[Misc] Update `gptq_marlin_24` to use vLLMParameters (#7762)

Co-authored-by: Michael Goin <michael@neuralmagic.com>

vllm/model_executor/layers/linear.py

@@ -23,7 +23,7 @@ logger = init_logger(__name__)
 WEIGHT_LOADER_V2_SUPPORTED = [
     "CompressedTensorsLinearMethod", "AWQMarlinLinearMethod",
     "AWQLinearMethod", "GPTQMarlinLinearMethod", "Fp8LinearMethod",
-    "MarlinLinearMethod", "QQQLinearMethod"
+    "MarlinLinearMethod", "QQQLinearMethod", "GPTQMarlin24LinearMethod"
 ]
 
 

vllm/model_executor/layers/quantization/gptq_marlin_24.py

@@ -8,7 +8,10 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
-from vllm.model_executor.utils import set_weight_attrs
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
 from vllm.scalar_type import scalar_types
 
 logger = init_logger(__name__)
@@ -149,7 +152,7 @@ class GPTQMarlin24LinearMethod(LinearMethodBase):
         **extra_weight_attrs,
     ):
         del output_size  # Unused.
-
+        weight_loader = extra_weight_attrs["weight_loader"]
         if params_dtype != torch.float16:
             raise ValueError(
                 f"The params dtype must be float16, but got {params_dtype}")
@@ -187,87 +190,80 @@ class GPTQMarlin24LinearMethod(LinearMethodBase):
                 "Each permutation group must reside on the same gpu")
 
         # Quantized 4Bit weights packed into Int32.
-        qweight = Parameter(
-            torch.empty(
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
                 input_size_per_partition // self.quant_config.tile_size // 2,
                 output_size_per_partition * self.quant_config.tile_size //
                 self.quant_config.pack_factor,
                 device="cuda",
                 dtype=torch.int32,
             ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            qweight,
-            {
-                "input_dim": 0,
-                "output_dim": 1,
-                "packed_dim": 1,
-                "pack_factor": self.quant_config.pack_factor,
-                "marlin_tile_size": self.quant_config.tile_size,
-            },
-        )
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            marlin_tile_size=self.quant_config.tile_size,
+            weight_loader=weight_loader)
 
         # Meta
-        meta = Parameter(
-            torch.empty(
+        meta = PackedvLLMParameter(data=torch.empty(
             input_size_per_partition // 8 // 2 // 2,
             output_size_per_partition * 2,
             device="cuda",
             dtype=torch.int16,
         ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            meta,
-            {
-                "input_dim": 0,
-                "packed_dim": 1,
-                "pack_factor": 1,
-                "output_dim": 1,
-                "marlin_tile_size": 2,
-            },
-        )
+                                   input_dim=0,
+                                   output_dim=1,
+                                   packed_dim=1,
+                                   packed_factor=1,
+                                   marlin_tile_size=2,
+                                   weight_loader=weight_loader)
 
         # Determine if channelwise or not
         input_groups = (1 if self.quant_config.group_size == -1 else
                         input_size_per_partition //
                         self.quant_config.group_size)
 
-        scales = Parameter(
+        weight_scale_args = {
+            "data":
             torch.empty(
                 input_groups,
                 output_size_per_partition,
                 device="cuda",
                 dtype=params_dtype,
             ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            scales,
-            {
-                "input_dim": None if input_groups == 1 else 0,
-                "output_dim": 1,
-            },
-        )
+            "weight_loader":
+            weight_loader
+        }
+        if input_groups == 1:
+            scales = ChannelQuantScaleParameter(output_dim=1,
+                                                **weight_scale_args)
+        else:
+            scales = GroupQuantScaleParameter(output_dim=1,
+                                              input_dim=0,
+                                              **weight_scale_args)
 
         # Allocate workspace (Used for internal locking mechanism)
         max_workspace_size = (
             output_size_per_partition //
             self.quant_config.min_n_threads) * self.quant_config.max_parallel
-        workspace = Parameter(torch.zeros(max_workspace_size,
+
+        workspace = BasevLLMParameter(data=torch.zeros(max_workspace_size,
                                                        device="cuda",
                                                        dtype=torch.int),
-                              requires_grad=False)
+                                      weight_loader=weight_loader)
 
         layer.register_parameter("B_24", qweight)
-        set_weight_attrs(qweight, extra_weight_attrs)
         layer.register_parameter("B_meta", meta)
-        set_weight_attrs(meta, extra_weight_attrs)
         layer.register_parameter("s", scales)
-        set_weight_attrs(scales, extra_weight_attrs)
         layer.register_parameter("workspace", workspace)
-        set_weight_attrs(workspace, extra_weight_attrs)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # required by torch.compile
+        layer.B_24 = Parameter(layer.B_24.data, requires_grad=False)
+        layer.s = Parameter(layer.s.data, requires_grad=False)
+        layer.B_meta = Parameter(layer.B_meta.data, requires_grad=False)
+        layer.workspace = Parameter(layer.workspace.data, requires_grad=False)
 
     def apply(
         self,