mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 23:12:07 +00:00
WIP, many bits are still missing...
So this won't work yet.
This commit is contained in:
Daniël de Kok
parent
c0f201c9d3
commit
4ed551abba
@ -146,7 +146,11 @@ class TensorParallelColumnLinear(SuperLayer):
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num_key_value_heads=num_key_value_heads,
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)
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if bias:
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raise NotImplementedError("packed_qkv only implemented for baichuan")
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bias = weights.get_packed_sharded(
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f"{prefix}.bias",
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dim=0,
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block_sizes=[num_heads, num_key_value_heads, num_key_value_heads],
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)
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else:
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bias = None
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linear = get_linear(weight, bias, config.quantize)
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@ -82,12 +82,14 @@ try:
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from text_generation_server.models.flash_phi import FlashPhi
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from text_generation_server.models.flash_starcoder2 import FlashStarcoder2
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from text_generation_server.models.flash_dbrx import FlashDbrx
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from text_generation_server.models.flash_phi3small import FlashPhi3Small
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from text_generation_server.layers.attention import SUPPORTS_WINDOWING
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except ImportError as e:
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logger.warning(f"Could not import Flash Attention enabled models: {e}")
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SUPPORTS_WINDOWING = False
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FLASH_ATTENTION = False
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if FLASH_ATTENTION:
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__all__.append(FlashGPT2)
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__all__.append(FlashNeoXSharded)
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@ -103,6 +105,7 @@ if FLASH_ATTENTION:
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__all__.append(FlashStarcoder2)
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__all__.append(FlashGemma)
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__all__.append(FlashCohere)
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__all__.append(FlashPhi3Small)
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MAMBA_AVAILABLE = True
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try:
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@ -138,6 +141,11 @@ class ModelType(enum.Enum):
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"name": "Phi 3",
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"url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct",
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}
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PHI3_SMALL = {
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"type": "phi3small",
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"name": "Phi 3 Small",
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"url": "https://huggingface.co/microsoft/Phi-3-small-8k-instruct",
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}
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GEMMA = {
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"type": "gemma",
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"name": "Gemma",
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@ -864,6 +872,30 @@ def get_model(
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else:
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raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("LlavaNext"))
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if model_type == PHI3_SMALL:
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if FLASH_ATTENTION:
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return FlashPhi3Small(
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model_id,
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revision,
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quantize=quantize,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif sharded:
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raise NotImplementedError(
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FLASH_ATT_ERROR_MESSAGE.format("Sharded Phi3 Small")
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)
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else:
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return CausalLM(
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model_id,
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revision,
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quantize=quantize,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if sharded:
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raise NotImplementedError("sharded is not supported for AutoModel")
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if quantize == "gptq":
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@ -0,0 +1,434 @@
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# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Optional, Tuple
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import torch
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import torch.distributed
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from torch import nn
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from transformers.activations import ACT2FN
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from text_generation_server.utils.import_utils import SYSTEM
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from text_generation_server.layers.attention import (
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paged_attention,
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attention,
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reshape_and_cache,
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)
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from text_generation_server.layers import (
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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SpeculativeHead,
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)
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from text_generation_server.layers.rotary import PositionRotaryEmbedding
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from text_generation_server.layers.layernorm import (
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FastLayerNorm,
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)
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if SYSTEM == "rocm":
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try:
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from vllm import _custom_C
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except Exception as e:
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raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
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def load_attention(config, prefix, weights):
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# Only defined in granite.
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bias = getattr(config, "attention_bias", False)
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bias = True
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return TensorParallelColumnLinear.load_qkv(
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config,
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prefix=f"{prefix}.query_key_value",
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weights=weights,
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bias=bias,
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num_heads=config.num_attention_heads,
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num_key_value_heads=config.num_key_value_heads,
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)
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class FlashPhi3SmallAttention(torch.nn.Module):
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def __init__(
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self,
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prefix: str,
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config,
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weights,
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):
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super().__init__()
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self.num_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.head_size = self.hidden_size // self.num_heads
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self.rotary_emb = PositionRotaryEmbedding.static(
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config=config,
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dim=self.head_size,
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base=config.rope_embedding_base,
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device=weights.device,
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)
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self.softmax_scale = self.head_size**-0.5
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if self.num_heads % weights.process_group.size() != 0:
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raise ValueError(
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f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
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f"and `num_shards`: {weights.process_group.size()}"
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)
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if config.num_key_value_heads % weights.process_group.size() != 0:
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raise ValueError(
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f"`num_key_value_heads` must be divisible by `num_shards` (got `num_key_value_heads`: {config.num_key_value_heads} "
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f"and `num_shards`: {weights.process_group.size()}"
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)
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self.num_heads = self.num_heads // weights.process_group.size()
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self.num_key_value_heads = (
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config.num_key_value_heads // weights.process_group.size()
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)
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self.query_key_value = load_attention(config, prefix, weights)
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self.o_proj = TensorParallelRowLinear.load(
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config,
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prefix=f"{prefix}.dense",
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weights=weights,
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bias=False,
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)
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self.num_groups = self.num_heads // self.num_key_value_heads
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self.kv_head_mapping = torch.arange(
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0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
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).repeat_interleave(self.num_groups)
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def forward(
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self,
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hidden_states,
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cos,
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sin,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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):
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qkv = self.query_key_value(hidden_states)
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query, kv = qkv.split(
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[
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self.head_size * self.num_heads,
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2 * self.head_size * self.num_key_value_heads,
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],
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dim=1,
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)
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query = query.view(-1, self.num_heads, self.head_size)
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kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
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self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
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reshape_and_cache(kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots)
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# output tensor
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attn_output = torch.empty_like(query)
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# Prefill
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if cu_seqlen_prefill is not None:
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# flash attention
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attention(
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query,
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torch.select(kv, dim=1, index=0),
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torch.select(kv, dim=1, index=1),
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attn_output,
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cu_seqlen_prefill,
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max_s,
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self.softmax_scale,
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)
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# Decode
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else:
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paged_attention(
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attn_output,
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query,
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kv_cache[0],
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kv_cache[1],
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self.kv_head_mapping,
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self.softmax_scale,
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block_tables,
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input_lengths,
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max_s,
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)
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return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
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# Copyright (c) Microsoft Corporation, MIT license
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# https://huggingface.co/microsoft/Phi-3-small-8k-instruct/blob/e6adf2a152c4cede5e9b88ede22ce5c0af2861fc/modeling_phi3_small.py#L88
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# Remove once this is in transformers.
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def gegelu(input: torch.Tensor, limit: Optional[float] = None):
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a_gelu, a_linear = input[..., ::2], input[..., 1::2]
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if limit is not None:
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a_gelu = torch.where(
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torch.isinf(a_gelu), a_gelu, a_gelu.clamp(min=None, max=limit)
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)
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a_linear = torch.where(
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torch.isinf(a_linear), a_linear, a_linear.clamp(min=-limit, max=limit)
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)
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out_gelu = a_gelu * torch.sigmoid(1.702 * a_gelu)
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return out_gelu * (a_linear + 1)
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class Phi3SmallMLP(nn.Module):
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def __init__(self, prefix, config, weights):
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super().__init__()
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self.hidden_act = config.hidden_act
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if self.hidden_act == "gegelu":
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self.act = lambda x: gegelu(x, config.gegelu_limit)
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elif "gelu" in self.hidden_act:
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self.act = lambda x: torch.nn.functional.gelu(
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x,
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approximate=(
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"tanh"
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if self.hidden_act in ["gelu_fast", "gelu_pytorch_tanh"]
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else "none"
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),
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)
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else:
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self.act = ACT2FN[self.hidden_act]
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bias = getattr(config, "mlp_bias", False)
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bias = True
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self.up_proj = TensorParallelColumnLinear.load(
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config, prefix=f"{prefix}.up_proj", weights=weights, bias=bias
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)
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self.down_proj = TensorParallelRowLinear.load(
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config,
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prefix=f"{prefix}.down_proj",
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weights=weights,
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bias=bias,
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)
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self.intermediate_size = (
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config.intermediate_size // weights.process_group.size()
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)
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# TODO: This is a hotfix to be removed & properly refactored.
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self.quantize = config.quantize
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def forward(self, hidden_states):
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if (
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SYSTEM == "rocm"
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and self.hidden_act == "silu"
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and hidden_states.shape[0] == 1
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and not self.quantize
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):
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out = torch.empty(
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hidden_states.shape[0],
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self.intermediate_size,
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dtype=hidden_states.dtype,
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device="cuda",
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)
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_custom_C.LLMM_Silu(self.gate_up_proj.linear.weight, hidden_states, out, 8)
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return self.down_proj(out)
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else:
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return self.down_proj(self.act(self.up_proj(hidden_states)))
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class FlashPhi3SmallLayer(nn.Module):
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def __init__(self, prefix, config, weights):
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super().__init__()
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self.self_attn = FlashPhi3SmallAttention(
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prefix=f"{prefix}.self_attn", config=config, weights=weights
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)
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self.mlp = Phi3SmallMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
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self.input_layernorm = FastLayerNorm.load(
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prefix=f"{prefix}.input_layernorm",
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weights=weights,
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eps=config.layer_norm_epsilon,
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)
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self.post_attention_layernorm = FastLayerNorm.load(
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prefix=f"{prefix}.post_attention_layernorm",
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weights=weights,
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eps=config.layer_norm_epsilon,
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)
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def forward(
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self,
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hidden_states,
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residual,
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cos,
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sin,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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):
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normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
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# Self Attention
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attn_output = self.self_attn(
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normed_hidden_states,
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cos,
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sin,
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cu_seqlen_prefill,
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kv_cache,
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block_tables,
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slots,
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input_lengths,
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max_s,
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)
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# faster post attention rms norm
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normed_attn_res_output, attn_res = self.post_attention_layernorm(
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attn_output, res
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)
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mlp_output = self.mlp(normed_attn_res_output)
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return mlp_output, attn_res
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class FlashPhi3SmallModel(torch.nn.Module):
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def __init__(self, prefix, config, weights):
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super().__init__()
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process_group = weights.process_group
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self.tp_rank = process_group.rank()
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self.tp_world_size = process_group.size()
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self.layers = nn.ModuleList(
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[
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FlashPhi3SmallLayer(
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prefix=(
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f"model.layers.{layer_id}"
|
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if not prefix
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else f"{prefix}.model.layers.{layer_id}"
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),
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config=config,
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weights=weights,
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)
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for layer_id in range(config.num_hidden_layers)
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]
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)
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self.norm = FastLayerNorm.load(
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prefix=(
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"model.final_layernorm"
|
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if not prefix
|
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else f"{prefix}.model.final_layernorm"
|
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),
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weights=weights,
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eps=config.layer_norm_epsilon,
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)
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self.gradient_checkpointing = False
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|
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self.head_size = self.layers[0].self_attn.head_size
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self.num_heads = self.layers[0].self_attn.num_heads
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self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
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|
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def forward(
|
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self,
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inputs_embeds: torch.Tensor,
|
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position_ids: torch.Tensor,
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cu_seqlen_prefill: Optional[torch.Tensor],
|
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kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
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block_tables: torch.Tensor,
|
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slots: torch.Tensor,
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input_lengths: torch.Tensor,
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max_s: int,
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true_max_s: int,
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prefill_cache_indices: Optional[torch.Tensor],
|
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) -> torch.Tensor:
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hidden_states = inputs_embeds
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|
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# Get rotary cos and sin for this forward
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# Avoid to index in each layer
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cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
|
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position_ids, max_s, hidden_states.dtype
|
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)
|
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|
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residual = None
|
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for i, layer in enumerate(self.layers):
|
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hidden_states, residual = layer(
|
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hidden_states,
|
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residual,
|
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cos,
|
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sin,
|
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cu_seqlen_prefill,
|
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kv_cache[i],
|
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block_tables,
|
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slots,
|
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input_lengths,
|
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max_s,
|
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)
|
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|
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hidden_states, _ = self.norm(hidden_states, residual)
|
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|
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return hidden_states
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|
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|
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class FlashPhi3SmallForCausalLM(torch.nn.Module):
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def __init__(self, prefix, config, weights):
|
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super().__init__()
|
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|
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self.embed_tokens = TensorParallelEmbedding(
|
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prefix=(
|
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"model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens"
|
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),
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weights=weights,
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)
|
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self.model = FlashPhi3SmallModel(prefix, config, weights)
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if config.tie_word_embeddings:
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suffix = "model.embed_tokens"
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else:
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suffix = "lm_head"
|
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|
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self.lm_head = SpeculativeHead.load(
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config,
|
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prefix=suffix if not prefix else f"{prefix}.{suffix}",
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weights=weights,
|
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)
|
||||
|
||||
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] = 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(
|
||||
inputs_embeds,
|
||||
position_ids,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
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]
|
||||
logits, speculative_logits = self.lm_head(hidden_states)
|
||||
return logits, speculative_logits
|
||||
86
server/text_generation_server/models/flash_phi3small.py
Normal file
86
server/text_generation_server/models/flash_phi3small.py
Normal file
@ -0,0 +1,86 @@
|
||||
import torch
|
||||
import torch.distributed
|
||||
|
||||
from opentelemetry import trace
|
||||
from transformers import AutoConfig, AutoTokenizer, GenerationConfig
|
||||
from typing import Optional
|
||||
|
||||
from text_generation_server.models import FlashCausalLM
|
||||
from text_generation_server.models.custom_modeling.flash_phi3small_modeling import (
|
||||
FlashPhi3SmallForCausalLM,
|
||||
)
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
Weights,
|
||||
)
|
||||
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
from text_generation_server.utils.import_utils import SYSTEM
|
||||
|
||||
|
||||
class FlashPhi3Small(FlashCausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
speculator: 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.float16 if dtype is None else dtype
|
||||
elif SYSTEM == "xpu":
|
||||
device = torch.device(f"xpu:{rank}")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
else:
|
||||
raise NotImplementedError("FlashPhi3Small is only available on GPU")
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
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 = AutoConfig.from_pretrained(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
config.quantize = quantize
|
||||
config.speculator = speculator
|
||||
|
||||
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", "exl2"]:
|
||||
weights._set_gptq_params(model_id, revision)
|
||||
|
||||
prefix = ""
|
||||
model = FlashPhi3SmallForCausalLM(prefix, config, weights)
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashPhi3Small, 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,
|
||||
)
|
||||
Loading…
Reference in New Issue
Block a user