format; update fbgemm path

Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

@@ -86,7 +86,7 @@ class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
                 act_q_group_shape=self.act_q_group_shape,
                 weight_quant_strategy=weight_quant_strategy,
                 out_dtype=self.out_dtype,
-                module_name=self.__class__.__name__
+                module_name=self.__class__.__name__,
             )
 
     @classmethod

vllm/model_executor/layers/quantization/fbgemm_fp8.py

@@ -18,6 +18,12 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
+from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
+    init_fp8_linear_kernel,
+)
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (
+    ScaledMMLinearQuantStrategy,
+)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     apply_fp8_marlin_linear,
     prepare_fp8_layer_for_marlin,
@@ -96,6 +102,14 @@ class FBGEMMFp8LinearMethod(LinearMethodBase):
         )
         self.out_dtype = torch.get_default_dtype()
 
+        self.fp8_linear_kernel = init_fp8_linear_kernel(
+            act_q_static=False,
+            act_q_group_shape=GroupShape.PER_TOKEN,
+            weight_quant_strategy=ScaledMMLinearQuantStrategy.CHANNEL,
+            out_dtype=self.out_dtype,
+            module_name=self.__class__.__name__,
+        )
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -184,12 +198,4 @@ class FBGEMMFp8LinearMethod(LinearMethodBase):
                 bias=bias,
             )
 
-        return self.fp8_linear.apply(
-            input=x,
-            weight=layer.weight,
-            weight_scale=layer.weight_scale,
-            out_dtype=self.out_dtype,
-            input_scale=None,
-            input_scale_ub=layer.input_scale_ub,
-            bias=bias,
-        )
+        return self.fp8_linear_kernel.apply_weights(layer, x, bias)

vllm/model_executor/layers/quantization/fp8.py

@@ -398,7 +398,7 @@ class Fp8LinearMethod(LinearMethodBase):
                 act_q_group_shape=self.act_q_group_shape,
                 weight_quant_strategy=ScaledMMLinearQuantStrategy.TENSOR,
                 out_dtype=self.out_dtype,
-                module_name=self.__class__.__name__
+                module_name=self.__class__.__name__,
             )
 
     def create_weights(

vllm/model_executor/layers/quantization/kernels/scaled_mm/__init__.py

@@ -15,6 +15,11 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm.cpu import (
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.cutlass import (
     CutlassScaledMMLinearKernel,
 )
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.pytorch import (
+    ChannelWiseTorchScaledMMLinearKernel,
+    PerTensorTorchScaledMMLinearKernel,
+    RowWiseTorchScaledMMLinearKernel,
+)
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.rocm import (
     ROCmScaledMMLinearKernel,
 )
@@ -25,19 +30,14 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKer
     ScaledMMLinearLayerConfig,
     ScaledMMLinearQuantStrategy,
 )
-from vllm.model_executor.layers.quantization.kernels.scaled_mm.pytorch import (
-    ChannelWiseTorchScaledMMLinearKernel,
-    PerTensorTorchScaledMMLinearKernel,
-    RowWiseTorchScaledMMLinearKernel,
-)
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.triton import (
     TritonScaledMMLinearKernel,
 )
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.xla import (
     XLAScaledMMLinearKernel,
 )
-from vllm.platforms import PlatformEnum, current_platform
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
+from vllm.platforms import PlatformEnum, current_platform
 
 logger = init_logger(__name__)
 
@@ -133,7 +133,7 @@ def choose_scaled_mm_linear_kernel(
 def init_fp8_linear_kernel(
     act_q_static: bool,
     act_q_group_shape: GroupShape,
-    weight_quant_strategy: ScaledMMLinearQuantStrategy, 
+    weight_quant_strategy: ScaledMMLinearQuantStrategy,
     out_dtype: torch.dtype,
     module_name: str,
 ) -> FP8ScaledMMLinearKernel:

vllm/model_executor/layers/quantization/ptpc_fp8.py

@@ -108,10 +108,9 @@ class PTPCFp8LinearMethod(Fp8LinearMethod):
             act_q_group_shape=GroupShape.PER_TOKEN,
             weight_quant_strategy=ScaledMMLinearQuantStrategy.CHANNEL,
             out_dtype=self.out_dtype,
-            module_name=self.__class__.__name__
+            module_name=self.__class__.__name__,
         )
 
-
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)
 
@@ -136,6 +135,4 @@ class PTPCFp8LinearMethod(Fp8LinearMethod):
         x: torch.Tensor,
         bias: torch.Tensor | None = None,
     ) -> torch.Tensor:
-        return self.fp8_linear_kernel.apply_weights(
-            layer, x, bias
-        )
+        return self.fp8_linear_kernel.apply_weights(layer, x, bias)

vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py

@@ -178,7 +178,7 @@ class QuarkW8A8Fp8(QuarkScheme):
             act_q_group_shape=self.act_quant_group_shape,
             weight_quant_strategy=weight_quant_strategy,
             out_dtype=self.out_dtype,
-            module_name=self.__class__.__name__
+            module_name=self.__class__.__name__,
         )
 
     def apply_weights(