464 lines
15 KiB
C++
464 lines
15 KiB
C++
/***************************************************************************************************
|
|
* Copyright (c) 2017 - 2022 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 Template for a pipelined fused activation's scale+bias+relu and Implicit GEMM kernel.
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include "cutlass/cutlass.h"
|
|
|
|
#include "cutlass/aligned_buffer.h"
|
|
#include "cutlass/array.h"
|
|
#include "cutlass/numeric_types.h"
|
|
#include "cutlass/matrix_shape.h"
|
|
#include "cutlass/semaphore.h"
|
|
#include "cutlass/tensor_ref.h"
|
|
#include "cutlass/layout/tensor.h"
|
|
#include "cutlass/gemm/gemm.h"
|
|
#include "cutlass/conv/convolution.h"
|
|
#include "cutlass/conv/conv2d_problem_size.h"
|
|
#include "cutlass/conv/conv3d_problem_size.h"
|
|
#include "cutlass/epilogue/threadblock/output_iterator_parameter.h"
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace cutlass {
|
|
namespace conv {
|
|
namespace kernel {
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
typename Mma_, ///! Threadblock-scoped matrix multiply-accumulate
|
|
typename Epilogue_, ///! Epilogue
|
|
typename ThreadblockSwizzle_, ///! Threadblock swizzling function
|
|
conv::Operator ConvOperator, ///! Convolutional operator (Fprop, Dgrad, Wgrad)
|
|
typename ConvProblemSize_ = Conv2dProblemSize ///! Convolutional operator on 2D or 3D problem
|
|
>
|
|
struct ImplicitGemmConvolutionFusion {
|
|
|
|
using Mma = Mma_;
|
|
using Epilogue = Epilogue_;
|
|
using EpilogueOutputOp = typename Epilogue::OutputOp;
|
|
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
|
static Operator const kConvolutionalOperator = ConvOperator;
|
|
|
|
using ElementA = typename Mma::IteratorA::Element;
|
|
using LayoutA = typename Mma::IteratorA::Layout;
|
|
using ElementB = typename Mma::IteratorB::Element;
|
|
using LayoutB = typename Mma::IteratorB::Layout;
|
|
|
|
using ElementScaleBias = typename Mma::IteratorScaleBias::Element;
|
|
using LayoutScaleBias = typename Mma::IteratorScaleBias::Layout;
|
|
|
|
using ElementC = typename EpilogueOutputOp::ElementOutput;
|
|
using LayoutC = LayoutA;
|
|
|
|
using ElementAccumulator = typename EpilogueOutputOp::ElementAccumulator;
|
|
using ElementCompute = typename EpilogueOutputOp::ElementCompute;
|
|
|
|
using WarpMmaOperator = typename Mma::Policy::Operator;
|
|
|
|
using ArchMmaOperator = typename WarpMmaOperator::ArchMmaOperator;
|
|
using MathOperator = typename ArchMmaOperator::Operator;
|
|
|
|
using OperatorClass = typename WarpMmaOperator::OperatorClass;
|
|
using ArchTag = typename WarpMmaOperator::ArchTag;
|
|
|
|
using ThreadblockShape = typename Mma::Shape;
|
|
using WarpShape = typename WarpMmaOperator::Shape;
|
|
using InstructionShape = typename ArchMmaOperator::Shape;
|
|
|
|
static int const kStages = Mma::kStages;
|
|
static IteratorAlgorithm const kIteratorAlgorithm = Mma::IteratorA::kIteratorAlgorithm;
|
|
|
|
/// Warp count (concept: GemmShape)
|
|
using WarpCount = typename Mma::WarpCount;
|
|
static int const kThreadCount = 32 * WarpCount::kCount;
|
|
|
|
using TensorRefA = typename Mma::IteratorA::TensorRef;
|
|
using TensorRefB = typename Mma::IteratorB::TensorRef;
|
|
using TensorRefScaleBias = typename Mma::IteratorScaleBias::TensorRef;
|
|
using TensorRefC = cutlass::TensorRef<ElementC, LayoutC>;
|
|
|
|
/// Check iterator A and B convolution dimension are the same and
|
|
// set device::ImplicitGemmConvolution::kConvDim
|
|
static_assert(Mma::IteratorA::kConvDim == Mma::IteratorB::kConvDim,
|
|
"Convolution on different different dimensions is not supported");
|
|
static int const kConvDim = Mma::IteratorA::kConvDim;
|
|
|
|
/// Conv dimension and problem size structure (Conv2d or Conv3d)
|
|
using ConvProblemSize = ConvProblemSize_;
|
|
|
|
static conv::GroupMode const kGroupMode = conv::GroupMode::kNone;
|
|
|
|
/// Wgrad C stride idx for implicit gemm algorithm
|
|
// Conv2d row-major matrix C (KxRSC)
|
|
// Conv3d row-major matrix C (KxTRSC)
|
|
static int const kWgradCStrideIdx =
|
|
platform::is_same<LayoutC, cutlass::layout::TensorNHWC>::value ? 2 : 3;
|
|
|
|
/// This chooses the appropriate stride element of the C tensor.
|
|
static int const kTensorCStrideIdx =
|
|
(kConvolutionalOperator == conv::Operator::kWgrad ? kWgradCStrideIdx : 0);
|
|
|
|
//
|
|
//
|
|
//
|
|
using ConvOutputIteratorParameter = epilogue::threadblock::ConvOutputIteratorParameter<
|
|
LayoutC,
|
|
typename Epilogue::OutputTileIterator::Layout,
|
|
TensorRefC,
|
|
ConvOperator,
|
|
ConvProblemSize
|
|
>;
|
|
|
|
/// Argument structure
|
|
struct Arguments {
|
|
|
|
//
|
|
// Data members
|
|
//
|
|
|
|
ConvProblemSize problem_size;
|
|
TensorRefA ref_A;
|
|
TensorRefB ref_B;
|
|
TensorRefScaleBias ref_scale;
|
|
TensorRefScaleBias ref_bias;
|
|
TensorRefC ref_C;
|
|
TensorRefC ref_D;
|
|
typename EpilogueOutputOp::Params output_op;
|
|
SplitKMode split_k_mode;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
/// Default ctor
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments() { }
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments(
|
|
ConvProblemSize const & problem_size
|
|
):
|
|
problem_size(problem_size) { }
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Arguments(
|
|
ConvProblemSize const & problem_size,
|
|
TensorRefA const & ref_A,
|
|
TensorRefB const & ref_B,
|
|
TensorRefScaleBias const & ref_scale,
|
|
TensorRefScaleBias const & ref_bias,
|
|
TensorRefC const & ref_C,
|
|
TensorRefC const & ref_D,
|
|
typename EpilogueOutputOp::Params const & output_op,
|
|
SplitKMode const & split_k_mode = SplitKMode::kSerial
|
|
):
|
|
problem_size(problem_size),
|
|
ref_A(ref_A),
|
|
ref_B(ref_B),
|
|
ref_scale(ref_scale),
|
|
ref_bias(ref_bias),
|
|
ref_C(ref_C),
|
|
ref_D(ref_D),
|
|
output_op(output_op),
|
|
split_k_mode(split_k_mode)
|
|
{
|
|
|
|
}
|
|
|
|
};
|
|
|
|
/// Parameters structure
|
|
struct Params {
|
|
ConvProblemSize problem_size;
|
|
cutlass::gemm::GemmCoord grid_tiled_shape;
|
|
gemm::GemmCoord implicit_gemm_problem_size;
|
|
int swizzle_log_tile;
|
|
int gemm_k_iterations;
|
|
typename Mma::IteratorA::Params iterator_A;
|
|
typename Mma::IteratorA::Element const *ptr_A;
|
|
typename Mma::IteratorB::Params iterator_B;
|
|
typename Mma::IteratorB::Element const *ptr_B;
|
|
typename Mma::IteratorScaleBias::Params iterator_scale_bias;
|
|
typename Mma::IteratorScaleBias::Element const *ptr_scale;
|
|
typename Mma::IteratorScaleBias::Element const *ptr_bias;
|
|
typename Epilogue::OutputTileIterator::Params iterator_C;
|
|
typename Epilogue::OutputTileIterator::Element *ptr_C;
|
|
typename Epilogue::OutputTileIterator::Params iterator_D;
|
|
typename Epilogue::OutputTileIterator::Element *ptr_D;
|
|
typename EpilogueOutputOp::Params output_op;
|
|
int *semaphore;
|
|
SplitKMode split_k_mode;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Params(): swizzle_log_tile(0), gemm_k_iterations(0) { }
|
|
|
|
///
|
|
CUTLASS_HOST_DEVICE
|
|
Params(
|
|
Arguments const &args,
|
|
int *semaphore = nullptr
|
|
):
|
|
problem_size(args.problem_size),
|
|
implicit_gemm_problem_size(cutlass::conv::implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size)),
|
|
iterator_A(Mma::IteratorA::getParams(args.problem_size, args.ref_A.layout())),
|
|
ptr_A(args.ref_A.data()),
|
|
iterator_B(args.problem_size, args.ref_B.layout()),
|
|
ptr_B(args.ref_B.data()),
|
|
iterator_scale_bias(args.problem_size, args.ref_scale.layout()),
|
|
ptr_scale(args.ref_scale.data()),
|
|
ptr_bias(args.ref_bias.data()),
|
|
iterator_C(ConvOutputIteratorParameter::layout(args.ref_C)),
|
|
ptr_C(args.ref_C.data()),
|
|
iterator_D(ConvOutputIteratorParameter::layout(args.ref_D)),
|
|
ptr_D(args.ref_D.data()),
|
|
output_op(args.output_op),
|
|
semaphore(semaphore),
|
|
split_k_mode(args.split_k_mode)
|
|
{
|
|
gemm_k_iterations = implicit_gemm_k_iterations(kConvolutionalOperator, ThreadblockShape::kK, args.problem_size);
|
|
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
grid_tiled_shape = threadblock_swizzle.get_tiled_shape(
|
|
implicit_gemm_problem_size,
|
|
{ThreadblockShape::kM, ThreadblockShape::kN, ThreadblockShape::kK},
|
|
args.problem_size.split_k_slices);
|
|
|
|
swizzle_log_tile = threadblock_swizzle.get_log_tile(grid_tiled_shape);
|
|
}
|
|
};
|
|
|
|
/// Shared memory storage structure
|
|
union SharedStorage {
|
|
typename Mma::SharedStorage main_loop;
|
|
typename Epilogue::SharedStorage epilogue;
|
|
};
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
ImplicitGemmConvolutionFusion() { }
|
|
|
|
/// Executes one ImplicitGEMM
|
|
CUTLASS_DEVICE
|
|
void operator()(Params const ¶ms, SharedStorage &shared_storage) {
|
|
|
|
// Compute threadblock location
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
cutlass::gemm::GemmCoord threadblock_tile_idx =
|
|
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
|
|
|
// Early exit if CTA is out of range
|
|
if (params.grid_tiled_shape.m() <= threadblock_tile_idx.m() ||
|
|
params.grid_tiled_shape.n() <= threadblock_tile_idx.n()) {
|
|
|
|
return;
|
|
}
|
|
|
|
// Compute position within threadblock
|
|
int thread_idx = threadIdx.x;
|
|
|
|
// Construct iterators to A operand
|
|
typename Mma::IteratorA iterator_A(
|
|
params.iterator_A,
|
|
params.problem_size,
|
|
params.ptr_A,
|
|
thread_idx,
|
|
MatrixCoord(
|
|
threadblock_tile_idx.m() * Mma::Shape::kM,
|
|
threadblock_tile_idx.k() * Mma::Shape::kK
|
|
)
|
|
);
|
|
|
|
// Construct iterators to B operand
|
|
typename Mma::IteratorB iterator_B(
|
|
params.iterator_B,
|
|
params.problem_size,
|
|
params.ptr_B,
|
|
thread_idx,
|
|
MatrixCoord(
|
|
threadblock_tile_idx.k() * Mma::Shape::kK,
|
|
threadblock_tile_idx.n() * Mma::Shape::kN
|
|
)
|
|
);
|
|
|
|
// Construct iterators to A scale/bias vector
|
|
typename Mma::IteratorScaleBias iterator_scale_bias(
|
|
params.iterator_scale_bias,
|
|
params.problem_size,
|
|
params.ptr_scale,
|
|
params.ptr_bias,
|
|
thread_idx,
|
|
MatrixCoord(
|
|
0, (kConvolutionalOperator == conv::Operator::kFprop) ?
|
|
(threadblock_tile_idx.k() * Mma::Shape::kK) :
|
|
// Wgrad
|
|
(threadblock_tile_idx.n() * Mma::Shape::kN)
|
|
)
|
|
);
|
|
|
|
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
|
// is compiled as warp-uniform.
|
|
int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
|
|
int lane_idx = threadIdx.x % 32;
|
|
|
|
//
|
|
// Main loop
|
|
//
|
|
|
|
// Construct thread-scoped matrix multiply
|
|
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
|
|
|
|
typename Mma::FragmentC accumulators;
|
|
|
|
accumulators.clear();
|
|
|
|
// Compute threadblock-scoped matrix multiply-add
|
|
mma(params.gemm_k_iterations, accumulators, iterator_A,
|
|
iterator_B, iterator_scale_bias, accumulators);
|
|
|
|
//
|
|
// Epilogue
|
|
//
|
|
|
|
EpilogueOutputOp output_op(params.output_op);
|
|
|
|
// Construct the semaphore.
|
|
int block_idx = threadblock_tile_idx.m() + threadblock_tile_idx.n() * params.grid_tiled_shape.m();
|
|
|
|
Semaphore semaphore(params.semaphore + block_idx, thread_idx);
|
|
|
|
// Compute logical position within grid
|
|
threadblock_tile_idx =
|
|
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
|
|
|
// If performing a reduction via split-K, fetch the initial synchronization
|
|
if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
|
|
|
|
// Fetch the synchronization lock initially but do not block.
|
|
semaphore.fetch();
|
|
|
|
// Indicate which position in a serial reduction the output operator is currently updating
|
|
output_op.set_k_partition(threadblock_tile_idx.k(), params.grid_tiled_shape.k());
|
|
}
|
|
|
|
MatrixCoord threadblock_offset(
|
|
threadblock_tile_idx.m() * Mma::Shape::kM,
|
|
threadblock_tile_idx.n() * Mma::Shape::kN
|
|
);
|
|
|
|
// Tile iterator writing to destination tensor
|
|
typename Epilogue::OutputTileIterator iterator_D(
|
|
params.iterator_D,
|
|
params.ptr_D,
|
|
ConvOutputIteratorParameter::extent(params.problem_size),
|
|
thread_idx,
|
|
threadblock_offset
|
|
);
|
|
|
|
// Tile iterator reading from source accumulator tensor
|
|
typename Epilogue::OutputTileIterator iterator_C(
|
|
params.iterator_C,
|
|
params.ptr_C,
|
|
ConvOutputIteratorParameter::extent(params.problem_size),
|
|
thread_idx,
|
|
threadblock_offset
|
|
);
|
|
|
|
// Construct the epilogue
|
|
Epilogue epilogue(
|
|
shared_storage.epilogue,
|
|
thread_idx,
|
|
warp_idx,
|
|
lane_idx);
|
|
|
|
// Wait on the semaphore - this latency may have been covered by iterator construction
|
|
if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
|
|
|
|
// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
|
|
if (threadblock_tile_idx.k()) {
|
|
iterator_C = iterator_D;
|
|
}
|
|
|
|
semaphore.wait(threadblock_tile_idx.k());
|
|
|
|
}
|
|
// Each split-k-slice writes to a unique tensor location
|
|
else if (params.split_k_mode == SplitKMode::kParallel) {
|
|
iterator_D.add_pointer_offset(threadblock_tile_idx.k() *
|
|
cutlass::conv::implicit_gemm_tensor_c_size(ConvOperator, params.problem_size));
|
|
}
|
|
|
|
// Run efficient epilogue
|
|
epilogue(output_op, iterator_D, accumulators, iterator_C);
|
|
|
|
//
|
|
// Release the semaphore
|
|
//
|
|
|
|
if (params.split_k_mode == SplitKMode::kSerial && params.grid_tiled_shape.k() > 1) {
|
|
|
|
int lock = 0;
|
|
if (params.grid_tiled_shape.k() == threadblock_tile_idx.k() + 1) {
|
|
|
|
// The final threadblock resets the semaphore for subsequent grids.
|
|
lock = 0;
|
|
}
|
|
else {
|
|
// Otherwise, the semaphore is incremented
|
|
lock = threadblock_tile_idx.k() + 1;
|
|
}
|
|
|
|
semaphore.release(lock);
|
|
}
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace kernel
|
|
} // namespace conv
|
|
} // namespace cutlass
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|