[refactor] refactor deepseek-related files (#2849)

### What this PR does / why we need it?
This PR deletes ~2K lines of code about deepseek modeling. It falls back
CustomDeepseekV2 modules to original vllm implementations and adapts
some modifications in vllm about deepseek and moe.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
E2E  vllm serving with torchair graph mode and eager mode.

- vLLM version: v0.10.2
- vLLM main:
759ef49b15

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: yiz-liu <136800916+yiz-liu@users.noreply.github.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/models/deepseek_mtp.py

@@ -23,7 +23,8 @@ import torch
 import torch.nn as nn
 from transformers import PretrainedConfig
 from vllm.attention.backends.abstract import AttentionMetadata
-from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.config import (CacheConfig, ModelConfig, VllmConfig,
+                         get_current_vllm_config)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
@@ -33,12 +34,11 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.models.deepseek_mtp import (
     DeepSeekMTP, DeepSeekMultiTokenPredictor, DeepSeekMultiTokenPredictorLayer,
     SharedHead)
+from vllm.model_executor.models.deepseek_v2 import DeepseekV2DecoderLayer
 from vllm.model_executor.models.utils import maybe_prefix
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
-from .deepseek_v2 import CustomDeepseekV2DecoderLayer
-
 
 class CustomDeepSeekShareHead(SharedHead):
 
@@ -65,6 +65,7 @@ class CustomDeepSeekMultiTokenPredictorLayer(DeepSeekMultiTokenPredictorLayer):
         quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         nn.Module.__init__(self)
+        vllm_config = get_current_vllm_config()
 
         self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -75,10 +76,8 @@ class CustomDeepSeekMultiTokenPredictorLayer(DeepSeekMultiTokenPredictorLayer):
                                                    quant_config=quant_config,
                                                    prefix=maybe_prefix(
                                                        prefix, "shared_head"))
-        self.mtp_block = CustomDeepseekV2DecoderLayer(config, prefix,
-                                                      model_config,
-                                                      cache_config,
-                                                      quant_config)
+        self.mtp_block = DeepseekV2DecoderLayer(vllm_config=vllm_config,
+                                                prefix=prefix)
 
     def forward(
         self,
@@ -103,8 +102,6 @@ class CustomDeepSeekMultiTokenPredictorLayer(DeepSeekMultiTokenPredictorLayer):
 
         hidden_states, residual = self.mtp_block(positions=positions,
                                                  hidden_states=hidden_states,
-                                                 kv_cache=kv_cache,
-                                                 attn_metadata=attn_metadata,
                                                  residual=None)
         hidden_states = residual + hidden_states
         return hidden_states