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https://github.com/handsomezhuzhu/vllm-npu-plugin.git
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364 lines
14 KiB
Python
364 lines
14 KiB
Python
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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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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# This file is a part of the vllm-ascend project.
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from collections.abc import Iterable
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from typing import Any, List, Optional, Union
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import torch
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import torch.nn.functional as F
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import vllm
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import vllm.envs as envs
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from torch import nn
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from transformers import Qwen2Config
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from vllm.attention import AttentionMetadata, AttentionType
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import (get_pp_group, tensor_model_parallel_all_gather,
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tensor_model_parallel_reduce_scatter)
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP
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from vllm.model_executor.models.qwen2 import Qwen2Attention # noqa: F401
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from vllm.model_executor.models.qwen2 import Qwen2ForCausalLM # noqa: F401
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from vllm.model_executor.models.qwen2 import Qwen2MLP, Qwen2Model
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from vllm.model_executor.models.utils import (AutoWeightsLoader,
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PPMissingLayer, maybe_prefix)
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from vllm.sequence import IntermediateTensors
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from vllm_npu.ascend_config import get_ascend_config
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from vllm_npu.attention.attention_v1 import AscendAttentionState
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def all_gather_and_maybe_unpad(
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hidden_states: torch.Tensor,
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pad_size: int,
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) -> torch.Tensor:
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hidden_states = tensor_model_parallel_all_gather(hidden_states, 0)
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if pad_size > 0:
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return hidden_states[:-pad_size, :]
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return hidden_states
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def maybe_pad_and_reduce_scatter(
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hidden_states: torch.Tensor,
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pad_size: int,
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) -> torch.Tensor:
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if pad_size > 0:
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hidden_states = F.pad(hidden_states, (0, 0, 0, pad_size))
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hidden_states = tensor_model_parallel_reduce_scatter(hidden_states, 0)
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return hidden_states
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class CustomQwen2Attention(Qwen2Attention):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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max_position: int = 4096 * 32,
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rope_theta: float = 10000,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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rope_scaling: Optional[tuple] = None,
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prefix: str = "",
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attn_type: str = AttentionType.DECODER,
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dual_chunk_attention_config: Optional[dict[str, Any]] = None,
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) -> None:
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super().__init__(
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hidden_size=hidden_size,
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num_heads=num_heads,
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num_kv_heads=num_kv_heads,
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max_position=max_position,
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rope_theta=rope_theta,
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cache_config=cache_config,
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quant_config=quant_config,
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rope_scaling=rope_scaling,
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prefix=prefix,
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attn_type=attn_type,
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dual_chunk_attention_config=dual_chunk_attention_config)
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ascend_config = get_ascend_config()
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self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: Optional[torch.Tensor] = None,
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attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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if self.torchair_graph_enabled and attn_metadata is not None and attn_metadata.attn_state == AscendAttentionState.DecodeOnly:
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q, k = self.rotary_emb(positions,
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q,
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k,
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is_prefill=False,
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is_qwen_torchair=True)
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forward_kwargs = {}
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if envs.VLLM_USE_V1:
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output_shape = q.shape
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output = torch.empty(output_shape,
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dtype=q.dtype,
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device=q.device)
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forward_kwargs['output'] = output
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attn_output = self.attn.impl.forward(self.attn,
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q,
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k,
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v,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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trace_flag=False,
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**forward_kwargs)
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output, _ = self.o_proj(attn_output)
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return output
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else:
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if type(self.rotary_emb) is RotaryEmbedding:
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q, k = self.rotary_emb(positions, q, k, is_qwen_torchair=True)
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else:
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class CustomQwen2DecoderLayer(nn.Module):
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def __init__(
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self,
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config: Qwen2Config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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# Requires transformers > 4.32.0
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rope_theta = getattr(config, "rope_theta", 1000000)
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rope_scaling = getattr(config, "rope_scaling", None)
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dual_chunk_attention_config = getattr(config,
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"dual_chunk_attention_config",
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None)
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# By default, Qwen2 uses causal attention as it is a decoder-only model.
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# You can override the HF config with `is_causal=False` to enable
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# bidirectional attention, which is used in some embedding models
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# (e.g. Alibaba-NLP/gte-Qwen2-7B-instruct)
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if getattr(config, "is_causal", True):
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attn_type = AttentionType.DECODER
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else:
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attn_type = AttentionType.ENCODER_ONLY
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self.self_attn = CustomQwen2Attention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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max_position=config.max_position_embeddings,
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num_kv_heads=config.num_key_value_heads,
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rope_theta=rope_theta,
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cache_config=cache_config,
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quant_config=quant_config,
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rope_scaling=rope_scaling,
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prefix=f"{prefix}.self_attn",
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attn_type=attn_type,
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dual_chunk_attention_config=dual_chunk_attention_config,
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)
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self.mlp = Qwen2MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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kv_cache: Optional[torch.Tensor] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile(
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dynamic_arg_dims={
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"input_ids": 0,
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# positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
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# otherwise (seq_len, ).
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"positions": -1,
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"intermediate_tensors": 0,
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"inputs_embeds": 0,
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})
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class CustomQwen2Model(Qwen2Model):
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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decoder_layer_type: type[nn.Module] = CustomQwen2DecoderLayer):
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super().__init__(vllm_config=vllm_config,
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prefix=prefix,
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decoder_layer_type=decoder_layer_type)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: Optional[List[torch.Tensor]] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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kv_cache = kv_caches[i - self.start_layer] \
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if kv_caches is not None else None
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hidden_states, residual = layer(positions,
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hidden_states,
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residual,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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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 CustomQwen2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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# add `CustomQwen2Model` to init self.model
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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self.config = config
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self.lora_config = lora_config
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self.quant_config = quant_config
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self.model = CustomQwen2Model(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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if get_pp_group().is_last_rank:
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if config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(
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prefix, "lm_head"))
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else:
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self.lm_head = PPMissingLayer()
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: Optional[List[torch.Tensor]] = None,
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attn_metadata: Optional[AttentionMetadata] = None,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata, intermediate_tensors,
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inputs_embeds)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata=None, # type: ignore
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=(["lm_head."]
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if self.config.tie_word_embeddings else None),
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)
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return loader.load_weights(weights)
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vllm.model_executor.models.qwen2.Qwen2ForCausalLM = CustomQwen2ForCausalLM
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