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predicated_tile_iterator.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2024 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 Unit tests for thread-level GEMM
*/
#include <fstream>
#include "../../common/cutlass_unit_test.h"
#include "cutlass/aligned_buffer.h"
#include "cutlass/half.h"
#include "cutlass/epilogue/threadblock/predicated_tile_iterator.h"
#include "cutlass/epilogue/threadblock/default_thread_map_tensor_op.h"
#include "cutlass/util/tensor_view_io.h"
#include "cutlass/util/host_tensor.h"
#include "cutlass/util/reference/host/tensor_fill.h"
/////////////////////////////////////////////////////////////////////////////////////////////////
namespace test {
namespace epilogue {
namespace threadblock {
/////////////////////////////////////////////////////////////////////////////////////////////////
template <typename TileIterator>
__global__ void kernel_store_iterator(
typename TileIterator::Params params,
typename TileIterator::TensorRef ref,
cutlass::MatrixCoord extent) {
TileIterator iterator(params, ref.data(), extent, threadIdx.x, {0, 0});
typename TileIterator::Fragment fragment;
CUTLASS_PRAGMA_NO_UNROLL
for (int iter = 0; iter < TileIterator::ThreadMap::Count::kTile; ++iter) {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < TileIterator::Fragment::kElements; ++i) {
typename TileIterator::Element tidx(iter + 1);
fragment[i] = tidx;
}
iterator.store(fragment);
++iterator;
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
}
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
template <typename T, typename Layout>
static bool verify_footprint(cutlass::TensorView<T, Layout> view, cutlass::MatrixCoord extent) {
for (int r = 0; r < view.extent().row(); ++r) {
for (int c = 0; c < view.extent().column(); ++c) {
cutlass::MatrixCoord coord{r, c};
bool within = coord < extent;
if (within) {
if (view.at(coord) == T(0)) {
return false;
}
}
else {
if (view.at(coord) != T(0)) {
return false;
}
}
}
}
return true;
}
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(PredicatedTileIterator, tensor_op_64x64x32_64x64x8) {
using Layout = cutlass::layout::RowMajor;
using Element = int;
static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
static int const kThreads = 32;
//
// The following tests were used to develop the OutputTileOptimalThreadMap
// metaprogram. The definitions in the disabled blocks of code in this and
// the following tests are hand-written quantities. They are expected to
// match what is defined in the ThreadMap.
//
#if 1
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
cutlass::epilogue::threadblock::OutputTileShape<64, 8, 1, 1, 1>,
cutlass::epilogue::threadblock::OutputTileShape<1, 8, 1, 1, 8>,
kThreads,
kElementsPerAccess,
cutlass::sizeof_bits<Element>::value
>;
#else
using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
cutlass::layout::PitchLinearShape<64, 64>,
kThreads,
kElementsPerAccess
>;
using Shape = cutlass::epilogue::threadblock::OutputTileShape<
64, // column
8, // row
1, // group
1, // cluster
1 // iterations
>;
using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
4, // row
1, // group
1, // cluster
1 // iterations
>;
using Delta = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
2, // row
1, // group
1, // cluster
1 // iterations
>;
using Count = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
8, // row
1, // group
1, // cluster
8 // iterations
>;
using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
InternalThreadMap,
Shape,
Iterations,
Delta,
Count
>;
#endif
using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
ThreadMap,
Element
>;
//
// Initialize workspace
//
cutlass::MatrixCoord tensor_extent{64, 64};
cutlass::MatrixCoord output_extent{62, 56};
//
// Configure parameters
//
cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
host_tensor.sync_device();
//
// Launch kernel
//
dim3 grid(1,1);
dim3 block(kThreads, 1);
test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
iterator_params, host_tensor.device_ref(), output_extent);
cudaError_t result = cudaDeviceSynchronize();
ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
//
// Verify results
//
host_tensor.sync_host();
bool passed = verify_footprint(host_tensor.host_view(), output_extent);
EXPECT_TRUE(passed);
if (!passed) {
std::ofstream output("tensor_op_64x64x32_64x64x8.csv");
output << host_tensor.host_view();
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(PredicatedTileIterator, tensor_op_128x64x32_64x64x8) {
using Layout = cutlass::layout::RowMajor;
using Element = int;
static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
static int const kThreads = 64;
#if 1
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
cutlass::epilogue::threadblock::OutputTileShape<128, 8, 2, 1, 1>,
cutlass::epilogue::threadblock::OutputTileShape<1, 8, 2, 1, 8>,
kThreads,
kElementsPerAccess,
cutlass::sizeof_bits<Element>::value
>;
#else
using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
cutlass::layout::PitchLinearShape<64, 128>,
kThreads,
kElementsPerAccess
>;
using Shape = cutlass::epilogue::threadblock::OutputTileShape<
64, // column
8, // row
2, // group
1, // cluster
8 // tile
>;
using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
2, // row
2, // group
1, // cluster
1 // iterations
>;
using Delta = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
4, // row
64, // group
1, // cluster
1 // tile
>;
using Count = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
8, // row
1, // group
1, // cluster
8 // iterations
>;
using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
InternalThreadMap,
Shape,
Iterations,
Delta,
Count
>;
#endif
using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
ThreadMap,
Element
>;
//
// Initialize workspace
//
cutlass::MatrixCoord tensor_extent{128, 64};
cutlass::MatrixCoord output_extent{125, 56};
//
// Configure parameters
//
cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
host_tensor.sync_device();
//
// Launch kernel
//
dim3 grid(1,1);
dim3 block(kThreads, 1);
test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
iterator_params, host_tensor.device_ref(), output_extent);
cudaError_t result = cudaDeviceSynchronize();
ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
//
// Verify results
//
host_tensor.sync_host();
bool passed = verify_footprint(host_tensor.host_view(), output_extent);
EXPECT_TRUE(passed);
if (!passed) {
std::ofstream output("tensor_op_128x64x32_64x64x8.csv");
output << host_tensor.host_view();
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(PredicatedTileIterator, tensor_op_128x256x32_64x64x8) {
using Layout = cutlass::layout::RowMajor;
using Element = int;
static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
static int const kThreads = 256;
#if 1
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
cutlass::epilogue::threadblock::OutputTileShape<256, 8, 2, 1, 1>,
cutlass::epilogue::threadblock::OutputTileShape<1, 8, 2, 1, 8>,
kThreads,
kElementsPerAccess,
cutlass::sizeof_bits<Element>::value
>;
#else
using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
cutlass::layout::PitchLinearShape<256, 128>,
kThreads,
kElementsPerAccess
>;
using Shape = cutlass::epilogue::threadblock::OutputTileShape<
256, // column
8, // row
2, // group
1, // cluster
8 // tile
>;
using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
2, // row
2, // group
1, // cluster
1 // iterations
>;
using Delta = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
4, // row
64, // group
1, // cluster
1 // tile
>;
using Count = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
8, // row
1, // group
1, // cluster
8 // iterations
>;
using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
InternalThreadMap,
Shape,
Iterations,
Delta,
Count
>;
#endif
using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
ThreadMap,
Element
>;
//
// Initialize workspace
//
cutlass::MatrixCoord tensor_extent{128, 256};
cutlass::MatrixCoord output_extent{123, 252};
//
// Configure parameters
//
cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
host_tensor.sync_device();
//
// Launch kernel
//
dim3 grid(1,1);
dim3 block(kThreads, 1);
test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
iterator_params, host_tensor.device_ref(), output_extent);
cudaError_t result = cudaDeviceSynchronize();
ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
//
// Verify results
//
host_tensor.sync_host();
bool passed = verify_footprint(host_tensor.host_view(), output_extent);
EXPECT_TRUE(passed);
if (!passed) {
std::ofstream output("tensor_op_128x256x32_64x64x8.csv");
output << host_tensor.host_view();
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(PredicatedTileIterator, volta_tensor_op_64x64x32_64x64x4) {
using Layout = cutlass::layout::RowMajor;
using Element = int;
static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
static int const kThreads = 32;
#if 1
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
cutlass::epilogue::threadblock::OutputTileShape<64, 2, 4, 1, 1>,
cutlass::epilogue::threadblock::OutputTileShape<1, 4, 2, 1, 8>,
kThreads,
kElementsPerAccess,
cutlass::sizeof_bits<Element>::value
>;
#else
using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
cutlass::layout::PitchLinearShape<64, 8>,
kThreads,
kElementsPerAccess
>;
using Shape = cutlass::epilogue::threadblock::OutputTileShape<
64, // column
2, // row
4, // group
1, // cluster
8 // iterations
>;
using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
1, // row
4, // group
1, // cluster
1 // iterations
>;
using Delta = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
1, // row
8, // group
1, // cluster
1 // iterations
>;
using Count = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
4, // row
2, // group
1, // cluster
8 // iterations
>;
using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
InternalThreadMap,
Shape,
Iterations,
Delta,
Count
>;
#endif
using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
ThreadMap,
Element
>;
//
// Initialize workspace
//
cutlass::MatrixCoord tensor_extent{64, 64};
cutlass::MatrixCoord output_extent{62, 56};
//
// Configure parameters
//
cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
host_tensor.sync_device();
//
// Launch kernel
//
dim3 grid(1,1);
dim3 block(kThreads, 1);
test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
iterator_params, host_tensor.device_ref(), output_extent);
cudaError_t result = cudaDeviceSynchronize();
ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
//
// Verify results
//
host_tensor.sync_host();
bool passed = verify_footprint(host_tensor.host_view(), output_extent);
EXPECT_TRUE(passed);
if (!passed) {
std::ofstream output("volta_tensor_op_64x64x32_64x64x4.csv");
output << host_tensor.host_view();
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////
TEST(PredicatedTileIterator, volta_tensor_op_64x128x32_32x64x4) {
using Layout = cutlass::layout::RowMajor;
using Element = int;
static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
static int const kThreads = 128;
#if 1
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
cutlass::epilogue::threadblock::OutputTileShape<128, 2, 4, 1, 1>,
cutlass::epilogue::threadblock::OutputTileShape<1, 4, 2, 1, 8>,
kThreads,
kElementsPerAccess,
cutlass::sizeof_bits<Element>::value
>;
#else
using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
cutlass::layout::PitchLinearShape<128, 8>,
kThreads,
kElementsPerAccess
>;
using Shape = cutlass::epilogue::threadblock::OutputTileShape<
128, // column
2, // row
2, // group
2, // cluster
8 // iterations
>;
using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
1, // row
1, // group
2, // cluster
1 // iterations
>;
using Delta = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
1, // row
8, // group
32, // cluster
1 // iterations
>;
using Count = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
4, // row
4, // group
1, // cluster
8 // iterations
>;
using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
InternalThreadMap,
Shape,
Iterations,
Delta,
Count
>;
#endif
using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
ThreadMap,
Element
>;
//
// Initialize workspace
//
cutlass::MatrixCoord tensor_extent{64, 128};
cutlass::MatrixCoord output_extent{57, 124};
//
// Configure parameters
//
cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
host_tensor.sync_device();
//
// Launch kernel
//
dim3 grid(1,1);
dim3 block(kThreads, 1);
test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
iterator_params, host_tensor.device_ref(), output_extent);
cudaError_t result = cudaDeviceSynchronize();
ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
//
// Verify results
//
host_tensor.sync_host();
bool passed = verify_footprint(host_tensor.host_view(), output_extent);
EXPECT_TRUE(passed);
if (!passed) {
std::ofstream output("volta_tensor_op_64x128x32_32x64x4.csv");
output << host_tensor.host_view();
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(PredicatedTileIterator, volta_tensor_op_128x256x32_64x64x4) {
using Layout = cutlass::layout::RowMajor;
using Element = int;
static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
static int const kThreads = 256;
#if 1
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
cutlass::epilogue::threadblock::OutputTileShape<256, 2, 4, 2, 1>,
cutlass::epilogue::threadblock::OutputTileShape<1, 4, 2, 1, 8>,
kThreads,
kElementsPerAccess,
cutlass::sizeof_bits<Element>::value
>;
#else
using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
cutlass::layout::PitchLinearShape<256, 16>,
kThreads,
kElementsPerAccess
>;
using Shape = cutlass::epilogue::threadblock::OutputTileShape<
256, // column
2, // row
4, // group
2, // cluster
8 // iterations
>;
using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
1, // row
2, // group
2, // cluster
1 // iterations
>;
using Delta = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
1, // row
16, // group
64, // cluster
1 // iterations
>;
using Count = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
4, // row
2, // group
1, // cluster
8 // iterations
>;
using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
InternalThreadMap,
Shape,
Iterations,
Delta,
Count
>;
#endif
using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
ThreadMap,
Element
>;
//
// Initialize workspace
//
cutlass::MatrixCoord tensor_extent{128, 256};
cutlass::MatrixCoord output_extent{128, 256};
//
// Configure parameters
//
cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
host_tensor.sync_device();
//
// Launch kernel
//
dim3 grid(1,1);
dim3 block(kThreads, 1);
test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
iterator_params, host_tensor.device_ref(), output_extent);
cudaError_t result = cudaDeviceSynchronize();
ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
//
// Verify results
//
host_tensor.sync_host();
bool passed = verify_footprint(host_tensor.host_view(), output_extent);
EXPECT_TRUE(passed);
if (!passed || true) {
std::ofstream output("volta_tensor_op_128x256x32_64x64x4.csv");
output << host_tensor.host_view();
}
}
TEST(PredicatedTileIterator, volta_tensor_op_256x128x32_64x64x4) {
using Layout = cutlass::layout::RowMajor;
using Element = int;
static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
static int const kThreads = 256;
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
cutlass::epilogue::threadblock::OutputTileShape<128, 2, 4, 4, 1>,
cutlass::epilogue::threadblock::OutputTileShape<1, 4, 2, 1, 8>,
kThreads,
kElementsPerAccess,
cutlass::sizeof_bits<Element>::value
>;
using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
ThreadMap,
Element
>;
//
// Initialize workspace
//
cutlass::MatrixCoord tensor_extent{ 256, 128 };
cutlass::MatrixCoord output_extent{ 256, 128 };
//
// Configure parameters
//
cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
host_tensor.sync_device();
//
// Launch kernel
//
dim3 grid(1, 1);
dim3 block(kThreads, 1);
test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator> <<< grid, block >>>(
iterator_params, host_tensor.device_ref(), output_extent);
cudaError_t result = cudaDeviceSynchronize();
ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
//
// Verify results
//
host_tensor.sync_host();
bool passed = verify_footprint(host_tensor.host_view(), output_extent);
EXPECT_TRUE(passed);
if (!passed || true) {
std::ofstream output("volta_tensor_op_256x128x32_64x64x4.csv");
output << host_tensor.host_view();
}
}
/////////////////////////////////////////////////////////////////////////////////////////////////
TEST(PredicatedTileIterator, simt_32x64x8_32x64x1) {
using Layout = cutlass::layout::RowMajor;
using Element = int;
static int const kElementsPerAccess = 32 / cutlass::sizeof_bits<Element>::value;
static int const kThreads = 32;
#if 1
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
cutlass::epilogue::threadblock::OutputTileShape<64, 1, 4, 1, 1>,
cutlass::epilogue::threadblock::OutputTileShape<1, 4, 2, 1, 8>,
kThreads,
kElementsPerAccess,
cutlass::sizeof_bits<Element>::value
>;
#else
using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
cutlass::layout::PitchLinearShape<64, 4>,
kThreads,
kElementsPerAccess
>;
using Shape = cutlass::epilogue::threadblock::OutputTileShape<
64, // column
1, // row
4, // group
1, // cluster
1 // iterations
>;
using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
2, // column
1, // row
4, // group
1, // cluster
1 // iterations
>;
using Delta = cutlass::epilogue::threadblock::OutputTileShape<
32, // column
1, // row
4, // group
16, // cluster
1 // iterations
>;
using Count = cutlass::epilogue::threadblock::OutputTileShape<
1, // column
4, // row
2, // group
1, // cluster
8 // iterations
>;
using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
InternalThreadMap,
Shape,
Iterations,
Delta,
Count
>;
#endif
using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
ThreadMap,
Element
>;
//
// Initialize workspace
//
cutlass::MatrixCoord tensor_extent{32, 64};
cutlass::MatrixCoord output_extent{27, 63};
//
// Configure parameters
//
cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
host_tensor.sync_device();
//
// Launch kernel
//
dim3 grid(1,1);
dim3 block(kThreads, 1);
test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
iterator_params, host_tensor.device_ref(), output_extent);
cudaError_t result = cudaDeviceSynchronize();
ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
//
// Verify results
//
host_tensor.sync_host();
bool passed = verify_footprint(host_tensor.host_view(), output_extent);
EXPECT_TRUE(passed);