fix int8 path

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

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

@@ -15,7 +15,7 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
     choose_scaled_mm_linear_kernel,
 )
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (
-    ScaledMMLinearLayerConfig,
+    FP8ScaledMMLinearLayerConfig,
     ScaledMMLinearQuantStrategy,
 )
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
@@ -91,10 +91,7 @@ class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
             elif self.strategy == QuantizationStrategy.CHANNEL:
                 weight_quant_strategy = ScaledMMLinearQuantStrategy.CHANNEL
 
-            scaled_mm_linear_kernel_config = ScaledMMLinearLayerConfig(
+            scaled_mm_linear_kernel_config = FP8ScaledMMLinearLayerConfig(
-                is_channelwise=(self.strategy == QuantizationStrategy.CHANNEL),
-                is_static_input_scheme=self.is_static_input_scheme,
-                input_symmetric=True,
                 weight_quant_strategy=weight_quant_strategy,
                 activation_group_shape=self.act_q_group_shape,
                 out_dtype=self.out_dtype,

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -11,8 +11,8 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme,
 )
 from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
-    ScaledMMLinearLayerConfig,
     choose_scaled_mm_linear_kernel,
+    _POSSIBLE_INT8_KERNELS
 )
 from vllm.model_executor.parameter import (
     BasevLLMParameter,
@@ -20,6 +20,7 @@ from vllm.model_executor.parameter import (
     ModelWeightParameter,
     PerTensorScaleParameter,
 )
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import Int8ScaledMMLinearLayerConfig
 
 logger = init_logger(__name__)
 
@@ -50,13 +51,16 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
     ):
         layer.logical_widths = output_partition_sizes
 
-        scaled_mm_linear_kernel_config = ScaledMMLinearLayerConfig(
+        scaled_mm_linear_kernel_config = Int8ScaledMMLinearLayerConfig(
             is_channelwise=(self.strategy == QuantizationStrategy.CHANNEL),
             is_static_input_scheme=self.is_static_input_scheme,
             input_symmetric=self.input_symmetric,
         )
 
-        kernel_type = choose_scaled_mm_linear_kernel(scaled_mm_linear_kernel_config)
+        kernel_type = choose_scaled_mm_linear_kernel(
+            scaled_mm_linear_kernel_config,
+            _POSSIBLE_INT8_KERNELS
+        )
 
         if kernel_type.__name__ not in self._kernel_backends_being_used:
             logger.info("Using %s for CompressedTensorsW8A8Int8", kernel_type.__name__)
@@ -90,12 +94,12 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
         layer.register_parameter("weight_scale", weight_scale)
 
         # INPUT SCALE
+        input_zero_point=None
+        input_scale=None
         if self.is_static_input_scheme:
             input_scale = BasevLLMParameter(
                 data=torch.empty(1, dtype=torch.float32), weight_loader=weight_loader
             )
-            layer.register_parameter("input_scale", input_scale)
-
             if not self.input_symmetric:
                 # Note: compressed-tensors stores the zp using the same dtype
                 # as the weights
@@ -103,15 +107,21 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
                 input_zero_point = BasevLLMParameter(
                     data=torch.empty(1, dtype=torch.int8), weight_loader=weight_loader
                 )
-                layer.register_parameter("input_zero_point", input_zero_point)
 
+        layer.register_parameter("input_zero_point", input_zero_point)
+        layer.register_parameter("input_scale", input_scale)
+        if not hasattr(layer, "azp_adj"):
+            layer.register_parameter("azp_adj", None)
+
+        param_name_list = ["weight", "weight_scale", "input_scale", "input_zero_point", "azp_adj"]
+
+        layer_mapping_function = lambda layer: (
+            tuple(getattr(layer, param_name) for param_name in param_name_list),
+            param_name_list,
+        )
         self.kernel = kernel_type(
             c=scaled_mm_linear_kernel_config,
-            w_q_param_name="weight",
+            layer_mapping_function = layer_mapping_function
-            w_s_param_name="weight_scale",
-            i_s_param_name="input_scale",
-            i_zp_param_name="input_zero_point",
-            azp_adj_param_name="azp_adj",
         )
 
     # Checkpoints are serialized in compressed-tensors format, which is

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

@@ -5,7 +5,7 @@ from abc import ABC, abstractmethod
 from collections.abc import Callable
 from dataclasses import dataclass
 from enum import Enum
-
+from typing import Generic, TypeVar
 import torch
 
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
@@ -25,16 +25,25 @@ class ScaledMMLinearQuantStrategy(Enum):
 
 @dataclass
 class ScaledMMLinearLayerConfig:
-    # TODO: remove is channelwise
+    pass
+
+@dataclass
+class Int8ScaledMMLinearLayerConfig(ScaledMMLinearLayerConfig):
     is_channelwise: bool
     is_static_input_scheme: bool
     input_symmetric: bool
-    out_dtype: torch.dtype | None
+
+@dataclass
+class FP8ScaledMMLinearLayerConfig(ScaledMMLinearLayerConfig):
     weight_quant_strategy: ScaledMMLinearQuantStrategy
-    activation_group_shape: GroupShape | None = GroupShape.PER_TENSOR
+    activation_group_shape: GroupShape
+    out_dtype: torch.dtype
 
 
-class ScaledMMLinearKernel(ABC):
+ConfigT = TypeVar('ConfigT', bound=ScaledMMLinearLayerConfig)
+
+
+class ScaledMMLinearKernel(Generic[ConfigT], ABC):
     @classmethod
     @abstractmethod
     def get_min_capability(cls) -> int:
@@ -42,11 +51,11 @@ class ScaledMMLinearKernel(ABC):
 
     @classmethod
     @abstractmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: ConfigT) -> tuple[bool, str | None]:
         raise NotImplementedError
 
     def __init__(
-        self, c: ScaledMMLinearLayerConfig, layer_mapping_function: Callable
+        self, c: ConfigT, layer_mapping_function: Callable
     ) -> None:
         assert self.can_implement(c)
         self.config = c
@@ -64,20 +73,3 @@ class ScaledMMLinearKernel(ABC):
         bias: torch.Tensor | None = None,
     ) -> torch.Tensor:
         raise NotImplementedError
-
-    # def _get_weight_params(
-    #     self, layer: torch.nn.Module
-    # ) -> tuple[
-    #     torch.Tensor,  # weight
-    #     torch.Tensor,  # weight_scale
-    #     torch.Tensor | None,  # input_scale,
-    #     torch.Tensor | None,  # input_zp
-    #     torch.Tensor | None,  # azp_adj
-    # ]:
-    #     return (
-    #         getattr(layer, self.w_q_name),
-    #         getattr(layer, self.w_s_name),
-    #         getattr(layer, self.i_s_name),
-    #         getattr(layer, self.i_zp_name),
-    #         getattr(layer, self.azp_adj_name),
-    #     )

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

@@ -19,6 +19,7 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKer
     ScaledMMLinearKernel,
     ScaledMMLinearLayerConfig,
 )
+
 from vllm.model_executor.layers.quantization.kernels.scaled_mm.torch import (
     ChannelWiseTorchScaledMMLinearKernel,
     PerTensorTorchScaledMMLinearKernel,

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

@@ -10,7 +10,7 @@ from vllm.platforms import current_platform
 from vllm.utils.torch_utils import direct_register_custom_op
 
 from .cutlass import process_weights_after_loading
-from .ScaledMMLinearKernel import ScaledMMLinearKernel, ScaledMMLinearLayerConfig
+from .ScaledMMLinearKernel import ScaledMMLinearKernel, Int8ScaledMMLinearLayerConfig
 
 
 def rocm_aiter_gemm_w8a8_impl(
@@ -58,7 +58,7 @@ class AiterScaledMMLinearKernel(ScaledMMLinearKernel):
         return 90
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: Int8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         if not current_platform.is_rocm():
             return (
                 False,

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

@@ -14,7 +14,7 @@ from vllm.model_executor.layers.utils import check_cpu_sgl_kernel
 from vllm.platforms import current_platform
 from vllm.platforms.interface import CpuArchEnum
 
-from .ScaledMMLinearKernel import ScaledMMLinearKernel, ScaledMMLinearLayerConfig
+from .ScaledMMLinearKernel import ScaledMMLinearKernel, Int8ScaledMMLinearLayerConfig
 
 
 class CPUScaledMMLinearKernel(ScaledMMLinearKernel):
@@ -23,7 +23,7 @@ class CPUScaledMMLinearKernel(ScaledMMLinearKernel):
         return 75
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: Int8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         if not current_platform.is_cpu():
             return False, "CPUScaledMM requires running on CPU."
 

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

@@ -15,7 +15,7 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
 )
 from vllm.platforms import current_platform
 
-from .ScaledMMLinearKernel import ScaledMMLinearKernel, ScaledMMLinearLayerConfig
+from .ScaledMMLinearKernel import ScaledMMLinearKernel, Int8ScaledMMLinearLayerConfig, FP8ScaledMMLinearLayerConfig
 
 
 def cutlass_w8a8_scaled_mm(
@@ -36,7 +36,7 @@ def cutlass_w8a8_scaled_mm(
 
 
 def process_weights_after_loading(
-    config: ScaledMMLinearLayerConfig,
+    config: Int8ScaledMMLinearLayerConfig,
     layer: torch.nn.Module,
     w_q_name: str,
     w_s_name: str,
@@ -98,9 +98,6 @@ def process_weights_after_loading(
                 layer, i_zp_name, torch.nn.Parameter(azp, requires_grad=False)
             )
 
-    else:
-        setattr(layer, i_s_name, None)
-        setattr(layer, i_zp_name, None)
 
     # azp_adj is the AZP adjustment term, used to account for weights.
     # It does not depend on scales or azp, so it is the same for
@@ -119,8 +116,6 @@ def process_weights_after_loading(
             azp_adj_name,
             torch.nn.Parameter(azp_adj, requires_grad=False),
         )
-    else:
-        setattr(layer, azp_adj_name, None)
 
 
 class CutlassScaledMMLinearKernel(ScaledMMLinearKernel):
@@ -129,7 +124,7 @@ class CutlassScaledMMLinearKernel(ScaledMMLinearKernel):
         return 75
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: Int8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         if not current_platform.is_cuda():
             return False, "CutlassScaledMM requires running on CUDA."
 
@@ -177,7 +172,7 @@ class CutlassScaledMMLinearKernel(ScaledMMLinearKernel):
 
 class CutlassFP8ScaledMMLinearKernel(ScaledMMLinearKernel):
     def __init__(
-        self, c: ScaledMMLinearLayerConfig, layer_mapping_function: Callable
+        self, c: FP8ScaledMMLinearLayerConfig, layer_mapping_function: Callable
     ) -> None:
         self.quant_fp8 = QuantFP8(
             static=c.is_static_input_scheme,
@@ -192,7 +187,7 @@ class CutlassFP8ScaledMMLinearKernel(ScaledMMLinearKernel):
         return 89
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         if not current_platform.is_cuda():
             return (
                 False,

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

@@ -11,7 +11,7 @@ from vllm.utils.flashinfer import flashinfer_scaled_fp8_mm, has_flashinfer
 
 from .ScaledMMLinearKernel import (
     ScaledMMLinearKernel,
-    ScaledMMLinearLayerConfig,
+    Int8ScaledMMLinearLayerConfig,
     ScaledMMLinearQuantStrategy,
 )
 
@@ -32,7 +32,7 @@ def flashinfer_w8a8_scaled_mm(
 
 class FlashInferScaledMMLinearKernel(ScaledMMLinearKernel):
     def __init__(
-        self, c: ScaledMMLinearLayerConfig, layer_mapping_function: Callable
+        self, c: Int8ScaledMMLinearLayerConfig, layer_mapping_function: Callable
     ) -> None:
         self.quant_fp8 = QuantFP8(
             static=c.is_static_input_scheme,
@@ -46,7 +46,7 @@ class FlashInferScaledMMLinearKernel(ScaledMMLinearKernel):
         return 100
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: Int8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         per_tensor_activation_scales = c.activation_group_shape.is_per_tensor()
         per_tensor_weight_scales = (
             c.weight_quant_strategy == ScaledMMLinearQuantStrategy.TENSOR

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

@@ -13,7 +13,7 @@ from vllm.utils.torch_utils import direct_register_custom_op
 
 from .ScaledMMLinearKernel import (
     ScaledMMLinearKernel,
-    ScaledMMLinearLayerConfig,
+    FP8ScaledMMLinearLayerConfig,
     ScaledMMLinearQuantStrategy,
 )
 
@@ -90,7 +90,7 @@ if current_platform.is_rocm():
 
 class ROCmScaledMMLinearKernel(ScaledMMLinearKernel):
     def __init__(
-        self, c: ScaledMMLinearLayerConfig, layer_mapping_function: Callable
+        self, c: FP8ScaledMMLinearLayerConfig, layer_mapping_function: Callable
     ) -> None:
         self.quant_fp8 = QuantFP8(
             static=c.is_static_input_scheme,
@@ -104,7 +104,7 @@ class ROCmScaledMMLinearKernel(ScaledMMLinearKernel):
         return 90
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         # TODO: check if this causes an issue on non-ROCM platforms
         from vllm.platforms.rocm import on_mi3xx
 

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

@@ -12,7 +12,7 @@ from vllm.platforms import current_platform
 
 from .ScaledMMLinearKernel import (
     ScaledMMLinearKernel,
-    ScaledMMLinearLayerConfig,
+    FP8ScaledMMLinearLayerConfig,
     ScaledMMLinearQuantStrategy,
 )
 
@@ -136,7 +136,7 @@ def torch_channelwise_w8a8_scaled_mm(
 
 class TorchScaledMMLinearKernel(ScaledMMLinearKernel):
     def __init__(
-        self, c: ScaledMMLinearLayerConfig, layer_mapping_function: Callable
+        self, c: FP8ScaledMMLinearLayerConfig, layer_mapping_function: Callable
     ) -> None:
         vllm_config = get_current_vllm_config().compilation_config
         pad_output = vllm_config.mode < CompilationMode.VLLM_COMPILE
@@ -161,7 +161,7 @@ class TorchScaledMMLinearKernel(ScaledMMLinearKernel):
 
 class PerTensorTorchScaledMMLinearKernel(TorchScaledMMLinearKernel):
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         assert c.activation_group_shape is not None
         per_tensor_activation_scales = c.activation_group_shape.is_per_tensor()
         per_tensor_weight_scales = (
@@ -218,7 +218,7 @@ class RowWiseTorchScaledMMLinearKernel(TorchScaledMMLinearKernel):
         return 94
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         assert c.activation_group_shape is not None
 
         per_tensor_activation_scales = c.activation_group_shape.is_per_tensor()
@@ -290,7 +290,7 @@ class ChannelWiseTorchScaledMMLinearKernel(TorchScaledMMLinearKernel):
         return 94
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: FP8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         assert c.activation_group_shape is not None
 
         per_tensor_activation_scales = c.activation_group_shape.is_per_tensor()

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

@@ -7,7 +7,7 @@ import torch
 from vllm.platforms import current_platform
 
 from .cutlass import CutlassScaledMMLinearKernel
-from .ScaledMMLinearKernel import ScaledMMLinearLayerConfig
+from .ScaledMMLinearKernel import Int8ScaledMMLinearLayerConfig
 
 
 class TritonScaledMMLinearKernel(CutlassScaledMMLinearKernel):
@@ -16,7 +16,7 @@ class TritonScaledMMLinearKernel(CutlassScaledMMLinearKernel):
         return 75
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: Int8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         if current_platform.is_cpu():
             return (
                 False,

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

@@ -12,7 +12,7 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
 )
 from vllm.platforms import current_platform
 
-from .ScaledMMLinearKernel import ScaledMMLinearKernel, ScaledMMLinearLayerConfig
+from .ScaledMMLinearKernel import ScaledMMLinearKernel, Int8ScaledMMLinearLayerConfig
 
 
 class XLAScaledMMLinearKernel(ScaledMMLinearKernel):
@@ -24,7 +24,7 @@ class XLAScaledMMLinearKernel(ScaledMMLinearKernel):
         )
 
     @classmethod
-    def can_implement(cls, c: ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
+    def can_implement(cls, c: Int8ScaledMMLinearLayerConfig) -> tuple[bool, str | None]:
         if not current_platform.is_tpu():
             return False, "ScaledMMXLA requires running on TPU."
 

vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_int8.py

@@ -7,9 +7,9 @@ import torch
 
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
-    ScaledMMLinearLayerConfig,
     choose_scaled_mm_linear_kernel,
 )
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import Int8ScaledMMLinearLayerConfig
 from vllm.model_executor.layers.quantization.quark.schemes import QuarkScheme
 from vllm.model_executor.parameter import (
     BasevLLMParameter,
@@ -50,7 +50,7 @@ class QuarkW8A8Int8(QuarkScheme):
     ):
         layer.logical_widths = output_partition_sizes
 
-        scaled_mm_linear_kernel_config = ScaledMMLinearLayerConfig(
+        scaled_mm_linear_kernel_config = Int8ScaledMMLinearLayerConfig(
             is_channelwise=(self.qscheme == "per_channel"),
             is_static_input_scheme=(self.is_static_input_scheme is True),
             input_symmetric=(self.input_symmetric is True),