[V1][Spec Decoding] Use model_loader.get_model() to load models (#18273)

Signed-off-by: Mark McLoughlin <markmc@redhat.com>

tests/v1/spec_decode/test_eagle.py

@@ -117,34 +117,13 @@ def test_prepare_inputs():
     ])
 @mock.patch('vllm.v1.spec_decode.eagle.get_pp_group')
 @mock.patch('vllm.v1.spec_decode.eagle.get_layers_from_vllm_config')
-@mock.patch('vllm.v1.spec_decode.eagle.ModelRegistry')
-@mock.patch('vllm.v1.spec_decode.eagle.get_model_loader')
-@mock.patch('vllm.v1.spec_decode.eagle.set_default_torch_dtype')
-@mock.patch('vllm.v1.spec_decode.eagle.set_current_vllm_config')
-def test_load_model(mock_set_config, mock_set_dtype, mock_get_loader,
-                    mock_registry, mock_get_layers, mock_get_pp_group, method,
+@mock.patch('vllm.v1.spec_decode.eagle.get_model')
+def test_load_model(mock_get_model, mock_get_layers, mock_get_pp_group, method,
                     proposer_helper, draft_model_dir, target_attribute_path):
 
-    # Setup mock for model class
-    mock_model_cls = mock.MagicMock()
-    mock_registry.resolve_model_cls.return_value = (mock_model_cls,
-                                                    "test_arch")
-
-    # Create a real context manager for mocks
-    class MockContextManager:
-
-        def __init__(self):
-            pass
-
-        def __enter__(self):
-            return None
-
-        def __exit__(self, exc_type, exc_val, exc_tb):
-            return False
-
-    # Make the mocks return actual context manager objects
-    mock_set_dtype.return_value = MockContextManager()
-    mock_set_config.return_value = MockContextManager()
+    # Setup model mock
+    mock_model = mock.MagicMock()
+    mock_get_model.return_value = mock_model
 
     # Setup mocks for attention layers
     target_attn_layers = {
@@ -164,25 +143,6 @@ def test_load_model(mock_set_config, mock_set_dtype, mock_get_loader,
     mock_pp_group.world_size = 2 if method == "eagle" else 1
     mock_get_pp_group.return_value = mock_pp_group
 
-    # Setup model loader mock
-    mock_loader = mock.MagicMock()
-    mock_get_loader.return_value = mock_loader
-
-    # Setup model mock
-    mock_model = mock.MagicMock()
-    mock_model_cls.return_value = mock_model
-    mock_model.to.return_value = mock_model
-
-    # Configure mock to test the attribute sharing path
-    if method == "eagle":
-        # For eagle, test the lm_head path
-        mock_model.load_weights.return_value = {
-            "model.embed_tokens.weight": torch.zeros(1)
-        }
-    else:
-        # For eagle3, test the embed_tokens path
-        mock_model.load_weights.return_value = {}
-
     # Setup target model with the appropriate attributes
     target_model = mock.MagicMock()
 
@@ -204,13 +164,7 @@ def test_load_model(mock_set_config, mock_set_dtype, mock_get_loader,
     proposer.load_model(target_model)
 
     # Verify common interactions
-    mock_get_loader.assert_called_once()
-    mock_model_cls.assert_called_once()
-    mock_model.to.assert_called_once()
-    mock_model.load_weights.assert_called_once()
-
-    # Verify the loader was called with the right config
-    mock_get_loader.assert_called_once_with(proposer.vllm_config.load_config)
+    mock_get_model.assert_called_once()
 
     # Verify the specific attribute sharing based on the method
     if method == "eagle":

vllm/model_executor/model_loader/__init__.py

@@ -1,8 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 
+from typing import Optional
+
 from torch import nn
 
-from vllm.config import LoadConfig, LoadFormat, VllmConfig
+from vllm.config import LoadConfig, LoadFormat, ModelConfig, VllmConfig
 from vllm.model_executor.model_loader.base_loader import BaseModelLoader
 from vllm.model_executor.model_loader.bitsandbytes_loader import (
     BitsAndBytesModelLoader)
@@ -47,9 +49,14 @@ def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
     return DefaultModelLoader(load_config)
 
 
-def get_model(*, vllm_config: VllmConfig) -> nn.Module:
+def get_model(*,
+              vllm_config: VllmConfig,
+              model_config: Optional[ModelConfig] = None) -> nn.Module:
     loader = get_model_loader(vllm_config.load_config)
-    return loader.load_model(vllm_config=vllm_config)
+    if model_config is None:
+        model_config = vllm_config.model_config
+    return loader.load_model(vllm_config=vllm_config,
+                             model_config=model_config)
 
 
 __all__ = [

vllm/model_executor/model_loader/base_loader.py

@@ -18,6 +18,7 @@ class BaseModelLoader(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def load_model(self, *, vllm_config: VllmConfig) -> nn.Module:
+    def load_model(self, *, vllm_config: VllmConfig,
+                   model_config: ModelConfig) -> nn.Module:
         """Load a model with the given configurations."""
         raise NotImplementedError

vllm/model_executor/model_loader/bitsandbytes_loader.py

@@ -569,10 +569,9 @@ class BitsAndBytesModelLoader(BaseModelLoader):
     def download_model(self, model_config: ModelConfig) -> None:
         self._prepare_weights(model_config.model, model_config.revision)
 
-    def load_model(self, vllm_config: VllmConfig) -> nn.Module:
+    def load_model(self, vllm_config: VllmConfig,
+                   model_config: ModelConfig) -> nn.Module:
         device_config = vllm_config.device_config
-        model_config = vllm_config.model_config
-
         with set_default_torch_dtype(model_config.dtype):
             with torch.device(device_config.device):
 

vllm/model_executor/model_loader/default_loader.py

@@ -264,13 +264,14 @@ class DefaultModelLoader(BaseModelLoader):
                               fall_back_to_pt=True,
                               allow_patterns_overrides=None)
 
-    def load_model(self, vllm_config: VllmConfig) -> nn.Module:
+    def load_model(self, vllm_config: VllmConfig,
+                   model_config: ModelConfig) -> nn.Module:
         device_config = vllm_config.device_config
-        model_config = vllm_config.model_config
         target_device = torch.device(device_config.device)
         with set_default_torch_dtype(model_config.dtype):
             with target_device:
-                model = initialize_model(vllm_config=vllm_config)
+                model = initialize_model(vllm_config=vllm_config,
+                                         model_config=model_config)
 
             weights_to_load = {name for name, _ in model.named_parameters()}
             loaded_weights = model.load_weights(

vllm/model_executor/model_loader/dummy_loader.py

@@ -22,9 +22,9 @@ class DummyModelLoader(BaseModelLoader):
     def download_model(self, model_config: ModelConfig) -> None:
         pass  # Nothing to download
 
-    def load_model(self, vllm_config: VllmConfig) -> nn.Module:
+    def load_model(self, vllm_config: VllmConfig,
+                   model_config: ModelConfig) -> nn.Module:
         device_config = vllm_config.device_config
-        model_config = vllm_config.model_config
         target_device = torch.device(device_config.device)
         with set_default_torch_dtype(model_config.dtype):
             with target_device:

vllm/model_executor/model_loader/gguf_loader.py

@@ -92,9 +92,9 @@ class GGUFModelLoader(BaseModelLoader):
     def download_model(self, model_config: ModelConfig) -> None:
         self._prepare_weights(model_config.model)
 
-    def load_model(self, vllm_config: VllmConfig) -> nn.Module:
+    def load_model(self, vllm_config: VllmConfig,
+                   model_config: ModelConfig) -> nn.Module:
         device_config = vllm_config.device_config
-        model_config = vllm_config.model_config
         local_model_path = self._prepare_weights(model_config.model)
         gguf_weights_map = self._get_gguf_weights_map(model_config)
         # we can only know if tie word embeddings after mapping weights

vllm/model_executor/model_loader/runai_streamer_loader.py

@@ -100,11 +100,10 @@ class RunaiModelStreamerLoader(BaseModelLoader):
         """Download model if necessary"""
         self._prepare_weights(model_config.model, model_config.revision)
 
-    def load_model(self, vllm_config: VllmConfig) -> nn.Module:
+    def load_model(self, vllm_config: VllmConfig,
+                   model_config: ModelConfig) -> nn.Module:
         """Perform streaming of the model to destination"""
         device_config = vllm_config.device_config
-        model_config = vllm_config.model_config
-
         target_device = torch.device(device_config.device)
         with set_default_torch_dtype(model_config.dtype):
             with target_device:

vllm/model_executor/model_loader/sharded_state_loader.py

@@ -100,9 +100,9 @@ class ShardedStateLoader(BaseModelLoader):
     def download_model(self, model_config: ModelConfig) -> None:
         self._prepare_weights(model_config.model, model_config.revision)
 
-    def load_model(self, vllm_config: VllmConfig) -> nn.Module:
+    def load_model(self, vllm_config: VllmConfig,
+                   model_config: ModelConfig) -> nn.Module:
         device_config = vllm_config.device_config
-        model_config = vllm_config.model_config
         target_device = torch.device(device_config.device)
 
         from vllm.distributed import get_tensor_model_parallel_rank

vllm/model_executor/model_loader/tensorizer_loader.py

@@ -93,8 +93,8 @@ class TensorizerLoader(BaseModelLoader):
         with self.tensorizer_config.open_stream():
             pass
 
-    def load_model(self, vllm_config: VllmConfig) -> nn.Module:
-        model_config = vllm_config.model_config
+    def load_model(self, vllm_config: VllmConfig,
+                   model_config: ModelConfig) -> nn.Module:
         parallel_config = vllm_config.parallel_config
         self._verify_config(model_config, parallel_config)
 

vllm/model_executor/model_loader/utils.py

@@ -42,8 +42,10 @@ def initialize_model(
     *,
     prefix: str = "",
     model_class: Optional[type[nn.Module]] = None,
+    model_config: Optional[ModelConfig] = None,
 ) -> nn.Module:
     """Initialize a model with the given configurations."""
+    if model_config is None:
         model_config = vllm_config.model_config
     if model_class is None:
         model_class, _ = get_model_architecture(model_config)

vllm/model_executor/models/llama_eagle.py

@@ -130,13 +130,15 @@ class LlamaModel(nn.Module):
 
 class EagleLlamaForCausalLM(LlamaForCausalLM):
 
-    def __init__(self, *, vllm_config: VllmConfig, start_layer_id: int = 0):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         nn.Module.__init__(self)
         self.config = vllm_config. \
             speculative_config.draft_model_config.hf_config
+        target_layer_num = vllm_config.model_config.get_num_layers(
+            vllm_config.parallel_config)
         self.model = LlamaModel(vllm_config=vllm_config,
                                 prefix="model",
-                                start_layer_id=start_layer_id)
+                                start_layer_id=target_layer_num)
 
         logit_scale = getattr(self.config, "logit_scale", 1.0)
         self.logits_processor = LogitsProcessor(self.config.vocab_size,

vllm/model_executor/models/llama_eagle3.py

@@ -175,13 +175,15 @@ class LlamaModel(nn.Module):
 
 class Eagle3LlamaForCausalLM(LlamaForCausalLM):
 
-    def __init__(self, *, vllm_config: VllmConfig, start_layer_id: int = 0):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         nn.Module.__init__(self)
         self.config = vllm_config. \
             speculative_config.draft_model_config.hf_config
+        target_layer_num = vllm_config.model_config.get_num_layers(
+            vllm_config.parallel_config)
         self.model = LlamaModel(vllm_config=vllm_config,
-                                start_layer_id=start_layer_id,
-                                prefix="model")
+                                prefix="model",
+                                start_layer_id=target_layer_num)
 
         logit_scale = getattr(self.config, "logit_scale", 1.0)
         self.lm_head = ParallelLMHead(
@@ -193,8 +195,7 @@ class Eagle3LlamaForCausalLM(LlamaForCausalLM):
         self.logits_processor = LogitsProcessor(self.config.draft_vocab_size,
                                                 scale=logit_scale)
         self.draft_id_to_target_id = nn.Parameter(
-            torch.zeros((self.config.draft_vocab_size),
-                        dtype=torch.long).type(torch.LongTensor),
+            torch.zeros(self.config.draft_vocab_size, dtype=torch.long),
             requires_grad=False,
         )
 

vllm/model_executor/models/medusa.py

@@ -51,10 +51,7 @@ class Medusa(nn.Module):
        needs to have truncated_vocab_size (=k) as an attribute."""
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
-        if hasattr(vllm_config, 'draft_model_config'):
-            config = vllm_config.draft_model_config.hf_config
-        else:
-            config = vllm_config.model_config.hf_config
+        config = vllm_config.speculative_config.draft_model_config.hf_config
         super().__init__()
         self.config = config
         self.blocks = nn.ModuleList([

vllm/v1/spec_decode/eagle.py

@@ -4,14 +4,11 @@ import torch.nn as nn
 
 from vllm.attention.layer import Attention
 from vllm.config import (CompilationLevel, VllmConfig,
-                         get_layers_from_vllm_config, set_current_vllm_config)
+                         get_layers_from_vllm_config)
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
-from vllm.model_executor.model_loader import get_model_loader
-from vllm.model_executor.model_loader.utils import (
-    process_weights_after_loading, set_default_torch_dtype)
-from vllm.model_executor.models import ModelRegistry
+from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
 from vllm.triton_utils import tl, triton
 from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
@@ -280,51 +277,28 @@ class EagleProposer:
         return cu_num_tokens, token_indices
 
     def load_model(self, target_model: nn.Module) -> None:
-        loader = get_model_loader(self.vllm_config.load_config)
-        target_layer_num = self.vllm_config.model_config.get_num_layers(
-            self.vllm_config.parallel_config)
+        draft_model_config = \
+            self.vllm_config.speculative_config.draft_model_config
         target_attn_layer_names = set(
             get_layers_from_vllm_config(self.vllm_config, Attention).keys())
 
-        draft_model_config = \
-            self.vllm_config.speculative_config.draft_model_config
-        # FIXME(lily): This does not handle with distributed inference.
-        target_device = self.vllm_config.device_config.device
-        # We need to set the vllm_config here to register attention
-        # layers in the forward context.
-        with set_default_torch_dtype(
-                draft_model_config.dtype), set_current_vllm_config(
-                    self.vllm_config):
-            draft_model_cls, arch = ModelRegistry.resolve_model_cls(
-                draft_model_config.architectures)
-            self.model = draft_model_cls(
-                vllm_config=self.vllm_config,
-                start_layer_id=target_layer_num).to(target_device)
+        self.model = get_model(vllm_config=self.vllm_config,
+                               model_config=draft_model_config)
 
         draft_attn_layer_names = (
             get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
             target_attn_layer_names)
         assert len(draft_attn_layer_names) == 1
         self.attn_layer_name = next(iter(draft_attn_layer_names))
-        loaded_weights = self.model.load_weights(
-            loader.get_all_weights(draft_model_config, self.model))
-
-        process_weights_after_loading(self.model, draft_model_config,
-                                      target_device)
 
         # share embed_tokens with the target model if needed
         if get_pp_group().world_size == 1:
-            assert "model.embed_tokens.weight" not in loaded_weights, \
-            "For PP = 1, Eagle draft should share embed with target model"
             logger.info(
                 "The EAGLE head shares the same vocab embedding" \
                 " with the target model."
             )
             self.model.model.embed_tokens = target_model.model.embed_tokens
         else:
-            assert "model.embed_tokens.weight" in loaded_weights, \
-            "For PP > 1, Eagle draft checkpoint should its own copy of "
-            " the model.embed_tokens.weight"
             logger.info(
                 "Since PP > 1, the EAGLE head loaded its own vocab embedding" \
                 " weights instead of sharing them with the target model."

vllm/v1/spec_decode/medusa.py

@@ -3,12 +3,10 @@
 import torch
 import torch.nn as nn
 
-from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.config import VllmConfig
 from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
-from vllm.model_executor.model_loader import get_model_loader
-from vllm.model_executor.model_loader.utils import set_default_torch_dtype
-from vllm.model_executor.models.medusa import Medusa
+from vllm.model_executor.model_loader import get_model
 from vllm.v1.sample.metadata import SamplingMetadata
 
 # Initialize logger
@@ -49,20 +47,9 @@ class MedusaProposer:
         return [list(row) for row in zip(*draft_tokens)]
 
     def load_model(self, target_model: nn.Module) -> None:
-        # Get model loader and config
-        loader = get_model_loader(self.vllm_config.load_config)
-        draft_config = self.vllm_config.speculative_config.draft_model_config
-
-        # Load model with proper dtype and config
-        with set_default_torch_dtype(draft_config.dtype), \
-                set_current_vllm_config(self.vllm_config):
-            self.model = Medusa(
-                vllm_config=self.vllm_config.speculative_config).to(
-                    self.device)
-
-        # Load model weights
-        weights = loader.get_all_weights(draft_config, self.model)
-        self.model.load_weights(weights)
+        self.model = get_model(vllm_config=self.vllm_config,
+                               model_config=self.vllm_config.
+                               speculative_config.draft_model_config)
 
     @torch.inference_mode()
     def dummy_run(self, num_tokens: int) -> None: