[Core] Support weight_loader_v2 for UnquantizedLinearMethod (#23036)

Signed-off-by: Kyle Sayers <kylesayrs@gmail.com>
Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/compilation/decorators.py

@@ -8,6 +8,7 @@ from unittest.mock import patch
 
 import torch
 import torch.nn as nn
+from packaging import version
 from torch._dynamo.symbolic_convert import InliningInstructionTranslator
 
 from vllm.compilation.counter import compilation_counter
@@ -300,13 +301,13 @@ def _support_torch_compile(
                 logger.debug(
                     "enable_cpp_symbolic_shape_guards config not available")
 
-            with patch.object(InliningInstructionTranslator, 'inline_call',
+            with patch.object(
+                    InliningInstructionTranslator, "inline_call",
                     patched_inline_call), torch._dynamo.config.patch(
                         **dynamo_config_patches
                     ), maybe_use_cudagraph_partition_wrapper(
-                                  self.vllm_config):
+                        self.vllm_config), _torch27_patch_tensor_subclasses():
                 output = self.compiled_callable(*args, **kwargs)
-
             return output
 
         # usually, capturing the model once is enough, and then we can
@@ -367,3 +368,33 @@ def maybe_use_cudagraph_partition_wrapper(vllm_config: VllmConfig):
     if (compilation_config.cudagraph_mode != CUDAGraphMode.NONE
             and compilation_config.use_inductor_graph_partition):
         torch._inductor.utils.set_customized_partition_wrappers(None)
+
+
+@contextlib.contextmanager
+def _torch27_patch_tensor_subclasses():
+    """
+    Add support for using tensor subclasses (ie `BasevLLMParameter`, ect) when
+    using torch 2.7.0. This enables using weight_loader_v2 and the use of
+    `BasevLLMParameters` without having to replace them with regular tensors
+    before `torch.compile`-time.
+    """
+    from vllm.model_executor.parameter import (BasevLLMParameter,
+                                               ModelWeightParameter,
+                                               RowvLLMParameter,
+                                               _ColumnvLLMParameter)
+
+    def return_false(*args, **kwargs):
+        return False
+
+    if version.parse("2.7") <= version.parse(
+            torch.__version__) < version.parse("2.8"):
+        yield
+        return
+
+    with (torch._dynamo.config.patch("traceable_tensor_subclasses", [
+            BasevLLMParameter, ModelWeightParameter, _ColumnvLLMParameter,
+            RowvLLMParameter
+    ]),
+          patch("torch._dynamo.variables.torch.can_dispatch_torch_function",
+                return_false)):
+        yield

vllm/model_executor/layers/linear.py

@@ -22,6 +22,7 @@ from vllm.model_executor.layers.utils import dispatch_unquantized_gemm
 # yapf: disable
 from vllm.model_executor.parameter import (BasevLLMParameter,
                                            BlockQuantScaleParameter,
+                                           ModelWeightParameter,
                                            PackedColumnParameter,
                                            PackedvLLMParameter,
                                            PerTensorScaleParameter,
@@ -34,6 +35,7 @@ from vllm.utils import GiB_bytes
 logger = init_logger(__name__)
 
 WEIGHT_LOADER_V2_SUPPORTED = [
+    "UnquantizedLinearMethod",
     "CompressedTensorsLinearMethod",
     "CompressedTensorsLinearTransformMethod",
     "BitBLASLinearMethod",
@@ -196,10 +198,14 @@ class UnquantizedLinearMethod(LinearMethodBase):
         # The amount of memory allocated for the weights is
         # sum(output_partition_sizes) * input_size_per_partition.
         try:
-            weight = Parameter(torch.empty(sum(output_partition_sizes),
+            weight_loader = extra_weight_attrs.pop("weight_loader")
+            weight = ModelWeightParameter(data=torch.empty(
+                sum(output_partition_sizes),
                 input_size_per_partition,
                 dtype=params_dtype),
-                               requires_grad=False)
+                                          input_dim=1,
+                                          output_dim=0,
+                                          weight_loader=weight_loader)
         except torch.cuda.OutOfMemoryError as e:
             logger.error("Failed to create unquantized linear weights: %s", e)
             if torch.cuda.is_available():
@@ -212,7 +218,7 @@ class UnquantizedLinearMethod(LinearMethodBase):
                 "Failed to create unquantized linear weights. "
                 "This may be caused by insufficient memory to allocate "
                 "the weight.") from e
-        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
+
         layer.register_parameter("weight", weight)
         set_weight_attrs(weight, extra_weight_attrs)
 

vllm/model_executor/parameter.py

@@ -61,9 +61,24 @@ class BasevLLMParameter(Parameter):
         self.tp_size = get_tensor_model_parallel_world_size()
 
     @property
-    def weight_loader(self):
+    def weight_loader(self) -> Callable:
+        # NOTE(@ksayers) some models such as mamba_mixer2 override the
+        # weight loader to support custom loading. In the future, model-specific
+        # weight loading should be implemented via Model.load_weights. In the
+        # meantime, support deleting and overriding `weight_loader`` attribute
+        if self._weight_loader is None:
+            raise AttributeError(f"{self.__class__.__name__} weight_loader "
+                                 "attribute has been deleted")
         return self._weight_loader
 
+    @weight_loader.setter
+    def weight_loader(self, value: Callable):
+        self._weight_loader = value
+
+    @weight_loader.deleter
+    def weight_loader(self):
+        self._weight_loader = None  # type: ignore[assignment]
+
     def _is_1d_and_scalar(self, loaded_weight: torch.Tensor):
         cond1 = self.data.ndim == 1 and self.data.numel() == 1
         cond2 = loaded_weight.ndim == 0 and loaded_weight.numel() == 1
@@ -97,6 +112,12 @@ class BasevLLMParameter(Parameter):
         assert shard_id in qkv_idxs
         return qkv_idxs[shard_id]
 
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+        return super().__torch_function__(func, types, args, kwargs)
+
 
 class _ColumnvLLMParameter(BasevLLMParameter):
     """