + "logprob": -12.9921875, "text": "" }, { "id": 32000, - "logprob": -12.2734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.2265625, - "text": "" - }, - { - "id": 32000, - "logprob": -17.5625, - "text": "" - }, - { - "id": 32000, - "logprob": -12.640625, - "text": "" - }, - { - "id": 32000, - "logprob": -15.1015625, - "text": "" - }, - { - "id": 32000, - "logprob": -16.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8203125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3203125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.265625, - "text": "" - }, - { - "id": 32000, - "logprob": 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"id": 32000, + "logprob": -11.609375, "text": "" }, { @@ -56779,17 +56779,17 @@ }, { "id": 32000, - "logprob": -15.6171875, + "logprob": -15.640625, "text": "" }, { "id": 32000, - "logprob": -12.1015625, + "logprob": -12.109375, "text": "" }, { "id": 32000, - "logprob": -14.25, + "logprob": -14.265625, "text": "" }, { @@ -56804,17 +56804,17 @@ }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.828125, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.6875, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0390625, "text": "" }, { @@ -56824,7 +56824,7 @@ }, { "id": 32000, - "logprob": -12.3046875, + "logprob": -12.3203125, "text": "" }, { @@ -56834,37 +56834,37 @@ }, { "id": 32000, - "logprob": -14.2265625, + "logprob": -14.3046875, "text": "" }, { "id": 32000, - "logprob": -14.890625, + "logprob": -14.90625, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8046875, "text": "" }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5390625, "text": "" }, { "id": 32000, - "logprob": -19.421875, + "logprob": -19.4375, "text": "" }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2265625, "text": "" }, { "id": 32000, - "logprob": -13.7109375, + "logprob": -13.6796875, "text": "" }, { @@ -56879,7 +56879,7 @@ }, { "id": 32000, - "logprob": -12.21875, + "logprob": -12.2109375, "text": "" }, { @@ -56889,27 +56889,27 @@ }, { "id": 32000, - "logprob": -12.3359375, + "logprob": -12.328125, "text": "" }, { "id": 32000, - "logprob": -17.65625, + "logprob": -17.625, "text": "" }, { "id": 32000, - "logprob": -17.703125, + "logprob": -17.75, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5234375, "text": "" }, { "id": 32000, - "logprob": -12.015625, + "logprob": -12.0234375, "text": "" }, { @@ -56919,7 +56919,7 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.5859375, "text": "" }, { @@ -56929,37 +56929,37 @@ }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3046875, "text": "" }, { "id": 32000, - "logprob": -13.5, + "logprob": -13.4921875, "text": "" }, { "id": 32000, - "logprob": -10.5625, + "logprob": -10.5703125, "text": "" }, { "id": 32000, - "logprob": -12.28125, + "logprob": -12.2734375, "text": "" }, { "id": 32000, - "logprob": -12.96875, + "logprob": -12.9375, "text": "" }, { "id": 32000, - "logprob": -13.2734375, + "logprob": -13.3046875, "text": "" }, { "id": 32000, - "logprob": -14.34375, + "logprob": -14.3359375, "text": "" }, { @@ -56974,17 +56974,17 @@ }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -14.859375, + "logprob": -14.9375, "text": "" }, { "id": 32000, - "logprob": -11.03125, + "logprob": -11.0390625, "text": "" }, { @@ -57009,27 +57009,27 @@ }, { "id": 32000, - "logprob": -11.8828125, + "logprob": -11.90625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.3359375, "text": "" }, { "id": 32000, - "logprob": 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"logprob": -11.53125, + "logprob": -11.5625, "text": "" }, { "id": 32000, - "logprob": -14.859375, + "logprob": -14.8125, "text": "" }, { @@ -57114,12 +57114,12 @@ }, { "id": 32000, - "logprob": -12.4375, + "logprob": -12.4453125, "text": "" }, { "id": 32000, - "logprob": -12.671875, + "logprob": -12.6484375, "text": "" }, { @@ -57129,12 +57129,12 @@ }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1484375, "text": "" }, { "id": 32000, - "logprob": -14.8828125, + "logprob": -14.8671875, "text": "" }, { @@ -57149,27 +57149,27 @@ }, { "id": 32000, - "logprob": -12.4453125, + "logprob": -12.453125, "text": "" }, { "id": 32000, - "logprob": -13.2578125, + "logprob": -13.21875, "text": "" }, { "id": 32000, - "logprob": -11.1328125, + "logprob": -11.1484375, "text": "" }, { "id": 32000, - "logprob": -16.9375, + "logprob": -16.890625, "text": "" }, { "id": 32000, - "logprob": -12.890625, + "logprob": -12.84375, "text": "" }, { @@ -57179,12 +57179,12 @@ }, { "id": 32000, - "logprob": -14.6484375, + "logprob": -14.625, "text": "" }, { "id": 32000, - "logprob": -10.9296875, + "logprob": -10.9375, "text": "" }, { @@ -57194,7 +57194,7 @@ }, { "id": 32000, - "logprob": -17.15625, + "logprob": -17.6875, "text": "" }, { @@ -57209,17 +57209,17 @@ }, { "id": 32000, - "logprob": -11.28125, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -11.2109375, + "logprob": -11.203125, "text": "" }, { @@ -57234,17 +57234,17 @@ }, { "id": 32000, - "logprob": -14.3046875, + "logprob": -14.3125, "text": "" }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -13.1875, + "logprob": -13.203125, "text": "" }, { @@ -57254,22 +57254,22 @@ }, { "id": 32000, - "logprob": -12.8671875, + "logprob": -12.8515625, "text": "" }, { "id": 32000, - "logprob": -11.0703125, + "logprob": -11.078125, "text": "" }, { "id": 32000, - "logprob": -11.59375, + "logprob": -11.5859375, "text": "" }, { "id": 32000, - "logprob": -12.578125, + "logprob": -12.6015625, "text": "" }, { @@ -57279,12 +57279,12 @@ }, { "id": 32000, - "logprob": -12.71875, + "logprob": -12.671875, "text": "" }, { "id": 32000, - "logprob": -13.4453125, + "logprob": -13.46875, "text": "" }, { @@ -57294,22 +57294,22 @@ }, { "id": 32000, - "logprob": -12.53125, + "logprob": -12.5703125, "text": "" }, { "id": 32000, - "logprob": -12.0390625, + "logprob": -12.03125, "text": "" }, { "id": 32000, - "logprob": -16.53125, + "logprob": -16.46875, "text": "" }, { "id": 32000, - "logprob": -13.1484375, + "logprob": -13.140625, "text": "" }, { @@ -57319,17 +57319,17 @@ }, { "id": 32000, - "logprob": -14.171875, + "logprob": -14.109375, "text": "" }, { "id": 32000, - "logprob": -11.828125, + "logprob": -11.84375, "text": "" }, { "id": 32000, - "logprob": -14.3359375, + "logprob": -14.34375, "text": "" }, { @@ -57339,47 +57339,47 @@ }, { "id": 32000, - "logprob": -11.9375, + "logprob": -11.921875, "text": "" }, { "id": 32000, - "logprob": -12.8203125, + "logprob": -12.8359375, "text": "" }, { "id": 32000, - "logprob": -12.890625, + "logprob": -12.8984375, "text": "" }, { "id": 32000, - "logprob": -11.46875, + "logprob": -11.4765625, "text": "" }, { "id": 32000, - "logprob": -11.609375, + "logprob": -11.625, "text": "" }, { "id": 32000, - "logprob": -14.234375, + "logprob": -14.1484375, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -14.71875, + "logprob": -14.8046875, "text": "" }, { "id": 32000, - "logprob": -12.6328125, + "logprob": -12.625, "text": "" }, { @@ -57394,12 +57394,12 @@ }, { "id": 32000, - "logprob": -15.0, + "logprob": -14.6484375, "text": "" }, { "id": 32000, - "logprob": -13.53125, + "logprob": -13.546875, "text": "" }, { @@ -57409,7 +57409,7 @@ }, { "id": 32000, - "logprob": -11.3828125, + "logprob": -11.390625, "text": "" }, { @@ -57424,7 +57424,7 @@ }, { "id": 32000, - "logprob": -12.921875, + "logprob": -12.9375, "text": "" }, { @@ -57434,12 +57434,12 @@ }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.921875, "text": "" }, { "id": 32000, - "logprob": -13.4453125, + "logprob": -13.46875, "text": "" }, { @@ -57449,17 +57449,17 @@ }, { "id": 32000, - "logprob": -12.4140625, + "logprob": -12.421875, "text": "" }, { "id": 32000, - "logprob": -13.78125, + "logprob": -13.7734375, "text": "" }, { "id": 32000, - "logprob": -12.171875, + "logprob": -12.1640625, "text": "" }, { @@ -57469,22 +57469,22 @@ }, { "id": 32000, - "logprob": -12.1953125, + "logprob": -12.2109375, "text": "" }, { "id": 32000, - "logprob": -12.9296875, + "logprob": -12.90625, "text": "" }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.7421875, + "logprob": -11.75, "text": "" }, { @@ -57494,7 +57494,7 @@ }, { "id": 32000, - "logprob": -16.40625, + "logprob": -16.390625, "text": "" }, { @@ -57504,17 +57504,17 @@ }, { "id": 32000, - "logprob": -11.96875, + "logprob": -11.984375, "text": "" }, { "id": 32000, - "logprob": -13.921875, + "logprob": -13.8984375, "text": "" }, { "id": 32000, - "logprob": -14.9375, + "logprob": -14.9921875, "text": "" }, { @@ -57524,27 +57524,27 @@ }, { "id": 32000, - "logprob": -12.0703125, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -16.03125, + "logprob": -16.046875, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.6484375, "text": "" }, { "id": 32000, - "logprob": -15.3125, + "logprob": -15.3203125, "text": "" }, { @@ -57554,17 +57554,17 @@ }, { "id": 32000, - "logprob": -11.703125, + "logprob": -11.7109375, "text": "" }, { "id": 32000, - "logprob": -12.390625, + "logprob": -12.3984375, "text": "" }, { "id": 32000, - "logprob": -14.484375, + "logprob": -15.4765625, "text": "" }, { @@ -57574,37 +57574,12 @@ }, { "id": 32000, - "logprob": -14.5703125, + "logprob": -14.5859375, "text": "" }, { "id": 32000, - "logprob": -11.6328125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.1171875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.203125, - "text": "" - }, - { - "id": 32000, - "logprob": -11.8046875, - "text": "" - }, - { - "id": 32000, - "logprob": -15.828125, - "text": "" - }, - { - "id": 32000, - "logprob": -17.640625, + "logprob": -11.5859375, "text": "" }, { @@ -57612,6 +57587,31 @@ "logprob": -12.1015625, "text": "" }, + { + "id": 32000, + "logprob": -12.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.796875, + "text": "" + }, + { + "id": 32000, + "logprob": -15.8359375, + "text": "" + }, + { + "id": 32000, + "logprob": -17.65625, + "text": "" + }, + { + "id": 32000, + "logprob": -12.09375, + "text": "" + }, { "id": 32000, "logprob": -11.84375, @@ -57619,17 +57619,17 @@ }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.71875, "text": "" }, { "id": 32000, - "logprob": -11.515625, + "logprob": -11.5546875, "text": "" }, { "id": 32000, - "logprob": -13.578125, + "logprob": -13.59375, "text": "" }, { @@ -57639,17 +57639,17 @@ }, { "id": 32000, - "logprob": -16.703125, + "logprob": -16.6875, "text": "" }, { "id": 32000, - "logprob": -13.0625, + "logprob": -13.03125, "text": "" }, { "id": 32000, - "logprob": -14.90625, + "logprob": -14.859375, "text": "" }, { @@ -57659,42 +57659,42 @@ }, { "id": 32000, - "logprob": -15.21875, + "logprob": -15.203125, "text": "" }, { "id": 32000, - "logprob": -11.84375, + "logprob": -11.8359375, "text": "" }, { "id": 32000, - "logprob": -11.359375, + "logprob": -11.4140625, "text": "" }, { "id": 32000, - "logprob": -11.328125, + "logprob": -11.265625, "text": "" }, { "id": 32000, - "logprob": -17.0, + "logprob": -16.8125, "text": "" }, { "id": 32000, - "logprob": -13.046875, + "logprob": -13.0625, "text": "" }, { "id": 32000, - "logprob": -14.734375, + "logprob": -14.8671875, "text": "" }, { "id": 32000, - "logprob": -12.125, + "logprob": -12.1171875, "text": "" }, { @@ -57709,7 +57709,7 @@ }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.359375, "text": "" }, { @@ -57719,7 +57719,7 @@ }, { "id": 32000, - "logprob": -13.75, + "logprob": -13.765625, "text": "" }, { @@ -57734,22 +57734,22 @@ }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.6015625, "text": "" }, { "id": 32000, - "logprob": -12.046875, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -11.6953125, + "logprob": -11.703125, "text": "" }, { "id": 32000, - "logprob": -11.390625, + "logprob": -11.40625, "text": "" }, { @@ -57759,17 +57759,17 @@ }, { "id": 32000, - "logprob": -11.8125, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.9375, + "logprob": -12.921875, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6875, "text": "" }, { @@ -57779,47 +57779,47 @@ }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9609375, "text": "" }, { "id": 32000, - "logprob": -11.671875, + "logprob": -11.6875, "text": "" }, { "id": 32000, - "logprob": -11.7734375, + "logprob": -11.765625, "text": "" }, { "id": 32000, - "logprob": -12.40625, + "logprob": -12.4140625, "text": "" }, { "id": 32000, - "logprob": -18.71875, + "logprob": -18.703125, "text": "" }, { "id": 32000, - "logprob": -15.59375, + "logprob": -15.6484375, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.0703125, "text": "" }, { "id": 32000, - "logprob": -13.6875, + "logprob": -13.703125, "text": "" }, { "id": 32000, - "logprob": -15.0703125, + "logprob": -15.0859375, "text": "" }, { @@ -57829,12 +57829,12 @@ }, { "id": 32000, - "logprob": -14.53125, + "logprob": -14.5234375, "text": "" }, { "id": 32000, - "logprob": -12.34375, + "logprob": -12.3671875, "text": "" }, { @@ -57844,7 +57844,7 @@ }, { "id": 32000, - "logprob": -13.65625, + "logprob": -13.640625, "text": "" }, { @@ -57854,37 +57854,37 @@ }, { "id": 32000, - "logprob": -13.0703125, + 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32000, - "logprob": -11.9375, + "logprob": -11.9453125, "text": "" }, { "id": 32000, - "logprob": -11.765625, + "logprob": -11.8203125, "text": "" }, { "id": 32000, - "logprob": -12.078125, + "logprob": -12.109375, "text": "" }, { @@ -57959,22 +57959,22 @@ }, { "id": 32000, - "logprob": -12.515625, + "logprob": -12.5078125, "text": "" }, { "id": 32000, - "logprob": -10.921875, + "logprob": -10.9140625, "text": "" }, { "id": 32000, - "logprob": -12.25, + "logprob": -12.2421875, "text": "" }, { "id": 32000, - "logprob": -12.3125, + "logprob": -12.3203125, "text": "" }, { @@ -57984,87 +57984,87 @@ }, { "id": 32000, - "logprob": -16.6875, + "logprob": -16.671875, "text": "" }, { "id": 32000, - "logprob": -11.9765625, + "logprob": -11.953125, "text": "" }, { "id": 32000, - "logprob": -12.8125, + "logprob": -12.8203125, "text": "" }, { "id": 32000, - "logprob": -15.0234375, + "logprob": -14.9453125, "text": "" }, { "id": 32000, - "logprob": -16.578125, + "logprob": -16.5625, "text": "" }, { 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"id": 32000, + "logprob": -12.0234375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.3828125, "text": "" }, { @@ -58519,17 +58519,17 @@ }, { "id": 32000, - "logprob": -14.2578125, + "logprob": -14.2265625, "text": "" }, { "id": 32000, - "logprob": -12.0625, + "logprob": -12.078125, "text": "" }, { "id": 32000, - "logprob": -12.609375, + "logprob": -12.6171875, "text": "" }, { @@ -58544,17 +58544,17 @@ }, { "id": 32000, - "logprob": -11.734375, + "logprob": -11.7265625, "text": "" }, { "id": 32000, - "logprob": -14.96875, + "logprob": -15.0703125, "text": "" }, { "id": 32000, - "logprob": -14.34375, + "logprob": -14.375, "text": "" }, { @@ -58564,7 +58564,7 @@ }, { "id": 32000, - "logprob": -13.203125, + "logprob": -13.1796875, "text": "" }, { @@ -58574,17 +58574,17 @@ }, { "id": 32000, - "logprob": -15.828125, + "logprob": -15.796875, "text": "" }, { "id": 32000, - "logprob": -12.7421875, + "logprob": -12.75, "text": "" }, { "id": 32000, - "logprob": -13.640625, + "logprob": 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{ "id": 32000, - "logprob": -12.0546875, + "logprob": -12.0390625, "text": "" }, { "id": 32000, - "logprob": -11.7890625, + "logprob": -11.796875, "text": "" }, { "id": 32000, - "logprob": -12.0078125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.8515625, - "text": "" - }, - { - "id": 32000, - "logprob": -16.796875, - "text": "" - }, - { - "id": 32000, - "logprob": -12.734375, - "text": "" - }, - { - "id": 32000, - "logprob": -12.453125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.3828125, - "text": "" - }, - { - "id": 32000, - "logprob": -13.0390625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.890625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.4453125, - "text": "" - }, - { - "id": 32000, - "logprob": -12.7265625, - "text": "" - }, - { - "id": 32000, - "logprob": -11.84375, - "text": "" - }, - { - "id": 32000, - "logprob": -11.28125, - "text": "" - }, - { - "id": 32000, - "logprob": -14.734375, - "text": "" - }, - { - "id": 32000, - "logprob": 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32000, + "logprob": -11.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.4375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.828125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.2734375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.734375, + "text": "" + }, + { + "id": 32000, + "logprob": -14.3984375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.0078125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.578125, + "text": "" + }, + { + "id": 32000, + "logprob": -11.3359375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.984375, + "text": "" + }, + { + "id": 32000, + "logprob": -13.421875, + "text": "" + }, + { + "id": 32000, + "logprob": -12.9140625, + "text": "" + }, + { + "id": 32000, + "logprob": -14.34375, + "text": "" + }, + { + "id": 32000, + "logprob": -12.8828125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.890625, + "text": "" + }, + { + "id": 32000, + "logprob": -13.3203125, + "text": "" + }, + { + "id": 32000, + "logprob": -13.2109375, + "text": "" + }, + { + "id": 32000, + "logprob": -11.9765625, + "text": "" + }, + { + "id": 32000, + "logprob": -15.140625, "text": "" }, { @@ -58974,12 +58974,12 @@ }, { "id": 32000, - "logprob": -14.421875, + "logprob": -14.40625, "text": "" }, { "id": 32000, - "logprob": -12.03125, + "logprob": -12.046875, "text": "" }, { @@ -58989,52 +58989,52 @@ }, { "id": 32000, - "logprob": -13.546875, + "logprob": -13.5078125, "text": "" }, { "id": 32000, - "logprob": -11.7109375, + "logprob": -11.734375, "text": "" }, { "id": 32000, - "logprob": -13.3828125, + "logprob": -13.390625, "text": "" }, { "id": 32000, - "logprob": -17.28125, + "logprob": -17.3125, "text": "" }, { "id": 32000, - "logprob": -12.59375, + "logprob": -12.5234375, "text": "" }, { "id": 32000, - "logprob": -17.59375, + "logprob": -17.625, "text": "" }, { "id": 32000, - "logprob": -11.9453125, + "logprob": -11.9296875, "text": "" }, { "id": 32000, - "logprob": -12.7109375, + "logprob": -12.71875, "text": "" }, { "id": 32000, - "logprob": -15.9375, + "logprob": -15.9140625, "text": "" }, { "id": 32000, - "logprob": -16.625, + "logprob": -16.65625, "text": "" }, { @@ -59044,27 +59044,27 @@ }, { "id": 2418, - "logprob": -19.078125, + "logprob": -19.0625, "text": "Can" }, { "id": 368, - "logprob": -0.19665527, + "logprob": -0.19726562, "text": "you" }, { "id": 1912, - "logprob": -1.5009766, + "logprob": -1.4990234, "text": "tell" }, { "id": 528, - "logprob": -0.31054688, + "logprob": -0.31152344, "text": "me" }, { "id": 264, - "logprob": -2.6269531, + "logprob": -2.6367188, "text": "a" }, { @@ -59074,12 +59074,12 @@ }, { "id": 2485, - "logprob": -0.99365234, + "logprob": -0.9941406, "text": "short" }, { "id": 2838, - "logprob": -0.45996094, + "logprob": -0.46118164, "text": "story" }, { @@ -59089,22 +59089,22 @@ }, { "id": 356, - "logprob": -0.029006958, + "logprob": -0.029129028, "text": "on" }, { "id": 272, - "logprob": -0.9897461, + "logprob": -0.9902344, "text": "the" }, { "id": 3469, - "logprob": -0.29125977, + "logprob": -0.29052734, "text": "image" }, { "id": 28804, - "logprob": -0.43017578, + "logprob": -0.43188477, "text": "?" } ], @@ -59112,13 +59112,13 @@ "tokens": [ { "id": 13, - "logprob": -0.007446289, + "logprob": -0.0076828003, "special": false, "text": "\n" }, { "id": 13, - "logprob": -0.20129395, + "logprob": -0.19958496, "special": false, "text": "\n" }, @@ -59130,43 +59130,43 @@ }, { "id": 3714, - "logprob": -0.20825195, + "logprob": -0.20861816, "special": false, "text": " upon" }, { "id": 264, - "logprob": -0.0017786026, + "logprob": -0.0017719269, "special": false, "text": " a" }, { "id": 727, - "logprob": -0.011955261, + "logprob": -0.011749268, "special": false, "text": " time" }, { "id": 28725, - "logprob": -0.17297363, + "logprob": -0.17529297, "special": false, "text": "," }, { "id": 736, - "logprob": -0.91015625, + "logprob": -0.9086914, "special": false, "text": " there" }, { "id": 403, - "logprob": -0.05758667, + "logprob": -0.056732178, "special": false, "text": " was" }, { "id": 264, - "logprob": -0.009544373, + "logprob": -0.00970459, "special": false, "text": " a" } diff --git a/integration-tests/models/test_idefics2.py b/integration-tests/models/test_idefics2.py new file mode 100644 index 00000000..d34cce34 --- /dev/null +++ b/integration-tests/models/test_idefics2.py @@ -0,0 +1,81 @@ +import pytest +import base64 + + +# TODO fix the server parsser to count inline image tokens correctly +def get_chicken(): + with open("integration-tests/images/chicken_on_money.png", "rb") as image_file: + encoded_string = base64.b64encode(image_file.read()) + return f"data:image/png;base64,{encoded_string.decode('utf-8')}" + + +@pytest.fixture(scope="module") +def flash_idefics2_next_handle(launcher): + with launcher( + "HuggingFaceM4/idefics2-8b", + ) as handle: + yield handle + + +@pytest.fixture(scope="module") +async def flash_idefics2_next(flash_idefics2_next_handle): + await flash_idefics2_next_handle.health(300) + return flash_idefics2_next_handle.client + + +@pytest.mark.asyncio +@pytest.mark.private +async def test_flash_idefics2_next_simple(flash_idefics2_next, response_snapshot): + chicken = get_chicken() + response = await flash_idefics2_next.generate( + f"User:![]({chicken})Write me a short story \nAssistant:", + max_new_tokens=10, + ) + assert ( + response.generated_text == " A chicken is sitting on a pile of money." + ), f"{repr(response.generated_text)}" + assert response.details.generated_tokens == 10 + assert response == response_snapshot + + +@pytest.mark.asyncio +@pytest.mark.private +async def test_flash_idefics2_next_all_params(flash_idefics2_next, response_snapshot): + response = await flash_idefics2_next.generate( + "Test request", + max_new_tokens=10, + repetition_penalty=1.2, + return_full_text=True, + stop_sequences=["test"], + temperature=0.5, + top_p=0.9, + top_k=10, + truncate=5, + typical_p=0.9, + watermark=True, + decoder_input_details=True, + seed=0, + ) + + assert response.details.generated_tokens == 10 + assert response == response_snapshot + + +@pytest.mark.asyncio +@pytest.mark.private +async def test_flash_idefics2_next_load( + flash_idefics2_next, generate_load, response_snapshot +): + chicken = get_chicken() + responses = await generate_load( + flash_idefics2_next, + f"User:![]({chicken})Write me a short story \nAssistant:", + max_new_tokens=10, + n=4, + ) + generated_texts = [r.generated_text for r in responses] + assert generated_texts[0] == " A chicken is sitting on a pile of money." + assert len(generated_texts) == 4 + assert all([r.generated_text == generated_texts[0] for r in responses]) + + assert responses == response_snapshot diff --git a/router/client/src/client.rs b/router/client/src/client.rs index ae926139..e8035106 100644 --- a/router/client/src/client.rs +++ b/router/client/src/client.rs @@ -114,8 +114,12 @@ impl Client { let truncate = min(max_input_length, max_prefill_tokens - n_tokens); let mut inputs = String::new(); - inputs.push_str("![](data:image/jpeg;base64,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"); inputs.push_str(&"_test ".to_string().repeat(max_input_length as usize)); + if n_tokens == 0 { + // 1 request is enough to test vision heads. + // Sending images on other queries messes up easily with truncation. + inputs.push_str("![](data:image/jpeg;base64,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)"); + } requests.push(Request { id: 0, diff --git a/router/src/config.rs b/router/src/config.rs index 9b5a2404..88cde69a 100644 --- a/router/src/config.rs +++ b/router/src/config.rs @@ -57,6 +57,31 @@ fn select_best_resolution( best_fit.unwrap_or((original_height, original_width)) } +fn get_unpadded_features( + height: usize, + width: usize, + npatches: usize, + num_patch_height: usize, + num_patch_width: usize, +) -> (usize, usize) { + let current_height = npatches * num_patch_height; + let current_width = npatches * num_patch_width; + + let aspect_ratio: f64 = width as f64 / height as f64; + let current_aspect_ratio: f64 = current_width as f64 / current_height as f64; + let (current_height, current_width) = if aspect_ratio > current_aspect_ratio { + let new_height = (height * current_width) / width; + (new_height, current_width) + } else { + let new_width = (width * current_height) / height; + (current_height, new_width) + }; + + let unpadded_features = current_height * current_width; + let newline_features = current_height; + (unpadded_features, newline_features) +} + impl LlavaNext { pub fn get_number_of_features(&self, height: usize, width: usize) -> usize { let image_size = self.vision_config.image_size; @@ -65,11 +90,9 @@ impl LlavaNext { let npatches = image_size / patch_size; let (num_patch_height, num_patch_width) = get_anyres_image_grid_shape(height, width, &self.image_grid_pinpoints, image_size); - // Ceil - let height_of_patch = (height * npatches + width - 1) / width; - let unpadded_features = npatches * height_of_patch * num_patch_height * num_patch_width; - // They are only added after width - let newline_features = height_of_patch * num_patch_width; + + let (unpadded_features, newline_features) = + get_unpadded_features(height, width, npatches, num_patch_height, num_patch_width); // The base patch covers the entire image let base_features = npatches.pow(2); unpadded_features + newline_features + base_features @@ -84,6 +107,17 @@ pub struct ClipVisionModel { patch_size: usize, } +#[derive(Clone, Debug, Serialize, Deserialize)] +#[serde(tag = "model_type")] +#[serde(rename_all = "snake_case")] +pub struct Idefics2 {} + +impl Idefics2 { + pub fn get_number_of_features(&self, _height: usize, _width: usize) -> usize { + 320 + } +} + #[derive(Clone, Debug, Serialize, Deserialize)] #[serde(tag = "model_type")] #[serde(rename_all = "snake_case")] @@ -92,6 +126,7 @@ pub enum Config { ClipVisionModel(ClipVisionModel), Mistral, Idefics, + Idefics2(Idefics2), Ssm, GptBigcode, Santacoder, @@ -146,13 +181,17 @@ mod test { ], }; + let slots = config.get_number_of_features(20, 20); + assert_eq!(slots, 1176); let slots = config.get_number_of_features(640, 640); assert_eq!(slots, 2928); let slots = config.get_number_of_features(480, 640); assert_eq!(slots, 2340); let slots = config.get_number_of_features(899, 1024); - assert_eq!(slots, 2732); + assert_eq!(slots, 2634); let slots = config.get_number_of_features(1024, 899); - assert_eq!(slots, 3320); + assert_eq!(slots, 2640); + let slots = config.get_number_of_features(1067, 1600); + assert_eq!(slots, 2144); } } diff --git a/router/src/validation.rs b/router/src/validation.rs index 2029c7e0..be4bef00 100644 --- a/router/src/validation.rs +++ b/router/src/validation.rs @@ -540,7 +540,57 @@ fn prepare_input( inputs = modified_inputs; tokenizer_query } - Some(Config::Idefics) => RE.replace_all(&inputs, "").into(), + Some(Config::Idefics2(config)) => { + let mut modified_inputs = String::with_capacity(inputs.len()); + let mut tokenizer_query = String::with_capacity(inputs.len()); + let mut start = 0; + for chunk in RE.find_iter(&inputs) { + let chunk_start = chunk.start(); + let chunk_end = chunk.end(); + if chunk_start != start { + modified_inputs.push_str(&inputs[start..chunk_start]); + tokenizer_query.push_str(&inputs[start..chunk_start]); + } + let (image_uri, height, width) = fetch_image(&inputs[chunk_start..chunk_end])?; + let slots = config.get_number_of_features(height, width); + tokenizer_query.push_str(""); + tokenizer_query.push_str(&"".repeat(slots)); + tokenizer_query.push_str(""); + + modified_inputs.push_str(&image_uri); + start = chunk_end; + } + if start != inputs.len() - 1 { + modified_inputs.push_str(&inputs[start..]); + tokenizer_query.push_str(&inputs[start..]); + } + inputs = modified_inputs; + tokenizer_query + } + Some(Config::Idefics) => { + let mut modified_inputs = String::with_capacity(inputs.len()); + let mut tokenizer_query = String::with_capacity(inputs.len()); + let mut start = 0; + for chunk in RE.find_iter(&inputs) { + let chunk_start = chunk.start(); + let chunk_end = chunk.end(); + if chunk_start != start { + modified_inputs.push_str(&inputs[start..chunk_start]); + tokenizer_query.push_str(&inputs[start..chunk_start]); + } + let (image_uri, _height, _width) = fetch_image(&inputs[chunk_start..chunk_end])?; + let slots = 1; + tokenizer_query.push_str(&"".repeat(slots)); + modified_inputs.push_str(&image_uri); + start = chunk_end; + } + if start != inputs.len() - 1 { + modified_inputs.push_str(&inputs[start..]); + tokenizer_query.push_str(&inputs[start..]); + } + inputs = modified_inputs; + tokenizer_query + } _ => inputs.clone(), }; diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index e4e8717d..b52765d7 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -68,6 +68,7 @@ try: ) from text_generation_server.models.idefics import IDEFICSSharded from text_generation_server.models.llava_next import LlavaNext + from text_generation_server.models.idefics2 import Idefics2 from text_generation_server.models.flash_mistral import FlashMistral from text_generation_server.models.flash_mixtral import FlashMixtral from text_generation_server.models.flash_phi import FlashPhi @@ -579,6 +580,18 @@ def get_model( ) else: raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics")) + if model_type == "idefics2": + if FLASH_ATTENTION: + return Idefics2( + model_id, + revision, + quantize=quantize, + use_medusa=use_medusa, + dtype=dtype, + trust_remote_code=trust_remote_code, + ) + else: + raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics")) if model_type == "llava_next": if FLASH_ATTENTION: diff --git a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py index ffaa0c32..c2445cda 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py @@ -409,23 +409,29 @@ class MistralModel(torch.nn.Module): class FlashMistralForCausalLM(torch.nn.Module): - def __init__(self, prefix, config, weights): + def __init__(self, prefix, config, weights, name=None): + if name is None: + name = "model" super().__init__() - self.embed_tokens = TensorParallelEmbedding( prefix=( - "model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens" + f"{name}.embed_tokens" + if not prefix + else f"{prefix}.{name}.embed_tokens" ), weights=weights, ) self.model = MistralModel( - prefix="model" if not prefix else f"{prefix}.model", + prefix=name if not prefix else f"{prefix}.{name}", config=config, weights=weights, ) self.lm_head = SpeculativeHead.load( config, - prefix="lm_head" if not prefix else f"{prefix}.lm_head", + # TODO dirty hack for idefics2. + prefix=( + "lm_head" if not prefix or name != "model" else f"{prefix}.lm_head" + ), weights=weights, ) self.max_past = config.sliding_window diff --git a/server/text_generation_server/models/custom_modeling/idefics2.py b/server/text_generation_server/models/custom_modeling/idefics2.py new file mode 100644 index 00000000..cb2ee7db --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/idefics2.py @@ -0,0 +1,829 @@ +# coding=utf-8 +# Copyright 2024 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Idefics2 model.""" + +from typing import List, Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +import math + +from transformers.activations import ACT2FN +from transformers.image_processing_utils import select_best_resolution +from text_generation_server.models.custom_modeling.vlm import ( + load_text_model, + load_vision_model, +) +from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask + +from text_generation_server.utils.layers import ( + TensorParallelColumnLinear, + TensorParallelEmbedding, + TensorParallelRowLinear, +) + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand( + batch, num_key_value_heads, n_rep, slen, head_dim + ) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class Idefics2VisionEmbeddings(nn.Module): + """ + This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings` to enable images of variable + resolution. + + The modifications are adapted from [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs/2307.06304) + which allows treating images in their native aspect ratio and without the need to resize them to the same + fixed size. In particular, we start from the original pre-trained SigLIP model + (which uses images of fixed-size square images) and adapt it by training on images of variable resolutions. + """ + + def __init__(self, prefix, config, weights): + super().__init__() + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + self.patch_embedding.weight = nn.Parameter( + weights.get_tensor(f"{prefix}.patch_embedding.weight"), requires_grad=False + ) + self.patch_embedding.bias = nn.Parameter( + weights.get_tensor(f"{prefix}.patch_embedding.bias"), requires_grad=False + ) + + self.num_patches_per_side = self.image_size // self.patch_size + self.num_patches = self.num_patches_per_side**2 + self.num_positions = self.num_patches + self.position_embedding = TensorParallelEmbedding( + prefix=f"{prefix}.position_embedding", weights=weights + ) + + def forward( + self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor + ) -> torch.Tensor: + batch_size, _, max_im_h, max_im_w = pixel_values.shape + + patch_embeds = self.patch_embedding(pixel_values) + embeddings = patch_embeds.flatten(2).transpose(1, 2) + + max_nb_patches_h, max_nb_patches_w = ( + max_im_h // self.patch_size, + max_im_w // self.patch_size, + ) + boundaries = torch.arange( + 1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side + ) + position_ids = torch.full( + size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0 + ) + + for batch_idx, p_attn_mask in enumerate(patch_attention_mask): + nb_patches_h = p_attn_mask[:, 0].sum() + nb_patches_w = p_attn_mask[0].sum() + + fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h) + fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w) + + bucket_coords_h = torch.bucketize( + fractional_coords_h, boundaries, right=True + ) + bucket_coords_w = torch.bucketize( + fractional_coords_w, boundaries, right=True + ) + + pos_ids = ( + bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w + ).flatten() + position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids + + position_ids = position_ids.to(self.position_embedding.weight.device) + embeddings = embeddings + self.position_embedding(position_ids) + return embeddings + + +class Idefics2VisionAttention(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_size = self.embed_dim // self.num_heads + if self.head_size * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_size**-0.5 + self.dropout = config.attention_dropout + + self.num_heads = self.num_heads // weights.process_group.size() + self.embed_dim = self.embed_dim // weights.process_group.size() + + self.qkv = TensorParallelColumnLinear.load_multi( + config, + prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"], + dim=0, + weights=weights, + bias=True, + ) + self.out_proj = TensorParallelRowLinear.load( + config=config, prefix=f"{prefix}.out_proj", weights=weights, bias=True + ) + self.is_causal = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + batch_size, q_len, _ = hidden_states.size() + + qkv = self.qkv(hidden_states) + query_states, key_states, value_states = qkv.split( + [ + self.head_size * self.num_heads, + self.head_size * self.num_heads, + self.head_size * self.num_heads, + ], + dim=2, + ) + + query_states = query_states.view( + batch_size, q_len, self.num_heads, self.head_size + ).transpose(1, 2) + key_states = key_states.view( + batch_size, q_len, self.num_heads, self.head_size + ).transpose(1, 2) + value_states = value_states.view( + batch_size, q_len, self.num_heads, self.head_size + ).transpose(1, 2) + + k_v_seq_len = key_states.shape[-2] + attn_weights = ( + torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale + ) + + if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len): + raise ValueError( + f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len): + raise ValueError( + f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax( + attn_weights, dim=-1, dtype=torch.float32 + ).to(query_states.dtype) + attn_weights = nn.functional.dropout( + attn_weights, p=self.dropout, training=self.training + ) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_size): + raise ValueError( + f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_size)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output + + +class Idefics2VisionMLP(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = TensorParallelColumnLinear.load( + prefix=f"{prefix}.fc1", config=config, weights=weights, bias=True + ) + self.fc2 = TensorParallelRowLinear.load( + prefix=f"{prefix}.fc2", config=config, weights=weights, bias=True + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class Idefics2EncoderLayer(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = Idefics2VisionAttention( + prefix=f"{prefix}.self_attn", config=config, weights=weights + ) + self.layer_norm1 = nn.LayerNorm.load( + prefix=f"{prefix}.layer_norm1", eps=config.layer_norm_eps, weights=weights + ) + self.layer_norm2 = nn.LayerNorm.load( + prefix=f"{prefix}.layer_norm2", eps=config.layer_norm_eps, weights=weights + ) + self.mlp = Idefics2VisionMLP( + prefix=f"{prefix}.mlp", config=config, weights=weights + ) + + # Copied from transformers.models.siglip.modeling_siglip.SiglipEncoderLayer.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + ) -> torch.Tensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class Idefics2Encoder(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + self.config = config + self.layers = nn.ModuleList( + [ + Idefics2EncoderLayer( + prefix=f"{prefix}.layers.{i}", config=config, weights=weights + ) + for i in range(config.num_hidden_layers) + ] + ) + + # Ignore copy + def forward( + self, + inputs_embeds, + attention_mask: Optional[torch.Tensor] = None, + ): + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + ) + return hidden_states + + +class Idefics2VisionTransformer(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + self.config = config + self.embeddings = Idefics2VisionEmbeddings( + prefix=f"{prefix}.embeddings", config=config, weights=weights + ) + self.encoder = Idefics2Encoder( + prefix=f"{prefix}.encoder", config=config, weights=weights + ) + self.post_layernorm = nn.LayerNorm.load( + prefix=f"{prefix}.post_layernorm", + weights=weights, + eps=config.layer_norm_eps, + ) + + def forward( + self, + pixel_values, + patch_attention_mask: Optional[torch.BoolTensor] = None, + ): + batch_size = pixel_values.size(0) + if patch_attention_mask is None: + patch_size = self.config.patch_size + patch_attention_mask = torch.ones( + ( + batch_size, + pixel_values.size(2) // patch_size, + pixel_values.size(3) // patch_size, + ) + ) + patch_attention_mask = patch_attention_mask.to( + dtype=torch.bool, device=pixel_values.device + ) + + hidden_states = self.embeddings( + pixel_values=pixel_values, patch_attention_mask=patch_attention_mask + ) + + patch_attention_mask = patch_attention_mask.view(batch_size, -1) + # The call to `_upad_input` in `_flash_attention_forward` is expensive + # So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence), + # avoiding passing the attention_mask, which is equivalent to attending to the full sequence + if not torch.any(~patch_attention_mask): + patch_attention_mask = None + else: + patch_attention_mask = _prepare_4d_attention_mask( + patch_attention_mask, hidden_states.dtype + ) + + encoder_outputs = self.encoder( + inputs_embeds=hidden_states, + attention_mask=patch_attention_mask, + ) + + last_hidden_state = encoder_outputs + last_hidden_state = self.post_layernorm(last_hidden_state) + + return last_hidden_state + + +class Idefics2MLP(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + act = config.text_config.hidden_act + self.act = ( + ACT2FN[act] + if "gelu" not in act + else lambda x: torch.nn.functional.gelu( + x, + approximate=( + "tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none" + ), + ) + ) + self.gate_up_proj = TensorParallelColumnLinear.load_multi( + config, + prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"], + weights=weights, + dim=0, + bias=False, + ) + self.down_proj = TensorParallelRowLinear.load( + config, + prefix=f"{prefix}.down_proj", + weights=weights, + bias=False, + ) + + def forward(self, hidden_states): + start_shape = hidden_states.shape[:-1] + gate_up_states = self.gate_up_proj(hidden_states) + intermediate_size = gate_up_states.shape[-1] // 2 + gate_up_states = gate_up_states.view(-1, 2, intermediate_size) + return self.down_proj( + self.act(gate_up_states[:, 0]) * gate_up_states[:, 1] + ).view(*start_shape, -1) + + +class Idefics2RMSNorm(nn.Module): + def __init__(self, prefix, weights, eps): + """ + Idefics2RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter( + weights.get_tensor(f"{prefix}.weight"), requires_grad=False + ) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + +class Idefics2PerceiverAttention(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + + self.layer_idx = None + self.hidden_size = config.text_config.hidden_size + self.num_heads = config.perceiver_config.resampler_n_heads + self.head_size = config.perceiver_config.resampler_head_dim + self.num_key_value_heads = config.perceiver_config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.attention_dropout = config.perceiver_config.attention_dropout + self.num_heads = self.num_heads // weights.process_group.size() + self.num_key_value_heads = ( + self.num_key_value_heads // weights.process_group.size() + ) + + self.q_proj = TensorParallelColumnLinear.load( + config, + prefix=f"{prefix}.q_proj", + weights=weights, + bias=False, + ) + self.kv = TensorParallelColumnLinear.load_multi( + config, + prefixes=[f"{prefix}.k_proj", f"{prefix}.v_proj"], + dim=0, + weights=weights, + bias=False, + ) + self.o_proj = TensorParallelRowLinear.load( + config=config, prefix=f"{prefix}.o_proj", weights=weights, bias=False + ) + + self.is_causal = False + + def forward( + self, + latents: torch.Tensor, + context: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = latents.size() + kv_seq_len = q_len + context.size()[1] + + hidden_states = torch.concat([context, latents], dim=-2) + query_states = self.q_proj(latents) + kv = self.kv(hidden_states) + key_states, value_states = kv.split( + [ + self.head_size * self.num_key_value_heads, + self.head_size * self.num_key_value_heads, + ], + dim=2, + ) + + query_states = query_states.view( + bsz, q_len, self.num_heads, self.head_size + ).transpose(1, 2) + key_states = key_states.view( + bsz, kv_seq_len, self.num_key_value_heads, self.head_size + ).transpose(1, 2) + value_states = value_states.view( + bsz, kv_seq_len, self.num_key_value_heads, self.head_size + ).transpose(1, 2) + + # repeat k/v heads if n_kv_heads < n_heads + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul( + query_states, key_states.transpose(2, 3) + ) / math.sqrt(self.head_size) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax( + attn_weights, dim=-1, dtype=torch.float32 + ).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_size): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_size)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.head_size) + + attn_output = self.o_proj(attn_output) + + return attn_output + + +class Idefics2PerceiverLayer(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + self.hidden_size = config.text_config.hidden_size + self.n_latents = config.perceiver_config.resampler_n_latents + self.depth = config.perceiver_config.resampler_depth + self.rms_norm_eps = config.text_config.rms_norm_eps + + self.input_latents_norm = Idefics2RMSNorm( + prefix=f"{prefix}.input_latents_norm", + weights=weights, + eps=self.rms_norm_eps, + ) + self.input_context_norm = Idefics2RMSNorm( + prefix=f"{prefix}.input_context_norm", + weights=weights, + eps=self.rms_norm_eps, + ) + self.self_attn = Idefics2PerceiverAttention( + prefix=f"{prefix}.self_attn", config=config, weights=weights + ) + self.post_attention_layernorm = Idefics2RMSNorm( + prefix=f"{prefix}.post_attention_layernorm", + weights=weights, + eps=self.rms_norm_eps, + ) + self.mlp = Idefics2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights) + + def forward( + self, + latents: torch.Tensor, + context: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + ): + """ + Args: + latents (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + context (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + """ + residual = latents + + latents = self.input_latents_norm(latents) + context = self.input_context_norm(context) + + latents = self.self_attn( + latents=latents, + context=context, + attention_mask=attention_mask, + ) + latents = residual + latents + residual = latents + + latents = self.post_attention_layernorm(latents) + latents = self.mlp(latents) + latents = residual + latents + + return latents + + +class Idefics2PerceiverResampler(nn.Module): + def __init__(self, prefix, config, weights) -> None: + super().__init__() + self.hidden_size = config.text_config.hidden_size + self.hidden_act = config.perceiver_config.hidden_act + self.n_latents = config.perceiver_config.resampler_n_latents + self.depth = config.perceiver_config.resampler_depth + self.rms_norm_eps = config.text_config.rms_norm_eps + + # Create Latents for Perceiver + self.latents = weights.get_tensor(f"{prefix}.latents") + + # Create Transformer Blocks + self.layers = nn.ModuleList( + [ + Idefics2PerceiverLayer( + prefix=f"{prefix}.layers.{idx}", config=config, weights=weights + ) + for idx in range(self.depth) + ] + ) + self.norm = Idefics2RMSNorm( + prefix=f"{prefix}.norm", + weights=weights, + eps=config.text_config.rms_norm_eps, + ) + + def forward( + self, + context: torch.Tensor, + attention_mask, + ) -> torch.Tensor: + # seq embed -> bsz seq embed + latents = self.latents.unsqueeze(0).expand( + (context.shape[0], *self.latents.size()) + ) + + latent_attention_mask = torch.ones( + (attention_mask.size(0), latents.size(1)), + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + attention_mask = torch.cat([attention_mask, latent_attention_mask], dim=-1) + attention_mask = _prepare_4d_attention_mask( + attention_mask, latents.dtype, tgt_len=self.n_latents + ) + + compressed_context = latents + for perceiver_layer in self.layers: + compressed_context = perceiver_layer( + compressed_context, + context, + attention_mask=attention_mask, + ) + compressed_context = self.norm(compressed_context) + + return compressed_context + + +class Idefics2Connector(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + self.modality_projection = Idefics2MLP( + prefix=f"{prefix}.modality_projection", config=config, weights=weights + ) + self.perceiver_resampler = Idefics2PerceiverResampler( + prefix=f"{prefix}.perceiver_resampler", config=config, weights=weights + ) + + def forward(self, image_hidden_states, attention_mask): + image_hidden_states = self.modality_projection(image_hidden_states) + image_hidden_states = self.perceiver_resampler( + context=image_hidden_states, attention_mask=attention_mask + ) + return image_hidden_states + + +class Idefics2ForConditionalGeneration(nn.Module): + def __init__(self, prefix, config, weights): + super().__init__() + config.vision_config.quantize = config.quantize + config.vision_config.use_medusa = config.use_medusa + config.text_config.quantize = config.quantize + config.text_config.use_medusa = config.use_medusa + + vision_config = config.vision_config + self.text_model = load_text_model( + prefix="model" if not prefix else f"{prefix}.model", + config=config.text_config, + weights=weights, + name="text_model", + ) + self.dtype = weights.dtype + self.vision_model = Idefics2VisionTransformer( + prefix=f"{prefix}.model.vision_model" if prefix else "model.vision_model", + config=vision_config, + weights=weights, + ) + self.connector = Idefics2Connector( + prefix=f"{prefix}.model.connector" if prefix else "model.connector", + config=config, + weights=weights, + ) + self.config = config + self.image_seq_len = config.perceiver_config.resampler_n_latents + self.image_token_id = config.image_token_id + self.pad_token_id = ( + config.pad_token_id if config.pad_token_id is not None else -1 + ) + + def _merge_input_ids_with_image_features( + self, + input_ids: torch.Tensor, + inputs_embeds: torch.Tensor, + image_features: torch.Tensor, + ): + """In place merges in vision_embeddings with inputs_embeds.""" + # mask = input_ids == self.config.image_token_index + mask = input_ids == self.config.image_token_id + # Let's pray we have enabled enough slots ! + inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1]) + return inputs_embeds + + def forward( + self, + input_ids: torch.Tensor, + position_ids: torch.Tensor, + cu_seqlen_prefill: Optional[torch.Tensor], + kv_cache: List[Tuple[torch.Tensor, torch.Tensor]], + block_tables: torch.Tensor, + slots: torch.Tensor, + input_lengths: torch.Tensor, + max_s: int, + prefill_cache_indices: Optional[torch.Tensor], + lm_head_indices: Optional[torch.Tensor] = None, + pixel_values: torch.FloatTensor = None, + pixel_attention_mask: Optional[torch.BoolTensor] = None, + # Unused here + image_sizes: Optional[torch.Tensor] = None, + ): + inputs_embeds = self.text_model.embed_tokens(input_ids) + if pixel_values is not None: + batch_size, num_images, num_channels, height, width = pixel_values.shape + all_states = [] + all_pixel_values = pixel_values + all_pixel_mask = pixel_attention_mask + for i in range(batch_size): + pixel_values = all_pixel_values.to( + dtype=self.dtype + ) # fp16 compatibility + pixel_values = pixel_values[i : i + 1] + pixel_values = pixel_values.view(num_images, *pixel_values.shape[2:]) + + # Remove padding images - padding images are full 0. + nb_values_per_image = pixel_values.shape[1:].numel() + real_images_inds = (pixel_values == 0.0).sum( + dim=(-1, -2, -3) + ) != nb_values_per_image + pixel_values = pixel_values[real_images_inds].contiguous() + + # Handle the vision attention mask + if pixel_attention_mask is None: + pixel_attention_mask = torch.ones( + size=( + pixel_values.size(0), + pixel_values.size(2), + pixel_values.size(3), + ), + dtype=torch.bool, + device=pixel_values.device, + ) + else: + # Remove padding images from the mask/pP p + pixel_attention_mask = all_pixel_mask[i : i + 1] + pixel_attention_mask = pixel_attention_mask.view( + 1 * num_images, *pixel_attention_mask.shape[2:] + ) + pixel_attention_mask = pixel_attention_mask[ + real_images_inds + ].contiguous() + + patch_size = self.config.vision_config.patch_size + patches_subgrid = pixel_attention_mask.unfold( + dimension=1, size=patch_size, step=patch_size + ) + patches_subgrid = patches_subgrid.unfold( + dimension=2, size=patch_size, step=patch_size + ) + patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool() + + # Get sequence from the vision encoder + image_hidden_states = self.vision_model( + pixel_values=pixel_values, + patch_attention_mask=patch_attention_mask, + ) + + # Modality projection & resampling + image_hidden_states = self.connector( + image_hidden_states, + attention_mask=patch_attention_mask.view(pixel_values.size(0), -1), + ) + all_states.append(image_hidden_states) + image_hidden_states = torch.stack(all_states, dim=0) + # When we generate, we don't want to replace the potential image_token_id that we generated by images + # that simply don't exist + inputs_embeds = self._merge_input_ids_with_image_features( + input_ids, inputs_embeds, image_hidden_states + ) + + hidden_states = self.text_model.model( + inputs_embeds=inputs_embeds, + position_ids=position_ids, + cu_seqlen_prefill=cu_seqlen_prefill, + kv_cache=kv_cache, + block_tables=block_tables, + slots=slots, + input_lengths=input_lengths, + max_s=max_s, + true_max_s=max_s, + prefill_cache_indices=None, + ) + if lm_head_indices is not None: + hidden_states = hidden_states[lm_head_indices] + logits, speculative_logits = self.text_model.lm_head(hidden_states) + return logits, speculative_logits diff --git a/server/text_generation_server/models/custom_modeling/llava_next.py b/server/text_generation_server/models/custom_modeling/llava_next.py index ed21a52b..0d93791f 100644 --- a/server/text_generation_server/models/custom_modeling/llava_next.py +++ b/server/text_generation_server/models/custom_modeling/llava_next.py @@ -23,6 +23,10 @@ from torch import nn from transformers.activations import ACT2FN from transformers.image_processing_utils import select_best_resolution +from text_generation_server.models.custom_modeling.vlm import ( + load_text_model, + load_vision_model, +) from text_generation_server.utils.layers import ( TensorParallelColumnLinear, TensorParallelRowLinear, @@ -105,36 +109,6 @@ class LlavaNextMultiModalProjector(nn.Module): return hidden_states -def load_vision_model(prefix, config, weights): - if config.model_type == "clip_vision_model": - from text_generation_server.models.custom_modeling.clip import ( - CLIPVisionTransformer, - ) - - return CLIPVisionTransformer( - prefix=f"{prefix}.vision_model", config=config, weights=weights - ) - else: - raise RuntimeError(f"Unsupported model type {config.model_type}") - - -def load_text_model(prefix, config, weights): - if config.model_type == "llama": - from text_generation_server.models.custom_modeling.flash_llama_modeling import ( - FlashLlamaForCausalLM, - ) - - return FlashLlamaForCausalLM(prefix, config, weights) - elif config.model_type == "mistral": - from text_generation_server.models.custom_modeling.flash_mistral_modeling import ( - FlashMistralForCausalLM, - ) - - return FlashMistralForCausalLM(prefix, config, weights) - else: - raise RuntimeError(f"Unsupported model type {config.model_type}") - - class LlavaNextForConditionalGeneration(nn.Module): def __init__(self, prefix, config, weights): super().__init__() @@ -180,7 +154,12 @@ class LlavaNextForConditionalGeneration(nn.Module): """In place merges in vision_embeddings with inputs_embeds.""" mask = input_ids == self.config.image_token_index # Let's pray we have enabled enough slots ! - inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1]) + try: + inputs_embeds[mask] = image_features.view(-1, image_features.shape[-1]) + except Exception as e: + raise RuntimeError( + f"Cannot fill images right now. If error happens at warmup, make sure you have enough `--max-input-tokens` to handle images. If error happens at regular runtime, please fill in an issue: {e}" + ) return inputs_embeds def forward( @@ -196,6 +175,8 @@ class LlavaNextForConditionalGeneration(nn.Module): prefill_cache_indices: Optional[torch.Tensor], lm_head_indices: Optional[torch.Tensor] = None, pixel_values: torch.FloatTensor = None, + # Unused for this model + pixel_attention_mask=None, image_sizes: Optional[torch.LongTensor] = None, ): inputs_embeds = self.language_model.embed_tokens(input_ids) diff --git a/server/text_generation_server/models/custom_modeling/vlm.py b/server/text_generation_server/models/custom_modeling/vlm.py new file mode 100644 index 00000000..690957d0 --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/vlm.py @@ -0,0 +1,28 @@ +def load_text_model(prefix, config, weights, name=None): + if config.model_type == "llama": + from text_generation_server.models.custom_modeling.flash_llama_modeling import ( + FlashLlamaForCausalLM, + ) + + return FlashLlamaForCausalLM(prefix, config, weights) + elif config.model_type == "mistral": + from text_generation_server.models.custom_modeling.flash_mistral_modeling import ( + FlashMistralForCausalLM, + ) + + return FlashMistralForCausalLM(prefix, config, weights, name=name) + else: + raise RuntimeError(f"Unsupported model type {config.model_type}") + + +def load_vision_model(prefix, config, weights): + if config.model_type == "clip_vision_model": + from text_generation_server.models.custom_modeling.clip import ( + CLIPVisionTransformer, + ) + + return CLIPVisionTransformer( + prefix=f"{prefix}.vision_model", config=config, weights=weights + ) + else: + raise RuntimeError(f"Unsupported model type {config.model_type}") diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py index ace7ea8e..52a30b5f 100644 --- a/server/text_generation_server/models/flash_mistral.py +++ b/server/text_generation_server/models/flash_mistral.py @@ -511,18 +511,33 @@ class BaseFlashMistral(FlashCausalLM): cuda_graph = self.cuda_graphs.get(padded_bs, None) if cu_seqlen_prefill is not None or cuda_graph is None: - logits, speculative_logits = self.model.forward( - input_ids=input_ids, - position_ids=position_ids, - cu_seqlen_prefill=cu_seqlen_prefill, - kv_cache=kv_cache, - block_tables=block_tables, - slots=slots, - input_lengths=input_lengths, - max_s=max_s, - prefill_cache_indices=batch.prefill_cache_indices, - lm_head_indices=lm_head_indices, - ) + + if cu_seqlen_prefill is None: + logits, speculative_logits = self.compiled_model( + input_ids=input_ids, + position_ids=position_ids, + cu_seqlen_prefill=cu_seqlen_prefill, + kv_cache=kv_cache, + block_tables=block_tables, + slots=slots, + input_lengths=input_lengths, + max_s=max_s, + prefill_cache_indices=batch.prefill_cache_indices, + lm_head_indices=lm_head_indices, + ) + else: + logits, speculative_logits = self.model.forward( + input_ids=input_ids, + position_ids=position_ids, + cu_seqlen_prefill=cu_seqlen_prefill, + kv_cache=kv_cache, + block_tables=block_tables, + slots=slots, + input_lengths=input_lengths, + max_s=max_s, + prefill_cache_indices=batch.prefill_cache_indices, + lm_head_indices=lm_head_indices, + ) if batch.prefill_cache_indices is not None: batch.prefill_cache_indices = None return logits, speculative_logits diff --git a/server/text_generation_server/models/idefics2.py b/server/text_generation_server/models/idefics2.py new file mode 100644 index 00000000..e831af89 --- /dev/null +++ b/server/text_generation_server/models/idefics2.py @@ -0,0 +1,51 @@ +import torch + +from typing import Optional, Tuple + +from transformers import ( + AutoProcessor, +) +from text_generation_server.models.custom_modeling.idefics2 import ( + Idefics2ForConditionalGeneration, +) + +from text_generation_server.models.vlm_causal_lm import VlmCausalLM + + +class Idefics2(VlmCausalLM): + def __init__( + self, + model_id: str, + revision: Optional[str] = None, + quantize: Optional[str] = None, + use_medusa: Optional[str] = None, + dtype: Optional[torch.dtype] = None, + trust_remote_code: bool = False, + ): + self.processor = AutoProcessor.from_pretrained( + model_id, + revision=revision, + trust_remote_code=trust_remote_code, + # XXX: Extremely important to cap resolution in order to limit + # VRAM usage. + size={"longest_edge": 448, "shortest_edge": 378}, + ) + super().__init__( + model_cls=Idefics2ForConditionalGeneration, + model_id=model_id, + revision=revision, + quantize=quantize, + use_medusa=use_medusa, + dtype=dtype, + trust_remote_code=trust_remote_code, + ) + + def get_layer_config(self, model) -> Tuple[int, int, int]: + return ( + len(model.text_model.model.layers), + model.text_model.model.num_key_value_heads, + model.text_model.model.head_size, + ) + + def max_past(self) -> Optional[int]: + return getattr(self.model.text_model, "max_past", None) diff --git a/server/text_generation_server/models/llava_next.py b/server/text_generation_server/models/llava_next.py index 0ae1b46d..3983bc85 100644 --- a/server/text_generation_server/models/llava_next.py +++ b/server/text_generation_server/models/llava_next.py @@ -1,6 +1,6 @@ import torch -from typing import Optional +from typing import Optional, Tuple from transformers import ( AutoProcessor, @@ -34,3 +34,13 @@ class LlavaNext(VlmCausalLM): dtype=dtype, trust_remote_code=trust_remote_code, ) + + def get_layer_config(self, model) -> Tuple[int, int, int]: + return ( + len(model.language_model.model.layers), + model.language_model.model.num_key_value_heads, + model.language_model.model.head_size, + ) + + def max_past(self) -> Optional[int]: + return getattr(self.model.language_model, "max_past", None) diff --git a/server/text_generation_server/models/vlm_causal_lm.py b/server/text_generation_server/models/vlm_causal_lm.py index 16042fc9..5394feb5 100644 --- a/server/text_generation_server/models/vlm_causal_lm.py +++ b/server/text_generation_server/models/vlm_causal_lm.py @@ -64,6 +64,46 @@ def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size): return height // patch_size, width // patch_size +def image_text_replacement(image_input, config, image_id) -> str: + if config.model_type == "idefics2": + # TODO technically depends on image splitting which is not implemented. + num_features = 320 + return ( + "" + + "" * num_features + + "" + ) + elif config.model_type == "llava_next": + height, width = image_input["image_sizes"][image_id] + num_features = get_number_of_features(height, width, config) + from loguru import logger + + logger.info(f"Found {num_features} in image of resolution {height}x{width}") + return "" * num_features + else: + raise RuntimeError(f"Unknown config {config.model_type} for multimodal") + + +def get_unpadded_features( + height: int, width: int, npatches: int, num_patch_height: int, num_patch_width: int +) -> Tuple[int, int]: + current_height = npatches * num_patch_height + current_width = npatches * num_patch_width + + aspect_ratio: float = width / height + current_aspect_ratio: float = current_width / current_height + if aspect_ratio > current_aspect_ratio: + new_height = (height * current_width) // width + current_height = new_height + else: + new_width = (width * current_height) // height + current_width = new_width + + unpadded_features = current_height * current_width + newline_features = current_height + return (unpadded_features, newline_features) + + def get_number_of_features(height: int, width: int, config) -> int: # From config # Hardcoded for CLIP for now @@ -81,12 +121,9 @@ def get_number_of_features(height: int, width: int, config) -> int: image_grid_pinpoints, image_size, ) - - height_of_patch = math.ceil(height / width * npatches) - - unpadded_features = npatches * height_of_patch * num_patch_height * num_patch_width - # They are only added after width - newline_features = height_of_patch * num_patch_width + unpadded_features, newline_features = get_unpadded_features( + height, width, npatches, num_patch_height, num_patch_width + ) # The base patch covers the entire image base_features = npatches**2 return unpadded_features + newline_features + base_features @@ -99,12 +136,9 @@ def load_data_uri(image_uri: str) -> Image.Image: return image -# assert get_number_of_features(889, 1024) == 2634, f"{get_number_of_features(889, 1024)}" -# assert get_number_of_features(640, 640) == 2928 - - class VlmCausalLMBatch(FlashMistralBatch): pixel_values: Optional[List[torch.Tensor]] + pixel_attention_mask: Optional[List[torch.Tensor]] image_sizes: Optional[List[Tuple[int, int]]] @classmethod @@ -112,6 +146,7 @@ class VlmCausalLMBatch(FlashMistralBatch): def concatenate(cls, batches): batch = super(VlmCausalLMBatch, cls).concatenate(batches) batch.pixel_values = None + batch.pixel_attention_mask = None batch.image_sizes = None return batch @@ -119,6 +154,7 @@ class VlmCausalLMBatch(FlashMistralBatch): def filter(self, request_ids: List[int]): batch = super().filter(request_ids) batch.pixel_values = None + batch.pixel_attention_mask = None batch.image_sizes = None return batch @@ -130,6 +166,7 @@ class VlmCausalLMBatch(FlashMistralBatch): for r in requests: chunks = split(r.inputs) full_text = "" + image_id = 0 for chunk in chunks: if chunk["type"] == "text": full_text += chunk["content"] @@ -147,9 +184,7 @@ class VlmCausalLMBatch(FlashMistralBatch): "Cannot process input image not starting with data:" ) image_input = processor.image_processor(image, return_tensors="pt") - height, width = image_input["image_sizes"][0] - num_features = get_number_of_features(height, width, config) - full_text += "" * num_features + full_text += image_text_replacement(image_input, config, image_id) image_inputs.append(image_input) else: raise RuntimeError(f"Invalid chunk type {chunk['type']}") @@ -161,12 +196,21 @@ class VlmCausalLMBatch(FlashMistralBatch): batch_inputs, truncation=True, max_length=max_truncation )["input_ids"] if image_inputs: - image_inputs = { + image_input = image_inputs[0] + new_image_inputs = { "pixel_values": torch.cat( [img["pixel_values"] for img in image_inputs], dim=0 ), - "image_sizes": torch.cat([img["image_sizes"] for img in image_inputs]), } + if "pixel_attention_mask" in image_input: + new_image_inputs["pixel_attention_mask"] = torch.cat( + [img["pixel_attention_mask"] for img in image_inputs], dim=0 + ) + if "image_sizes" in image_input: + new_image_inputs["image_sizes"] = torch.cat( + [img["image_sizes"] for img in image_inputs], dim=0 + ) + image_inputs = new_image_inputs else: image_inputs = None return batch_tokenized_inputs, image_inputs @@ -187,9 +231,19 @@ class VlmCausalLMBatch(FlashMistralBatch): batch = cls.from_tokenized(pb, tokenizer, batch_tokenized_inputs, dtype, device) if image_inputs is not None: batch.pixel_values = image_inputs["pixel_values"].to(device=device) - batch.image_sizes = image_inputs["image_sizes"].to(device=device) + if "pixel_attention_mask" in image_inputs: + batch.pixel_attention_mask = image_inputs["pixel_attention_mask"].to( + device=device + ) + else: + batch.pixel_attention_mask = None + if "image_sizes" in image_inputs: + batch.image_sizes = image_inputs["image_sizes"].to(device=device) + else: + batch.image_sizes = None else: batch.pixel_values = None + batch.pixel_attention_mask = None batch.image_sizes = None return batch @@ -199,16 +253,6 @@ class VlmCausalLM(BaseFlashMistral): def batch_type(self) -> Type[VlmCausalLMBatch]: return VlmCausalLMBatch - def get_layer_config(self, model) -> Tuple[int, int, int]: - return ( - len(model.language_model.model.layers), - model.language_model.model.num_key_value_heads, - model.language_model.model.head_size, - ) - - def max_past(self) -> Optional[int]: - return getattr(self.model.language_model, "max_past", None) - def forward( self, batch: VlmCausalLMBatch ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: @@ -270,17 +314,14 @@ class VlmCausalLM(BaseFlashMistral): max_s = min(self.max_past(), max_s) bs = input_ids.shape[0] - padded_bs = bs - if bs == 3: - padded_bs = 4 - elif 3 < bs <= 8: - padded_bs = 8 - elif bs > 8: - padded_bs = (bs + 7) // 8 * 8 - # Try to find an associated cuda graph - cuda_graph = self.cuda_graphs.get(padded_bs, None) - + bs = input_ids.shape[0] + sorted_padded_bs = sorted([k for k in self.cuda_graphs.keys() if k >= bs]) + if sorted_padded_bs: + # Get associated cuda graph + cuda_graph = self.cuda_graphs[sorted_padded_bs[0]] + else: + cuda_graph = None if cu_seqlen_prefill is not None or cuda_graph is None: logits, speculative_logits = self.model.forward( input_ids=input_ids, @@ -294,12 +335,15 @@ class VlmCausalLM(BaseFlashMistral): prefill_cache_indices=batch.prefill_cache_indices, lm_head_indices=lm_head_indices, pixel_values=batch.pixel_values, + pixel_attention_mask=batch.pixel_attention_mask, image_sizes=batch.image_sizes, ) if batch.prefill_cache_indices is not None: batch.prefill_cache_indices = None if batch.pixel_values is not None: batch.pixel_values = None + if batch.pixel_attention_mask is not None: + batch.pixel_attention_mask = None if batch.image_sizes is not None: batch.image_sizes = None return logits, speculative_logits From 23d82b8fb6e65642f55843f0c64ec90094074ed7 Mon Sep 17 00:00:00 2001 From: drbh Date: Tue, 23 Apr 2024 17:19:16 -0400 Subject: [PATCH 39/74] fix: avoid frequency and repetition penalty on padding tokens (#1765) This PR resolves an issue with the penalty processors during batched generation where extra padding tokens incorrectly impact the penalty scores. generation is impacted in the case where at least one item in the batch includes a `frequency_penalty` reproduction script below ```python import requests from concurrent import futures import time headers = { "Content-Type": "application/json", } json_data = { "inputs": "[INST] Whats the capitol of France? [/INST]", "parameters": { "max_new_tokens": 100, "seed": 20, "do_sample": False, }, } json_data2 = { "inputs": "[INST]Write a mind bending story: I saw a puppy a cat a rat and a raccoon during my bike ride in the park[/INST]", "parameters": { "max_new_tokens": 100, "seed": 2, "do_sample": False, # OFFENDING LINE "frequency_penalty": 1.05, }, } base_url = "http://localhost:3000/generate" def req(): response = requests.post(base_url, headers=headers, json=json_data) print("[req ]", response.json()) def req2(): response = requests.post(base_url, headers=headers, json=json_data2) print("[req2]", response.json()) n = 1 for i in range(0, 3): print(f"- {n} threads -") with futures.ThreadPoolExecutor(max_workers=n) as executor: executor.submit(req) for i in range(3): executor.submit(req2) n += 1 # - 1 threads - # [req ] {'generated_text': ' The capital of France is Paris.'} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # - 2 threads - # [req ] {'generated_text': ' The capital city'} # [req2] {'generated_text': ' As""%\n================'} # [req2] {'generated_text': ' As""%%$\n================'} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # output with this PR's changes: # - 1 threads - # [req ] {'generated_text': ' The capital of France is Paris.'} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # - 2 threads - # [req ] {'generated_text': ' The capital city'} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} # [req2] {'generated_text': " As you were riding your bicycle through Central Park, enjoying some fresh air on an otherwise gloomy day. You couldn't help but notice that it was eerily quiet for this time of year - usually there would be hordes"} ``` **divergence from expected generation is easier to reproduce with batched grammar requests as they are more sensitive to unexpected outputs. this PR resolves the issue by setting the penalty score to 0 where input ids are padding tokens (0). --------- Co-authored-by: OlivierDehaene --- server/text_generation_server/utils/logits_process.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/server/text_generation_server/utils/logits_process.py b/server/text_generation_server/utils/logits_process.py index 6d8cb71a..2decee53 100644 --- a/server/text_generation_server/utils/logits_process.py +++ b/server/text_generation_server/utils/logits_process.py @@ -143,6 +143,8 @@ class FrequencyPenaltyLogitsProcessor(LogitsProcessor): score = torch.gather(scores, 1, input_ids) # if score < 0 then penalty has to be multiplied to reduce the previous token probability score = -torch.where(score < 0, score * self.penalty, score / self.penalty) + # set score to 0 where input_ids is a padding token + score *= input_ids.ne(0) return scores.scatter_add_(1, input_ids, score) @@ -168,6 +170,8 @@ class HeterogeneousFrequencyPenaltyLogitsProcessor(LogitsProcessor): score = -torch.where( score < 0, score * self.penalty_tensor, score / self.penalty_tensor ) + # set score to 0 where input_ids is a padding token + score *= input_ids.ne(0) return scores.scatter_add_(1, input_ids, score) From 4c698fa6c2ea17ae3372b3d6b6971654817d1969 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Tue, 23 Apr 2024 23:38:30 +0200 Subject: [PATCH 40/74] Adding support for `HF_HUB_OFFLINE` support in the router. (#1789) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- router/src/lib.rs | 6 +- router/src/main.rs | 199 ++++++++++++++++++++++----------------------- 2 files changed, 102 insertions(+), 103 deletions(-) diff --git a/router/src/lib.rs b/router/src/lib.rs index 5e207a03..ecd8e2e0 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -73,9 +73,9 @@ pub struct HubTokenizerConfig { } impl HubTokenizerConfig { - pub fn from_file(filename: &std::path::Path) -> Self { - let content = std::fs::read_to_string(filename).unwrap(); - serde_json::from_str(&content).unwrap_or_default() + pub fn from_file>(filename: P) -> Option { + let content = std::fs::read_to_string(filename).ok()?; + serde_json::from_str(&content).ok() } } diff --git a/router/src/main.rs b/router/src/main.rs index c7e3f90b..63347b78 100644 --- a/router/src/main.rs +++ b/router/src/main.rs @@ -1,7 +1,7 @@ use axum::http::HeaderValue; use clap::Parser; use hf_hub::api::tokio::{Api, ApiBuilder, ApiRepo}; -use hf_hub::{Repo, RepoType}; +use hf_hub::{Cache, Repo, RepoType}; use opentelemetry::sdk::propagation::TraceContextPropagator; use opentelemetry::sdk::trace; use opentelemetry::sdk::trace::Sampler; @@ -11,7 +11,7 @@ use opentelemetry_otlp::WithExportConfig; use std::fs::File; use std::io::BufReader; use std::net::{IpAddr, Ipv4Addr, SocketAddr}; -use std::path::Path; +use std::path::{Path, PathBuf}; use text_generation_client::{ClientError, ShardedClient}; use text_generation_router::config::Config; use text_generation_router::{server, HubModelInfo, HubTokenizerConfig}; @@ -162,7 +162,6 @@ async fn main() -> Result<(), RouterError> { // Tokenizer instance // This will only be used to validate payloads let local_path = Path::new(&tokenizer_name); - let local_model = local_path.exists() && local_path.is_dir(); // Shared API builder initialization let api_builder = || { @@ -181,112 +180,113 @@ async fn main() -> Result<(), RouterError> { let use_api = revision.is_some() || !local_path.exists() || !local_path.is_dir(); // Initialize API if needed + #[derive(Clone)] + enum Type { + Api(Api), + Cache(Cache), + None, + } let api = if use_api { - tracing::info!("Using the Hugging Face API"); - match api_builder().build() { - Ok(api) => Some(api), - Err(_) => { - tracing::warn!("Unable to build the Hugging Face API"); - None + if std::env::var("HF_HUB_OFFLINE") == Ok("1".to_string()) { + let cache = Cache::default(); + tracing::warn!("Offline mode active using cache defaults"); + Type::Cache(cache) + } else { + tracing::info!("Using the Hugging Face API"); + match api_builder().build() { + Ok(api) => Type::Api(api), + Err(_) => { + tracing::warn!("Unable to build the Hugging Face API"); + Type::None + } } } } else { - None + Type::None }; // Load tokenizer and model info - let (tokenizer, model_info, config) = if local_model { - let tokenizer = Tokenizer::from_file(local_path.join("tokenizer.json")).ok(); - let model_info = HubModelInfo { - model_id: tokenizer_name.to_string(), - sha: None, - pipeline_tag: None, - }; - let config: Option = std::fs::read_to_string(local_path.join("config.json")) - .ok() - .as_ref() - .and_then(|c| serde_json::from_str(c).ok()); + let (tokenizer_filename, config_filename, tokenizer_config_filename, model_info) = match api { + Type::None => ( + Some(local_path.join("tokenizer.json")), + Some(local_path.join("config.json")), + Some(local_path.join("tokenizer_config.json")), + None, + ), + Type::Api(api) => { + let api_repo = api.repo(Repo::with_revision( + tokenizer_name.to_string(), + RepoType::Model, + revision.clone().unwrap_or_else(|| "main".to_string()), + )); - (tokenizer, model_info, config) - } else if let Some(api) = api.clone() { - let api_repo = api.repo(Repo::with_revision( - tokenizer_name.to_string(), - RepoType::Model, - revision.clone().unwrap_or_else(|| "main".to_string()), - )); + let tokenizer_filename = match api_repo.get("tokenizer.json").await { + Ok(tokenizer_filename) => Some(tokenizer_filename), + Err(_) => get_base_tokenizer(&api, &api_repo).await, + }; + let config_filename = api_repo.get("config.json").await.ok(); + let tokenizer_config_filename = api_repo.get("tokenizer_config.json").await.ok(); - let tokenizer = match api_repo.get("tokenizer.json").await { - Ok(tokenizer_filename) => Tokenizer::from_file(tokenizer_filename).ok(), - Err(_) => get_base_tokenizer(&api, &api_repo).await, - }; - - let config: Option = api_repo.get("config.json").await.ok().and_then(|filename| { - std::fs::read_to_string(filename) - .ok() - .as_ref() - .and_then(|c| { - let config: Result = serde_json::from_str(c); - if let Err(err) = &config { - tracing::warn!("Could not parse config {err:?}"); - } - config.ok() - }) - }); - - let model_info = get_model_info(&api_repo).await.unwrap_or_else(|| { - tracing::warn!("Could not retrieve model info from the Hugging Face hub."); - HubModelInfo { - model_id: tokenizer_name.to_string(), - sha: None, - pipeline_tag: None, - } - }); - - (tokenizer, model_info, config) - } else { - // No API and no local model - return Err(RouterError::ArgumentValidation( - "No local model found and no revision specified".to_string(), - )); - }; - - tracing::info!("Using config {config:?}"); - - // Load tokenizer config if found locally, or check if we can get it from the API if needed - let tokenizer_config = if let Some(path) = tokenizer_config_path { - tracing::info!( - "Using local tokenizer config from user specified path {}", - path - ); - HubTokenizerConfig::from_file(&std::path::PathBuf::from(path)) - } else if local_model { - tracing::info!("Using local tokenizer config"); - HubTokenizerConfig::from_file(&local_path.join("tokenizer_config.json")) - } else { - match api { - Some(api) => { - tracing::info!("Using the Hugging Face API to retrieve tokenizer config"); - let repo = Repo::with_revision( - tokenizer_name.to_string(), - RepoType::Model, - revision.unwrap_or("main".to_string()), - ); - get_tokenizer_config(&api.repo(repo)) - .await - .unwrap_or_else(|| { - tracing::warn!( - "Could not retrieve tokenizer config from the Hugging Face hub." - ); - HubTokenizerConfig::default() - }) - } - None => { - tracing::warn!("Could not find tokenizer config locally and no API specified"); - HubTokenizerConfig::default() - } + let model_info = if let Some(model_info) = get_model_info(&api_repo).await { + Some(model_info) + } else { + tracing::warn!("Could not retrieve model info from the Hugging Face hub."); + None + }; + ( + tokenizer_filename, + config_filename, + tokenizer_config_filename, + model_info, + ) + } + Type::Cache(cache) => { + let repo = cache.repo(Repo::with_revision( + tokenizer_name.to_string(), + RepoType::Model, + revision.clone().unwrap_or_else(|| "main".to_string()), + )); + ( + repo.get("tokenizer.json"), + repo.get("config.json"), + repo.get("tokenizer_config.json"), + None, + ) } }; + let tokenizer: Option = + tokenizer_filename.and_then(|filename| Tokenizer::from_file(filename).ok()); + let config: Option = config_filename.and_then(|filename| { + std::fs::read_to_string(filename) + .ok() + .as_ref() + .and_then(|c| { + let config: Result = serde_json::from_str(c); + if let Err(err) = &config { + tracing::warn!("Could not parse config {err:?}"); + } + config.ok() + }) + }); + let model_info = model_info.unwrap_or_else(|| HubModelInfo { + model_id: tokenizer_name.to_string(), + sha: None, + pipeline_tag: None, + }); + // Read the JSON contents of the file as an instance of 'HubTokenizerConfig'. + let tokenizer_config: Option = if let Some(filename) = tokenizer_config_path + { + HubTokenizerConfig::from_file(filename) + } else { + tokenizer_config_filename.and_then(HubTokenizerConfig::from_file) + }; + let tokenizer_config = tokenizer_config.unwrap_or_else(|| { + tracing::warn!("Could not find tokenizer config locally and no API specified"); + HubTokenizerConfig::default() + }); + + tracing::info!("Using config {config:?}"); if tokenizer.is_none() { tracing::warn!("Could not find a fast tokenizer implementation for {tokenizer_name}"); tracing::warn!("Rust input length validation and truncation is disabled"); @@ -483,7 +483,7 @@ pub async fn get_model_info(api: &ApiRepo) -> Option { } /// get base tokenizer -pub async fn get_base_tokenizer(api: &Api, api_repo: &ApiRepo) -> Option { +pub async fn get_base_tokenizer(api: &Api, api_repo: &ApiRepo) -> Option { let config_filename = api_repo.get("config.json").await.ok()?; // Open the file in read-only mode with buffer. @@ -500,8 +500,7 @@ pub async fn get_base_tokenizer(api: &Api, api_repo: &ApiRepo) -> Option Date: Thu, 25 Apr 2024 09:31:35 -0400 Subject: [PATCH 41/74] feat: improve temperature logic in chat (#1749) This PR adds support for `do_sample` to chat to enable greedy sampling --------- Co-authored-by: Nicolas Patry --- router/src/server.rs | 10 ++++++++-- server/text_generation_server/utils/tokens.py | 8 ++++---- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/router/src/server.rs b/router/src/server.rs index 302a4753..03d184c3 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -1000,6 +1000,7 @@ async fn chat_completions( tools, tool_choice, tool_prompt, + temperature, .. } = req; @@ -1008,6 +1009,11 @@ async fn chat_completions( let logprobs = logprobs.unwrap_or(false); let tool_prompt = tool_prompt.unwrap_or_default(); let stop = stop.unwrap_or_default(); + // enable greedy only when temperature is 0 + let (do_sample, temperature) = match temperature { + Some(temperature) if temperature == 0.0 => (false, None), + other => (true, other), + }; // extract tool grammar if present let tool_grammar = match ToolGrammar::apply(tools, tool_choice) { @@ -1054,13 +1060,13 @@ async fn chat_completions( inputs: inputs.to_string(), parameters: GenerateParameters { best_of: None, - temperature: req.temperature, + temperature, repetition_penalty, frequency_penalty: req.frequency_penalty, top_k: None, top_p: req.top_p, typical_p: None, - do_sample: true, + do_sample, max_new_tokens, return_full_text: None, stop, diff --git a/server/text_generation_server/utils/tokens.py b/server/text_generation_server/utils/tokens.py index 7c8a18f0..8ef1ca0d 100644 --- a/server/text_generation_server/utils/tokens.py +++ b/server/text_generation_server/utils/tokens.py @@ -273,7 +273,7 @@ class HeterogeneousNextTokenChooser: else None ) - if any([x != 1.0 for x in temperature]): + if any(x != 1.0 for x in temperature): do_sample = [ sample or x != 1.0 for x, sample in zip(temperature, do_sample) ] @@ -281,15 +281,15 @@ class HeterogeneousNextTokenChooser: HeterogeneousTemperatureLogitsWarper(temperature, dtype, device) ) - if any([x != 0 for x in top_k]): + if any(x != 0 for x in top_k): do_sample = [sample or x != 0 for x, sample in zip(top_k, do_sample)] warpers.append(HeterogeneousTopKLogitsWarper(top_k, device)) - if any([x < 1.0 for x in top_p]): + if any(x < 1.0 for x in top_p): do_sample = [sample or x < 1.0 for x, sample in zip(top_p, do_sample)] warpers.append(HeterogeneousTopPLogitsWarper(top_p, dtype, device)) - if any([x < 1.0 for x in typical_p]): + if any(x < 1.0 for x in typical_p): do_sample = [sample or x < 1.0 for x, sample in zip(typical_p, do_sample)] warpers.append(HeterogeneousTypicalLogitsWarper(typical_p, dtype, device)) From fccf5edf45836491d8cdd9e2c98d5cde9bae76ab Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 25 Apr 2024 15:42:17 +0200 Subject: [PATCH 42/74] Updating the benchmarks so everyone uses openai compat layer. (#1800) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- load_tests/common.js | 83 ++++++++++++++++++++++++++++---------------- load_tests/tgi.js | 17 --------- load_tests/vllm.js | 17 --------- 3 files changed, 53 insertions(+), 64 deletions(-) delete mode 100644 load_tests/tgi.js delete mode 100644 load_tests/vllm.js diff --git a/load_tests/common.js b/load_tests/common.js index 06d2506f..80728214 100644 --- a/load_tests/common.js +++ b/load_tests/common.js @@ -1,71 +1,94 @@ -import { check, randomSeed } from 'k6'; +import { check } from 'k6'; +import { scenario } from 'k6/execution'; import http from 'k6/http'; import { Trend, Counter } from 'k6/metrics'; -import { randomItem } from 'https://jslib.k6.io/k6-utils/1.2.0/index.js'; -const seed = 0; - -const host = __ENV.HOST || '127.0.0.1:8000'; +const host = __ENV.HOST; +const model_id = __ENV.MODEL_ID; const timePerToken = new Trend('time_per_token', true); const tokens = new Counter('tokens'); const new_tokens = new Counter('new_tokens'); const input_tokens = new Counter('input_tokens'); +const max_new_tokens = 50; -randomSeed(seed); // const shareGPT = JSON.parse(open("ShareGPT_V3_unfiltered_cleaned_split.json")) const shareGPT = JSON.parse(open("small.json")) -export function get_options(reference_latency_ms){ +export function get_options() { return { thresholds: { http_req_failed: ['rate==0'], - time_per_token: [{ - threshold: `p(50)<${5 * reference_latency_ms}`, - abortOnFail: true, - delayAbortEval: '10s' - }], + // time_per_token: [{ + // threshold: `p(50)<${5 * reference_latency_ms}`, + // abortOnFail: true, + // delayAbortEval: '10s' + // }], }, scenarios: { - load_test: { + single_user: { executor: 'constant-arrival-rate', duration: '60s', - preAllocatedVUs: 10, - rate: 10, + preAllocatedVUs: 1, + rate: 1, timeUnit: '1s', }, + // load_test: { + // executor: 'constant-arrival-rate', + // duration: '60s', + // preAllocatedVUs: 100, + // rate: 1, + // timeUnit: '1s', + // }, + // breakpoint: { + // executor: 'ramping-arrival-rate', //Assure load increase if the system slows + // preAllocatedVUs: 1000, + // stages: [ + // { duration: '60s', target: 100 }, // just slowly ramp-up to a HUGE load + // ], + // }, + // throughput: { + // executor: 'shared-iterations', + // vus: 100, + // iterations: 200, + // maxDuration: '40s', + // }, }, }; } +function generate_payload(gpt, max_new_tokens) { + const input = gpt["conversations"][0]["value"]; + return { "messages": [{ "role": "user", "content": input }], "temperature": 0, "model": `${model_id}`, "max_tokens": max_new_tokens } +} -export function run(host, generate_payload, max_new_tokens) { - const headers = {'Content-Type': 'application/json'}; - const query = randomItem(shareGPT); - const payload = JSON.stringify(generate_payload(query)); - const res = http.post(`http://${host}/generate`, payload, { +export const options = get_options(); + +export default function run() { + const headers = { 'Content-Type': 'application/json' }; + const query = shareGPT[scenario.iterationInTest % shareGPT.length]; + const payload = JSON.stringify(generate_payload(query, max_new_tokens)); + const res = http.post(`http://${host}/v1/chat/completions`, payload, { headers, }); - if(res.status >= 400 && res.status < 500){ + if (res.status >= 400 && res.status < 500) { return; } check(res, { - 'Post status is 200': (r) => res.status === 200, + 'Post status is 200': (res) => res.status === 200, }); const duration = res.timings.duration; if (res.status === 200) { const body = res.json(); - const n_tokens = body.details.tokens.length; - const latency_ms_per_token = duration / n_tokens; + const completion_tokens = body.usage.completion_tokens; + const latency_ms_per_token = duration / completion_tokens; timePerToken.add(latency_ms_per_token); - const latency_in_s = latency_ms_per_token / 1000; - const individual_throughput = 1 / latency_in_s; - const _input_tokens = body.details.prefill.length; - tokens.add(n_tokens + _input_tokens); - input_tokens.add(_input_tokens); - new_tokens.add(n_tokens); + const prompt_tokens = body.usage.prompt_tokens; + input_tokens.add(prompt_tokens); + new_tokens.add(completion_tokens); + tokens.add(completion_tokens + prompt_tokens); } } diff --git a/load_tests/tgi.js b/load_tests/tgi.js deleted file mode 100644 index 6c559a9f..00000000 --- a/load_tests/tgi.js +++ /dev/null @@ -1,17 +0,0 @@ -import { get_options, run } from "./common.js"; - -const reference_latency_ms = 70; -const host = __ENV.HOST || '127.0.0.1:8000'; -const max_new_tokens = 50; - - -function generate_payload(gpt){ - const input = gpt["conversations"][0]["value"]; - return {"inputs": input, "parameters": {"max_new_tokens": max_new_tokens, "decoder_input_details": true}} -} - -export const options = get_options(reference_latency_ms); - -export default function(){ - run(host, generate_payload, max_new_tokens); -} diff --git a/load_tests/vllm.js b/load_tests/vllm.js deleted file mode 100644 index 1edc039a..00000000 --- a/load_tests/vllm.js +++ /dev/null @@ -1,17 +0,0 @@ -import { get_options, run } from "./common.js"; - -const reference_latency_ms = 22; -const host = __ENV.HOST || '127.0.0.1:8000'; -const max_new_tokens = 50; - - -function generate_payload(gpt){ - const input = gpt["conversations"][0]["value"]; - return {"prompt": input, "temperature": 0.5, "ignore_eos": true} -} - -export const options = get_options(reference_latency_ms); - -export default function(){ - run(host, generate_payload, max_new_tokens); -} From eb08b9faeff36e74c6929f3ca7d64e57efaacc15 Mon Sep 17 00:00:00 2001 From: dr3s Date: Thu, 25 Apr 2024 13:11:26 -0400 Subject: [PATCH 43/74] Update guidance docs to reflect grammar support in API (#1775) # What does this PR do? Update guidance docs to reflect grammar support in API. The previous wording was vague and made it sound like openai API supported the grammar parameter. https://github.com/huggingface/text-generation-inference/blob/main/router/src/server.rs#L654 confirms that support for grammar is TGI only at this time. ## Before submitting - [ x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ x] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ x] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- docs/source/conceptual/guidance.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/source/conceptual/guidance.md b/docs/source/conceptual/guidance.md index a9ff61d8..cecb3f03 100644 --- a/docs/source/conceptual/guidance.md +++ b/docs/source/conceptual/guidance.md @@ -2,7 +2,9 @@ Text Generation Inference (TGI) now supports [JSON and regex grammars](#grammar-and-constraints) and [tools and functions](#tools-and-functions) to help developers guide LLM responses to fit their needs. -These feature are available starting from version `1.4.3`. They are accessible via the [text_generation](https://pypi.org/project/text-generation/) library and is compatible with OpenAI's client libraries. The following guide will walk you through the new features and how to use them! +These feature are available starting from version `1.4.3`. They are accessible via the [text_generation](https://pypi.org/project/text-generation/) library. The tool support is compatible with OpenAI's client libraries. The following guide will walk you through the new features and how to use them! + +> The Grammar guidance support is currently only available in the TGI API due to lack of support in Open AI API. ## Quick Start From ee47973a2f2d2152a6c32b69a93573040271c10b Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 25 Apr 2024 19:41:50 +0200 Subject: [PATCH 44/74] Use the generation config. (#1808) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- router/src/lib.rs | 43 ++++++++++----- router/src/server.rs | 8 ++- .../custom_modeling/flash_llama_modeling.py | 52 ------------------- .../models/flash_llama.py | 14 +++-- server/text_generation_server/models/model.py | 7 +++ server/text_generation_server/utils/tokens.py | 25 +++++++-- 6 files changed, 74 insertions(+), 75 deletions(-) diff --git a/router/src/lib.rs b/router/src/lib.rs index ecd8e2e0..9b9097f6 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -589,7 +589,9 @@ pub(crate) struct ChatCompletionChoice { #[derive(Clone, Debug, Deserialize, Serialize, ToSchema)] pub(crate) struct ChatCompletionDelta { #[schema(example = "user")] - pub role: String, + // TODO Modify this to a true enum. + #[serde(default, skip_serializing_if = "Option::is_none")] + pub role: Option, #[serde(default, skip_serializing_if = "Option::is_none")] #[schema(example = "What is Deep Learning?")] pub content: Option, @@ -623,6 +625,31 @@ impl ChatCompletionChunk { logprobs: Option, finish_reason: Option, ) -> Self { + let delta = match (delta, tool_calls) { + (Some(delta), _) => ChatCompletionDelta { + role: Some("assistant".to_string()), + content: Some(delta), + tool_calls: None, + }, + (None, Some(tool_calls)) => ChatCompletionDelta { + role: Some("assistant".to_string()), + content: None, + tool_calls: Some(DeltaToolCall { + index: 0, + id: String::new(), + r#type: "function".to_string(), + function: Function { + name: None, + arguments: tool_calls[0].to_string(), + }, + }), + }, + (None, None) => ChatCompletionDelta { + role: None, + content: None, + tool_calls: None, + }, + }; Self { id: String::new(), object: "text_completion".to_string(), @@ -631,19 +658,7 @@ impl ChatCompletionChunk { system_fingerprint, choices: vec![ChatCompletionChoice { index: 0, - delta: ChatCompletionDelta { - role: "assistant".to_string(), - content: delta, - tool_calls: tool_calls.map(|tc| DeltaToolCall { - index: 0, - id: String::new(), - r#type: "function".to_string(), - function: Function { - name: None, - arguments: tc[0].to_string(), - }, - }), - }, + delta, logprobs, finish_reason, }], diff --git a/router/src/server.rs b/router/src/server.rs index 03d184c3..8657b779 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -1103,7 +1103,13 @@ async fn chat_completions( let (content, tool_calls) = if tool_grammar.is_some() { (None, Some(vec![stream_token.token.text])) } else { - (Some(stream_token.token.text), None) + let content = if !stream_token.token.special { + Some(stream_token.token.text) + } else { + None + }; + + (content, None) }; event diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index 6d796ac3..6fa85d4e 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -38,58 +38,6 @@ from text_generation_server.utils.layers import ( ) -class LlamaConfig(PretrainedConfig): - def __init__( - self, - vocab_size=32000, - hidden_size=4096, - intermediate_size=11008, - num_hidden_layers=32, - num_attention_heads=32, - num_key_value_heads=None, - hidden_act="silu", - max_position_embeddings=2048, - initializer_range=0.02, - rms_norm_eps=1e-6, - use_cache=True, - pad_token_id=0, - bos_token_id=1, - eos_token_id=2, - pretraining_tp=1, - tie_word_embeddings=False, - rope_scaling=None, - rope_theta=10000.0, - **kwargs, - ): - self.vocab_size = vocab_size - self.max_position_embeddings = max_position_embeddings - self.hidden_size = hidden_size - self.intermediate_size = intermediate_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - - # for backward compatibility - if num_key_value_heads is None: - num_key_value_heads = num_attention_heads - - self.num_key_value_heads = num_key_value_heads - self.hidden_act = hidden_act - self.initializer_range = initializer_range - self.rms_norm_eps = rms_norm_eps - self.pretraining_tp = pretraining_tp - self.use_cache = use_cache - self.rope_scaling = rope_scaling - self.rope_theta = rope_theta - - super().__init__( - pad_token_id=pad_token_id, - bos_token_id=bos_token_id, - eos_token_id=eos_token_id, - tie_word_embeddings=tie_word_embeddings, - **kwargs, - ) - - def load_attention(config, prefix, weights): if config.num_attention_heads != config.num_key_value_heads: return _load_gqa(config, prefix, weights) diff --git a/server/text_generation_server/models/flash_llama.py b/server/text_generation_server/models/flash_llama.py index 56768942..f3578f88 100644 --- a/server/text_generation_server/models/flash_llama.py +++ b/server/text_generation_server/models/flash_llama.py @@ -2,14 +2,13 @@ import torch import torch.distributed from opentelemetry import trace -from transformers import AutoConfig, AutoTokenizer +from transformers import AutoConfig, AutoTokenizer, GenerationConfig from transformers.models.llama import LlamaTokenizer from typing import Optional from text_generation_server.models import FlashCausalLM from text_generation_server.models.custom_modeling.flash_llama_modeling import ( FlashLlamaForCausalLM, - LlamaConfig, ) from text_generation_server.utils import ( initialize_torch_distributed, @@ -53,8 +52,17 @@ class FlashLlama(FlashCausalLM): truncation_side="left", trust_remote_code=trust_remote_code, ) + try: + generation_config = GenerationConfig.from_pretrained( + model_id, revision=revision, trust_remote_code=trust_remote_code + ) + if isinstance(generation_config.eos_token_id, (list, set)): + # TODO Huge hack + tokenizer._eos_token_ids = set(generation_config.eos_token_id) + except Exception: + pass - config = LlamaConfig.from_pretrained( + config = AutoConfig.from_pretrained( model_id, revision=revision, trust_remote_code=trust_remote_code ) config.quantize = quantize diff --git a/server/text_generation_server/models/model.py b/server/text_generation_server/models/model.py index cec9eafa..4f35b0aa 100644 --- a/server/text_generation_server/models/model.py +++ b/server/text_generation_server/models/model.py @@ -27,7 +27,14 @@ class Model(ABC): ): self.model = model.eval() self.tokenizer = tokenizer + + # all_special_ids is not set correctly if the rust tokenizer is unpacked + # TODO report this to transformers. + other_special_ids = { + id for id, token in tokenizer.added_tokens_decoder.items() if token.special + } self.all_special_ids = set(tokenizer.all_special_ids) + self.all_special_ids.update(other_special_ids) self.requires_padding = requires_padding self.dtype = dtype self.device = device diff --git a/server/text_generation_server/utils/tokens.py b/server/text_generation_server/utils/tokens.py index 8ef1ca0d..22f86b60 100644 --- a/server/text_generation_server/utils/tokens.py +++ b/server/text_generation_server/utils/tokens.py @@ -1,5 +1,5 @@ import re -from typing import List, Optional, Tuple +from typing import List, Optional, Tuple, Set, Union import math import torch @@ -143,12 +143,22 @@ class StopSequenceCriteria: class StoppingCriteria: def __init__( self, - eos_token_id: int, + eos_token_ids: Optional[Union[Set[int], int]], stop_sequence_criterias: List[StopSequenceCriteria], max_new_tokens: int = 20, ignore_eos_token: bool = False, ): - self.eos_token_id = eos_token_id + if eos_token_ids is None: + eos_token_ids = set() + elif isinstance(eos_token_ids, int): + eos_token_ids = set([eos_token_ids]) + elif isinstance(eos_token_ids, set): + eos_token_ids = eos_token_ids + else: + raise RuntimeError( + f"eos_token_ids is of invalid type {type(eos_token_ids)}, expected int, None or set[int]" + ) + self.eos_token_ids = eos_token_ids self.stop_sequence_criterias = stop_sequence_criterias self.max_new_tokens = max_new_tokens self.current_tokens = 0 @@ -160,7 +170,10 @@ class StoppingCriteria: if self.current_tokens >= self.max_new_tokens: return True, FinishReason.FINISH_REASON_LENGTH - if not self.ignore_eos_token and last_token == self.eos_token_id: + if isinstance(last_token, torch.Tensor): + last_token = last_token.item() + + if not self.ignore_eos_token and last_token in self.eos_token_ids: return True, FinishReason.FINISH_REASON_EOS_TOKEN if self.stop_sequence_criterias: @@ -184,8 +197,10 @@ class StoppingCriteria: stop_sequence_criterias = [ StopSequenceCriteria(sequence) for sequence in pb.stop_sequences ] + # TODO Hack because eos_token_id cannot be what we want. + eos_token_id = getattr(tokenizer, "_eos_token_ids", tokenizer.eos_token_id) return StoppingCriteria( - tokenizer.eos_token_id, + eos_token_id, stop_sequence_criterias, pb.max_new_tokens, pb.ignore_eos_token, From bbc547ad8de76398d625253a3b25d097b88fff48 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 26 Apr 2024 15:39:00 +0200 Subject: [PATCH 45/74] 2nd round of benchmark modifications (tiny adjustements to avoid overloading the host). (#1816) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- load_tests/common.js | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/load_tests/common.js b/load_tests/common.js index 80728214..e0a10595 100644 --- a/load_tests/common.js +++ b/load_tests/common.js @@ -26,23 +26,23 @@ export function get_options() { // }], }, scenarios: { - single_user: { + // single_user: { + // executor: 'constant-arrival-rate', + // duration: '60s', + // preAllocatedVUs: 1, + // rate: 20, + // timeUnit: '1s', + // }, + load_test: { executor: 'constant-arrival-rate', duration: '60s', - preAllocatedVUs: 1, + preAllocatedVUs: 100, rate: 1, timeUnit: '1s', }, - // load_test: { - // executor: 'constant-arrival-rate', - // duration: '60s', - // preAllocatedVUs: 100, - // rate: 1, - // timeUnit: '1s', - // }, // breakpoint: { // executor: 'ramping-arrival-rate', //Assure load increase if the system slows - // preAllocatedVUs: 1000, + // preAllocatedVUs: 300, // stages: [ // { duration: '60s', target: 100 }, // just slowly ramp-up to a HUGE load // ], From f9cf3456250e420af65e1d813ccee6af749658ad Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 26 Apr 2024 15:44:44 +0200 Subject: [PATCH 46/74] Adding new env variables for TPU backends. (#1755) # What does this PR do? On TPU (and probably inferentia). The model needs to know right off the bat about BATCH_SIZE and MAX_TOTAL_TOKENS (since the entire cache will be determined by both). This PR sends that information to the shards to they can allocate accordingly. Should be no-op for other backends. Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- launcher/src/main.rs | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 40e7364f..f264e000 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -448,6 +448,8 @@ fn shard_manager( cuda_memory_fraction: f32, rope_scaling: Option, rope_factor: Option, + max_total_tokens: usize, + max_batch_size: Option, otlp_endpoint: Option, status_sender: mpsc::Sender, shutdown: Arc, @@ -512,6 +514,7 @@ fn shard_manager( (Some(scaling), Some(factor)) => Some((scaling, factor)), (None, Some(factor)) => Some((RopeScaling::Linear, factor)), }; + // OpenTelemetry if let Some(otlp_endpoint) = otlp_endpoint { shard_args.push("--otlp-endpoint".to_string()); @@ -564,6 +567,14 @@ fn shard_manager( envs.push(("ROPE_FACTOR".into(), factor.to_string().into())); } + envs.push(( + "MAX_TOTAL_TOKENS".into(), + max_total_tokens.to_string().into(), + )); + if let Some(max_batch_size) = max_batch_size { + envs.push(("MAX_BATCH_SIZE".into(), max_batch_size.to_string().into())); + } + // If huggingface_hub_cache is some, pass it to the shard // Useful when running inside a docker container if let Some(huggingface_hub_cache) = huggingface_hub_cache { @@ -967,6 +978,7 @@ fn spawn_shards( num_shard: usize, args: &Args, cuda_graphs: Vec, + max_total_tokens: usize, shutdown: Arc, shutdown_receiver: &mpsc::Receiver<()>, shutdown_sender: mpsc::Sender<()>, @@ -998,6 +1010,7 @@ fn spawn_shards( let cuda_memory_fraction = args.cuda_memory_fraction; let rope_scaling = args.rope_scaling; let rope_factor = args.rope_factor; + let max_batch_size = args.max_batch_size; thread::spawn(move || { shard_manager( model_id, @@ -1020,6 +1033,8 @@ fn spawn_shards( cuda_memory_fraction, rope_scaling, rope_factor, + max_total_tokens, + max_batch_size, otlp_endpoint, status_sender, shutdown, @@ -1474,6 +1489,7 @@ fn main() -> Result<(), LauncherError> { num_shard, &args, cuda_graphs, + max_total_tokens, shutdown.clone(), &shutdown_receiver, shutdown_sender, From 45ecf9d04060d2acfbb3286c261b3c5a476ec297 Mon Sep 17 00:00:00 2001 From: "Wang, Yi" Date: Fri, 26 Apr 2024 21:48:58 +0800 Subject: [PATCH 47/74] add intel xpu support for TGI (#1475) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --------- Signed-off-by: Wang, Yi A Co-authored-by: Morgan Funtowicz Co-authored-by: Nicolas Patry --- .github/workflows/build.yaml | 93 ++++++++++++ Dockerfile_intel | 105 +++++++++++++ launcher/src/env_runtime.rs | 13 +- .../models/cache_manager.py | 6 +- .../custom_modeling/flash_dbrx_modeling.py | 4 +- .../custom_modeling/flash_mixtral_modeling.py | 5 +- .../models/flash_causal_lm.py | 28 +++- .../models/flash_llama.py | 4 + .../models/flash_mistral.py | 6 +- .../models/flash_neox.py | 5 +- .../text_generation_server/models/flash_rw.py | 5 +- .../models/flash_santacoder.py | 4 + .../text_generation_server/models/globals.py | 4 +- server/text_generation_server/utils/dist.py | 9 +- .../utils/flash_attn.py | 142 +++++++++++------- .../utils/import_utils.py | 9 ++ server/text_generation_server/utils/layers.py | 39 ++++- .../utils/paged_attention.py | 30 +++- 18 files changed, 434 insertions(+), 77 deletions(-) create mode 100644 Dockerfile_intel diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index 066ea889..f1131450 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -274,12 +274,105 @@ jobs: cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache-rocm,mode=min cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache-rocm,mode=min + build-and-push-image-intel: + concurrency: + group: ${{ github.workflow }}-build-and-push-image-intel-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + needs: + - start-runner + - build-and-push-image # Wait for the main docker image to be built + - integration-tests # Wait for the main integration-tests + runs-on: ${{ needs.start-runner.outputs.label }} # run the job on the newly created runner + permissions: + contents: write + packages: write + # This is used to complete the identity challenge + # with sigstore/fulcio when running outside of PRs. + id-token: write + security-events: write + steps: + - name: Checkout repository + uses: actions/checkout@v3 + - name: Initialize Docker Buildx + uses: docker/setup-buildx-action@v2.0.0 + with: + install: true + - name: Inject slug/short variables + uses: rlespinasse/github-slug-action@v4.4.1 + - name: Tailscale + uses: tailscale/github-action@7bd8039bf25c23c4ab1b8d6e2cc2da2280601966 + with: + authkey: ${{ secrets.TAILSCALE_AUTHKEY }} + - name: Login to GitHub Container Registry + if: github.event_name != 'pull_request' + uses: docker/login-action@v2 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + - name: Login to internal Container Registry + uses: docker/login-action@v2.1.0 + with: + username: ${{ secrets.TAILSCALE_DOCKER_USERNAME }} + password: ${{ secrets.TAILSCALE_DOCKER_PASSWORD }} + registry: registry.internal.huggingface.tech + - name: Login to Azure Container Registry + if: github.event_name != 'pull_request' + uses: docker/login-action@v2.1.0 + with: + username: ${{ secrets.AZURE_DOCKER_USERNAME }} + password: ${{ secrets.AZURE_DOCKER_PASSWORD }} + registry: db4c2190dd824d1f950f5d1555fbadf0.azurecr.io + # If pull request + - name: Extract metadata (tags, labels) for Docker + if: ${{ github.event_name == 'pull_request' }} + id: meta-pr + uses: docker/metadata-action@v4.3.0 + with: + images: | + registry.internal.huggingface.tech/api-inference/community/text-generation-inference + tags: | + type=raw,value=sha-${{ env.GITHUB_SHA_SHORT }}-intel + # If main, release or tag + - name: Extract metadata (tags, labels) for Docker + if: ${{ github.event_name != 'pull_request' }} + id: meta + uses: docker/metadata-action@v4.3.0 + with: + flavor: | + latest=false + images: | + registry.internal.huggingface.tech/api-inference/community/text-generation-inference + ghcr.io/huggingface/text-generation-inference + db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation-inference + tags: | + type=semver,pattern={{version}}-intel + type=semver,pattern={{major}}.{{minor}}-intel + type=raw,value=latest-intel,enable=${{ github.ref == format('refs/heads/{0}', github.event.repository.default_branch) }} + type=raw,value=sha-${{ env.GITHUB_SHA_SHORT }}-intel + - name: Build and push Docker image + id: build-and-push + uses: docker/build-push-action@v4 + with: + context: . + file: Dockerfile_intel + push: true + platforms: 'linux/amd64' + build-args: | + GIT_SHA=${{ env.GITHUB_SHA }} + DOCKER_LABEL=sha-${{ env.GITHUB_SHA_SHORT }}-intel + tags: ${{ steps.meta.outputs.tags || steps.meta-pr.outputs.tags }} + labels: ${{ steps.meta.outputs.labels || steps.meta-pr.outputs.labels }} + cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache-intel,mode=min + cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache-intel,mode=min + stop-runner: name: Stop self-hosted EC2 runner needs: - start-runner - build-and-push-image - build-and-push-image-rocm + - build-and-push-image-intel - integration-tests runs-on: ubuntu-latest env: diff --git a/Dockerfile_intel b/Dockerfile_intel new file mode 100644 index 00000000..d0791cac --- /dev/null +++ b/Dockerfile_intel @@ -0,0 +1,105 @@ +FROM lukemathwalker/cargo-chef:latest-rust-1.75 AS chef +WORKDIR /usr/src + +ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse + +FROM chef as planner +COPY Cargo.toml Cargo.toml +COPY rust-toolchain.toml rust-toolchain.toml +COPY proto proto +COPY benchmark benchmark +COPY router router +COPY launcher launcher +RUN cargo chef prepare --recipe-path recipe.json + +FROM chef AS builder + +ARG GIT_SHA +ARG DOCKER_LABEL + +RUN PROTOC_ZIP=protoc-21.12-linux-x86_64.zip && \ + curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP && \ + unzip -o $PROTOC_ZIP -d /usr/local bin/protoc && \ + unzip -o $PROTOC_ZIP -d /usr/local 'include/*' && \ + rm -f $PROTOC_ZIP + +COPY --from=planner /usr/src/recipe.json recipe.json +RUN cargo chef cook --release --recipe-path recipe.json + +COPY Cargo.toml Cargo.toml +COPY rust-toolchain.toml rust-toolchain.toml +COPY proto proto +COPY benchmark benchmark +COPY router router +COPY launcher launcher +RUN cargo build --release + + +# Text Generation Inference base image for Intel +FROM intel/intel-extension-for-pytorch:2.1.10-xpu as base + +USER root +# libssl.so.1.1 is not installed on Ubuntu 22.04 by default, install it +RUN wget http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb && \ + dpkg -i ./libssl1.1_1.1.1f-1ubuntu2_amd64.deb + + +RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \ +| gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list + +RUN apt-get update && apt install -y intel-basekit xpu-smi cmake python3-dev ninja-build + +# Text Generation Inference base env +ENV HUGGINGFACE_HUB_CACHE=/data \ + HF_HUB_ENABLE_HF_TRANSFER=1 \ + PORT=80 + + +WORKDIR /usr/src +# Build pytorch and ipex +RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout -b xpu_main origin/xpu-main +RUN git clone https://github.com/pytorch/pytorch.git && cd pytorch && git checkout 209f2fa8ff86652f67d75c2f19bf9cb9942fd018 && git apply /usr/src/intel-extension-for-pytorch/torch_patches/00*.patch + +# Install server +COPY proto proto +COPY server server +COPY server/Makefile server/Makefile +RUN cd server && \ + make gen-server && \ + pip install -r requirements_cuda.txt && \ + pip install ".[accelerate, peft, outlines]" --no-cache-dir + +ENV CCL_ROOT=/opt/intel/oneapi/ccl/latest +ENV I_MPI_ROOT=/opt/intel/oneapi/mpi/latest +ENV FI_PROVIDER_PATH=/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/lib/prov:/usr/lib/x86_64-linux-gnu/libfabric +ENV DIAGUTIL_PATH=/opt/intel/oneapi/compiler/latest/etc/compiler/sys_check/sys_check.sh +ENV CCL_CONFIGURATION=cpu_gpu_dpcpp +ENV MANPATH=/opt/intel/oneapi/mpi/latest/share/man:/opt/intel/oneapi/mpi/latest/share/man:/opt/intel/oneapi/compiler/latest/share/man +ENV CMAKE_PREFIX_PATH=/opt/intel/oneapi/mkl/latest/lib/cmake:/opt/intel/oneapi/compiler/latest +ENV CMPLR_ROOT=/opt/intel/oneapi/compiler/latest +ENV LIBRARY_PATH=/opt/intel/oneapi/mpi/latest/lib:/opt/intel/oneapi/ccl/latest/lib/:/opt/intel/oneapi/mkl/latest/lib/:/opt/intel/oneapi/compiler/latest/lib +ENV OCL_ICD_FILENAMES=libintelocl_emu.so:libalteracl.so:/opt/intel/oneapi/compiler/latest/lib/libintelocl.so +ENV CLASSPATH=/opt/intel/oneapi/mpi/latest/share/java/mpi.jar:/opt/intel/oneapi/mpi/latest/share/java/mpi.jar +ENV LD_LIBRARY_PATH=/opt/intel/oneapi/ccl/latest/lib/:/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/lib:/opt/intel/oneapi/mpi/latest/lib:/opt/intel/oneapi/mkl/latest/lib:/opt/intel/oneapi/compiler/latest/opt/compiler/lib:/opt/intel/oneapi/compiler/latest/lib:/opt/intel/oneapi/lib:/opt/intel/oneapi/lib/intel64: +ENV MKLROOT=/opt/intel/oneapi/mkl/latest +ENV NLSPATH=/opt/intel/oneapi/mkl/latest/share/locale/%l_%t/%N:/opt/intel/oneapi/compiler/latest/lib/locale/%l_%t/%N +ENV PATH=/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/bin:/opt/intel/oneapi/mpi/latest/bin:/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/bin:/opt/intel/oneapi/mkl/latest/bin/:/opt/intel/oneapi/compiler/latest/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin +ENV CPATH=/opt/intel/oneapi/mpi/latest/include:/opt/intel/oneapi/ccl/latest/include:/opt/intel/oneapi/mkl/latest/include +ENV CCL_ZE_IPC_EXCHANGE=sockets + + +RUN pip uninstall -y torch && cd pytorch && git submodule update --init --recursive && python setup.py install +RUN pip uninstall -y intel-extension-for-pytorch && cd intel-extension-for-pytorch && git submodule update --init --recursive && USE_AOT_DEVLIST='pvc' BUILD_SEPARATE_OPS=ON BUILD_WITH_CPU=ON USE_XETLA=ON python setup.py install + +# Install benchmarker +COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark +# Install router +COPY --from=builder /usr/src/target/release/text-generation-router /usr/local/bin/text-generation-router +# Install launcher +COPY --from=builder /usr/src/target/release/text-generation-launcher /usr/local/bin/text-generation-launcher + +# Final image +FROM base + +ENTRYPOINT ["text-generation-launcher"] +CMD ["--json-output"] diff --git a/launcher/src/env_runtime.rs b/launcher/src/env_runtime.rs index 9dbc83f7..08fb301c 100644 --- a/launcher/src/env_runtime.rs +++ b/launcher/src/env_runtime.rs @@ -7,14 +7,17 @@ pub(crate) struct Env { git_sha: &'static str, docker_label: &'static str, nvidia_env: String, + xpu_env: String, } impl Env { pub fn new() -> Self { let nvidia_env = nvidia_smi(); + let xpu_env = xpu_smi(); Self { nvidia_env: nvidia_env.unwrap_or("N/A".to_string()), + xpu_env: xpu_env.unwrap_or("N/A".to_string()), cargo_target: env!("VERGEN_CARGO_TARGET_TRIPLE"), cargo_version: env!("VERGEN_RUSTC_SEMVER"), git_sha: option_env!("VERGEN_GIT_SHA").unwrap_or("N/A"), @@ -31,7 +34,8 @@ impl fmt::Display for Env { writeln!(f, "Cargo version: {}", self.cargo_version)?; writeln!(f, "Commit sha: {}", self.git_sha)?; writeln!(f, "Docker label: {}", self.docker_label)?; - write!(f, "nvidia-smi:\n{}", self.nvidia_env)?; + writeln!(f, "nvidia-smi:\n{}", self.nvidia_env)?; + write!(f, "xpu-smi:\n{}", self.xpu_env)?; Ok(()) } @@ -43,3 +47,10 @@ fn nvidia_smi() -> Option { let output = nvidia_smi.replace('\n', "\n "); Some(output.trim().to_string()) } + +fn xpu_smi() -> Option { + let output = Command::new("xpu-smi").arg("discovery").output().ok()?; + let xpu_smi = String::from_utf8(output.stdout).ok()?; + let output = xpu_smi.replace('\n', "\n "); + Some(output.trim().to_string()) +} diff --git a/server/text_generation_server/models/cache_manager.py b/server/text_generation_server/models/cache_manager.py index 85e1b19b..4c65e2dd 100644 --- a/server/text_generation_server/models/cache_manager.py +++ b/server/text_generation_server/models/cache_manager.py @@ -2,6 +2,7 @@ import math import torch from typing import Optional, List, Tuple +from text_generation_server.utils.import_utils import IS_XPU_SYSTEM BLOCK_SIZE: int = 16 # Will be set in warmup @@ -24,7 +25,10 @@ class CacheManager: self.repeat_slots = repeat_slots element_size = torch.tensor([], dtype=dtype).element_size() - x = self.block_size // element_size + if IS_XPU_SYSTEM: + x = 1 + else: + x = self.block_size // element_size self.kv_cache = [ ( diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py index d04ce39e..d0978bef 100644 --- a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py @@ -21,8 +21,10 @@ from transformers.activations import ACT2FN from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple, Any from loguru import logger +from text_generation_server.utils.import_utils import IS_XPU_SYSTEM -from vllm.model_executor.layers.fused_moe import fused_moe +if not IS_XPU_SYSTEM: + from vllm.model_executor.layers.fused_moe import fused_moe from text_generation_server.utils import paged_attention, flash_attn from text_generation_server.utils.layers import ( FastLinear, diff --git a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py index be8cb965..3f6c8e03 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py @@ -24,7 +24,10 @@ import torch.distributed import numpy as np from torch import nn -from vllm.model_executor.layers.fused_moe import fused_moe +from text_generation_server.utils.import_utils import IS_XPU_SYSTEM + +if not IS_XPU_SYSTEM: + from vllm.model_executor.layers.fused_moe import fused_moe from transformers.activations import ACT2FN from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index 1189ccdd..94518b8f 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -33,7 +33,7 @@ from text_generation_server.utils import StoppingCriteria, HeterogeneousNextToke from text_generation_server.utils.dist import MEMORY_FRACTION tracer = trace.get_tracer(__name__) - +from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM, IS_XPU_SYSTEM @dataclass class FlashCausalLMBatch(Batch): @@ -752,7 +752,10 @@ class FlashCausalLM(Model): def warmup(self, batch: FlashCausalLMBatch): # The warmup batch is the biggest batch we could ever receive - torch.cuda.empty_cache() + if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: + torch.cuda.empty_cache() + elif IS_XPU_SYSTEM: + torch.xpu.empty_cache() try: cache_manager = set_cache_manager( batch.blocks, @@ -772,7 +775,10 @@ class FlashCausalLM(Model): f"You need to decrease `--max-batch-prefill-tokens`" ) from e - torch.cuda.synchronize(self.device) + if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: + torch.cuda.synchronize(self.device) + elif IS_XPU_SYSTEM: + torch.xpu.synchronize(self.device) # Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm) # Calculate the number of blocks that can be allocated with the free memory @@ -780,12 +786,18 @@ class FlashCausalLM(Model): cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size - total_free_memory, _ = torch.cuda.mem_get_info(self.device) - total_gpu_memory = torch.cuda.get_device_properties(self.device).total_memory + if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: + total_free_memory, _ = torch.cuda.mem_get_info(self.device) + total_gpu_memory = torch.cuda.get_device_properties(self.device).total_memory - free_memory = max( - 0, total_free_memory - (1 - MEMORY_FRACTION) * total_gpu_memory - ) + free_memory = max( + 0, total_free_memory - (1 - MEMORY_FRACTION) * total_gpu_memory + ) + elif IS_XPU_SYSTEM: + total_gpu_memory = torch.xpu.get_device_properties(self.device).total_memory + free_memory = int(total_gpu_memory *0.5) + else: + raise NotImplementedError("FlashModel is only available on GPU") num_blocks = ( # Leave 5% for some wiggle room diff --git a/server/text_generation_server/models/flash_llama.py b/server/text_generation_server/models/flash_llama.py index f3578f88..f37fc542 100644 --- a/server/text_generation_server/models/flash_llama.py +++ b/server/text_generation_server/models/flash_llama.py @@ -18,6 +18,7 @@ from text_generation_server.utils import ( tracer = trace.get_tracer(__name__) +from text_generation_server.utils.import_utils import IS_XPU_SYSTEM class FlashLlama(FlashCausalLM): def __init__( @@ -33,6 +34,9 @@ class FlashLlama(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype + elif IS_XPU_SYSTEM: + device = torch.device(f"xpu:{rank}") + dtype = torch.float16 if dtype is None else dtype else: raise NotImplementedError("FlashLlama is only available on GPU") diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py index 52a30b5f..e2ad78d9 100644 --- a/server/text_generation_server/models/flash_mistral.py +++ b/server/text_generation_server/models/flash_mistral.py @@ -33,8 +33,9 @@ tracer = trace.get_tracer(__name__) # Will be set in init SLIDING_WINDOW: Optional[int] = None SLIDING_WINDOW_BLOCKS: Optional[int] = None +from text_generation_server.utils.import_utils import IS_XPU_SYSTEM -MEM_POOL = torch.cuda.graph_pool_handle() +MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None def set_sliding_window(sliding_window: int, sliding_window_blocks: int): @@ -316,6 +317,9 @@ class BaseFlashMistral(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype + elif IS_XPU_SYSTEM: + device = torch.device(f"xpu:{rank}") + dtype = torch.float16 if dtype is None else dtype else: raise NotImplementedError("FlashMistral is only available on GPU") diff --git a/server/text_generation_server/models/flash_neox.py b/server/text_generation_server/models/flash_neox.py index 5a351bd7..70c978de 100644 --- a/server/text_generation_server/models/flash_neox.py +++ b/server/text_generation_server/models/flash_neox.py @@ -14,7 +14,7 @@ from text_generation_server.utils import ( weight_files, Weights, ) - +from text_generation_server.utils.import_utils import IS_XPU_SYSTEM tracer = trace.get_tracer(__name__) @@ -32,6 +32,9 @@ class FlashNeoXSharded(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype + elif IS_XPU_SYSTEM: + device = torch.device(f"xpu:{rank}") + dtype = torch.float16 if dtype is None else dtype else: raise NotImplementedError("FlashNeoX is only available on GPU") diff --git a/server/text_generation_server/models/flash_rw.py b/server/text_generation_server/models/flash_rw.py index fc1e26bd..6eb25f22 100644 --- a/server/text_generation_server/models/flash_rw.py +++ b/server/text_generation_server/models/flash_rw.py @@ -15,7 +15,7 @@ from text_generation_server.utils import ( weight_files, Weights, ) - +from text_generation_server.utils.import_utils import IS_XPU_SYSTEM tracer = trace.get_tracer(__name__) @@ -33,6 +33,9 @@ class FlashRWSharded(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype + elif IS_XPU_SYSTEM: + device = torch.device(f"xpu:{rank}") + dtype = torch.float16 if dtype is None else dtype else: raise NotImplementedError("FlashRW is only available on GPU") diff --git a/server/text_generation_server/models/flash_santacoder.py b/server/text_generation_server/models/flash_santacoder.py index 034949f9..6147398a 100644 --- a/server/text_generation_server/models/flash_santacoder.py +++ b/server/text_generation_server/models/flash_santacoder.py @@ -18,6 +18,7 @@ from text_generation_server.utils import ( Weights, ) +from text_generation_server.utils.import_utils import IS_XPU_SYSTEM tracer = trace.get_tracer(__name__) @@ -35,6 +36,9 @@ class FlashSantacoderSharded(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype + elif IS_XPU_SYSTEM: + device = torch.device(f"xpu:{rank}") + dtype = torch.float16 if dtype is None else dtype else: raise NotImplementedError("FlashSantacoderSharded is only available on GPU") diff --git a/server/text_generation_server/models/globals.py b/server/text_generation_server/models/globals.py index 91b4225a..b92aa65b 100644 --- a/server/text_generation_server/models/globals.py +++ b/server/text_generation_server/models/globals.py @@ -1,10 +1,10 @@ import torch import os -MEM_POOL = torch.cuda.graph_pool_handle() +MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None # This is overridden by the cli cuda_graphs = os.getenv("CUDA_GRAPHS") -if cuda_graphs is not None and cuda_graphs != "0": +if torch.cuda.is_available() and cuda_graphs is not None and cuda_graphs != "0": try: cuda_graphs = [int(item) for item in cuda_graphs.split(",")] except Exception as e: diff --git a/server/text_generation_server/utils/dist.py b/server/text_generation_server/utils/dist.py index d02bfc5b..3625e6f2 100644 --- a/server/text_generation_server/utils/dist.py +++ b/server/text_generation_server/utils/dist.py @@ -57,7 +57,14 @@ def initialize_torch_distributed(): options.is_high_priority_stream = True options._timeout = timedelta(seconds=60) else: - backend = "gloo" + try: + import oneccl_bindings_for_pytorch + + backend = "ccl" + if os.getenv("CCL_WORKER_COUNT", None) is None: + os.environ["CCL_WORKER_COUNT"] = str(1) + except ImportError: + backend = "gloo" options = None if WORLD_SIZE == 1: diff --git a/server/text_generation_server/utils/flash_attn.py b/server/text_generation_server/utils/flash_attn.py index 45090c64..583a8f91 100644 --- a/server/text_generation_server/utils/flash_attn.py +++ b/server/text_generation_server/utils/flash_attn.py @@ -2,69 +2,81 @@ import os import torch from loguru import logger +import math -from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM +from text_generation_server.utils.import_utils import ( + IS_CUDA_SYSTEM, + IS_ROCM_SYSTEM, + IS_XPU_SYSTEM, +) if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false": raise ImportError("`USE_FLASH_ATTENTION` is false.") - -if not torch.cuda.is_available(): - raise ImportError("CUDA is not available") - -major, minor = torch.cuda.get_device_capability() -is_sm75 = major == 7 and minor == 5 -is_sm8x = major == 8 and minor >= 0 -is_sm90 = major == 9 and minor == 0 - -HAS_FLASH_ATTN = False +HAS_FLASH_ATTN = True HAS_FLASH_ATTN_V2_CUDA = False HAS_FLASH_ATTN_V2_ROCM = False -try: + +if IS_XPU_SYSTEM: + import intel_extension_for_pytorch as ipex + +if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: + if not torch.cuda.is_available(): + raise ImportError("CUDA is not available") + + major, minor = torch.cuda.get_device_capability() + is_sm75 = major == 7 and minor == 5 + is_sm8x = major == 8 and minor >= 0 + is_sm90 = major == 9 and minor == 0 + + HAS_FLASH_ATTN = False + HAS_FLASH_ATTN_V2_CUDA = False + HAS_FLASH_ATTN_V2_ROCM = False try: - import flash_attn_2_cuda - except ImportError: - architecture_suffix = "" - if IS_CUDA_SYSTEM: - architecture_suffix = "-cuda" + try: + import flash_attn_2_cuda + except ImportError: + architecture_suffix = "" + if IS_CUDA_SYSTEM: + architecture_suffix = "-cuda" + elif IS_ROCM_SYSTEM: + architecture_suffix = "-rocm" + raise ImportError( + "Flash Attention V2 is not installed.\n" + "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) " + f"or install flash attention v2 with `cd server && make install install-flash-attention-v2{architecture_suffix}`" + ) + if not (is_sm8x or is_sm90): + raise ImportError( + f"GPU with CUDA capability {major} {minor} is not supported for " + "Flash Attention V2" + ) + HAS_FLASH_ATTN_V2_CUDA = IS_CUDA_SYSTEM + HAS_FLASH_ATTN_V2_ROCM = IS_ROCM_SYSTEM + except ImportError as e: + try: + import flash_attn_cuda + except ImportError: + raise ImportError( + "Flash Attention is not installed.\n" + "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) " + "or install flash attention with `cd server && make install install-flash-attention`" + ) from e + + if IS_CUDA_SYSTEM and not (is_sm75 or is_sm8x or is_sm90): + raise ImportError( + f"GPU with CUDA capability {major} {minor} is not supported" + ) from e elif IS_ROCM_SYSTEM: - architecture_suffix = "-rocm" - raise ImportError( - "Flash Attention V2 is not installed.\n" - "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) " - f"or install flash attention v2 with `cd server && make install install-flash-attention-v2{architecture_suffix}`" - ) - if not (is_sm8x or is_sm90): - raise ImportError( - f"GPU with CUDA capability {major} {minor} is not supported for " - "Flash Attention V2" - ) - HAS_FLASH_ATTN_V2_CUDA = IS_CUDA_SYSTEM - HAS_FLASH_ATTN_V2_ROCM = IS_ROCM_SYSTEM -except ImportError as e: - try: - import flash_attn_cuda - except ImportError: - raise ImportError( - "Flash Attention is not installed.\n" - "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) " - "or install flash attention with `cd server && make install install-flash-attention`" - ) from e + for idx in range(torch.cuda.device_count()): + if "MI210" not in torch.cuda.get_device_name( + idx + ) and "MI250" not in torch.cuda.get_device_name(idx): + raise ImportError( + f"AMD GPU {torch.cuda.get_device_name(idx)} does not support flash-attention" + ) - if IS_CUDA_SYSTEM and not (is_sm75 or is_sm8x or is_sm90): - raise ImportError( - f"GPU with CUDA capability {major} {minor} is not supported" - ) from e - elif IS_ROCM_SYSTEM: - for idx in range(torch.cuda.device_count()): - if "MI210" not in torch.cuda.get_device_name( - idx - ) and "MI250" not in torch.cuda.get_device_name(idx): - raise ImportError( - f"AMD GPU {torch.cuda.get_device_name(idx)} does not support flash-attention" - ) - - logger.warning(f"Unable to use Flash Attention V2: {e}") - HAS_FLASH_ATTN = True + logger.warning(f"Unable to use Flash Attention V2: {e}") + HAS_FLASH_ATTN = True def attention( @@ -80,6 +92,28 @@ def attention( if window_size_left <= 0 and window_size_left != -1: raise ValueError("`window_size_left` must be > 0 or -1") + if IS_XPU_SYSTEM: + if window_size_left != -1: + raise ValueError( + f"XPU version of Flash Attention does not support window attention (window_size_left != -1, got window_size_left={window_size_left})." + ) + return ipex.llm.functional.varlen_attention( + q, + k, + v, + out, + cu_seqlens, + cu_seqlens, + max_s, + max_s, + 0.0, + softmax_scale, + False, + True, + False, + None, + ) + if HAS_FLASH_ATTN_V2_CUDA: return flash_attn_2_cuda.varlen_fwd( q, diff --git a/server/text_generation_server/utils/import_utils.py b/server/text_generation_server/utils/import_utils.py index 428c9f3e..7c0d8001 100644 --- a/server/text_generation_server/utils/import_utils.py +++ b/server/text_generation_server/utils/import_utils.py @@ -1,4 +1,13 @@ import torch +def is_xpu_available(): + try: + import intel_extension_for_pytorch + except ImportError: + return False + + return hasattr(torch, "xpu") and torch.xpu.is_available() + IS_ROCM_SYSTEM = torch.version.hip is not None IS_CUDA_SYSTEM = torch.version.cuda is not None +IS_XPU_SYSTEM = is_xpu_available() diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index 69bd5e88..638cb0a0 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -18,7 +18,14 @@ except ImportError: from accelerate import init_empty_weights from text_generation_server.utils.gptq.quant_linear import QuantLinear -from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM +from text_generation_server.utils.import_utils import ( + IS_CUDA_SYSTEM, + IS_ROCM_SYSTEM, + IS_XPU_SYSTEM, +) + +if IS_XPU_SYSTEM: + import intel_extension_for_pytorch as ipex HAS_AWQ = True try: @@ -812,7 +819,15 @@ try: class FastLayerNorm(nn.LayerNorm): def forward(self, hidden_states, residual=None): - if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM: + if IS_XPU_SYSTEM: + res_out = hidden_states + out = ipex.llm.functional.add_layer_norm( + residual, hidden_states, self.weight, self.bias, self.eps, True + ) + if residual is not None: + res_out = residual + return out, res_out + elif hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM: if residual is not None: hidden_states += residual residual = hidden_states @@ -858,7 +873,20 @@ try: return cls(weight, eps) def forward(self, hidden_states, residual=None): - if hidden_states.shape[-1] > 8192: + if IS_XPU_SYSTEM: + residual_out = hidden_states + out = ipex.llm.functional.add_rms_norm( + residual, + hidden_states, + self.weight, + None, + self.variance_epsilon, + True, + ) + if residual is not None: + residual_out = residual + return out, residual_out + elif hidden_states.shape[-1] > 8192: if residual is not None: hidden_states += residual residual = hidden_states @@ -984,6 +1012,10 @@ try: # Inplace operation, updating query and key. pos_encoding_ops.rotary_embedding(query, key, head_size, cos, sin, True) + elif IS_XPU_SYSTEM: + ipex.llm.functional.rotary_embedding( + query, key, sin, cos, query.size(-1), True + ) else: raise ValueError( "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction." @@ -1103,6 +1135,7 @@ try: cos = torch.index_select(self._cos_cached, 0, position_ids) sin = torch.index_select(self._sin_cached, 0, position_ids) + # Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow. return cos.unsqueeze(1), sin.unsqueeze(1) diff --git a/server/text_generation_server/utils/paged_attention.py b/server/text_generation_server/utils/paged_attention.py index 487a3a72..62c0c893 100644 --- a/server/text_generation_server/utils/paged_attention.py +++ b/server/text_generation_server/utils/paged_attention.py @@ -1,9 +1,15 @@ import torch - -from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM +from text_generation_server.utils.import_utils import ( + IS_CUDA_SYSTEM, + IS_ROCM_SYSTEM, + IS_XPU_SYSTEM, +) _PARTITION_SIZE = 512 +if IS_XPU_SYSTEM: + import intel_extension_for_pytorch as ipex + def reshape_and_cache( key: torch.Tensor, @@ -22,6 +28,10 @@ def reshape_and_cache( from vllm import cache_ops cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots) + elif IS_XPU_SYSTEM: + ipex.llm.modules.PagedAttention.reshape_and_cache( + key, value, key_cache, value_cache, slots + ) else: raise ValueError("vllm is not supported on your system") @@ -58,6 +68,22 @@ def attention( block_size = value_cache.shape[3] num_seqs, num_heads, head_size = query.shape max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE + if IS_XPU_SYSTEM: + query = query.contiguous() + return ipex.llm.modules.PagedAttention.single_query_cached_kv_attention( + out, + query, + key_cache, + value_cache, + kv_head_mapping, + softmax_scale, + block_tables, + input_lengths, + block_size, + max_s, + None, + ) + # NOTE(woosuk): We use a simple heuristic to decide whether to use # PagedAttention V1 or V2. If the number of partitions is 1, we use # V1 to avoid the overhead of reduction. Also, if the number of From a8fd4236eb3421c0c9e8845de5cd8183e9793ef2 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 26 Apr 2024 15:51:09 +0200 Subject: [PATCH 48/74] Blunder (#1815) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- .../models/flash_mistral.py | 39 ++++++------------- 1 file changed, 12 insertions(+), 27 deletions(-) diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py index e2ad78d9..6959e2ec 100644 --- a/server/text_generation_server/models/flash_mistral.py +++ b/server/text_generation_server/models/flash_mistral.py @@ -515,33 +515,18 @@ class BaseFlashMistral(FlashCausalLM): cuda_graph = self.cuda_graphs.get(padded_bs, None) if cu_seqlen_prefill is not None or cuda_graph is None: - - if cu_seqlen_prefill is None: - logits, speculative_logits = self.compiled_model( - input_ids=input_ids, - position_ids=position_ids, - cu_seqlen_prefill=cu_seqlen_prefill, - kv_cache=kv_cache, - block_tables=block_tables, - slots=slots, - input_lengths=input_lengths, - max_s=max_s, - prefill_cache_indices=batch.prefill_cache_indices, - lm_head_indices=lm_head_indices, - ) - else: - logits, speculative_logits = self.model.forward( - input_ids=input_ids, - position_ids=position_ids, - cu_seqlen_prefill=cu_seqlen_prefill, - kv_cache=kv_cache, - block_tables=block_tables, - slots=slots, - input_lengths=input_lengths, - max_s=max_s, - prefill_cache_indices=batch.prefill_cache_indices, - lm_head_indices=lm_head_indices, - ) + logits, speculative_logits = self.model.forward( + input_ids=input_ids, + position_ids=position_ids, + cu_seqlen_prefill=cu_seqlen_prefill, + kv_cache=kv_cache, + block_tables=block_tables, + slots=slots, + input_lengths=input_lengths, + max_s=max_s, + prefill_cache_indices=batch.prefill_cache_indices, + lm_head_indices=lm_head_indices, + ) if batch.prefill_cache_indices is not None: batch.prefill_cache_indices = None return logits, speculative_logits From 8b8e8f663286b8a5f4d5c5fb14cd02a60cb43652 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 26 Apr 2024 19:19:08 +0200 Subject: [PATCH 49/74] Fixing qwen2. (#1818) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- server/text_generation_server/models/flash_qwen2.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/server/text_generation_server/models/flash_qwen2.py b/server/text_generation_server/models/flash_qwen2.py index c3c63516..cb3cf6b0 100644 --- a/server/text_generation_server/models/flash_qwen2.py +++ b/server/text_generation_server/models/flash_qwen2.py @@ -4,7 +4,7 @@ import torch import torch.distributed from opentelemetry import trace -from transformers.models.qwen2 import Qwen2Tokenizer +from transformers import AutoTokenizer, AutoConfig from typing import Optional from text_generation_server.models.cache_manager import BLOCK_SIZE @@ -15,7 +15,6 @@ from text_generation_server.models.flash_mistral import ( from text_generation_server.models.custom_modeling.flash_qwen2_modeling import ( Qwen2ForCausalLM, ) -from transformers.models.qwen2 import Qwen2Config from text_generation_server.utils import ( initialize_torch_distributed, weight_files, @@ -42,7 +41,7 @@ class FlashQwen2(BaseFlashMistral): else: raise NotImplementedError("FlashQwen2 is only available on GPU") - tokenizer = Qwen2Tokenizer.from_pretrained( + tokenizer = AutoTokenizer.from_pretrained( model_id, revision=revision, padding_side="left", @@ -50,7 +49,7 @@ class FlashQwen2(BaseFlashMistral): trust_remote_code=trust_remote_code, ) - config = Qwen2Config.from_pretrained( + config = AutoConfig.from_pretrained( model_id, revision=revision, trust_remote_code=trust_remote_code ) config.quantize = quantize From e9f03f822a766f071620457bd977f7987e65b20e Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Fri, 26 Apr 2024 19:19:55 +0200 Subject: [PATCH 50/74] Dummy CI run. (#1817) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- .../models/flash_causal_lm.py | 13 ++++++++++--- server/text_generation_server/models/flash_llama.py | 1 + server/text_generation_server/models/flash_neox.py | 1 + server/text_generation_server/models/flash_rw.py | 1 + .../models/flash_santacoder.py | 1 + server/text_generation_server/utils/import_utils.py | 2 ++ server/text_generation_server/utils/layers.py | 1 + 7 files changed, 17 insertions(+), 3 deletions(-) diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index 94518b8f..a6d0204f 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -33,7 +33,12 @@ from text_generation_server.utils import StoppingCriteria, HeterogeneousNextToke from text_generation_server.utils.dist import MEMORY_FRACTION tracer = trace.get_tracer(__name__) -from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM, IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import ( + IS_CUDA_SYSTEM, + IS_ROCM_SYSTEM, + IS_XPU_SYSTEM, +) + @dataclass class FlashCausalLMBatch(Batch): @@ -788,14 +793,16 @@ class FlashCausalLM(Model): if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: total_free_memory, _ = torch.cuda.mem_get_info(self.device) - total_gpu_memory = torch.cuda.get_device_properties(self.device).total_memory + total_gpu_memory = torch.cuda.get_device_properties( + self.device + ).total_memory free_memory = max( 0, total_free_memory - (1 - MEMORY_FRACTION) * total_gpu_memory ) elif IS_XPU_SYSTEM: total_gpu_memory = torch.xpu.get_device_properties(self.device).total_memory - free_memory = int(total_gpu_memory *0.5) + free_memory = int(total_gpu_memory * 0.5) else: raise NotImplementedError("FlashModel is only available on GPU") diff --git a/server/text_generation_server/models/flash_llama.py b/server/text_generation_server/models/flash_llama.py index f37fc542..609a188d 100644 --- a/server/text_generation_server/models/flash_llama.py +++ b/server/text_generation_server/models/flash_llama.py @@ -20,6 +20,7 @@ tracer = trace.get_tracer(__name__) from text_generation_server.utils.import_utils import IS_XPU_SYSTEM + class FlashLlama(FlashCausalLM): def __init__( self, diff --git a/server/text_generation_server/models/flash_neox.py b/server/text_generation_server/models/flash_neox.py index 70c978de..f82e27db 100644 --- a/server/text_generation_server/models/flash_neox.py +++ b/server/text_generation_server/models/flash_neox.py @@ -15,6 +15,7 @@ from text_generation_server.utils import ( Weights, ) from text_generation_server.utils.import_utils import IS_XPU_SYSTEM + tracer = trace.get_tracer(__name__) diff --git a/server/text_generation_server/models/flash_rw.py b/server/text_generation_server/models/flash_rw.py index 6eb25f22..ccf38a0c 100644 --- a/server/text_generation_server/models/flash_rw.py +++ b/server/text_generation_server/models/flash_rw.py @@ -16,6 +16,7 @@ from text_generation_server.utils import ( Weights, ) from text_generation_server.utils.import_utils import IS_XPU_SYSTEM + tracer = trace.get_tracer(__name__) diff --git a/server/text_generation_server/models/flash_santacoder.py b/server/text_generation_server/models/flash_santacoder.py index 6147398a..e66f1bf8 100644 --- a/server/text_generation_server/models/flash_santacoder.py +++ b/server/text_generation_server/models/flash_santacoder.py @@ -19,6 +19,7 @@ from text_generation_server.utils import ( ) from text_generation_server.utils.import_utils import IS_XPU_SYSTEM + tracer = trace.get_tracer(__name__) diff --git a/server/text_generation_server/utils/import_utils.py b/server/text_generation_server/utils/import_utils.py index 7c0d8001..db205f4d 100644 --- a/server/text_generation_server/utils/import_utils.py +++ b/server/text_generation_server/utils/import_utils.py @@ -1,5 +1,6 @@ import torch + def is_xpu_available(): try: import intel_extension_for_pytorch @@ -8,6 +9,7 @@ def is_xpu_available(): return hasattr(torch, "xpu") and torch.xpu.is_available() + IS_ROCM_SYSTEM = torch.version.hip is not None IS_CUDA_SYSTEM = torch.version.cuda is not None IS_XPU_SYSTEM = is_xpu_available() diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index 638cb0a0..8c46ea49 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -8,6 +8,7 @@ from typing import List, Tuple, Optional from loguru import logger from functools import lru_cache +# Dummy comment. HAS_BITS_AND_BYTES = True try: import bitsandbytes as bnb From 007d5e54aa76be74925501011fa8029bc5034f89 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Sun, 28 Apr 2024 17:54:19 +0200 Subject: [PATCH 51/74] Changing the waiting_served_ratio default (stack more aggressively by default). (#1820) # What does this PR do? This should enable more aggressive by default stacking, meaning better throughput (in throughput constrained environements). Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- docs/source/basic_tutorials/launcher.md | 2 +- launcher/src/main.rs | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/basic_tutorials/launcher.md b/docs/source/basic_tutorials/launcher.md index de7c995d..1e5b6fd2 100644 --- a/docs/source/basic_tutorials/launcher.md +++ b/docs/source/basic_tutorials/launcher.md @@ -162,7 +162,7 @@ Options: This setting is only applied if there is room in the batch as defined by `max_batch_total_tokens`. [env: WAITING_SERVED_RATIO=] - [default: 1.2] + [default: 0.3] ``` ## MAX_BATCH_PREFILL_TOKENS diff --git a/launcher/src/main.rs b/launcher/src/main.rs index f264e000..28226fb4 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -251,7 +251,7 @@ struct Args { /// /// This setting is only applied if there is room in the batch /// as defined by `max_batch_total_tokens`. - #[clap(default_value = "1.2", long, env)] + #[clap(default_value = "0.3", long, env)] waiting_served_ratio: f32, /// Limits the number of tokens for the prefill operation. From eade7377140a680a79bd2ce3f2d486314cf5a9b9 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Mon, 29 Apr 2024 17:23:40 +0200 Subject: [PATCH 52/74] Better graceful shutdown. (#1827) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- launcher/src/main.rs | 6 +----- server/text_generation_server/server.py | 23 ++++++++++++++++++----- 2 files changed, 19 insertions(+), 10 deletions(-) diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 28226fb4..ca6aa8dd 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -683,9 +683,7 @@ fn shard_manager( // We received a shutdown signal if shutdown.load(Ordering::SeqCst) { - p.kill().unwrap(); - let _ = p.wait(); - tracing::info!("Shard terminated"); + terminate("shard", p, Duration::from_secs(90)).unwrap(); return; } @@ -1245,7 +1243,6 @@ fn terminate(process_name: &str, mut process: Child, timeout: Duration) -> io::R signal::kill(Pid::from_raw(process.id() as i32), Signal::SIGTERM).unwrap(); tracing::info!("Waiting for {process_name} to gracefully shutdown"); - while terminate_time.elapsed() < timeout { if let Some(status) = process.try_wait()? { tracing::info!("{process_name} terminated"); @@ -1253,7 +1250,6 @@ fn terminate(process_name: &str, mut process: Child, timeout: Duration) -> io::R } sleep(Duration::from_millis(100)); } - tracing::info!("Killing {process_name}"); process.kill()?; diff --git a/server/text_generation_server/server.py b/server/text_generation_server/server.py index 495c2c0c..158966e3 100644 --- a/server/text_generation_server/server.py +++ b/server/text_generation_server/server.py @@ -2,6 +2,7 @@ import asyncio import os import torch import time +import signal from grpc import aio from loguru import logger @@ -19,6 +20,21 @@ from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch +class SignalHandler: + KEEP_PROCESSING = True + + def __init__(self): + signal.signal(signal.SIGINT, self.exit_gracefully) + signal.signal(signal.SIGTERM, self.exit_gracefully) + + def exit_gracefully(self, signum, frame): + print(f"Exiting gracefully: Signal {signum}") + self.KEEP_PROCESSING = False + + +signal_handler = SignalHandler() + + class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer): def __init__( self, @@ -231,11 +247,8 @@ def serve( logger.info("Server started at {}".format(local_url)) - try: - await server.wait_for_termination() - except KeyboardInterrupt: - logger.info("Signal received. Shutting down") - await server.stop(0) + while signal_handler.KEEP_PROCESSING: + await asyncio.sleep(0.5) asyncio.run( serve_inner( From f75c1a5b264f5ec6b2e5e1b29a2070e6193cdf6d Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Tue, 30 Apr 2024 10:52:37 +0200 Subject: [PATCH 53/74] Prepare release. --- Cargo.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Cargo.toml b/Cargo.toml index 593fd950..34e55652 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -9,7 +9,7 @@ members = [ resolver = "2" [workspace.package] -version = "2.0.1" +version = "2.0.2" edition = "2021" authors = ["Olivier Dehaene"] homepage = "https://github.com/huggingface/text-generation-inference" From 51ee60da74700d051a2b35ca236be53dac0caea2 Mon Sep 17 00:00:00 2001 From: Maziyar Panahi Date: Tue, 30 Apr 2024 11:07:17 +0200 Subject: [PATCH 54/74] Add the missing `tool_prompt` parameter to Python client (#1825) # What does this PR do? This PR adds the missing `tool_prompt` parameter in Python client ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [x] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. @Narsil --- clients/python/text_generation/client.py | 8 ++++++++ clients/python/text_generation/types.py | 2 ++ 2 files changed, 10 insertions(+) diff --git a/clients/python/text_generation/client.py b/clients/python/text_generation/client.py index 95d23901..0e86901d 100644 --- a/clients/python/text_generation/client.py +++ b/clients/python/text_generation/client.py @@ -80,6 +80,7 @@ class Client: temperature: Optional[float] = None, top_p: Optional[float] = None, tools: Optional[List[Tool]] = None, + tool_prompt: Optional[str] = None, tool_choice: Optional[str] = None, ): """ @@ -119,6 +120,8 @@ class Client: higher are kept for generation tools (`List[Tool]`): List of tools to use + tool_prompt (`str`): + A prompt to be appended before the tools tool_choice (`str`): The tool to use @@ -139,6 +142,7 @@ class Client: temperature=temperature, top_p=top_p, tools=tools, + tool_prompt=tool_prompt, tool_choice=tool_choice, ) if not stream: @@ -466,6 +470,7 @@ class AsyncClient: temperature: Optional[float] = None, top_p: Optional[float] = None, tools: Optional[List[Tool]] = None, + tool_prompt: Optional[str] = None, tool_choice: Optional[str] = None, ) -> Union[ChatComplete, AsyncIterator[ChatCompletionChunk]]: """ @@ -505,6 +510,8 @@ class AsyncClient: higher are kept for generation tools (`List[Tool]`): List of tools to use + tool_prompt (`str`): + A prompt to be appended before the tools tool_choice (`str`): The tool to use @@ -525,6 +532,7 @@ class AsyncClient: temperature=temperature, top_p=top_p, tools=tools, + tool_prompt=tool_prompt, tool_choice=tool_choice, ) if not stream: diff --git a/clients/python/text_generation/types.py b/clients/python/text_generation/types.py index cfa2a9ed..5e32bc6f 100644 --- a/clients/python/text_generation/types.py +++ b/clients/python/text_generation/types.py @@ -159,6 +159,8 @@ class ChatRequest(BaseModel): top_p: Optional[float] = None # List of tools to be used tools: Optional[List[Tool]] = None + # A prompt to be appended before the tools + tool_prompt: Optional[str] = None # Choice of tool to be used tool_choice: Optional[str] = None From 04d4765bad5707458955189fbf39e8b485de5cbd Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Tue, 30 Apr 2024 11:39:38 +0200 Subject: [PATCH 55/74] Small CI cleanup. (#1801) # What does this PR do? Just unifying some branches and making intentions clearer (no cuda graph when 0 all the way in the launcher) Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- launcher/src/main.rs | 12 ++++++++---- server/text_generation_server/models/globals.py | 2 +- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/launcher/src/main.rs b/launcher/src/main.rs index ca6aa8dd..23944f40 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -1284,7 +1284,7 @@ fn main() -> Result<(), LauncherError> { tracing::info!("{}", env_runtime); } - tracing::info!("{:?}", args); + tracing::info!("{:#?}", args); let get_max_position_embeddings = || -> Result> { let model_id = args.model_id.clone(); @@ -1317,7 +1317,12 @@ fn main() -> Result<(), LauncherError> { (Some(max_position_embeddings), _) | (None, Some(max_position_embeddings)) => { if max_position_embeddings > max_default { let max = max_position_embeddings; - tracing::info!("Model supports up to {max} but tgi will now set its default to {max_default} instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens={} --max-total-tokens={max} --max-input-tokens={}`.", max + 50, max - 1); + if args.max_input_tokens.is_none() + && args.max_total_tokens.is_none() + && args.max_batch_prefill_tokens.is_none() + { + tracing::info!("Model supports up to {max} but tgi will now set its default to {max_default} instead. This is to save VRAM by refusing large prompts in order to allow more users on the same hardware. You can increase that size using `--max-batch-prefill-tokens={} --max-total-tokens={max} --max-input-tokens={}`.", max + 50, max - 1); + } max_default } else { max_position_embeddings @@ -1389,8 +1394,7 @@ fn main() -> Result<(), LauncherError> { } let cuda_graphs = match (&args.cuda_graphs, &args.quantize) { - (Some(cuda_graphs), Some(_q)) => cuda_graphs.clone(), - (Some(cuda_graphs), None) => cuda_graphs.clone(), + (Some(cuda_graphs), _) => cuda_graphs.iter().cloned().filter(|&c| c > 0).collect(), #[allow(deprecated)] ( None, diff --git a/server/text_generation_server/models/globals.py b/server/text_generation_server/models/globals.py index b92aa65b..6f8d1017 100644 --- a/server/text_generation_server/models/globals.py +++ b/server/text_generation_server/models/globals.py @@ -4,7 +4,7 @@ import os MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None # This is overridden by the cli cuda_graphs = os.getenv("CUDA_GRAPHS") -if torch.cuda.is_available() and cuda_graphs is not None and cuda_graphs != "0": +if cuda_graphs is not None: try: cuda_graphs = [int(item) for item in cuda_graphs.split(",")] except Exception as e: From 743ecbca3aa5a708f438ae473f17ede3c15452b6 Mon Sep 17 00:00:00 2001 From: Brandon Royal <2762697+brandonroyal@users.noreply.github.com> Date: Tue, 30 Apr 2024 05:39:52 -0400 Subject: [PATCH 56/74] Add reference to TPU support (#1760) # What does this PR do? This PR makes a small addition to the readme that reference new TGI support for TPUs via Optimum TPU (https://huggingface.co/docs/optimum-tpu/howto/serving) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ad66e328..74616748 100644 --- a/README.md +++ b/README.md @@ -64,6 +64,7 @@ Text Generation Inference (TGI) is a toolkit for deploying and serving Large Lan - [Inferentia](https://github.com/huggingface/optimum-neuron/tree/main/text-generation-inference) - [Intel GPU](https://github.com/huggingface/text-generation-inference/pull/1475) - [Gaudi](https://github.com/huggingface/tgi-gaudi) +- [Google TPU](https://huggingface.co/docs/optimum-tpu/howto/serving) ## Get Started From 8332fc490804a83d3ac050bc825f3175c4b7fc51 Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Tue, 30 Apr 2024 11:45:26 +0200 Subject: [PATCH 57/74] fix: use get_speculate to the number of layers (#1737) --- server/text_generation_server/utils/layers.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index 8c46ea49..6e4a13cd 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -8,7 +8,8 @@ from typing import List, Tuple, Optional from loguru import logger from functools import lru_cache -# Dummy comment. +from text_generation_server.utils.speculate import get_speculate + HAS_BITS_AND_BYTES = True try: import bitsandbytes as bnb @@ -445,7 +446,7 @@ class MedusaModel(torch.nn.Module): self.heads = torch.nn.ModuleList( [ MedusaHead(config, medusa_config, prefix=f"{i}", weights=weights) - for i in range(medusa_config["medusa_num_heads"]) + for i in range(get_speculate()) ] ) @@ -542,7 +543,7 @@ class MedusaHeadV2(nn.Module): ) routing[k] = filename - self.n_medusa_heads = medusa_config["medusa_num_heads"] + self.n_medusa_heads = get_speculate() assert medusa_config["medusa_num_layers"] == 1 self.linear = TensorParallelColumnLinear.load_multi( From f6615080b9290a89d9e83d29257d510c8c994762 Mon Sep 17 00:00:00 2001 From: drbh Date: Tue, 30 Apr 2024 05:45:49 -0400 Subject: [PATCH 58/74] feat: add how it works section (#1773) This PR adds a short "how it works" section to guidance and includes a mention to the outlines library that enables grammars/tools *and a small formatting change --------- Co-authored-by: Mishig --- docs/source/conceptual/guidance.md | 108 +++++++++++++++-------------- 1 file changed, 56 insertions(+), 52 deletions(-) diff --git a/docs/source/conceptual/guidance.md b/docs/source/conceptual/guidance.md index cecb3f03..d3ec5fb9 100644 --- a/docs/source/conceptual/guidance.md +++ b/docs/source/conceptual/guidance.md @@ -12,6 +12,10 @@ Before we jump into the deep end, ensure your system is using TGI version `1.4.3 If you're not up to date, grab the latest version and let's get started! +## How it works + +TGI leverages the [outlines](https://github.com/outlines-dev/outlines) library to efficiently parse and compile the grammatical structures and tools specified by users. This integration transforms the defined grammars into an intermediate representation that acts as a framework to guide and constrain content generation, ensuring that outputs adhere to the specified grammatical rules. + ## Table of Contents 📚 ### Grammar and Constraints @@ -270,58 +274,58 @@ curl localhost:3000/v1/chat/completions \
Tools used in example below - ```python - tools = [ - { - "type": "function", - "function": { - "name": "get_current_weather", - "description": "Get the current weather", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - }, - "required": ["location", "format"], - }, - }, - }, - { - "type": "function", - "function": { - "name": "get_n_day_weather_forecast", - "description": "Get an N-day weather forecast", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - "num_days": { - "type": "integer", - "description": "The number of days to forecast", - }, - }, - "required": ["location", "format", "num_days"], - }, - }, - } - ] - ``` +```python + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + "required": ["location", "format"], + }, + }, + }, + { + "type": "function", + "function": { + "name": "get_n_day_weather_forecast", + "description": "Get an N-day weather forecast", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + "num_days": { + "type": "integer", + "description": "The number of days to forecast", + }, + }, + "required": ["location", "format", "num_days"], + }, + }, + } + ] +```
From 9192de57cc6802508db41c489a9e0ee9df569de5 Mon Sep 17 00:00:00 2001 From: Martin Iglesias Goyanes Date: Tue, 30 Apr 2024 12:13:23 +0200 Subject: [PATCH 59/74] Fixing frequency penalty (#1811) Thank you so much for the work you are doing, this is my little contribution to this great thing you have built. I hope it is useful and helpful, please don't hesitate to discuss any matters that are not clear! I am basing my implementation of frequency penalty on OpenAI's implementation: https://platform.openai.com/docs/guides/text-generation/parameter-details The problem I see with TGI's current implementation is that is not taking into account the frequency of tokens which have already been sampled in the current generation stream. Also, the scaling is of the adjusted token logits is done differently for positive and negative logits. While in OpenAI's implementation token frequency is taking into account and the scaling is always done with a subtraction (if penalty is positive) or add operation (if penalty is negative). This leads to corrupt generations as I mentioned in issue #1810 . Moreover, after my tests, other issues are also gone like the one about some request's with ``penalty_frequency = 1.0`` overruling other requests (with ``frequency_penalty = 0.0``) in the same batch and therefore corrupting all generations in the batch. Basically, padding does not affect this implementation so I believe this ``score *= input_ids.ne(0)`` is not needed anymore. Frequency penalty | -1.0 | 0.0 | 1.0 -- | -- | -- | -- Before my change | https://paste.mozilla.org/JxqGJkWY | https://paste.mozilla.org/hrztJ56h | https://paste.mozilla.org/pBSEH2zw After my change | https://paste.mozilla.org/7gXCi7zo | https://paste.mozilla.org/ZR9rJ92g | https://paste.mozilla.org/gHaD2YnC --------- Co-authored-by: martini --- .gitignore | 2 ++ .../utils/logits_process.py | 23 +++++++++++-------- 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/.gitignore b/.gitignore index b3ca772b..2ac2f6b4 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,5 @@ server/exllama_kernels/exllama_kernels/hip_func/ *_hip.cuh server/exllama_kernels/exllama_kernels/hip_buffers.cuh server/exllama_kernels/exllama_kernels/exllama_ext_hip.cpp + +data/ diff --git a/server/text_generation_server/utils/logits_process.py b/server/text_generation_server/utils/logits_process.py index 2decee53..6b915437 100644 --- a/server/text_generation_server/utils/logits_process.py +++ b/server/text_generation_server/utils/logits_process.py @@ -151,7 +151,8 @@ class FrequencyPenaltyLogitsProcessor(LogitsProcessor): class HeterogeneousFrequencyPenaltyLogitsProcessor(LogitsProcessor): r""" - Frequency penalty as defined by OpenAI + Frequency penalty as defined by OpenAI in + https://platform.openai.com/docs/guides/text-generation/parameter-details Args: frequency_penalty (`List[float]`): @@ -165,15 +166,19 @@ class HeterogeneousFrequencyPenaltyLogitsProcessor(LogitsProcessor): ).unsqueeze(1) def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor: - score = torch.gather(scores, 1, input_ids) - # if score < 0 then penalty has to be multiplied to reduce the previous token probability - score = -torch.where( - score < 0, score * self.penalty_tensor, score / self.penalty_tensor - ) - # set score to 0 where input_ids is a padding token - score *= input_ids.ne(0) + batch_size, input_size = input_ids.size() + vocab_size = scores.size(1) - return scores.scatter_add_(1, input_ids, score) + # Calculate the frequency for each token so far + token_freq = torch.zeros(batch_size, vocab_size, device=input_ids.device) + token_freq.scatter_add_( + 1, input_ids, torch.ones_like(input_ids, dtype=torch.float) + ) + token_freq /= input_size + + # Apply the frequency penalty to logits + scores -= token_freq * self.penalty_tensor + return scores def filter(self, indices): self.penalty = [self.penalty[i] for i in indices] From b2c982750a398a562fe7e442d289d629e62b4303 Mon Sep 17 00:00:00 2001 From: drbh Date: Tue, 30 Apr 2024 06:14:39 -0400 Subject: [PATCH 60/74] feat: add vlm docs and simple examples (#1812) This PR start to add documentation for visual language models --- docs/source/_toctree.yml | 3 + .../basic_tutorials/visual_language_models.md | 170 ++++++++++++++++++ 2 files changed, 173 insertions(+) create mode 100644 docs/source/basic_tutorials/visual_language_models.md diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index 1598c248..79a2102f 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -25,6 +25,8 @@ title: Non-core Model Serving - local: basic_tutorials/safety title: Safety + - local: basic_tutorials/visual_language_models + title: Visual Language Models title: Tutorials - sections: - local: conceptual/streaming @@ -43,4 +45,5 @@ title: Speculation (Medusa, ngram) - local: conceptual/guidance title: Guidance, JSON, tools (using outlines) + title: Conceptual Guides diff --git a/docs/source/basic_tutorials/visual_language_models.md b/docs/source/basic_tutorials/visual_language_models.md new file mode 100644 index 00000000..e804ef09 --- /dev/null +++ b/docs/source/basic_tutorials/visual_language_models.md @@ -0,0 +1,170 @@ +# Vision Language Model Inference in TGI + +Visual Language Model (VLM) are models that consume both image and text inputs to generate text. + +VLM's are trained on a combination of image and text data and can handle a wide range of tasks, such as image captioning, visual question answering, and visual dialog. + +> What distinguishes VLMs from other text and image models is their ability to handle long context and generate text that is coherent and relevant to the image even after multiple turns or in some cases, multiple images. + +Below are couple of common use cases for vision language models: + +- **Image Captioning**: Given an image, generate a caption that describes the image. +- **Visual Question Answering (VQA)**: Given an image and a question about the image, generate an answer to the question. +- **Mulimodal Dialog**: Generate response to multiple turns of images and conversations. +- **Image Information Retrieval**: Given an image, retrieve information from the image. + +## How to Use a Vision Language Model? + +### Hugging Face Hub Python Library + +To infer with vision language models through Python, you can use the [`huggingface_hub`](https://pypi.org/project/huggingface-hub/) library. The `InferenceClient` class provides a simple way to interact with the [Inference API](https://huggingface.co/docs/api-inference/index). Images can be passed as URLs or base64-encoded strings. The `InferenceClient` will automatically detect the image format. + +```python +from huggingface_hub import InferenceClient + +client = InferenceClient("http://127.0.0.1:3000") +image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png" +prompt = f"![]({image})What is this a picture of?\n\n" +for token in client.text_generation(prompt, max_new_tokens=16, stream=True): + print(token) + +# This is a picture of an anthropomorphic rabbit in a space suit. +``` + +```python +from huggingface_hub import InferenceClient +import base64 +import requests +import io + +client = InferenceClient("http://127.0.0.1:3000") + +# read image from local file +image_path = "rabbit.png" +with open(image_path, "rb") as f: + image = base64.b64encode(f.read()).decode("utf-8") + +image = f"data:image/png;base64,{image}" +prompt = f"![]({image})What is this a picture of?\n\n" + +for token in client.text_generation(prompt, max_new_tokens=10, stream=True): + print(token) + +# This is a picture of an anthropomorphic rabbit in a space suit. +``` + +If you want additional details, you can add `details=True`. In this case, you get a `TextGenerationStreamResponse` which contains additional information such as the probabilities and the tokens. For the final response in the stream, it also returns the full generated text. + +### Inference Through Sending `cURL` Requests + +To use the `generate_stream` endpoint with curl, you can add the `-N` flag. This flag disables curl default buffering and shows data as it arrives from the server. + +```bash +curl -N 127.0.0.1:3000/generate_stream \ + -X POST \ + -d '{"inputs":"![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)What is this a picture of?\n\n","parameters":{"max_new_tokens":16, "seed": 42}}' \ + -H 'Content-Type: application/json' + +# ... +# data:{"index":16,"token":{"id":28723,"text":".","logprob":-0.6196289,"special":false},"generated_text":"This is a picture of an anthropomorphic rabbit in a space suit.","details":null} +``` + +### Inference Through JavaScript + +First, we need to install the `@huggingface/inference` library. + +```bash +npm install @huggingface/inference +``` + +If you're using the free Inference API, you can use [Huggingface.js](https://huggingface.co/docs/huggingface.js/inference/README)'s `HfInference`. If you're using inference endpoints, you can use `HfInferenceEndpoint` class to easily interact with the Inference API. + +We can create a `HfInferenceEndpoint` providing our endpoint URL and We can create a `HfInferenceEndpoint` providing our endpoint URL and [Hugging Face access token](https://huggingface.co/settings/tokens). + +```js +import { HfInferenceEndpoint } from "@huggingface/inference"; + +const hf = new HfInferenceEndpoint("http://127.0.0.1:3000", "HF_TOKEN"); + +const prompt = + "![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)What is this a picture of?\n\n"; + +const stream = hf.textGenerationStream({ + inputs: prompt, + parameters: { max_new_tokens: 16, seed: 42 }, +}); +for await (const r of stream) { + // yield the generated token + process.stdout.write(r.token.text); +} + +// This is a picture of an anthropomorphic rabbit in a space suit. +``` + +## Combining Vision Language Models with Other Features + +VLMs in TGI have several advantages, for example these models can be used in tandem with other features for more complex tasks. For example, you can use VLMs with [Guided Generation](/docs/conceptual/guided-generation) to generate specific JSON data from an image. + +
+ +
+ +For example we can extract information from the rabbit image and generate a JSON object with the location, activity, number of animals seen, and the animals seen. That would look like this: + +```json +{ + "activity": "Standing", + "animals": ["Rabbit"], + "animals_seen": 1, + "location": "Rocky surface with mountains in the background and a red light on the rabbit's chest" +} +``` + +All we need to do is provide a JSON schema to the VLM model and it will generate the JSON object for us. + +```bash +curl localhost:3000/generate \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "inputs":"![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)What is this a picture of?\n\n", + "parameters": { + "max_new_tokens": 100, + "seed": 42, + "grammar": { + "type": "json", + "value": { + "properties": { + "location": { + "type": "string" + }, + "activity": { + "type": "string" + }, + "animals_seen": { + "type": "integer", + "minimum": 1, + "maximum": 5 + }, + "animals": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "required": ["location", "activity", "animals_seen", "animals"] + } + } + } +}' + +# { +# "generated_text": "{ \"activity\": \"Standing\", \"animals\": [ \"Rabbit\" ], \"animals_seen\": 1, \"location\": \"Rocky surface with mountains in the background and a red light on the rabbit's chest\" }" +# } +``` + +Want to learn more about how Vision Language Models work? Check out the [awesome blog post on the topic](https://huggingface.co/blog/vlms). From c99ecd77ecc079a67c176b46b61c7a2d85ac068f Mon Sep 17 00:00:00 2001 From: drbh Date: Tue, 30 Apr 2024 06:18:32 -0400 Subject: [PATCH 61/74] Handle images in chat api (#1828) This PR allows for messages to be formatted as simple strings, or as an array of objects including image urls. This is done by formatting content arrays into a simple string. Example using `llava-hf/llava-v1.6-mistral-7b-hf` ```bash curl localhost: 3000/v1/chat/completions \ -X POST \ -H 'Content-Type: application/json' \ -d '{ "model": "tgi", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Whats in this image?" }, { "type": "image_url", "image_url": { "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png" } } ] } ], "stream": false, "max_tokens": 20, "seed": 42 }' ``` is equivlant to this more simple request ```bash curl localhost: 3000/v1/chat/completions \ -X POST \ -H 'Content-Type: application/json' \ -d '{ "model": "tgi", "messages": [ { "role": "user", "content": "Whats in this image?\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png)" } ], "stream": false, "max_tokens": 20, "seed": 42 }' ``` output ``` # {"id":"","object":"text_completion","created":1714406985,"model":"llava-hf/llava-v1.6-mistral-7b-hf","system_fingerprint":"2.0.1-native","choices":[{"index":0,"message":{"role":"assistant","content":" This is an illustration of an anthropomorphic rabbit in a spacesuit, standing on what"},"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":2945,"completion_tokens":20,"total_tokens":2965}}% ``` --------- Co-authored-by: Nicolas Patry --- router/src/lib.rs | 65 ++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 64 insertions(+), 1 deletion(-) diff --git a/router/src/lib.rs b/router/src/lib.rs index 9b9097f6..fac4c14e 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -884,12 +884,75 @@ pub(crate) struct ToolCall { pub function: FunctionDefinition, } -#[derive(Clone, Deserialize, ToSchema, Serialize)] +#[derive(Clone, Deserialize, Serialize, ToSchema, Default, Debug)] +pub(crate) struct Text { + #[serde(default)] + pub text: String, +} + +#[derive(Clone, Deserialize, Serialize, ToSchema, Default, Debug)] +pub(crate) struct ImageUrl { + #[serde(default)] + pub url: String, +} + +#[derive(Clone, Deserialize, Serialize, ToSchema, Default, Debug)] +pub(crate) struct Content { + pub r#type: String, + #[serde(default, skip_serializing_if = "Option::is_none")] + pub text: Option, + #[serde(default, skip_serializing_if = "Option::is_none")] + pub image_url: Option, +} + +mod message_content_serde { + use super::*; + use serde::de; + use serde::Deserializer; + use serde_json::Value; + + pub fn deserialize<'de, D>(deserializer: D) -> Result, D::Error> + where + D: Deserializer<'de>, + { + let value = Value::deserialize(deserializer)?; + match value { + Value::String(s) => Ok(Some(s)), + Value::Array(arr) => { + let results: Result, _> = arr + .into_iter() + .map(|v| { + let content: Content = + serde_json::from_value(v).map_err(de::Error::custom)?; + match content.r#type.as_str() { + "text" => Ok(content.text.unwrap_or_default()), + "image_url" => { + if let Some(url) = content.image_url { + Ok(format!("![]({})", url.url)) + } else { + Ok(String::new()) + } + } + _ => Err(de::Error::custom("invalid content type")), + } + }) + .collect(); + + results.map(|strings| Some(strings.join(""))) + } + Value::Null => Ok(None), + _ => Err(de::Error::custom("invalid token format")), + } + } +} + +#[derive(Clone, Deserialize, ToSchema, Serialize, Debug)] pub(crate) struct Message { #[schema(example = "user")] pub role: String, #[serde(skip_serializing_if = "Option::is_none")] #[schema(example = "My name is David and I")] + #[serde(deserialize_with = "message_content_serde::deserialize")] pub content: Option, #[serde(default, skip_serializing_if = "Option::is_none")] #[schema(example = "\"David\"")] From b4ef038837c9264fa7665d5a6baf130f8dbb145b Mon Sep 17 00:00:00 2001 From: OlivierDehaene Date: Tue, 30 Apr 2024 14:04:28 +0200 Subject: [PATCH 62/74] chore: update torch (#1730) Co-authored-by: Nicolas Patry --- Dockerfile | 7 +++++-- server/Makefile-vllm | 2 +- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/Dockerfile b/Dockerfile index c2ae4c85..333f7c91 100644 --- a/Dockerfile +++ b/Dockerfile @@ -39,7 +39,7 @@ RUN cargo build --release # Adapted from: https://github.com/pytorch/pytorch/blob/master/Dockerfile FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 as pytorch-install -ARG PYTORCH_VERSION=2.1.1 +ARG PYTORCH_VERSION=2.3.0 ARG PYTHON_VERSION=3.10 # Keep in sync with `server/pyproject.toml ARG CUDA_VERSION=12.1 @@ -149,6 +149,8 @@ FROM kernel-builder as vllm-builder WORKDIR /usr/src +ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX" + COPY server/Makefile-vllm Makefile # Build specific version of vllm @@ -210,7 +212,7 @@ COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-310 /opt/c COPY --from=mamba-builder /usr/src/mamba/build/lib.linux-x86_64-cpython-310/ /opt/conda/lib/python3.10/site-packages COPY --from=mamba-builder /usr/src/causal-conv1d/build/lib.linux-x86_64-cpython-310/ /opt/conda/lib/python3.10/site-packages -# Install vllm/flash-attention dependencies +# Install flash-attention dependencies RUN pip install einops --no-cache-dir # Install server @@ -246,6 +248,7 @@ ENTRYPOINT ["./entrypoint.sh"] FROM base COPY ./tgi-entrypoint.sh /tgi-entrypoint.sh +RUN chmod +x /tgi-entrypoint.sh ENTRYPOINT ["/tgi-entrypoint.sh"] CMD ["--json-output"] diff --git a/server/Makefile-vllm b/server/Makefile-vllm index ada484a6..9999ad89 100644 --- a/server/Makefile-vllm +++ b/server/Makefile-vllm @@ -4,7 +4,7 @@ vllm-cuda: git clone https://github.com/OlivierDehaene/vllm.git vllm build-vllm-cuda: vllm-cuda - cd vllm && git fetch && git checkout 4bec8cee87f6bb8cebaec297029713cd2082e0b2 + cd vllm && git fetch && git checkout 3d4693536dcb69f036c26b016a35839b99ebed59 cd vllm && python setup.py build install-vllm-cuda: build-vllm-cuda From dccab72549635c7eb5ddb17f43f0b7cdff07c214 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Tue, 30 Apr 2024 18:15:35 +0200 Subject: [PATCH 63/74] (chore): torch 2.3.0 (#1833) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- server/Makefile-vllm | 4 +- server/poetry.lock | 922 ++++++++++++++++++----------------- server/pyproject.toml | 6 +- server/requirements_cuda.txt | 16 +- server/requirements_rocm.txt | 16 +- 5 files changed, 507 insertions(+), 457 deletions(-) diff --git a/server/Makefile-vllm b/server/Makefile-vllm index 9999ad89..6f36c679 100644 --- a/server/Makefile-vllm +++ b/server/Makefile-vllm @@ -1,10 +1,10 @@ vllm-cuda: # Clone vllm pip install -U ninja packaging --no-cache-dir - git clone https://github.com/OlivierDehaene/vllm.git vllm + git clone https://github.com/Narsil/vllm.git vllm build-vllm-cuda: vllm-cuda - cd vllm && git fetch && git checkout 3d4693536dcb69f036c26b016a35839b99ebed59 + cd vllm && git fetch && git checkout b5dfc61db88a81069e45b44f7cc99bd9e62a60fa cd vllm && python setup.py build install-vllm-cuda: build-vllm-cuda diff --git a/server/poetry.lock b/server/poetry.lock index aea9c4c2..3a554af0 100644 --- a/server/poetry.lock +++ b/server/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "accelerate" @@ -359,45 +359,43 @@ files = [ [[package]] name = "datasets" -version = "2.18.0" +version = "2.14.4" description = "HuggingFace community-driven open-source library of datasets" optional = true python-versions = ">=3.8.0" files = [ - {file = "datasets-2.18.0-py3-none-any.whl", hash = "sha256:f1bbf0e2896917a914de01cbd37075b14deea3837af87ad0d9f697388ccaeb50"}, - {file = "datasets-2.18.0.tar.gz", hash = "sha256:cdf8b8c6abf7316377ba4f49f9589a4c74556d6b481afd0abd2284f3d69185cb"}, + {file = "datasets-2.14.4-py3-none-any.whl", hash = "sha256:29336bd316a7d827ccd4da2236596279b20ca2ac78f64c04c9483da7cbc2459b"}, + {file = "datasets-2.14.4.tar.gz", hash = "sha256:ef29c2b5841de488cd343cfc26ab979bff77efa4d2285af51f1ad7db5c46a83b"}, ] [package.dependencies] aiohttp = "*" -dill = ">=0.3.0,<0.3.9" -filelock = "*" -fsspec = {version = ">=2023.1.0,<=2024.2.0", extras = ["http"]} -huggingface-hub = ">=0.19.4" +dill = ">=0.3.0,<0.3.8" +fsspec = {version = ">=2021.11.1", extras = ["http"]} +huggingface-hub = ">=0.14.0,<1.0.0" multiprocess = "*" numpy = ">=1.17" packaging = "*" pandas = "*" -pyarrow = ">=12.0.0" -pyarrow-hotfix = "*" +pyarrow = ">=8.0.0" pyyaml = ">=5.1" requests = ">=2.19.0" tqdm = ">=4.62.1" xxhash = "*" [package.extras] -apache-beam = ["apache-beam (>=2.26.0)"] +apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"] audio = ["librosa", "soundfile (>=0.12.1)"] benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"] -dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", 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"3.13" +fsspec==2024.3.1 ; python_version >= "3.9" and python_version < "3.13" googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13" grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" -grpcio-reflection==1.62.1 ; python_version >= "3.9" and python_version < "3.13" -grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13" -grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13" +grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13" +grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13" +grpcio==1.62.2 ; python_version >= "3.9" and python_version < "3.13" hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13" huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13" idna==3.7 ; python_version >= "3.9" and python_version < "3.13" @@ -28,9 +28,11 @@ opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13" opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13" packaging==24.0 ; python_version >= "3.9" and python_version < "3.13" pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13" +prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13" protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13" +py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13" -regex==2024.4.16 ; python_version >= "3.9" and python_version < "3.13" +regex==2024.4.28 ; python_version >= "3.9" and python_version < "3.13" requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13" safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13" scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13" @@ -38,7 +40,7 @@ sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13" setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13" tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.40.0 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.40.1 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13" diff --git a/server/requirements_rocm.txt b/server/requirements_rocm.txt index 5cd03130..c2714764 100644 --- a/server/requirements_rocm.txt +++ b/server/requirements_rocm.txt @@ -5,13 +5,13 @@ click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13" einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13" -filelock==3.13.4 ; python_version >= "3.9" and python_version < "3.13" -fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13" +filelock==3.14.0 ; python_version >= "3.9" and python_version < "3.13" +fsspec==2024.3.1 ; python_version >= "3.9" and python_version < "3.13" googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13" grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" -grpcio-reflection==1.62.1 ; python_version >= "3.9" and python_version < "3.13" -grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13" -grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13" +grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13" +grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13" +grpcio==1.62.2 ; python_version >= "3.9" and python_version < "3.13" hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13" huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13" idna==3.7 ; python_version >= "3.9" and python_version < "3.13" @@ -28,9 +28,11 @@ opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13" opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13" packaging==24.0 ; python_version >= "3.9" and python_version < "3.13" pillow==10.3.0 ; python_version >= "3.9" and python_version < "3.13" +prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13" protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13" +py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13" pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13" -regex==2024.4.16 ; python_version >= "3.9" and python_version < "3.13" +regex==2024.4.28 ; python_version >= "3.9" and python_version < "3.13" requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13" safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13" scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13" @@ -38,7 +40,7 @@ sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13" setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13" tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13" tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13" -transformers==4.40.0 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.40.1 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13" From 6073ece4fc2d7180c2057cb49b9ea436463fd52b Mon Sep 17 00:00:00 2001 From: drbh Date: Wed, 1 May 2024 03:03:25 -0400 Subject: [PATCH 64/74] fix: split docs and start conceptual page (#1836) This PR improves the guidance docs and adds a section that explains how grammars are applied on a technical level --- docs/source/_toctree.yml | 4 +- docs/source/basic_tutorials/using_guidance.md | 419 ++++++++++++++++ docs/source/conceptual/guidance.md | 459 +++--------------- 3 files changed, 482 insertions(+), 400 deletions(-) create mode 100644 docs/source/basic_tutorials/using_guidance.md diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index 79a2102f..c815b535 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -25,6 +25,8 @@ title: Non-core Model Serving - local: basic_tutorials/safety title: Safety + - local: basic_tutorials/using_guidance + title: Using Guidance, JSON, tools - local: basic_tutorials/visual_language_models title: Visual Language Models title: Tutorials @@ -44,6 +46,6 @@ - local: conceptual/speculation title: Speculation (Medusa, ngram) - local: conceptual/guidance - title: Guidance, JSON, tools (using outlines) + title: How Guidance Works (via outlines) title: Conceptual Guides diff --git a/docs/source/basic_tutorials/using_guidance.md b/docs/source/basic_tutorials/using_guidance.md new file mode 100644 index 00000000..606f2453 --- /dev/null +++ b/docs/source/basic_tutorials/using_guidance.md @@ -0,0 +1,419 @@ +# Guidance + +Text Generation Inference (TGI) now supports [JSON and regex grammars](#grammar-and-constraints) and [tools and functions](#tools-and-functions) to help developers guide LLM responses to fit their needs. + +These feature are available starting from version `1.4.3`. They are accessible via the [text_generation](https://pypi.org/project/text-generation/) library. The tool support is compatible with OpenAI's client libraries. The following guide will walk you through the new features and how to use them! + +_note: guidance is supported as grammar in the `/generate` endpoint and as tools in the `/chat/completions` endpoint._ + +## How it works + +TGI leverages the [outlines](https://github.com/outlines-dev/outlines) library to efficiently parse and compile the grammatical structures and tools specified by users. This integration transforms the defined grammars into an intermediate representation that acts as a framework to guide and constrain content generation, ensuring that outputs adhere to the specified grammatical rules. + +If you are interested in the technical details on how outlines is used in TGI, you can check out the [conceptual guidance documentation](../conceptual/guidance). + +## Table of Contents 📚 + +### Grammar and Constraints + +- [The Grammar Parameter](#the-grammar-parameter): Shape your AI's responses with precision. +- [Constrain with Pydantic](#constrain-with-pydantic): Define a grammar using Pydantic models. +- [JSON Schema Integration](#json-schema-integration): Fine-grained control over your requests via JSON schema. +- [Using the client](#using-the-client): Use TGI's client libraries to shape the AI's responses. + +### Tools and Functions + +- [The Tools Parameter](#the-tools-parameter): Enhance the AI's capabilities with predefined functions. +- [Via the client](#text-generation-inference-client): Use TGI's client libraries to interact with the Messages API and Tool functions. +- [OpenAI integration](#openai-integration): Use OpenAI's client libraries to interact with TGI's Messages API and Tool functions. + +## Grammar and Constraints 🛣️ + +### The Grammar Parameter + +In TGI `1.4.3`, we've introduced the grammar parameter, which allows you to specify the format of the response you want from the LLM. + +Using curl, you can make a request to TGI's Messages API with the grammar parameter. This is the most primitive way to interact with the API and using [Pydantic](#constrain-with-pydantic) is recommended for ease of use and readability. + +```json +curl localhost:3000/generate \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "inputs": "I saw a puppy a cat and a raccoon during my bike ride in the park", + "parameters": { + "repetition_penalty": 1.3, + "grammar": { + "type": "json", + "value": { + "properties": { + "location": { + "type": "string" + }, + "activity": { + "type": "string" + }, + "animals_seen": { + "type": "integer", + "minimum": 1, + "maximum": 5 + }, + "animals": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "required": ["location", "activity", "animals_seen", "animals"] + } + } + } +}' +// {"generated_text":"{ \n\n\"activity\": \"biking\",\n\"animals\": [\"puppy\",\"cat\",\"raccoon\"],\n\"animals_seen\": 3,\n\"location\": \"park\"\n}"} + +``` + +A grammar can be defined using Pydantic models, JSON schemas, or regular expressions. The LLM will then generate a response that conforms to the specified grammar. + +> Note: A grammar must compile to an intermediate representation to constrain the output. Grammar compilation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster. + +### Constrain with Pydantic + +Using Pydantic models we can define a similar grammar as the previous example in a shorter and more readable way. + +```python +import requests +from pydantic import BaseModel, conint +from typing import List + +class Animals(BaseModel): + location: str + activity: str + animals_seen: conint(ge=1, le=5) # Constrained integer type + animals: List[str] + +prompt = "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park" + +data = { + "inputs": prompt, + "parameters": { + "repetition_penalty": 1.3, + "grammar": { + "type": "json", + "value": Animals.schema() + } + } +} + +headers = { + "Content-Type": "application/json", +} + +response = requests.post( + 'http://127.0.0.1:3000/generate', + headers=headers, + json=data +) +print(response.json()) +# {'generated_text': '{ "activity": "bike riding", "animals": ["puppy","cat","raccoon"],"animals_seen": 3, "location":"park" }'} + +``` + +### JSON Schema Integration + +If Pydantic's not your style, go raw with direct JSON Schema integration. This is similar to the first example but with programmatic control. + +```python +import requests + +json_schema = { + "properties": { + "location": { + "type": "string" + }, + "activity": { + "type": "string" + }, + "animals_seen": { + "type": "integer", + "minimum": 1, + "maximum": 5 + }, + "animals": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "required": ["location", "activity", "animals_seen", "animals"] +} + +data = { + "inputs": "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park", + "parameters": { + "max_new_tokens": 200, + "repetition_penalty": 1.3, + "grammar": { + "type": "json", + "value": json_schema + } + } +} + +headers = { + "Content-Type": "application/json", +} + +response = requests.post( + 'http://127.0.0.1:3000/generate', + headers=headers, + json=data +) +print(response.json()) +# {'generated_text': '{\n"activity": "biking",\n"animals": ["puppy","cat","raccoon"]\n , "animals_seen": 3,\n "location":"park"}'} + +``` + +### Using the client + +TGI provides a client library to that make it easy to send requests with all of the parameters we've discussed above. Here's an example of how to use the client to send a request with a grammar parameter. + +```python +from text_generation import AsyncClient +from text_generation.types import GrammarType + +# NOTE: tools defined above and removed for brevity + +# Define an async function to encapsulate the async operation +async def main(): + client = AsyncClient(base_url="http://localhost:3000") + + # Use 'await' to wait for the async method 'chat' to complete + response = await client.generate( + "Whats Googles DNS", + max_new_tokens=10, + decoder_input_details=True, + seed=1, + grammar={ + "type": GrammarType.Regex, + "value": "((25[0-5]|2[0-4]\\d|[01]?\\d\\d?)\\.){3}(25[0-5]|2[0-4]\\d|[01]?\\d\\d?)", + }, + ) + + # Once the response is received, you can process it + print(response.generated_text) + +# Ensure the main async function is run in the event loop +if __name__ == "__main__": + import asyncio + asyncio.run(main()) + +# 118.8.0.84 + +``` + +## Tools and Functions 🛠️ + +### The Tools Parameter + +In addition to the grammar parameter, we've also introduced a set of tools and functions to help you get the most out of the Messages API. + +Tools are a set of user defined functions that can be used in tandem with the chat functionality to enhance the LLM's capabilities. Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API. + +Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API. + +```json +curl localhost:3000/v1/chat/completions \ + -X POST \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "tgi", + "messages": [ + { + "role": "user", + "content": "What is the weather like in New York?" + } + ], + "tools": [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location." + } + }, + "required": ["location", "format"] + } + } + } + ], + "tool_choice": "get_current_weather" +}' +// {"id":"","object":"text_completion","created":1709051640,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":19,"total_tokens":176}} +``` + +### Text Generation Inference Client + +TGI provides a client library to interact with the Messages API and Tool functions. The client library is available in both synchronous and asynchronous versions. + +```python +from text_generation import AsyncClient + +# NOTE: tools defined above and removed for brevity + +# Define an async function to encapsulate the async operation +async def main(): + client = AsyncClient(base_url="http://localhost:3000") + + # Use 'await' to wait for the async method 'chat' to complete + response = await client.chat( + max_tokens=100, + seed=1, + tools=tools, + presence_penalty=-1.1, + messages=[ + { + "role": "system", + "content": "You're a helpful assistant! Answer the users question best you can.", + }, + { + "role": "user", + "content": "What is the weather like in Brooklyn, New York?", + }, + ], + ) + + # Once the response is received, you can process it + print(response.choices[0].message.tool_calls) + +# Ensure the main async function is run in the event loop +if __name__ == "__main__": + import asyncio + asyncio.run(main()) + +# {"id":"","object":"text_completion","created":1709051942,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":20,"total_tokens":177}} + +``` + +
+ Tools used in example above + +```python + tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + "required": ["location", "format"], + }, + }, + }, + { + "type": "function", + "function": { + "name": "get_n_day_weather_forecast", + "description": "Get an N-day weather forecast", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + "num_days": { + "type": "integer", + "description": "The number of days to forecast", + }, + }, + "required": ["location", "format", "num_days"], + }, + }, + } + ] +``` + +
+ +### OpenAI integration + +TGI exposes an OpenAI-compatible API, which means you can use OpenAI's client libraries to interact with TGI's Messages API and Tool functions. + +However there are some minor differences in the API, for example `tool_choice="auto"` will ALWAYS choose the tool for you. This is different from OpenAI's API where `tool_choice="auto"` will choose a tool if the model thinks it's necessary. + +```python +from openai import OpenAI + +# Initialize the client, pointing it to one of the available models +client = OpenAI( + base_url="http://localhost:3000/v1", + api_key="_", +) + +# NOTE: tools defined above and removed for brevity + +chat_completion = client.chat.completions.create( + model="tgi", + messages=[ + { + "role": "system", + "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.", + }, + { + "role": "user", + "content": "What's the weather like the next 3 days in San Francisco, CA?", + }, + ], + tools=tools, + tool_choice="auto", # tool selected by model + max_tokens=500, +) + + +called = chat_completion.choices[0].message.tool_calls +print(called) +# { +# "id": 0, +# "type": "function", +# "function": { +# "description": None, +# "name": "tools", +# "parameters": { +# "format": "celsius", +# "location": "San Francisco, CA", +# "num_days": 3, +# }, +# }, +# } +``` diff --git a/docs/source/conceptual/guidance.md b/docs/source/conceptual/guidance.md index d3ec5fb9..0ce34f2f 100644 --- a/docs/source/conceptual/guidance.md +++ b/docs/source/conceptual/guidance.md @@ -1,425 +1,86 @@ # Guidance -Text Generation Inference (TGI) now supports [JSON and regex grammars](#grammar-and-constraints) and [tools and functions](#tools-and-functions) to help developers guide LLM responses to fit their needs. +## What is Guidance? -These feature are available starting from version `1.4.3`. They are accessible via the [text_generation](https://pypi.org/project/text-generation/) library. The tool support is compatible with OpenAI's client libraries. The following guide will walk you through the new features and how to use them! +Guidance is a feature that allows users to constrain the generation of a large language model with a specified grammar. This feature is particularly useful when you want to generate text that follows a specific structure or uses a specific set of words or produce output in a specific format. -> The Grammar guidance support is currently only available in the TGI API due to lack of support in Open AI API. +## How is it used? -## Quick Start +Guidance can be in many ways and the community is always finding new ways to use it. Here are some examples of how you can use guidance: -Before we jump into the deep end, ensure your system is using TGI version `1.4.3` or later to access all the features we're about to explore in this guide. +Technically, guidance can be used to generate: -If you're not up to date, grab the latest version and let's get started! +- a specific JSON object +- a function signature +- typed output like a list of integers -## How it works +However these use cases can span a wide range of applications, such as: -TGI leverages the [outlines](https://github.com/outlines-dev/outlines) library to efficiently parse and compile the grammatical structures and tools specified by users. This integration transforms the defined grammars into an intermediate representation that acts as a framework to guide and constrain content generation, ensuring that outputs adhere to the specified grammatical rules. +- extracting structured data from unstructured text +- summarizing text into a specific format +- limit output to specific classes of words (act as a LLM powered classifier) +- generate the input to specific APIs or services +- provide reliable and consistent output for downstream tasks +- extract data from multimodal inputs -## Table of Contents 📚 +## How it works? -### Grammar and Constraints +Diving into the details, guidance is enabled by including a grammar with a generation request that is compiled, and used to modify the chosen tokens. -- [The Grammar Parameter](#the-grammar-parameter): Shape your AI's responses with precision. -- [Constrain with Pydantic](#constrain-with-pydantic): Define a grammar using Pydantic models. -- [JSON Schema Integration](#json-schema-integration): Fine-grained control over your requests via JSON schema. -- [Using the client](#using-the-client): Use TGI's client libraries to shape the AI's responses. +This process can be broken down into the following steps: -### Tools and Functions +1. A request is sent to the backend, it is processed and placed in batch. Processing includes compiling the grammar into a finite state machine and a grammar state. -- [The Tools Parameter](#the-tools-parameter): Enhance the AI's capabilities with predefined functions. -- [Via the client](#text-generation-inference-client): Use TGI's client libraries to interact with the Messages API and Tool functions. -- [OpenAI integration](#openai-integration): Use OpenAI's client libraries to interact with TGI's Messages API and Tool functions. +
+ + +
-## Grammar and Constraints 🛣️ +2. The model does a forward pass over the batch. This returns probabilities for each token in the vocabulary for each request in the batch. -### The Grammar Parameter +3. The process of choosing one of those tokens is called `sampling`. The model samples from the distribution of probabilities to choose the next token. In TGI all of the steps before sampling are called `processor`. Grammars are applied as a processor that masks out tokens that are not allowed by the grammar. -In TGI `1.4.3`, we've introduced the grammar parameter, which allows you to specify the format of the response you want from the AI. This is a game-changer for those who need precise control over the AI's output. +
+ + +
-Using curl, you can make a request to TGI's Messages API with the grammar parameter. This is the most primitive way to interact with the API and using [Pydantic](#constrain-with-pydantic) is recommended for ease of use and readability. +4. The grammar mask is applied and the model samples from the remaining tokens. Once a token is chosen, we update the grammar state with the new token, to prepare it for the next pass. -```json -curl localhost:3000/generate \ - -X POST \ - -H 'Content-Type: application/json' \ - -d '{ - "inputs": "I saw a puppy a cat and a raccoon during my bike ride in the park", - "parameters": { - "repetition_penalty": 1.3, - "grammar": { - "type": "json", - "value": { - "properties": { - "location": { - "type": "string" - }, - "activity": { - "type": "string" - }, - "animals_seen": { - "type": "integer", - "minimum": 1, - "maximum": 5 - }, - "animals": { - "type": "array", - "items": { - "type": "string" - } - } - }, - "required": ["location", "activity", "animals_seen", "animals"] - } - } - } -}' -// {"generated_text":"{ \n\n\"activity\": \"biking\",\n\"animals\": [\"puppy\",\"cat\",\"raccoon\"],\n\"animals_seen\": 3,\n\"location\": \"park\"\n}"} +
+ + +
-``` +## How to use Guidance? -A grammar can be defined using Pydantic models, JSON schemas, or regular expressions. The AI will then generate a response that conforms to the specified grammar. +There are two main ways to use guidance; you can either use the `/generate` endpoint with a grammar or use the `/chat/completion` endpoint with tools. -> Note: A grammar must compile to an intermediate representation to constrain the output. Grammar compilation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster. +Under the hood tools are a special case of grammars that allows the model to choose one or none of the provided tools. -### Constrain with Pydantic +Please refer to [using guidance](../basic_tutorial/using_guidance) for more examples and details on how to use guidance in Python, JavaScript, and cURL. -Pydantic is a powerful library for data validation and settings management. It's the perfect tool for crafting the a specific response format. +### Getting the most out of guidance -Using Pydantic models we can define a similar grammar as the previous example in a shorter and more readable way. +Depending on how you are using guidance, you may want to make use of different features. Here are some tips to get the most out of guidance: -```python -import requests -from pydantic import BaseModel, conint -from typing import List - -class Animals(BaseModel): - location: str - activity: str - animals_seen: conint(ge=1, le=5) # Constrained integer type - animals: List[str] - -prompt = "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park" - -data = { - "inputs": prompt, - "parameters": { - "repetition_penalty": 1.3, - "grammar": { - "type": "json", - "value": Animals.schema() - } - } -} - -headers = { - "Content-Type": "application/json", -} - -response = requests.post( - 'http://127.0.0.1:3000/generate', - headers=headers, - json=data -) -print(response.json()) -# {'generated_text': '{ "activity": "bike riding", "animals": ["puppy","cat","raccoon"],"animals_seen": 3, "location":"park" }'} - -``` - -### JSON Schema Integration - -If Pydantic's not your style, go raw with direct JSON Schema integration. It's like having a conversation with the AI in its own language. This is simliar to the first example but with programmatic control. - -```python -import requests - -json_schema = { - "properties": { - "location": { - "type": "string" - }, - "activity": { - "type": "string" - }, - "animals_seen": { - "type": "integer", - "minimum": 1, - "maximum": 5 - }, - "animals": { - "type": "array", - "items": { - "type": "string" - } - } - }, - "required": ["location", "activity", "animals_seen", "animals"] -} - -data = { - "inputs": "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park", - "parameters": { - "max_new_tokens": 200, - "repetition_penalty": 1.3, - "grammar": { - "type": "json", - "value": json_schema - } - } -} - -headers = { - "Content-Type": "application/json", -} - -response = requests.post( - 'http://127.0.0.1:3000/generate', - headers=headers, - json=data -) -print(response.json()) -# {'generated_text': '{\n"activity": "biking",\n"animals": ["puppy","cat","raccoon"]\n , "animals_seen": 3,\n "location":"park"}'} - -``` - -### Using the client - -TGI provides a client library to that make it easy to send requests with all of the parameters we've discussed above. Here's an example of how to use the client to send a request with a grammar parameter. - -```python -from text_generation import AsyncClient -from text_generation.types import GrammarType - -# NOTE: tools defined above and removed for brevity - -# Define an async function to encapsulate the async operation -async def main(): - client = AsyncClient(base_url="http://localhost:3000") - - # Use 'await' to wait for the async method 'chat' to complete - response = await client.generate( - "Whats Googles DNS", - max_new_tokens=10, - decoder_input_details=True, - seed=1, - grammar={ - "type": GrammarType.Regex, - "value": "((25[0-5]|2[0-4]\\d|[01]?\\d\\d?)\\.){3}(25[0-5]|2[0-4]\\d|[01]?\\d\\d?)", - }, - ) - - # Once the response is received, you can process it - print(response.generated_text) - -# Ensure the main async function is run in the event loop -if __name__ == "__main__": - import asyncio - asyncio.run(main()) - -# 118.8.0.84 - -``` - -## Tools and Functions 🛠️ - -### The Tools Parameter - -In addition to the grammar parameter, we've also introduced a set of tools and functions to help you get the most out of the Messages API. - -Tools are a set of user defined functions that can be used in tandem with the chat functionality to enhance the AI's capabilities. You can use these tools to perform a variety of tasks, such as data manipulation, formatting, and more. - -Functions, similar to grammar are defined as JSON schema and can be passed as part of the parameters to the Messages API. - -```json -curl localhost:3000/v1/chat/completions \ - -X POST \ - -H 'Content-Type: application/json' \ - -d '{ - "model": "tgi", - "messages": [ - { - "role": "user", - "content": "What is the weather like in New York?" - } - ], - "tools": [ - { - "type": "function", - "function": { - "name": "get_current_weather", - "description": "Get the current weather", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA" - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location." - } - }, - "required": ["location", "format"] - } - } - } - ], - "tool_choice": "get_current_weather" -}' -// {"id":"","object":"text_completion","created":1709051640,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":19,"total_tokens":176}} -``` - -
- Tools used in example below - -```python - tools = [ - { - "type": "function", - "function": { - "name": "get_current_weather", - "description": "Get the current weather", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - }, - "required": ["location", "format"], - }, - }, - }, - { - "type": "function", - "function": { - "name": "get_n_day_weather_forecast", - "description": "Get an N-day weather forecast", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - "num_days": { - "type": "integer", - "description": "The number of days to forecast", - }, - }, - "required": ["location", "format", "num_days"], - }, - }, - } - ] -``` - -
- -### Text Generation Inference Client - -TGI provides a client library to interact with the Messages API and Tool functions. The client library is available in both synchronous and asynchronous versions. - -```python -from text_generation import AsyncClient - -# NOTE: tools defined above and removed for brevity - -# Define an async function to encapsulate the async operation -async def main(): - client = AsyncClient(base_url="http://localhost:3000") - - # Use 'await' to wait for the async method 'chat' to complete - response = await client.chat( - max_tokens=100, - seed=1, - tools=tools, - presence_penalty=-1.1, - messages=[ - { - "role": "system", - "content": "You're a helpful assistant! Answer the users question best you can.", - }, - { - "role": "user", - "content": "What is the weather like in Brooklyn, New York?", - }, - ], - ) - - # Once the response is received, you can process it - print(response.choices[0].message.tool_calls) - -# Ensure the main async function is run in the event loop -if __name__ == "__main__": - import asyncio - asyncio.run(main()) - -# {"id":"","object":"text_completion","created":1709051942,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":20,"total_tokens":177}} - -``` - -### OpenAI integration - -TGI exposes an OpenAI-compatible API, which means you can use OpenAI's client libraries to interact with TGI's Messages API and Tool functions. - -However there are some minor differences in the API, for example `tool_choice="auto"` will ALWAYS choose the tool for you. This is different from OpenAI's API where `tool_choice="auto"` will choose a tool if the model thinks it's necessary. - -```python -from openai import OpenAI - -# Initialize the client, pointing it to one of the available models -client = OpenAI( - base_url="http://localhost:3000/v1", - api_key="_", -) - -# NOTE: tools defined above and removed for brevity - -chat_completion = client.chat.completions.create( - model="tgi", - messages=[ - { - "role": "system", - "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.", - }, - { - "role": "user", - "content": "What's the weather like the next 3 days in San Francisco, CA?", - }, - ], - tools=tools, - tool_choice="auto", # tool selected by model - max_tokens=500, -) - - -called = chat_completion.choices[0].message.tool_calls -print(called) -# { -# "id": 0, -# "type": "function", -# "function": { -# "description": None, -# "name": "tools", -# "parameters": { -# "format": "celsius", -# "location": "San Francisco, CA", -# "num_days": 3, -# }, -# }, -# } -``` +- If you are using the `/generate` with a `grammar` it is recommended to include the grammar in the prompt prefixed by something like `Please use the following JSON schema to generate the output:`. This will help the model understand the context of the grammar and generate the output accordingly. +- If you are getting a response with many repeated tokens, please use the `frequency_penalty` or `repetition_penalty` to reduce the number of repeated tokens in the output. From 27b3a2c9fc3fd85e8c52ac0ed9c5a2aefe395f97 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Wed, 1 May 2024 18:21:17 +0200 Subject: [PATCH 65/74] Fix: "Fixing" double BOS for mistral too. (#1843) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- server/text_generation_server/models/flash_mistral.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py index 6959e2ec..85e93543 100644 --- a/server/text_generation_server/models/flash_mistral.py +++ b/server/text_generation_server/models/flash_mistral.py @@ -121,6 +121,11 @@ class FlashMistralBatch(FlashCausalLMBatch): requests_idx_mapping[r.id] = i tokenized_input = tokenized_input[-r.truncate :] + if ( + tokenized_input[0] == tokenizer.bos_token_id + and tokenized_input[1] == tokenizer.bos_token_id + ): + tokenized_input = tokenized_input[1:] input_length = len(tokenized_input) input_lengths.append(input_length) From 0038e6020fec4650ce6c35de37440ab7c7bb206e Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Wed, 1 May 2024 21:48:06 +0200 Subject: [PATCH 66/74] Adding scripts to prepare load data. (#1841) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- .gitignore | 1 + load_tests/Makefile | 9 ++++++ load_tests/filter.py | 26 +++++++++++++++ load_tests/orca.py | 27 ++++++++++++++++ load_tests/starcoder_load.js | 63 ------------------------------------ 5 files changed, 63 insertions(+), 63 deletions(-) create mode 100644 load_tests/Makefile create mode 100644 load_tests/filter.py create mode 100644 load_tests/orca.py delete mode 100644 load_tests/starcoder_load.js diff --git a/.gitignore b/.gitignore index 2ac2f6b4..e9ad1808 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,4 @@ server/exllama_kernels/exllama_kernels/hip_buffers.cuh server/exllama_kernels/exllama_kernels/exllama_ext_hip.cpp data/ +load_tests/*.json diff --git a/load_tests/Makefile b/load_tests/Makefile new file mode 100644 index 00000000..9199aa3b --- /dev/null +++ b/load_tests/Makefile @@ -0,0 +1,9 @@ + +ShareGPT_V3_unfiltered_cleaned_split.json: + wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json + +prepare_share: ShareGPT_V3_unfiltered_cleaned_split.json + python filter.py + +prepare_orca: + python orca.py diff --git a/load_tests/filter.py b/load_tests/filter.py new file mode 100644 index 00000000..a00226ed --- /dev/null +++ b/load_tests/filter.py @@ -0,0 +1,26 @@ +import json + + +def main(): + with open("./ShareGPT_V3_unfiltered_cleaned_split.json", "r") as f: + data = json.load(f) + + # Select only the first 2k conversations that start with a human. + max = 2000 + conversations = [] + for conversation in data: + conv = conversation.get("conversations") + if conv and conv[0]["from"] == "human": + # Trim the rest of the output + conversation["conversations"] = conversation["conversations"][:1] + conversations.append(conversation) + + if len(conversation) >= max: + break + + with open("./small.json", "w") as f: + data = json.dump(conversations, f, indent=4) + + +if __name__ == "__main__": + main() diff --git a/load_tests/orca.py b/load_tests/orca.py new file mode 100644 index 00000000..e607d27c --- /dev/null +++ b/load_tests/orca.py @@ -0,0 +1,27 @@ +import json +import datasets +import tqdm + + +def main(): + dataset = datasets.load_dataset("Open-Orca/OpenOrca", split="train") + # Select only the first 2k conversations that start with a human. + max = min(2000, len(dataset)) + conversations = [] + for item in tqdm.tqdm(dataset, total=max): + conversation = { + "conversations": [ + {"from": "human", "value": item["question"]}, + ], + "id": item["id"], + } + conversations.append(conversation) + if len(conversations) >= max: + break + + with open("./small.json", "w") as f: + data = json.dump(conversations, f, indent=4) + + +if __name__ == "__main__": + main() diff --git a/load_tests/starcoder_load.js b/load_tests/starcoder_load.js deleted file mode 100644 index 2f6cb3d6..00000000 --- a/load_tests/starcoder_load.js +++ /dev/null @@ -1,63 +0,0 @@ -import {check} from 'k6'; -import http from 'k6/http'; -import {Trend} from 'k6/metrics'; - -const host = __ENV.HOST || '127.0.0.1:3000'; - -const totalTime = new Trend('total_time', true); -const validationTime = new Trend('validation_time', true); -const queueTime = new Trend('queue_time', true); -const inferenceTime = new Trend('inference_time', true); -const timePerToken = new Trend('time_per_token', true); - -const example = { - payload: JSON.stringify({ - inputs: '# This is a fibonacci function written in the Python programming language.' + - 'def fibonacci', - parameters: { - details: true, - max_new_tokens: 60, - temperature: 0.2, - top_p: 0.95, - seed: 0, - }, - }), - generated_tokens: 60 -}; - -export const options = { - thresholds: { - http_req_failed: ['rate==0'], - time_per_token: ['p(95)<90'], - queue_time: ['p(95)<1500'], - }, - scenarios: { - load_test: { - executor: 'constant-arrival-rate', - duration: '60s', - preAllocatedVUs: 100, - rate: 10, - timeUnit: '1s', - }, - }, -}; - -export default function () { - const headers = {'Content-Type': 'application/json'}; - const res = http.post(`http://${host}/generate`, example.payload, { - headers, - }); - - check(res, { - 'Post status is 200': (r) => res.status === 200, - 'Post response generated tokens': (r) => res.status === 200 && res.json().details.generated_tokens === example.generated_tokens, - }); - - if (res.status === 200) { - totalTime.add(res.headers["X-Total-Time"]); - validationTime.add(res.headers["X-Validation-Time"]); - queueTime.add(res.headers["X-Queue-Time"]); - inferenceTime.add(res.headers["X-Inference-Time"]); - timePerToken.add(res.headers["X-Time-Per-Token"]); - } -} From de079d607a3af4ae893c12c9ddc339e32b3fb95e Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 2 May 2024 15:09:46 +0200 Subject: [PATCH 67/74] Remove misleading warning (not that important nowadays anyway). (#1848) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- .../models/custom_modeling/neox_modeling.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/server/text_generation_server/models/custom_modeling/neox_modeling.py b/server/text_generation_server/models/custom_modeling/neox_modeling.py index 1b060060..f060ec0e 100644 --- a/server/text_generation_server/models/custom_modeling/neox_modeling.py +++ b/server/text_generation_server/models/custom_modeling/neox_modeling.py @@ -60,9 +60,6 @@ if ( except ImportError: pass -if not CUSTOM_KERNELS_ENABLED: - logger.warning("We're not using custom kernels.") - def make_causal_mask( input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int From 65539b743ee2d4dd9b85a9961c33f11b4f687566 Mon Sep 17 00:00:00 2001 From: drbh Date: Thu, 2 May 2024 10:56:24 -0400 Subject: [PATCH 68/74] feat: prefer huggingface_hub in docs and show image api (#1844) This PR prefers the `huggingface_hub` library, refactors the grammar docs and adds the new image_url api to the vlm docs. --- docs/source/basic_tutorials/using_guidance.md | 332 +++++++----------- .../basic_tutorials/visual_language_models.md | 62 +++- 2 files changed, 197 insertions(+), 197 deletions(-) diff --git a/docs/source/basic_tutorials/using_guidance.md b/docs/source/basic_tutorials/using_guidance.md index 606f2453..d0008fdb 100644 --- a/docs/source/basic_tutorials/using_guidance.md +++ b/docs/source/basic_tutorials/using_guidance.md @@ -2,7 +2,7 @@ Text Generation Inference (TGI) now supports [JSON and regex grammars](#grammar-and-constraints) and [tools and functions](#tools-and-functions) to help developers guide LLM responses to fit their needs. -These feature are available starting from version `1.4.3`. They are accessible via the [text_generation](https://pypi.org/project/text-generation/) library. The tool support is compatible with OpenAI's client libraries. The following guide will walk you through the new features and how to use them! +These feature are available starting from version `1.4.3`. They are accessible via the [`huggingface_hub`](https://pypi.org/project/huggingface-hub/) library. The tool support is compatible with OpenAI's client libraries. The following guide will walk you through the new features and how to use them! _note: guidance is supported as grammar in the `/generate` endpoint and as tools in the `/chat/completions` endpoint._ @@ -74,6 +74,45 @@ curl localhost:3000/generate \ ``` +### Hugging Face Hub Python Library + +The Hugging Face Hub Python library provides a client that makes it easy to interact with the Messages API. Here's an example of how to use the client to send a request with a grammar parameter. + +```python +from huggingface_hub import InferenceClient + +client = InferenceClient("http://localhost:3000") + +schema = { + "properties": { + "location": {"title": "Location", "type": "string"}, + "activity": {"title": "Activity", "type": "string"}, + "animals_seen": { + "maximum": 5, + "minimum": 1, + "title": "Animals Seen", + "type": "integer", + }, + "animals": {"items": {"type": "string"}, "title": "Animals", "type": "array"}, + }, + "required": ["location", "activity", "animals_seen", "animals"], + "title": "Animals", + "type": "object", +} + +user_input = "I saw a puppy a cat and a raccoon during my bike ride in the park" +resp = client.text_generation( + f"convert to JSON: 'f{user_input}'. please use the following schema: {schema}", + max_new_tokens=100, + seed=42, + grammar={"type": "json", "value": schema}, +) + +print(resp) +# { "activity": "bike ride", "animals": ["puppy", "cat", "raccoon"], "animals_seen": 3, "location": "park" } + +``` + A grammar can be defined using Pydantic models, JSON schemas, or regular expressions. The LLM will then generate a response that conforms to the specified grammar. > Note: A grammar must compile to an intermediate representation to constrain the output. Grammar compilation is a computationally expensive and may take a few seconds to complete on the first request. Subsequent requests will use the cached grammar and will be much faster. @@ -83,134 +122,55 @@ A grammar can be defined using Pydantic models, JSON schemas, or regular express Using Pydantic models we can define a similar grammar as the previous example in a shorter and more readable way. ```python -import requests +from huggingface_hub import InferenceClient from pydantic import BaseModel, conint from typing import List + class Animals(BaseModel): location: str activity: str animals_seen: conint(ge=1, le=5) # Constrained integer type animals: List[str] -prompt = "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park" -data = { - "inputs": prompt, - "parameters": { - "repetition_penalty": 1.3, - "grammar": { - "type": "json", - "value": Animals.schema() - } - } -} +client = InferenceClient("http://localhost:3000") -headers = { - "Content-Type": "application/json", -} - -response = requests.post( - 'http://127.0.0.1:3000/generate', - headers=headers, - json=data +user_input = "I saw a puppy a cat and a raccoon during my bike ride in the park" +resp = client.text_generation( + f"convert to JSON: 'f{user_input}'. please use the following schema: {Animals.schema()}", + max_new_tokens=100, + seed=42, + grammar={"type": "json", "value": Animals.schema()}, ) -print(response.json()) -# {'generated_text': '{ "activity": "bike riding", "animals": ["puppy","cat","raccoon"],"animals_seen": 3, "location":"park" }'} + +print(resp) +# { "activity": "bike ride", "animals": ["puppy", "cat", "raccoon"], "animals_seen": 3, "location": "park" } + ``` -### JSON Schema Integration - -If Pydantic's not your style, go raw with direct JSON Schema integration. This is similar to the first example but with programmatic control. +defining a grammar as regular expressions ```python -import requests +from huggingface_hub import InferenceClient -json_schema = { - "properties": { - "location": { - "type": "string" - }, - "activity": { - "type": "string" - }, - "animals_seen": { - "type": "integer", - "minimum": 1, - "maximum": 5 - }, - "animals": { - "type": "array", - "items": { - "type": "string" - } - } +client = InferenceClient("http://localhost:3000") + +regexp = "((25[0-5]|2[0-4]\\d|[01]?\\d\\d?)\\.){3}(25[0-5]|2[0-4]\\d|[01]?\\d\\d?)" + +resp = client.text_generation( + f"Whats Googles DNS? Please use the following regex: {regexp}", + seed=42, + grammar={ + "type": "regex", + "value": regexp, }, - "required": ["location", "activity", "animals_seen", "animals"] -} - -data = { - "inputs": "convert to JSON: I saw a puppy a cat and a raccoon during my bike ride in the park", - "parameters": { - "max_new_tokens": 200, - "repetition_penalty": 1.3, - "grammar": { - "type": "json", - "value": json_schema - } - } -} - -headers = { - "Content-Type": "application/json", -} - -response = requests.post( - 'http://127.0.0.1:3000/generate', - headers=headers, - json=data ) -print(response.json()) -# {'generated_text': '{\n"activity": "biking",\n"animals": ["puppy","cat","raccoon"]\n , "animals_seen": 3,\n "location":"park"}'} -``` -### Using the client - -TGI provides a client library to that make it easy to send requests with all of the parameters we've discussed above. Here's an example of how to use the client to send a request with a grammar parameter. - -```python -from text_generation import AsyncClient -from text_generation.types import GrammarType - -# NOTE: tools defined above and removed for brevity - -# Define an async function to encapsulate the async operation -async def main(): - client = AsyncClient(base_url="http://localhost:3000") - - # Use 'await' to wait for the async method 'chat' to complete - response = await client.generate( - "Whats Googles DNS", - max_new_tokens=10, - decoder_input_details=True, - seed=1, - grammar={ - "type": GrammarType.Regex, - "value": "((25[0-5]|2[0-4]\\d|[01]?\\d\\d?)\\.){3}(25[0-5]|2[0-4]\\d|[01]?\\d\\d?)", - }, - ) - - # Once the response is received, you can process it - print(response.generated_text) - -# Ensure the main async function is run in the event loop -if __name__ == "__main__": - import asyncio - asyncio.run(main()) - -# 118.8.0.84 +print(resp) +# 7.1.1.1 ``` @@ -265,107 +225,87 @@ curl localhost:3000/v1/chat/completions \ // {"id":"","object":"text_completion","created":1709051640,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":19,"total_tokens":176}} ``` -### Text Generation Inference Client +### Chat Completion with Tools -TGI provides a client library to interact with the Messages API and Tool functions. The client library is available in both synchronous and asynchronous versions. +Grammars are supported in the `/generate` endpoint, while tools are supported in the `/chat/completions` endpoint. Here's an example of how to use the client to send a request with a tool parameter. ```python -from text_generation import AsyncClient +from huggingface_hub import InferenceClient -# NOTE: tools defined above and removed for brevity +client = InferenceClient("http://localhost:3000") -# Define an async function to encapsulate the async operation -async def main(): - client = AsyncClient(base_url="http://localhost:3000") - - # Use 'await' to wait for the async method 'chat' to complete - response = await client.chat( - max_tokens=100, - seed=1, - tools=tools, - presence_penalty=-1.1, - messages=[ - { - "role": "system", - "content": "You're a helpful assistant! Answer the users question best you can.", +tools = [ + { + "type": "function", + "function": { + "name": "get_current_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + "required": ["location", "format"], }, - { - "role": "user", - "content": "What is the weather like in Brooklyn, New York?", + }, + }, + { + "type": "function", + "function": { + "name": "get_n_day_weather_forecast", + "description": "Get an N-day weather forecast", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + "num_days": { + "type": "integer", + "description": "The number of days to forecast", + }, + }, + "required": ["location", "format", "num_days"], }, - ], - ) + }, + }, +] - # Once the response is received, you can process it - print(response.choices[0].message.tool_calls) +chat = client.chat_completion( + messages=[ + { + "role": "system", + "content": "You're a helpful assistant! Answer the users question best you can.", + }, + { + "role": "user", + "content": "What is the weather like in Brooklyn, New York?", + }, + ], + tools=tools, + seed=42, + max_tokens=100, +) -# Ensure the main async function is run in the event loop -if __name__ == "__main__": - import asyncio - asyncio.run(main()) - -# {"id":"","object":"text_completion","created":1709051942,"model":"HuggingFaceH4/zephyr-7b-beta","system_fingerprint":"1.4.3-native","choices":[{"index":0,"message":{"role":"assistant","tool_calls":{"id":0,"type":"function","function":{"description":null,"name":"tools","parameters":{"format":"celsius","location":"New York"}}}},"logprobs":null,"finish_reason":"eos_token"}],"usage":{"prompt_tokens":157,"completion_tokens":20,"total_tokens":177}} +print(chat.choices[0].message.tool_calls) +# [ChatCompletionOutputToolCall(function=ChatCompletionOutputFunctionDefinition(arguments={'format': 'fahrenheit', 'location': 'Brooklyn, New York', 'num_days': 7}, name='get_n_day_weather_forecast', description=None), id=0, type='function')] ``` -
- Tools used in example above - -```python - tools = [ - { - "type": "function", - "function": { - "name": "get_current_weather", - "description": "Get the current weather", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - }, - "required": ["location", "format"], - }, - }, - }, - { - "type": "function", - "function": { - "name": "get_n_day_weather_forecast", - "description": "Get an N-day weather forecast", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - "num_days": { - "type": "integer", - "description": "The number of days to forecast", - }, - }, - "required": ["location", "format", "num_days"], - }, - }, - } - ] -``` - -
- ### OpenAI integration TGI exposes an OpenAI-compatible API, which means you can use OpenAI's client libraries to interact with TGI's Messages API and Tool functions. diff --git a/docs/source/basic_tutorials/visual_language_models.md b/docs/source/basic_tutorials/visual_language_models.md index e804ef09..3770db0b 100644 --- a/docs/source/basic_tutorials/visual_language_models.md +++ b/docs/source/basic_tutorials/visual_language_models.md @@ -53,7 +53,67 @@ for token in client.text_generation(prompt, max_new_tokens=10, stream=True): # This is a picture of an anthropomorphic rabbit in a space suit. ``` -If you want additional details, you can add `details=True`. In this case, you get a `TextGenerationStreamResponse` which contains additional information such as the probabilities and the tokens. For the final response in the stream, it also returns the full generated text. +or via the `chat_completion` endpoint: + +```python +from huggingface_hub import InferenceClient + +client = InferenceClient("http://127.0.0.1:3000") + +chat = client.chat_completion( + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "Whats in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png" + }, + }, + ], + }, + ], + seed=42, + max_tokens=100, +) + +print(chat) +# ChatCompletionOutput(choices=[ChatCompletionOutputComplete(finish_reason='length', index=0, message=ChatCompletionOutputMessage(role='assistant', content=" The image you've provided features an anthropomorphic rabbit in spacesuit attire. This rabbit is depicted with human-like posture and movement, standing on a rocky terrain with a vast, reddish-brown landscape in the background. The spacesuit is detailed with mission patches, circuitry, and a helmet that covers the rabbit's face and ear, with an illuminated red light on the chest area.\n\nThe artwork style is that of a", name=None, tool_calls=None), logprobs=None)], created=1714589614, id='', model='llava-hf/llava-v1.6-mistral-7b-hf', object='text_completion', system_fingerprint='2.0.2-native', usage=ChatCompletionOutputUsage(completion_tokens=100, prompt_tokens=2943, total_tokens=3043)) + +``` + +or with OpenAi's library: + +```python +from openai import OpenAI + +# init the client but point it to TGI +client = OpenAI(base_url="http://localhost:3000/v1", api_key="-") + +chat_completion = client.chat.completions.create( + model="tgi", + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "Whats in this image?"}, + { + "type": "image_url", + "image_url": { + "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png" + }, + }, + ], + }, + ], + stream=False, +) + +print(chat_completion) +# ChatCompletion(id='', choices=[Choice(finish_reason='eos_token', index=0, logprobs=None, message=ChatCompletionMessage(content=' The image depicts an anthropomorphic rabbit dressed in a space suit with gear that resembles NASA attire. The setting appears to be a solar eclipse with dramatic mountain peaks and a partial celestial body in the sky. The artwork is detailed and vivid, with a warm color palette and a sense of an adventurous bunny exploring or preparing for a journey beyond Earth. ', role='assistant', function_call=None, tool_calls=None))], created=1714589732, model='llava-hf/llava-v1.6-mistral-7b-hf', object='text_completion', system_fingerprint='2.0.2-native', usage=CompletionUsage(completion_tokens=84, prompt_tokens=2943, total_tokens=3027)) +``` ### Inference Through Sending `cURL` Requests From a25737139d302390edc40ee2d9d92109c7720c04 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 2 May 2024 19:07:10 +0200 Subject: [PATCH 69/74] Updating Phi3 (long context). (#1849) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- Cargo.lock | 8 +- router/src/config.rs | 1 + server/text_generation_server/utils/layers.py | 92 ++++++++++++++++++- 3 files changed, 96 insertions(+), 5 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index de54c536..3c6515ad 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -3393,7 +3393,7 @@ dependencies = [ [[package]] name = "text-generation-benchmark" -version = "2.0.1" +version = "2.0.2" dependencies = [ "average", "clap", @@ -3414,7 +3414,7 @@ dependencies = [ [[package]] name = "text-generation-client" -version = "2.0.1" +version = "2.0.2" dependencies = [ "futures", "grpc-metadata", @@ -3430,7 +3430,7 @@ dependencies = [ [[package]] name = "text-generation-launcher" -version = "2.0.1" +version = "2.0.2" dependencies = [ "clap", "ctrlc", @@ -3448,7 +3448,7 @@ dependencies = [ [[package]] name = "text-generation-router" -version = "2.0.1" +version = "2.0.2" dependencies = [ "async-stream", "axum", diff --git a/router/src/config.rs b/router/src/config.rs index 88cde69a..8640ede9 100644 --- a/router/src/config.rs +++ b/router/src/config.rs @@ -136,6 +136,7 @@ pub enum Config { Phi, #[serde(rename = "phi-msft")] PhiMsft, + Phi3, Llama, Baichuan, Gemma, diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py index 6e4a13cd..7d339fe5 100644 --- a/server/text_generation_server/utils/layers.py +++ b/server/text_generation_server/utils/layers.py @@ -1029,10 +1029,10 @@ try: scaling_factor = None rope_scaling = _get_rope_config(config) if rope_scaling is not None: - scaling_factor = rope_scaling["factor"] if rope_scaling["type"] == "linear": pass elif rope_scaling["type"] == "dynamic": + scaling_factor = rope_scaling["factor"] return DynamicPositionRotaryEmbedding( dim=dim, max_position_embeddings=config.max_position_embeddings, @@ -1041,6 +1041,7 @@ try: scaling_factor=scaling_factor, ) elif rope_scaling["type"] == "yarn": + scaling_factor = rope_scaling["factor"] return YarnPositionRotaryEmbedding( dim=2 * inv_freq.shape[0], max_position_embeddings=rope_scaling[ @@ -1054,6 +1055,52 @@ try: beta_fast=32, beta_slow=1, ) + elif rope_scaling["type"] == "su": + short_factor = torch.tensor( + rope_scaling["short_factor"], dtype=torch.float32, device=device + ) + short_inv_freq = 1.0 / ( + short_factor + * base + ** ( + torch.arange(0, dim, 2, device=device, dtype=torch.float32) + / dim + ) + ) + long_factor = torch.tensor( + rope_scaling["long_factor"], dtype=torch.float32, device=device + ) + long_inv_freq = 1.0 / ( + long_factor + * base + ** ( + torch.arange(0, dim, 2, device=device, dtype=torch.float32) + / dim + ) + ) + + original_max_position_embeddings = ( + config.original_max_position_embeddings + ) + max_position_embeddings = config.max_position_embeddings + if max_position_embeddings <= original_max_position_embeddings: + scaling_factor = 1.0 + else: + scale = ( + max_position_embeddings / original_max_position_embeddings + ) + scaling_factor = math.sqrt( + 1 + + math.log(scale) + / math.log(original_max_position_embeddings) + ) + + return SuRotaryEmbedding( + short_inv_freq=short_inv_freq, + long_inv_freq=long_inv_freq, + scaling_factor=scaling_factor, + original_max_position_embeddings=original_max_position_embeddings, + ) else: raise NotImplementedError( f"rope scaling type {rope_scaling['type']} is not implemented or invalid" @@ -1141,6 +1188,49 @@ try: # Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow. return cos.unsqueeze(1), sin.unsqueeze(1) + class SuRotaryEmbedding(PositionRotaryEmbedding): + def __init__( + self, + short_inv_freq, + long_inv_freq, + scaling_factor, + original_max_position_embeddings, + ): + super(PositionRotaryEmbedding, self).__init__() + self.short_inv_freq = short_inv_freq + self.long_inv_freq = long_inv_freq + self.scaling_factor = scaling_factor + self.original_max_position_embeddings = original_max_position_embeddings + self._seq_len_cached = 0 + self._cos_cached = None + self._sin_cached = None + self._cos_k_cached = None + self._sin_k_cached = None + self.dynamic_args = None + + def _update_cos_sin_cache(self, dtype, device, seqlen): + # Reset the tables if the sequence length has changed, + # or if we're on a new device (possibly due to tracing for instance) + if ( + seqlen > self._seq_len_cached + or self._cos_cached.device != device + or self._cos_cached.dtype != dtype + ): + self._seq_len_cached = seqlen + if seqlen > self.original_max_position_embeddings: + inv_freq = self.long_inv_freq + else: + inv_freq = self.short_inv_freq + t = torch.arange(seqlen, device=device, dtype=inv_freq.dtype) + if self.scaling_factor is not None: + t /= self.scaling_factor + # Don't do einsum, it converts fp32 to fp16 + # freqs = torch.einsum("i,j->ij", t, self.inv_freq) + + freqs = torch.outer(t, inv_freq.to(device=t.device)) + self._cos_cached = torch.cos(freqs).to(dtype) + self._sin_cached = torch.sin(freqs).to(dtype) + class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding): def __init__(self, dim, max_position_embeddings, base, device, scaling_factor): inv_freq = _create_inv_freq(dim, base, device) From bb2b2959a222594dd8a41b8bb18d7b6fe280730a Mon Sep 17 00:00:00 2001 From: Lucain Date: Fri, 3 May 2024 16:39:04 +0200 Subject: [PATCH 70/74] Add router name to /info endpoint (#1854) Add `router` key in `/info` endpoint and set it to `env!("CARGO_PKG_NAME")` => so always set to `"text-generation-router"` in TGI. Happy to change the naming if you think of a better one (framework? package_name?) The goal is to use this information in `InferenceClient` to know the model is served with TGI. At the moment we can use https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2/info to infer it is TGI-served because it returns information but having a proper key would be better. For context, a transformers-served model is only outputting `{"ok": "ok"}` (see [here](https://api-inference.huggingface.co/models/microsoft/DialoGPT-large/info)). --- router/src/lib.rs | 2 ++ router/src/server.rs | 1 + 2 files changed, 3 insertions(+) diff --git a/router/src/lib.rs b/router/src/lib.rs index fac4c14e..96a9fdf6 100644 --- a/router/src/lib.rs +++ b/router/src/lib.rs @@ -159,6 +159,8 @@ pub struct Info { #[schema(example = "32")] pub max_client_batch_size: usize, /// Router Info + #[schema(example = "text-generation-router")] + pub router: &'static str, #[schema(example = "0.5.0")] pub version: &'static str, #[schema(nullable = true, example = "null")] diff --git a/router/src/server.rs b/router/src/server.rs index 8657b779..5dbde21e 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -1564,6 +1564,7 @@ pub async fn run( max_batch_size, validation_workers, max_client_batch_size, + router: env!("CARGO_PKG_NAME"), version: env!("CARGO_PKG_VERSION"), sha: option_env!("VERGEN_GIT_SHA"), docker_label: option_env!("DOCKER_LABEL"), From ac7076b64db3c3896ca9f84e9484fb16788c7f2c Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Mon, 6 May 2024 13:48:11 +0200 Subject: [PATCH 71/74] Upgrading to rust 1.78. (#1851) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- .github/workflows/tests.yaml | 7 +- Cargo.lock | 171 +++++++++++++++++--------------- Dockerfile | 2 +- Dockerfile_amd | 2 +- Dockerfile_intel | 2 +- benchmark/src/event.rs | 6 +- launcher/Cargo.toml | 1 + launcher/src/main.rs | 19 ++-- router/grpc-metadata/src/lib.rs | 23 +---- router/src/infer.rs | 4 +- router/src/server.rs | 13 +-- rust-toolchain.toml | 7 +- 12 files changed, 124 insertions(+), 133 deletions(-) diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index 29ff6d45..37dc8305 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -33,10 +33,9 @@ jobs: - name: Install Rust uses: actions-rs/toolchain@v1 with: - # Released on: 28 December, 2023 - # Branched from master on: 10 November, 2023 - # https://releases.rs/docs/1.75.0/ - toolchain: 1.75.0 + # Released on: 02 May, 2024 + # https://releases.rs/docs/1.78.0/ + toolchain: 1.78.0 override: true components: rustfmt, clippy - name: Install Protoc diff --git a/Cargo.lock b/Cargo.lock index 3c6515ad..138b6676 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -48,47 +48,48 @@ checksum = "4aa90d7ce82d4be67b64039a3d588d38dbcc6736577de4a847025ce5b0c468d1" [[package]] name = "anstream" -version = "0.6.13" +version = "0.6.14" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d96bd03f33fe50a863e394ee9718a706f988b9079b20c3784fb726e7678b62fb" +checksum = "418c75fa768af9c03be99d17643f93f79bbba589895012a80e3452a19ddda15b" dependencies = [ "anstyle", "anstyle-parse", "anstyle-query", "anstyle-wincon", "colorchoice", + "is_terminal_polyfill", "utf8parse", ] [[package]] name = "anstyle" -version = "1.0.6" +version = "1.0.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8901269c6307e8d93993578286ac0edf7f195079ffff5ebdeea6a59ffb7e36bc" +checksum = "038dfcf04a5feb68e9c60b21c9625a54c2c0616e79b72b0fd87075a056ae1d1b" [[package]] name = "anstyle-parse" -version = "0.2.3" +version = "0.2.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c75ac65da39e5fe5ab759307499ddad880d724eed2f6ce5b5e8a26f4f387928c" +checksum = "c03a11a9034d92058ceb6ee011ce58af4a9bf61491aa7e1e59ecd24bd40d22d4" dependencies = [ "utf8parse", ] [[package]] name = "anstyle-query" -version = "1.0.2" +version = "1.0.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e28923312444cdd728e4738b3f9c9cac739500909bb3d3c94b43551b16517648" +checksum = "a64c907d4e79225ac72e2a354c9ce84d50ebb4586dee56c82b3ee73004f537f5" dependencies = [ "windows-sys 0.52.0", ] [[package]] name = "anstyle-wincon" -version = "3.0.2" +version = "3.0.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1cd54b81ec8d6180e24654d0b371ad22fc3dd083b6ff8ba325b72e00c87660a7" +checksum = "61a38449feb7068f52bb06c12759005cf459ee52bb4adc1d5a7c4322d716fb19" dependencies = [ "anstyle", "windows-sys 0.52.0", @@ -321,9 +322,9 @@ checksum = "9d297deb1925b89f2ccc13d7635fa0714f12c87adce1c75356b39ca9b7178567" [[package]] name = "base64" -version = "0.22.0" +version = "0.22.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9475866fec1451be56a3c2400fd081ff546538961565ccb5b7142cbd22bc7a51" +checksum = "72b3254f16251a8381aa12e40e3c4d2f0199f8c6508fbecb9d91f575e0fbb8c6" [[package]] name = "bit-set" @@ -387,9 +388,9 @@ checksum = "79296716171880943b8470b5f8d03aa55eb2e645a4874bdbb28adb49162e012c" [[package]] name = "bytecount" -version = "0.6.7" +version = "0.6.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e1e5f035d16fc623ae5f74981db80a439803888314e3a555fd6f04acd51a3205" +checksum = "5ce89b21cab1437276d2650d57e971f9d548a2d9037cc231abdc0562b97498ce" [[package]] name = "bytemuck" @@ -403,6 +404,12 @@ version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "1fd0f2584146f6f2ef48085050886acf353beff7305ebd1ae69500e27c67f64b" +[[package]] +name = "byteorder-lite" +version = "0.1.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8f1fe948ff07f4bd06c30984e69f5b4899c516a3ef74f34df92a2df2ab535495" + [[package]] name = "bytes" version = "1.6.0" @@ -449,12 +456,13 @@ checksum = "df8670b8c7b9dae1793364eafadf7239c40d669904660c5960d74cfd80b46a53" [[package]] name = "cc" -version = "1.0.94" +version = "1.0.96" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "17f6e324229dc011159fcc089755d1e2e216a90d43a7dea6853ca740b84f35e7" +checksum = "065a29261d53ba54260972629f9ca6bffa69bac13cd1fed61420f7fa68b9f8bd" dependencies = [ "jobserver", "libc", + "once_cell", ] [[package]] @@ -527,9 +535,9 @@ checksum = "3d7b894f5411737b7867f4827955924d7c254fc9f4d91a6aad6b097804b1018b" [[package]] name = "colorchoice" -version = "1.0.0" +version = "1.0.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "acbf1af155f9b9ef647e42cdc158db4b64a1b61f743629225fde6f3e0be2a7c7" +checksum = "0b6a852b24ab71dffc585bcb46eaf7959d175cb865a7152e35b348d1b2960422" [[package]] name = "console" @@ -872,9 +880,9 @@ dependencies = [ [[package]] name = "fastrand" -version = "2.0.2" +version = "2.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "658bd65b1cf4c852a3cc96f18a8ce7b5640f6b703f905c7d74532294c2a63984" +checksum = "9fc0510504f03c51ada170672ac806f1f105a88aa97a5281117e1ddc3368e51a" [[package]] name = "fdeflate" @@ -893,9 +901,9 @@ checksum = "0ce7134b9999ecaf8bcd65542e436736ef32ddca1b3e06094cb6ec5755203b80" [[package]] name = "flate2" -version = "1.0.28" +version = "1.0.30" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "46303f565772937ffe1d394a4fac6f411c6013172fadde9dcdb1e147a086940e" +checksum = "5f54427cfd1c7829e2a139fcefea601bf088ebca651d2bf53ebc600eac295dae" dependencies = [ "crc32fast", "miniz_oxide", @@ -1155,9 +1163,9 @@ dependencies = [ [[package]] name = "hashbrown" -version = "0.14.3" +version = "0.14.5" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "290f1a1d9242c78d09ce40a5e87e7554ee637af1351968159f4952f028f75604" +checksum = "e5274423e17b7c9fc20b6e7e208532f9b19825d82dfd615708b70edd83df41f1" [[package]] name = "heck" @@ -1339,11 +1347,11 @@ dependencies = [ [[package]] name = "image-webp" -version = "0.1.1" +version = "0.1.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7a84a25dcae3ac487bc24ef280f9e20c79c9b1a3e5e32cbed3041d1c514aa87c" +checksum = "d730b085583c4d789dfd07fdcf185be59501666a90c97c40162b37e4fdad272d" dependencies = [ - "byteorder", + "byteorder-lite", "thiserror", ] @@ -1370,7 +1378,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "168fb715dda47215e360912c096649d23d58bf392ac62f73919e831745e40f26" dependencies = [ "equivalent", - "hashbrown 0.14.3", + "hashbrown 0.14.5", "serde", ] @@ -1432,6 +1440,12 @@ version = "2.9.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "8f518f335dce6725a761382244631d86cf0ccb2863413590b31338feb467f9c3" +[[package]] +name = "is_terminal_polyfill" +version = "1.70.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f8478577c03552c21db0e2724ffb8986a5ce7af88107e6be5d2ee6e158c12800" + [[package]] name = "iso8601" version = "0.6.1" @@ -1476,9 +1490,9 @@ checksum = "49f1f14873335454500d59611f1cf4a4b0f786f9ac11f4312a78e4cf2566695b" [[package]] name = "jobserver" -version = "0.1.30" +version = "0.1.31" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "685a7d121ee3f65ae4fddd72b25a04bb36b6af81bc0828f7d5434c0fe60fa3a2" +checksum = "d2b099aaa34a9751c5bf0878add70444e1ed2dd73f347be99003d4577277de6e" dependencies = [ "libc", ] @@ -1542,9 +1556,9 @@ checksum = "03087c2bad5e1034e8cace5926dec053fb3790248370865f5117a7d0213354c8" [[package]] name = "libc" -version = "0.2.153" +version = "0.2.154" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9c198f91728a82281a64e1f4f9eeb25d82cb32a5de251c6bd1b5154d63a8e7bd" +checksum = "ae743338b92ff9146ce83992f766a31066a91a8c84a45e0e9f21e7cf6de6d346" [[package]] name = "libfuzzer-sys" @@ -1581,9 +1595,9 @@ checksum = "01cda141df6706de531b6c46c3a33ecca755538219bd484262fa09410c13539c" [[package]] name = "lock_api" -version = "0.4.11" +version = "0.4.12" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3c168f8615b12bc01f9c17e2eb0cc07dcae1940121185446edc3744920e8ef45" +checksum = "07af8b9cdd281b7915f413fa73f29ebd5d55d0d3f0155584dade1ff18cea1b17" dependencies = [ "autocfg", "scopeguard", @@ -2267,9 +2281,9 @@ dependencies = [ [[package]] name = "parking_lot" -version = "0.12.1" +version = "0.12.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3742b2c103b9f06bc9fff0a37ff4912935851bee6d36f3c02bcc755bcfec228f" +checksum = "7e4af0ca4f6caed20e900d564c242b8e5d4903fdacf31d3daf527b66fe6f42fb" dependencies = [ "lock_api", "parking_lot_core", @@ -2277,15 +2291,15 @@ dependencies = [ [[package]] name = "parking_lot_core" -version = "0.9.9" +version = "0.9.10" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4c42a9226546d68acdd9c0a280d17ce19bfe27a46bf68784e4066115788d008e" +checksum = "1e401f977ab385c9e4e3ab30627d6f26d00e2c73eef317493c4ec6d468726cf8" dependencies = [ "cfg-if", "libc", "redox_syscall", "smallvec", - "windows-targets 0.48.5", + "windows-targets 0.52.5", ] [[package]] @@ -2696,11 +2710,11 @@ dependencies = [ [[package]] name = "redox_syscall" -version = "0.4.1" +version = "0.5.1" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4722d768eff46b75989dd134e5c353f0d6296e5aaa3132e776cbdb56be7731aa" +checksum = "469052894dcb553421e483e4209ee581a45100d31b4018de03e5a7ad86374a7e" dependencies = [ - "bitflags 1.3.2", + "bitflags 2.5.0", ] [[package]] @@ -2889,9 +2903,9 @@ dependencies = [ [[package]] name = "rustix" -version = "0.38.32" +version = "0.38.34" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "65e04861e65f21776e67888bfbea442b3642beaa0138fdb1dd7a84a52dffdb89" +checksum = "70dc5ec042f7a43c4a73241207cecc9873a06d45debb38b329f8541d85c2730f" dependencies = [ "bitflags 2.5.0", "errno", @@ -2914,9 +2928,9 @@ dependencies = [ [[package]] name = "rustls" -version = "0.22.3" +version = "0.22.4" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "99008d7ad0bbbea527ec27bddbc0e432c5b87d8175178cee68d2eec9c4a1813c" +checksum = "bf4ef73721ac7bcd79b2b315da7779d8fc09718c6b3d2d1b2d94850eb8c18432" dependencies = [ "log", "ring 0.17.8", @@ -2937,15 +2951,15 @@ dependencies = [ [[package]] name = "rustls-pki-types" -version = "1.4.1" +version = "1.5.0" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ecd36cc4259e3e4514335c4a138c6b43171a8d61d8f5c9348f9fc7529416f247" +checksum = "beb461507cee2c2ff151784c52762cf4d9ff6a61f3e80968600ed24fa837fa54" [[package]] name = "rustls-webpki" -version = "0.102.2" +version = "0.102.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "faaa0a62740bedb9b2ef5afa303da42764c012f743917351dc9a237ea1663610" +checksum = "f3bce581c0dd41bce533ce695a1437fa16a7ab5ac3ccfa99fe1a620a7885eabf" dependencies = [ "ring 0.17.8", "rustls-pki-types", @@ -3032,18 +3046,18 @@ dependencies = [ [[package]] name = "serde" -version = "1.0.198" +version = "1.0.200" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9846a40c979031340571da2545a4e5b7c4163bdae79b301d5f86d03979451fcc" +checksum = "ddc6f9cc94d67c0e21aaf7eda3a010fd3af78ebf6e096aa6e2e13c79749cce4f" dependencies = [ "serde_derive", ] [[package]] name = "serde_derive" -version = "1.0.198" +version = "1.0.200" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e88edab869b01783ba905e7d0153f9fc1a6505a96e4ad3018011eedb838566d9" +checksum = "856f046b9400cee3c8c94ed572ecdb752444c24528c035cd35882aad6f492bcb" dependencies = [ "proc-macro2", "quote", @@ -3144,9 +3158,9 @@ dependencies = [ [[package]] name = "signal-hook-registry" -version = "1.4.1" +version = "1.4.2" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d8229b473baa5980ac72ef434c4415e70c4b5e71b423043adb4ba059f89c99a1" +checksum = "a9e9e0b4211b72e7b8b6e85c807d36c212bdb33ea8587f7569562a84df5465b1" dependencies = [ "libc", ] @@ -3198,9 +3212,9 @@ checksum = "3c5e1a9a646d36c3599cd173a41282daf47c44583ad367b8e6837255952e5c67" [[package]] name = "socket2" -version = "0.5.6" +version = "0.5.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "05ffd9c0a93b7543e062e759284fcf5f5e3b098501104bfbdde4d404db792871" +checksum = "ce305eb0b4296696835b71df73eb912e0f1ffd2556a501fcede6e0c50349191c" dependencies = [ "libc", "windows-sys 0.52.0", @@ -3303,9 +3317,9 @@ checksum = "2047c6ded9c721764247e62cd3b03c09ffc529b2ba5b10ec482ae507a4a70160" [[package]] name = "sysinfo" -version = "0.30.10" +version = "0.30.11" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "26d7c217777061d5a2d652aea771fb9ba98b6dade657204b08c4b9604d11555b" +checksum = "87341a165d73787554941cd5ef55ad728011566fe714e987d1b976c15dbc3a83" dependencies = [ "cfg-if", "core-foundation-sys", @@ -3441,6 +3455,7 @@ dependencies = [ "reqwest", "serde", "serde_json", + "thiserror", "tracing", "tracing-subscriber", "vergen", @@ -3453,7 +3468,7 @@ dependencies = [ "async-stream", "axum", "axum-tracing-opentelemetry", - "base64 0.22.0", + "base64 0.22.1", "clap", "futures", "futures-util", @@ -3490,18 +3505,18 @@ dependencies = [ [[package]] name = "thiserror" -version = "1.0.58" +version = "1.0.59" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "03468839009160513471e86a034bb2c5c0e4baae3b43f79ffc55c4a5427b3297" +checksum = "f0126ad08bff79f29fc3ae6a55cc72352056dfff61e3ff8bb7129476d44b23aa" dependencies = [ "thiserror-impl", ] [[package]] name = "thiserror-impl" -version = "1.0.58" +version = "1.0.59" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c61f3ba182994efc43764a46c018c347bc492c79f024e705f46567b418f6d4f7" +checksum = "d1cd413b5d558b4c5bf3680e324a6fa5014e7b7c067a51e69dbdf47eb7148b66" dependencies = [ "proc-macro2", "quote", @@ -3720,9 +3735,9 @@ dependencies = [ [[package]] name = "toml_edit" -version = "0.22.9" +version = "0.22.12" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8e40bb779c5187258fd7aad0eb68cb8706a0a81fa712fbea808ab43c4b8374c4" +checksum = "d3328d4f68a705b2a4498da1d580585d39a6510f98318a2cec3018a7ec61ddef" dependencies = [ "indexmap 2.2.6", "serde", @@ -4023,9 +4038,9 @@ checksum = "d4c87d22b6e3f4a18d4d40ef354e97c90fcb14dd91d7dc0aa9d8a1172ebf7202" [[package]] name = "unicode-width" -version = "0.1.11" +version = "0.1.12" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e51733f11c9c4f72aa0c160008246859e340b00807569a0da0e7a1079b27ba85" +checksum = "68f5e5f3158ecfd4b8ff6fe086db7c8467a2dfdac97fe420f2b7c4aa97af66d6" [[package]] name = "unicode_categories" @@ -4047,16 +4062,16 @@ checksum = "8ecb6da28b8a351d773b68d5825ac39017e680750f980f3a1a85cd8dd28a47c1" [[package]] name = "ureq" -version = "2.9.6" +version = "2.9.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "11f214ce18d8b2cbe84ed3aa6486ed3f5b285cf8d8fbdbce9f3f767a724adc35" +checksum = "d11a831e3c0b56e438a28308e7c810799e3c118417f342d30ecec080105395cd" dependencies = [ - "base64 0.21.7", + "base64 0.22.1", "flate2", "log", "native-tls", "once_cell", - "rustls 0.22.3", + "rustls 0.22.4", "rustls-pki-types", "rustls-webpki", "serde", @@ -4330,11 +4345,11 @@ checksum = "ac3b87c63620426dd9b991e5ce0329eff545bccbbb34f3be09ff6fb6ab51b7b6" [[package]] name = "winapi-util" -version = "0.1.6" +version = "0.1.8" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f29e6f9198ba0d26b4c9f07dbe6f9ed633e1f3d5b8b414090084349e46a52596" +checksum = "4d4cc384e1e73b93bafa6fb4f1df8c41695c8a91cf9c4c64358067d15a7b6c6b" dependencies = [ - "winapi", + "windows-sys 0.52.0", ] [[package]] @@ -4569,9 +4584,9 @@ checksum = "bec47e5bfd1bff0eeaf6d8b485cc1074891a197ab4225d504cb7a1ab88b02bf0" [[package]] name = "winnow" -version = "0.6.6" +version = "0.6.7" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f0c976aaaa0e1f90dbb21e9587cdaf1d9679a1cde8875c0d6bd83ab96a208352" +checksum = "14b9415ee827af173ebb3f15f9083df5a122eb93572ec28741fb153356ea2578" dependencies = [ "memchr", ] diff --git a/Dockerfile b/Dockerfile index 333f7c91..0471450f 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,5 +1,5 @@ # Rust builder -FROM lukemathwalker/cargo-chef:latest-rust-1.75 AS chef +FROM lukemathwalker/cargo-chef:latest-rust-1.78 AS chef WORKDIR /usr/src ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse diff --git a/Dockerfile_amd b/Dockerfile_amd index fb820116..57a7c637 100644 --- a/Dockerfile_amd +++ b/Dockerfile_amd @@ -1,5 +1,5 @@ # Rust builder -FROM lukemathwalker/cargo-chef:latest-rust-1.75 AS chef +FROM lukemathwalker/cargo-chef:latest-rust-1.78 AS chef WORKDIR /usr/src ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse diff --git a/Dockerfile_intel b/Dockerfile_intel index d0791cac..8c7478c1 100644 --- a/Dockerfile_intel +++ b/Dockerfile_intel @@ -1,4 +1,4 @@ -FROM lukemathwalker/cargo-chef:latest-rust-1.75 AS chef +FROM lukemathwalker/cargo-chef:latest-rust-1.78 AS chef WORKDIR /usr/src ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse diff --git a/benchmark/src/event.rs b/benchmark/src/event.rs index 91ce8400..07482aed 100644 --- a/benchmark/src/event.rs +++ b/benchmark/src/event.rs @@ -11,7 +11,7 @@ pub(crate) enum Event { /// Key press. Key(event::KeyEvent), /// Terminal resize. - Resize(u16, u16), + Resize, } pub(crate) async fn terminal_event_task( @@ -47,8 +47,8 @@ async fn event_loop(fps: u32, event_sender: mpsc::Sender) { if event::poll(Duration::from_secs(0)).expect("no events available") { match event::read().expect("unable to read event") { event::Event::Key(e) => event_sender.send(Event::Key(e)).await.unwrap_or(()), - event::Event::Resize(w, h) => { - event_sender.send(Event::Resize(w, h)).await.unwrap_or(()) + event::Event::Resize(_w, _h) => { + event_sender.send(Event::Resize).await.unwrap_or(()) } _ => (), } diff --git a/launcher/Cargo.toml b/launcher/Cargo.toml index 6b6fd58e..eb219423 100644 --- a/launcher/Cargo.toml +++ b/launcher/Cargo.toml @@ -14,6 +14,7 @@ nix = { version = "0.28.0", features = ["signal"] } once_cell = "1.19.0" serde = { version = "1.0.188", features = ["derive"] } serde_json = "1.0.107" +thiserror = "1.0.59" tracing = "0.1.37" tracing-subscriber = { version = "0.3.17", features = ["json", "env-filter"] } diff --git a/launcher/src/main.rs b/launcher/src/main.rs index 23944f40..f2f5a99b 100644 --- a/launcher/src/main.rs +++ b/launcher/src/main.rs @@ -16,6 +16,7 @@ use std::thread; use std::thread::sleep; use std::time::{Duration, Instant}; use std::{fs, io}; +use thiserror::Error; use tracing_subscriber::EnvFilter; mod env_runtime; @@ -819,26 +820,26 @@ fn find_num_shards( Ok(num_shard) } -#[derive(Debug)] +#[derive(Debug, Error)] enum LauncherError { + #[error("Invalid argument: {0}")] ArgumentValidation(String), + #[error("not enough cuda devices: {0}")] NotEnoughCUDADevices(String), + #[error("Download error")] DownloadError, + #[error("Shard cannot start")] ShardCannotStart, + #[error("Shard disconnected")] ShardDisconnected, + #[error("Shard failed")] ShardFailed, + #[error("Webserver failed")] WebserverFailed, + #[error("Webserver cannot start")] WebserverCannotStart, } -impl core::fmt::Display for LauncherError { - fn fmt(&self, f: &mut core::fmt::Formatter) -> core::fmt::Result { - write!(f, "{self:?}") - } -} - -impl std::error::Error for LauncherError {} - fn download_convert_model(args: &Args, running: Arc) -> Result<(), LauncherError> { // Enter download tracing span let _span = tracing::span!(tracing::Level::INFO, "download").entered(); diff --git a/router/grpc-metadata/src/lib.rs b/router/grpc-metadata/src/lib.rs index 7ba353fa..3068a61c 100644 --- a/router/grpc-metadata/src/lib.rs +++ b/router/grpc-metadata/src/lib.rs @@ -2,30 +2,9 @@ //! Inspired by: https://github.com/open-telemetry/opentelemetry-rust gRPC examples use opentelemetry::global; -use opentelemetry::propagation::{Extractor, Injector}; +use opentelemetry::propagation::Injector; use tracing_opentelemetry::OpenTelemetrySpanExt; -/// Extract context metadata from a gRPC request's metadata -struct MetadataExtractor<'a>(pub &'a tonic::metadata::MetadataMap); - -impl<'a> Extractor for MetadataExtractor<'a> { - /// Get a value for a key from the MetadataMap. If the value can't be converted to &str, returns None - fn get(&self, key: &str) -> Option<&str> { - self.0.get(key).and_then(|metadata| metadata.to_str().ok()) - } - - /// Collect all the keys from the MetadataMap. - fn keys(&self) -> Vec<&str> { - self.0 - .keys() - .map(|key| match key { - tonic::metadata::KeyRef::Ascii(v) => v.as_str(), - tonic::metadata::KeyRef::Binary(v) => v.as_str(), - }) - .collect::>() - } -} - /// Inject context in the metadata of a gRPC request. struct MetadataInjector<'a>(pub &'a mut tonic::metadata::MetadataMap); diff --git a/router/src/infer.rs b/router/src/infer.rs index da0db072..a4945512 100644 --- a/router/src/infer.rs +++ b/router/src/infer.rs @@ -1249,7 +1249,7 @@ mod tests { }, ]; - let example_chat_with_system = vec![Message { + let example_chat_with_system = [Message { role: "system".to_string(), content: Some( "You are a friendly chatbot who always responds in the style of a pirate" @@ -1373,7 +1373,7 @@ mod tests { { let mut env = Environment::new(); env.add_function("raise_exception", raise_exception); - let tmpl = env.template_from_str(&chat_template); + let tmpl = env.template_from_str(chat_template); let result = tmpl.unwrap().render(input).unwrap(); assert_eq!(result, target); } diff --git a/router/src/server.rs b/router/src/server.rs index 5dbde21e..cb55d897 100644 --- a/router/src/server.rs +++ b/router/src/server.rs @@ -696,7 +696,7 @@ async fn completions( model: model_id.clone(), system_fingerprint: system_fingerprint.clone(), }) - .map_or_else(|_e| Event::default(), |data| data) + .unwrap_or_else(|_e| Event::default()) }; let (header_tx, header_rx) = oneshot::channel(); @@ -1122,13 +1122,10 @@ async fn chat_completions( logprobs, stream_token.details.map(|d| d.finish_reason.to_string()), )) - .map_or_else( - |e| { - println!("Failed to serialize ChatCompletionChunk: {:?}", e); - Event::default() - }, - |data| data, - ) + .unwrap_or_else(|e| { + println!("Failed to serialize ChatCompletionChunk: {:?}", e); + Event::default() + }) }; let (headers, response_stream) = generate_stream_internal( diff --git a/rust-toolchain.toml b/rust-toolchain.toml index 67982433..507ee859 100644 --- a/rust-toolchain.toml +++ b/rust-toolchain.toml @@ -1,6 +1,5 @@ [toolchain] -# Released on: 28 December, 2023 -# Branched from master on: 10 November, 2023 -# https://releases.rs/docs/1.75.0/ -channel = "1.75.0" +# Released on: 02 May, 2024 +# https://releases.rs/docs/1.78.0/ +channel = "1.78.0" components = ["rustfmt", "clippy"] From 59b3ffea1442e40b37ba92e84687ea9784212556 Mon Sep 17 00:00:00 2001 From: "Wang, Yi" Date: Mon, 6 May 2024 22:05:43 +0800 Subject: [PATCH 72/74] update xpu docker image and use public ipex whel (#1860) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. Signed-off-by: Wang, Yi A --- Dockerfile_intel | 23 ++++------------------- 1 file changed, 4 insertions(+), 19 deletions(-) diff --git a/Dockerfile_intel b/Dockerfile_intel index 8c7478c1..5bc39d64 100644 --- a/Dockerfile_intel +++ b/Dockerfile_intel @@ -36,7 +36,7 @@ RUN cargo build --release # Text Generation Inference base image for Intel -FROM intel/intel-extension-for-pytorch:2.1.10-xpu as base +FROM intel/intel-extension-for-pytorch:2.1.30-xpu as base USER root # libssl.so.1.1 is not installed on Ubuntu 22.04 by default, install it @@ -47,7 +47,7 @@ RUN wget http://nz2.archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1. RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \ | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list -RUN apt-get update && apt install -y intel-basekit xpu-smi cmake python3-dev ninja-build +RUN apt-get update && apt install -y intel-basekit xpu-smi # Text Generation Inference base env ENV HUGGINGFACE_HUB_CACHE=/data \ @@ -56,9 +56,8 @@ ENV HUGGINGFACE_HUB_CACHE=/data \ WORKDIR /usr/src -# Build pytorch and ipex -RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout -b xpu_main origin/xpu-main -RUN git clone https://github.com/pytorch/pytorch.git && cd pytorch && git checkout 209f2fa8ff86652f67d75c2f19bf9cb9942fd018 && git apply /usr/src/intel-extension-for-pytorch/torch_patches/00*.patch +RUN wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.1.30a0-cp310-cp310-linux_x86_64.whl +RUN pip install intel_extension_for_pytorch-2.1.30a0-cp310-cp310-linux_x86_64.whl # Install server COPY proto proto @@ -72,25 +71,11 @@ RUN cd server && \ ENV CCL_ROOT=/opt/intel/oneapi/ccl/latest ENV I_MPI_ROOT=/opt/intel/oneapi/mpi/latest ENV FI_PROVIDER_PATH=/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/lib/prov:/usr/lib/x86_64-linux-gnu/libfabric -ENV DIAGUTIL_PATH=/opt/intel/oneapi/compiler/latest/etc/compiler/sys_check/sys_check.sh -ENV CCL_CONFIGURATION=cpu_gpu_dpcpp -ENV MANPATH=/opt/intel/oneapi/mpi/latest/share/man:/opt/intel/oneapi/mpi/latest/share/man:/opt/intel/oneapi/compiler/latest/share/man -ENV CMAKE_PREFIX_PATH=/opt/intel/oneapi/mkl/latest/lib/cmake:/opt/intel/oneapi/compiler/latest -ENV CMPLR_ROOT=/opt/intel/oneapi/compiler/latest ENV LIBRARY_PATH=/opt/intel/oneapi/mpi/latest/lib:/opt/intel/oneapi/ccl/latest/lib/:/opt/intel/oneapi/mkl/latest/lib/:/opt/intel/oneapi/compiler/latest/lib -ENV OCL_ICD_FILENAMES=libintelocl_emu.so:libalteracl.so:/opt/intel/oneapi/compiler/latest/lib/libintelocl.so -ENV CLASSPATH=/opt/intel/oneapi/mpi/latest/share/java/mpi.jar:/opt/intel/oneapi/mpi/latest/share/java/mpi.jar ENV LD_LIBRARY_PATH=/opt/intel/oneapi/ccl/latest/lib/:/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/lib:/opt/intel/oneapi/mpi/latest/lib:/opt/intel/oneapi/mkl/latest/lib:/opt/intel/oneapi/compiler/latest/opt/compiler/lib:/opt/intel/oneapi/compiler/latest/lib:/opt/intel/oneapi/lib:/opt/intel/oneapi/lib/intel64: -ENV MKLROOT=/opt/intel/oneapi/mkl/latest -ENV NLSPATH=/opt/intel/oneapi/mkl/latest/share/locale/%l_%t/%N:/opt/intel/oneapi/compiler/latest/lib/locale/%l_%t/%N ENV PATH=/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/bin:/opt/intel/oneapi/mpi/latest/bin:/opt/intel/oneapi/mpi/latest/opt/mpi/libfabric/bin:/opt/intel/oneapi/mkl/latest/bin/:/opt/intel/oneapi/compiler/latest/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin -ENV CPATH=/opt/intel/oneapi/mpi/latest/include:/opt/intel/oneapi/ccl/latest/include:/opt/intel/oneapi/mkl/latest/include ENV CCL_ZE_IPC_EXCHANGE=sockets - -RUN pip uninstall -y torch && cd pytorch && git submodule update --init --recursive && python setup.py install -RUN pip uninstall -y intel-extension-for-pytorch && cd intel-extension-for-pytorch && git submodule update --init --recursive && USE_AOT_DEVLIST='pvc' BUILD_SEPARATE_OPS=ON BUILD_WITH_CPU=ON USE_XETLA=ON python setup.py install - # Install benchmarker COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark # Install router From fd89d9dfaef408e4482ba5e230473de5bb203a99 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Mon, 13 May 2024 12:44:30 +0200 Subject: [PATCH 73/74] Refactor layers. (#1866) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. --- server/tests/utils/test_layers.py | 2 +- .../text_generation_server/layers/__init__.py | 14 + .../{utils => layers}/awq/conversion_utils.py | 0 .../{utils => layers}/awq/quantize/qmodule.py | 0 server/text_generation_server/layers/bnb.py | 106 ++ server/text_generation_server/layers/conv.py | 41 + server/text_generation_server/layers/eetq.py | 25 + server/text_generation_server/layers/fp8.py | 43 + .../layers/gptq/__init__.py | 39 + .../{utils => layers}/gptq/custom_autotune.py | 0 .../{utils => layers}/gptq/exllama.py | 0 .../{utils => layers}/gptq/exllamav2.py | 2 + .../layers/gptq/quant_linear.py | 356 +++++ .../{utils => layers}/gptq/quantize.py | 0 .../layers/layernorm.py | 185 +++ .../text_generation_server/layers/linear.py | 153 ++ .../text_generation_server/layers/medusa.py | 186 +++ .../text_generation_server/layers/rotary.py | 419 +++++ .../layers/speculative.py | 35 + .../layers/tensor_parallel.py | 188 +++ .../models/cache_manager.py | 4 +- .../models/custom_modeling/bloom_modeling.py | 2 +- .../models/custom_modeling/clip.py | 2 +- .../custom_modeling/flash_cohere_modeling.py | 18 +- .../custom_modeling/flash_dbrx_modeling.py | 19 +- .../custom_modeling/flash_gemma_modeling.py | 6 +- .../custom_modeling/flash_llama_modeling.py | 6 +- .../custom_modeling/flash_mistral_modeling.py | 6 +- .../custom_modeling/flash_mixtral_modeling.py | 14 +- .../custom_modeling/flash_neox_modeling.py | 10 +- .../custom_modeling/flash_phi_modeling.py | 8 +- .../custom_modeling/flash_qwen2_modeling.py | 6 +- .../custom_modeling/flash_rw_modeling.py | 10 +- .../flash_santacoder_modeling.py | 10 +- .../flash_starcoder2_modeling.py | 10 +- .../models/custom_modeling/idefics2.py | 2 +- .../custom_modeling/idefics_modeling.py | 16 +- .../custom_modeling/idefics_perceiver.py | 2 +- .../models/custom_modeling/idefics_vision.py | 2 +- .../models/custom_modeling/llava_next.py | 2 +- .../models/custom_modeling/mamba_modeling.py | 4 +- .../models/custom_modeling/mpt_modeling.py | 2 +- .../models/custom_modeling/neox_modeling.py | 2 +- .../models/custom_modeling/opt_modeling.py | 2 +- .../models/custom_modeling/phi_modeling.py | 2 +- .../models/custom_modeling/t5_modeling.py | 2 +- .../models/flash_causal_lm.py | 36 +- .../models/flash_llama.py | 4 +- .../models/flash_mistral.py | 4 +- .../models/flash_neox.py | 4 +- .../text_generation_server/models/flash_rw.py | 4 +- .../models/flash_santacoder.py | 4 +- server/text_generation_server/server.py | 2 +- .../utils/flash_attn.py | 197 ++- .../utils/gptq/quant_linear.py | 359 ----- .../utils/import_utils.py | 41 +- server/text_generation_server/utils/layers.py | 1374 ----------------- .../utils/paged_attention.py | 24 +- .../text_generation_server/utils/weights.py | 10 +- 59 files changed, 2085 insertions(+), 1941 deletions(-) create mode 100644 server/text_generation_server/layers/__init__.py rename server/text_generation_server/{utils => layers}/awq/conversion_utils.py (100%) rename server/text_generation_server/{utils => layers}/awq/quantize/qmodule.py (100%) create mode 100644 server/text_generation_server/layers/bnb.py create mode 100644 server/text_generation_server/layers/conv.py create mode 100644 server/text_generation_server/layers/eetq.py create mode 100644 server/text_generation_server/layers/fp8.py create mode 100644 server/text_generation_server/layers/gptq/__init__.py rename server/text_generation_server/{utils => layers}/gptq/custom_autotune.py (100%) rename server/text_generation_server/{utils => layers}/gptq/exllama.py (100%) rename server/text_generation_server/{utils => layers}/gptq/exllamav2.py (96%) create mode 100644 server/text_generation_server/layers/gptq/quant_linear.py rename server/text_generation_server/{utils => layers}/gptq/quantize.py (100%) create mode 100644 server/text_generation_server/layers/layernorm.py create mode 100644 server/text_generation_server/layers/linear.py create mode 100644 server/text_generation_server/layers/medusa.py create mode 100644 server/text_generation_server/layers/rotary.py create mode 100644 server/text_generation_server/layers/speculative.py create mode 100644 server/text_generation_server/layers/tensor_parallel.py delete mode 100644 server/text_generation_server/utils/gptq/quant_linear.py delete mode 100644 server/text_generation_server/utils/layers.py diff --git a/server/tests/utils/test_layers.py b/server/tests/utils/test_layers.py index 93a0e982..9a8da0d6 100644 --- a/server/tests/utils/test_layers.py +++ b/server/tests/utils/test_layers.py @@ -1,5 +1,5 @@ import torch -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelEmbedding, ) diff --git a/server/text_generation_server/layers/__init__.py b/server/text_generation_server/layers/__init__.py new file mode 100644 index 00000000..c3a6c921 --- /dev/null +++ b/server/text_generation_server/layers/__init__.py @@ -0,0 +1,14 @@ +from text_generation_server.layers.tensor_parallel import ( + TensorParallelColumnLinear, + TensorParallelRowLinear, + TensorParallelEmbedding, +) +from text_generation_server.layers.speculative import SpeculativeHead +from text_generation_server.layers.linear import ( + get_linear, + FastLinear, +) + +# Just to add the `load` methods. +from text_generation_server.layers.layernorm import load_layer_norm +from text_generation_server.layers.conv import load_conv2d diff --git a/server/text_generation_server/utils/awq/conversion_utils.py b/server/text_generation_server/layers/awq/conversion_utils.py similarity index 100% rename from server/text_generation_server/utils/awq/conversion_utils.py rename to server/text_generation_server/layers/awq/conversion_utils.py diff --git a/server/text_generation_server/utils/awq/quantize/qmodule.py b/server/text_generation_server/layers/awq/quantize/qmodule.py similarity index 100% rename from server/text_generation_server/utils/awq/quantize/qmodule.py rename to server/text_generation_server/layers/awq/quantize/qmodule.py diff --git a/server/text_generation_server/layers/bnb.py b/server/text_generation_server/layers/bnb.py new file mode 100644 index 00000000..d27a33a1 --- /dev/null +++ b/server/text_generation_server/layers/bnb.py @@ -0,0 +1,106 @@ +import torch +from loguru import logger +from functools import lru_cache +import bitsandbytes as bnb +from bitsandbytes.nn import Int8Params, Params4bit + + +@lru_cache(1) +def warn_deprecate_bnb(): + logger.warning( + "Bitsandbytes 8bit is deprecated, using `eetq` is a drop-in replacement, and has much better performnce" + ) + + +class Linear8bitLt(torch.nn.Module): + def __init__( + self, + weight, + bias, + has_fp16_weights=True, + memory_efficient_backward=False, + threshold=0.0, + index=None, + ): + super().__init__() + assert ( + not memory_efficient_backward + ), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0" + self.state = bnb.MatmulLtState() + self.index = index + + # Necessary for stacked layers + self.state.threshold = threshold + self.state.has_fp16_weights = has_fp16_weights + self.state.memory_efficient_backward = memory_efficient_backward + if threshold > 0.0 and not has_fp16_weights: + self.state.use_pool = True + + self.weight = Int8Params( + weight.data, + has_fp16_weights=has_fp16_weights, + requires_grad=has_fp16_weights, + ) + self.weight.cuda(weight.device) + self.bias = bias + + def init_8bit_state(self): + self.state.CB = self.weight.CB + self.state.SCB = self.weight.SCB + self.weight.CB = None + self.weight.SCB = None + + def forward(self, x: torch.Tensor): + self.state.is_training = self.training + if self.weight.CB is not None: + self.init_8bit_state() + + # weights are cast automatically as Int8Params, but the bias has to be cast manually + if self.bias is not None and self.bias.dtype != x.dtype: + self.bias.data = self.bias.data.to(x.dtype) + + out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state) + + if not self.state.has_fp16_weights: + if self.state.CB is not None and self.state.CxB is not None: + # we converted 8-bit row major to turing/ampere format in the first inference pass + # we no longer need the row-major weight + del self.state.CB + self.weight.data = self.state.CxB + return out + + +class Linear4bit(nn.Module): + def __init__(self, weight, bias, quant_type): + super().__init__() + self.weight = Params4bit( + weight.data, + requires_grad=False, + compress_statistics=True, + quant_type=quant_type, + ) + self.compute_dtype = None + self.weight.cuda(weight.device) + self.bias = bias + + def forward(self, x: torch.Tensor): + # weights are cast automatically as Int8Params, but the bias has to be cast manually + if self.bias is not None and self.bias.dtype != x.dtype: + self.bias.data = self.bias.data.to(x.dtype) + + if getattr(self.weight, "quant_state", None) is None: + print( + "FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first." + ) + inp_dtype = x.dtype + if self.compute_dtype is not None: + x = x.to(self.compute_dtype) + + bias = None if self.bias is None else self.bias.to(self.compute_dtype) + out = bnb.matmul_4bit( + x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state + ) + + out = out.to(inp_dtype) + + return out diff --git a/server/text_generation_server/layers/conv.py b/server/text_generation_server/layers/conv.py new file mode 100644 index 00000000..7fb18ab3 --- /dev/null +++ b/server/text_generation_server/layers/conv.py @@ -0,0 +1,41 @@ +from accelerate import init_empty_weights +import torch + + +@classmethod +def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, stride): + weight = weights.get_tensor(f"{prefix}.weight") + bias = weights.get_tensor(f"{prefix}.bias") + with init_empty_weights(): + conv2d = cls( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + ) + + conv2d.weight = torch.nn.Parameter(weight) + conv2d.bias = torch.nn.Parameter(bias) + return conv2d + + +@classmethod +def load_conv2d_no_bias( + cls, prefix, weights, in_channels, out_channels, kernel_size, stride +): + weight = weights.get_tensor(f"{prefix}.weight") + with init_empty_weights(): + conv2d = cls( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + ) + + conv2d.weight = torch.nn.Parameter(weight) + conv2d.bias = None + return conv2d + + +torch.nn.Conv2d.load = load_conv2d +torch.nn.Conv2d.load_no_bias = load_conv2d_no_bias diff --git a/server/text_generation_server/layers/eetq.py b/server/text_generation_server/layers/eetq.py new file mode 100644 index 00000000..fd22b5c6 --- /dev/null +++ b/server/text_generation_server/layers/eetq.py @@ -0,0 +1,25 @@ +import torch +from EETQ import quant_weights, w8_a16_gemm + + +class EETQLinear(torch.nn.Module): + def __init__( + self, + weight, + bias, + ) -> None: + super().__init__() + device = weight.device + if weight.dtype != torch.float16: + weight = weight.to(dtype=torch.float16) + weight = torch.t(weight).contiguous().cpu() + weight, scale = quant_weights(weight, torch.int8, False) + + self.weight = weight.cuda(device) + self.scale = scale.cuda(device) + self.bias = bias.cuda(device) if bias is not None else None + + def forward(self, input: torch.Tensor) -> torch.Tensor: + output = w8_a16_gemm(input, self.weight, self.scale) + output = output + self.bias if self.bias is not None else output + return output diff --git a/server/text_generation_server/layers/fp8.py b/server/text_generation_server/layers/fp8.py new file mode 100644 index 00000000..dd61d081 --- /dev/null +++ b/server/text_generation_server/layers/fp8.py @@ -0,0 +1,43 @@ +import torch + + +def fp8_quantize(weight, qdtype=torch.float8_e4m3fn): + device = weight.device + # weight, scale = quant_weights(weight, torch.int8, False) + finfo = torch.finfo(qdtype) + # Calculate the scale as dtype max divided by absmax + scale = finfo.max / weight.abs().max().clamp(min=1e-12) + # scale and clamp the tensor to bring it to + # the representative range of float8 data type + # (as default cast is unsaturated) + qweight = (weight * scale).clamp(min=finfo.min, max=finfo.max) + # Return both float8 data and the inverse scale (as float), + # as both required as inputs to torch._scaled_mm + qweight = qweight.to(qdtype) + scale = scale.float().reciprocal() + return qweight, scale + + +class Fp8Linear(torch.nn.Module): + def __init__( + self, + weight, + bias, + ) -> None: + super().__init__() + self.dtype = weight.dtype + self.qweight, self.scale = fp8_quantize(weight) + + self.bias = bias if bias is not None else None + + def forward(self, input: torch.Tensor) -> torch.Tensor: + qinput, scale = fp8_quantize(input) + output, _ = torch._scaled_mm( + qinput, + self.qweight.t(), + out_dtype=self.dtype, + scale_a=scale, + scale_b=self.scale, + bias=self.bias, + ) + return output diff --git a/server/text_generation_server/layers/gptq/__init__.py b/server/text_generation_server/layers/gptq/__init__.py new file mode 100644 index 00000000..1c46f493 --- /dev/null +++ b/server/text_generation_server/layers/gptq/__init__.py @@ -0,0 +1,39 @@ +import os +import torch +from text_generation_server.utils.import_utils import ( + SYSTEM, +) + +try: + major, _minor = torch.cuda.get_device_capability() +except Exception: + major = 1 + +HAS_EXLLAMA = False +CAN_EXLLAMA = major >= 8 or SYSTEM == "rocm" +V2 = os.getenv("EXLLAMA_VERSION", "2") == "2" +if os.getenv("DISABLE_EXLLAMA") == "True": + HAS_EXLLAMA = False +elif CAN_EXLLAMA: + try: + if V2: + from text_generation_server.layers.gptq.exllamav2 import ( + QuantLinear as ExllamaQuantLinear, + create_exllama_buffers, + set_device, + ) + + HAS_EXLLAMA = "2" + else: + from text_generation_server.layers.gptq.exllama import ( + Ex4bitLinear as ExllamaQuantLinear, + create_exllama_buffers, + set_device, + ) + + HAS_EXLLAMA = "1" + + except ImportError: + pass + +from text_generation_server.layers.gptq.quant_linear import QuantLinear diff --git a/server/text_generation_server/utils/gptq/custom_autotune.py b/server/text_generation_server/layers/gptq/custom_autotune.py similarity index 100% rename from server/text_generation_server/utils/gptq/custom_autotune.py rename to server/text_generation_server/layers/gptq/custom_autotune.py diff --git a/server/text_generation_server/utils/gptq/exllama.py b/server/text_generation_server/layers/gptq/exllama.py similarity index 100% rename from server/text_generation_server/utils/gptq/exllama.py rename to server/text_generation_server/layers/gptq/exllama.py diff --git a/server/text_generation_server/utils/gptq/exllamav2.py b/server/text_generation_server/layers/gptq/exllamav2.py similarity index 96% rename from server/text_generation_server/utils/gptq/exllamav2.py rename to server/text_generation_server/layers/gptq/exllamav2.py index 80836a95..321ced97 100644 --- a/server/text_generation_server/utils/gptq/exllamav2.py +++ b/server/text_generation_server/layers/gptq/exllamav2.py @@ -119,6 +119,8 @@ def ext_make_q_matrix(w: dict, temp_dq, key: str = None): none_tensor, temp_dq, ) + else: + RuntimeError("Cannot create handle") DEVICE = None diff --git a/server/text_generation_server/layers/gptq/quant_linear.py b/server/text_generation_server/layers/gptq/quant_linear.py new file mode 100644 index 00000000..f60758b6 --- /dev/null +++ b/server/text_generation_server/layers/gptq/quant_linear.py @@ -0,0 +1,356 @@ +import math +import numpy as np +import torch +import torch.nn as nn +from torch.cuda.amp import custom_fwd + +import triton +import triton.language as tl +from . import custom_autotune + + +# code based https://github.com/fpgaminer/GPTQ-triton +@custom_autotune.autotune( + configs=[ + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=4, + num_warps=4, + ), + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + }, + num_stages=2, + num_warps=8, + ), + triton.Config( + { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + }, + num_stages=3, + num_warps=8, + ), + triton.Config( + { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + }, + num_stages=2, + num_warps=4, + ), + ], + key=["M", "N", "K"], + nearest_power_of_two=True, + prune_configs_by={ + "early_config_prune": custom_autotune.matmul248_kernel_config_pruner, + "perf_model": None, + "top_k": None, + }, +) +@triton.jit +def matmul_248_kernel( + a_ptr, + b_ptr, + c_ptr, + scales_ptr, + zeros_ptr, + g_ptr, + M, + N, + K, + bits, + maxq, + stride_am, + stride_ak, + stride_bk, + stride_bn, + stride_cm, + stride_cn, + stride_scales, + stride_zeros, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, + GROUP_SIZE_M: tl.constexpr, +): + """ + Compute the matrix multiplication C = A x B. + A is of shape (M, K) float16 + B is of shape (K//8, N) int32 + C is of shape (M, N) float16 + scales is of shape (G, N) float16 + zeros is of shape (G, N) float16 + g_ptr is of shape (K) int32 + """ + infearure_per_bits = 32 // bits + + pid = tl.program_id(axis=0) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + num_pid_k = tl.cdiv(K, BLOCK_SIZE_K) + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + (pid % group_size_m) + pid_n = (pid % num_pid_in_group) // group_size_m + + offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + offs_k = tl.arange(0, BLOCK_SIZE_K) + a_ptrs = a_ptr + ( + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak + ) # (BLOCK_SIZE_M, BLOCK_SIZE_K) + a_mask = offs_am[:, None] < M + # b_ptrs is set up such that it repeats elements along the K axis 8 times + b_ptrs = b_ptr + ( + (offs_k[:, None] // infearure_per_bits) * stride_bk + + offs_bn[None, :] * stride_bn + ) # (BLOCK_SIZE_K, BLOCK_SIZE_N) + g_ptrs = g_ptr + offs_k + # shifter is used to extract the N bits of each element in the 32-bit word from B + scales_ptrs = scales_ptr + offs_bn[None, :] + zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits) + + shifter = (offs_k % infearure_per_bits) * bits + zeros_shifter = (offs_bn % infearure_per_bits) * bits + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + + for k in range(0, num_pid_k): + g_idx = tl.load(g_ptrs) + + # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop + scales = tl.load( + scales_ptrs + g_idx[:, None] * stride_scales + ) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) + zeros = tl.load( + zeros_ptrs + g_idx[:, None] * stride_zeros + ) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) + + zeros = (zeros >> zeros_shifter[None, :]) & maxq + zeros = (zeros + 1) & maxq # eventually avoid overflow + + a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K) + b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated + + # Now we need to unpack b (which is N-bit values) into 32-bit values + b = (b >> shifter[:, None]) & maxq # Extract the N-bit values + b = (b - zeros) * scales # Scale and shift + + accumulator += tl.dot(a, b) + a_ptrs += BLOCK_SIZE_K + b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk + g_ptrs += BLOCK_SIZE_K + + c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] + c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N) + tl.store(c_ptrs, accumulator, mask=c_mask) + + +def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq): + with torch.cuda.device(input.device): + output = torch.empty( + (input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16 + ) + grid = lambda META: ( + triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"]) + * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]), + ) + matmul_248_kernel[grid]( + input, + qweight, + output, + scales, + qzeros, + g_idx, + input.shape[0], + qweight.shape[1], + input.shape[1], + bits, + maxq, + input.stride(0), + input.stride(1), + qweight.stride(0), + qweight.stride(1), + output.stride(0), + output.stride(1), + scales.stride(0), + qzeros.stride(0), + ) + return output + + +class QuantLinearFunction(torch.autograd.Function): + @staticmethod + @custom_fwd(cast_inputs=torch.float16) + def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq): + output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq) + return output + + +class QuantLinear(nn.Module): + def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize): + super().__init__() + self.register_buffer("qweight", qweight) + self.register_buffer("qzeros", qzeros) + self.register_buffer("scales", scales) + self.register_buffer("g_idx", g_idx) + if bias is not None: + self.register_buffer("bias", bias) + else: + self.bias = None + if bits not in [2, 4, 8]: + raise NotImplementedError("Only 2,4,8 bits are supported.") + self.bits = bits + self.maxq = 2**self.bits - 1 + self.groupsize = groupsize + + self.outfeatures = qweight.shape[1] + self.infeatures = qweight.shape[0] * 32 // bits + + @classmethod + def new(cls, bits, groupsize, infeatures, outfeatures, bias): + if bits not in [2, 4, 8]: + raise NotImplementedError("Only 2,4,8 bits are supported.") + + qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32) + qzeros = torch.zeros( + (math.ceil(infeatures / groupsize), outfeatures // 32 * bits), + dtype=torch.int32, + ) + scales = torch.zeros( + (math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16 + ) + g_idx = torch.tensor( + [i // groupsize for i in range(infeatures)], dtype=torch.int32 + ) + if bias: + bias = torch.zeros((outfeatures), dtype=torch.float16) + else: + bias = None + return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize) + + def pack(self, linear, scales, zeros, g_idx=None): + self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx + + scales = scales.t().contiguous() + zeros = zeros.t().contiguous() + scale_zeros = zeros * scales + self.scales = scales.clone().half() + if linear.bias is not None: + self.bias = linear.bias.clone().half() + + intweight = [] + for idx in range(self.infeatures): + intweight.append( + torch.round( + (linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) + / self.scales[self.g_idx[idx]] + ).to(torch.int)[:, None] + ) + intweight = torch.cat(intweight, dim=1) + intweight = intweight.t().contiguous() + intweight = intweight.numpy().astype(np.uint32) + qweight = np.zeros( + (intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32 + ) + i = 0 + row = 0 + while row < qweight.shape[0]: + if self.bits in [2, 4, 8]: + for j in range(i, i + (32 // self.bits)): + qweight[row] |= intweight[j] << (self.bits * (j - i)) + i += 32 // self.bits + row += 1 + else: + raise NotImplementedError("Only 2,4,8 bits are supported.") + + qweight = qweight.astype(np.int32) + self.qweight = torch.from_numpy(qweight) + + zeros -= 1 + zeros = zeros.numpy().astype(np.uint32) + qzeros = np.zeros( + (zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32 + ) + i = 0 + col = 0 + while col < qzeros.shape[1]: + if self.bits in [2, 4, 8]: + for j in range(i, i + (32 // self.bits)): + qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i)) + i += 32 // self.bits + col += 1 + else: + raise NotImplementedError("Only 2,4,8 bits are supported.") + + qzeros = qzeros.astype(np.int32) + self.qzeros = torch.from_numpy(qzeros) + + def forward(self, x): + out_shape = x.shape[:-1] + (self.outfeatures,) + out = QuantLinearFunction.apply( + x.reshape(-1, x.shape[-1]), + self.qweight, + self.scales, + self.qzeros, + self.g_idx, + self.bits, + self.maxq, + ) + out = out + self.bias if self.bias is not None else out + return out.reshape(out_shape) diff --git a/server/text_generation_server/utils/gptq/quantize.py b/server/text_generation_server/layers/gptq/quantize.py similarity index 100% rename from server/text_generation_server/utils/gptq/quantize.py rename to server/text_generation_server/layers/gptq/quantize.py diff --git a/server/text_generation_server/layers/layernorm.py b/server/text_generation_server/layers/layernorm.py new file mode 100644 index 00000000..15d24e80 --- /dev/null +++ b/server/text_generation_server/layers/layernorm.py @@ -0,0 +1,185 @@ +import torch +from torch import nn +from accelerate import init_empty_weights +from text_generation_server.utils.import_utils import ( + SYSTEM, +) + + +# Monkey patching +@classmethod +def load_layer_norm(cls, prefix, weights, eps): + weight = weights.get_tensor(f"{prefix}.weight") + bias = weights.get_tensor(f"{prefix}.bias") + with init_empty_weights(): + ln = cls(weight.shape, eps=eps) + + ln.weight = torch.nn.Parameter(weight) + ln.bias = torch.nn.Parameter(bias) + return ln + + +@classmethod +def load_layer_norm_no_bias(cls, prefix, weights, eps): + weight = weights.get_tensor(f"{prefix}.weight") + with init_empty_weights(): + ln = cls(weight.shape, eps=eps) + + ln.weight = torch.nn.Parameter(weight) + ln.bias = None + return ln + + +torch.nn.LayerNorm.load = load_layer_norm +torch.nn.LayerNorm.load_no_bias = load_layer_norm_no_bias + +if SYSTEM == "cuda": + import dropout_layer_norm + + class FastLayerNorm(nn.LayerNorm): + def forward(self, hidden_states, residual=None): + if hidden_states.shape[-1] > 8192: + if residual is not None: + hidden_states += residual + residual = hidden_states + + return super(FastLayerNorm, self).forward(hidden_states), residual + else: + ( + normed_hidden_states, + residual, + *rest, + ) = dropout_layer_norm.dropout_add_ln_fwd( + hidden_states, + residual, + self.weight, + self.bias, + None, + None, + None, + None, + 0.0, + self.eps, + 1.0, + 0, + None, + False, + False, + ) + if residual is None: + residual = hidden_states + + return normed_hidden_states, residual + +elif SYSTEM == "rocm": + from vllm import layernorm_ops + + class FastLayerNorm(nn.LayerNorm): + def forward(self, hidden_states, residual=None): + if residual is not None: + hidden_states += residual + residual = hidden_states + + return super().forward(hidden_states), residual + +elif SYSTEM == "xpu": + import intel_extension_for_pytorch as ipex + + class FastLayerNorm(nn.LayerNorm): + def forward(self, hidden_states, residual=None): + res_out = hidden_states + out = ipex.llm.functional.add_layer_norm( + residual, hidden_states, self.weight, self.bias, self.eps, True + ) + if residual is not None: + res_out = residual + return out, res_out + + +class FastRMSNorm(nn.Module): + def __init__(self, weight: torch.Tensor, eps: float): + super().__init__() + + self.weight = nn.Parameter(weight) + self.variance_epsilon = eps + + @classmethod + def load(cls, prefix, weights, eps=1e-6): + weight = weights.get_tensor(f"{prefix}.weight") + return cls(weight, eps) + + def forward(self, hidden_states, residual=None): + if SYSTEM == "xpu": + residual_out = hidden_states + out = ipex.llm.functional.add_rms_norm( + residual, + hidden_states, + self.weight, + None, + self.variance_epsilon, + True, + ) + if residual is not None: + residual_out = residual + return out, residual_out + elif hidden_states.shape[-1] > 8192: + if residual is not None: + hidden_states += residual + residual = hidden_states + + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt( + variance + self.variance_epsilon + ) + + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + + return self.weight * hidden_states, residual + elif SYSTEM == "cuda": + # faster post attention rms norm + ( + normed_hidden_states, + res, + *rest, + ) = dropout_layer_norm.dropout_add_ln_fwd( + hidden_states, + residual, + self.weight, + None, + None, + None, + None, + None, + 0.0, + self.variance_epsilon, + 1.0, + 0, + None, + False, + True, # Activate RMSNorm + ) + if res is None: + res = hidden_states + + return normed_hidden_states, res + elif SYSTEM == "rocm": + # We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not. + if residual is not None: + hidden_states += residual + residual = hidden_states + + out = torch.empty_like(hidden_states) + layernorm_ops.rms_norm( + out, + hidden_states, + self.weight.data, + self.variance_epsilon, + ) + return out, residual + else: + raise ValueError( + "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction." + ) diff --git a/server/text_generation_server/layers/linear.py b/server/text_generation_server/layers/linear.py new file mode 100644 index 00000000..d137a500 --- /dev/null +++ b/server/text_generation_server/layers/linear.py @@ -0,0 +1,153 @@ +import torch +from torch.nn import functional as F +from text_generation_server.utils.import_utils import SYSTEM + + +class FastLinear(torch.nn.Module): + def __init__( + self, + weight, + bias, + ) -> None: + super().__init__() + self.weight = torch.nn.Parameter(weight) + if bias is not None: + self.bias = torch.nn.Parameter(bias) + else: + self.bias = None + + @classmethod + def load(cls, config, prefix: str, weights, bias: bool): + weight = weights.get_tensor(f"{prefix}.weight") + if bias: + bias = weights.get_tensor(f"{prefix}.bias") + else: + bias = None + return cls(weight, bias) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return F.linear(input, self.weight, self.bias) + + +def get_linear(weight, bias, quantize): + if quantize is None: + linear = FastLinear(weight, bias) + elif quantize == "eetq": + try: + from text_generation_server.layers.eetq import EETQLinear + + linear = EETQLinear(weight, bias) + except ImportError: + raise ImportError( + "Please install EETQ from https://github.com/NetEase-FuXi/EETQ" + ) + elif quantize == "fp8": + from text_generation_server.layers.fp8 import Fp8Linear + + linear = Fp8Linear(weight, bias) + elif quantize == "bitsandbytes": + try: + from text_generation_server.layers.bnb import ( + warn_deprecate_bnb, + Linear8bitLt, + ) + except ImportError: + raise NotImplementedError( + f"Bitsandbytes is missing install it with `pip install bitsandbytes`." + ) + warn_deprecate_bnb() + linear = Linear8bitLt( + weight, + bias, + has_fp16_weights=False, + threshold=6.0, + ) + if bias is not None: + linear.bias = nn.Parameter(bias) + elif quantize == "bitsandbytes-fp4": + try: + from text_generation_server.layers.bnb import Linear4bit + except ImportError: + raise NotImplementedError( + f"Bitsandbytes is missing install it with `pip install bitsandbytes`." + ) + linear = Linear4bit( + weight, + bias, + quant_type="fp4", + ) + elif quantize == "bitsandbytes-nf4": + try: + from text_generation_server.layers.bnb import Linear4bit + except ImportError: + raise NotImplementedError( + f"Bitsandbytes is missing install it with `pip install bitsandbytes`." + ) + linear = Linear4bit( + weight, + bias, + quant_type="nf4", + ) + elif quantize == "gptq": + try: + qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama = weight + except Exception: + raise NotImplementedError( + f"The passed weight is not `gptq` compatible, loader needs to be updated." + ) + + if use_exllama: + try: + from text_generation_server.layers.gptq import ( + ExllamaQuantLinear, + ) + except ImportError: + raise NotImplementedError( + f"Exllama gptq kernels are not installed. Install them `cd server/exllama_kernels && python setup.py install && cd ../exllamav2_kernels && python setup.py install`" + ) + + linear = ExllamaQuantLinear( + qweight, qzeros, scales, g_idx, bias, bits, groupsize + ) + else: + from text_generation_server.layers.gptq.quant_linear import QuantLinear + + linear = QuantLinear( + qweight, + qzeros, + scales, + g_idx, + bias, + bits, + groupsize, + ) + elif quantize == "awq": + try: + qweight, qzeros, scales, _, bits, groupsize, _ = weight + except Exception: + raise NotImplementedError( + f"The passed weight is not `awq` compatible, loader needs to be updated." + ) + if SYSTEM == "rocm": + raise NotImplementedError( + "AWQ GEMM kernel can't be used on ROCm systems, please use `--quantize gptq` instead " + "to use Exllama/GPTQ kernels for AWQ inference." + ) + try: + from text_generation_server.layers.awq.quantize.qmodule import WQLinear + + linear = WQLinear( + w_bit=bits, + group_size=groupsize, + qweight=qweight, + qzeros=qzeros, + scales=scales, + bias=bias is not None, + ) + except ImportError: + raise NotImplementedError( + "You do not seem to have awq installed, either install it (cd server && make install-awq), or try using GPTQ `---quantize gptq` a conversion AWQ->GPTQ will happen on the fly" + ) + else: + raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.") + return linear diff --git a/server/text_generation_server/layers/medusa.py b/server/text_generation_server/layers/medusa.py new file mode 100644 index 00000000..4ac86978 --- /dev/null +++ b/server/text_generation_server/layers/medusa.py @@ -0,0 +1,186 @@ +import torch +from torch import nn +from typing import Tuple, Optional +from text_generation_server.utils.speculate import get_speculate +from text_generation_server.layers.linear import FastLinear +from text_generation_server.layers.tensor_parallel import ( + TensorParallelHead, + TensorParallelColumnLinear, +) + + +class ResBlock(torch.nn.Module): + def __init__(self, config, prefix, weights): + super().__init__() + self.linear = FastLinear.load( + config, prefix=f"{prefix}.linear", weights=weights, bias=True + ) + self.act = torch.nn.SiLU() + + def forward(self, x): + return x + self.act(self.linear(x)) + + +class MedusaModel(torch.nn.Module): + def __init__(self, config, medusa_config, weights): + super().__init__() + self.heads = torch.nn.ModuleList( + [ + MedusaHead(config, medusa_config, prefix=f"{i}", weights=weights) + for i in range(get_speculate()) + ] + ) + + def forward(self, x): + speculative_logits = torch.stack([head(x) for head in self.heads], dim=1) + return speculative_logits + + +class MedusaHead(torch.nn.Module): + def __init__(self, config, medusa_config, prefix, weights): + super().__init__() + self.blocks = torch.nn.ModuleList( + [ + ResBlock(config, prefix=f"{prefix}.{i}", weights=weights) + for i in range(medusa_config["medusa_num_layers"]) + ] + ) + n = len(self.blocks) + self.out = FastLinear.load( + config, prefix=f"{prefix}.{n}", weights=weights, bias=False + ) + + def forward(self, x): + for block in self.blocks: + x = block(x) + x = self.out(x) + return x + + +class MedusaHeadV1(nn.Module): + def __init__(self, lm_head, medusa): + super().__init__() + self.lm_head = lm_head + self.medusa = medusa + + @staticmethod + def load(config, prefix: str, weights): + from pathlib import Path + from safetensors import safe_open + import json + + use_medusa = config.use_medusa + + medusa_config = str(Path(use_medusa) / "config.json") + filename = str(Path(use_medusa) / "medusa_lm_head.safetensors") + + with open(medusa_config, "r") as f: + medusa_config = json.load(f) + routing = weights.routing + with safe_open(filename, framework="pytorch") as f: + for k in f.keys(): + if k in routing and routing[k] != filename: + raise RuntimeError( + f"Key {k} was found in multiple files: {filename} and {routing[k]}" + ) + routing[k] = filename + + medusa = MedusaModel(config, medusa_config, weights) + lm_head = TensorParallelHead.load(config, prefix, weights) + return MedusaHeadV1(lm_head, medusa) + + def forward( + self, input: torch.Tensor + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + logits = self.lm_head(input) + # If we have too many tokens, we skip speculative logits + if input.shape[0] > 128: + return logits, None + + speculative_logits = self.medusa(input) + return logits, speculative_logits + + +class MedusaHeadV2(nn.Module): + def __init__(self, config, prefix, weights): + super().__init__() + from pathlib import Path + from safetensors import safe_open + import json + + use_medusa = config.use_medusa + + medusa_config = str(Path(use_medusa) / "config.json") + filename = str(Path(use_medusa) / "medusa_lm_head.safetensors") + + with open(medusa_config, "r") as f: + medusa_config = json.load(f) + routing = weights.routing + with safe_open(filename, framework="pytorch") as f: + for k in f.keys(): + if k in routing and routing[k] != filename: + raise RuntimeError( + f"Key {k} was found in multiple files: {filename} and {routing[k]}" + ) + routing[k] = filename + + self.n_medusa_heads = get_speculate() + + assert medusa_config["medusa_num_layers"] == 1 + self.linear = TensorParallelColumnLinear.load_multi( + config, + prefixes=[f"{i}.0.linear" for i in range(self.n_medusa_heads)], + dim=0, + weights=weights, + bias=True, + ) + self.process_group = weights.process_group + self.world_size = self.process_group.size() + self.rank = self.process_group.rank() + + self.act = torch.nn.SiLU() + + self.lm_head = TensorParallelHead.load(config, prefix, weights) + + def forward(self, x): + # If we have too many tokens, we skip speculative logits + if x.shape[0] > 128: + logits = self.lm_head(x) + return logits, None + + size = x.shape[-1] + block_size = (size + self.world_size - 1) // self.world_size + start = self.rank * block_size + stop = (self.rank + 1) * block_size + + x_block = x[:, start:stop] + + # Compute all medusa heads at the same time, then reshape and move the n_medusa_heads dim to dim 1 + medusa_res = self.act(self.linear(x)).reshape( + *x_block.shape[:-1], self.n_medusa_heads, x_block.shape[-1] + ) + + # Apply all residual medusa heads + output = x[:, start:stop].unsqueeze(-2) + medusa_res + + # Gather medusa heads + world_output = [ + torch.empty_like(output) for _ in range(self.process_group.size()) + ] + torch.distributed.all_gather(world_output, output, group=self.process_group) + world_output = torch.cat(world_output, dim=-1) + + # Stack x and medusa residual x + stacked_x = torch.cat([x.unsqueeze(-2), world_output], dim=-2) + + # Compute lm head on x + medusa residual x + logits = self.lm_head(stacked_x) + + # Finally, split logits from speculative logits + logits, speculative_logits = torch.split( + logits, [1, self.n_medusa_heads], dim=-2 + ) + # Squeeze added dimension + logits = logits.squeeze(-2) + + return logits, speculative_logits diff --git a/server/text_generation_server/layers/rotary.py b/server/text_generation_server/layers/rotary.py new file mode 100644 index 00000000..503dd554 --- /dev/null +++ b/server/text_generation_server/layers/rotary.py @@ -0,0 +1,419 @@ +import os +import torch +from torch import nn + +from text_generation_server.utils.import_utils import SYSTEM + +if SYSTEM == "cuda": + from flash_attn.layers.rotary import RotaryEmbedding + import rotary_emb +elif SYSTEM == "rocm": + from vllm import pos_encoding_ops + + +def _create_inv_freq(dim, base, device): + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim) + ) + return inv_freq + + +def _get_rope_config(config): + if os.getenv("ROPE_SCALING", None) is not None: + rope_scaling = { + "type": os.environ["ROPE_SCALING"], + "factor": float(os.environ["ROPE_FACTOR"]), + } + return rope_scaling + return getattr(config, "rope_scaling", None) + + +class PositionRotaryEmbedding(nn.Module): + def __init__(self, inv_freq, scaling_factor): + super().__init__() + self.inv_freq = inv_freq + self._seq_len_cached = 0 + self._cos_cached = None + self._sin_cached = None + self._cos_k_cached = None + self._sin_k_cached = None + self.scaling_factor = scaling_factor + self.dynamic_args = None + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + ): + # Such controlflows may add some overhead. + if SYSTEM == "cuda": + rotary_dim = cos.shape[-1] + q1 = query[..., :rotary_dim] + q2 = query[..., rotary_dim : 2 * rotary_dim] + + rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False) + + k1 = key[..., :rotary_dim] + k2 = key[..., rotary_dim : 2 * rotary_dim] + + rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False) + elif SYSTEM == "rocm": + # NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems. + # Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773 + + head_size = query.shape[-1] + + # Inplace operation, updating query and key. + pos_encoding_ops.rotary_embedding(query, key, head_size, cos, sin, True) + elif SYSTEM == "xpu": + ipex.llm.functional.rotary_embedding( + query, key, sin, cos, query.size(-1), True + ) + else: + raise ValueError( + "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction." + ) + + @classmethod + def static(cls, config, dim, base, device): + inv_freq = _create_inv_freq(dim, base, device) + scaling_factor = None + rope_scaling = _get_rope_config(config) + if rope_scaling is not None: + if rope_scaling["type"] == "linear": + pass + elif rope_scaling["type"] == "dynamic": + scaling_factor = rope_scaling["factor"] + return DynamicPositionRotaryEmbedding( + dim=dim, + max_position_embeddings=config.max_position_embeddings, + base=base, + device=inv_freq.device, + scaling_factor=scaling_factor, + ) + elif rope_scaling["type"] == "yarn": + scaling_factor = rope_scaling["factor"] + return YarnPositionRotaryEmbedding( + dim=2 * inv_freq.shape[0], + max_position_embeddings=rope_scaling[ + "original_max_position_embeddings" + ], + base=10000.0, + device=inv_freq.device, + scaling_factor=scaling_factor, + extrapolation_factor=1, + attn_factor=1, + beta_fast=32, + beta_slow=1, + ) + elif rope_scaling["type"] == "su": + short_factor = torch.tensor( + rope_scaling["short_factor"], dtype=torch.float32, device=device + ) + short_inv_freq = 1.0 / ( + short_factor + * base + ** ( + torch.arange(0, dim, 2, device=device, dtype=torch.float32) + / dim + ) + ) + long_factor = torch.tensor( + rope_scaling["long_factor"], dtype=torch.float32, device=device + ) + long_inv_freq = 1.0 / ( + long_factor + * base + ** ( + torch.arange(0, dim, 2, device=device, dtype=torch.float32) + / dim + ) + ) + + original_max_position_embeddings = ( + config.original_max_position_embeddings + ) + max_position_embeddings = config.max_position_embeddings + if max_position_embeddings <= original_max_position_embeddings: + scaling_factor = 1.0 + else: + scale = max_position_embeddings / original_max_position_embeddings + scaling_factor = math.sqrt( + 1 + math.log(scale) / math.log(original_max_position_embeddings) + ) + + return SuRotaryEmbedding( + short_inv_freq=short_inv_freq, + long_inv_freq=long_inv_freq, + scaling_factor=scaling_factor, + original_max_position_embeddings=original_max_position_embeddings, + ) + else: + raise NotImplementedError( + f"rope scaling type {rope_scaling['type']} is not implemented or invalid" + ) + return cls(inv_freq, scaling_factor) + + @classmethod + def load(cls, config, prefix, weights): + # XXX: Always load this in float32 ! + dtype = weights.dtype + weights.dtype = torch.float32 + inv_freq = weights.get_tensor(f"{prefix}.inv_freq") + weights.dtype = dtype + + scaling_factor = None + rope_scaling = _get_rope_config(config) + if rope_scaling is not None: + scaling_factor = rope_scaling["factor"] + if rope_scaling["type"] == "linear": + pass + elif rope_scaling["type"] == "dynamic": + return DynamicPositionRotaryEmbedding( + dim=2 * inv_freq.shape[0], + max_position_embeddings=config.max_position_embeddings, + base=10000.0, + device=inv_freq.device, + scaling_factor=scaling_factor, + ) + elif rope_scaling["type"] == "yarn": + return YarnPositionRotaryEmbedding( + dim=2 * inv_freq.shape[0], + max_position_embeddings=rope_scaling[ + "original_max_position_embeddings" + ], + base=10000.0, + device=inv_freq.device, + scaling_factor=scaling_factor, + extrapolation_factor=1, + attn_factor=1, + beta_fast=32, + beta_slow=1, + ) + else: + raise NotImplementedError( + f"rope scaling type {rope_scaling['type']} is not implemented or invalid" + ) + return cls(inv_freq, scaling_factor) + + def _update_cos_sin_cache(self, dtype, device, seqlen): + # Reset the tables if the sequence length has changed, + # or if we're on a new device (possibly due to tracing for instance) + if ( + seqlen > self._seq_len_cached + or self._cos_cached.device != device + or self._cos_cached.dtype != dtype + ): + self._seq_len_cached = seqlen + t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) + if self.scaling_factor is not None: + t /= self.scaling_factor + # Don't do einsum, it converts fp32 to fp16 + # freqs = torch.einsum("i,j->ij", t, self.inv_freq) + + freqs = torch.outer(t, self.inv_freq.to(device=t.device)) + self._cos_cached = torch.cos(freqs).to(dtype) + self._sin_cached = torch.sin(freqs).to(dtype) + + def get_cos_sin(self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype): + """ + Return cos and sin for the asked position ids + """ + if SYSTEM == "rocm": + # For RoCm, we always use float cos/sin to avoid a cast. + # For NVIDIA, for some reason, the flash-attn rotary kernel requires cos/sin and query/key to be of same dtype: https://github.com/Dao-AILab/flash-attention/blob/017716451d446e464dde9aca3a3c1ed2209caaa9/csrc/rotary/rotary.cpp#L26 + # But later on goes and cast cos/sin to float anyway: https://github.com/Dao-AILab/flash-attention/blob/017716451d446e464dde9aca3a3c1ed2209caaa9/csrc/rotary/rotary_cuda.cu#L29, which looks suboptimal. + dtype = torch.float32 + + self._update_cos_sin_cache(dtype, position_ids.device, max_s) + + cos = torch.index_select(self._cos_cached, 0, position_ids) + sin = torch.index_select(self._sin_cached, 0, position_ids) + + # Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow. + return cos.unsqueeze(1), sin.unsqueeze(1) + + +class SuRotaryEmbedding(PositionRotaryEmbedding): + def __init__( + self, + short_inv_freq, + long_inv_freq, + scaling_factor, + original_max_position_embeddings, + ): + super(PositionRotaryEmbedding, self).__init__() + self.short_inv_freq = short_inv_freq + self.long_inv_freq = long_inv_freq + self.scaling_factor = scaling_factor + self.original_max_position_embeddings = original_max_position_embeddings + self._seq_len_cached = 0 + self._cos_cached = None + self._sin_cached = None + self._cos_k_cached = None + self._sin_k_cached = None + self.dynamic_args = None + + def _update_cos_sin_cache(self, dtype, device, seqlen): + # Reset the tables if the sequence length has changed, + # or if we're on a new device (possibly due to tracing for instance) + if ( + seqlen > self._seq_len_cached + or self._cos_cached.device != device + or self._cos_cached.dtype != dtype + ): + self._seq_len_cached = seqlen + if seqlen > self.original_max_position_embeddings: + inv_freq = self.long_inv_freq + else: + inv_freq = self.short_inv_freq + t = torch.arange(seqlen, device=device, dtype=inv_freq.dtype) + if self.scaling_factor is not None: + t /= self.scaling_factor + # Don't do einsum, it converts fp32 to fp16 + # freqs = torch.einsum("i,j->ij", t, self.inv_freq) + + freqs = torch.outer(t, inv_freq.to(device=t.device)) + self._cos_cached = torch.cos(freqs).to(dtype) + self._sin_cached = torch.sin(freqs).to(dtype) + + +class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding): + def __init__(self, dim, max_position_embeddings, base, device, scaling_factor): + inv_freq = _create_inv_freq(dim, base, device) + super().__init__(inv_freq, scaling_factor) + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + + def _update_cos_sin_cache(self, dtype, device, seqlen): + # Reset the tables if the sequence length has changed, + # or if we're on a new device (possibly due to tracing for instance) + if ( + seqlen > self._seq_len_cached + or self._cos_cached.device != device + or self._cos_cached.dtype != dtype + ): + if seqlen > self.max_position_embeddings: + newbase = self.base * ( + (self.scaling_factor * seqlen / self.max_position_embeddings) + - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + self.inv_freq = _create_inv_freq( + self.dim, newbase, self.inv_freq.device + ) + self._seq_len_cached = seqlen + t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) + # Don't do einsum, it converts fp32 to fp16 + # freqs = torch.einsum("i,j->ij", t, self.inv_freq) + + freqs = torch.outer(t, self.inv_freq.to(device=t.device)) + self._cos_cached = torch.cos(freqs).to(dtype) + self._sin_cached = torch.sin(freqs).to(dtype) + + +# Inverse dim formula to find dim based on number of rotations +import math + + +def find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048): + return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / ( + 2 * math.log(base) + ) + + +# Find dim range bounds based on rotations +def find_correction_range( + low_rot, high_rot, dim, base=10000, max_position_embeddings=2048 +): + low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings)) + high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings)) + return max(low, 0), min(high, dim - 1) # Clamp values just in case + + +def linear_ramp_mask(min, max, dim): + if min == max: + max += 0.001 # Prevent singularity + + linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min) + ramp_func = torch.clamp(linear_func, 0, 1) + return ramp_func + + +def get_mscale(scale=1): + if scale <= 1: + return 1.0 + return 0.1 * math.log(scale) + 1.0 + + +class YarnPositionRotaryEmbedding(PositionRotaryEmbedding): + def __init__( + self, + dim, + max_position_embeddings, + base, + device, + scaling_factor, + *, + extrapolation_factor, + attn_factor, + beta_fast, + beta_slow, + ): + inv_freq = _create_inv_freq(dim, base, device) + super().__init__(inv_freq, scaling_factor) + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + self.extrapolation_factor = extrapolation_factor + self.attn_factor = attn_factor + self.beta_fast = beta_fast + self.beta_slow = beta_slow + self.mscale = float( + get_mscale(self.scaling_factor) * self.attn_factor + ) # Get n-d magnitude scaling corrected for interpolation + + def _update_cos_sin_cache(self, dtype, device, seqlen): + # Reset the tables if the sequence length has changed, + # or if we're on a new device (possibly due to tracing for instance) + if ( + seqlen > self._seq_len_cached + or self._cos_cached.device != device + or self._cos_cached.dtype != dtype + ): + if seqlen > self.max_position_embeddings: + inv_freq_extrapolation = _create_inv_freq( + self.dim, self.base, self.inv_freq.device + ) + freqs = 1.0 / inv_freq_extrapolation + inv_freq_interpolation = 1.0 / (self.scaling_factor * freqs) + low, high = find_correction_range( + self.beta_fast, + self.beta_slow, + self.dim, + self.base, + self.max_position_embeddings, + ) + inv_freq_mask = ( + 1 - linear_ramp_mask(low, high, self.dim // 2).float().to(device) + ) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation + inv_freq = ( + inv_freq_interpolation * (1 - inv_freq_mask) + + inv_freq_extrapolation * inv_freq_mask + ) + + self.inv_freq = inv_freq + self.mscale = float( + get_mscale(self.scaling_factor) * self.attn_factor + ) # Get n-d magnitude scaling corrected for interpolation + + self._seq_len_cached = seqlen + t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) + # Don't do einsum, it converts fp32 to fp16 + # freqs = torch.einsum("i,j->ij", t, self.inv_freq) + + freqs = torch.outer(t, self.inv_freq.to(device=t.device)) + self._cos_cached = (torch.cos(freqs) * self.mscale).to(dtype) + self._sin_cached = (torch.sin(freqs) * self.mscale).to(dtype) diff --git a/server/text_generation_server/layers/speculative.py b/server/text_generation_server/layers/speculative.py new file mode 100644 index 00000000..663f8c2e --- /dev/null +++ b/server/text_generation_server/layers/speculative.py @@ -0,0 +1,35 @@ +import torch +from typing import Tuple, Optional +from text_generation_server.layers.medusa import MedusaHeadV1, MedusaHeadV2 +from text_generation_server.layers.tensor_parallel import TensorParallelHead + + +class SpeculativeHead(torch.nn.Module): + def __init__(self, lm_head, medusa): + super().__init__() + self.head = lm_head + self.medusa = medusa + + @staticmethod + def load(config, prefix: str, weights): + use_medusa = config.use_medusa + if use_medusa: + lm_head = None + try: + medusa = MedusaHeadV1.load(config, prefix, weights) + except: + medusa = MedusaHeadV2(config, prefix, weights) + else: + lm_head = TensorParallelHead.load(config, prefix, weights) + medusa = None + return SpeculativeHead(lm_head, medusa) + + def forward( + self, input: torch.Tensor + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + if self.medusa is not None: + return self.medusa(input) + + assert self.head is not None + logits = self.head(input) + return logits, None diff --git a/server/text_generation_server/layers/tensor_parallel.py b/server/text_generation_server/layers/tensor_parallel.py new file mode 100644 index 00000000..34b9c51e --- /dev/null +++ b/server/text_generation_server/layers/tensor_parallel.py @@ -0,0 +1,188 @@ +import torch +from torch.nn import functional as F +from typing import List +from text_generation_server.layers.linear import get_linear, FastLinear + + +class SuperLayer(torch.nn.Module): + def __init__(self, linear): + super().__init__() + self.linear = linear + + def forward(self, x): + return self.linear.forward(x) + + +class TensorParallelHead(SuperLayer): + def __init__(self, linear, process_group, should_gather: bool): + super().__init__(linear) + self.process_group = process_group + self.should_gather = should_gather + + @staticmethod + def load(config, prefix: str, weights): + if weights.process_group.size() > 1: + try: + weight = weights.get_sharded(f"{prefix}.weight", dim=0) + should_gather = True + except AssertionError: + # If the vocab size is not divisible by number of shards + # just load the entire thing. + weight = weights.get_tensor(f"{prefix}.weight") + should_gather = False + else: + weight = weights.get_tensor(f"{prefix}.weight") + should_gather = False + + # GPTQ,AWQ,EETQ don't quantize heads (nor embeddings) + if config.quantize in ["gptq", "awq", "eetq"]: + quantize = None + else: + quantize = config.quantize + return TensorParallelHead( + get_linear(weight, bias=None, quantize=quantize), + process_group=weights.process_group, + should_gather=should_gather, + ) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + if not self.should_gather: + return super().forward(input) + + world_size = self.process_group.size() + if len(input.shape) == 2 and isinstance(self.linear, FastLinear): + out_dim = self.linear.weight.shape[0] + + if input.shape[0] == 1: + world_out = input.new_empty(1, out_dim * world_size) + local_out = input.new_empty(1, out_dim) + gather_input = local_out + else: + world_out = input.new_empty(out_dim * world_size, input.shape[0]) + gather_input = input.new_empty(out_dim, input.shape[0]) + local_out = gather_input.T + + torch.mm(input, self.linear.weight.T, out=local_out) + + torch.distributed.all_gather_into_tensor( + world_out, gather_input, group=self.process_group + ) + + if input.shape[0] == 1: + return world_out + return world_out.T + + output = super().forward(input) + world_output = [ + torch.empty_like(output) for _ in range(self.process_group.size()) + ] + torch.distributed.all_gather(world_output, output, group=self.process_group) + world_output = torch.cat(world_output, dim=-1) + return world_output + + +class TensorParallelColumnLinear(SuperLayer): + @classmethod + def load_gate_up(cls, config, prefix: str, weights, bias: bool): + """Specific method when the QKV was joined after the fact""" + weight = weights.get_weights_col_packed_gate_up( + prefix, quantize=config.quantize + ) + if bias: + raise NotImplementedError("packed_gate_up only implemented without bias") + else: + bias = None + linear = get_linear(weight, bias, config.quantize) + return cls(linear) + + @classmethod + def load_qkv(cls, config, prefix: str, weights, bias: bool): + """Specific method when the QKV was joined after the fact""" + weight = weights.get_weights_col_packed_qkv(prefix, quantize=config.quantize) + if bias: + raise NotImplementedError("packed_qkv only implemented for baichuan") + else: + bias = None + linear = get_linear(weight, bias, config.quantize) + return cls(linear) + + @classmethod + def load(cls, config, prefix: str, weights, bias: bool): + return cls.load_multi(config, [prefix], weights, bias, dim=0) + + @classmethod + def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int): + weight = weights.get_multi_weights_col( + prefixes, quantize=config.quantize, dim=dim + ) + + if bias: + b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes] + bias = torch.cat(b, dim=dim) + else: + bias = None + linear = get_linear(weight, bias, config.quantize) + return cls(linear) + + +class TensorParallelRowLinear(SuperLayer): + def __init__(self, linear, process_group): + super().__init__(linear) + self.process_group = process_group + + @classmethod + def load(cls, config, prefix: str, weights, bias: bool): + weight = weights.get_multi_weights_row(prefix, quantize=config.quantize) + + if bias and weights.process_group.rank() == 0: + # Rank is only on the first rank process + bias = weights.get_tensor(f"{prefix}.bias") + else: + bias = None + return cls( + get_linear(weight, bias, config.quantize), + process_group=weights.process_group, + ) + + def forward(self, input: torch.Tensor, reduce: bool = True) -> torch.Tensor: + out = super().forward(input) + if self.process_group.size() > 1 and reduce: + torch.distributed.all_reduce(out, group=self.process_group) + return out + + +class TensorParallelEmbedding(torch.nn.Module): + def __init__(self, prefix: str, weights, reduce=True): + super().__init__() + weight = weights.get_partial_sharded(f"{prefix}.weight", dim=0) + num_embeddings = weights.get_shape(f"{prefix}.weight")[0] + + process_group = weights.process_group + + world_size = process_group.size() + rank = process_group.rank() + + block_size = (num_embeddings + world_size - 1) // world_size + self.min_id = rank * block_size + self.max_id = min(num_embeddings, (rank + 1) * block_size) + self.null_idx = weight.shape[ + 0 + ] # Usually block_size, might be less in non even vocab_size. + self.process_group = weights.process_group + self.reduce = reduce + + """Additional 0 entry used for masking""" + self.weight = torch.nn.Parameter(F.pad(weight, (0, 0, 0, 1))) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + # default all out of bounds values to `self.null_idx` that will then be mapped to 0 + # translate for [0, self.max_id - self.min_id[ + input = torch.where( + (self.min_id > input) | (input >= self.max_id), + self.null_idx, + input - self.min_id, + ) + out = torch.nn.functional.embedding(input, self.weight) + if self.reduce and self.process_group.size() > 1: + torch.distributed.all_reduce(out, group=self.process_group) + return out diff --git a/server/text_generation_server/models/cache_manager.py b/server/text_generation_server/models/cache_manager.py index 4c65e2dd..c7705fe8 100644 --- a/server/text_generation_server/models/cache_manager.py +++ b/server/text_generation_server/models/cache_manager.py @@ -2,7 +2,7 @@ import math import torch from typing import Optional, List, Tuple -from text_generation_server.utils.import_utils import IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import SYSTEM BLOCK_SIZE: int = 16 # Will be set in warmup @@ -25,7 +25,7 @@ class CacheManager: self.repeat_slots = repeat_slots element_size = torch.tensor([], dtype=dtype).element_size() - if IS_XPU_SYSTEM: + if SYSTEM == "xpu": x = 1 else: x = self.block_size // element_size diff --git a/server/text_generation_server/models/custom_modeling/bloom_modeling.py b/server/text_generation_server/models/custom_modeling/bloom_modeling.py index c8f02bca..0d8a1b59 100644 --- a/server/text_generation_server/models/custom_modeling/bloom_modeling.py +++ b/server/text_generation_server/models/custom_modeling/bloom_modeling.py @@ -32,7 +32,7 @@ from transformers.modeling_outputs import ( ) from transformers import BloomConfig, PreTrainedModel -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, diff --git a/server/text_generation_server/models/custom_modeling/clip.py b/server/text_generation_server/models/custom_modeling/clip.py index c4917733..56618bf1 100644 --- a/server/text_generation_server/models/custom_modeling/clip.py +++ b/server/text_generation_server/models/custom_modeling/clip.py @@ -15,7 +15,7 @@ from transformers.modeling_outputs import ( ) from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelEmbedding, TensorParallelColumnLinear, TensorParallelRowLinear, diff --git a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py index 56d9a966..8c423eaf 100644 --- a/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py @@ -26,18 +26,22 @@ from transformers.activations import ACT2FN from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.import_utils import IS_ROCM_SYSTEM, IS_CUDA_SYSTEM -from text_generation_server.utils.layers import ( +from text_generation_server.utils.import_utils import SYSTEM +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, +) +from text_generation_server.layers.layernorm import ( FastLayerNorm, ) +from text_generation_server.layers.rotary import ( + PositionRotaryEmbedding, +) -if IS_CUDA_SYSTEM: +if SYSTEM == "cuda": import dropout_layer_norm else: dropout_layer_norm = None @@ -52,7 +56,7 @@ class CohereRotary(PositionRotaryEmbedding): sin: torch.Tensor, ): # Such controlflows may add some overhead. - if IS_CUDA_SYSTEM: + if SYSTEM == "cuda": import rotary_emb q1 = query[..., ::2] @@ -64,7 +68,7 @@ class CohereRotary(PositionRotaryEmbedding): k2 = key[..., 1::2] rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False) - elif IS_ROCM_SYSTEM: + elif SYSTEM == "rocm": from vllm import pos_encoding_ops # NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems. @@ -90,7 +94,7 @@ class CohereLayerNorm(nn.Module): self.eps = eps def forward(self, hidden_states): - if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM: + if hidden_states.shape[-1] > 8192 or SYSTEM == "rocm": hidden_states = hidden_states.reshape( -1, self.weight.shape[0], self.weight.shape[1] ) diff --git a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py index d0978bef..9d652b67 100644 --- a/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py @@ -21,21 +21,26 @@ from transformers.activations import ACT2FN from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple, Any from loguru import logger -from text_generation_server.utils.import_utils import IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import SYSTEM -if not IS_XPU_SYSTEM: +if SYSTEM != "xpu": from vllm.model_executor.layers.fused_moe import fused_moe + from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( FastLinear, - FastLayerNorm, TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, ) +from text_generation_server.layers.rotary import ( + PositionRotaryEmbedding, +) +from text_generation_server.layers.layernorm import ( + FastLayerNorm, +) from text_generation_server.utils.log import log_once @@ -216,7 +221,7 @@ def _load_gqa(config, prefix: str, weights): bits, groupsize, desc_act, quant_method = weights._get_gptq_params() - from text_generation_server.utils.layers import HAS_EXLLAMA + from text_generation_server.layers import HAS_EXLLAMA use_exllama = ( bits == 4 and HAS_EXLLAMA and config.quantize == "gptq" and not desc_act @@ -236,7 +241,7 @@ def _load_gqa(config, prefix: str, weights): log_once( logger.info, "Converting AWQ model to Exllama/GPTQ packing format." ) - from text_generation_server.utils.awq.conversion_utils import ( + from text_generation_server.layers.awq.conveersion_utils import ( fast_awq_to_gptq, ) diff --git a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py index bd7596db..43b90bdd 100644 --- a/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py @@ -27,13 +27,15 @@ from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, +) +from text_generation_server.layers.rotary import PositionRotaryEmbedding +from text_generation_server.layers.layernorm import ( FastRMSNorm, ) diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index 6fa85d4e..a7969494 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -27,13 +27,15 @@ from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, +) +from text_generation_server.layers.rotary import PositionRotaryEmbedding +from text_generation_server.layers.layernorm import ( FastRMSNorm, ) diff --git a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py index c2445cda..3e13c26d 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py @@ -27,13 +27,15 @@ from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, +) +from text_generation_server.layers.rotary import PositionRotaryEmbedding +from text_generation_server.layers.layernorm import ( FastRMSNorm, ) diff --git a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py index 3f6c8e03..be2d6c45 100644 --- a/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py @@ -24,9 +24,9 @@ import torch.distributed import numpy as np from torch import nn -from text_generation_server.utils.import_utils import IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import SYSTEM -if not IS_XPU_SYSTEM: +if SYSTEM != "xpu": from vllm.model_executor.layers.fused_moe import fused_moe from transformers.activations import ACT2FN from transformers.configuration_utils import PretrainedConfig @@ -34,16 +34,20 @@ from typing import Optional, List, Tuple from loguru import logger from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( FastLinear, - FastRMSNorm, TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, ) +from text_generation_server.layers.layernorm import ( + FastRMSNorm, +) +from text_generation_server.layers.rotary import ( + PositionRotaryEmbedding, +) class MixtralConfig(PretrainedConfig): diff --git a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py index ee062d3d..d45cab2e 100644 --- a/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_neox_modeling.py @@ -29,15 +29,19 @@ from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn from text_generation_server.utils.flash_attn import attention -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, SpeculativeHead, - FastLayerNorm, - PositionRotaryEmbedding, get_linear, ) +from text_generation_server.layers.layernorm import ( + FastLayerNorm, +) +from text_generation_server.layers.rotary import ( + PositionRotaryEmbedding, +) def load_row(config, prefix: str, weights, bias: bool): diff --git a/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py b/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py index cfe447a7..f2efb538 100644 --- a/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_phi_modeling.py @@ -7,15 +7,19 @@ from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, +) +from text_generation_server.layers.layernorm import ( FastLayerNorm, ) +from text_generation_server.layers.rotary import ( + PositionRotaryEmbedding, +) class PhiConfig(PretrainedConfig): diff --git a/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py index 94023b33..3a6d2db5 100644 --- a/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py @@ -6,13 +6,15 @@ from transformers.activations import ACT2FN from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, +) +from text_generation_server.layers.rotary import PositionRotaryEmbedding +from text_generation_server.layers.layernorm import ( FastRMSNorm, ) diff --git a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py index a9127d1f..52ea3ae1 100644 --- a/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_rw_modeling.py @@ -8,15 +8,19 @@ from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn from text_generation_server.utils.flash_attn import attention -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, SpeculativeHead, - FastLayerNorm, - PositionRotaryEmbedding, get_linear, ) +from text_generation_server.layers.layernorm import ( + FastLayerNorm, +) +from text_generation_server.layers.rotary import ( + PositionRotaryEmbedding, +) def load_row(config, prefix: str, weights, bias: bool): diff --git a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py index bbb603a7..d2f6d9af 100644 --- a/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py @@ -6,14 +6,16 @@ from transformers.activations import ACT2FN from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, SpeculativeHead, TensorParallelEmbedding, - FastLayerNorm, get_linear, ) +from text_generation_server.layers.layernorm import ( + FastLayerNorm, +) def load_multi_mqa( @@ -80,13 +82,13 @@ def _load_multi_mqa_gptq( g_idx = g_idx.to(device=weights.device) elif quant_method == "awq": g_idx = None - from text_generation_server.utils.awq.conversion_utils import ( + from text_generation_server.layers.awq.conversion_utils import ( fast_awq_to_gptq, ) qweight, qzeros = fast_awq_to_gptq(qweight, qzeros) - from text_generation_server.utils.layers import HAS_EXLLAMA + from text_generation_server.layers.gptq import HAS_EXLLAMA use_exllama = HAS_EXLLAMA weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama) diff --git a/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py b/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py index ed77af78..3e2ce4f9 100644 --- a/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py @@ -27,15 +27,19 @@ from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, - PositionRotaryEmbedding, SpeculativeHead, get_linear, - FastRMSNorm, +) +from text_generation_server.layers.layernorm import ( FastLayerNorm, + FastRMSNorm, +) +from text_generation_server.layers.rotary import ( + PositionRotaryEmbedding, ) diff --git a/server/text_generation_server/models/custom_modeling/idefics2.py b/server/text_generation_server/models/custom_modeling/idefics2.py index cb2ee7db..935f049b 100644 --- a/server/text_generation_server/models/custom_modeling/idefics2.py +++ b/server/text_generation_server/models/custom_modeling/idefics2.py @@ -29,7 +29,7 @@ from text_generation_server.models.custom_modeling.vlm import ( ) from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, diff --git a/server/text_generation_server/models/custom_modeling/idefics_modeling.py b/server/text_generation_server/models/custom_modeling/idefics_modeling.py index ee4cdb08..ec3f900b 100644 --- a/server/text_generation_server/models/custom_modeling/idefics_modeling.py +++ b/server/text_generation_server/models/custom_modeling/idefics_modeling.py @@ -47,20 +47,22 @@ from text_generation_server.models.custom_modeling.idefics_vision import ( from text_generation_server.models.custom_modeling.idefics_perceiver import ( IdeficsPerceiverResampler, ) -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, SpeculativeHead, - PositionRotaryEmbedding, FastLinear, ) -from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM +from text_generation_server.layers.rotary import PositionRotaryEmbedding +from text_generation_server.utils.import_utils import SYSTEM -if IS_CUDA_SYSTEM: +if SYSTEM == "cuda": import dropout_layer_norm -elif IS_ROCM_SYSTEM: +elif SYSTEM == "rocm": from vllm import layernorm_ops +else: + raise RuntimeError(f"Unsupported system {SYSTEM}") @dataclass @@ -373,7 +375,7 @@ class IdeficsRMSNorm(nn.Module): hidden_states = hidden_states.to(self.weight.dtype) return self.weight * hidden_states - elif IS_CUDA_SYSTEM: + elif SYSTEM == "cuda": # faster post attention rms norm unwrap = False if len(hidden_states.shape) > 2: @@ -405,7 +407,7 @@ class IdeficsRMSNorm(nn.Module): normed_hidden_states = normed_hidden_states.view(*shape) return normed_hidden_states - elif IS_ROCM_SYSTEM: + elif SYSTEM == "rocm": # We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not. if residual is not None: hidden_states += residual diff --git a/server/text_generation_server/models/custom_modeling/idefics_perceiver.py b/server/text_generation_server/models/custom_modeling/idefics_perceiver.py index 477d4d70..af44490b 100644 --- a/server/text_generation_server/models/custom_modeling/idefics_perceiver.py +++ b/server/text_generation_server/models/custom_modeling/idefics_perceiver.py @@ -41,7 +41,7 @@ from typing import Optional, Tuple import torch import torch.nn as nn -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelRowLinear, ) diff --git a/server/text_generation_server/models/custom_modeling/idefics_vision.py b/server/text_generation_server/models/custom_modeling/idefics_vision.py index c521dd0a..30c5997f 100644 --- a/server/text_generation_server/models/custom_modeling/idefics_vision.py +++ b/server/text_generation_server/models/custom_modeling/idefics_vision.py @@ -28,7 +28,7 @@ from transformers.utils import ( ModelOutput, logging, ) -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelRowLinear, TensorParallelEmbedding, diff --git a/server/text_generation_server/models/custom_modeling/llava_next.py b/server/text_generation_server/models/custom_modeling/llava_next.py index 0d93791f..a049f756 100644 --- a/server/text_generation_server/models/custom_modeling/llava_next.py +++ b/server/text_generation_server/models/custom_modeling/llava_next.py @@ -27,7 +27,7 @@ from text_generation_server.models.custom_modeling.vlm import ( load_text_model, load_vision_model, ) -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelRowLinear, ) diff --git a/server/text_generation_server/models/custom_modeling/mamba_modeling.py b/server/text_generation_server/models/custom_modeling/mamba_modeling.py index c58a617f..293051c2 100644 --- a/server/text_generation_server/models/custom_modeling/mamba_modeling.py +++ b/server/text_generation_server/models/custom_modeling/mamba_modeling.py @@ -8,12 +8,12 @@ from typing import Optional, Tuple, Any from transformers.configuration_utils import PretrainedConfig import torch.nn.functional as F -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( SpeculativeHead, TensorParallelEmbedding, - FastRMSNorm, FastLinear, ) +from text_generation_server.layers.layernorm import FastRMSNorm from einops import rearrange from causal_conv1d import causal_conv1d_fn, causal_conv1d_update diff --git a/server/text_generation_server/models/custom_modeling/mpt_modeling.py b/server/text_generation_server/models/custom_modeling/mpt_modeling.py index 9b0f8b92..f7981bf5 100644 --- a/server/text_generation_server/models/custom_modeling/mpt_modeling.py +++ b/server/text_generation_server/models/custom_modeling/mpt_modeling.py @@ -17,7 +17,7 @@ from transformers.modeling_outputs import ( ) from einops import rearrange from packaging import version -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelEmbedding, TensorParallelColumnLinear, TensorParallelRowLinear, diff --git a/server/text_generation_server/models/custom_modeling/neox_modeling.py b/server/text_generation_server/models/custom_modeling/neox_modeling.py index f060ec0e..fcad32fa 100644 --- a/server/text_generation_server/models/custom_modeling/neox_modeling.py +++ b/server/text_generation_server/models/custom_modeling/neox_modeling.py @@ -40,7 +40,7 @@ from transformers.modeling_outputs import ( from transformers.modeling_utils import PreTrainedModel from transformers import GPTNeoXConfig from loguru import logger -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, diff --git a/server/text_generation_server/models/custom_modeling/opt_modeling.py b/server/text_generation_server/models/custom_modeling/opt_modeling.py index 7a5cf917..83d62dea 100644 --- a/server/text_generation_server/models/custom_modeling/opt_modeling.py +++ b/server/text_generation_server/models/custom_modeling/opt_modeling.py @@ -27,7 +27,7 @@ from transformers.modeling_outputs import ( ) from transformers.modeling_utils import PreTrainedModel from transformers import OPTConfig -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( FastLinear, TensorParallelColumnLinear, TensorParallelEmbedding, diff --git a/server/text_generation_server/models/custom_modeling/phi_modeling.py b/server/text_generation_server/models/custom_modeling/phi_modeling.py index 1571f9fd..04b470eb 100644 --- a/server/text_generation_server/models/custom_modeling/phi_modeling.py +++ b/server/text_generation_server/models/custom_modeling/phi_modeling.py @@ -9,7 +9,7 @@ from typing import Optional, List, Tuple, Any from transformers.configuration_utils import PretrainedConfig from transformers.modeling_outputs import CausalLMOutputWithPast -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelRowLinear, TensorParallelColumnLinear, TensorParallelEmbedding, diff --git a/server/text_generation_server/models/custom_modeling/t5_modeling.py b/server/text_generation_server/models/custom_modeling/t5_modeling.py index 2773fb15..0b899fba 100644 --- a/server/text_generation_server/models/custom_modeling/t5_modeling.py +++ b/server/text_generation_server/models/custom_modeling/t5_modeling.py @@ -38,7 +38,7 @@ from transformers.utils import ( is_torch_fx_proxy, ) from transformers import T5Config -from text_generation_server.utils.layers import ( +from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py index a6d0204f..f567bea9 100644 --- a/server/text_generation_server/models/flash_causal_lm.py +++ b/server/text_generation_server/models/flash_causal_lm.py @@ -12,7 +12,6 @@ from dataclasses import dataclass from opentelemetry import trace from transformers import PreTrainedTokenizerBase from typing import Optional, Tuple, List, Type, Dict - from text_generation_server.models import Model from text_generation_server.utils.tokens import batch_top_tokens from text_generation_server.utils.speculate import get_speculate @@ -32,13 +31,14 @@ from text_generation_server.models.globals import MEM_POOL, CUDA_GRAPHS from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser from text_generation_server.utils.dist import MEMORY_FRACTION -tracer = trace.get_tracer(__name__) from text_generation_server.utils.import_utils import ( - IS_CUDA_SYSTEM, - IS_ROCM_SYSTEM, - IS_XPU_SYSTEM, + empty_cache, + synchronize, + get_free_memory, ) +tracer = trace.get_tracer(__name__) + @dataclass class FlashCausalLMBatch(Batch): @@ -757,10 +757,8 @@ class FlashCausalLM(Model): def warmup(self, batch: FlashCausalLMBatch): # The warmup batch is the biggest batch we could ever receive - if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: - torch.cuda.empty_cache() - elif IS_XPU_SYSTEM: - torch.xpu.empty_cache() + empty_cache() + try: cache_manager = set_cache_manager( batch.blocks, @@ -780,10 +778,7 @@ class FlashCausalLM(Model): f"You need to decrease `--max-batch-prefill-tokens`" ) from e - if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: - torch.cuda.synchronize(self.device) - elif IS_XPU_SYSTEM: - torch.xpu.synchronize(self.device) + synchronize(self.device) # Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm) # Calculate the number of blocks that can be allocated with the free memory @@ -791,20 +786,7 @@ class FlashCausalLM(Model): cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size - if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: - total_free_memory, _ = torch.cuda.mem_get_info(self.device) - total_gpu_memory = torch.cuda.get_device_properties( - self.device - ).total_memory - - free_memory = max( - 0, total_free_memory - (1 - MEMORY_FRACTION) * total_gpu_memory - ) - elif IS_XPU_SYSTEM: - total_gpu_memory = torch.xpu.get_device_properties(self.device).total_memory - free_memory = int(total_gpu_memory * 0.5) - else: - raise NotImplementedError("FlashModel is only available on GPU") + free_memory = get_free_memory(self.device, MEMORY_FRACTION) num_blocks = ( # Leave 5% for some wiggle room diff --git a/server/text_generation_server/models/flash_llama.py b/server/text_generation_server/models/flash_llama.py index 609a188d..8ea70713 100644 --- a/server/text_generation_server/models/flash_llama.py +++ b/server/text_generation_server/models/flash_llama.py @@ -18,7 +18,7 @@ from text_generation_server.utils import ( tracer = trace.get_tracer(__name__) -from text_generation_server.utils.import_utils import IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import SYSTEM class FlashLlama(FlashCausalLM): @@ -35,7 +35,7 @@ class FlashLlama(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype - elif IS_XPU_SYSTEM: + elif SYSTEM == "xpu": device = torch.device(f"xpu:{rank}") dtype = torch.float16 if dtype is None else dtype else: diff --git a/server/text_generation_server/models/flash_mistral.py b/server/text_generation_server/models/flash_mistral.py index 85e93543..48304ad8 100644 --- a/server/text_generation_server/models/flash_mistral.py +++ b/server/text_generation_server/models/flash_mistral.py @@ -33,7 +33,7 @@ tracer = trace.get_tracer(__name__) # Will be set in init SLIDING_WINDOW: Optional[int] = None SLIDING_WINDOW_BLOCKS: Optional[int] = None -from text_generation_server.utils.import_utils import IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import SYSTEM MEM_POOL = torch.cuda.graph_pool_handle() if torch.cuda.is_available() else None @@ -322,7 +322,7 @@ class BaseFlashMistral(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype - elif IS_XPU_SYSTEM: + elif SYSTEM == "xpu": device = torch.device(f"xpu:{rank}") dtype = torch.float16 if dtype is None else dtype else: diff --git a/server/text_generation_server/models/flash_neox.py b/server/text_generation_server/models/flash_neox.py index f82e27db..1119bdae 100644 --- a/server/text_generation_server/models/flash_neox.py +++ b/server/text_generation_server/models/flash_neox.py @@ -14,7 +14,7 @@ from text_generation_server.utils import ( weight_files, Weights, ) -from text_generation_server.utils.import_utils import IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import SYSTEM tracer = trace.get_tracer(__name__) @@ -33,7 +33,7 @@ class FlashNeoXSharded(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype - elif IS_XPU_SYSTEM: + elif SYSTEM == "xpu": device = torch.device(f"xpu:{rank}") dtype = torch.float16 if dtype is None else dtype else: diff --git a/server/text_generation_server/models/flash_rw.py b/server/text_generation_server/models/flash_rw.py index ccf38a0c..33298e1a 100644 --- a/server/text_generation_server/models/flash_rw.py +++ b/server/text_generation_server/models/flash_rw.py @@ -15,7 +15,7 @@ from text_generation_server.utils import ( weight_files, Weights, ) -from text_generation_server.utils.import_utils import IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import SYSTEM tracer = trace.get_tracer(__name__) @@ -34,7 +34,7 @@ class FlashRWSharded(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype - elif IS_XPU_SYSTEM: + elif SYSTEM == "xpu": device = torch.device(f"xpu:{rank}") dtype = torch.float16 if dtype is None else dtype else: diff --git a/server/text_generation_server/models/flash_santacoder.py b/server/text_generation_server/models/flash_santacoder.py index e66f1bf8..66698a3a 100644 --- a/server/text_generation_server/models/flash_santacoder.py +++ b/server/text_generation_server/models/flash_santacoder.py @@ -18,7 +18,7 @@ from text_generation_server.utils import ( Weights, ) -from text_generation_server.utils.import_utils import IS_XPU_SYSTEM +from text_generation_server.utils.import_utils import SYSTEM tracer = trace.get_tracer(__name__) @@ -37,7 +37,7 @@ class FlashSantacoderSharded(FlashCausalLM): if torch.cuda.is_available(): device = torch.device(f"cuda:{rank}") dtype = torch.float16 if dtype is None else dtype - elif IS_XPU_SYSTEM: + elif SYSTEM == "xpu": device = torch.device(f"xpu:{rank}") dtype = torch.float16 if dtype is None else dtype else: diff --git a/server/text_generation_server/server.py b/server/text_generation_server/server.py index 158966e3..9d0571a6 100644 --- a/server/text_generation_server/server.py +++ b/server/text_generation_server/server.py @@ -85,7 +85,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer): # When using GPTQ, Exllama kernels need some global kernels # For which we have the finale shapes only after the model has loaded # This will allocate those buffers. - from text_generation_server.utils.layers import ( + from text_generation_server.layers.gptq import ( create_exllama_buffers, set_device, ) diff --git a/server/text_generation_server/utils/flash_attn.py b/server/text_generation_server/utils/flash_attn.py index 583a8f91..0830656d 100644 --- a/server/text_generation_server/utils/flash_attn.py +++ b/server/text_generation_server/utils/flash_attn.py @@ -2,13 +2,8 @@ import os import torch from loguru import logger -import math -from text_generation_server.utils.import_utils import ( - IS_CUDA_SYSTEM, - IS_ROCM_SYSTEM, - IS_XPU_SYSTEM, -) +from text_generation_server.utils.import_utils import SYSTEM if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false": raise ImportError("`USE_FLASH_ATTENTION` is false.") @@ -16,83 +11,22 @@ HAS_FLASH_ATTN = True HAS_FLASH_ATTN_V2_CUDA = False HAS_FLASH_ATTN_V2_ROCM = False -if IS_XPU_SYSTEM: +if SYSTEM == "xpu": import intel_extension_for_pytorch as ipex -if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: - if not torch.cuda.is_available(): - raise ImportError("CUDA is not available") + def attention( + q, + k, + v, + out, + cu_seqlens, + max_s, + softmax_scale, + window_size_left=-1, + ): + if window_size_left <= 0 and window_size_left != -1: + raise ValueError("`window_size_left` must be > 0 or -1") - major, minor = torch.cuda.get_device_capability() - is_sm75 = major == 7 and minor == 5 - is_sm8x = major == 8 and minor >= 0 - is_sm90 = major == 9 and minor == 0 - - HAS_FLASH_ATTN = False - HAS_FLASH_ATTN_V2_CUDA = False - HAS_FLASH_ATTN_V2_ROCM = False - try: - try: - import flash_attn_2_cuda - except ImportError: - architecture_suffix = "" - if IS_CUDA_SYSTEM: - architecture_suffix = "-cuda" - elif IS_ROCM_SYSTEM: - architecture_suffix = "-rocm" - raise ImportError( - "Flash Attention V2 is not installed.\n" - "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) " - f"or install flash attention v2 with `cd server && make install install-flash-attention-v2{architecture_suffix}`" - ) - if not (is_sm8x or is_sm90): - raise ImportError( - f"GPU with CUDA capability {major} {minor} is not supported for " - "Flash Attention V2" - ) - HAS_FLASH_ATTN_V2_CUDA = IS_CUDA_SYSTEM - HAS_FLASH_ATTN_V2_ROCM = IS_ROCM_SYSTEM - except ImportError as e: - try: - import flash_attn_cuda - except ImportError: - raise ImportError( - "Flash Attention is not installed.\n" - "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) " - "or install flash attention with `cd server && make install install-flash-attention`" - ) from e - - if IS_CUDA_SYSTEM and not (is_sm75 or is_sm8x or is_sm90): - raise ImportError( - f"GPU with CUDA capability {major} {minor} is not supported" - ) from e - elif IS_ROCM_SYSTEM: - for idx in range(torch.cuda.device_count()): - if "MI210" not in torch.cuda.get_device_name( - idx - ) and "MI250" not in torch.cuda.get_device_name(idx): - raise ImportError( - f"AMD GPU {torch.cuda.get_device_name(idx)} does not support flash-attention" - ) - - logger.warning(f"Unable to use Flash Attention V2: {e}") - HAS_FLASH_ATTN = True - - -def attention( - q, - k, - v, - out, - cu_seqlens, - max_s, - softmax_scale, - window_size_left=-1, -): - if window_size_left <= 0 and window_size_left != -1: - raise ValueError("`window_size_left` must be > 0 or -1") - - if IS_XPU_SYSTEM: if window_size_left != -1: raise ValueError( f"XPU version of Flash Attention does not support window attention (window_size_left != -1, got window_size_left={window_size_left})." @@ -114,7 +48,77 @@ def attention( None, ) - if HAS_FLASH_ATTN_V2_CUDA: + +if SYSTEM in {"cuda", "rocm"}: + if not torch.cuda.is_available(): + raise ImportError("CUDA is not available") + + major, minor = torch.cuda.get_device_capability() + is_sm75 = major == 7 and minor == 5 + is_sm8x = major == 8 and minor >= 0 + is_sm90 = major == 9 and minor == 0 + + HAS_FLASH_ATTN = False + HAS_FLASH_ATTN_V2_CUDA = False + HAS_FLASH_ATTN_V2_ROCM = False + try: + try: + import flash_attn_2_cuda + except ImportError: + architecture_suffix = f"-{SYSTEM}" + raise ImportError( + "Flash Attention V2 is not installed.\n" + "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) " + f"or install flash attention v2 with `cd server && make install install-flash-attention-v2{architecture_suffix}`" + ) + if not (is_sm8x or is_sm90): + raise ImportError( + f"GPU with CUDA capability {major} {minor} is not supported for " + "Flash Attention V2" + ) + HAS_FLASH_ATTN_V2_CUDA = SYSTEM == "cuda" + HAS_FLASH_ATTN_V2_ROCM = SYSTEM == "rocm" + except ImportError as e: + try: + import flash_attn_cuda + except ImportError: + raise ImportError( + "Flash Attention is not installed.\n" + "Use the official Docker image (ghcr.io/huggingface/text-generation-inference:latest) " + "or install flash attention with `cd server && make install install-flash-attention`" + ) from e + + if SYSTEM == "cuda" and not (is_sm75 or is_sm8x or is_sm90): + raise ImportError( + f"GPU with CUDA capability {major} {minor} is not supported" + ) from e + elif SYSTEM == "rocm": + for idx in range(torch.cuda.device_count()): + if "MI210" not in torch.cuda.get_device_name( + idx + ) and "MI250" not in torch.cuda.get_device_name(idx): + raise ImportError( + f"AMD GPU {torch.cuda.get_device_name(idx)} does not support flash-attention" + ) + + logger.warning(f"Unable to use Flash Attention V2: {e}") + HAS_FLASH_ATTN = True + + +if HAS_FLASH_ATTN_V2_CUDA: + + def attention( + q, + k, + v, + out, + cu_seqlens, + max_s, + softmax_scale, + window_size_left=-1, + ): + if window_size_left <= 0 and window_size_left != -1: + raise ValueError("`window_size_left` must be > 0 or -1") return flash_attn_2_cuda.varlen_fwd( q, k, @@ -136,7 +140,21 @@ def attention( False, None, ) - elif HAS_FLASH_ATTN_V2_ROCM: + +elif HAS_FLASH_ATTN_V2_ROCM: + + def attention( + q, + k, + v, + out, + cu_seqlens, + max_s, + softmax_scale, + window_size_left=-1, + ): + if window_size_left <= 0 and window_size_left != -1: + raise ValueError("`window_size_left` must be > 0 or -1") if window_size_left != -1: raise ValueError( f"RoCm version of Flash Attention v2 does not support window attention (window_size_left != -1, got window_size_left={window_size_left})." @@ -159,7 +177,19 @@ def attention( False, None, ) - elif HAS_FLASH_ATTN: + +elif HAS_FLASH_ATTN: + + def attention( + q, + k, + v, + out, + cu_seqlens, + max_s, + softmax_scale, + window_size_left=-1, + ): if window_size_left != -1: raise NotImplementedError( "window_size_left is only available with flash attn v2" @@ -209,4 +239,5 @@ def attention( None, ) +else: raise NotImplementedError("flash attention is not installed") diff --git a/server/text_generation_server/utils/gptq/quant_linear.py b/server/text_generation_server/utils/gptq/quant_linear.py deleted file mode 100644 index a832f755..00000000 --- a/server/text_generation_server/utils/gptq/quant_linear.py +++ /dev/null @@ -1,359 +0,0 @@ -import math -import numpy as np -import torch -import torch.nn as nn -from torch.cuda.amp import custom_bwd, custom_fwd - -try: - import triton - import triton.language as tl - from . import custom_autotune - - # code based https://github.com/fpgaminer/GPTQ-triton - @custom_autotune.autotune( - configs=[ - triton.Config( - { - "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 256, - "BLOCK_SIZE_K": 32, - "GROUP_SIZE_M": 8, - }, - num_stages=4, - num_warps=4, - ), - triton.Config( - { - "BLOCK_SIZE_M": 128, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 32, - "GROUP_SIZE_M": 8, - }, - num_stages=4, - num_warps=4, - ), - triton.Config( - { - "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 32, - "GROUP_SIZE_M": 8, - }, - num_stages=4, - num_warps=4, - ), - triton.Config( - { - "BLOCK_SIZE_M": 128, - "BLOCK_SIZE_N": 32, - "BLOCK_SIZE_K": 32, - "GROUP_SIZE_M": 8, - }, - num_stages=4, - num_warps=4, - ), - triton.Config( - { - "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 32, - "GROUP_SIZE_M": 8, - }, - num_stages=4, - num_warps=4, - ), - triton.Config( - { - "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 32, - "GROUP_SIZE_M": 8, - }, - num_stages=2, - num_warps=8, - ), - triton.Config( - { - "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 8, - }, - num_stages=3, - num_warps=8, - ), - triton.Config( - { - "BLOCK_SIZE_M": 32, - "BLOCK_SIZE_N": 32, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 8, - }, - num_stages=2, - num_warps=4, - ), - ], - key=["M", "N", "K"], - nearest_power_of_two=True, - prune_configs_by={ - "early_config_prune": custom_autotune.matmul248_kernel_config_pruner, - "perf_model": None, - "top_k": None, - }, - ) - @triton.jit - def matmul_248_kernel( - a_ptr, - b_ptr, - c_ptr, - scales_ptr, - zeros_ptr, - g_ptr, - M, - N, - K, - bits, - maxq, - stride_am, - stride_ak, - stride_bk, - stride_bn, - stride_cm, - stride_cn, - stride_scales, - stride_zeros, - BLOCK_SIZE_M: tl.constexpr, - BLOCK_SIZE_N: tl.constexpr, - BLOCK_SIZE_K: tl.constexpr, - GROUP_SIZE_M: tl.constexpr, - ): - """ - Compute the matrix multiplication C = A x B. - A is of shape (M, K) float16 - B is of shape (K//8, N) int32 - C is of shape (M, N) float16 - scales is of shape (G, N) float16 - zeros is of shape (G, N) float16 - g_ptr is of shape (K) int32 - """ - infearure_per_bits = 32 // bits - - pid = tl.program_id(axis=0) - num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) - num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) - num_pid_k = tl.cdiv(K, BLOCK_SIZE_K) - num_pid_in_group = GROUP_SIZE_M * num_pid_n - group_id = pid // num_pid_in_group - first_pid_m = group_id * GROUP_SIZE_M - group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) - pid_m = first_pid_m + (pid % group_size_m) - pid_n = (pid % num_pid_in_group) // group_size_m - - offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - offs_k = tl.arange(0, BLOCK_SIZE_K) - a_ptrs = a_ptr + ( - offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak - ) # (BLOCK_SIZE_M, BLOCK_SIZE_K) - a_mask = offs_am[:, None] < M - # b_ptrs is set up such that it repeats elements along the K axis 8 times - b_ptrs = b_ptr + ( - (offs_k[:, None] // infearure_per_bits) * stride_bk - + offs_bn[None, :] * stride_bn - ) # (BLOCK_SIZE_K, BLOCK_SIZE_N) - g_ptrs = g_ptr + offs_k - # shifter is used to extract the N bits of each element in the 32-bit word from B - scales_ptrs = scales_ptr + offs_bn[None, :] - zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits) - - shifter = (offs_k % infearure_per_bits) * bits - zeros_shifter = (offs_bn % infearure_per_bits) * bits - accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) - - for k in range(0, num_pid_k): - g_idx = tl.load(g_ptrs) - - # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop - scales = tl.load( - scales_ptrs + g_idx[:, None] * stride_scales - ) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) - zeros = tl.load( - zeros_ptrs + g_idx[:, None] * stride_zeros - ) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) - - zeros = (zeros >> zeros_shifter[None, :]) & maxq - zeros = (zeros + 1) & maxq # eventually avoid overflow - - a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K) - b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated - - # Now we need to unpack b (which is N-bit values) into 32-bit values - b = (b >> shifter[:, None]) & maxq # Extract the N-bit values - b = (b - zeros) * scales # Scale and shift - - accumulator += tl.dot(a, b) - a_ptrs += BLOCK_SIZE_K - b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk - g_ptrs += BLOCK_SIZE_K - - c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] - c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N) - tl.store(c_ptrs, accumulator, mask=c_mask) - -except: - print("triton not installed.") - - -def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq): - with torch.cuda.device(input.device): - output = torch.empty( - (input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16 - ) - grid = lambda META: ( - triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"]) - * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]), - ) - matmul_248_kernel[grid]( - input, - qweight, - output, - scales, - qzeros, - g_idx, - input.shape[0], - qweight.shape[1], - input.shape[1], - bits, - maxq, - input.stride(0), - input.stride(1), - qweight.stride(0), - qweight.stride(1), - output.stride(0), - output.stride(1), - scales.stride(0), - qzeros.stride(0), - ) - return output - - -class QuantLinearFunction(torch.autograd.Function): - @staticmethod - @custom_fwd(cast_inputs=torch.float16) - def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq): - output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq) - return output - - -class QuantLinear(nn.Module): - def __init__(self, qweight, qzeros, scales, g_idx, bias, bits, groupsize): - super().__init__() - self.register_buffer("qweight", qweight) - self.register_buffer("qzeros", qzeros) - self.register_buffer("scales", scales) - self.register_buffer("g_idx", g_idx) - if bias is not None: - self.register_buffer("bias", bias) - else: - self.bias = None - if bits not in [2, 4, 8]: - raise NotImplementedError("Only 2,4,8 bits are supported.") - self.bits = bits - self.maxq = 2**self.bits - 1 - self.groupsize = groupsize - - self.outfeatures = qweight.shape[1] - self.infeatures = qweight.shape[0] * 32 // bits - - @classmethod - def new(cls, bits, groupsize, infeatures, outfeatures, bias): - if bits not in [2, 4, 8]: - raise NotImplementedError("Only 2,4,8 bits are supported.") - - qweight = torch.zeros((infeatures // 32 * bits, outfeatures), dtype=torch.int32) - qzeros = torch.zeros( - (math.ceil(infeatures / groupsize), outfeatures // 32 * bits), - dtype=torch.int32, - ) - scales = torch.zeros( - (math.ceil(infeatures / groupsize), outfeatures), dtype=torch.float16 - ) - g_idx = torch.tensor( - [i // groupsize for i in range(infeatures)], dtype=torch.int32 - ) - if bias: - bias = torch.zeros((outfeatures), dtype=torch.float16) - else: - bias = None - return cls(qweight, qzeros, scales, g_idx, bias, bits, groupsize) - - def pack(self, linear, scales, zeros, g_idx=None): - self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx - - scales = scales.t().contiguous() - zeros = zeros.t().contiguous() - scale_zeros = zeros * scales - self.scales = scales.clone().half() - if linear.bias is not None: - self.bias = linear.bias.clone().half() - - intweight = [] - for idx in range(self.infeatures): - intweight.append( - torch.round( - (linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) - / self.scales[self.g_idx[idx]] - ).to(torch.int)[:, None] - ) - intweight = torch.cat(intweight, dim=1) - intweight = intweight.t().contiguous() - intweight = intweight.numpy().astype(np.uint32) - qweight = np.zeros( - (intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32 - ) - i = 0 - row = 0 - while row < qweight.shape[0]: - if self.bits in [2, 4, 8]: - for j in range(i, i + (32 // self.bits)): - qweight[row] |= intweight[j] << (self.bits * (j - i)) - i += 32 // self.bits - row += 1 - else: - raise NotImplementedError("Only 2,4,8 bits are supported.") - - qweight = qweight.astype(np.int32) - self.qweight = torch.from_numpy(qweight) - - zeros -= 1 - zeros = zeros.numpy().astype(np.uint32) - qzeros = np.zeros( - (zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32 - ) - i = 0 - col = 0 - while col < qzeros.shape[1]: - if self.bits in [2, 4, 8]: - for j in range(i, i + (32 // self.bits)): - qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i)) - i += 32 // self.bits - col += 1 - else: - raise NotImplementedError("Only 2,4,8 bits are supported.") - - qzeros = qzeros.astype(np.int32) - self.qzeros = torch.from_numpy(qzeros) - - def forward(self, x): - out_shape = x.shape[:-1] + (self.outfeatures,) - out = QuantLinearFunction.apply( - x.reshape(-1, x.shape[-1]), - self.qweight, - self.scales, - self.qzeros, - self.g_idx, - self.bits, - self.maxq, - ) - out = out + self.bias if self.bias is not None else out - return out.reshape(out_shape) diff --git a/server/text_generation_server/utils/import_utils.py b/server/text_generation_server/utils/import_utils.py index db205f4d..f54987eb 100644 --- a/server/text_generation_server/utils/import_utils.py +++ b/server/text_generation_server/utils/import_utils.py @@ -10,6 +10,41 @@ def is_xpu_available(): return hasattr(torch, "xpu") and torch.xpu.is_available() -IS_ROCM_SYSTEM = torch.version.hip is not None -IS_CUDA_SYSTEM = torch.version.cuda is not None -IS_XPU_SYSTEM = is_xpu_available() +def get_cuda_free_memory(device, memory_fraction): + total_free_memory, _ = torch.cuda.mem_get_info(device) + total_gpu_memory = torch.cuda.get_device_properties(device).total_memory + free_memory = max(0, total_free_memory - (1 - memory_fraction) * total_gpu_memory) + return free_memory + + +def get_xpu_free_memory(device): + total_gpu_memory = torch.xpu.get_device_properties(device).total_memory + free_memory = int(total_gpu_memory * 0.5) + return free_memory + + +SYSTEM = None +if torch.version.hip is not None: + SYSTEM = "rocm" + empty_cache = torch.cuda.empty_cache + synchronize = torch.cuda.synchronize + get_free_memory = get_cuda_free_memory +elif torch.version.cuda is not None and torch.cuda.is_available(): + SYSTEM = "cuda" + empty_cache = torch.cuda.empty_cache + synchronize = torch.cuda.synchronize + get_free_memory = get_cuda_free_memory +elif is_xpu_available(): + SYSTEM = "xpu" + empty_cache = torch.xpu.empty_cache + synchronize = torch.xpu.synchronize + get_free_memory = get_xpu_free_memory +else: + SYSTEM = "cpu" + + def noop(*args, **kwargs): + pass + + empty_cache = noop + synchronize = noop + get_free_memory = noop diff --git a/server/text_generation_server/utils/layers.py b/server/text_generation_server/utils/layers.py deleted file mode 100644 index 7d339fe5..00000000 --- a/server/text_generation_server/utils/layers.py +++ /dev/null @@ -1,1374 +0,0 @@ -import os -import torch -import torch.distributed - -from torch import nn -from torch.nn import functional as F -from typing import List, Tuple, Optional -from loguru import logger -from functools import lru_cache - -from text_generation_server.utils.speculate import get_speculate - -HAS_BITS_AND_BYTES = True -try: - import bitsandbytes as bnb - from bitsandbytes.nn import Int8Params, Params4bit -except ImportError: - HAS_BITS_AND_BYTES = False - -from accelerate import init_empty_weights - -from text_generation_server.utils.gptq.quant_linear import QuantLinear -from text_generation_server.utils.import_utils import ( - IS_CUDA_SYSTEM, - IS_ROCM_SYSTEM, - IS_XPU_SYSTEM, -) - -if IS_XPU_SYSTEM: - import intel_extension_for_pytorch as ipex - -HAS_AWQ = True -try: - from text_generation_server.utils.awq.quantize.qmodule import WQLinear -except ImportError: - HAS_AWQ = False - -try: - major, _minor = torch.cuda.get_device_capability() -except Exception: - major = 1 - -HAS_EXLLAMA = False -CAN_EXLLAMA = major >= 8 or IS_ROCM_SYSTEM -V2 = os.getenv("EXLLAMA_VERSION", "2") == "2" - -if os.getenv("DISABLE_EXLLAMA") == "True": - HAS_EXLLAMA = False -elif CAN_EXLLAMA: - try: - if V2: - from text_generation_server.utils.gptq.exllamav2 import ( - QuantLinear as ExllamaQuantLinear, - create_exllama_buffers, - set_device, - ) - - HAS_EXLLAMA = "2" - else: - from text_generation_server.utils.gptq.exllama import ( - Ex4bitLinear as ExllamaQuantLinear, - create_exllama_buffers, - set_device, - ) - - HAS_EXLLAMA = "1" - - except ImportError: - pass - -HAS_EETQ = False -try: - from EETQ import quant_weights, w8_a16_gemm - - HAS_EETQ = True -except ImportError: - pass - - -# Monkey patching -@classmethod -def load_layer_norm(cls, prefix, weights, eps): - weight = weights.get_tensor(f"{prefix}.weight") - bias = weights.get_tensor(f"{prefix}.bias") - with init_empty_weights(): - ln = cls(weight.shape, eps=eps) - - ln.weight = nn.Parameter(weight) - ln.bias = nn.Parameter(bias) - return ln - - -@classmethod -def load_layer_norm_no_bias(cls, prefix, weights, eps): - weight = weights.get_tensor(f"{prefix}.weight") - with init_empty_weights(): - ln = cls(weight.shape, eps=eps) - - ln.weight = nn.Parameter(weight) - ln.bias = None - return ln - - -@classmethod -def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, stride): - weight = weights.get_tensor(f"{prefix}.weight") - bias = weights.get_tensor(f"{prefix}.bias") - with init_empty_weights(): - conv2d = cls( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - ) - - conv2d.weight = nn.Parameter(weight) - conv2d.bias = nn.Parameter(bias) - return conv2d - - -@classmethod -def load_conv2d_no_bias( - cls, prefix, weights, in_channels, out_channels, kernel_size, stride -): - weight = weights.get_tensor(f"{prefix}.weight") - with init_empty_weights(): - conv2d = cls( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - ) - - conv2d.weight = nn.Parameter(weight) - conv2d.bias = None - return conv2d - - -torch.nn.Conv2d.load = load_conv2d -torch.nn.Conv2d.load_no_bias = load_conv2d_no_bias -torch.nn.LayerNorm.load = load_layer_norm -torch.nn.LayerNorm.load_no_bias = load_layer_norm_no_bias - - -class FastLinear(nn.Module): - def __init__( - self, - weight, - bias, - ) -> None: - super().__init__() - self.weight = nn.Parameter(weight) - if bias is not None: - self.bias = nn.Parameter(bias) - else: - self.bias = None - - @classmethod - def load(cls, config, prefix: str, weights, bias: bool): - weight = weights.get_tensor(f"{prefix}.weight") - if bias: - bias = weights.get_tensor(f"{prefix}.bias") - else: - bias = None - return cls(weight, bias) - - def forward(self, input: torch.Tensor) -> torch.Tensor: - return F.linear(input, self.weight, self.bias) - - -class EETQLinear(nn.Module): - def __init__( - self, - weight, - bias, - ) -> None: - super().__init__() - device = weight.device - if weight.dtype != torch.float16: - weight = weight.to(dtype=torch.float16) - weight = torch.t(weight).contiguous().cpu() - weight, scale = quant_weights(weight, torch.int8, False) - - self.weight = weight.cuda(device) - self.scale = scale.cuda(device) - self.bias = bias.cuda(device) if bias is not None else None - - def forward(self, input: torch.Tensor) -> torch.Tensor: - output = w8_a16_gemm(input, self.weight, self.scale) - output = output + self.bias if self.bias is not None else output - return output - - -def fp8_quantize(weight, qdtype=torch.float8_e4m3fn): - device = weight.device - # weight, scale = quant_weights(weight, torch.int8, False) - finfo = torch.finfo(qdtype) - # Calculate the scale as dtype max divided by absmax - scale = finfo.max / weight.abs().max().clamp(min=1e-12) - # scale and clamp the tensor to bring it to - # the representative range of float8 data type - # (as default cast is unsaturated) - qweight = (weight * scale).clamp(min=finfo.min, max=finfo.max) - # Return both float8 data and the inverse scale (as float), - # as both required as inputs to torch._scaled_mm - qweight = qweight.to(qdtype) - scale = scale.float().reciprocal() - return qweight, scale - - -class Fp8Linear(nn.Module): - def __init__( - self, - weight, - bias, - ) -> None: - super().__init__() - self.dtype = weight.dtype - self.qweight, self.scale = fp8_quantize(weight) - - self.bias = bias if bias is not None else None - - def forward(self, input: torch.Tensor) -> torch.Tensor: - qinput, scale = fp8_quantize(input) - output, _ = torch._scaled_mm( - qinput, - self.qweight.t(), - out_dtype=self.dtype, - scale_a=scale, - scale_b=self.scale, - bias=self.bias, - ) - return output - - -class Linear8bitLt(nn.Module): - def __init__( - self, - weight, - bias, - has_fp16_weights=True, - memory_efficient_backward=False, - threshold=0.0, - index=None, - ): - super().__init__() - assert ( - not memory_efficient_backward - ), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0" - self.state = bnb.MatmulLtState() - self.index = index - - # Necessary for stacked layers - self.state.threshold = threshold - self.state.has_fp16_weights = has_fp16_weights - self.state.memory_efficient_backward = memory_efficient_backward - if threshold > 0.0 and not has_fp16_weights: - self.state.use_pool = True - - self.weight = Int8Params( - weight.data, - has_fp16_weights=has_fp16_weights, - requires_grad=has_fp16_weights, - ) - self.weight.cuda(weight.device) - self.bias = bias - - def init_8bit_state(self): - self.state.CB = self.weight.CB - self.state.SCB = self.weight.SCB - self.weight.CB = None - self.weight.SCB = None - - def forward(self, x: torch.Tensor): - self.state.is_training = self.training - if self.weight.CB is not None: - self.init_8bit_state() - - # weights are cast automatically as Int8Params, but the bias has to be cast manually - if self.bias is not None and self.bias.dtype != x.dtype: - self.bias.data = self.bias.data.to(x.dtype) - - out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state) - - if not self.state.has_fp16_weights: - if self.state.CB is not None and self.state.CxB is not None: - # we converted 8-bit row major to turing/ampere format in the first inference pass - # we no longer need the row-major weight - del self.state.CB - self.weight.data = self.state.CxB - return out - - -class Linear4bit(nn.Module): - def __init__(self, weight, bias, quant_type): - super().__init__() - self.weight = Params4bit( - weight.data, - requires_grad=False, - compress_statistics=True, - quant_type=quant_type, - ) - self.compute_dtype = None - self.weight.cuda(weight.device) - self.bias = bias - - def forward(self, x: torch.Tensor): - # weights are cast automatically as Int8Params, but the bias has to be cast manually - if self.bias is not None and self.bias.dtype != x.dtype: - self.bias.data = self.bias.data.to(x.dtype) - - if getattr(self.weight, "quant_state", None) is None: - print( - "FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first." - ) - inp_dtype = x.dtype - if self.compute_dtype is not None: - x = x.to(self.compute_dtype) - - bias = None if self.bias is None else self.bias.to(self.compute_dtype) - out = bnb.matmul_4bit( - x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state - ) - - out = out.to(inp_dtype) - - return out - - -@lru_cache(1) -def warn_deprecate_bnb(): - logger.warning( - "Bitsandbytes 8bit is deprecated, using `eetq` is a drop-in replacement, and has much better performnce" - ) - - -def get_linear(weight, bias, quantize): - if quantize is None: - linear = FastLinear(weight, bias) - elif quantize == "eetq": - if HAS_EETQ: - linear = EETQLinear(weight, bias) - else: - raise ImportError( - "Please install EETQ from https://github.com/NetEase-FuXi/EETQ" - ) - elif quantize == "fp8": - linear = Fp8Linear(weight, bias) - elif quantize == "bitsandbytes": - warn_deprecate_bnb() - linear = Linear8bitLt( - weight, - bias, - has_fp16_weights=False, - threshold=6.0, - ) - if bias is not None: - linear.bias = nn.Parameter(bias) - elif quantize == "bitsandbytes-fp4": - linear = Linear4bit( - weight, - bias, - quant_type="fp4", - ) - elif quantize == "bitsandbytes-nf4": - linear = Linear4bit( - weight, - bias, - quant_type="nf4", - ) - elif quantize == "gptq": - try: - qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama = weight - except Exception: - raise NotImplementedError( - f"The passed weight is not `gptq` compatible, loader needs to be updated." - ) - - if use_exllama: - linear = ExllamaQuantLinear( - qweight, qzeros, scales, g_idx, bias, bits, groupsize - ) - else: - linear = QuantLinear( - qweight, - qzeros, - scales, - g_idx, - bias, - bits, - groupsize, - ) - elif quantize == "awq": - try: - qweight, qzeros, scales, _, bits, groupsize, _ = weight - except Exception: - raise NotImplementedError( - f"The passed weight is not `awq` compatible, loader needs to be updated." - ) - if IS_ROCM_SYSTEM: - raise NotImplementedError( - "AWQ GEMM kernel can't be used on ROCm systems, please use `--quantize gptq` instead " - "to use Exllama/GPTQ kernels for AWQ inference." - ) - if not HAS_AWQ: - raise NotImplementedError( - "You do not seem to have awq installed, either install it (cd server && make install-awq), or try using GPTQ `---quantize gptq` a conversion AWQ->GPTQ will happen on the fly" - ) - linear = WQLinear( - w_bit=bits, - group_size=groupsize, - qweight=qweight, - qzeros=qzeros, - scales=scales, - bias=bias is not None, - ) - else: - raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.") - return linear - - -class SuperLayer(nn.Module): - def __init__(self, linear): - super().__init__() - self.linear = linear - - def forward(self, x): - return self.linear.forward(x) - - -class ResBlock(torch.nn.Module): - def __init__(self, config, prefix, weights): - super().__init__() - self.linear = FastLinear.load( - config, prefix=f"{prefix}.linear", weights=weights, bias=True - ) - self.act = torch.nn.SiLU() - - def forward(self, x): - return x + self.act(self.linear(x)) - - -class MedusaModel(torch.nn.Module): - def __init__(self, config, medusa_config, weights): - super().__init__() - self.heads = torch.nn.ModuleList( - [ - MedusaHead(config, medusa_config, prefix=f"{i}", weights=weights) - for i in range(get_speculate()) - ] - ) - - def forward(self, x): - speculative_logits = torch.stack([head(x) for head in self.heads], dim=1) - return speculative_logits - - -class MedusaHead(torch.nn.Module): - def __init__(self, config, medusa_config, prefix, weights): - super().__init__() - self.blocks = torch.nn.ModuleList( - [ - ResBlock(config, prefix=f"{prefix}.{i}", weights=weights) - for i in range(medusa_config["medusa_num_layers"]) - ] - ) - n = len(self.blocks) - self.out = FastLinear.load( - config, prefix=f"{prefix}.{n}", weights=weights, bias=False - ) - - def forward(self, x): - for block in self.blocks: - x = block(x) - x = self.out(x) - return x - - -class MedusaHeadV1(nn.Module): - def __init__(self, lm_head, medusa): - super().__init__() - self.lm_head = lm_head - self.medusa = medusa - - @staticmethod - def load(config, prefix: str, weights): - from pathlib import Path - from safetensors import safe_open - import json - - use_medusa = config.use_medusa - - medusa_config = str(Path(use_medusa) / "config.json") - filename = str(Path(use_medusa) / "medusa_lm_head.safetensors") - - with open(medusa_config, "r") as f: - medusa_config = json.load(f) - routing = weights.routing - with safe_open(filename, framework="pytorch") as f: - for k in f.keys(): - if k in routing and routing[k] != filename: - raise RuntimeError( - f"Key {k} was found in multiple files: {filename} and {routing[k]}" - ) - routing[k] = filename - - medusa = MedusaModel(config, medusa_config, weights) - lm_head = TensorParallelHead.load(config, prefix, weights) - return MedusaHeadV1(lm_head, medusa) - - def forward( - self, input: torch.Tensor - ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - logits = self.lm_head(input) - # If we have too many tokens, we skip speculative logits - if input.shape[0] > 128: - return logits, None - - speculative_logits = self.medusa(input) - return logits, speculative_logits - - -class MedusaHeadV2(nn.Module): - def __init__(self, config, prefix, weights): - super().__init__() - from pathlib import Path - from safetensors import safe_open - import json - - use_medusa = config.use_medusa - - medusa_config = str(Path(use_medusa) / "config.json") - filename = str(Path(use_medusa) / "medusa_lm_head.safetensors") - - with open(medusa_config, "r") as f: - medusa_config = json.load(f) - routing = weights.routing - with safe_open(filename, framework="pytorch") as f: - for k in f.keys(): - if k in routing and routing[k] != filename: - raise RuntimeError( - f"Key {k} was found in multiple files: {filename} and {routing[k]}" - ) - routing[k] = filename - - self.n_medusa_heads = get_speculate() - - assert medusa_config["medusa_num_layers"] == 1 - self.linear = TensorParallelColumnLinear.load_multi( - config, - prefixes=[f"{i}.0.linear" for i in range(self.n_medusa_heads)], - dim=0, - weights=weights, - bias=True, - ) - self.process_group = weights.process_group - self.world_size = self.process_group.size() - self.rank = self.process_group.rank() - - self.act = torch.nn.SiLU() - - self.lm_head = TensorParallelHead.load(config, prefix, weights) - - def forward(self, x): - # If we have too many tokens, we skip speculative logits - if x.shape[0] > 128: - logits = self.lm_head(x) - return logits, None - - size = x.shape[-1] - block_size = (size + self.world_size - 1) // self.world_size - start = self.rank * block_size - stop = (self.rank + 1) * block_size - - x_block = x[:, start:stop] - - # Compute all medusa heads at the same time, then reshape and move the n_medusa_heads dim to dim 1 - medusa_res = self.act(self.linear(x)).reshape( - *x_block.shape[:-1], self.n_medusa_heads, x_block.shape[-1] - ) - - # Apply all residual medusa heads - output = x[:, start:stop].unsqueeze(-2) + medusa_res - - # Gather medusa heads - world_output = [ - torch.empty_like(output) for _ in range(self.process_group.size()) - ] - torch.distributed.all_gather(world_output, output, group=self.process_group) - world_output = torch.cat(world_output, dim=-1) - - # Stack x and medusa residual x - stacked_x = torch.cat([x.unsqueeze(-2), world_output], dim=-2) - - # Compute lm head on x + medusa residual x - logits = self.lm_head(stacked_x) - - # Finally, split logits from speculative logits - logits, speculative_logits = torch.split( - logits, [1, self.n_medusa_heads], dim=-2 - ) - # Squeeze added dimension - logits = logits.squeeze(-2) - - return logits, speculative_logits - - -class SpeculativeHead(nn.Module): - def __init__(self, lm_head, medusa): - super().__init__() - self.head = lm_head - self.medusa = medusa - - @staticmethod - def load(config, prefix: str, weights): - use_medusa = config.use_medusa - if use_medusa: - lm_head = None - try: - medusa = MedusaHeadV1.load(config, prefix, weights) - except: - medusa = MedusaHeadV2(config, prefix, weights) - else: - lm_head = TensorParallelHead.load(config, prefix, weights) - medusa = None - return SpeculativeHead(lm_head, medusa) - - def forward( - self, input: torch.Tensor - ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: - if self.medusa is not None: - return self.medusa(input) - - assert self.head is not None - logits = self.head(input) - return logits, None - - -class TensorParallelHead(SuperLayer): - def __init__(self, linear, process_group, should_gather: bool): - super().__init__(linear) - self.process_group = process_group - self.should_gather = should_gather - - @staticmethod - def load(config, prefix: str, weights): - if weights.process_group.size() > 1: - try: - weight = weights.get_sharded(f"{prefix}.weight", dim=0) - should_gather = True - except AssertionError: - # If the vocab size is not divisible by number of shards - # just load the entire thing. - weight = weights.get_tensor(f"{prefix}.weight") - should_gather = False - else: - weight = weights.get_tensor(f"{prefix}.weight") - should_gather = False - - # GPTQ,AWQ,EETQ don't quantize heads (nor embeddings) - if config.quantize in ["gptq", "awq", "eetq"]: - quantize = None - else: - quantize = config.quantize - return TensorParallelHead( - get_linear(weight, bias=None, quantize=quantize), - process_group=weights.process_group, - should_gather=should_gather, - ) - - def forward(self, input: torch.Tensor) -> torch.Tensor: - if not self.should_gather: - return super().forward(input) - - world_size = self.process_group.size() - if len(input.shape) == 2 and isinstance(self.linear, FastLinear): - out_dim = self.linear.weight.shape[0] - - if input.shape[0] == 1: - world_out = input.new_empty(1, out_dim * world_size) - local_out = input.new_empty(1, out_dim) - gather_input = local_out - else: - world_out = input.new_empty(out_dim * world_size, input.shape[0]) - gather_input = input.new_empty(out_dim, input.shape[0]) - local_out = gather_input.T - - torch.mm(input, self.linear.weight.T, out=local_out) - - torch.distributed.all_gather_into_tensor( - world_out, gather_input, group=self.process_group - ) - - if input.shape[0] == 1: - return world_out - return world_out.T - - output = super().forward(input) - world_output = [ - torch.empty_like(output) for _ in range(self.process_group.size()) - ] - torch.distributed.all_gather(world_output, output, group=self.process_group) - world_output = torch.cat(world_output, dim=-1) - return world_output - - -class TensorParallelColumnLinear(SuperLayer): - @classmethod - def load_gate_up(cls, config, prefix: str, weights, bias: bool): - """Specific method when the QKV was joined after the fact""" - weight = weights.get_weights_col_packed_gate_up( - prefix, quantize=config.quantize - ) - if bias: - raise NotImplementedError("packed_gate_up only implemented without bias") - else: - bias = None - linear = get_linear(weight, bias, config.quantize) - return cls(linear) - - @classmethod - def load_qkv(cls, config, prefix: str, weights, bias: bool): - """Specific method when the QKV was joined after the fact""" - weight = weights.get_weights_col_packed_qkv(prefix, quantize=config.quantize) - if bias: - raise NotImplementedError("packed_qkv only implemented for baichuan") - else: - bias = None - linear = get_linear(weight, bias, config.quantize) - return cls(linear) - - @classmethod - def load(cls, config, prefix: str, weights, bias: bool): - return cls.load_multi(config, [prefix], weights, bias, dim=0) - - @classmethod - def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int): - weight = weights.get_multi_weights_col( - prefixes, quantize=config.quantize, dim=dim - ) - - if bias: - b = [weights.get_sharded(f"{p}.bias", dim=0) for p in prefixes] - bias = torch.cat(b, dim=dim) - else: - bias = None - linear = get_linear(weight, bias, config.quantize) - return cls(linear) - - -class TensorParallelRowLinear(SuperLayer): - def __init__(self, linear, process_group): - super().__init__(linear) - self.process_group = process_group - - @classmethod - def load(cls, config, prefix: str, weights, bias: bool): - weight = weights.get_multi_weights_row(prefix, quantize=config.quantize) - - if bias and weights.process_group.rank() == 0: - # Rank is only on the first rank process - bias = weights.get_tensor(f"{prefix}.bias") - else: - bias = None - return cls( - get_linear(weight, bias, config.quantize), - process_group=weights.process_group, - ) - - def forward(self, input: torch.Tensor, reduce: bool = True) -> torch.Tensor: - out = super().forward(input) - if self.process_group.size() > 1 and reduce: - torch.distributed.all_reduce(out, group=self.process_group) - return out - - -class TensorParallelEmbedding(nn.Module): - def __init__(self, prefix: str, weights, reduce=True): - super().__init__() - weight = weights.get_partial_sharded(f"{prefix}.weight", dim=0) - num_embeddings = weights.get_shape(f"{prefix}.weight")[0] - - process_group = weights.process_group - - world_size = process_group.size() - rank = process_group.rank() - - block_size = (num_embeddings + world_size - 1) // world_size - self.min_id = rank * block_size - self.max_id = min(num_embeddings, (rank + 1) * block_size) - self.null_idx = weight.shape[ - 0 - ] # Usually block_size, might be less in non even vocab_size. - self.process_group = weights.process_group - self.reduce = reduce - - """Additional 0 entry used for masking""" - self.weight = nn.Parameter(F.pad(weight, (0, 0, 0, 1))) - - def forward(self, input: torch.Tensor) -> torch.Tensor: - # default all out of bounds values to `self.null_idx` that will then be mapped to 0 - # translate for [0, self.max_id - self.min_id[ - input = torch.where( - (self.min_id > input) | (input >= self.max_id), - self.null_idx, - input - self.min_id, - ) - out = torch.nn.functional.embedding(input, self.weight) - if self.reduce and self.process_group.size() > 1: - torch.distributed.all_reduce(out, group=self.process_group) - return out - - -try: - if IS_CUDA_SYSTEM: - import dropout_layer_norm - elif IS_ROCM_SYSTEM: - from vllm import layernorm_ops - else: - dropout_layer_norm = None - - class FastLayerNorm(nn.LayerNorm): - def forward(self, hidden_states, residual=None): - if IS_XPU_SYSTEM: - res_out = hidden_states - out = ipex.llm.functional.add_layer_norm( - residual, hidden_states, self.weight, self.bias, self.eps, True - ) - if residual is not None: - res_out = residual - return out, res_out - elif hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM: - if residual is not None: - hidden_states += residual - residual = hidden_states - - return super(FastLayerNorm, self).forward(hidden_states), residual - else: - ( - normed_hidden_states, - residual, - *rest, - ) = dropout_layer_norm.dropout_add_ln_fwd( - hidden_states, - residual, - self.weight, - self.bias, - None, - None, - None, - None, - 0.0, - self.eps, - 1.0, - 0, - None, - False, - False, - ) - if residual is None: - residual = hidden_states - - return normed_hidden_states, residual - - class FastRMSNorm(nn.Module): - def __init__(self, weight: torch.Tensor, eps: float): - super().__init__() - - self.weight = nn.Parameter(weight) - self.variance_epsilon = eps - - @classmethod - def load(cls, prefix, weights, eps=1e-6): - weight = weights.get_tensor(f"{prefix}.weight") - return cls(weight, eps) - - def forward(self, hidden_states, residual=None): - if IS_XPU_SYSTEM: - residual_out = hidden_states - out = ipex.llm.functional.add_rms_norm( - residual, - hidden_states, - self.weight, - None, - self.variance_epsilon, - True, - ) - if residual is not None: - residual_out = residual - return out, residual_out - elif hidden_states.shape[-1] > 8192: - if residual is not None: - hidden_states += residual - residual = hidden_states - - hidden_states = hidden_states.to(torch.float32) - variance = hidden_states.pow(2).mean(-1, keepdim=True) - hidden_states = hidden_states * torch.rsqrt( - variance + self.variance_epsilon - ) - - # convert into half-precision if necessary - if self.weight.dtype in [torch.float16, torch.bfloat16]: - hidden_states = hidden_states.to(self.weight.dtype) - - return self.weight * hidden_states, residual - elif IS_CUDA_SYSTEM: - # faster post attention rms norm - ( - normed_hidden_states, - res, - *rest, - ) = dropout_layer_norm.dropout_add_ln_fwd( - hidden_states, - residual, - self.weight, - None, - None, - None, - None, - None, - 0.0, - self.variance_epsilon, - 1.0, - 0, - None, - False, - True, # Activate RMSNorm - ) - if res is None: - res = hidden_states - - return normed_hidden_states, res - elif IS_ROCM_SYSTEM: - # We use VLLM RMSNorm kernel that can be compiled for RoCm, instead of Flash Attention ones that can not. - if residual is not None: - hidden_states += residual - residual = hidden_states - - out = torch.empty_like(hidden_states) - layernorm_ops.rms_norm( - out, - hidden_states, - self.weight.data, - self.variance_epsilon, - ) - return out, residual - else: - raise ValueError( - "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction." - ) - -except ImportError: - pass - -try: - if IS_CUDA_SYSTEM: - from flash_attn.layers.rotary import RotaryEmbedding - import rotary_emb - elif IS_ROCM_SYSTEM: - from vllm import pos_encoding_ops - - def _create_inv_freq(dim, base, device): - inv_freq = 1.0 / ( - base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim) - ) - return inv_freq - - def _get_rope_config(config): - if os.getenv("ROPE_SCALING", None) is not None: - rope_scaling = { - "type": os.environ["ROPE_SCALING"], - "factor": float(os.environ["ROPE_FACTOR"]), - } - return rope_scaling - return getattr(config, "rope_scaling", None) - - class PositionRotaryEmbedding(nn.Module): - def __init__(self, inv_freq, scaling_factor): - super().__init__() - self.inv_freq = inv_freq - self._seq_len_cached = 0 - self._cos_cached = None - self._sin_cached = None - self._cos_k_cached = None - self._sin_k_cached = None - self.scaling_factor = scaling_factor - self.dynamic_args = None - - def forward( - self, - query: torch.Tensor, - key: torch.Tensor, - cos: torch.Tensor, - sin: torch.Tensor, - ): - # Such controlflows may add some overhead. - if IS_CUDA_SYSTEM: - rotary_dim = cos.shape[-1] - q1 = query[..., :rotary_dim] - q2 = query[..., rotary_dim : 2 * rotary_dim] - - rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False) - - k1 = key[..., :rotary_dim] - k2 = key[..., rotary_dim : 2 * rotary_dim] - - rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False) - elif IS_ROCM_SYSTEM: - # NOTE: On RoCm systems, we use a ROPE implementatation adapted from VLLM which launches a single kernel for both query/key, contrary to flash-attn implementation used on NVIDIA systems. - # Compiling flash-attn rotary on RoCm, it appears hipcc is unable to unroll loops, resulting in an even slower inference compared to eager: https://github.com/pytorch/pytorch/issues/113773 - - head_size = query.shape[-1] - - # Inplace operation, updating query and key. - pos_encoding_ops.rotary_embedding(query, key, head_size, cos, sin, True) - elif IS_XPU_SYSTEM: - ipex.llm.functional.rotary_embedding( - query, key, sin, cos, query.size(-1), True - ) - else: - raise ValueError( - "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction." - ) - - @classmethod - def static(cls, config, dim, base, device): - inv_freq = _create_inv_freq(dim, base, device) - scaling_factor = None - rope_scaling = _get_rope_config(config) - if rope_scaling is not None: - if rope_scaling["type"] == "linear": - pass - elif rope_scaling["type"] == "dynamic": - scaling_factor = rope_scaling["factor"] - return DynamicPositionRotaryEmbedding( - dim=dim, - max_position_embeddings=config.max_position_embeddings, - base=base, - device=inv_freq.device, - scaling_factor=scaling_factor, - ) - elif rope_scaling["type"] == "yarn": - scaling_factor = rope_scaling["factor"] - return YarnPositionRotaryEmbedding( - dim=2 * inv_freq.shape[0], - max_position_embeddings=rope_scaling[ - "original_max_position_embeddings" - ], - base=10000.0, - device=inv_freq.device, - scaling_factor=scaling_factor, - extrapolation_factor=1, - attn_factor=1, - beta_fast=32, - beta_slow=1, - ) - elif rope_scaling["type"] == "su": - short_factor = torch.tensor( - rope_scaling["short_factor"], dtype=torch.float32, device=device - ) - short_inv_freq = 1.0 / ( - short_factor - * base - ** ( - torch.arange(0, dim, 2, device=device, dtype=torch.float32) - / dim - ) - ) - long_factor = torch.tensor( - rope_scaling["long_factor"], dtype=torch.float32, device=device - ) - long_inv_freq = 1.0 / ( - long_factor - * base - ** ( - torch.arange(0, dim, 2, device=device, dtype=torch.float32) - / dim - ) - ) - - original_max_position_embeddings = ( - config.original_max_position_embeddings - ) - max_position_embeddings = config.max_position_embeddings - if max_position_embeddings <= original_max_position_embeddings: - scaling_factor = 1.0 - else: - scale = ( - max_position_embeddings / original_max_position_embeddings - ) - scaling_factor = math.sqrt( - 1 - + math.log(scale) - / math.log(original_max_position_embeddings) - ) - - return SuRotaryEmbedding( - short_inv_freq=short_inv_freq, - long_inv_freq=long_inv_freq, - scaling_factor=scaling_factor, - original_max_position_embeddings=original_max_position_embeddings, - ) - else: - raise NotImplementedError( - f"rope scaling type {rope_scaling['type']} is not implemented or invalid" - ) - return cls(inv_freq, scaling_factor) - - @classmethod - def load(cls, config, prefix, weights): - # XXX: Always load this in float32 ! - dtype = weights.dtype - weights.dtype = torch.float32 - inv_freq = weights.get_tensor(f"{prefix}.inv_freq") - weights.dtype = dtype - - scaling_factor = None - rope_scaling = _get_rope_config(config) - if rope_scaling is not None: - scaling_factor = rope_scaling["factor"] - if rope_scaling["type"] == "linear": - pass - elif rope_scaling["type"] == "dynamic": - return DynamicPositionRotaryEmbedding( - dim=2 * inv_freq.shape[0], - max_position_embeddings=config.max_position_embeddings, - base=10000.0, - device=inv_freq.device, - scaling_factor=scaling_factor, - ) - elif rope_scaling["type"] == "yarn": - return YarnPositionRotaryEmbedding( - dim=2 * inv_freq.shape[0], - max_position_embeddings=rope_scaling[ - "original_max_position_embeddings" - ], - base=10000.0, - device=inv_freq.device, - scaling_factor=scaling_factor, - extrapolation_factor=1, - attn_factor=1, - beta_fast=32, - beta_slow=1, - ) - else: - raise NotImplementedError( - f"rope scaling type {rope_scaling['type']} is not implemented or invalid" - ) - return cls(inv_freq, scaling_factor) - - def _update_cos_sin_cache(self, dtype, device, seqlen): - # Reset the tables if the sequence length has changed, - # or if we're on a new device (possibly due to tracing for instance) - if ( - seqlen > self._seq_len_cached - or self._cos_cached.device != device - or self._cos_cached.dtype != dtype - ): - self._seq_len_cached = seqlen - t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) - if self.scaling_factor is not None: - t /= self.scaling_factor - # Don't do einsum, it converts fp32 to fp16 - # freqs = torch.einsum("i,j->ij", t, self.inv_freq) - - freqs = torch.outer(t, self.inv_freq.to(device=t.device)) - self._cos_cached = torch.cos(freqs).to(dtype) - self._sin_cached = torch.sin(freqs).to(dtype) - - def get_cos_sin( - self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype - ): - """ - Return cos and sin for the asked position ids - """ - if IS_ROCM_SYSTEM: - # For RoCm, we always use float cos/sin to avoid a cast. - # For NVIDIA, for some reason, the flash-attn rotary kernel requires cos/sin and query/key to be of same dtype: https://github.com/Dao-AILab/flash-attention/blob/017716451d446e464dde9aca3a3c1ed2209caaa9/csrc/rotary/rotary.cpp#L26 - # But later on goes and cast cos/sin to float anyway: https://github.com/Dao-AILab/flash-attention/blob/017716451d446e464dde9aca3a3c1ed2209caaa9/csrc/rotary/rotary_cuda.cu#L29, which looks suboptimal. - dtype = torch.float32 - - self._update_cos_sin_cache(dtype, position_ids.device, max_s) - - cos = torch.index_select(self._cos_cached, 0, position_ids) - sin = torch.index_select(self._sin_cached, 0, position_ids) - - # Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow. - return cos.unsqueeze(1), sin.unsqueeze(1) - - class SuRotaryEmbedding(PositionRotaryEmbedding): - def __init__( - self, - short_inv_freq, - long_inv_freq, - scaling_factor, - original_max_position_embeddings, - ): - super(PositionRotaryEmbedding, self).__init__() - self.short_inv_freq = short_inv_freq - self.long_inv_freq = long_inv_freq - self.scaling_factor = scaling_factor - self.original_max_position_embeddings = original_max_position_embeddings - self._seq_len_cached = 0 - self._cos_cached = None - self._sin_cached = None - self._cos_k_cached = None - self._sin_k_cached = None - self.dynamic_args = None - - def _update_cos_sin_cache(self, dtype, device, seqlen): - # Reset the tables if the sequence length has changed, - # or if we're on a new device (possibly due to tracing for instance) - if ( - seqlen > self._seq_len_cached - or self._cos_cached.device != device - or self._cos_cached.dtype != dtype - ): - self._seq_len_cached = seqlen - if seqlen > self.original_max_position_embeddings: - inv_freq = self.long_inv_freq - else: - inv_freq = self.short_inv_freq - t = torch.arange(seqlen, device=device, dtype=inv_freq.dtype) - if self.scaling_factor is not None: - t /= self.scaling_factor - # Don't do einsum, it converts fp32 to fp16 - # freqs = torch.einsum("i,j->ij", t, self.inv_freq) - - freqs = torch.outer(t, inv_freq.to(device=t.device)) - self._cos_cached = torch.cos(freqs).to(dtype) - self._sin_cached = torch.sin(freqs).to(dtype) - - class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding): - def __init__(self, dim, max_position_embeddings, base, device, scaling_factor): - inv_freq = _create_inv_freq(dim, base, device) - super().__init__(inv_freq, scaling_factor) - self.dim = dim - self.max_position_embeddings = max_position_embeddings - self.base = base - - def _update_cos_sin_cache(self, dtype, device, seqlen): - # Reset the tables if the sequence length has changed, - # or if we're on a new device (possibly due to tracing for instance) - if ( - seqlen > self._seq_len_cached - or self._cos_cached.device != device - or self._cos_cached.dtype != dtype - ): - if seqlen > self.max_position_embeddings: - newbase = self.base * ( - (self.scaling_factor * seqlen / self.max_position_embeddings) - - (self.scaling_factor - 1) - ) ** (self.dim / (self.dim - 2)) - self.inv_freq = _create_inv_freq( - self.dim, newbase, self.inv_freq.device - ) - self._seq_len_cached = seqlen - t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) - # Don't do einsum, it converts fp32 to fp16 - # freqs = torch.einsum("i,j->ij", t, self.inv_freq) - - freqs = torch.outer(t, self.inv_freq.to(device=t.device)) - self._cos_cached = torch.cos(freqs).to(dtype) - self._sin_cached = torch.sin(freqs).to(dtype) - - # Inverse dim formula to find dim based on number of rotations - import math - - def find_correction_dim( - num_rotations, dim, base=10000, max_position_embeddings=2048 - ): - return ( - dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi)) - ) / (2 * math.log(base)) - - # Find dim range bounds based on rotations - def find_correction_range( - low_rot, high_rot, dim, base=10000, max_position_embeddings=2048 - ): - low = math.floor( - find_correction_dim(low_rot, dim, base, max_position_embeddings) - ) - high = math.ceil( - find_correction_dim(high_rot, dim, base, max_position_embeddings) - ) - return max(low, 0), min(high, dim - 1) # Clamp values just in case - - def linear_ramp_mask(min, max, dim): - if min == max: - max += 0.001 # Prevent singularity - - linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min) - ramp_func = torch.clamp(linear_func, 0, 1) - return ramp_func - - def get_mscale(scale=1): - if scale <= 1: - return 1.0 - return 0.1 * math.log(scale) + 1.0 - - class YarnPositionRotaryEmbedding(PositionRotaryEmbedding): - def __init__( - self, - dim, - max_position_embeddings, - base, - device, - scaling_factor, - *, - extrapolation_factor, - attn_factor, - beta_fast, - beta_slow, - ): - inv_freq = _create_inv_freq(dim, base, device) - super().__init__(inv_freq, scaling_factor) - self.dim = dim - self.max_position_embeddings = max_position_embeddings - self.base = base - self.extrapolation_factor = extrapolation_factor - self.attn_factor = attn_factor - self.beta_fast = beta_fast - self.beta_slow = beta_slow - self.mscale = float( - get_mscale(self.scaling_factor) * self.attn_factor - ) # Get n-d magnitude scaling corrected for interpolation - - def _update_cos_sin_cache(self, dtype, device, seqlen): - # Reset the tables if the sequence length has changed, - # or if we're on a new device (possibly due to tracing for instance) - if ( - seqlen > self._seq_len_cached - or self._cos_cached.device != device - or self._cos_cached.dtype != dtype - ): - if seqlen > self.max_position_embeddings: - inv_freq_extrapolation = _create_inv_freq( - self.dim, self.base, self.inv_freq.device - ) - freqs = 1.0 / inv_freq_extrapolation - inv_freq_interpolation = 1.0 / (self.scaling_factor * freqs) - low, high = find_correction_range( - self.beta_fast, - self.beta_slow, - self.dim, - self.base, - self.max_position_embeddings, - ) - inv_freq_mask = ( - 1 - - linear_ramp_mask(low, high, self.dim // 2).float().to(device) - ) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation - inv_freq = ( - inv_freq_interpolation * (1 - inv_freq_mask) - + inv_freq_extrapolation * inv_freq_mask - ) - - self.inv_freq = inv_freq - self.mscale = float( - get_mscale(self.scaling_factor) * self.attn_factor - ) # Get n-d magnitude scaling corrected for interpolation - - self._seq_len_cached = seqlen - t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype) - # Don't do einsum, it converts fp32 to fp16 - # freqs = torch.einsum("i,j->ij", t, self.inv_freq) - - freqs = torch.outer(t, self.inv_freq.to(device=t.device)) - self._cos_cached = (torch.cos(freqs) * self.mscale).to(dtype) - self._sin_cached = (torch.sin(freqs) * self.mscale).to(dtype) - -except ImportError: - pass diff --git a/server/text_generation_server/utils/paged_attention.py b/server/text_generation_server/utils/paged_attention.py index 62c0c893..1b31f7e7 100644 --- a/server/text_generation_server/utils/paged_attention.py +++ b/server/text_generation_server/utils/paged_attention.py @@ -1,13 +1,9 @@ import torch -from text_generation_server.utils.import_utils import ( - IS_CUDA_SYSTEM, - IS_ROCM_SYSTEM, - IS_XPU_SYSTEM, -) +from text_generation_server.utils.import_utils import SYSTEM _PARTITION_SIZE = 512 -if IS_XPU_SYSTEM: +if SYSTEM == "xpu": import intel_extension_for_pytorch as ipex @@ -18,17 +14,17 @@ def reshape_and_cache( value_cache: torch.Tensor, slots: torch.Tensor, ): - if IS_CUDA_SYSTEM: + if SYSTEM == "cuda": from vllm._C import cache_ops cache_ops.reshape_and_cache( key, value, key_cache, value_cache, slots, "auto", 1.0 ) - elif IS_ROCM_SYSTEM: + elif SYSTEM == "rocm": from vllm import cache_ops cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots) - elif IS_XPU_SYSTEM: + elif SYSTEM == "xpu": ipex.llm.modules.PagedAttention.reshape_and_cache( key, value, key_cache, value_cache, slots ) @@ -68,7 +64,7 @@ def attention( block_size = value_cache.shape[3] num_seqs, num_heads, head_size = query.shape max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE - if IS_XPU_SYSTEM: + if SYSTEM == "xpu": query = query.contiguous() return ipex.llm.modules.PagedAttention.single_query_cached_kv_attention( out, @@ -91,7 +87,7 @@ def attention( # to parallelize. use_v1 = max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512) if use_v1: - if IS_CUDA_SYSTEM: + if SYSTEM == "cuda": from vllm._C import ops ops.paged_attention_v1( @@ -109,7 +105,7 @@ def attention( "auto", 1.0, ) - elif IS_ROCM_SYSTEM: + elif SYSTEM == "rocm": from vllm import attention_ops attention_ops.paged_attention_v1( @@ -143,7 +139,7 @@ def attention( ) max_logits = torch.empty_like(exp_sums) - if IS_CUDA_SYSTEM: + if SYSTEM == "cuda": from vllm._C import ops ops.paged_attention_v2( @@ -164,7 +160,7 @@ def attention( "auto", 1.0, ) - elif IS_ROCM_SYSTEM: + elif SYSTEM == "rocm": from vllm import attention_ops attention_ops.paged_attention_v2( diff --git a/server/text_generation_server/utils/weights.py b/server/text_generation_server/utils/weights.py index da7aed1a..6af7d3fb 100644 --- a/server/text_generation_server/utils/weights.py +++ b/server/text_generation_server/utils/weights.py @@ -171,7 +171,7 @@ class Weights: log_once( logger.info, "Converting AWQ model to Exllama/GPTQ packing format." ) - from text_generation_server.utils.awq.conversion_utils import ( + from text_generation_server.layers.awq.conversion_utils import ( fast_awq_to_gptq, ) @@ -227,7 +227,7 @@ class Weights: bits, groupsize, desc_act, quant_method = self._get_gptq_params() - from text_generation_server.utils.layers import HAS_EXLLAMA + from text_generation_server.layers.gptq import HAS_EXLLAMA use_exllama = ( bits == 4 and HAS_EXLLAMA and quantize == "gptq" and not desc_act @@ -242,7 +242,7 @@ class Weights: log_once( logger.info, "Converting AWQ model to Exllama/GPTQ packing format." ) - from text_generation_server.utils.awq.conversion_utils import ( + from text_generation_server.layers.awq.conversion_utils import ( fast_awq_to_gptq, ) @@ -321,7 +321,7 @@ class Weights: # it would require to reorder input activations that are split unto several GPUs use_exllama = False - from text_generation_server.utils.layers import HAS_EXLLAMA, CAN_EXLLAMA + from text_generation_server.layers.gptq import HAS_EXLLAMA, CAN_EXLLAMA if use_exllama: if not HAS_EXLLAMA: @@ -348,7 +348,7 @@ class Weights: log_once( logger.info, "Converting AWQ model to Exllama/GPTQ packing format." ) - from text_generation_server.utils.awq.conversion_utils import ( + from text_generation_server.layers.awq.conversion_utils import ( fast_awq_to_gptq, ) From d348d2b28feeaab7a8f6bd44cc8924b6b4ae7868 Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Mon, 13 May 2024 13:46:29 +0200 Subject: [PATCH 74/74] Granite support? (#1882) # What does this PR do? Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. 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100644 --- a/server/requirements_cuda.txt +++ b/server/requirements_cuda.txt @@ -11,7 +11,7 @@ googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13" grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13" grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13" -grpcio==1.62.2 ; python_version >= "3.9" and python_version < "3.13" +grpcio==1.63.0 ; python_version >= "3.9" and python_version < "3.13" hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13" huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13" idna==3.7 ; python_version >= "3.9" and python_version < "3.13" @@ -32,15 +32,15 @@ prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13" protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13" py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < 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-transformers==4.40.1 ; python_version >= "3.9" and python_version < "3.13" +tqdm==4.66.4 ; python_version >= "3.9" and python_version < "3.13" +transformers==4.40.2 ; python_version >= "3.9" and python_version < "3.13" typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13" typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13" urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13" diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index a7969494..6a6b2e0a 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -23,7 +23,6 @@ import torch.distributed from torch import nn from transformers.activations import ACT2FN -from transformers.configuration_utils import PretrainedConfig from typing import Optional, List, Tuple from text_generation_server.utils import paged_attention, flash_attn @@ -32,7 +31,6 @@ from text_generation_server.layers import ( TensorParallelColumnLinear, TensorParallelEmbedding, SpeculativeHead, - get_linear, ) from text_generation_server.layers.rotary import PositionRotaryEmbedding from text_generation_server.layers.layernorm import ( @@ -41,22 +39,29 @@ from text_generation_server.layers.layernorm import ( def load_attention(config, prefix, weights): + bias = config.attention_bias if config.num_attention_heads != config.num_key_value_heads: - return _load_gqa(config, prefix, weights) + return TensorParallelColumnLinear.load_multi( + config, + prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"], + dim=0, + weights=weights, + bias=bias, + ) else: if config.model_type == "baichuan": return TensorParallelColumnLinear.load_qkv( config, prefix=f"{prefix}.W_pack", weights=weights, - bias=False, + bias=bias, ) elif config.model_type == "phi3": return TensorParallelColumnLinear.load_qkv( config, prefix=f"{prefix}.qkv_proj", weights=weights, - bias=False, + bias=bias, ) else: return TensorParallelColumnLinear.load_multi( @@ -64,36 +69,10 @@ def load_attention(config, prefix, weights): prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"], dim=0, weights=weights, - bias=False, + bias=bias, ) -def _load_gqa(config, prefix: str, weights): - assert config.hidden_size % config.num_attention_heads == 0 - assert config.num_attention_heads % weights.process_group.size() == 0 - - weight = weights.get_multi_weights_col( - prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"], - quantize=config.quantize, - dim=0, - ) - - if config.quantize not in ["gptq", "awq"]: - weight = weight.to(dtype=weights.dtype).to(device=weights.device) - - head_size = config.hidden_size // config.num_attention_heads - num_heads = config.num_attention_heads // weights.process_group.size() - num_key_value_heads = config.num_key_value_heads // weights.process_group.size() - assert list(weight.shape) == [ - (num_heads + 2 * num_key_value_heads) * head_size, - config.hidden_size, - ], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}" - - return TensorParallelColumnLinear( - get_linear(weight, bias=None, quantize=config.quantize) - ) - - class FlashLlamaAttention(torch.nn.Module): def __init__( self, @@ -214,12 +193,13 @@ class LlamaMLP(nn.Module): ) ) # Fuse gate and up proj + bias = getattr(config, "mlp_bias", False) if config.model_type == "phi3": self.gate_up_proj = TensorParallelColumnLinear.load_gate_up( config, prefix=f"{prefix}.gate_up_proj", weights=weights, - bias=False, + bias=bias, ) else: self.gate_up_proj = TensorParallelColumnLinear.load_multi( @@ -227,13 +207,13 @@ class LlamaMLP(nn.Module): prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"], weights=weights, dim=0, - bias=False, + bias=bias, ) self.down_proj = TensorParallelRowLinear.load( config, prefix=f"{prefix}.down_proj", weights=weights, - bias=False, + bias=bias, ) self.intermediate_size = ( config.intermediate_size // weights.process_group.size() @@ -385,9 +365,14 @@ class FlashLlamaForCausalLM(torch.nn.Module): weights=weights, ) self.model = FlashLlamaModel(prefix, config, weights) + if config.tie_word_embeddings: + suffix = "model.embed_tokens" + else: + suffix = "lm_head" + self.lm_head = SpeculativeHead.load( config, - prefix="lm_head" if not prefix else f"{prefix}.lm_head", + prefix=suffix if not prefix else f"{prefix}.suffix", weights=weights, )