381 lines
13 KiB
C++
381 lines
13 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 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"
|
|
#include "cutlass/arch/arch.h"
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
namespace cutlass {
|
|
namespace gemm {
|
|
namespace kernel {
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
typename Mma_, ///! 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 Gemm {
|
|
|
|
using Mma = Mma_;
|
|
using Epilogue = Epilogue_;
|
|
using OutputOp = typename Epilogue::OutputOp;
|
|
using ThreadblockSwizzle = ThreadblockSwizzle_;
|
|
static bool const kSplitKSerial = SplitKSerial;
|
|
|
|
/// Warp count (concept: GemmShape)
|
|
using WarpCount = typename Mma::WarpCount;
|
|
static int const kThreadCount = 32 * WarpCount::kCount;
|
|
|
|
/// Parameters structure
|
|
struct Params {
|
|
cutlass::gemm::GemmCoord problem_size;
|
|
cutlass::gemm::GemmCoord grid_tiled_shape;
|
|
int swizzle_log_tile;
|
|
typename Mma::IteratorA::Params params_A;
|
|
typename Mma::IteratorA::TensorRef ref_A;
|
|
typename Mma::IteratorB::Params params_B;
|
|
typename Mma::IteratorB::TensorRef ref_B;
|
|
typename Epilogue::OutputTileIterator::Params params_C;
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C;
|
|
typename Epilogue::OutputTileIterator::Params params_D;
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_D;
|
|
typename OutputOp::Params output_op;
|
|
int *semaphore;
|
|
int gemm_k_size;
|
|
// For gather+scatter operations
|
|
int const *gather_A_indices;
|
|
int const *gather_B_indices;
|
|
int const *scatter_D_indices;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Params(): swizzle_log_tile(0), semaphore(0), gemm_k_size(0) { }
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Params(
|
|
cutlass::gemm::GemmCoord const & problem_size,
|
|
cutlass::gemm::GemmCoord const & grid_tiled_shape,
|
|
typename Mma::IteratorA::TensorRef ref_A,
|
|
typename Mma::IteratorB::TensorRef ref_B,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_D,
|
|
typename OutputOp::Params output_op = typename OutputOp::Params(),
|
|
int *workspace = nullptr,
|
|
int const *gather_A_indices = nullptr,
|
|
int const *gather_B_indices = nullptr,
|
|
int const *scatter_D_indices = nullptr
|
|
):
|
|
problem_size(problem_size),
|
|
grid_tiled_shape(grid_tiled_shape),
|
|
swizzle_log_tile(ThreadblockSwizzle().get_log_tile(grid_tiled_shape)),
|
|
params_A(ref_A.layout()),
|
|
ref_A(ref_A),
|
|
params_B(ref_B.layout()),
|
|
ref_B(ref_B),
|
|
params_C(ref_C.layout()),
|
|
ref_C(ref_C),
|
|
params_D(ref_D.layout()),
|
|
ref_D(ref_D),
|
|
output_op(output_op),
|
|
gather_A_indices(gather_A_indices),
|
|
gather_B_indices(gather_B_indices),
|
|
scatter_D_indices(scatter_D_indices) {
|
|
|
|
int total_gemm_k_iterations = (problem_size.k() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
|
int gemm_k_iterations = (total_gemm_k_iterations + grid_tiled_shape.k() - 1) / grid_tiled_shape.k();
|
|
|
|
gemm_k_size = gemm_k_iterations * Mma::Shape::kK;
|
|
|
|
semaphore = workspace;
|
|
}
|
|
};
|
|
|
|
/// Shared memory storage structure
|
|
union SharedStorage {
|
|
typename Mma::SharedStorage main_loop;
|
|
typename Epilogue::SharedStorage epilogue;
|
|
};
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
|
|
CUTLASS_HOST_DEVICE
|
|
Gemm() { }
|
|
|
|
/// Determines whether kernel satisfies alignment
|
|
CUTLASS_HOST_DEVICE
|
|
static Status can_implement(
|
|
cutlass::gemm::GemmCoord const & problem_size,
|
|
typename Mma::IteratorA::TensorRef ref_A,
|
|
typename Mma::IteratorB::TensorRef ref_B,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_C,
|
|
typename Epilogue::OutputTileIterator::TensorRef ref_D) {
|
|
|
|
static int const kAlignmentA = (platform::is_same<typename Mma::IteratorA::Layout,
|
|
layout::ColumnMajorInterleaved<32>>::value)
|
|
? 32
|
|
: (platform::is_same<typename Mma::IteratorA::Layout,
|
|
layout::ColumnMajorInterleaved<64>>::value)
|
|
? 64
|
|
: Mma::IteratorA::AccessType::kElements;
|
|
static int const kAlignmentB = (platform::is_same<typename Mma::IteratorB::Layout,
|
|
layout::RowMajorInterleaved<32>>::value)
|
|
? 32
|
|
: (platform::is_same<typename Mma::IteratorB::Layout,
|
|
layout::RowMajorInterleaved<64>>::value)
|
|
? 64
|
|
: Mma::IteratorB::AccessType::kElements;
|
|
static int const kAlignmentC = (platform::is_same<typename Epilogue::OutputTileIterator::Layout,
|
|
layout::ColumnMajorInterleaved<32>>::value)
|
|
? 32
|
|
: (platform::is_same<typename Epilogue::OutputTileIterator::Layout,
|
|
layout::ColumnMajorInterleaved<64>>::value)
|
|
? 64
|
|
: Epilogue::OutputTileIterator::kElementsPerAccess;
|
|
|
|
if (!TensorRef_aligned(ref_A, kAlignmentA)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_B, kAlignmentB)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_C, kAlignmentC)) {
|
|
return Status::kErrorMisalignedOperand;
|
|
}
|
|
|
|
if (!TensorRef_aligned(ref_D, 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.swizzle_log_tile);
|
|
|
|
// 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_A{
|
|
threadblock_tile_offset.m() * Mma::Shape::kM,
|
|
threadblock_tile_offset.k() * params.gemm_k_size,
|
|
};
|
|
|
|
cutlass::MatrixCoord tb_offset_B{
|
|
threadblock_tile_offset.k() * params.gemm_k_size,
|
|
threadblock_tile_offset.n() * Mma::Shape::kN
|
|
};
|
|
|
|
// Problem size is a function of threadblock index in the K dimension
|
|
int problem_size_k = min(
|
|
params.problem_size.k(),
|
|
(threadblock_tile_offset.k() + 1) * params.gemm_k_size);
|
|
|
|
// Compute threadblock-scoped matrix multiply-add
|
|
int gemm_k_iterations = (problem_size_k - tb_offset_A.column() + Mma::Shape::kK - 1) / Mma::Shape::kK;
|
|
|
|
// Compute position within threadblock
|
|
int thread_idx = threadIdx.x;
|
|
|
|
// Construct iterators to A and B operands
|
|
typename Mma::IteratorA iterator_A(
|
|
params.params_A,
|
|
params.ref_A.data(),
|
|
{params.problem_size.m(), problem_size_k},
|
|
thread_idx,
|
|
tb_offset_A,
|
|
params.gather_A_indices);
|
|
|
|
typename Mma::IteratorB iterator_B(
|
|
params.params_B,
|
|
params.ref_B.data(),
|
|
{problem_size_k, params.problem_size.n()},
|
|
thread_idx,
|
|
tb_offset_B,
|
|
params.gather_B_indices);
|
|
|
|
// Broadcast the warp_id computed by lane 0 to ensure dependent code
|
|
// is compiled as warp-uniform.
|
|
int warp_idx = canonical_warp_idx();
|
|
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();
|
|
|
|
if (!kSplitKSerial || gemm_k_iterations > 0) {
|
|
// Compute threadblock-scoped matrix multiply-add
|
|
mma(gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators);
|
|
}
|
|
|
|
//
|
|
// Epilogue
|
|
//
|
|
|
|
OutputOp output_op(params.output_op);
|
|
|
|
//
|
|
// Masked tile iterators constructed from members
|
|
//
|
|
|
|
threadblock_tile_offset =
|
|
threadblock_swizzle.get_tile_offset(params.swizzle_log_tile);
|
|
|
|
//assume identity swizzle
|
|
MatrixCoord threadblock_offset(
|
|
threadblock_tile_offset.m() * Mma::Shape::kM,
|
|
threadblock_tile_offset.n() * Mma::Shape::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.set_k_partition(threadblock_tile_offset.k(), params.grid_tiled_shape.k());
|
|
}
|
|
|
|
// Tile iterator loading from source tensor.
|
|
typename Epilogue::OutputTileIterator iterator_C(
|
|
params.params_C,
|
|
params.ref_C.data(),
|
|
params.problem_size.mn(),
|
|
thread_idx,
|
|
threadblock_offset,
|
|
params.scatter_D_indices
|
|
);
|
|
|
|
// Tile iterator writing to destination tensor.
|
|
typename Epilogue::OutputTileIterator iterator_D(
|
|
params.params_D,
|
|
params.ref_D.data(),
|
|
params.problem_size.mn(),
|
|
thread_idx,
|
|
threadblock_offset,
|
|
params.scatter_D_indices
|
|
);
|
|
|
|
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_C = iterator_D;
|
|
}
|
|
|
|
semaphore.wait(threadblock_tile_offset.k());
|
|
|
|
}
|
|
|
|
// Execute the epilogue operator to update the destination tensor.
|
|
epilogue(output_op, iterator_D, accumulators, iterator_C);
|
|
|
|
//
|
|
// 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;
|
|
}
|
|
|
|
semaphore.release(lock);
|
|
}
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace kernel
|
|
} // namespace gemm
|
|
} // namespace cutlass
|
|
|