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e5d51840e8
cutlass/include/cutlass/gemm/device/gemm_splitk_parallel.h
Manish Gupta e5d51840e8
CUTLASS 2.6 (#298) 
CUTLASS 2.6
2021-07-23 00:40:53 -04:00

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/***************************************************************************************************
* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice, this list of
* conditions and the following disclaimer.
* * 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.
* * Neither the name of the NVIDIA CORPORATION 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 NVIDIA CORPORATION 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 TOR (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 Template for GEMM performing a reduction over K partitions in parallel.
*/
#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/kernel/gemm.h"
#include "cutlass/gemm/kernel/default_gemm_splitk_parallel.h"
#include "cutlass/gemm/device/default_gemm_configuration.h"
#include "cutlass/epilogue/thread/conversion_op.h"
#include "cutlass/reduction/kernel/reduce_split_k.h"
#include "cutlass/reduction/thread/reduction_operators.h"
////////////////////////////////////////////////////////////////////////////////
namespace cutlass {
namespace gemm {
namespace device {
////////////////////////////////////////////////////////////////////////////////
/*!
Gemm device-level operator performing parallel reduction over the K partition.
*/
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 B matrix operand
typename LayoutB_,
/// 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_ = ElementC_,
/// Operator class tag
typename OperatorClass_ = arch::OpClassSimt,
/// Tag indicating architecture to tune for. This is the minimum SM that
/// supports the intended feature. The device kernel can be built
/// targeting any SM larger than this number.
typename ArchTag_ = arch::Sm70,
/// Threadblock-level tile size (concept: GemmShape)
typename ThreadblockShape_ = typename DefaultGemmConfiguration<
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
ElementAccumulator_>::ThreadblockShape,
/// Warp-level tile size (concept: GemmShape)
typename WarpShape_ = typename DefaultGemmConfiguration<
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
ElementAccumulator_>::WarpShape,
/// Instruction-level tile size (concept: GemmShape)
typename InstructionShape_ = typename DefaultGemmConfiguration<
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
ElementAccumulator_>::InstructionShape,
/// Epilogue output operator
typename EpilogueOutputOp_ = typename DefaultGemmConfiguration<
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
ElementAccumulator_>::EpilogueOutputOp,
/// Epilogue output operator
typename ConvertScaledOp_ = cutlass::epilogue::thread::Convert<
ElementAccumulator_,
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
ElementAccumulator_,
ElementAccumulator_>::EpilogueOutputOp::kCount,
ElementAccumulator_>,
/// Reduction operator
typename ReductionOp_ = cutlass::reduction::thread::ReduceAdd<
ElementAccumulator_, typename EpilogueOutputOp_::ElementAccumulator,
EpilogueOutputOp_::kCount>,
/// Threadblock-level swizzling operator
typename ThreadblockSwizzle_ =
threadblock::GemmSplitKHorizontalThreadblockSwizzle,
/// Number of stages used in the pipelined mainloop
int Stages =
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
ElementC_, ElementAccumulator_>::kStages,
/// Access granularity of A matrix in units of elements
int kAlignmentA =
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
ElementC_, ElementAccumulator_>::kAlignmentA,
/// Access granularity of B matrix in units of elements
int kAlignmentB =
DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
ElementC_, ElementAccumulator_>::kAlignmentB,
/// Operation performed by GEMM
typename Operator_ = typename DefaultGemmConfiguration<
OperatorClass_, ArchTag_, ElementA_, ElementB_, ElementC_,
ElementAccumulator_>::Operator>
class GemmSplitKParallel {
public:
using ElementA = ElementA_;
using LayoutA = LayoutA_;
using ElementB = ElementB_;
using LayoutB = LayoutB_;
using ElementC = ElementC_;
using LayoutC = LayoutC_;
using ElementAccumulator = ElementAccumulator_;
using OperatorClass = OperatorClass_;
using ArchTag = ArchTag_;
using ThreadblockShape = ThreadblockShape_;
using WarpShape = WarpShape_;
using InstructionShape = InstructionShape_;
using ConvertScaledOp = ConvertScaledOp_;
using EpilogueOutputOp = EpilogueOutputOp_;
using ReductionOp = ReductionOp_;
using ThreadblockSwizzle = ThreadblockSwizzle_;
using Operator = Operator_;
static int const kStages = Stages;
/// GEMM kernel
using GemmKernel = typename kernel::DefaultGemmSplitKParallel<
ElementA,
LayoutA,
kAlignmentA,
ElementB,
LayoutB,
kAlignmentB,
ElementAccumulator,
LayoutC,
ElementAccumulator,
OperatorClass,
ArchTag,
ThreadblockShape,
WarpShape,
InstructionShape,
ConvertScaledOp,
ThreadblockSwizzle,
kStages,
Operator
>::GemmKernel;
/// Reduction kernel
using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
cutlass::MatrixShape<4, 32 * EpilogueOutputOp::kCount>,
EpilogueOutputOp,
ReductionOp
>;
//
//
//
/// Argument structure
struct Arguments {
//
// Data members
//
GemmCoord problem_size;
TensorRef<ElementA const, LayoutA> ref_A;
TensorRef<ElementB const, LayoutB> ref_B;
TensorRef<ElementC const, LayoutC> ref_C;
TensorRef<ElementC, LayoutC> ref_D;
typename EpilogueOutputOp::Params epilogue;
int split_k_slices;
typename ConvertScaledOp::Params convert;
typename ReductionOp::Params reduction;
//
// Methods
//
/// Default ctor
CUTLASS_HOST_DEVICE
Arguments() { }
/// Constructs an Arguments structure
CUTLASS_HOST_DEVICE
Arguments(
GemmCoord problem_size_,
TensorRef<ElementA const, LayoutA> ref_A_,
TensorRef<ElementB const, LayoutB> ref_B_,
TensorRef<ElementC const, LayoutC> ref_C_,
TensorRef<ElementC, LayoutC> ref_D_,
typename EpilogueOutputOp::Params epilogue_ =
typename EpilogueOutputOp::Params(),
int split_k_slices = 1,
typename ConvertScaledOp::Params convert_ =
typename ConvertScaledOp::Params(),
typename ReductionOp::Params reduction_ =
typename ReductionOp::Params()
):
problem_size(problem_size_),
ref_A(ref_A_),
ref_B(ref_B_),
ref_C(ref_C_),
ref_D(ref_D_),
epilogue(epilogue_),
split_k_slices(split_k_slices),
convert(convert_),
reduction(reduction_) { }
};
private:
/// Kernel parameters object
typename GemmKernel::Params gemm_params_;
/// Reduction kernel parameters object
typename ReductionKernel::Params reduction_params_;
public:
/// Constructs the GEMM.
GemmSplitKParallel() { }
/// Determines whether the GEMM can execute the given problem.
static Status can_implement(Arguments const &args) {
// TODO
return Status::kSuccess;
}
/// Gets the workspace size
static size_t get_workspace_size(Arguments const &args) {
// 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.split_k_slices);
return sizeof(ElementAccumulator_) * size_t(args.problem_size.m()) * size_t(args.problem_size.n()) * grid_shape.k();
}
/// Initializes GEMM state from arguments.
Status initialize(Arguments const &args, void *workspace) {
// 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.split_k_slices);
// Define a reference to the workspace - this is an aligned region in device memory.
if (!workspace) {
return Status::kErrorWorkspaceNull;
}
TensorRef<ElementAccumulator_, layout::RowMajor> ref_workspace(
static_cast<ElementAccumulator_ *>(workspace),
args.problem_size.n());
int64_t partition_stride = int64_t(args.problem_size.m()) * int64_t(args.problem_size.n());
// Initialize the Params structure
gemm_params_ = typename GemmKernel::Params{
args.problem_size,
grid_shape,
args.ref_A.non_const_ref(),
args.ref_B.non_const_ref(),
ref_workspace,
args.convert,
partition_stride
};
reduction_params_ = typename ReductionKernel::Params(
args.problem_size.mn(),
grid_shape.k(),
partition_stride,
ref_workspace,
args.ref_D,
args.ref_C.non_const_ref(),
args.epilogue
);
return Status::kSuccess;
}
/// Lightweight update given a subset of arguments
Status update(Arguments const &args, void *workspace = nullptr) {
if (!workspace) {
return Status::kErrorWorkspaceNull;
}
gemm_params_.ref_A.reset(args.ref_A.data());
gemm_params_.ref_B.reset(args.ref_B.data());
gemm_params_.ref_D.reset(workspace);
reduction_params_.ref_D.reset(args.ref_D.data());
reduction_params_.ref_C.reset(args.ref_C.data());
return Status::kSuccess;
}
/// Runs the kernel using initialized state.
Status run(cudaStream_t stream = nullptr) {
//
// Launch GEMM kernel
//
ThreadblockSwizzle threadblock_swizzle;
dim3 grid = threadblock_swizzle.get_grid_shape(gemm_params_.grid_tiled_shape);
dim3 block(GemmKernel::kThreadCount, 1, 1);
cudaError_t result;
int smem_size = int(sizeof(typename GemmKernel::SharedStorage));
if (smem_size >= (48 << 10)) {
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributeMaxDynamicSharedMemorySize,
smem_size);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
result = cudaFuncSetAttribute(
Kernel<GemmKernel>,
cudaFuncAttributePreferredSharedMemoryCarveout, 100);
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
}
Kernel<GemmKernel><<<grid, block, smem_size, stream>>>(gemm_params_);
result = cudaGetLastError();
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
//
// Launch reduction kernel
//
block = ReductionKernel::block_shape();
grid = ReductionKernel::grid_shape(gemm_params_.problem_size.mn());
Kernel<ReductionKernel><<< grid, block, 0, stream >>>(reduction_params_);
result = cudaGetLastError();
if (result != cudaSuccess) {
return Status::kErrorInternal;
}
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);
if (status == Status::kSuccess) {
status = run(stream);
}
return status;
}
};
////////////////////////////////////////////////////////////////////////////////
/// Partial specialization for column-major output
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 B matrix operand
typename LayoutB_,
/// Element type for C and D matrix operands
typename ElementC_,
/// Element type for internal accumulation
typename ElementAccumulator_,
/// Operator class tag
typename OperatorClass_,
/// Tag indicating architecture to tune for. This is the minimum SM that
/// supports the intended feature. The device kernel can be built
/// targeting any SM larger than this number.
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 EpilogueOutputOp_,
/// Epilogue output operator
typename ConvertScaledOp_,
/// Reduction operator
typename ReductionOp_,
/// Threadblock-level swizzling operator
typename ThreadblockSwizzle_,
/// Number of stages used in the pipelined mainloop
int Stages, int kAlignmentA, int kAlignmentB,
/// Operation performed by GEMM
typename Operator_>
class GemmSplitKParallel<ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_,
layout::ColumnMajor, ElementAccumulator_,
OperatorClass_, ArchTag_, ThreadblockShape_,
WarpShape_, InstructionShape_, EpilogueOutputOp_,
ConvertScaledOp_, ReductionOp_, ThreadblockSwizzle_,
Stages, kAlignmentA, kAlignmentB, Operator_> {
public:
using ElementA = ElementA_;
using LayoutA = LayoutA_;
using ElementB = ElementB_;
using LayoutB = LayoutB_;
using ElementC = ElementC_;
using LayoutC = layout::ColumnMajor;
using ElementAccumulator = ElementAccumulator_;
using OperatorClass = OperatorClass_;
using ArchTag = ArchTag_;
using ThreadblockShape = ThreadblockShape_;
using WarpShape = WarpShape_;
using InstructionShape = InstructionShape_;
using ConvertScaledOp = ConvertScaledOp_;
using EpilogueOutputOp = EpilogueOutputOp_;
using ReductionOp = ReductionOp_;
using ThreadblockSwizzle = ThreadblockSwizzle_;
using Operator = Operator_;
static int const kStages = Stages;
using UnderlyingOperator = GemmSplitKParallel<
ElementB,
typename layout::LayoutTranspose<LayoutB>::type,
ElementA,
typename layout::LayoutTranspose<LayoutA>::type,
ElementC,
layout::RowMajor,
ElementAccumulator,
OperatorClass,
ArchTag,
ThreadblockShape,
WarpShape,
InstructionShape,
EpilogueOutputOp,
ConvertScaledOp,
ReductionOp,
ThreadblockSwizzle,
Stages,
kAlignmentA,
kAlignmentB,
Operator
>;
using UnderlyingArguments = typename UnderlyingOperator::Arguments;
using GemmKernel = typename UnderlyingOperator::GemmKernel;
using ReductionKernel = typename UnderlyingOperator::ReductionKernel;
/// Argument structure
struct Arguments {
//
// Data members
//
GemmCoord problem_size;
TensorRef<ElementA const, LayoutA> ref_A;
TensorRef<ElementB const, LayoutB> ref_B;
TensorRef<ElementC const, LayoutC> ref_C;
TensorRef<ElementC, LayoutC> ref_D;
typename EpilogueOutputOp::Params epilogue;
int split_k_slices;
typename ConvertScaledOp::Params convert;
typename ReductionOp::Params reduction;
//
// Methods
//
/// Default ctor
CUTLASS_HOST_DEVICE
Arguments() { }
/// Constructs an Arguments structure
CUTLASS_HOST_DEVICE
Arguments(
GemmCoord problem_size_,
TensorRef<ElementA const, LayoutA> ref_A_,
TensorRef<ElementB const, LayoutB> ref_B_,
TensorRef<ElementC const, LayoutC> ref_C_,
TensorRef<ElementC, LayoutC> ref_D_,
typename EpilogueOutputOp::Params epilogue_ =
typename EpilogueOutputOp::Params(),
int split_k_slices = 1,
typename ConvertScaledOp::Params convert_ =
typename ConvertScaledOp::Params(),
typename ReductionOp::Params reduction_ =
typename ReductionOp::Params()
):
problem_size(problem_size_),
ref_A(ref_A_),
ref_B(ref_B_),
ref_C(ref_C_),
ref_D(ref_D_),
epilogue(epilogue_),
split_k_slices(split_k_slices),
convert(convert_),
reduction(reduction_) { }
};
private:
/// Kernel parameters object
UnderlyingOperator underlying_operator_;
public:
/// Constructs the GEMM.
GemmSplitKParallel() { }
/// Helper to construct a transposed equivalent for the underying GEMM operator
static UnderlyingArguments to_underlying_arguments(Arguments const &args) {
return UnderlyingArguments(
{args.problem_size.n(), args.problem_size.m(), args.problem_size.k()},
{args.ref_B.data(), args.ref_B.stride(0)},
{args.ref_A.data(), args.ref_A.stride(0)},
{args.ref_C.data(), args.ref_C.stride(0)},
{args.ref_D.data(), args.ref_D.stride(0)},
args.epilogue,
args.split_k_slices,
args.convert,
args.reduction
);
}
/// Determines whether the GEMM can execute the given problem.
static Status can_implement(Arguments const &args) {
return UnderlyingOperator::can_implement(to_underlying_arguments(args));
}
/// Gets the workspace size
static size_t get_workspace_size(Arguments const &args) {
return UnderlyingOperator::get_workspace_size(to_underlying_arguments(args));
}
/// Initializes GEMM state from arguments.
Status initialize(Arguments const &args, void *workspace) {
return underlying_operator_.initialize(to_underlying_arguments(args), workspace);
}
/// Lightweight update given a subset of arguments
Status update(Arguments const &args, void *workspace = nullptr) {
return underlying_operator_.update(to_underlying_arguments(args), workspace);
}
/// Runs the kernel using initialized state.
Status run(cudaStream_t stream = nullptr) {
return underlying_operator_.run(stream);
}
/// 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
////////////////////////////////////////////////////////////////////////////////
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