[Misc] Move weights mapper (#11443)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_gemma_embedding.py

@@ -13,6 +13,7 @@ from vllm.sequence import IntermediateTensors, PoolerOutput
 
 
 class MyGemma2Embedding(nn.Module):
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -62,8 +63,8 @@ class MyGemma2Embedding(nn.Module):
         return self._pooler(hidden_states, pooling_metadata)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
-        weights = hf_to_vllm_mapper.apply(weights)
+
+        weights = self.hf_to_vllm_mapper.apply(weights)
         weights = ((name, data) for name, data in weights
                    if not name.startswith("lm_head."))
         return self.model.load_weights(weights)

vllm/model_executor/models/aria.py

@@ -521,6 +521,15 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
     This model combines a vision tower, a multi-modal projector, and a language
     model to perform tasks that involve both image and text inputs.
     """
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "language_model.model": "language_model",
+            "language_model.lm_head": "lm_head",
+        },
+        orig_to_new_suffix={
+            "router.weight": "router_weight",
+        },
+    )
 
     def __init__(
         self,
@@ -662,15 +671,6 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
         return next_tokens
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        hf_to_vllm_mapper = WeightsMapper(
-            orig_to_new_prefix={
-                "language_model.model": "language_model",
-                "language_model.lm_head": "lm_head",
-            },
-            orig_to_new_suffix={
-                "router.weight": "router_weight",
-            },
-        )
 
         loader = AutoWeightsLoader(self)
-        loader.load_weights(weights, mapper=hf_to_vllm_mapper)
+        loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

vllm/model_executor/models/bert.py

@@ -409,6 +409,7 @@ class BertEmbeddingModel(nn.Module):
        model: An instance of BertModel used for forward operations.
        _pooler: An instance of Pooler used for pooling operations.
    """
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -441,8 +442,7 @@ class BertEmbeddingModel(nn.Module):
         return self._pooler(hidden_states, pooling_metadata)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
-        weights = hf_to_vllm_mapper.apply(weights)
+        weights = self.hf_to_vllm_mapper.apply(weights)
         weights = ((name, data) for name, data in weights
                    if not name.startswith("lm_head."))
         self.model.load_weights(weights)

vllm/model_executor/models/molmo.py

@@ -1123,6 +1123,34 @@ def input_processor_for_molmo(ctx: InputContext, inputs: DecoderOnlyInputs):
 @INPUT_REGISTRY.register_input_processor(input_processor_for_molmo)
 class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_substr={
+            # vision backbone mapping
+            "image_projector.w1.": "image_projector.gate_proj.",
+            "image_projector.w3.": "image_projector.up_proj.",
+            "image_projector.w2.": "image_projector.down_proj.",
+            # language backbone mapping
+            "att_proj": "self_attn.qkv_proj",
+            "attn_out": "self_attn.o_proj",
+            "q_norm": "self_attn.q_norm",
+            "k_norm": "self_attn.k_norm",
+            "ff_proj": "mlp.gate_up_proj",
+            "ff_out": "mlp.down_proj",
+            "attn_norm": "input_layernorm",
+            "ff_norm": "post_attention_layernorm",
+        },
+        orig_to_new_prefix={
+            # vision backbone mapping
+            "model.vision_backbone.": "vision_backbone.",
+            # language backbone mapping
+            "model.transformer.blocks.": "model.layers.",
+            "model.transformer.ln_f.": "model.norm.",
+            # lm_head is renamed to model.transformer.mlp.down_proj firstly,
+            # we need to run a second renaming for it
+            "model.transformer.mlp.down_proj.": "lm_head.",
+        },
+    )
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -1298,36 +1326,10 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
         return next_tokens
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        hf_to_vllm_mapper = WeightsMapper(
-            orig_to_new_substr={
-                # vision backbone mapping
-                "image_projector.w1.": "image_projector.gate_proj.",
-                "image_projector.w3.": "image_projector.up_proj.",
-                "image_projector.w2.": "image_projector.down_proj.",
-                # language backbone mapping
-                "att_proj": "self_attn.qkv_proj",
-                "attn_out": "self_attn.o_proj",
-                "q_norm": "self_attn.q_norm",
-                "k_norm": "self_attn.k_norm",
-                "ff_proj": "mlp.gate_up_proj",
-                "ff_out": "mlp.down_proj",
-                "attn_norm": "input_layernorm",
-                "ff_norm": "post_attention_layernorm",
-            },
-            orig_to_new_prefix={
-                # vision backbone mapping
-                "model.vision_backbone.": "vision_backbone.",
-                # language backbone mapping
-                "model.transformer.blocks.": "model.layers.",
-                "model.transformer.ln_f.": "model.norm.",
-                # lm_head is renamed to model.transformer.mlp.down_proj firstly,
-                # we need to run a second renaming for it
-                "model.transformer.mlp.down_proj.": "lm_head.",
-            },
-        )
+
         loader = AutoWeightsLoader(self)
         weights = _get_weights_with_merged_embedding(weights)
-        return loader.load_weights(weights, mapper=hf_to_vllm_mapper)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 
 
 def _get_weights_with_merged_embedding(

vllm/model_executor/models/phi3v.py

@@ -408,6 +408,13 @@ class Phi3VMultiModalProcessor(BaseMultiModalProcessor):
 @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_phi3v_image_tokens)
 @MULTIMODAL_REGISTRY.register_processor(Phi3VMultiModalProcessor)
 class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "model.vision_embed_tokens.wte": "embed_tokens",
+            "model.vision_embed_tokens.": "vision_embed_tokens.",
+            "lm_head.": "language_model.lm_head.",
+            "model.": "language_model.model.",
+        })
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
@@ -616,17 +623,10 @@ class Phi3VForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
-        hf_to_vllm_mapper = WeightsMapper(
-            orig_to_new_prefix={
-                "model.vision_embed_tokens.wte": "embed_tokens",
-                "model.vision_embed_tokens.": "vision_embed_tokens.",
-                "lm_head.": "language_model.lm_head.",
-                "model.": "language_model.model.",
-            })
 
         loader = AutoWeightsLoader(self)
         autoloaded_weights = loader.load_weights(weights,
-                                                 mapper=hf_to_vllm_mapper)
+                                                 mapper=self.hf_to_vllm_mapper)
 
         # The HF config doesn't specify whether these are tied,
         # so we detect it this way

vllm/model_executor/models/qwen2.py

@@ -529,6 +529,8 @@ class Qwen2EmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
     embedding_modules = {}
     embedding_padding_modules = []
 
+    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -577,8 +579,7 @@ class Qwen2EmbeddingModel(nn.Module, SupportsLoRA, SupportsPP):
         return self._pooler(hidden_states, pooling_metadata)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
-        weights = hf_to_vllm_mapper.apply(weights)
+        weights = self.hf_to_vllm_mapper.apply(weights)
         weights = ((name, data) for name, data in weights
                    if not name.startswith("lm_head."))
         self.model.load_weights(weights)

vllm/model_executor/models/telechat2.py

@@ -31,6 +31,19 @@ from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
 
 class TeleChat2Model(LlamaModel):
 
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "transformer.": "model.",
+        },
+        orig_to_new_substr={
+            ".h.": ".layers.",
+            ".self_attention.": ".self_attn.",
+            ".word_embeddings.": ".embed_tokens.",
+            ".dense.": ".o_proj.",
+            ".ln_f.": ".norm.",
+        },
+    )
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         # 1. Initialize the LlamaModel with bias
         vllm_config.model_config.hf_config.bias = True
@@ -111,21 +124,9 @@ class TeleChat2ForCausalLM(LlamaForCausalLM):
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
 
-        hf_to_vllm_mapper = WeightsMapper(
-            orig_to_new_prefix={
-                "transformer.": "model.",
-            },
-            orig_to_new_substr={
-                ".h.": ".layers.",
-                ".self_attention.": ".self_attn.",
-                ".word_embeddings.": ".embed_tokens.",
-                ".dense.": ".o_proj.",
-                ".ln_f.": ".norm.",
-            },
-        )
         loader = AutoWeightsLoader(
             self,
             skip_prefixes=(["lm_head."]
                            if self.config.tie_word_embeddings else None),
         )
-        return loader.load_weights(weights, mapper=hf_to_vllm_mapper)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

vllm/model_executor/models/ultravox.py

@@ -302,6 +302,9 @@ class ModifiedWhisperEncoder(WhisperEncoder):
 @MULTIMODAL_REGISTRY.register_processor(UltravoxMultiModalProcessor)
 class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP):
 
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={"audio_tower.model.encoder.": "audio_tower."})
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -494,9 +497,7 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP):
 
     def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
-        hf_to_vllm_mapper = WeightsMapper(
-            orig_to_new_prefix={"audio_tower.model.encoder.": "audio_tower."})
 
         loader = AutoWeightsLoader(self,
                                    ignore_unexpected_prefixes=["audio_tower."])
-        return loader.load_weights(weights, mapper=hf_to_vllm_mapper)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)