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Add note for deepseek related docs and remove unnecessary comments (#590)

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### What this PR does / why we need it?
Add notes for deepseek's patch and remove some of the unnecessary
comments

---------

Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
This commit is contained in:
Pleaplusone
2025-04-22 09:59:09 +08:00
committed by GitHub
parent c5850d302d
commit d12a057df8
6 changed files with 78 additions and 199 deletions
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48
vllm_ascend/models/deepseek_v2.py
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@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
# Copyright 2023 DeepSeek-AI and the HuggingFace Inc. team. All rights reserved.
#
@@ -19,31 +19,11 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# <<<<<<< HEAD
# # Adapted from
# # vllm-project/vllm/blob/main/vllm/model_executor/models/deepseek_v2.py
# # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# # vllm-project/vllm/vllm/model_executor/models/deepseek_v2.py
# """Inference-only DeepseekV2/DeepseekV3 model."""
# from typing import Optional, Union
# import torch
# from torch import nn
# from transformers import PretrainedConfig
# from vllm.config import CacheConfig, ModelConfig, VllmConfig
# from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
# from vllm.model_executor.layers.fused_moe import FusedMoE
# from vllm.model_executor.layers.layernorm import RMSNorm
# from vllm.model_executor.layers.linear import ReplicatedLinear
# from vllm.model_executor.layers.logits_processor import LogitsProcessor
# from vllm.model_executor.layers.quantization import QuantizationConfig
# from vllm.model_executor.layers.sampler import get_sampler
# from vllm.model_executor.layers.vocab_parallel_embedding import (
# ParallelLMHead, VocabParallelEmbedding)
# from vllm.model_executor.models.deepseek_v2 import ( # noqa
# DeepseekV2Attention, DeepseekV2DecoderLayer, DeepseekV2ForCausalLM,
# DeepseekV2MLAAttention, DeepseekV2MLP, DeepseekV2MoE)
# =======
import os
from typing import Any, Dict, Optional, Union
@@ -173,9 +153,6 @@ class CustomDeepseekV2MoE(nn.Module):
if (self.tp_size > 1 and self.enable_mc2
and attn_metadata.num_prefills == 0):
# hidden_states = dist._functional_collectives.reduce_scatter_tensor(
# hidden_states, "sum", scatter_dim=0, group=self.tp_group
# )
chunks = torch.chunk(hidden_states,
get_tp_group().world_size,
dim=0)
@@ -365,29 +342,6 @@ class CustomDeepseekV2MLAAttention(DeepseekV2MLAAttention):
k_pe,
output_shape=hidden_states.shape)
# def forward(
# self,
# positions: torch.Tensor,
# hidden_states: torch.Tensor,
# # torchair should pass below two parameters
# kv_cache: torch.Tensor = None,
# attn_metadata: AttentionMetadata = None,
# ) -> torch.Tensor:
# if self.q_lora_rank is not None:
# ckq = self.q_a_proj(hidden_states)[0]
# hidden_states_or_q_c = self.q_a_layernorm(ckq)
# else:
# hidden_states_or_q_c = hidden_states
# if VLLM_ENABLE_GRAPH_MODE == '1':
# return self.mla_attn(hidden_states_or_q_c, hidden_states, None,
# kv_cache, attn_metadata)
# else:
# kv_c, k_pe = self.kv_a_proj_with_mqa(hidden_states)[0].split(
# [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
# kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
# return self.mla_attn(hidden_states_or_q_c, kv_c_normed, k_pe, output_shape=hidden_states.shape)
# kv_cache, attn_metadata)
class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):