Checkpointing CUTLASS 1.1 release.
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/***************************************************************************************************
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* Copyright (c) 2017-2018, 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 TOR (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
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These tests are intended to demonstrate the CUTLASS reference implementation for basic for-each
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operators on the index space of TensorView objects. They instantiate a HostMatrix, initialize
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its elements with random data according to specified random distributions, and clamp the
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elements using a TensorForEach() operation.
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Both device-side and host-side reference implementations are called.
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*/
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#include "cutlass_unit_test.h"
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#include "cutlass/matrix_traits.h"
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#include "tools/util/tensor_view_io.h"
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#include "tools/util/host_tensor.h"
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#include "tools/util/host_matrix.h"
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#include "tools/util/reference/device/tensor_foreach.h"
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#include "tools/util/reference/device/tensor_elementwise.h"
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#include "tools/util/reference/host/tensor_foreach.h"
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#include "tools/util/reference/host/tensor_elementwise.h"
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///////////////////////////////////////////////////////////////////////////////////////////////////
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namespace test {
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/// Define a functor that computes the ReLu operation on a tensor.
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template <typename View>
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struct ReLuFunc {
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/// Coordinate of index space
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typedef typename View::TensorCoord TensorCoord;
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/// Scalar type
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typedef typename View::Storage T;
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//
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// Data members
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//
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/// Tensor view
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View view;
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/// ReLu threshold
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T threshold;
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//
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// Methods
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//
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/// Constructor
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CUTLASS_HOST_DEVICE
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ReLuFunc(View const &view, T threshold): view(view), threshold(threshold) { }
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/// ReLu function
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CUTLASS_HOST_DEVICE
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void operator()(TensorCoord const &coord) {
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T value = view.at(coord);
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if (value < threshold) {
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value = threshold;
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}
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view.at(coord) = value;
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}
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};
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} // namespace test
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///////////////////////////////////////////////////////////////////////////////////////////////////
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/// This tests models the computation of ReLu using reference utility code.
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TEST(TensorForEach, ReLu_device) {
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// Define HostMatrix type
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typedef cutlass::HostMatrix<float> HostMatrix;
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typedef typename HostMatrix::DeviceTensorView View;
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// Define the problem size
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int const M = 517;
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int const N = 117;
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float threshold = 0;
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// Construct the host matrix
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HostMatrix source(cutlass::MatrixCoord(M, N), cutlass::MatrixLayout::kRowMajor);
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source.fill(0);
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// Initialize the source matrix with a uniform distribution
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cutlass::Distribution dist;
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dist.set_uniform(-16, 16);
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// RNG seed is hard-coded for determinism in the test.
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int64_t seed = 2080;
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cutlass::reference::device::TensorInitialize(source.device_view(), seed, dist);
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// Define a functor called by TensorForEach<>
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typedef test::ReLuFunc<View> ReLuFunc;
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// Instantiate on host with TensorView and threshold value
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ReLuFunc relu_func(source.device_view(), threshold);
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// Launch kernel that applies the element-wise operator over the tensor's index space.
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cutlass::reference::device::TensorForEach<
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ReLuFunc,
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View::kRank,
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ReLuFunc>(source.size(), relu_func);
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// Verify no element is less than the ReLu threshold.
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source.sync_host();
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int errors = 0;
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for (cutlass::MatrixCoord coord(0, 0); coord.row() < M; ++coord.row()) {
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for (coord.column() = 0; coord.column() < N; ++coord.column()) {
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if (source.at(coord) < threshold) {
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++errors;
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if (errors < 10) {
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std::cout << "Error - source(" << coord << ") = "
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<< source.at(coord) << " is less than threshold " << threshold << std::endl;
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}
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}
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}
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}
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EXPECT_EQ(errors, 0)
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<< "Result: " << source;
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////
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/// Test to apply the ReLu operation using host-side utilities
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TEST(TensorForEach, ReLu_host) {
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// Define HostMatrix type
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typedef cutlass::HostMatrix<float> HostMatrix;
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typedef typename HostMatrix::HostTensorView View;
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// Define the problem size
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int const M = 517;
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int const N = 117;
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float threshold = 0;
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bool const kDeviceBacked = false;
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// Construct the host matrix
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HostMatrix source(cutlass::MatrixCoord(M, N), cutlass::MatrixLayout::kRowMajor, kDeviceBacked);
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source.fill(0);
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// Initialize the source matrix with a uniform distribution
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cutlass::Distribution dist;
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dist.set_gaussian(-1, 4);
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// RNG seed is hard-coded for determinism in the test.
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unsigned seed = 2080;
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cutlass::reference::host::TensorInitialize(source.host_view(), seed, dist);
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// Define a functor called by TensorForEach<>
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typedef test::ReLuFunc<View> ReLuFunc;
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// Instantiate on host with TensorView and threshold value
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ReLuFunc relu_func(source.host_view(), threshold);
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// Invoke host-side for-each computation on the tensor
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cutlass::reference::host::TensorForEach<
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ReLuFunc,
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View::kRank,
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ReLuFunc>(source.size(), relu_func);
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int errors = 0;
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for (cutlass::MatrixCoord coord(0, 0); coord.row() < M; ++coord.row()) {
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for (coord.column() = 0; coord.column() < N; ++coord.column()) {
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if (source.at(coord) < threshold) {
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++errors;
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if (errors < 10) {
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std::cout << "Error - source(" << coord << ") = "
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<< source.at(coord) << " is less than threshold " << threshold << std::endl;
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}
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}
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}
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}
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EXPECT_EQ(errors, 0)
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<< "Result: " << source;
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////
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