Simplify weight loading in Transformers backend (#21382)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/distributed/test_pipeline_parallel.py

@@ -177,7 +177,7 @@ TEXT_GENERATION_MODELS = {
     "ai21labs/Jamba-tiny-dev": PPTestSettings.fast(),
     "meta-llama/Llama-3.2-1B-Instruct": PPTestSettings.detailed(),
     # Tests TransformersForCausalLM
-    "ArthurZ/Ilama-3.2-1B": PPTestSettings.fast(),
+    "hmellor/Ilama-3.2-1B": PPTestSettings.fast(),
     "openbmb/MiniCPM-2B-sft-bf16": PPTestSettings.fast(),
     "openbmb/MiniCPM3-4B": PPTestSettings.fast(),
     # Uses Llama
@@ -249,7 +249,7 @@ TEST_MODELS = [
     # [LANGUAGE GENERATION]
     "microsoft/Phi-3.5-MoE-instruct",
     "meta-llama/Llama-3.2-1B-Instruct",
-    "ArthurZ/Ilama-3.2-1B",
+    "hmellor/Ilama-3.2-1B",
     "ibm/PowerLM-3b",
     "deepseek-ai/DeepSeek-V2-Lite-Chat",
     # [LANGUAGE EMBEDDING]

tests/lora/test_transformers_model.py

@@ -9,7 +9,7 @@ from vllm.platforms import current_platform
 
 from ..utils import create_new_process_for_each_test, multi_gpu_test
 
-MODEL_PATH = "ArthurZ/ilama-3.2-1B"
+MODEL_PATH = "hmellor/Ilama-3.2-1B"
 
 PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:"""  # noqa: E501
 

tests/models/registry.py

@@ -500,7 +500,7 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
 }
 
 _TRANSFORMERS_MODELS = {
-    "TransformersForCausalLM": _HfExamplesInfo("ArthurZ/Ilama-3.2-1B", trust_remote_code=True),  # noqa: E501
+    "TransformersForCausalLM": _HfExamplesInfo("hmellor/Ilama-3.2-1B", trust_remote_code=True),  # noqa: E501
     "TransformersForMultimodalLM": _HfExamplesInfo("OpenGVLab/InternVL3-1B-hf"),
 }
 

tests/models/test_transformers.py

@@ -56,7 +56,7 @@ def check_implementation(
     "model,model_impl",
     [
         ("meta-llama/Llama-3.2-1B-Instruct", "transformers"),
-        ("ArthurZ/Ilama-3.2-1B", "auto"),  # CUSTOM CODE
+        ("hmellor/Ilama-3.2-1B", "auto"),  # CUSTOM CODE
     ])  # trust_remote_code=True by default
 def test_models(
     hf_runner: type[HfRunner],

vllm/model_executor/models/interfaces.py

@@ -624,13 +624,9 @@ class SupportsQuant:
             instance.quant_config = quant_config
 
             # apply model mappings to config for proper config-model matching
-            # NOTE: `TransformersForCausalLM` is not supported due to how this
+            if (hf_to_vllm_mapper := instance.hf_to_vllm_mapper) is not None:
-            # class defines `hf_to_vllm_mapper` as a post-init `@property`.
+                instance.quant_config.apply_vllm_mapper(hf_to_vllm_mapper)
-            # After this is fixed, get `instance.hf_to_vllm_mapper` directly
+            if instance.packed_modules_mapping is not None:
-            if getattr(instance, "hf_to_vllm_mapper", None) is not None:
-                instance.quant_config.apply_vllm_mapper(
-                    instance.hf_to_vllm_mapper)
-            if getattr(instance, "packed_modules_mapping", None) is not None:
                 instance.quant_config.packed_modules_mapping.update(
                     instance.packed_modules_mapping)
 

vllm/model_executor/models/transformers.py

@@ -414,7 +414,7 @@ class ConfigOverride:
                 setattr(self.config, key, value)
 
 
-class TransformersModel(nn.Module):
+class TransformersModel:
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -454,9 +454,6 @@ class TransformersModel(nn.Module):
         # method after v4.54.0 is released
         self.text_config._attn_implementation = "vllm"
         with init_on_device_without_buffers("meta"), config_override:
-            # FIXME(Isotr0py): We need to refactor this part in the future to
-            # avoid registering an extra model layer, otherwise we will need a
-            # weights mapper to rename weights.
             self.model: PreTrainedModel = AutoModel.from_config(
                 config,
                 torch_dtype=model_config.dtype,
@@ -620,9 +617,6 @@ class TransformersModel(nn.Module):
         for child in module.children():
             self.init_parameters(child)
 
-    def get_input_embeddings(self) -> nn.Module:
-        return self.model.get_input_embeddings()
-
     def forward(
         self,
         input_ids: Optional[torch.Tensor],
@@ -694,7 +688,9 @@ class TransformersForCausalLM(nn.Module, SupportsQuant, SupportsLoRA,
 
         self.config = config
 
-        self.model = TransformersModel(vllm_config=vllm_config, prefix=prefix)
+        self.transformers_model = TransformersModel(vllm_config=vllm_config,
+                                                    prefix=prefix)
+        self.model = self.transformers_model.model
 
         if get_pp_group().is_last_rank:
             self.unpadded_vocab_size = config.vocab_size
@@ -716,22 +712,7 @@ class TransformersForCausalLM(nn.Module, SupportsQuant, SupportsLoRA,
             self.lm_head = PPMissingLayer()
 
         self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors)
+            self.transformers_model.make_empty_intermediate_tensors)
-
-    # FIXME(Isotr0py): Don't use any weights mapper for Transformers backend,
-    # this makes thing complicated. We need to remove this mapper after refactor
-    # `TransformersModel` in the future.
-    # NOTE: `SupportsQuant` can be updated after property decorator is removed
-    @property
-    def hf_to_vllm_mapper(self):
-        prefix_mapper = {
-            name: "model." + name
-            for name, _ in self.model.model.named_children()
-        }
-        return WeightsMapper(
-            orig_to_new_substr={"model.": "model.model."},
-            orig_to_new_prefix=prefix_mapper,
-        )
 
     def forward(
         self,
@@ -740,8 +721,9 @@ class TransformersForCausalLM(nn.Module, SupportsQuant, SupportsLoRA,
         intermediate_tensors: Optional[IntermediateTensors] = None,
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
-        model_output = self.model(input_ids, positions, intermediate_tensors,
+        model_output = self.transformers_model.forward(input_ids, positions,
-                                  inputs_embeds)
+                                                       intermediate_tensors,
+                                                       inputs_embeds)
         return model_output
 
     def compute_logits(
@@ -755,12 +737,10 @@ class TransformersForCausalLM(nn.Module, SupportsQuant, SupportsLoRA,
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
-        loader = AutoWeightsLoader(
+        skip_prefixes = ["lm_head."
-            self,
+                         ] if self.config.tie_word_embeddings else None
-            skip_prefixes=(["lm_head."]
+        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
-                           if self.config.tie_word_embeddings else None),
+        return loader.load_weights(weights)
-        )
-        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 
 
 @MULTIMODAL_REGISTRY.register_processor(
@@ -772,6 +752,29 @@ class TransformersForMultimodalLM(nn.Module, SupportsQuant, SupportsLoRA,
     embedding_padding_modules = ["lm_head"]
     embedding_modules = ["embed_tokens"]
 
+    # Backwards compatibility for prev released models. State dicts back then
+    # had different formats and cannot be loaded with `AutoModel` mapping as is
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "language_model.model": "model.language_model",
+            "text_model.model": "model.text_model",
+            "vision_tower": "model.vision_tower",
+            "vqmodel": "model.vqmodel",
+            "visual": "model.visual",
+            "vision_model": "model.vision_model",
+            "vision_embed_tokens": "model.vision_embed_tokens",
+            "image_newline": "model.image_newline",
+            "multi_modal_projector": "model.multi_modal_projector",
+            "text_model.lm_head": "lm_head",
+            "language_model.lm_head": "lm_head",
+            # Qwen models used "model" as the name for the language model.
+            # Therefore, we must map each of submodule explicitly to avoid
+            # conflicts with newer models that use "model.language_model".
+            "model.embed_tokens": "model.language_model.embed_tokens",
+            "model.layers": "model.language_model.layers",
+            "model.norm": "model.language_model.norm",
+        })
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config: PretrainedConfig = vllm_config.model_config.hf_config
@@ -780,7 +783,9 @@ class TransformersForMultimodalLM(nn.Module, SupportsQuant, SupportsLoRA,
         self.config = config
         self.dtype = vllm_config.model_config.dtype
 
-        self.model = TransformersModel(vllm_config=vllm_config, prefix=prefix)
+        self.transformers_model = TransformersModel(vllm_config=vllm_config,
+                                                    prefix=prefix)
+        self.model = self.transformers_model.model
         text_config = config.get_text_config()
 
         if get_pp_group().is_last_rank:
@@ -803,32 +808,7 @@ class TransformersForMultimodalLM(nn.Module, SupportsQuant, SupportsLoRA,
             self.lm_head = PPMissingLayer()
 
         self.make_empty_intermediate_tensors = (
-            self.model.make_empty_intermediate_tensors)
+            self.transformers_model.make_empty_intermediate_tensors)
-
-    @property
-    def hf_to_vllm_mapper(self):
-        # Backwards compatibility for prev released models
-        # State dicts back then had different formats
-        # and cannot be loaded with `AutoModel` mapping
-        # as is
-        prefix_mapper = {
-            "language_model.model": "model.language_model",
-            "text_model.model": "model.text_model",
-            "vision_tower": "model.vision_tower",
-            "vqmodel": "model.vqmodel",
-            "vision_model": "model.vision_model",
-            "vision_embed_tokens": "model.vision_embed_tokens",
-            "image_newline": "model.image_newline",
-            "multi_modal_projector": "model.multi_modal_projector",
-            "text_model.lm_head": "lm_head",
-            "language_model.lm_head": "lm_head",
-        }
-        # Don't change the order for QwenVL
-        if 'Qwen2' in self.config.__class__.__name__:
-            prefix_mapper["model"] = "model.language_model"
-            prefix_mapper["visual"] = "model.visual"
-
-        return WeightsMapper(orig_to_new_prefix=prefix_mapper, )
 
     def forward(
         self,
@@ -848,8 +828,9 @@ class TransformersForMultimodalLM(nn.Module, SupportsQuant, SupportsLoRA,
                     input_ids, multimodal_embeds)
                 input_ids = None
 
-        model_output = self.model(input_ids, positions, intermediate_tensors,
+        model_output = self.transformers_model.forward(input_ids, positions,
-                                  inputs_embeds)
+                                                       intermediate_tensors,
+                                                       inputs_embeds)
         return model_output
 
     def compute_logits(
@@ -898,7 +879,7 @@ class TransformersForMultimodalLM(nn.Module, SupportsQuant, SupportsLoRA,
             if isinstance(num_image_patches, list):
                 num_image_patches = torch.cat(num_image_patches)
 
-            vision_embeddings = self.model.model.get_image_features(
+            vision_embeddings = self.model.get_image_features(
                 pixel_values,
                 **{
                     k: v.flatten(0, 1)
@@ -928,7 +909,7 @@ class TransformersForMultimodalLM(nn.Module, SupportsQuant, SupportsLoRA,
         input_ids: torch.Tensor,
         multimodal_embeddings=None,
     ) -> torch.Tensor:
-        inputs_embeds = self.model.model.get_input_embeddings()(input_ids)
+        inputs_embeds = self.model.get_input_embeddings()(input_ids)
         if (multimodal_embeddings is not None
                 and len(multimodal_embeddings) != 0):
             mask = (input_ids == self.config.image_token_id)

vllm/test_utils.py

@@ -10,7 +10,7 @@ MODELS_ON_S3 = [
     "allenai/OLMoE-1B-7B-0924-Instruct",
     "amd/Llama-3.1-8B-Instruct-FP8-KV-Quark-test",
     "AMead10/Llama-3.2-1B-Instruct-AWQ",
-    "ArthurZ/Ilama-3.2-1B",
+    "hmellor/Ilama-3.2-1B",
     "BAAI/bge-base-en-v1.5",
     "BAAI/bge-multilingual-gemma2",
     "BAAI/bge-reranker-v2-m3",