[Bugfix] Refactor composite weight loading logic (vllm-project#8656)

vllm/model_executor/models/internvl.py

@@ -4,7 +4,6 @@
 # Copyright (c) 2023 OpenGVLab
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
-import itertools
 import re
 from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                     TypedDict, Union)
@@ -33,8 +32,8 @@
 from .clip import (dummy_image_for_clip, dummy_seq_data_for_clip,
                    get_clip_num_patches)
 from .interfaces import SupportsMultiModal
-from .utils import (filter_weights, flatten_bn, init_vllm_registered_model,
-                    merge_multimodal_embeddings)
+from .utils import (flatten_bn, group_weights_with_prefix,
+                    init_vllm_registered_model, merge_multimodal_embeddings)
 
 IMG_START = '<img>'
 IMG_END = '</img>'
@@ -518,21 +517,18 @@ def sample(
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         # prepare weight iterators for components
-        vit_weights, mlp_weights, llm_weights = itertools.tee(weights, 3)
+        weights_group = group_weights_with_prefix(weights)
 
         # load vision encoder
-        vit_weights = filter_weights(vit_weights, "vision_model")
-        self.vision_model.load_weights(vit_weights)
+        self.vision_model.load_weights(weights_group["vision_model"])
 
         # load mlp projector
-        mlp_weights = filter_weights(mlp_weights, "mlp1")
         mlp_params_dict = dict(self.mlp1.named_parameters())
-        for name, loaded_weight in mlp_weights:
+        for name, loaded_weight in weights_group["mlp1"]:
             param = mlp_params_dict[name]
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)
             weight_loader(param, loaded_weight)
 
         # load llm backbone
-        llm_weights = filter_weights(llm_weights, "language_model")
-        self.language_model.load_weights(llm_weights)
+        self.language_model.load_weights(weights_group["language_model"])

vllm/model_executor/models/llava.py

@@ -1,4 +1,3 @@
-import itertools
 from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                     TypedDict, Union)
 
@@ -26,8 +25,8 @@
 from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                      dummy_seq_data_for_siglip, get_max_siglip_image_tokens,
                      input_processor_for_siglip)
-from .utils import (filter_weights, flatten_bn, init_vllm_registered_model,
-                    merge_multimodal_embeddings)
+from .utils import (flatten_bn, group_weights_with_prefix,
+                    init_vllm_registered_model, merge_multimodal_embeddings)
 
 
 class LlavaImagePixelInputs(TypedDict):
@@ -393,21 +392,18 @@ def sample(
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         # prepare weight iterators for components
-        vit_weights, mlp_weights, llm_weights = itertools.tee(weights, 3)
+        weights_group = group_weights_with_prefix(weights)
 
         # load vision encoder
-        vit_weights = filter_weights(vit_weights, "vision_tower")
-        self.vision_tower.load_weights(vit_weights)
+        self.vision_tower.load_weights(weights_group["vision_tower"])
 
         # load mlp projector
-        mlp_weights = filter_weights(mlp_weights, "multi_modal_projector")
         mlp_params_dict = dict(self.multi_modal_projector.named_parameters())
-        for name, loaded_weight in mlp_weights:
+        for name, loaded_weight in weights_group["multi_modal_projector"]:
             param = mlp_params_dict[name]
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)
             weight_loader(param, loaded_weight)
 
         # load llm backbone
-        llm_weights = filter_weights(llm_weights, "language_model")
-        self.language_model.load_weights(llm_weights)
+        self.language_model.load_weights(weights_group["language_model"])

vllm/model_executor/models/llava_next.py

@@ -1,4 +1,3 @@
-import itertools
 from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                     TypedDict, Union)
 
@@ -30,8 +29,8 @@
 from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                      dummy_seq_data_for_siglip, get_siglip_image_feature_size,
                      get_siglip_patch_grid_length, input_processor_for_siglip)
-from .utils import (filter_weights, flatten_bn, init_vllm_registered_model,
-                    merge_multimodal_embeddings)
+from .utils import (flatten_bn, group_weights_with_prefix,
+                    init_vllm_registered_model, merge_multimodal_embeddings)
 
 logger = init_logger(__name__)
 
@@ -637,31 +636,26 @@ def sample(
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         # prepare weight iterators for components
-        vit_weights, mlp_weights, newline_weights, llm_weights = itertools.tee(
-            weights, 4)
+        weights_group = group_weights_with_prefix(weights)
 
         # load vision encoder
-        vit_weights = filter_weights(vit_weights, "vision_tower")
-        self.vision_tower.load_weights(vit_weights)
+        self.vision_tower.load_weights(weights_group["vision_tower"])
 
         # load mlp projector
-        mlp_weights = filter_weights(mlp_weights, "multi_modal_projector")
         mlp_params_dict = dict(self.multi_modal_projector.named_parameters())
-        for name, loaded_weight in mlp_weights:
+        for name, loaded_weight in weights_group["multi_modal_projector"]:
             param = mlp_params_dict[name]
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)
             weight_loader(param, loaded_weight)
 
         # load newline
-        newline_weights = filter_weights(newline_weights, "image_newline")
-        for name, loaded_weight in newline_weights:
+        for name, loaded_weight in weights_group["image_newline"]:
             assert name == ""
             param = self.image_newline
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)
             weight_loader(param, loaded_weight)
 
         # load llm backbone
-        llm_weights = filter_weights(llm_weights, "language_model")
-        self.language_model.load_weights(llm_weights)
+        self.language_model.load_weights(weights_group["language_model"])

vllm/model_executor/models/llava_next_video.py

@@ -1,4 +1,3 @@
-import itertools
 import math
 from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                     TypedDict, Union)
@@ -30,7 +29,7 @@
 from .interfaces import SupportsMultiModal
 from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                      dummy_seq_data_for_siglip)
-from .utils import (filter_weights, init_vllm_registered_model,
+from .utils import (group_weights_with_prefix, init_vllm_registered_model,
                     merge_multimodal_embeddings)
 
 logger = init_logger(__name__)
@@ -449,23 +448,19 @@ def sample(
         return self.language_model.sample(logits, sampling_metadata)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
-        # prepare weight iterators
-        vit_weights, mlp_weights, newline_weights, llm_weights = itertools.tee(
-            weights, 4)
+        # prepare weight iterators for components
+        weights_group = group_weights_with_prefix(weights)
 
         # load vision encoder
-        vit_weights = filter_weights(vit_weights, "vision_tower")
-        self.vision_tower.load_weights(vit_weights)
+        self.vision_tower.load_weights(weights_group["vision_tower"])
 
         # load mlp projector
-        mlp_weights = filter_weights(mlp_weights, "multi_modal_projector")
         mlp_params_dict = dict(self.multi_modal_projector.named_parameters())
-        for name, loaded_weight in mlp_weights:
+        for name, loaded_weight in weights_group["multi_modal_projector"]:
             param = mlp_params_dict[name]
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)
             weight_loader(param, loaded_weight)
 
         # load llm backbone
-        llm_weights = filter_weights(llm_weights, "language_model")
-        self.language_model.load_weights(llm_weights)
+        self.language_model.load_weights(weights_group["language_model"])

vllm/model_executor/models/paligemma.py

@@ -1,4 +1,3 @@
-import itertools
 from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                     TypedDict, Union)
 
@@ -23,7 +22,7 @@
 from .interfaces import SupportsMultiModal
 from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
                      dummy_seq_data_for_siglip, get_max_siglip_image_tokens)
-from .utils import filter_weights, merge_multimodal_embeddings
+from .utils import group_weights_with_prefix, merge_multimodal_embeddings
 
 logger = init_logger(__name__)
 
@@ -286,21 +285,18 @@ def sample(
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         # prepare weight iterators for components
-        vit_weights, mlp_weights, llm_weights = itertools.tee(weights, 3)
+        weights_group = group_weights_with_prefix(weights)
 
         # load vision tower
-        vit_weights = filter_weights(vit_weights, "vision_tower")
-        self.vision_tower.load_weights(vit_weights)
+        self.vision_tower.load_weights(weights_group["vision_tower"])
 
         # load mlp projector
-        mlp_weights = filter_weights(mlp_weights, "multi_modal_projector")
         mlp_params_dict = dict(self.multi_modal_projector.named_parameters())
-        for name, loaded_weight in mlp_weights:
+        for name, loaded_weight in weights_group["multi_modal_projector"]:
             param = mlp_params_dict[name]
             weight_loader = getattr(param, "weight_loader",
                                     default_weight_loader)
             weight_loader(param, loaded_weight)
 
         # load llm backbone
-        llm_weights = filter_weights(llm_weights, "language_model")
-        self.language_model.load_weights(llm_weights)
+        self.language_model.load_weights(weights_group["language_model"])

vllm/model_executor/models/ultravox.py

@@ -1,7 +1,6 @@
 # Adapted from https://github.com/fixie-ai/ultravox/blob/ecd58c4041030bae2ad15aa6bcf04ab43199ea02/ultravox/model/ultravox_model.py
 """PyTorch Ultravox model."""
 
-import itertools
 import math
 from array import array
 from functools import lru_cache
@@ -29,7 +28,8 @@
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.interfaces import SupportsMultiModal
-from vllm.model_executor.models.utils import (filter_weights, flatten_bn,
+from vllm.model_executor.models.utils import (flatten_bn,
+                                              group_weights_with_prefix,
                                               init_vllm_registered_model,
                                               merge_multimodal_embeddings)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
@@ -467,11 +467,10 @@ def sample(
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         # prepare weight iterators for components
-        projector_weights, llm_weights = itertools.tee(weights, 2)
+        weights_group = group_weights_with_prefix(weights)
 
         # load projector weights
-        projector_weights = filter_weights(projector_weights,
-                                           "multi_modal_projector")
+        projector_weights = weights_group["multi_modal_projector"]
         projector_params_dict = dict(
             self.multi_modal_projector.named_parameters())
         for name, loaded_weight in projector_weights:
@@ -481,5 +480,4 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
             weight_loader(param, loaded_weight)
 
         # load llm backbone
-        llm_weights = filter_weights(llm_weights, "language_model")
-        self.language_model.load_weights(llm_weights)
+        self.language_model.load_weights(weights_group["language_model"])

vllm/model_executor/models/utils.py

@@ -1,3 +1,5 @@
+import itertools
+from collections import UserDict
 from typing import (Dict, Iterable, List, Literal, Optional, Protocol, Tuple,
                     Union, overload)
 
@@ -16,7 +18,23 @@
 from vllm.utils import is_pin_memory_available
 
 
-def filter_weights(weights: Iterable[Tuple[str, torch.Tensor]], prefix: str):
+class WeightsGroup(UserDict):
+    """
+    Wraps grouped weights dictionary for a more informative error message
+    when attempting to access a weight component that does not exist.
+    """
+
+    def __getitem__(self, key: str) -> int:
+        try:
+            return super().__getitem__(key)
+        except KeyError as exc:
+            msg = (f"There is no weights named with the prefix: {key}. "
+                   f"Available prefix: {set(self.keys())}")
+            raise KeyError(msg) from exc
+
+
+def filter_weights(weights: Iterable[Tuple[str, torch.Tensor]],
+                   prefix: str) -> Iterable[Tuple[str, torch.Tensor]]:
     """
     Helper function to load weights for inner vLLM models.
 
@@ -30,6 +48,22 @@ def filter_weights(weights: Iterable[Tuple[str, torch.Tensor]], prefix: str):
             yield name, loaded_weight
 
 
+def group_weights_with_prefix(
+    weights: Iterable[Tuple[str, torch.Tensor]]
+) -> Dict[str, Iterable[Tuple[str, torch.Tensor]]]:
+    """
+    Helper function to group weights with prefix
+    """
+    init_weights, repeated_weights = itertools.tee(weights, 2)
+    weights_prefix = {name.split(".")[0] for name, _ in init_weights}
+    repeated_weights = itertools.tee(repeated_weights, len(weights_prefix))
+
+    return WeightsGroup({
+        prefix: filter_weights(component, prefix)
+        for component, prefix in zip(repeated_weights, weights_prefix)
+    })
+
+
 def init_vllm_registered_model(
     hf_config: PretrainedConfig,
     cache_config: Optional[CacheConfig],