1120 lines
28 KiB
Plaintext
1120 lines
28 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Unit tests for thread-level GEMM
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*/
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#include <fstream>
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/aligned_buffer.h"
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#include "cutlass/half.h"
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#include "cutlass/epilogue/threadblock/predicated_tile_iterator.h"
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#include "cutlass/epilogue/threadblock/default_thread_map_tensor_op.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace test {
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namespace epilogue {
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namespace threadblock {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename TileIterator>
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__global__ void kernel_store_iterator(
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typename TileIterator::Params params,
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typename TileIterator::TensorRef ref,
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cutlass::MatrixCoord extent) {
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TileIterator iterator(params, ref.data(), extent, threadIdx.x, {0, 0});
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typename TileIterator::Fragment fragment;
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CUTLASS_PRAGMA_NO_UNROLL
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for (int iter = 0; iter < TileIterator::ThreadMap::Count::kTile; ++iter) {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < TileIterator::Fragment::kElements; ++i) {
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typename TileIterator::Element tidx(iter + 1);
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fragment[i] = tidx;
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}
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iterator.store(fragment);
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++iterator;
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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}
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename T, typename Layout>
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static bool verify_footprint(cutlass::TensorView<T, Layout> view, cutlass::MatrixCoord extent) {
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for (int r = 0; r < view.extent().row(); ++r) {
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for (int c = 0; c < view.extent().column(); ++c) {
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cutlass::MatrixCoord coord{r, c};
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bool within = coord < extent;
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if (within) {
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if (view.at(coord) == T(0)) {
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return false;
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}
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}
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else {
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if (view.at(coord) != T(0)) {
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return false;
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}
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}
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}
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}
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return true;
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(PredicatedTileIterator, tensor_op_64x64x32_64x64x8) {
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using Layout = cutlass::layout::RowMajor;
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using Element = int;
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static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
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static int const kThreads = 32;
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//
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// The following tests were used to develop the OutputTileOptimalThreadMap
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// metaprogram. The definitions in the disabled blocks of code in this and
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// the following tests are hand-written quantities. They are expected to
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// match what is defined in the ThreadMap.
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//
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#if 1
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using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
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cutlass::epilogue::threadblock::OutputTileShape<64, 8, 1, 1, 1>,
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cutlass::epilogue::threadblock::OutputTileShape<1, 8, 1, 1, 8>,
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kThreads,
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kElementsPerAccess,
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cutlass::sizeof_bits<Element>::value
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>;
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#else
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using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
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cutlass::layout::PitchLinearShape<64, 64>,
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kThreads,
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kElementsPerAccess
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>;
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using Shape = cutlass::epilogue::threadblock::OutputTileShape<
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64, // column
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8, // row
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1, // group
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1, // cluster
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1 // iterations
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>;
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using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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4, // row
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1, // group
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1, // cluster
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1 // iterations
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>;
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using Delta = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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2, // row
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1, // group
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1, // cluster
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1 // iterations
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>;
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using Count = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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8, // row
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1, // group
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1, // cluster
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8 // iterations
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>;
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using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
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InternalThreadMap,
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Shape,
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Iterations,
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Delta,
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Count
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>;
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#endif
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using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
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ThreadMap,
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Element
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>;
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//
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// Initialize workspace
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//
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cutlass::MatrixCoord tensor_extent{64, 64};
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cutlass::MatrixCoord output_extent{62, 56};
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//
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// Configure parameters
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//
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cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
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typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
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host_tensor.sync_device();
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//
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// Launch kernel
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//
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dim3 grid(1,1);
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dim3 block(kThreads, 1);
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test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
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iterator_params, host_tensor.device_ref(), output_extent);
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cudaError_t result = cudaDeviceSynchronize();
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ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
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//
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// Verify results
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//
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host_tensor.sync_host();
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bool passed = verify_footprint(host_tensor.host_view(), output_extent);
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EXPECT_TRUE(passed);
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if (!passed) {
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std::ofstream output("tensor_op_64x64x32_64x64x8.csv");
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output << host_tensor.host_view();
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(PredicatedTileIterator, tensor_op_128x64x32_64x64x8) {
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using Layout = cutlass::layout::RowMajor;
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using Element = int;
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static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
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static int const kThreads = 64;
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#if 1
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using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
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cutlass::epilogue::threadblock::OutputTileShape<128, 8, 2, 1, 1>,
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cutlass::epilogue::threadblock::OutputTileShape<1, 8, 2, 1, 8>,
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kThreads,
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kElementsPerAccess,
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cutlass::sizeof_bits<Element>::value
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>;
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#else
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using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
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cutlass::layout::PitchLinearShape<64, 128>,
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kThreads,
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kElementsPerAccess
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>;
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using Shape = cutlass::epilogue::threadblock::OutputTileShape<
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64, // column
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8, // row
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2, // group
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1, // cluster
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8 // tile
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>;
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using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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2, // row
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2, // group
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1, // cluster
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1 // iterations
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>;
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using Delta = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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4, // row
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64, // group
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1, // cluster
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1 // tile
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>;
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using Count = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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8, // row
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1, // group
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1, // cluster
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8 // iterations
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>;
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using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
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InternalThreadMap,
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Shape,
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Iterations,
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Delta,
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Count
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>;
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#endif
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using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
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ThreadMap,
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Element
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>;
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//
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// Initialize workspace
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//
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cutlass::MatrixCoord tensor_extent{128, 64};
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cutlass::MatrixCoord output_extent{125, 56};
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//
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// Configure parameters
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//
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cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
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typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
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host_tensor.sync_device();
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//
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// Launch kernel
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//
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dim3 grid(1,1);
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dim3 block(kThreads, 1);
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test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
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iterator_params, host_tensor.device_ref(), output_extent);
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cudaError_t result = cudaDeviceSynchronize();
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ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
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//
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// Verify results
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//
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host_tensor.sync_host();
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bool passed = verify_footprint(host_tensor.host_view(), output_extent);
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EXPECT_TRUE(passed);
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if (!passed) {
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std::ofstream output("tensor_op_128x64x32_64x64x8.csv");
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output << host_tensor.host_view();
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(PredicatedTileIterator, tensor_op_128x256x32_64x64x8) {
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using Layout = cutlass::layout::RowMajor;
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using Element = int;
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static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
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static int const kThreads = 256;
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#if 1
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using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
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cutlass::epilogue::threadblock::OutputTileShape<256, 8, 2, 1, 1>,
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cutlass::epilogue::threadblock::OutputTileShape<1, 8, 2, 1, 8>,
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kThreads,
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kElementsPerAccess,
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cutlass::sizeof_bits<Element>::value
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>;
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#else
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using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
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cutlass::layout::PitchLinearShape<256, 128>,
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kThreads,
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kElementsPerAccess
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>;
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using Shape = cutlass::epilogue::threadblock::OutputTileShape<
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256, // column
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8, // row
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2, // group
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1, // cluster
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8 // tile
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>;
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using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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2, // row
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2, // group
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1, // cluster
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1 // iterations
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>;
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using Delta = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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4, // row
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64, // group
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1, // cluster
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1 // tile
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>;
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using Count = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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8, // row
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1, // group
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1, // cluster
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8 // iterations
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>;
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using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
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InternalThreadMap,
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Shape,
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Iterations,
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Delta,
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Count
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>;
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#endif
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using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
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ThreadMap,
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Element
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>;
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//
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// Initialize workspace
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//
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cutlass::MatrixCoord tensor_extent{128, 256};
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cutlass::MatrixCoord output_extent{123, 252};
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//
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// Configure parameters
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//
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cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
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typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
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host_tensor.sync_device();
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//
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// Launch kernel
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//
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dim3 grid(1,1);
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dim3 block(kThreads, 1);
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test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
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iterator_params, host_tensor.device_ref(), output_extent);
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cudaError_t result = cudaDeviceSynchronize();
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ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
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//
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// Verify results
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//
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host_tensor.sync_host();
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bool passed = verify_footprint(host_tensor.host_view(), output_extent);
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EXPECT_TRUE(passed);
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if (!passed) {
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std::ofstream output("tensor_op_128x256x32_64x64x8.csv");
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output << host_tensor.host_view();
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(PredicatedTileIterator, volta_tensor_op_64x64x32_64x64x4) {
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using Layout = cutlass::layout::RowMajor;
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using Element = int;
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static int const kElementsPerAccess = 128 / cutlass::sizeof_bits<Element>::value;
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static int const kThreads = 32;
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#if 1
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using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
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cutlass::epilogue::threadblock::OutputTileShape<64, 2, 4, 1, 1>,
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cutlass::epilogue::threadblock::OutputTileShape<1, 4, 2, 1, 8>,
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kThreads,
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kElementsPerAccess,
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cutlass::sizeof_bits<Element>::value
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>;
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#else
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using InternalThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<
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cutlass::layout::PitchLinearShape<64, 8>,
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kThreads,
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kElementsPerAccess
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>;
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using Shape = cutlass::epilogue::threadblock::OutputTileShape<
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64, // column
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2, // row
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4, // group
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1, // cluster
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8 // iterations
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>;
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using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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1, // row
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4, // group
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1, // cluster
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1 // iterations
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>;
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using Delta = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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1, // row
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8, // group
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1, // cluster
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1 // iterations
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>;
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using Count = cutlass::epilogue::threadblock::OutputTileShape<
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1, // column
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4, // row
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2, // group
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1, // cluster
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8 // iterations
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>;
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using ThreadMap = cutlass::epilogue::threadblock::OutputTileThreadMap<
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InternalThreadMap,
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Shape,
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Iterations,
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Delta,
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Count
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>;
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#endif
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using PredicatedTileIterator = cutlass::epilogue::threadblock::PredicatedTileIterator<
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ThreadMap,
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Element
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>;
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//
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// Initialize workspace
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//
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cutlass::MatrixCoord tensor_extent{64, 64};
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cutlass::MatrixCoord output_extent{62, 56};
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|
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//
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// Configure parameters
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//
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cutlass::HostTensor<Element, Layout> host_tensor(tensor_extent);
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typename PredicatedTileIterator::Params iterator_params(host_tensor.layout());
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host_tensor.sync_device();
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//
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// Launch kernel
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//
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dim3 grid(1,1);
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dim3 block(kThreads, 1);
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test::epilogue::threadblock::kernel_store_iterator<PredicatedTileIterator><<< grid, block >>>(
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iterator_params, host_tensor.device_ref(), output_extent);
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cudaError_t result = cudaDeviceSynchronize();
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ASSERT_EQ(result, cudaSuccess) << cudaGetErrorString(result);
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|
|
//
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// Verify results
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//
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host_tensor.sync_host();
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bool passed = verify_footprint(host_tensor.host_view(), output_extent);
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EXPECT_TRUE(passed);
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|
|
if (!passed) {
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std::ofstream output("volta_tensor_op_64x64x32_64x64x4.csv");
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output << host_tensor.host_view();
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}
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}
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|
|
///////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(PredicatedTileIterator, volta_tensor_op_64x128x32_32x64x4) {
|
|
|
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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);
|
|
|
|
if (!passed) {
|
|
std::ofstream output("simt_32x64x8_32x64x1.csv");
|
|
output << host_tensor.host_view();
|
|
}
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
TEST(PredicatedTileIterator, simt_128x128x8_32x64x1) {
|
|
|
|
using Layout = cutlass::layout::RowMajor;
|
|
using Element = int;
|
|
|
|
static int const kElementsPerAccess = 32 / cutlass::sizeof_bits<Element>::value;
|
|
static int const kThreads = 256;
|
|
|
|
#if 1
|
|
|
|
using ThreadMap = cutlass::epilogue::threadblock::OutputTileOptimalThreadMap <
|
|
cutlass::epilogue::threadblock::OutputTileShape<128, 1, 4, 4, 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, 16>,
|
|
kThreads,
|
|
kElementsPerAccess
|
|
>;
|
|
|
|
using Shape = cutlass::epilogue::threadblock::OutputTileShape<
|
|
128, // column
|
|
1, // row
|
|
4, // group
|
|
4, // cluster
|
|
1 // iterations
|
|
>;
|
|
|
|
using Iterations = cutlass::epilogue::threadblock::OutputTileShape<
|
|
2, // column
|
|
1, // row
|
|
2, // group
|
|
4, // cluster
|
|
1 // iterations
|
|
>;
|
|
|
|
using Delta = cutlass::epilogue::threadblock::OutputTileShape<
|
|
32, // column
|
|
1, // row
|
|
8, // group
|
|
32, // 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, 128};
|
|
cutlass::MatrixCoord output_extent{123, 121};
|
|
|
|
//
|
|
// 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("simt_128x128x8_32x64x1.csv");
|
|
output << host_tensor.host_view();
|
|
}
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////////////////////////////////////
|