hunyuan分类模型适配

vllm/model_executor/layers/linear.py

@@ -3,7 +3,7 @@
 
 import itertools
 from abc import abstractmethod
-from typing import Any, Literal, Optional, Union
+from typing import Any, Iterable,Literal, Optional, Union
 import vllm.envs as envs
 import torch
 import torch.nn as nn
@@ -414,6 +414,53 @@ class ReplicatedLinear(LinearBase):
         else:
             self.register_parameter("bias", None)
         self.is_quantization = not isinstance(self.quant_method, UnquantizedLinearMethod)
+    def load_weights(
+        self,
+        weights: Iterable[tuple[str, torch.Tensor]],
+    ) -> set[str]:
+        """Load parameters from (name, tensor) pairs into this layer."""
+        params = dict(self.named_parameters(recurse=False))
+        buffers = dict(self.named_buffers(recurse=False))
+        loaded: set[str] = set()
+
+        for weight_name, loaded_weight in weights:
+            # Default to the primary weight parameter if no suffix is given.
+            target_name = weight_name or "weight"
+
+            if target_name in params:
+                param = params[target_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        self.weight_loader)
+                weight_loader(param, loaded_weight)
+                loaded.add(target_name)
+                continue
+
+            if target_name in buffers:
+                buffer = buffers[target_name]
+                if buffer.shape != loaded_weight.shape:
+                    raise ValueError(
+                        f"Shape mismatch when loading buffer '{target_name}': "
+                        f"expected {buffer.shape}, got {loaded_weight.shape}")
+                buffer.copy_(loaded_weight)
+                loaded.add(target_name)
+                continue
+
+            attr = getattr(self, target_name, None)
+            if isinstance(attr, torch.Tensor):
+                if attr.shape != loaded_weight.shape:
+                    raise ValueError(
+                        f"Shape mismatch when loading tensor '{target_name}': "
+                        f"expected {attr.shape}, got {loaded_weight.shape}")
+                attr.copy_(loaded_weight)
+                loaded.add(target_name)
+                continue
+
+            raise ValueError(
+                f"Unexpected weight '{target_name}' for "
+                f"{type(self).__name__}")
+
+        return loaded
+
 
     def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         # If the weight on disk does not have a shape, give it one

vllm/model_executor/model_loader/utils.py

@@ -30,7 +30,12 @@ from vllm.utils import is_pin_memory_available
 import vllm.envs as envs
 
 logger = init_logger(__name__)
-
+from ..models.adapters_custom.adapters_classify import (
+    as_hunyuan_seq_cls_model,
+)
+CLASSIFY_CLASSIFY_REGISTRY = {
+    "HunYuanForCausalLM": as_hunyuan_seq_cls_model,
+}
 
 @contextlib.contextmanager
 def set_default_torch_dtype(dtype: torch.dtype):
@@ -257,8 +262,11 @@ def _get_model_architecture(
         logger.debug_once("Converting to embedding model.")
         model_cls = as_embedding_model(model_cls)
     elif convert_type == "classify":
-        logger.debug_once("Converting to sequence classification model.")
-        model_cls = as_seq_cls_model(model_cls)
+        if arch in CLASSIFY_CLASSIFY_REGISTRY.keys():
+            model_cls = CLASSIFY_CLASSIFY_REGISTRY[arch](model_cls)
+        else:
+            logger.debug_once("Converting to sequence classification model.")
+            model_cls = as_seq_cls_model(model_cls)
     elif convert_type == "reward":
         logger.debug_once("Converting to reward model.")
         model_cls = as_reward_model(model_cls)

vllm/model_executor/models/hunyuan.py

@@ -36,6 +36,9 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
                                                ReplicatedLinear,
                                                RowParallelLinear)
+from vllm.model_executor.layers.pooler import (ClassifierPooler,
+                                               DispatchPooler, Pooler,
+                                               PoolingMethod, PoolingType)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
@@ -553,23 +556,23 @@ class HunYuanDecoderLayer(nn.Module):
         residual: Optional[torch.Tensor],
         kv_states: Optional[Tuple[torch.Tensor]] = None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Self Attention
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.input_layernorm(hidden_states)
-        else:
-            hidden_states, residual = self.input_layernorm(
-                hidden_states, residual)
+    
+    
+        residual=hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
         hidden_states, ori_kv_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
             kv_states=kv_states,
         )
+        hidden_states =residual+hidden_states
+        residual=hidden_states
+
+        hidden_states= self.post_attention_layernorm(hidden_states)
+        hidden_states=self.mlp(hidden_states)
+        hidden_states=hidden_states+residual
 
-        # Fully Connected
-        hidden_states, residual = self.post_attention_layernorm(
-            hidden_states, residual)
-        hidden_states = self.mlp(hidden_states)
         return hidden_states, residual, ori_kv_states
 
 
@@ -614,11 +617,13 @@ class HunYuanModel(nn.Module):
                 prefix=prefix,
             ),
             prefix=f"{prefix}.layers")
+        
         if get_pp_group().is_last_rank:
             self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         else:
             self.norm = PPMissingLayer()
 
+
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
@@ -650,7 +655,7 @@ class HunYuanModel(nn.Module):
                 residual,
                 prev_kv_states,
             )
-
+            
             if (getattr(self.config, "use_cla", False)
                     and (i - self.start_layer) % cla_factor == 0):
                 prev_kv_states = kv_states
@@ -662,8 +667,8 @@ class HunYuanModel(nn.Module):
                 "hidden_states": hidden_states,
                 "residual": residual
             })
-
-        hidden_states, _ = self.norm(hidden_states, residual)
+        if not self.config.add_classification_head:
+            hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
     def _split_qkv_weight(self, qkv: torch.Tensor):
@@ -732,6 +737,10 @@ class HunYuanModel(nn.Module):
         loaded_params: set[str] = set()
         expert_params_mapping = self.get_expert_mapping()
         for name, loaded_weight in weights:
+            if name.startswith("norm."):
+                # Some checkpoints omit the final norm; treat as handled.
+                loaded_params.add(name)
+                continue
             if "rotary_emb.inv_freq" in name:
                 continue
             if "gate_proj_bias" in name:
@@ -880,8 +889,16 @@ class HunYuanModel(nn.Module):
                                             default_weight_loader)
                     weight_loader(param, loaded_weight)
             loaded_params.add(name)
+        if "norm.weight" in params_dict:
+            loaded_params.add("norm.weight")
         return loaded_params
-    
+
+
+        
+
+        
+
+
 class HunYuanForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     packed_modules_mapping = {
         "qkv_proj": [
@@ -902,8 +919,11 @@ class HunYuanForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         quant_config = vllm_config.quant_config
         self.config = config
         self.quant_config = quant_config
-
+        self.pad_id = self.config.pad_id
+        
         self.model = HunYuanModel(vllm_config=vllm_config, prefix="model")
+        
+            
         if get_pp_group().is_last_rank:
             self.unpadded_vocab_size = config.vocab_size
             self.lm_head = ParallelLMHead(
@@ -924,6 +944,7 @@ class HunYuanForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         else:
             self.lm_head = PPMissingLayer()
 
+
     def set_eplb_state(
         self,
         expert_load_view: torch.Tensor,
@@ -957,18 +978,26 @@ class HunYuanForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 moe.n_physical_experts = num_physical_experts
                 moe.n_redundant_experts = self.num_redundant_experts
                 moe.experts.update_expert_map()
-                
+
+
+
+
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         model_output = self.model(input_ids, positions, intermediate_tensors,
                                   inputs_embeds)
+
+
         return model_output
+        
     
+
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
@@ -992,14 +1021,13 @@ class HunYuanForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
-        loader = AutoWeightsLoader(
-            self,
-            skip_prefixes=(["lm_head."]
-                           if self.config.tie_word_embeddings else None),
-        )
+        skip_prefixes = []
+        if self.config.tie_word_embeddings:
+            skip_prefixes.append("lm_head.")
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
         return loader.load_weights(weights)
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.model.get_input_embeddings(input_ids)
 
-    
\ No newline at end of file
+