500 lines
17 KiB
C++
500 lines
17 KiB
C++
/***************************************************************************************************
|
|
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
* SPDX-License-Identifier: BSD-3-Clause
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions are met:
|
|
*
|
|
* 1. Redistributions of source code must retain the above copyright notice, this
|
|
* list of conditions and the following disclaimer.
|
|
*
|
|
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
|
* this list of conditions and the following disclaimer in the documentation
|
|
* and/or other materials provided with the distribution.
|
|
*
|
|
* 3. Neither the name of the copyright holder nor the names of its
|
|
* contributors may be used to endorse or promote products derived from
|
|
* this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
|
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
|
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
|
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*
|
|
**************************************************************************************************/
|
|
/*! \file
|
|
\brief Performs a dual gemm in one fused kernel:
|
|
```
|
|
D0 = epilogue0(X @ B0, C0)
|
|
D1 = epilogue1(X @ B1, C1)
|
|
D2 = element_wise(D0, D1)
|
|
```
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include "cutlass/cutlass.h"
|
|
#include "cutlass/numeric_types.h"
|
|
#include "cutlass/arch/arch.h"
|
|
#include "cutlass/device_kernel.h"
|
|
|
|
#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
|
|
|
|
#include "cutlass/gemm/device/default_gemm_configuration.h"
|
|
#include "cutlass/gemm/threadblock/default_mma.h"
|
|
#include "cutlass/epilogue/thread/linear_combination_relu.h"
|
|
#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
|
|
|
|
#include "../kernel/dual_gemm.h"
|
|
#include "../dual_gemm_common.h"
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace cutlass {
|
|
namespace gemm {
|
|
namespace device {
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
/// Element type for A matrix operand
|
|
typename ElementA_,
|
|
/// Layout type for A matrix operand
|
|
typename LayoutA_,
|
|
/// Element type for B matrix operand
|
|
typename ElementB_,
|
|
/// Layout type for B0 matrix operand
|
|
typename LayoutB0_,
|
|
/// Layout type for B1 matrix operand
|
|
typename LayoutB1_,
|
|
/// Element type for C and D matrix operands
|
|
typename ElementC_,
|
|
/// Layout type for C and D matrix operands
|
|
typename LayoutC_,
|
|
/// Element type for internal accumulation
|
|
typename ElementAccumulator_,
|
|
/// Operator class tag
|
|
typename OperatorClass_,
|
|
/// Tag indicating architecture to tune for
|
|
typename ArchTag_,
|
|
/// Threadblock-level tile size (concept: GemmShape)
|
|
typename ThreadblockShape_,
|
|
/// Warp-level tile size (concept: GemmShape)
|
|
typename WarpShape_,
|
|
/// Instruction-level tile size (concept: GemmShape)
|
|
typename InstructionShape_,
|
|
/// Epilogue output operator
|
|
typename EpilogueOutputOp0_,
|
|
typename EpilogueOutputOp1_,
|
|
typename EpilogueOutputOp2_,
|
|
/// Threadblock-level swizzling operator
|
|
typename ThreadblockSwizzle_ = threadblock::GemmIdentityThreadblockSwizzle<>,
|
|
/// Number of stages used in the pipelined mainloop
|
|
int Stages =
|
|
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
|
ElementC_, ElementAccumulator_>::kStages,
|
|
bool StoreD0 = true,
|
|
bool StoreD1 = true,
|
|
/// If true, kernel supports split-K with serial reduction
|
|
bool SplitKSerial = false,
|
|
/// Access granularity of A matrix in units of elements
|
|
int AlignmentA =
|
|
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
|
ElementC_, ElementAccumulator_>::kAlignmentA,
|
|
/// Access granularity of B matrix in units of elements
|
|
int AlignmentB =
|
|
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
|
|
ElementC_, ElementAccumulator_>::kAlignmentB,
|
|
/// Operation performed by GEMM
|
|
typename Operator_ = typename DefaultGemmConfiguration<
|
|
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
|
|
ElementAccumulator_>::Operator>
|
|
class DualGemm {
|
|
public:
|
|
|
|
using ElementA = ElementA_;
|
|
using LayoutA = LayoutA_;
|
|
using TensorRefA = TensorRef<ElementA const, LayoutA>;
|
|
using ElementB = ElementB_;
|
|
using LayoutB0 = LayoutB0_;
|
|
using LayoutB1 = LayoutB1_;
|
|
using TensorRefB0 = TensorRef<ElementB const, LayoutB0>;
|
|
using TensorRefB1 = TensorRef<ElementB const, LayoutB1>;
|
|
using ElementC = ElementC_;
|
|
using LayoutC = LayoutC_;
|
|
using TensorRefC = TensorRef<ElementC const, LayoutC>;
|
|
using TensorRefD = TensorRef<ElementC, LayoutC>;
|
|
using ElementAccumulator = ElementAccumulator_;
|
|
using OperatorClass = OperatorClass_;
|
|
using ArchTag = ArchTag_;
|
|
using ThreadblockShape = ThreadblockShape_;
|
|
using WarpShape = WarpShape_;
|
|
using InstructionShape = InstructionShape_;
|
|
using EpilogueOutputOp0 = EpilogueOutputOp0_;
|
|
using EpilogueOutputOp1 = EpilogueOutputOp1_;
|
|
using EpilogueOutputOp2 = EpilogueOutputOp2_;
|
|
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
|
using Operator = Operator_;
|
|
static int const kStages = Stages;
|
|
static int const kAlignmentA = AlignmentA;
|
|
static int const kAlignmentB = AlignmentB;
|
|
static int const kAlignmentC = EpilogueOutputOp1::kCount;
|
|
static bool const kSplitKSerial = SplitKSerial;
|
|
static bool constexpr kStoreD0 = StoreD0;
|
|
static bool constexpr kStoreD1 = StoreD1;
|
|
static ComplexTransform const kTransformA = ComplexTransform::kNone;
|
|
static ComplexTransform const kTransformB = ComplexTransform::kNone;
|
|
|
|
using LayoutScaleBias = layout::RowMajor;
|
|
/// Define the kernel
|
|
/// Define the threadblock-scoped matrix multiply-accumulate
|
|
static_assert(ArchTag::kMinComputeCapability >= 80, "Only multistage is implemented");
|
|
static_assert(kStages >= 3, "Only multistage is implemented");
|
|
using Mma0 = typename cutlass::gemm::threadblock::DefaultMma<
|
|
ElementA, LayoutA, kAlignmentA, ElementB, LayoutB0, kAlignmentB,
|
|
ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, ArchTag,
|
|
ThreadblockShape, WarpShape,
|
|
InstructionShape, Stages, Operator>::ThreadblockMma;
|
|
using Mma1 = typename cutlass::gemm::threadblock::DefaultMma<
|
|
ElementA, LayoutA, kAlignmentA, ElementB, LayoutB1, kAlignmentB,
|
|
ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, ArchTag,
|
|
ThreadblockShape, WarpShape,
|
|
InstructionShape, Stages, Operator>::ThreadblockMma;
|
|
using DualMma = threadblock::DualMmaMultistage<
|
|
typename Mma0::Shape,
|
|
typename Mma0::IteratorA,
|
|
typename Mma0::SmemIteratorA,
|
|
Mma0::kCacheOpA,
|
|
typename Mma0::IteratorB,
|
|
typename Mma0::SmemIteratorB,
|
|
Mma0::kCacheOpB,
|
|
typename Mma1::IteratorB,
|
|
typename Mma1::SmemIteratorB,
|
|
typename Mma0::ElementC,
|
|
typename Mma0::LayoutC,
|
|
typename Mma0::Policy,
|
|
typename Mma1::Policy,
|
|
Mma0::kStages,
|
|
SharedMemoryClearOption::kNone
|
|
>;
|
|
|
|
static const int kPartitionsK = ThreadblockShape::kK / WarpShape::kK;
|
|
|
|
/// Define the epilogue
|
|
using Epilogue0 =
|
|
typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
ThreadblockShape, typename DualMma::Operator0, kPartitionsK, EpilogueOutputOp0,
|
|
EpilogueOutputOp0::kCount>::Epilogue;
|
|
using Epilogue1 =
|
|
typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
|
|
ThreadblockShape, typename DualMma::Operator1, kPartitionsK, EpilogueOutputOp1,
|
|
EpilogueOutputOp1::kCount>::Epilogue;
|
|
|
|
/// Define the kernel-level GEMM operator.
|
|
using DualGemmKernel = kernel::DualGemm<
|
|
DualMma,
|
|
Epilogue0, Epilogue1, EpilogueOutputOp2,
|
|
ThreadblockSwizzle, kSplitKSerial,
|
|
kStoreD0, kStoreD1>;
|
|
|
|
/// Argument structure
|
|
struct Arguments {
|
|
|
|
//
|
|
// Data members
|
|
//
|
|
|
|
DualGemmMode mode;
|
|
GemmCoord problem_size;
|
|
TensorRef<ElementA const, LayoutA> ref_A0;
|
|
TensorRef<ElementB const, LayoutB0> ref_B0;
|
|
TensorRef<ElementC const, LayoutC> ref_C0;
|
|
TensorRef<ElementC, LayoutC> ref_D0;
|
|
TensorRef<ElementB const, LayoutB1> ref_B1;
|
|
TensorRef<ElementC const, LayoutC> ref_C1;
|
|
TensorRef<ElementC, LayoutC> ref_D1;
|
|
TensorRef<ElementC, LayoutC> ref_D2;
|
|
typename EpilogueOutputOp0::Params epilogue0;
|
|
typename EpilogueOutputOp1::Params epilogue1;
|
|
typename EpilogueOutputOp2::Params epilogue2;
|
|
int split_k_slices;
|
|
|
|
int batch_count;
|
|
int64_t batch_stride_A;
|
|
int64_t batch_stride_B0;
|
|
int64_t batch_stride_B1;
|
|
int64_t batch_stride_C;
|
|
int64_t batch_stride_D;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Default ctor
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments(): problem_size(0, 0, 0), split_k_slices(1) {
|
|
|
|
}
|
|
|
|
/// Constructs an Arguments structure
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments(
|
|
DualGemmMode mode,
|
|
GemmCoord problem_size_,
|
|
TensorRef<ElementA const, LayoutA> ref_A0_,
|
|
TensorRef<ElementB const, LayoutB0> ref_B0_,
|
|
TensorRef<ElementC const, LayoutC> ref_C0_,
|
|
TensorRef<ElementC, LayoutC> ref_D0_,
|
|
TensorRef<ElementB const, LayoutB1> ref_B1_,
|
|
TensorRef<ElementC const, LayoutC> ref_C1_,
|
|
TensorRef<ElementC, LayoutC> ref_D1_,
|
|
TensorRef<ElementC, LayoutC> ref_D2_,
|
|
typename EpilogueOutputOp0::Params epilogue0_ =
|
|
typename EpilogueOutputOp0::Params(),
|
|
typename EpilogueOutputOp1::Params epilogue1_ =
|
|
typename EpilogueOutputOp1::Params(),
|
|
typename EpilogueOutputOp2::Params epilogue2_ =
|
|
typename EpilogueOutputOp2::Params(),
|
|
int split_k_slices_ = 1,
|
|
int batch_count = 1,
|
|
int64_t batch_stride_A = 0,
|
|
int64_t batch_stride_B0 = 0,
|
|
int64_t batch_stride_B1 = 0,
|
|
int64_t batch_stride_C = 0,
|
|
int64_t batch_stride_D = 0
|
|
):
|
|
mode(mode),
|
|
problem_size(problem_size_),
|
|
ref_A0(ref_A0_),
|
|
ref_B0(ref_B0_),
|
|
ref_C0(ref_C0_),
|
|
ref_D0(ref_D0_),
|
|
ref_B1(ref_B1_),
|
|
ref_C1(ref_C1_),
|
|
ref_D1(ref_D1_),
|
|
ref_D2(ref_D2_),
|
|
epilogue0(epilogue0_),
|
|
epilogue1(epilogue1_),
|
|
epilogue2(epilogue2_),
|
|
split_k_slices(split_k_slices_),
|
|
batch_count(batch_count),
|
|
batch_stride_A(batch_stride_A),
|
|
batch_stride_B0(batch_stride_B0),
|
|
batch_stride_B1(batch_stride_B1),
|
|
batch_stride_C(batch_stride_C),
|
|
batch_stride_D(batch_stride_D) {
|
|
|
|
}
|
|
};
|
|
|
|
private:
|
|
|
|
/// Kernel parameters object
|
|
typename DualGemmKernel::Params params_;
|
|
|
|
public:
|
|
|
|
/// Constructs the GEMM.
|
|
DualGemm() = default;
|
|
|
|
/// Determines whether the GEMM can execute the given problem.
|
|
static Status can_implement(Arguments const &args) {
|
|
|
|
if (args.mode == DualGemmMode::kBatched && kSplitKSerial) {
|
|
return Status::kErrorInvalidProblem;
|
|
}
|
|
if (!kSplitKSerial && args.split_k_slices > 1) {
|
|
return Status::kErrorInvalidProblem;
|
|
}
|
|
if (kStoreD0 != (args.ref_D0.data() != nullptr)) {
|
|
return Status::kErrorInternal;
|
|
}
|
|
if (kStoreD1 != (args.ref_D1.data() != nullptr)) {
|
|
return Status::kErrorInternal;
|
|
}
|
|
|
|
Status status = DualGemmKernel::can_implement(
|
|
args.problem_size,
|
|
args.ref_A0.non_const_ref(),
|
|
args.ref_B0.non_const_ref(),
|
|
args.ref_C0.non_const_ref(),
|
|
args.ref_D0,
|
|
args.ref_B1.non_const_ref(),
|
|
args.ref_C1.non_const_ref(),
|
|
args.ref_D1,
|
|
args.ref_D2
|
|
);
|
|
|
|
if (status != Status::kSuccess) {
|
|
return status;
|
|
}
|
|
|
|
return Status::kSuccess;
|
|
}
|
|
|
|
/// Gets the workspace size
|
|
static size_t get_workspace_size(Arguments const &args) {
|
|
|
|
size_t bytes = 0;
|
|
|
|
if (kSplitKSerial && args.split_k_slices > 1) {
|
|
// Determine grid shape
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
cutlass::gemm::GemmCoord tiled_shape = threadblock_swizzle.get_tiled_shape(
|
|
args.problem_size,
|
|
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
|
args.split_k_slices);
|
|
|
|
bytes += sizeof(int) * size_t(tiled_shape.m()) * size_t(tiled_shape.n());
|
|
}
|
|
|
|
return bytes;
|
|
}
|
|
|
|
/// Initializes GEMM state from arguments.
|
|
Status initialize(Arguments const &args, void *workspace = nullptr, cudaStream_t stream = nullptr) {
|
|
|
|
// Determine grid shape
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
cutlass::gemm::GemmCoord grid_shape = threadblock_swizzle.get_tiled_shape(
|
|
args.problem_size,
|
|
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
|
args.mode == DualGemmMode::kBatched ? args.batch_count : args.split_k_slices);
|
|
|
|
if (kSplitKSerial) {
|
|
if (args.split_k_slices > 1) {
|
|
if (!workspace) {
|
|
return Status::kErrorWorkspaceNull;
|
|
}
|
|
|
|
size_t bytes = get_workspace_size(args);
|
|
|
|
cudaError_t result = cudaMemsetAsync(workspace, 0, bytes, stream);
|
|
|
|
if (result != cudaSuccess) {
|
|
return Status::kErrorInternal;
|
|
}
|
|
}
|
|
}
|
|
else {
|
|
|
|
if (args.split_k_slices > 1) {
|
|
return Status::kErrorInvalidProblem;
|
|
}
|
|
}
|
|
|
|
// Initialize the Params structure
|
|
params_ = typename DualGemmKernel::Params{
|
|
args.mode,
|
|
args.problem_size,
|
|
grid_shape,
|
|
args.ref_A0.non_const_ref(),
|
|
args.ref_B0.non_const_ref(),
|
|
args.ref_C0.non_const_ref(),
|
|
args.ref_D0,
|
|
args.ref_B1.non_const_ref(),
|
|
args.ref_C1.non_const_ref(),
|
|
args.ref_D1,
|
|
args.ref_D2,
|
|
args.epilogue0,
|
|
args.epilogue1,
|
|
args.epilogue2,
|
|
reinterpret_cast<int *>(workspace),
|
|
args.batch_stride_A,
|
|
args.batch_stride_B0,
|
|
args.batch_stride_B1,
|
|
args.batch_stride_C,
|
|
args.batch_stride_D,
|
|
};
|
|
|
|
return Status::kSuccess;
|
|
}
|
|
|
|
/// Lightweight update given a subset of arguments
|
|
Status update(Arguments const &args, void *workspace = nullptr) {
|
|
|
|
if (kSplitKSerial && args.split_k_slices > 1) {
|
|
if (!workspace) {
|
|
return Status::kErrorWorkspaceNull;
|
|
}
|
|
}
|
|
|
|
params_.ref_A0.reset(args.ref_A0.non_const_ref().data());
|
|
params_.ref_B0.reset(args.ref_B0.non_const_ref().data());
|
|
params_.ref_C0.reset(args.ref_C0.non_const_ref().data());
|
|
params_.ref_D0.reset(args.ref_D0.data());
|
|
params_.ref_B1.reset(args.ref_B1.non_const_ref().data());
|
|
params_.ref_C1.reset(args.ref_C1.non_const_ref().data());
|
|
params_.ref_D1.reset(args.ref_D1.data());
|
|
params_.ref_D2.reset(args.ref_D2.data());
|
|
params_.output_op_0 = args.epilogue0;
|
|
params_.output_op_1 = args.epilogue1;
|
|
params_.output_op_2 = args.epilogue2;
|
|
params_.semaphore = reinterpret_cast<int *>(workspace);
|
|
|
|
return Status::kSuccess;
|
|
}
|
|
|
|
/// Runs the kernel using initialized state.
|
|
Status run(cudaStream_t stream = nullptr) {
|
|
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
dim3 grid = threadblock_swizzle.get_grid_shape(params_.grid_tiled_shape);
|
|
dim3 block(DualGemmKernel::kThreadCount, 1, 1);
|
|
|
|
cudaError_t result;
|
|
|
|
int smem_size = int(sizeof(typename DualGemmKernel::SharedStorage));
|
|
if (smem_size >= (48 << 10)) {
|
|
result = cudaFuncSetAttribute(Kernel<DualGemmKernel>,
|
|
cudaFuncAttributeMaxDynamicSharedMemorySize,
|
|
smem_size);
|
|
|
|
if (result != cudaSuccess) {
|
|
return Status::kErrorInternal;
|
|
}
|
|
}
|
|
|
|
cutlass::Kernel<DualGemmKernel><<<grid, block, smem_size, stream>>>(params_);
|
|
|
|
result = cudaGetLastError();
|
|
|
|
return result == cudaSuccess ? Status::kSuccess : Status::kErrorInternal;
|
|
}
|
|
|
|
/// Runs the kernel using initialized state.
|
|
Status operator()(cudaStream_t stream = nullptr) {
|
|
return run(stream);
|
|
}
|
|
|
|
/// Runs the kernel using initialized state.
|
|
Status operator()(
|
|
Arguments const &args,
|
|
void *workspace = nullptr,
|
|
cudaStream_t stream = nullptr) {
|
|
|
|
Status status = initialize(args, workspace, stream);
|
|
|
|
if (status == Status::kSuccess) {
|
|
status = run(stream);
|
|
}
|
|
|
|
return status;
|
|
}
|
|
};
|
|
|
|
} // namespace device
|
|
} // namespace gemm
|
|
} // namespace cutlass
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|