mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 12:24:53 +00:00
Added Qwen2 but generation is wrong
removed unnecessary imports
This commit is contained in:
Jason Cheng
parent
fa8a8e05af
commit
bb57cb34e0
@ -52,6 +52,9 @@ try:
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from text_generation_server.models.flash_llama import (
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FlashLlama,
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)
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from text_generation_server.models.flash_qwen2 import (
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FlashQwen2,
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)
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from text_generation_server.models.flash_santacoder import (
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FlashSantacoderSharded,
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)
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@ -75,6 +78,7 @@ if FLASH_ATTENTION:
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__all__.append(FlashMistral)
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__all__.append(FlashMixtral)
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__all__.append(FlashPhi)
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__all__.append(FlashQwen2)
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MAMBA_AVAILABLE = True
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try:
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@ -312,6 +316,25 @@ def get_model(
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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 model_type == "qwen2":
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if FLASH_ATTENTION:
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return FlashQwen2(
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model_id,
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revision,
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quantize=quantize,
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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(FLASH_ATT_ERROR_MESSAGE.format("Sharded Qwen2"))
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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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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_type in ["RefinedWeb", "RefinedWebModel", "falcon"]:
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if sharded:
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@ -0,0 +1,390 @@
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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 typing import Optional, List, Tuple
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from text_generation_server.utils import paged_attention, flash_attn
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from text_generation_server.utils.layers import (
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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PositionRotaryEmbedding,
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TensorParallelHead,
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get_linear,
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FastRMSNorm,
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)
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def load_attention(config, prefix, weights):
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if config.num_attention_heads != config.num_key_value_heads:
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return _load_gqa(config, prefix, weights)
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else:
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return TensorParallelColumnLinear.load_multi(
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config,
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prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
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dim=0,
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weights=weights,
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bias=False,
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)
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def _load_gqa(config, prefix: str, weights):
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assert config.hidden_size % config.num_attention_heads == 0
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assert config.num_attention_heads % weights.process_group.size() == 0
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weight = weights.get_multi_weights_col(
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prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
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quantize=config.quantize,
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dim=0,
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)
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if config.quantize not in ["gptq", "awq"]:
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weight = weight.to(dtype=weights.dtype).to(device=weights.device)
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head_size = config.hidden_size // config.num_attention_heads
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num_heads = config.num_attention_heads // weights.process_group.size()
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num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
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assert list(weight.shape) == [
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(num_heads + 2 * num_key_value_heads) * head_size,
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config.hidden_size,
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], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
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return TensorParallelColumnLinear(
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get_linear(weight, bias=None, quantize=config.quantize)
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)
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class Qwen2Attention(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.max_past = (
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config.sliding_window if config.sliding_window is not None else -1
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)
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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_theta,
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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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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}.o_proj",
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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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prefill_cache_indices,
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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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if prefill_cache_indices is not None:
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kv_to_cache = kv[prefill_cache_indices]
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else:
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kv_to_cache = kv
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paged_attention.reshape_and_cache(
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kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
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)
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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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flash_attn.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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window_size_left=self.max_past,
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)
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# Decode
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else:
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paged_attention.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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class Qwen2MLP(nn.Module):
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def __init__(self, prefix, config, weights):
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super().__init__()
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act = config.hidden_act
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self.act = (
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ACT2FN[act]
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if "gelu" not in act
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else lambda x: torch.nn.functional.gelu(
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x,
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approximate=(
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"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
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),
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)
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)
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# Fuse gate and up proj
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self.gate_up_proj = TensorParallelColumnLinear.load_multi(
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config,
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prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
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weights=weights,
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dim=0,
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bias=False,
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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=False,
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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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def forward(self, hidden_states):
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gate_up_states = self.gate_up_proj(hidden_states)
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gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
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return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1])
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class Qwen2Layer(nn.Module):
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def __init__(self, layer_id, config, weights):
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super().__init__()
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prefix = f"model.layers.{layer_id}"
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self.self_attn = Qwen2Attention(prefix=f"{prefix}.self_attn", config=config, weights=weights)
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self.mlp = Qwen2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
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self.input_layernorm = FastRMSNorm.load(
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prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
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)
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self.post_attention_layernorm = FastRMSNorm.load(
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prefix=f"{prefix}.post_attention_layernorm",
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weights=weights,
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eps=config.rms_norm_eps,
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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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prefill_cache_indices,
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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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prefill_cache_indices,
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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 Qwen2Model(torch.nn.Module):
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def __init__(self, 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.embed_tokens = TensorParallelEmbedding(
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prefix="model.embed_tokens", weights=weights
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)
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self.layers = nn.ModuleList(
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[
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Qwen2Layer(
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layer_id,
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config,
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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 = FastRMSNorm.load(
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prefix="model.norm", weights=weights, eps=config.rms_norm_eps
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)
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self.gradient_checkpointing = False
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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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def forward(
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self,
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input_ids: 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 = self.embed_tokens(input_ids)
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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, true_max_s, hidden_states.dtype
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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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prefill_cache_indices,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class Qwen2ForCausalLM(torch.nn.Module):
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def __init__(self, config, weights):
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super().__init__()
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self.model = Qwen2Model(config, weights)
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self.lm_head = TensorParallelHead.load(
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config,
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prefix="lm_head",
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weights=weights,
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)
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self.max_past = config.sliding_window
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self.max_past_tensor = (
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torch.tensor(config.sliding_window, device=weights.device)
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if self.max_past is not None
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else None
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)
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def forward(
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self,
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input_ids: 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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prefill_cache_indices: Optional[torch.Tensor] = None,
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lm_head_indices: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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true_max_s = max_s
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if prefill_cache_indices is not None:
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# Slots also need to be sliced as it has the same size as the whole kv tensor
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slots = slots[prefill_cache_indices]
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elif self.max_past is not None:
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# Clamp in decode mode as paged attention requires clamped values whereas the flash attention
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# kernel requires the true values
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input_lengths = torch.clamp(input_lengths, max=self.max_past_tensor)
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hidden_states = self.model(
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input_ids,
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position_ids,
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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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true_max_s,
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prefill_cache_indices,
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)
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if lm_head_indices is not None:
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hidden_states = hidden_states[lm_head_indices]
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logits = self.lm_head(hidden_states)
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return logits
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77
server/text_generation_server/models/flash_qwen2.py
Normal file
77
server/text_generation_server/models/flash_qwen2.py
Normal file
@ -0,0 +1,77 @@
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import torch
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import torch.distributed
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from opentelemetry import trace
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from transformers import AutoTokenizer
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from transformers.models.qwen2 import Qwen2Tokenizer
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from typing import Optional
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
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Qwen2ForCausalLM,
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)
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from transformers.models.qwen2 import Qwen2Config
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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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Weights,
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)
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tracer = trace.get_tracer(__name__)
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class FlashQwen2(FlashCausalLM):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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quantize: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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trust_remote_code: bool = False,
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):
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self.process_group, rank, world_size = initialize_torch_distributed()
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if torch.cuda.is_available():
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device = torch.device(f"cuda:{rank}")
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dtype = torch.float16 if dtype is None else dtype
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else:
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raise NotImplementedError("FlashQwen2 is only available on GPU")
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try:
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tokenizer = Qwen2Tokenizer.from_pretrained(
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model_id,
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revision=revision,
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trust_remote_code=trust_remote_code,
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)
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except Exception:
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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revision=revision,
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trust_remote_code=trust_remote_code,
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)
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||||
|
||||
config = Qwen2Config.from_pretrained(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
config.quantize = quantize
|
||||
|
||||
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 = Qwen2ForCausalLM(config, weights)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashQwen2, 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