[Bugfix][Model] Fix api in DeepSeek model (#545)

### What this PR does / why we need it?
Fix api in DeepSeekV2, aligning with the latest code of the main branch
in vllm.

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
Test locally with deepseek-v2-lite, and will add CI by @Potabk.
Plz update the model UT after this pr is merged, thx! cc @Potabk

Signed-off-by: MengqingCao <cmq0113@163.com>

vllm_ascend/models/deepseek_v2.py

@@ -24,12 +24,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only DeepseekV2/DeepseekV3 model."""
-from typing import List, Optional, Union
+from typing import Optional, Union
 
 import torch
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.attention import AttentionMetadata
 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
@@ -61,11 +60,6 @@ class CustomDeepseekV2MoE(DeepseekV2MoE):
         self.tp_size = get_tensor_model_parallel_world_size()
         self.routed_scaling_factor = config.routed_scaling_factor
         self.n_shared_experts = config.n_shared_experts
-        self.routed_scaling_factor = config.routed_scaling_factor
-        if self.tp_size > config.n_routed_experts:
-            raise ValueError(
-                f"Tensor parallel size {self.tp_size} is greater than "
-                f"the number of experts {config.n_routed_experts}.")
 
         if config.hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {config.hidden_act}. "
@@ -129,6 +123,7 @@ class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):
         # DecoderLayers are created with `make_layers` which passes the prefix
         # with the layer's index.
         layer_idx = int(prefix.split(sep='.')[-1])
+        self.layer_idx = layer_idx
         if model_config.use_mla:
             attn_cls = DeepseekV2MLAAttention
         else:
@@ -171,6 +166,7 @@ class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):
                                        eps=config.rms_norm_eps)
         self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                 eps=config.rms_norm_eps)
+        self.routed_scaling_factor = config.routed_scaling_factor
 
 
 class CustomDeepseekV2Model(nn.Module):
@@ -184,8 +180,8 @@ class CustomDeepseekV2Model(nn.Module):
         model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
+        self.config = config
 
-        self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
         if get_pp_group().is_first_rank:
@@ -223,8 +219,6 @@ class CustomDeepseekV2Model(nn.Module):
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
-        kv_caches: List[torch.Tensor],
-        attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors],
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
@@ -239,11 +233,8 @@ class CustomDeepseekV2Model(nn.Module):
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
 
-        for i in range(self.start_layer, self.end_layer):
-            layer = self.layers[i]
-            hidden_states, residual = layer(positions, hidden_states,
-                                            kv_caches[i - self.start_layer],
-                                            attn_metadata, residual)
+        for layer in self.layers[self.start_layer:self.end_layer]:
+            hidden_states, residual = layer(positions, hidden_states, residual)
 
         if not get_pp_group().is_last_rank:
             return IntermediateTensors({
@@ -272,9 +263,12 @@ class CustomDeepseekV2ForCausalLM(DeepseekV2ForCausalLM):
         self.model = CustomDeepseekV2Model(vllm_config=vllm_config,
                                            prefix=maybe_prefix(
                                                prefix, "model"))
-        self.lm_head = ParallelLMHead(config.vocab_size,
-                                      config.hidden_size,
-                                      quant_config=quant_config)
+        if get_pp_group().is_last_rank:
+            self.lm_head = ParallelLMHead(config.vocab_size,
+                                          config.hidden_size,
+                                          quant_config=quant_config)
+        else:
+            self.lm_head = PPMissingLayer()
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = get_sampler()
         self.make_empty_intermediate_tensors = (