[Misc] Update marlin to use vLLMParameters (#7803)

Author: dsikka

tests/weight_loading/models.txt

@@ -15,4 +15,6 @@ compressed-tensors, nm-testing/Phi-3-mini-128k-instruct-FP8, main
 compressed-tensors, neuralmagic/Phi-3-medium-128k-instruct-quantized.w4a16, main
 awq, casperhansen/mixtral-instruct-awq, main
 awq_marlin, casperhansen/mixtral-instruct-awq, main
-fp8, neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV, main
+fp8, neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV, main
+marlin, nm-testing/zephyr-beta-7b-marlin-g128, main
+marlin, robertgshaw2/zephyr-7b-beta-channelwise-marlin, main

vllm/model_executor/layers/linear.py

@@ -22,7 +22,8 @@
 
 WEIGHT_LOADER_V2_SUPPORTED = [
     "CompressedTensorsLinearMethod", "AWQMarlinLinearMethod",
-    "AWQLinearMethod", "GPTQMarlinLinearMethod", "Fp8LinearMethod"
+    "AWQLinearMethod", "GPTQMarlinLinearMethod", "Fp8LinearMethod",
+    "MarlinLinearMethod"
 ]
 
 

vllm/model_executor/layers/quantization/marlin.py

@@ -9,7 +9,10 @@
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
-from vllm.model_executor.utils import set_weight_attrs
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
 
 logger = init_logger(__name__)
 
@@ -132,6 +135,7 @@ def create_weights(
         **extra_weight_attrs,
     ):
         del output_size  # Unused.
+        weight_loader = extra_weight_attrs["weight_loader"]
 
         if params_dtype != torch.float16:
             raise ValueError(
@@ -170,64 +174,64 @@ def create_weights(
                 "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,
                 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)
 
         # 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,
-                                          device="cuda",
-                                          dtype=torch.int),
-                              requires_grad=False)
+
+        workspace = BasevLLMParameter(data=torch.zeros(max_workspace_size,
+                                                       device="cuda",
+                                                       dtype=torch.int),
+                                      weight_loader=weight_loader)
 
         layer.register_parameter("B", qweight)
-        set_weight_attrs(qweight, 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 = Parameter(layer.B.data, requires_grad=False)
+        layer.s = Parameter(layer.s.data, requires_grad=False)
+        layer.workspace = Parameter(layer.workspace.data, requires_grad=False)
 
     def apply(
         self,