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https://github.com/vllm-project/vllm-ascend.git
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46e62efd44
### What this PR does / why we need it? Currently, MTP Model in deepseek can not be capture in ACLGraph. This PR is use to allow MTP to be captured in ACLGraph mode. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
210 lines
8.5 KiB
Python
210 lines
8.5 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Adapted from vllm/model_executor/models/deepseek_mtp.py
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# Copyright 2023 The vLLM team.
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#
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# This file is a part of the vllm-ascend project.
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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
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.attention.backends.abstract import AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import (CacheConfig, ModelConfig, VllmConfig,
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get_current_vllm_config)
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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.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.models.deepseek_mtp import (
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DeepSeekMTP, DeepSeekMultiTokenPredictor, DeepSeekMultiTokenPredictorLayer,
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SharedHead)
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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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from vllm.sequence import IntermediateTensors
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class CustomDeepSeekShareHead(SharedHead):
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def __init__(self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "") -> None:
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nn.Module.__init__(self)
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.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(prefix, "head"))
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class CustomDeepSeekMultiTokenPredictorLayer(DeepSeekMultiTokenPredictorLayer):
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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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model_config: ModelConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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nn.Module.__init__(self)
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vllm_config = get_current_vllm_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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self.shared_head = CustomDeepSeekShareHead(config=config,
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quant_config=quant_config,
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prefix=maybe_prefix(
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prefix, "shared_head"))
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self.mtp_block = DeepseekV2DecoderLayer(vllm_config=vllm_config,
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prefix=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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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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inputs_embeds = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
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inputs_embeds, True)
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# masking inputs at position 0, as not needed by MTP
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inputs_embeds = torch.where((positions == 0).unsqueeze(-1),
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torch.zeros_like(inputs_embeds),
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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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class CustomDeepSeekMultiTokenPredictor(DeepSeekMultiTokenPredictor):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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nn.Module.__init__(self)
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config = vllm_config.model_config.hf_config
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self.mtp_start_layer_idx = config.num_hidden_layers
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self.num_mtp_layers = config.num_nextn_predict_layers
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# to map the exact layer index from weights
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self.layers = torch.nn.ModuleDict({
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str(idx):
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CustomDeepSeekMultiTokenPredictorLayer(
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config,
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f"{prefix}.layers.{idx}",
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model_config=vllm_config.model_config,
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cache_config=vllm_config.cache_config,
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quant_config=vllm_config.quant_config,
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)
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for idx in range(self.mtp_start_layer_idx,
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self.mtp_start_layer_idx + self.num_mtp_layers)
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})
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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# Note: torch._dynamo.exc.Unsupported: builtin: str
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self.layers_list = [
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self.layers[str(idx)]
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for idx in range(self.mtp_start_layer_idx,
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self.mtp_start_layer_idx + self.num_mtp_layers)
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]
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self.logits_processor = LogitsProcessor(config.vocab_size)
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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: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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_idx: int = 0,
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) -> torch.Tensor:
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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current_step_idx = (spec_step_idx % self.num_mtp_layers)
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step_kv_cache = kv_caches[
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current_step_idx] if kv_caches is not None else None
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return self.layers_list[current_step_idx](
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input_ids,
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positions,
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step_kv_cache,
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attn_metadata,
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previous_hidden_states,
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inputs_embeds,
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current_step_idx,
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)
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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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spec_step_idx: int = 0,
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) -> torch.Tensor:
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current_step_idx = (spec_step_idx % self.num_mtp_layers)
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mtp_layer = self.layers_list[current_step_idx]
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logits = self.logits_processor(mtp_layer.shared_head.head,
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mtp_layer.shared_head(hidden_states),
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sampling_metadata)
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return logits
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@support_torch_compile
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class CustomDeepSeekMTP(DeepSeekMTP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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nn.Module.__init__(self)
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self.config = vllm_config.model_config.hf_config
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self.model = CustomDeepSeekMultiTokenPredictor(vllm_config=vllm_config,
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prefix=maybe_prefix(
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prefix, "model"))
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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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previous_hidden_states: Optional[torch.Tensor] = None,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata, previous_hidden_states,
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inputs_embeds, spec_step_idx)
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hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
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hidden_states, True)
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return hidden_states
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