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cutlass/include/cutlass/gemm/kernel/ell_gemm.h
ANIKET SHIVAM 66d9cddc83
New updates for 2.11 (#775) 
* New updates.

* Minor profiler updates

Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
2023-01-20 16:32:57 -05:00

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/***************************************************************************************************
* 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 Block-Ell sparse gemm kernel.
*/
#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"
#include "cutlass/transform/threadblock/ell_iterator.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.
bool IsASparse ///! If true, A is sparse matrix
>
struct EllGemm {
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_iterations;
int gemm_k_size;
const int* ell_idx;
int ell_ncol;
int ell_blocksize;
int ell_base_idx;
//
// Methods
//
CUTLASS_HOST_DEVICE
Params(): swizzle_log_tile(0), semaphore(0), gemm_k_iterations(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,
const int* ell_idx,
int ell_ncol,
int ell_blocksize,
int ell_base_idx,
typename OutputOp::Params output_op = typename OutputOp::Params(),
int *workspace = 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),
ell_idx(ell_idx),
ell_ncol(ell_ncol),
ell_blocksize(ell_blocksize),
ell_base_idx(ell_base_idx)
{
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
struct SharedStorage {
union{
typename Mma::SharedStorage main_loop;
typename Epilogue::SharedStorage epilogue;
};
typename cutlass::transform::threadblock::ell::SharedStorage ell;
};
//
// Methods
//
CUTLASS_HOST_DEVICE
EllGemm() { }
/// Determines whether kernel satisfies alignment
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 = 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;
}
if ((problem_size.m() % kAlignmentA) || (problem_size.k() % kAlignmentA) ||
(problem_size.n() % kAlignmentB) || (problem_size.k() % kAlignmentB) ||
(problem_size.m() % kAlignmentC) || (problem_size.n() % kAlignmentC)) {
return Status::kErrorMisalignedOperand;
}
return Status::kSuccess;
}
/// Executes one GEMM
CUTLASS_DEVICE
void operator()(Params const &params, 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;
}
int tile_in_ell_block = (params.ell_blocksize + Mma::Shape::kM - 1 ) / Mma::Shape::kM;
int ell_block_offset_m = threadblock_tile_offset.m() / tile_in_ell_block;
int tile_offset_m = threadblock_tile_offset.m() % tile_in_ell_block;
// Compute position within threadblock
int thread_idx = threadIdx.x;
// 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;
typename Mma::FragmentC accumulators;
accumulators.clear();
// skip computation if matrix is 0
if (params.ell_ncol > 0) {
// Compute initial location in logical coordinates
cutlass::MatrixCoord tb_offset_A{
ell_block_offset_m * params.ell_blocksize
+ 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
};
int ell_idx_start =
(threadblock_tile_offset.m() / tile_in_ell_block) *
(params.ell_ncol / params.ell_blocksize);
const int* ell_idx_ptr = &(params.ell_idx[ell_idx_start]);
// 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);
problem_size_k = min(problem_size_k, params.ell_ncol);
// Compute threadblock-scoped matrix multiply-add
int gemm_k_iterations =
(problem_size_k - tb_offset_A.column() + Mma::Shape::kK - 1) / Mma::Shape::kK;
// 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);
typename Mma::IteratorB iterator_B(
params.params_B,
params.ref_B.data(),
{problem_size_k, params.problem_size.n()},
thread_idx,
tb_offset_B);
// Define coef for ELL index depending on LayoutB
int ell_stride = iterator_B.get_stride();
typename cutlass::transform::threadblock::ell::Iterator ell_iterator(
shared_storage.ell,
ell_idx_ptr,
params.ell_blocksize,
params.ell_base_idx,
Mma::Shape::kK,
problem_size_k,
ell_stride,
thread_idx
);
//
// Main loop
//
// Construct thread-scoped matrix multiply
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
if (!kSplitKSerial || gemm_k_iterations > 0) {
// check if index computations can be skipped
static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
constexpr bool is_double = (sizeof(Mma::IteratorA::Element) == 8);
constexpr bool is_multiple_alignment =
(kAlignmentA > 1) && (kAlignmentB > 1) && (kAlignmentC > 1);
const bool is_specialized_blocksize =
((params.ell_blocksize) & (params.ell_blocksize-1)) == 0
&& params.ell_blocksize >= Mma::Shape::kK;
// Compute threadblock-scoped matrix multiply-add
if ((is_double || is_multiple_alignment) && is_specialized_blocksize) {
mma.operator()<true, true>(
gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators, ell_iterator);
}
else {
mma.operator()<true, false>(
gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators, ell_iterator);
}
}
} // if (params.ell_ncols > 0)
//
// 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);
ell_block_offset_m = threadblock_tile_offset.m() / tile_in_ell_block;
tile_offset_m = threadblock_tile_offset.m() % tile_in_ell_block;
//assume identity swizzle
MatrixCoord threadblock_offset(
ell_block_offset_m * params.ell_blocksize
+ tile_offset_m * Mma::Shape::kM,
threadblock_tile_offset.n() * Mma::Shape::kN
);
//avoid out of bounds
MatrixCoord threadblock_extent(
min(params.problem_size.m(),
ell_block_offset_m * params.ell_blocksize
+ min((tile_offset_m + 1) * Mma::Shape::kM, params.ell_blocksize)),
min(params.problem_size.n(),
(threadblock_tile_offset.n()+1) * 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(),
threadblock_extent,
thread_idx,
threadblock_offset
);
// Tile iterator writing to destination tensor.
typename Epilogue::OutputTileIterator iterator_D(
params.params_D,
params.ref_D.data(),
threadblock_extent,
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_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);
}
}
};
// B is Sparse
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 EllGemm<Mma_, Epilogue_, ThreadblockSwizzle_, SplitKSerial, false> {
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_iterations;
int gemm_k_size;
const int* ell_idx;
int ell_ncol;
int ell_blocksize;
int ell_base_idx;
//
// Methods
//
CUTLASS_HOST_DEVICE
Params(): swizzle_log_tile(0), semaphore(0), gemm_k_iterations(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,
const int* ell_idx,
int ell_ncol,
int ell_blocksize,
int ell_base_idx,
typename OutputOp::Params output_op = typename OutputOp::Params(),
int *workspace = 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),
ell_idx(ell_idx),
ell_ncol(ell_ncol),
ell_blocksize(ell_blocksize),
ell_base_idx(ell_base_idx)
{
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
struct SharedStorage {
union{
typename Mma::SharedStorage main_loop;
typename Epilogue::SharedStorage epilogue;
};
typename cutlass::transform::threadblock::ell::SharedStorage ell;
};
//
// Methods
//
CUTLASS_HOST_DEVICE
EllGemm() { }
/// Determines whether kernel satisfies alignment
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 = 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;
}
if ((problem_size.m() % kAlignmentA) || (problem_size.k() % kAlignmentA) ||
(problem_size.n() % kAlignmentB) || (problem_size.k() % kAlignmentB) ||
(problem_size.m() % kAlignmentC) || (problem_size.n() % kAlignmentC)) {
return Status::kErrorMisalignedOperand;
}
return Status::kSuccess;
}
/// Executes one GEMM
CUTLASS_DEVICE
void operator()(Params const &params, 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;
}
int tile_in_ell_block = (params.ell_blocksize + Mma::Shape::kN - 1 ) / Mma::Shape::kN;
int ell_block_offset_n = threadblock_tile_offset.n() / tile_in_ell_block;
int tile_offset_n = threadblock_tile_offset.n() % tile_in_ell_block;
// Compute position within threadblock
int thread_idx = threadIdx.x;
// 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;
typename Mma::FragmentC accumulators;
accumulators.clear();
// skip computation if matrix is 0
if (params.ell_ncol > 0) {
// 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,
ell_block_offset_n * params.ell_blocksize
+ tile_offset_n * Mma::Shape::kN,
};
int ell_idx_start =
(threadblock_tile_offset.n() / tile_in_ell_block) *
(params.ell_ncol / params.ell_blocksize);
const int* ell_idx_ptr = &(params.ell_idx[ell_idx_start]);
// 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);
problem_size_k = min(problem_size_k, params.ell_ncol);
// Compute threadblock-scoped matrix multiply-add
int gemm_k_iterations =
(problem_size_k - tb_offset_A.column() + Mma::Shape::kK - 1) / Mma::Shape::kK;
// 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);
typename Mma::IteratorB iterator_B(
params.params_B,
params.ref_B.data(),
{problem_size_k, params.problem_size.n()},
thread_idx,
tb_offset_B);
// Define coef for ELL index depending on LayoutA
int ell_stride = iterator_A.get_stride();
typename cutlass::transform::threadblock::ell::Iterator ell_iterator(
shared_storage.ell,
ell_idx_ptr,
params.ell_blocksize,
params.ell_base_idx,
Mma::Shape::kK,
problem_size_k,
ell_stride,
thread_idx
);
//
// Main loop
//
// Construct thread-scoped matrix multiply
Mma mma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
if (!kSplitKSerial || gemm_k_iterations > 0) {
// check if index computations can be skipped
static int const kAlignmentA = Mma::IteratorA::AccessType::kElements;
static int const kAlignmentB = Mma::IteratorB::AccessType::kElements;
static int const kAlignmentC = Epilogue::OutputTileIterator::kElementsPerAccess;
constexpr bool is_double = (sizeof(Mma::IteratorA::Element) == 8);
constexpr bool is_multiple_alignment =
(kAlignmentA > 1) && (kAlignmentB > 1) && (kAlignmentC > 1);
const bool is_specialized_blocksize =
((params.ell_blocksize) & (params.ell_blocksize-1)) == 0
&& params.ell_blocksize >= Mma::Shape::kK;
// Compute threadblock-scoped matrix multiply-add
if ((is_double || is_multiple_alignment) && is_specialized_blocksize) {
mma.operator()<false, true>(
gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators, ell_iterator);
}
else {
mma.operator()<false, false>(
gemm_k_iterations, accumulators, iterator_A, iterator_B, accumulators, ell_iterator);
}
}
} // if (params.ell_ncols > 0)
//
// 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);
ell_block_offset_n = threadblock_tile_offset.n() / tile_in_ell_block;
tile_offset_n = threadblock_tile_offset.n() % tile_in_ell_block;
//assume identity swizzle
MatrixCoord threadblock_offset(
threadblock_tile_offset.m() * Mma::Shape::kM,
ell_block_offset_n * params.ell_blocksize
+ tile_offset_n * Mma::Shape::kN
);
//avoid out of bounds
MatrixCoord threadblock_extent(
min(params.problem_size.m(),
(threadblock_tile_offset.m()+1) * Mma::Shape::kM),
min(params.problem_size.n(),
ell_block_offset_n * params.ell_blocksize
+ min((tile_offset_n + 1) * Mma::Shape::kN, params.ell_blocksize))
);
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(),
threadblock_extent,
thread_idx,
threadblock_offset
);
// Tile iterator writing to destination tensor.
typename Epilogue::OutputTileIterator iterator_D(
params.params_D,
params.ref_D.data(),
threadblock_extent,
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_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
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