mirror of
https://github.com/handsomezhuzhu/vllm-npu-plugin.git
synced 2026-02-20 19:50:15 +00:00
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
from typing import Optional
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import torch
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import torch.nn as nn
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import vllm
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from transformers import PretrainedConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.models.deepseek_mtp import (
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DeepSeekMTP, DeepSeekMultiTokenPredictorLayer)
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from vllm.model_executor.models.deepseek_v2 import DeepseekV2DecoderLayer
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from vllm.model_executor.models.utils import maybe_prefix
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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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previous_hidden_states: torch.Tensor,
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inputs_embeds: Optional[torch.Tensor] = None,
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spec_step_index: int = 0,
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) -> torch.Tensor:
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assert inputs_embeds is not None
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# masking inputs at position 0, as not needed by MTP
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# Patch this for aclgraph support, as the original operation introduced d2h sync,
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# which breaks aclgraph
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inputs_embeds = torch.where(positions.unsqueeze(-1) == 0, 0, inputs_embeds)
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inputs_embeds = self.enorm(inputs_embeds)
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previous_hidden_states = self.hnorm(previous_hidden_states)
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hidden_states = self.eh_proj(
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torch.cat([inputs_embeds, previous_hidden_states], dim=-1))
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hidden_states, residual = self.mtp_block(positions=positions,
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hidden_states=hidden_states,
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residual=None)
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hidden_states = residual + hidden_states
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return hidden_states
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# Patch this only for aclgraph support, as this is not support in vLLM 0.11.0
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@support_torch_compile
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class AscendDeepSeekMTP(DeepSeekMTP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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class SharedHead(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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prefix: str,
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quant_config: QuantizationConfig = None,
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) -> None:
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super().__init__()
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.head = ParallelLMHead(
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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(prefix, "head"),
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return self.norm(hidden_states)
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def predictor_init(self, vllm_config: VllmConfig, prefix: str) -> None:
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nn.Module.__init__(self)
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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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self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.eh_proj = nn.Linear(config.hidden_size * 2,
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config.hidden_size,
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bias=False)
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# We don't need topk_indices_buffer in Ascend
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topk_indices_buffer = None
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self.shared_head = SharedHead(config=config,
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prefix=prefix,
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quant_config=quant_config)
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self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix,
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topk_indices_buffer)
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DeepSeekMultiTokenPredictorLayer.__init__ = predictor_init
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vllm.model_executor.models.deepseek_mtp.DeepSeekMultiTokenPredictorLayer.forward = forward
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