[Model] use AutoWeightsLoader for gpt2

vllm/model_executor/models/gpt2.py

@@ -43,7 +43,7 @@
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsPP
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -235,6 +235,35 @@ def forward(
         hidden_states = self.ln_f(hidden_states)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if ".attn.bias" in name or ".attn.masked_bias" in name:
+                # Skip attention mask.
+                # NOTE: "c_attn.bias" should not be skipped.
+                continue
+
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            # The HF's GPT-2 implementation uses Conv1D instead of Linear.
+            # Because of this, we need to transpose the weights.
+            # Note(zhuohan): the logic below might break quantized models.
+            for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
+                if conv1d_weight_name not in name:
+                    continue
+                if not name.endswith(".weight"):
+                    continue
+                loaded_weight = loaded_weight.t()
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class GPT2LMHeadModel(nn.Module, SupportsPP):
 
@@ -283,32 +312,16 @@ def compute_logits(
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
-        loaded_params: set[str] = set()
-        for name, loaded_weight in weights:
-            if ".attn.bias" in name or ".attn.masked_bias" in name:
-                # Skip attention mask.
-                # NOTE: "c_attn.bias" should not be skipped.
-                continue
-            if not name.startswith("transformer.") and not name.startswith(
-                    "lm_head"):
-                name = "transformer." + name
-
-            if is_pp_missing_parameter(name, self):
-                continue
-
-            param = params_dict[name]
-            # The HF's GPT-2 implementation uses Conv1D instead of Linear.
-            # Because of this, we need to transpose the weights.
-            # Note(zhuohan): the logic below might break quantized models.
-            for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
-                if conv1d_weight_name not in name:
-                    continue
-                if not name.endswith(".weight"):
-                    continue
-                loaded_weight = loaded_weight.t()
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params
+        loader = AutoWeightsLoader(self)
+        weights = _add_transformer_prefix(weights)
+        return loader.load_weights(weights)
+
+
+def _add_transformer_prefix(
+    weights: Iterable[tuple[str, torch.Tensor]]
+) -> Iterable[tuple[str, torch.Tensor]]:
+    for name, tensor in weights:
+        if not name.startswith('transformer.') and not name.startswith(
+                "lm_head"):
+            name = 'transformer.' + name
+        yield name, tensor