[Model] Refactor Molmo weights loading to use AutoWeightsLoader (vllm…

…-project#10771)

Signed-off-by: Isotr0py <[email protected]>

vllm/model_executor/models/molmo.py

@@ -3,7 +3,7 @@
 from array import array
 from dataclasses import dataclass
 from functools import lru_cache, partial
-from typing import Iterable, List, Mapping, Optional, Tuple, TypedDict
+from typing import Iterable, List, Mapping, Optional, Set, Tuple, TypedDict
 
 import torch
 from einops import rearrange
@@ -44,7 +44,8 @@
 from vllm.transformers_utils.processor import get_processor
 
 from .interfaces import SupportsMultiModal, SupportsPP
-from .utils import (get_vit_attn_backend,
+from .utils import (AutoWeightsLoader, WeightsMapper, get_vit_attn_backend,
+                    is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -720,6 +721,42 @@ def forward(
         # image_features: (batch_size, num_image, num_patch, d_model)
         return image_features
 
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+
+        for name, loaded_weight in weights:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+                if is_pp_missing_parameter(name, self):
+                    continue
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
 
 @support_torch_compile
 class MolmoModel(nn.Module):
@@ -804,6 +841,28 @@ def forward(
             hidden_states = self.norm(hidden_states)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: Set[str] = set()
+
+        for name, loaded_weight in weights:
+            if "gate_up_proj" in name:
+                up_proj, gate_proj = loaded_weight.chunk(2, dim=0)
+                loaded_weight = torch.cat([gate_proj, up_proj], dim=0)
+
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
 
 cached_get_processor = lru_cache(get_processor)
 
@@ -1200,103 +1259,53 @@ def sample(
         return next_tokens
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-
-        params_mapping = [
-            ("model.transformer.ln_f.weight", "model.norm.weight"),
-            ("attn_out", "self_attn.o_proj"),
-            ("att_proj", "self_attn.qkv_proj"),
-            ("q_norm", "self_attn.q_norm"),
-            ("k_norm", "self_attn.k_norm"),
-            ("attn_norm", "input_layernorm"),
-            ("ff_norm", "post_attention_layernorm"),
-        ]
-
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
-
-        embedding_weight = dict()
-        projector_weight = dict()
-        for name, loaded_weight in weights:
-            if "rotary_emb.inv_freq" in name:
-                continue
-            if self.config.tie_word_embeddings and "lm_head.weight" in name:
-                continue
-
-            if "wte.embedding" in name:
-                embedding_weight["embedding"] = loaded_weight
-                continue
-
-            if "wte.new_embedding" in name:
-                embedding_weight["new_embedding"] = loaded_weight
-                continue
-
-            if "vision_backbone" in name:
-                if name.startswith("model"):
-                    name = name[len("model."):]
-                if 'image_projector' in name:
-                    if 'w1' in name:
-                        projector_weight['gate_proj'] = loaded_weight
-                    elif 'w3' in name:
-                        projector_weight['up_proj'] = loaded_weight
-                    elif 'w2' in name:
-                        projector_weight['down_proj'] = loaded_weight
-                    else:
-                        raise ValueError(
-                            f"Unexpected projector weight: {name}")
-                    continue
-            else:
-                if "transformer.blocks" in name:
-                    name = name.replace("transformer.blocks", "layers")
-
-                if "ff_proj" in name:
-                    name = name.replace("ff_proj", "mlp.gate_up_proj")
-                    assert 'weight' in name
-                    up_weight, gate_weight = loaded_weight.chunk(2, dim=0)
-                    loaded_weight = torch.cat([gate_weight, up_weight], dim=0)
-
-                elif "ff_out" in name:
-                    if "layers" in name:
-                        name = name.replace("ff_out", "mlp.down_proj")
-                    else:
-                        # lm head
-                        name = name.replace("model.transformer.ff_out",
-                                            "lm_head")
-
-                else:
-                    for (param_name, weight_name) in params_mapping:
-                        if param_name in name:
-                            name = name.replace(param_name, weight_name)
-                            break
-
-            try:
-                # Skip loading extra bias for GPTQ models.
-                if name.endswith(".bias") and name not in params_dict:
-                    continue
-                param = params_dict[name]
-            except KeyError:
-                raise ValueError(f"Unexpected weight: {name}") from None
-
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
-
-        gate_up_proj_weight = torch.cat(
-            [projector_weight["gate_proj"], projector_weight["up_proj"]],
-            dim=0)
-        name = "vision_backbone.image_projector.gate_up_proj.weight"
-        param = params_dict[name]
-        weight_loader = getattr(param, "weight_loader", default_weight_loader)
-        weight_loader(param, gate_up_proj_weight)
-
-        down_proj_weight = projector_weight["down_proj"]
-        name = "vision_backbone.image_projector.down_proj.weight"
-        param = params_dict[name]
-        weight_loader = getattr(param, "weight_loader", default_weight_loader)
-        weight_loader(param, down_proj_weight)
-
-        embedding_weight = torch.cat(
-            [embedding_weight["embedding"], embedding_weight["new_embedding"]],
-            dim=0)
-        name = "model.embed_tokens.weight"
-        param = params_dict[name]
-        weight_loader = getattr(param, "weight_loader", default_weight_loader)
-        weight_loader(param, embedding_weight)
+        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)
+
+
+def _get_weights_with_merged_embedding(
+    weights: Iterable[Tuple[str, torch.Tensor]]
+) -> Iterable[Tuple[str, torch.Tensor]]:
+    embedding_weights = {}
+    for name, weight in weights:
+        if "wte.embedding" in name:
+            embedding_weights["embedding"] = weight
+        elif "wte.new_embedding" in name:
+            embedding_weights["new_embedding"] = weight
+        else:
+            yield (name, weight)
+    # this is compatible with most of quantization,
+    # because they won't quantize embed_tokens
+    embedding_weights = torch.cat(
+        [embedding_weights["embedding"], embedding_weights["new_embedding"]],
+        dim=0,
+    )
+    yield ("model.embed_tokens.weight", embedding_weights)