6615010cd0
CUTLASS 2.4 (Implicit GEMM Convolution) Co-authored-by: Manish Gupta <manigupta@nvidia.com>, Haicheng Wu <haichengw@nvidia.com>, Dustyn Blasig <dblasig@nvidia.com>, Andrew Kerr <akerr@nvidia.com>
410 lines
14 KiB
C++
410 lines
14 KiB
C++
/***************************************************************************************************
|
|
* Copyright (c) 2017-2020, 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 a pipelined GEMM kernel. Does not compute batching or support split-K.
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include "cutlass/cutlass.h"
|
|
|
|
#include "cutlass/gemm/gemm.h"
|
|
#include "cutlass/matrix_coord.h"
|
|
#include "cutlass/semaphore.h"
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace cutlass {
|
|
namespace gemm {
|
|
namespace kernel {
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
typename B2bMma_, ///! Threadblock-scoped matrix multiply-accumulate
|
|
typename Epilogue_, ///! Epilogue
|
|
typename ThreadblockSwizzle_, ///! Threadblock swizzling function
|
|
bool SplitKSerial ///! If true, code supporting split-K via serial reduction is enabled.
|
|
>
|
|
struct B2bGemm {
|
|
|
|
using B2bMma = B2bMma_;
|
|
using Epilogue = Epilogue_;
|
|
using OutputOp0 = typename B2bMma::OutputOp;
|
|
using OutputOp1 = typename Epilogue::OutputOp;
|
|
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
|
static bool const kSplitKSerial = SplitKSerial;
|
|
|
|
/// Warp count (concept: GemmShape)
|
|
using WarpCount0 = typename B2bMma::WarpCount0;
|
|
static int const kThreadCount = 32 * WarpCount0::kCount;
|
|
|
|
/// Parameters structure
|
|
struct Params {
|
|
cutlass::gemm::GemmCoord problem_size_0;
|
|
cutlass::gemm::GemmCoord problem_size_1;
|
|
cutlass::gemm::GemmCoord grid_tiled_shape;
|
|
typename B2bMma::IteratorA0::Params params_A0;
|
|
typename B2bMma::IteratorA0::TensorRef ref_A0;
|
|
typename B2bMma::IteratorB0::Params params_B0;
|
|
typename B2bMma::IteratorB0::TensorRef ref_B0;
|
|
typename Epilogue::OutputTileIterator::Params params_C0;
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C0;
|
|
typename B2bMma::IteratorB1::Params params_B1;
|
|
typename B2bMma::IteratorB1::TensorRef ref_B1;
|
|
typename Epilogue::OutputTileIterator::Params params_C1;
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C1;
|
|
typename Epilogue::OutputTileIterator::Params params_D1;
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_D1;
|
|
typename OutputOp0::Params output_op_0;
|
|
typename OutputOp1::Params output_op_1;
|
|
int *semaphore;
|
|
int gemm_k_iterations_0;
|
|
int gemm_k_size_0;
|
|
int gemm_k_iterations_1;
|
|
int gemm_k_size_1;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Params(): semaphore(0), gemm_k_iterations_0(0), gemm_k_size_0(0),
|
|
gemm_k_iterations_1(0), gemm_k_size_1(0) { }
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Params(
|
|
cutlass::gemm::GemmCoord const & problem_size_0,
|
|
cutlass::gemm::GemmCoord const & problem_size_1,
|
|
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
|
typename B2bMma::IteratorA0::TensorRef ref_A0,
|
|
typename B2bMma::IteratorB0::TensorRef ref_B0,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C0,
|
|
typename B2bMma::IteratorB1::TensorRef ref_B1,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C1,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_D1,
|
|
typename OutputOp0::Params output_op_0 = typename OutputOp0::Params(),
|
|
typename OutputOp1::Params output_op_1 = typename OutputOp1::Params(),
|
|
int *workspace = nullptr
|
|
):
|
|
problem_size_0(problem_size_0),
|
|
problem_size_1(problem_size_1),
|
|
grid_tiled_shape(grid_tiled_shape),
|
|
params_A0(ref_A0.layout()),
|
|
ref_A0(ref_A0),
|
|
params_B0(ref_B0.layout()),
|
|
ref_B0(ref_B0),
|
|
params_C0(ref_C0.layout()),
|
|
ref_C0(ref_C0),
|
|
params_B1(ref_B1.layout()),
|
|
ref_B1(ref_B1),
|
|
params_C1(ref_C1.layout()),
|
|
ref_C1(ref_C1),
|
|
params_D1(ref_D1.layout()),
|
|
ref_D1(ref_D1),
|
|
output_op_0(output_op_0),
|
|
output_op_1(output_op_1) {
|
|
|
|
int total_gemm_k_iterations_0 = (problem_size_0.k() + B2bMma::Shape0::kK - 1) / B2bMma::Shape0::kK;
|
|
int gemm_k_iterations_0 = (total_gemm_k_iterations_0 + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
|
|
gemm_k_size_0 = gemm_k_iterations_0 * B2bMma::Shape0::kK;
|
|
int total_gemm_k_iterations_1 = (problem_size_1.k() + B2bMma::Shape1::kK - 1) / B2bMma::Shape1::kK;
|
|
int gemm_k_iterations_1 = (total_gemm_k_iterations_1 + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
|
|
gemm_k_size_1 = gemm_k_iterations_1 * B2bMma::Shape1::kK;
|
|
|
|
semaphore = workspace;
|
|
}
|
|
};
|
|
|
|
/// Shared memory storage structure
|
|
union SharedStorage {
|
|
typename B2bMma::B2bMmaSharedStorage main_loop;
|
|
typename Epilogue::SharedStorage epilogue;
|
|
};
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
B2bGemm() { }
|
|
|
|
/// Determines whether kernel satisfies alignment
|
|
static Status can_implement(
|
|
cutlass::gemm::GemmCoord const & problem_size_0,
|
|
cutlass::gemm::GemmCoord const & problem_size_1,
|
|
typename B2bMma::IteratorA0::TensorRef ref_A0,
|
|
typename B2bMma::IteratorB0::TensorRef ref_B0,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C0,
|
|
typename B2bMma::IteratorB1::TensorRef ref_B1,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C1,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_D1) {
|
|
|
|
static int const kAlignmentA = B2bMma::IteratorA0::AccessType::kElements;
|
|
static int const kAlignmentB = B2bMma::IteratorB0::AccessType::kElements;
|
|
static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
|
|
|
|
if (!TensorRef_aligned(ref_A0, kAlignmentA)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_B0, kAlignmentB)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_C0, kAlignmentC)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_B1, kAlignmentB)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_C1, kAlignmentC)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_D1, kAlignmentC)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if ((problem_size_0.m() % kAlignmentA) || (problem_size_0.k() % kAlignmentA) ||
|
|
(problem_size_0.n() % kAlignmentB) || (problem_size_0.k() % kAlignmentB) ||
|
|
(problem_size_0.m() % kAlignmentC) || (problem_size_0.n() % kAlignmentC) ||
|
|
(problem_size_1.m() % kAlignmentA) || (problem_size_1.k() % kAlignmentA) ||
|
|
(problem_size_1.n() % kAlignmentB) || (problem_size_1.k() % kAlignmentB) ||
|
|
(problem_size_1.m() % kAlignmentC) || (problem_size_1.n() % kAlignmentC)) {
|
|
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
return Status::kSuccess;
|
|
}
|
|
|
|
/// Executes one GEMM
|
|
CUTLASS_DEVICE
|
|
void operator()(Params const ¶ms, SharedStorage &shared_storage) {
|
|
|
|
// Compute threadblock location
|
|
ThreadblockSwizzle threadblock_swizzle;
|
|
|
|
cutlass::gemm::GemmCoord threadblock_tile_offset =
|
|
threadblock_swizzle.get_tile_offset(params.grid_tiled_shape);
|
|
|
|
// Early exit if CTA is out of range
|
|
if (params.grid_tiled_shape.m() <= threadblock_tile_offset.m() ||
|
|
params.grid_tiled_shape.n() <= threadblock_tile_offset.n()) {
|
|
|
|
return;
|
|
}
|
|
|
|
// Compute initial location in logical coordinates
|
|
cutlass::MatrixCoord tb_offset_A0{
|
|
threadblock_tile_offset.m() * B2bMma::Shape0::kM,
|
|
threadblock_tile_offset.k() * params.gemm_k_size_0,
|
|
};
|
|
|
|
cutlass::MatrixCoord tb_offset_B0{
|
|
threadblock_tile_offset.k() * params.gemm_k_size_0,
|
|
threadblock_tile_offset.n() * B2bMma::Shape0::kN
|
|
};
|
|
|
|
cutlass::MatrixCoord tb_offset_B1{
|
|
threadblock_tile_offset.k() * params.gemm_k_size_1,
|
|
threadblock_tile_offset.n() * B2bMma::Shape1::kN
|
|
};
|
|
|
|
// Problem size is a function of threadblock index in the K dimension
|
|
int problem_size_k_0 = min(
|
|
params.problem_size_0.k(),
|
|
(threadblock_tile_offset.k() + 1) * params.gemm_k_size_0);
|
|
|
|
// Compute threadblock-scoped matrix multiply-add
|
|
int gemm_k_iterations_0 = (problem_size_k_0 - tb_offset_A0.column() + B2bMma::Shape0::kK - 1) / B2bMma::Shape0::kK;
|
|
|
|
// Problem size is a function of threadblock index in the K dimension
|
|
int problem_size_k_1 = min(
|
|
params.problem_size_1.k(),
|
|
(threadblock_tile_offset.k() + 1) * params.gemm_k_size_1);
|
|
|
|
// Compute threadblock-scoped matrix multiply-add
|
|
// int gemm_k_iterations_1 = (problem_size_k_1 - tb_offset_B1.row() + B2bMma::Shape1::kK - 1) / B2bMma::Shape1::kK;
|
|
|
|
|
|
// Compute position within threadblock
|
|
int thread_idx = threadIdx.x;
|
|
|
|
// Construct iterators to A and B operands
|
|
typename B2bMma::IteratorA0 iterator_A0(
|
|
params.params_A0,
|
|
params.ref_A0.data(),
|
|
{params.problem_size_0.m(), problem_size_k_0},
|
|
thread_idx,
|
|
tb_offset_A0);
|
|
|
|
typename B2bMma::IteratorB0 iterator_B0(
|
|
params.params_B0,
|
|
params.ref_B0.data(),
|
|
{problem_size_k_0, params.problem_size_0.n()},
|
|
thread_idx,
|
|
tb_offset_B0);
|
|
|
|
typename B2bMma::IteratorB1 iterator_B1(
|
|
params.params_B1,
|
|
params.ref_B1.data(),
|
|
{problem_size_k_1, params.problem_size_1.n()},
|
|
thread_idx,
|
|
tb_offset_B1);
|
|
|
|
|
|
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
|
// is compiled as warp-uniform.
|
|
int warp_idx = __shfl_sync(0x1f, threadIdx.x / 32, 0);
|
|
int lane_idx = threadIdx.x % 32;
|
|
|
|
//
|
|
// Main loop
|
|
//
|
|
|
|
OutputOp0 output_op_0(params.output_op_0);
|
|
|
|
// Construct thread-scoped matrix multiply
|
|
B2bMma b2bMma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
|
|
|
|
typename B2bMma::FragmentC0 src_accum;
|
|
typename B2bMma::FragmentC1 accumulators;
|
|
|
|
src_accum.clear();
|
|
accumulators.clear();
|
|
|
|
if (!kSplitKSerial || gemm_k_iterations_0 > 0) {
|
|
// Compute threadblock-scoped matrix multiply-add
|
|
b2bMma(gemm_k_iterations_0, accumulators, iterator_A0, iterator_B0, iterator_B1, src_accum, output_op_0);
|
|
}
|
|
|
|
//
|
|
// Epilogue
|
|
//
|
|
|
|
OutputOp1 output_op_1(params.output_op_1);
|
|
|
|
//
|
|
// Masked tile iterators constructed from members
|
|
//
|
|
|
|
threadblock_tile_offset =
|
|
threadblock_swizzle.get_tile_offset(params.grid_tiled_shape);
|
|
|
|
//assume identity swizzle
|
|
MatrixCoord threadblock_offset(
|
|
threadblock_tile_offset.m() * B2bMma::Shape1::kM,
|
|
threadblock_tile_offset.n() * B2bMma::Shape1::kN
|
|
);
|
|
|
|
int block_idx = threadblock_tile_offset.m() + threadblock_tile_offset.n() * params.grid_tiled_shape.m();
|
|
|
|
// Construct the semaphore.
|
|
Semaphore semaphore(params.semaphore + block_idx, thread_idx);
|
|
|
|
// If performing a reduction via split-K, fetch the initial synchronization
|
|
if (kSplitKSerial && 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_1.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
|
|
}
|
|
|
|
// Tile iterator loading from source tensor.
|
|
typename Epilogue::OutputTileIterator iterator_C1(
|
|
params.params_C1,
|
|
params.ref_C1.data(),
|
|
params.problem_size_1.mn(),
|
|
thread_idx,
|
|
threadblock_offset
|
|
);
|
|
|
|
// Tile iterator writing to destination tensor.
|
|
typename Epilogue::OutputTileIterator iterator_D1(
|
|
params.params_D1,
|
|
params.ref_D1.data(),
|
|
params.problem_size_1.mn(),
|
|
thread_idx,
|
|
threadblock_offset
|
|
);
|
|
|
|
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 (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
|
|
|
// For subsequent threadblocks, the source matrix is held in the 'D' tensor.
|
|
if (threadblock_tile_offset.k()) {
|
|
iterator_C1 = iterator_D1;
|
|
}
|
|
|
|
semaphore.wait(threadblock_tile_offset.k());
|
|
|
|
__threadfence();
|
|
}
|
|
|
|
// Execute the epilogue operator to update the destination tensor.
|
|
epilogue(output_op_1, iterator_D1, accumulators, iterator_C1);
|
|
|
|
//
|
|
// Release the semaphore
|
|
//
|
|
|
|
if (kSplitKSerial && params.grid_tiled_shape.k() > 1) {
|
|
|
|
int lock = 0;
|
|
if (params.grid_tiled_shape.k() == threadblock_tile_offset.k() + 1) {
|
|
|
|
// The final threadblock resets the semaphore for subsequent grids.
|
|
lock = 0;
|
|
}
|
|
else {
|
|
// Otherwise, the semaphore is incremented
|
|
lock = threadblock_tile_offset.k() + 1;
|
|
}
|
|
|
|
__threadfence();
|
|
semaphore.release(lock);
|
|
}
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace kernel
|
|
} // namespace gemm
|
|
} // namespace cutlass
|
|
|