update ptpc path; bug fixes

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

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

@@ -82,10 +82,11 @@ class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
         else:
             weight_quant_strategy = QUANT_STRATEGY_MAP[self.strategy]
             self.fp8_linear_kernel = init_fp8_linear_kernel(
-                is_static_input_scheme=self.is_static_input_scheme,
+                act_q_static=self.is_static_input_scheme,
+                act_q_group_shape=self.act_q_group_shape,
                 weight_quant_strategy=weight_quant_strategy,
-                activation_group_shape=self.act_q_group_shape,
                 out_dtype=self.out_dtype,
+                module_name=self.__class__.__name__
             )
 
     @classmethod

vllm/model_executor/layers/quantization/fp8.py

@@ -394,10 +394,11 @@ class Fp8LinearMethod(LinearMethodBase):
             )
         else:
             self.fp8_linear_kernel = init_fp8_linear_kernel(
-                is_static_input_scheme=self.act_q_static,
+                act_q_static=self.act_q_static,
+                act_q_group_shape=self.act_q_group_shape,
                 weight_quant_strategy=ScaledMMLinearQuantStrategy.TENSOR,
-                activation_group_shape=self.act_q_group_shape,
                 out_dtype=self.out_dtype,
+                module_name=self.__class__.__name__
             )
 
     def create_weights(

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

@@ -25,7 +25,7 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKer
     ScaledMMLinearLayerConfig,
     ScaledMMLinearQuantStrategy,
 )
-from vllm.model_executor.layers.quantization.kernels.scaled_mm.torch import (
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.pytorch import (
     ChannelWiseTorchScaledMMLinearKernel,
     PerTensorTorchScaledMMLinearKernel,
     RowWiseTorchScaledMMLinearKernel,
@@ -37,7 +37,7 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm.xla import (
     XLAScaledMMLinearKernel,
 )
 from vllm.platforms import PlatformEnum, current_platform
-from vllm.vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
+from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 
 logger = init_logger(__name__)
 
@@ -133,12 +133,13 @@ def choose_scaled_mm_linear_kernel(
 def init_fp8_linear_kernel(
     act_q_static: bool,
     act_q_group_shape: GroupShape,
+    weight_quant_strategy: ScaledMMLinearQuantStrategy, 
     out_dtype: torch.dtype,
     module_name: str,
 ) -> FP8ScaledMMLinearKernel:
     scaled_mm_linear_kernel_config = FP8ScaledMMLinearLayerConfig(
         is_static_input_scheme=act_q_static,
-        weight_quant_strategy=ScaledMMLinearQuantStrategy.TENSOR,
+        weight_quant_strategy=weight_quant_strategy,
         activation_group_shape=act_q_group_shape,
         out_dtype=out_dtype,
     )

vllm/model_executor/layers/quantization/ptpc_fp8.py

@@ -16,11 +16,16 @@ from vllm.model_executor.layers.quantization.fp8 import (
     Fp8KVCacheMethod,
     Fp8LinearMethod,
 )
+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 (  # noqa E501
+    ScaledMMLinearQuantStrategy,
+)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape,
     is_layer_skipped,
 )
-from vllm.model_executor.layers.quantization.utils.w8a8_utils import Fp8LinearOp
 from vllm.platforms import current_platform
 
 ACTIVATION_SCHEMES = ["static", "dynamic"]
@@ -98,11 +103,15 @@ class PTPCFp8LinearMethod(Fp8LinearMethod):
         )
         super().__init__(quant_config=quant_config)
         # Force weight quantization
-        self.quant_config.is_checkpoint_fp8_serialized = False
-        self.fp8_linear = Fp8LinearOp(
-            act_quant_static=False, act_quant_group_shape=GroupShape.PER_TOKEN
+        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 process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)
 
@@ -127,11 +136,6 @@ class PTPCFp8LinearMethod(Fp8LinearMethod):
         x: torch.Tensor,
         bias: torch.Tensor | None = None,
     ) -> torch.Tensor:
-        return self.fp8_linear.apply(
-            input=x,
-            weight=layer.weight,
-            weight_scale=layer.weight_scale,
-            input_scale=None,
-            input_scale_ub=None,
-            bias=bias,
+        return self.fp8_linear_kernel.apply_weights(
+            layer, x, bias
         )

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

@@ -174,10 +174,11 @@ class QuarkW8A8Fp8(QuarkScheme):
 
         weight_quant_strategy = QUANT_STRATEGY_MAP[self.weight_qscheme]
         self.fp8_linear_kernel = init_fp8_linear_kernel(
-            is_static_input_scheme=self.is_static_input_scheme,
+            act_q_static=self.is_static_input_scheme,
+            act_q_group_shape=self.act_quant_group_shape,
             weight_quant_strategy=weight_quant_strategy,
-            activation_group_shape=self.act_quant_group_shape,
             out_dtype=self.out_dtype,
+            module_name=self.__class__.__name__
         )
 
     def apply_weights(