225 lines
6.6 KiB
Plaintext
225 lines
6.6 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* 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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#include "../common/cutlass_unit_test.h"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/layout/matrix.h"
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////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(TensorRef, basic_rank2) {
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int const M = 8;
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int const N = 16;
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int matrix_data[M * N] = {0};
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cutlass::TensorRef<
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int,
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cutlass::IdentityTensorLayout<2> > matrix_ref(matrix_data, cutlass::make_Coord(N, 1));
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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matrix_ref.at(cutlass::make_Coord(m, n)) = m * N + n;
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}
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}
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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EXPECT_EQ(matrix_data[m * N + n], int(m * N + n));
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(TensorRef, rank2_column_major) {
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int const M = 8;
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int const N = 8;
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int matrix_data[M * N];
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cutlass::TensorRef<int, cutlass::layout::ColumnMajor> ref(matrix_data, M);
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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ref.at(cutlass::make_Coord(m, n)) = m * N + n;
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}
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}
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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EXPECT_EQ(matrix_data[m + n * M], int(m * N + n));
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(TensorRef, rank2_row_major) {
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int const M = 8;
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int const N = 16;
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int matrix_data[M * N] = { 0 };
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cutlass::TensorRef<int, cutlass::layout::RowMajor> ref(matrix_data, N);
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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ref.at(cutlass::make_Coord(m, n)) = m * N + n;
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}
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}
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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EXPECT_EQ(matrix_data[m * N + n], int(m * N + n));
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(TensorRef, rank2_contiguous_dynamic) {
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int const M = 8;
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int const N = 16;
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typedef cutlass::TensorRef<int, cutlass::layout::ContiguousMatrix> ContiguousTensorRef;
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cutlass::layout::Matrix layouts[] = {
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cutlass::layout::Matrix::kColumnMajor,
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cutlass::layout::Matrix::kRowMajor
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};
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for (int i = 0; i < 2; ++i) {
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int matrix_data[M * N] = { 0 };
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int row_stride;
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int col_stride;
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if (layouts[i] == cutlass::layout::Matrix::kColumnMajor) {
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row_stride = 1;
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col_stride = M;
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}
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else {
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row_stride = N;
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col_stride = 1;
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}
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// Use helper to determine stride vector from leading dimension
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ContiguousTensorRef ref(
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matrix_data,
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cutlass::layout::ContiguousMatrix::packed(cutlass::make_Coord(M, N), layouts[i]));
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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ref.at(cutlass::make_Coord(m, n)) = m * N + n;
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}
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}
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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EXPECT_EQ(matrix_data[m * row_stride + n * col_stride], int(m * N + n));
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}
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(TensorRef, rank2_column_major_interleaved) {
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int const M = 16;
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int const N = 16;
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int const kInterleave = 4;
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int matrix_data[M * N] = {0};
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// Define the Layout for a column-major interleaved matrix format
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using Layout = cutlass::layout::ColumnMajorInterleaved<kInterleave>;
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// Construct a TensorRef
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cutlass::TensorRef<
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int,
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Layout> ref(matrix_data, Layout::packed(cutlass::make_Coord(M, N)));
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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ref.at(cutlass::make_Coord(m, n)) = m + n * M;
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}
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}
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// Verify
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; n += kInterleave) {
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for (int i = 0; i < kInterleave; ++i) {
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EXPECT_EQ(matrix_data[m * kInterleave + n * M + i], int(m + (n + i) * M));
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}
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(TensorRef, rank2_row_major_interleaved) {
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int const M = 16;
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int const N = 16;
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int const kInterleave = 4;
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int matrix_data[M * N] = {0};
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// Define the Layout for a row-major interleaved matrix format
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using Layout = cutlass::layout::RowMajorInterleaved<kInterleave>;
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// Construct a TensorRef
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cutlass::TensorRef<
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int,
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Layout> ref(matrix_data, Layout::packed(cutlass::make_Coord(M, N)));
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for (int m = 0; m < M; ++m) {
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for (int n = 0; n < N; ++n) {
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ref.at(cutlass::make_Coord(m, n)) = m + n * M;
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}
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}
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// Verify
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for (int m = 0; m < M; m += kInterleave) {
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for (int n = 0; n < N; ++n) {
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for (int i = 0; i < kInterleave; ++i) {
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EXPECT_EQ(matrix_data[m * N + i + n * kInterleave], int((m + i) + n * M));
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}
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}
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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