[Kernel] Factor out epilogues from cutlass kernels (#5391)

Co-authored-by: Michael Goin <michael@neuralmagic.com>
Co-authored-by: youkaichao <youkaichao@gmail.com>
Co-authored-by: zifeitong <zifei.tong@parasail.io>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-neuralmagic@users.noreply.github.com>

CMakeLists.txt

@@ -179,9 +179,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     "csrc/quantization/gptq_marlin/gptq_marlin.cu"
     "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
     "csrc/custom_all_reduce.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_dq_entry.cu"
+    "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c2x.cu"
+    "csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c3x.cu")
+    "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu")
 
   #
   # The CUTLASS kernels for Hopper require sm90a to be enabled.
@@ -189,7 +189,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # That adds an extra 17MB to compiled binary, so instead we selectively enable it.
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0)
     set_source_files_properties(
-          "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c3x.cu"
+          "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu"
           PROPERTIES
           COMPILE_FLAGS
           "-gencode arch=compute_90a,code=sm_90a")

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -76,11 +76,7 @@ def pytorch_fp8_impl_fast_accum(a: torch.tensor, b: torch.tensor,
 def cutlass_impl(a: torch.tensor, b: torch.tensor, scale_a: torch.tensor,
                  scale_b: torch.tensor,
                  out_dtype: torch.dtype) -> torch.tensor:
-    return ops.cutlass_scaled_mm_dq(a,
+    return ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype=out_dtype)
-                                    b,
-                                    scale_a,
-                                    scale_b,
-                                    out_dtype=out_dtype)
 
 
 # bench

csrc/ops.h

@@ -90,7 +90,7 @@ torch::Tensor gptq_marlin_repack(torch::Tensor& b_q_weight, torch::Tensor& perm,
                                  int64_t size_k, int64_t size_n,
                                  int64_t num_bits);
 
-void cutlass_scaled_mm_dq(torch::Tensor& out, torch::Tensor const& a,
+void cutlass_scaled_mm(torch::Tensor& out, torch::Tensor const& a,
                        torch::Tensor const& b, torch::Tensor const& a_scales,
                        torch::Tensor const& b_scales);
 

csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cu

@@ -29,21 +29,14 @@
 using namespace cute;
 
 /*
-   This defines a quantized GEMM operation with dequantized output, similar to
+   This file defines quantized GEMM operations using the CUTLASS 2.x API, for
-   torch._scaled_mm. It is defined using the CUTLASS 2.x API, and is used for
    NVIDIA GPUs with SM versions prior to sm90 (Hopper).
 
-   A and B may be both either int8 or fp8_e4m3. A can be quantized per-tensor or
+   Epilogue functions can be defined to post-process the output before it is
-   per-row. B can be quantized per-tensor or per-column.
+   written to GPU memory.
-   Any combination of per-tensor and per-row or column is supported.
+   Epilogues must contain a public type named EVTCompute of type Sm80EVT,
-   A and B must have symmetric quantization (zero point == 0).
+   as well as a static prepare_args function that constructs an
-
+   EVTCompute::Arguments struct.
-   So the GEMM operation is D = (a_scales * A) (b_scales * B), where the
-   scales are applied elementwise with numpy-style broadcasting.
-
-   ScaleA and ScaleB define the epilogue functions that apply the scales for
-   the A and B operands respectively. These scales may be either per-tensor or
-   per row or column.
 */
 
 namespace {
@@ -83,27 +76,25 @@ struct enable_sm89_to_sm90 : Kernel {
   }
 };
 
-template <typename Arch, template <typename> typename ArchGuard,
+/*
-          typename ElementAB_, typename ElementD_, typename TileShape,
+   This epilogue function defines a quantized GEMM operation similar to
-          typename WarpShape, typename InstructionShape, int32_t MainLoopStages>
+   torch._scaled_mm.
-struct cutlass_2x_gemm {
-  using ElementAB = ElementAB_;
-  using ElementD = ElementD_;
 
-  using ElementAcc =
+   A and B may be both either int8 or fp8_e4m3. A can be quantized per-tensor or
-      typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
+   per-row. B can be quantized per-tensor or per-column.
-                                float>::type;
+   Any combination of per-tensor and per-row or column is supported.
+   A and B must have symmetric quantization (zero point == 0).
 
-  using Operator =
+   So the GEMM operation is D = (a_scales * A) (b_scales * B), where the
-      typename std::conditional<std::is_same_v<ElementAB, int8_t>,
+   scales are applied elementwise with numpy-style broadcasting.
-                                cutlass::arch::OpMultiplyAddSaturate,
-                                cutlass::arch::OpMultiplyAdd>::type;
-
-  using OutputTileThreadMap =
-      cutlass::epilogue::threadblock::OutputTileThreadLayout<
-          TileShape, WarpShape, float, 4, 1 /* epilogue stages */
-          >;
 
+   ScaleA and ScaleB define the epilogue functions that apply the scales for
+   the A and B operands respectively. These scales may be either per-tensor or
+   per row or column.
+*/
+template <typename ElementD, typename OutputTileThreadMap>
+struct ScaledEpilogue {
+ private:
   using Accum = cutlass::epilogue::threadblock::VisitorAccFetch;
 
   using ScaleA = cutlass::epilogue::threadblock::VisitorColOrScalarBroadcast<
@@ -123,14 +114,56 @@ struct cutlass_2x_gemm {
       cutlass::multiplies, ElementD, float,
       cutlass::FloatRoundStyle::round_to_nearest>;
 
-  using EVTCompute1 =
+ public:
+  using EVTCompute =
       cutlass::epilogue::threadblock::Sm80EVT<Compute1, ScaleA, EVTCompute0>;
+  using ArgumentType = typename EVTCompute::Arguments;
+
+  static ArgumentType prepare_args(torch::Tensor const& a_scales,
+                                   torch::Tensor const& b_scales) {
+    using ScaleAArgs = typename ScaleA::Arguments;
+    using ScaleBArgs = typename ScaleB::Arguments;
+
+    ScaleBArgs b_args{b_scales.data_ptr<float>(), b_scales.numel() != 1, {}};
+    ScaleAArgs a_args{a_scales.data_ptr<float>(), a_scales.numel() != 1, {}};
+
+    typename EVTCompute0::Arguments evt0_compute_args{b_args};
+
+    typename EVTCompute::Arguments evt_compute_args{a_args, evt0_compute_args};
+    return evt_compute_args;
+  }
+};
+
+template <typename Arch, template <typename> typename ArchGuard,
+          typename ElementAB_, typename ElementD_,
+          template <typename, typename> typename Epilogue_, typename TileShape,
+          typename WarpShape, typename InstructionShape, int32_t MainLoopStages>
+struct cutlass_2x_gemm {
+  using ElementAB = ElementAB_;
+  using ElementD = ElementD_;
+
+  using ElementAcc =
+      typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
+                                float>::type;
+
+  using Operator =
+      typename std::conditional<std::is_same_v<ElementAB, int8_t>,
+                                cutlass::arch::OpMultiplyAddSaturate,
+                                cutlass::arch::OpMultiplyAdd>::type;
+
+  using OutputTileThreadMap =
+      cutlass::epilogue::threadblock::OutputTileThreadLayout<
+          TileShape, WarpShape, float, 4, 1 /* epilogue stages */
+          >;
+
+  using Epilogue = Epilogue_<ElementD, OutputTileThreadMap>;
+  using EVTCompute = typename Epilogue::EVTCompute;
 
   using D = cutlass::epilogue::threadblock::VisitorAuxStore<
       OutputTileThreadMap, ElementD, cutlass::FloatRoundStyle::round_to_nearest,
       Stride<int64_t, Int<1>, Int<0>>>;
 
-  using EVTD = cutlass::epilogue::threadblock::Sm80EVT<D, EVTCompute1>;
+  using EVTD = cutlass::epilogue::threadblock::Sm80EVT<D, EVTCompute>;
 
   // clang-format off
   using RowMajor = typename cutlass::layout::RowMajor;
@@ -153,11 +186,10 @@ struct cutlass_2x_gemm {
   using Op = cutlass::gemm::device::GemmUniversalAdapter<KernelType>;
 };
 
-template <typename Gemm>
+template <typename Gemm, typename... EpilogueArgs>
-void cutlass_scaled_mm_dq_dispatcher(torch::Tensor& out, torch::Tensor const& a,
+void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
                          torch::Tensor const& b,
-                                     torch::Tensor const& a_scales,
+                         EpilogueArgs&&... epilogue_params) {
-                                     torch::Tensor const& b_scales) {
   using ElementAB = typename Gemm::ElementAB;
   using ElementD = typename Gemm::ElementD;
 
@@ -177,23 +209,14 @@ void cutlass_scaled_mm_dq_dispatcher(torch::Tensor& out, torch::Tensor const& a,
   auto b_ptr = static_cast<ElementAB const*>(b.data_ptr());
   auto c_ptr = static_cast<ElementD*>(out.data_ptr());
 
-  auto a_scales_ptr = a_scales.data_ptr<float>();
-  auto b_scales_ptr = b_scales.data_ptr<float>();
-
-  using ScaleAArgs = typename Gemm::ScaleA::Arguments;
-  using ScaleBArgs = typename Gemm::ScaleB::Arguments;
-
-  ScaleBArgs b_args{b_scales.data_ptr<float>(), b_scales.numel() != 1, {}};
-  ScaleAArgs a_args{a_scales.data_ptr<float>(), a_scales.numel() != 1, {}};
-
-  typename Gemm::EVTCompute0::Arguments evt0_compute_args{b_args};
-
-  typename Gemm::EVTCompute1::Arguments evt1_compute_args{a_args,
-                                                          evt0_compute_args};
   typename Gemm::D::Arguments d_args{c_ptr, c_stride};
 
+  using Epilogue = typename Gemm::Epilogue;
+  auto evt_args =
+      Epilogue::prepare_args(std::forward<EpilogueArgs>(epilogue_params)...);
+
   typename Gemm::EVTD::Arguments epilogue_args{
-      evt1_compute_args,
+      evt_args,
       d_args,
   };
 
@@ -229,7 +252,7 @@ void cutlass_scaled_mm_dq_dispatcher(torch::Tensor& out, torch::Tensor const& a,
 
 }  // namespace
 
-void cutlass_scaled_mm_dq_sm75(torch::Tensor& out, torch::Tensor const& a,
+void cutlass_scaled_mm_sm75(torch::Tensor& out, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales) {
@@ -243,20 +266,20 @@ void cutlass_scaled_mm_dq_sm75(torch::Tensor& out, torch::Tensor const& a,
   using InstructionShape = typename cutlass::gemm::GemmShape<8, 8, 16>;
 
   if (out.dtype() == torch::kBFloat16) {
-    return cutlass_scaled_mm_dq_dispatcher<cutlass_2x_gemm<
+    return cutlass_gemm_caller<cutlass_2x_gemm<
         cutlass::arch::Sm75, enable_sm75_to_sm80, int8_t, cutlass::bfloat16_t,
-        TileShape, WarpShape, InstructionShape, 2>>(out, a, b, a_scales,
+        ScaledEpilogue, TileShape, WarpShape, InstructionShape, 2>>(
-                                                    b_scales);
+        out, a, b, a_scales, b_scales);
   } else {
     TORCH_CHECK(out.dtype() == torch::kFloat16);
-    return cutlass_scaled_mm_dq_dispatcher<cutlass_2x_gemm<
+    return cutlass_gemm_caller<cutlass_2x_gemm<
         cutlass::arch::Sm75, enable_sm75_to_sm80, int8_t, cutlass::half_t,
-        TileShape, WarpShape, InstructionShape, 2>>(out, a, b, a_scales,
+        ScaledEpilogue, TileShape, WarpShape, InstructionShape, 2>>(
-                                                    b_scales);
+        out, a, b, a_scales, b_scales);
   }
 }
 
-void cutlass_scaled_mm_dq_sm80(torch::Tensor& out, torch::Tensor const& a,
+void cutlass_scaled_mm_sm80(torch::Tensor& out, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales) {
@@ -270,20 +293,20 @@ void cutlass_scaled_mm_dq_sm80(torch::Tensor& out, torch::Tensor const& a,
   using InstructionShape = typename cutlass::gemm::GemmShape<16, 8, 32>;
 
   if (out.dtype() == torch::kBFloat16) {
-    return cutlass_scaled_mm_dq_dispatcher<cutlass_2x_gemm<
+    return cutlass_gemm_caller<cutlass_2x_gemm<
         cutlass::arch::Sm80, enable_sm80_to_sm89, int8_t, cutlass::bfloat16_t,
-        TileShape, WarpShape, InstructionShape, 5>>(out, a, b, a_scales,
+        ScaledEpilogue, TileShape, WarpShape, InstructionShape, 5>>(
-                                                    b_scales);
+        out, a, b, a_scales, b_scales);
   } else {
     TORCH_CHECK(out.dtype() == torch::kFloat16);
-    return cutlass_scaled_mm_dq_dispatcher<cutlass_2x_gemm<
+    return cutlass_gemm_caller<cutlass_2x_gemm<
         cutlass::arch::Sm80, enable_sm80_to_sm89, int8_t, cutlass::half_t,
-        TileShape, WarpShape, InstructionShape, 5>>(out, a, b, a_scales,
+        ScaledEpilogue, TileShape, WarpShape, InstructionShape, 5>>(
-                                                    b_scales);
+        out, a, b, a_scales, b_scales);
   }
 }
 
-void cutlass_scaled_mm_dq_sm89(torch::Tensor& out, torch::Tensor const& a,
+void cutlass_scaled_mm_sm89(torch::Tensor& out, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales) {
@@ -298,32 +321,32 @@ void cutlass_scaled_mm_dq_sm89(torch::Tensor& out, torch::Tensor const& a,
     TORCH_CHECK(b.dtype() == torch::kInt8);
 
     if (out.dtype() == torch::kBFloat16) {
-      return cutlass_scaled_mm_dq_dispatcher<cutlass_2x_gemm<
+      return cutlass_gemm_caller<cutlass_2x_gemm<
           cutlass::arch::Sm89, enable_sm89_to_sm90, int8_t, cutlass::bfloat16_t,
-          TileShape, WarpShape, InstructionShape, 5>>(out, a, b, a_scales,
+          ScaledEpilogue, TileShape, WarpShape, InstructionShape, 5>>(
-                                                      b_scales);
+          out, a, b, a_scales, b_scales);
     } else {
       assert(out.dtype() == torch::kFloat16);
-      return cutlass_scaled_mm_dq_dispatcher<cutlass_2x_gemm<
+      return cutlass_gemm_caller<cutlass_2x_gemm<
           cutlass::arch::Sm89, enable_sm89_to_sm90, int8_t, cutlass::half_t,
-          TileShape, WarpShape, InstructionShape, 5>>(out, a, b, a_scales,
+          ScaledEpilogue, TileShape, WarpShape, InstructionShape, 5>>(
-                                                      b_scales);
+          out, a, b, a_scales, b_scales);
     }
   } else {
     TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
     TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
 
     if (out.dtype() == torch::kBFloat16) {
-      return cutlass_scaled_mm_dq_dispatcher<cutlass_2x_gemm<
+      return cutlass_gemm_caller<cutlass_2x_gemm<
           cutlass::arch::Sm89, enable_sm89_to_sm90, cutlass::float_e4m3_t,
-          cutlass::bfloat16_t, TileShape, WarpShape, InstructionShape, 5>>(
+          cutlass::bfloat16_t, ScaledEpilogue, TileShape, WarpShape,
-          out, a, b, a_scales, b_scales);
+          InstructionShape, 5>>(out, a, b, a_scales, b_scales);
     } else {
       TORCH_CHECK(out.dtype() == torch::kFloat16);
-      return cutlass_scaled_mm_dq_dispatcher<cutlass_2x_gemm<
+      return cutlass_gemm_caller<cutlass_2x_gemm<
           cutlass::arch::Sm89, enable_sm89_to_sm90, cutlass::float_e4m3_t,
-          cutlass::half_t, TileShape, WarpShape, InstructionShape, 5>>(
+          cutlass::half_t, ScaledEpilogue, TileShape, WarpShape,
-          out, a, b, a_scales, b_scales);
+          InstructionShape, 5>>(out, a, b, a_scales, b_scales);
     }
   }
 }

csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu

@@ -32,21 +32,14 @@
 using namespace cute;
 
 /*
-   This defines a quantized GEMM operation with dequantized output, similar to
+   This file defines quantized GEMM operations using the CUTLASS 3.x API, for
-   torch._scaled_mm. It is defined using the CUTLASS 3.x API, and is used for
    NVIDIA GPUs with sm90a (Hopper) or later.
 
-   A and B may be both either int8 or fp8_e4m3. A can be quantized per-tensor or
+   Epilogue functions can be defined to post-process the output before it is
-   per-row. B can be quantized per-tensor or per-column.
+   written to GPU memory.
-   Any combination of per-tensor and per-row or column is supported.
+   Epilogues must contain a public type named EVTCompute of type Sm90EVT,
-   A and B must have symmetric quantization (zero point == 0).
+   as well as a static prepare_args function that constructs an
-
+   EVTCompute::Arguments struct.
-   So the GEMM operation is D = (a_scales * A) (b_scales * B), where the
-   scales are applied elementwise with numpy-style broadcasting.
-
-   ScaleA and ScaleB define the epilogue functions that apply the scales for
-   the A and B operands respectively. These scales may be either per-tensor or
-   per row or column.
 */
 
 namespace {
@@ -71,21 +64,25 @@ struct enable_sm90_or_later : Kernel {
   }
 };
 
-template <typename ElementAB_, typename ElementD_, typename TileShape,
+/*
-          typename ClusterShape, typename KernelSchedule,
+   This epilogue function defines a quantized GEMM operation similar to
-          typename EpilogueSchedule>
+   torch.scaled_mm_.
-struct cutlass_3x_gemm {
-  using ElementAB = ElementAB_;
-  using ElementD = ElementD_;
-  using ElementAcc =
-      typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
-                                float>::type;
 
-  using EpilogueDescriptor =
+   A and B may be both either int8 or fp8_e4m3. A can be
-      cutlass::epilogue::collective::detail::EpilogueDescriptor<
+   quantized per-tensor or per-row. B can be quantized per-tensor or per-column.
-          TileShape, cutlass::epilogue::collective::EpilogueTileAuto, ElementD,
+   Any combination of per-tensor and per-row or column is supported.
-          ElementD, EpilogueSchedule>;
+   A and B must have symmetric quantization (zero point == 0).
 
+   So the GEMM operation is D = (a_scales * A) (b_scales * B), where the
+   scales are applied elementwise with numpy-style broadcasting.
+
+   ScaleA and ScaleB define the epilogue functions that apply the scales for
+   the A and B operands respectively. These scales may be either per-tensor or
+   per row or column.
+*/
+template <typename ElementAcc, typename ElementD, typename EpilogueDescriptor>
+struct ScaledEpilogue {
+ private:
   using Accum = cutlass::epilogue::fusion::Sm90AccFetch;
 
   using ScaleA = cutlass::epilogue::fusion::Sm90ColOrScalarBroadcast<
@@ -111,19 +108,53 @@ struct cutlass_3x_gemm {
       cutlass::multiplies, ElementD, float,
       cutlass::FloatRoundStyle::round_to_nearest>;
 
-  using EVTCompute1 =
+ public:
+  using EVTCompute =
       cutlass::epilogue::fusion::Sm90EVT<Compute1, ScaleA, EVTCompute0>;
+  using ArgumentType = typename EVTCompute::Arguments;
+
+  static ArgumentType prepare_args(torch::Tensor const& a_scales,
+                                   torch::Tensor const& b_scales) {
+    using ScaleA_Args = typename ScaleA::Arguments;
+    using ScaleB_Args = typename ScaleB::Arguments;
+
+    ScaleA_Args a_args{a_scales.data_ptr<float>(), a_scales.numel() != 1, {}};
+    ScaleB_Args b_args{b_scales.data_ptr<float>(), b_scales.numel() != 1, {}};
+
+    return ArgumentType{a_args, {b_args}};
+  }
+};
+
+template <typename ElementAB_, typename ElementD_,
+          template <typename, typename, typename> typename Epilogue_,
+          typename TileShape, typename ClusterShape, typename KernelSchedule,
+          typename EpilogueSchedule>
+struct cutlass_3x_gemm {
+  using ElementAB = ElementAB_;
+  using ElementD = ElementD_;
+  using ElementAcc =
+      typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
+                                float>::type;
+
+  using EpilogueDescriptor =
+      cutlass::epilogue::collective::detail::EpilogueDescriptor<
+          TileShape, cutlass::epilogue::collective::EpilogueTileAuto, ElementD,
+          ElementD, EpilogueSchedule>;
+
+  using Epilogue = Epilogue_<ElementAcc, ElementD, EpilogueDescriptor>;
 
   using StrideD = Stride<int64_t, Int<1>, Int<0>>;
   using ElementC = void;
   using StrideC = StrideD;
 
+  using EVTCompute = typename Epilogue::EVTCompute;
+
   using CollectiveEpilogue =
       typename cutlass::epilogue::collective::CollectiveBuilder<
           cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape,
           ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
           ElementAcc, float, ElementC, StrideC, 4, ElementD, StrideD, 4,
-          EpilogueSchedule, EVTCompute1>::CollectiveOp;
+          EpilogueSchedule, EVTCompute>::CollectiveOp;
 
   static constexpr size_t CEStorageSize =
       sizeof(typename CollectiveEpilogue::SharedStorage);
@@ -148,11 +179,10 @@ struct cutlass_3x_gemm {
   struct GemmKernel : public KernelType {};
 };
 
-template <typename Gemm>
+template <typename Gemm, typename... EpilogueArgs>
-void cutlass_scaled_mm_dq_dispatcher(torch::Tensor& out, torch::Tensor const& a,
+void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
                          torch::Tensor const& b,
-                                     torch::Tensor const& a_scales,
+                         EpilogueArgs&&... epilogue_params) {
-                                     torch::Tensor const& b_scales) {
   using ElementAB = typename Gemm::ElementAB;
   using ElementD = typename Gemm::ElementD;
 
@@ -182,19 +212,13 @@ void cutlass_scaled_mm_dq_dispatcher(torch::Tensor& out, torch::Tensor const& a,
 
   auto c_ptr = static_cast<ElementD*>(out.data_ptr());
   typename GemmKernel::EpilogueArguments epilogue_args{
-      {}, c_ptr, c_stride, c_ptr, c_stride};
+      Gemm::Epilogue::prepare_args(
+          std::forward<EpilogueArgs>(epilogue_params)...),
+      c_ptr, c_stride, c_ptr, c_stride};
 
   typename GemmKernel::Arguments args{cutlass::gemm::GemmUniversalMode::kGemm,
                                       prob_shape, mainloop_args, epilogue_args};
 
-  using ScaleA_Args = typename Gemm::ScaleA::Arguments;
-  using ScaleB_Args = typename Gemm::ScaleB::Arguments;
-
-  ScaleA_Args a_args{a_scales.data_ptr<float>(), a_scales.numel() != 1, {}};
-  ScaleB_Args b_args{b_scales.data_ptr<float>(), b_scales.numel() != 1, {}};
-
-  args.epilogue.thread = {a_args, {b_args}};
-
   // Launch the CUTLASS GEMM kernel.
   using GemmOp = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
   GemmOp gemm_op;
@@ -209,7 +233,8 @@ void cutlass_scaled_mm_dq_dispatcher(torch::Tensor& out, torch::Tensor const& a,
   CUTLASS_CHECK(status);
 }
 
-template <typename InType, typename OutType, int32_t M>
+template <typename InType, typename OutType,
+          template <typename, typename, typename> typename Epilogue, int32_t M>
 struct sm90_fp8_config {
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
   using KernelSchedule =
@@ -219,12 +244,13 @@ struct sm90_fp8_config {
   using ClusterShape = Shape<_2, _1, _1>;
 
   using Cutlass3xGemm =
-      cutlass_3x_gemm<InType, OutType, TileShape, ClusterShape, KernelSchedule,
+      cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                      EpilogueSchedule>;
+                      KernelSchedule, EpilogueSchedule>;
 };
 
-template <typename InType, typename OutType>
+template <typename InType, typename OutType,
-struct sm90_fp8_config<InType, OutType, 128> {
+          template <typename, typename, typename> typename Epilogue>
+struct sm90_fp8_config<InType, OutType, Epilogue, 128> {
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
   using KernelSchedule =
       cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
@@ -233,12 +259,13 @@ struct sm90_fp8_config<InType, OutType, 128> {
   using ClusterShape = Shape<_2, _1, _1>;
 
   using Cutlass3xGemm =
-      cutlass_3x_gemm<InType, OutType, TileShape, ClusterShape, KernelSchedule,
+      cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                      EpilogueSchedule>;
+                      KernelSchedule, EpilogueSchedule>;
 };
 
-template <typename InType, typename OutType>
+template <typename InType, typename OutType,
-struct sm90_fp8_config<InType, OutType, 64> {
+          template <typename, typename, typename> typename Epilogue>
+struct sm90_fp8_config<InType, OutType, Epilogue, 64> {
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
   using KernelSchedule =
       cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
@@ -247,30 +274,28 @@ struct sm90_fp8_config<InType, OutType, 64> {
   using ClusterShape = Shape<_1, _8, _1>;
 
   using Cutlass3xGemm =
-      cutlass_3x_gemm<InType, OutType, TileShape, ClusterShape, KernelSchedule,
+      cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                      EpilogueSchedule>;
+                      KernelSchedule, EpilogueSchedule>;
 };
 
 }  // namespace
 
-template <typename InType, typename OutType>
+template <typename InType, typename OutType,
-void cutlass_scaled_mm_dq_sm90_fp8_dispatch(torch::Tensor& out,
+          template <typename, typename, typename> typename Epilogue,
-                                            torch::Tensor const& a,
+          typename... EpilogueArgs>
+void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out, torch::Tensor const& a,
                                     torch::Tensor const& b,
-                                            torch::Tensor const& a_scales,
+                                    EpilogueArgs&&... args) {
-                                            torch::Tensor const& b_scales) {
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
   TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
   TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
-  TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
-  TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
 
   using Cutlass3xGemmDefault =
-      typename sm90_fp8_config<InType, OutType, 0>::Cutlass3xGemm;
+      typename sm90_fp8_config<InType, OutType, Epilogue, 0>::Cutlass3xGemm;
   using Cutlass3xGemmM64 =
-      typename sm90_fp8_config<InType, OutType, 64>::Cutlass3xGemm;
+      typename sm90_fp8_config<InType, OutType, Epilogue, 64>::Cutlass3xGemm;
   using Cutlass3xGemmM128 =
-      typename sm90_fp8_config<InType, OutType, 128>::Cutlass3xGemm;
+      typename sm90_fp8_config<InType, OutType, Epilogue, 128>::Cutlass3xGemm;
 
   uint32_t const m = a.size(0);
   uint32_t const mp2 =
@@ -278,20 +303,20 @@ void cutlass_scaled_mm_dq_sm90_fp8_dispatch(torch::Tensor& out,
 
   if (mp2 <= 64) {
     // m in [1, 64]
-    return cutlass_scaled_mm_dq_dispatcher<Cutlass3xGemmM64>(
+    return cutlass_gemm_caller<Cutlass3xGemmM64>(
-        out, a, b, a_scales, b_scales);
+        out, a, b, std::forward<EpilogueArgs>(args)...);
   } else if (mp2 <= 128) {
     // m in (64, 128]
-    return cutlass_scaled_mm_dq_dispatcher<Cutlass3xGemmM128>(
+    return cutlass_gemm_caller<Cutlass3xGemmM128>(
-        out, a, b, a_scales, b_scales);
+        out, a, b, std::forward<EpilogueArgs>(args)...);
   } else {
     // m in (128, inf)
-    return cutlass_scaled_mm_dq_dispatcher<Cutlass3xGemmDefault>(
+    return cutlass_gemm_caller<Cutlass3xGemmDefault>(
-        out, a, b, a_scales, b_scales);
+        out, a, b, std::forward<EpilogueArgs>(args)...);
   }
 }
 
-void cutlass_scaled_mm_dq_sm90(torch::Tensor& out, torch::Tensor const& a,
+void cutlass_scaled_mm_sm90(torch::Tensor& out, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales) {
@@ -308,16 +333,15 @@ void cutlass_scaled_mm_dq_sm90(torch::Tensor& out, torch::Tensor const& a,
     using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
 
     if (out.dtype() == torch::kBFloat16) {
-      return cutlass_scaled_mm_dq_dispatcher<
+      return cutlass_gemm_caller<cutlass_3x_gemm<
-          cutlass_3x_gemm<int8_t, cutlass::bfloat16_t, TileShape, ClusterShape,
+          int8_t, cutlass::bfloat16_t, ScaledEpilogue, TileShape, ClusterShape,
-                          KernelSchedule, EpilogueSchedule>>(
+          KernelSchedule, EpilogueSchedule>>(out, a, b, a_scales, b_scales);
-          out, a, b, a_scales, b_scales);
     } else {
       TORCH_CHECK(out.dtype() == torch::kFloat16);
 
-      return cutlass_scaled_mm_dq_dispatcher<
+      return cutlass_gemm_caller<
-          cutlass_3x_gemm<int8_t, cutlass::half_t, TileShape, ClusterShape,
+          cutlass_3x_gemm<int8_t, cutlass::half_t, ScaledEpilogue, TileShape,
-                          KernelSchedule, EpilogueSchedule>>(
+                          ClusterShape, KernelSchedule, EpilogueSchedule>>(
           out, a, b, a_scales, b_scales);
     }
   } else {
@@ -325,13 +349,13 @@ void cutlass_scaled_mm_dq_sm90(torch::Tensor& out, torch::Tensor const& a,
     TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
 
     if (out.dtype() == torch::kBFloat16) {
-      return cutlass_scaled_mm_dq_sm90_fp8_dispatch<cutlass::float_e4m3_t,
+      return cutlass_gemm_sm90_fp8_dispatch<
-                                                    cutlass::bfloat16_t>(
+          cutlass::float_e4m3_t, cutlass::bfloat16_t, ScaledEpilogue>(
           out, a, b, a_scales, b_scales);
     } else {
       TORCH_CHECK(out.dtype() == torch::kFloat16);
-      return cutlass_scaled_mm_dq_sm90_fp8_dispatch<cutlass::float_e4m3_t,
+      return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
-                                                    cutlass::half_t>(
+                                            cutlass::half_t, ScaledEpilogue>(
           out, a, b, a_scales, b_scales);
     }
   }

csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu

@@ -3,29 +3,29 @@
 #include <c10/cuda/CUDAGuard.h>
 #include <torch/all.h>
 
-void cutlass_scaled_mm_dq_sm75(torch::Tensor& c, torch::Tensor const& a,
+void cutlass_scaled_mm_sm75(torch::Tensor& c, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales);
 
-void cutlass_scaled_mm_dq_sm80(torch::Tensor& c, torch::Tensor const& a,
+void cutlass_scaled_mm_sm80(torch::Tensor& c, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales);
 
-void cutlass_scaled_mm_dq_sm89(torch::Tensor& c, torch::Tensor const& a,
+void cutlass_scaled_mm_sm89(torch::Tensor& c, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales);
 
 #if defined CUDA_VERSION && CUDA_VERSION >= 12000
-void cutlass_scaled_mm_dq_sm90(torch::Tensor& c, torch::Tensor const& a,
+void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales);
 #endif
 
-void cutlass_scaled_mm_dq(torch::Tensor& c, torch::Tensor const& a,
+void cutlass_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
                        torch::Tensor const& b, torch::Tensor const& a_scales,
                        torch::Tensor const& b_scales) {
   int32_t major_capability;
@@ -57,19 +57,19 @@ void cutlass_scaled_mm_dq(torch::Tensor& c, torch::Tensor const& a,
 
     // Guard against compilation issues for sm90 kernels
 #if defined CUDA_VERSION && CUDA_VERSION >= 12000
-    cutlass_scaled_mm_dq_sm90(c, a, b, a_scales, b_scales);
+    cutlass_scaled_mm_sm90(c, a, b, a_scales, b_scales);
 #else
-    cutlass_scaled_mm_dq_sm80(c, a, b, a_scales, b_scales);
+    cutlass_scaled_mm_sm80(c, a, b, a_scales, b_scales);
 #endif
   } else if (version_num == 89) {
     // Ada Lovelace
-    cutlass_scaled_mm_dq_sm89(c, a, b, a_scales, b_scales);
+    cutlass_scaled_mm_sm89(c, a, b, a_scales, b_scales);
   } else if (version_num >= 80) {
     // Ampere
-    cutlass_scaled_mm_dq_sm80(c, a, b, a_scales, b_scales);
+    cutlass_scaled_mm_sm80(c, a, b, a_scales, b_scales);
   } else {
     // Turing
     TORCH_CHECK(version_num >= 75);
-    cutlass_scaled_mm_dq_sm75(c, a, b, a_scales, b_scales);
+    cutlass_scaled_mm_sm75(c, a, b, a_scales, b_scales);
   }
 }

csrc/torch_bindings.cpp

@@ -136,10 +136,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // CUTLASS w8a8 GEMM, supporting symmetric per-tensor or per-row/column
   // quantization.
   ops.def(
-      "cutlass_scaled_mm_dq(Tensor! out, Tensor a,"
+      "cutlass_scaled_mm(Tensor! out, Tensor a,"
       "                  Tensor b, Tensor a_scales,"
       "                  Tensor b_scales) -> ()");
-  ops.impl("cutlass_scaled_mm_dq", torch::kCUDA, &cutlass_scaled_mm_dq);
+  ops.impl("cutlass_scaled_mm", torch::kCUDA, &cutlass_scaled_mm);
 #endif
 
   // Quantized GEMM for GPTQ.

tests/kernels/test_cutlass.py

@@ -47,7 +47,7 @@ def cutlass_fp8_gemm_helper(m: int,
     scale_b = (torch.randn(
         (1, n_b_scales), device=device, dtype=torch.float32) / 10)
 
-    out = ops.cutlass_scaled_mm_dq(a, b, scale_a, scale_b, out_dtype)
+    out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype)
     baseline = torch.mm(scale_a * a.to(dtype=torch.float32),
                         scale_b * b.to(dtype=torch.float32)).to(out_dtype)
 
@@ -74,7 +74,7 @@ def cutlass_int8_gemm_helper(m: int,
     scale_b = (torch.randn(
         (1, n_b_scales), device=device, dtype=torch.float32) / 10)
 
-    out = ops.cutlass_scaled_mm_dq(a, b, scale_a, scale_b, out_dtype)
+    out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype)
     baseline = torch.mm(scale_a * a.to(dtype=torch.float32),
                         scale_b *
                         b.to(dtype=torch.float32)).to(dtype=out_dtype)
@@ -180,7 +180,7 @@ def test_cutlass_subset():
     scale_a = torch.randn((1, 1), device="cuda", dtype=torch.float32) / 10
     scale_b = torch.randn((1, 1), device="cuda", dtype=torch.float32) / 10
 
-    out = ops.cutlass_scaled_mm_dq(a,
+    out = ops.cutlass_scaled_mm(a,
                                 b,
                                 scale_a,
                                 scale_b,
@@ -203,7 +203,7 @@ class CutlassLayer(torch.nn.Module):
         self.out_dtype = out_dtype
 
     def forward(self, a):
-        return ops.cutlass_scaled_mm_dq(a, self.b, self.scale_a, self.scale_b,
+        return ops.cutlass_scaled_mm(a, self.b, self.scale_a, self.scale_b,
                                      self.out_dtype)
 
 

vllm/_custom_ops.py

@@ -212,8 +212,8 @@ def gptq_marlin_24_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
 
 
 # cutlass
-def cutlass_scaled_mm_dq(a: torch.Tensor, b: torch.Tensor,
+def cutlass_scaled_mm(a: torch.Tensor, b: torch.Tensor, scale_a: torch.Tensor,
-                         scale_a: torch.Tensor, scale_b: torch.Tensor,
+                      scale_b: torch.Tensor,
                       out_dtype: Type[torch.dtype]) -> torch.Tensor:
     assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
     assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
@@ -222,8 +222,7 @@ def cutlass_scaled_mm_dq(a: torch.Tensor, b: torch.Tensor,
     n = b.shape[1]
     out = torch.empty((m, n), dtype=out_dtype, device=a.device)
 
-    torch.ops._C.cutlass_scaled_mm_dq(out, a, b, scale_a, scale_b)
+    torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b)
-
     return out
 
 

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py

@@ -81,5 +81,5 @@ class CompressedTensorsW8A8DynamicToken(CompressedTensorsScheme):
         weight_scale = layer.weight_scale
 
         x_q, input_scales = custom_ops.scaled_int8_quant(x)
-        return custom_ops.cutlass_scaled_mm_dq(x_q, weight.t(), input_scales,
+        return custom_ops.cutlass_scaled_mm(x_q, weight.t(), input_scales,
                                             weight_scale, x.dtype)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py

@@ -99,5 +99,5 @@ class CompressedTensorsW8A8StaticTensor(CompressedTensorsScheme):
         # Input quantize
         x_q, _ = custom_ops.scaled_int8_quant(x, act_scale)
 
-        return custom_ops.cutlass_scaled_mm_dq(x_q, weight.t(), act_scale,
+        return custom_ops.cutlass_scaled_mm(x_q, weight.t(), act_scale,
                                             weight_scale, x.dtype)

vllm/model_executor/layers/quantization/fp8.py

@@ -261,7 +261,7 @@ class Fp8LinearMethod(LinearMethodBase):
             qinput, x_scale = ops.scaled_fp8_quant(x, layer.input_scale)
 
             # Fused GEMM_DQ
-            output = ops.cutlass_scaled_mm_dq(
+            output = ops.cutlass_scaled_mm(
                 qinput,
                 layer.weight,
                 out_dtype=x.dtype,