384 lines
11 KiB
Plaintext
384 lines
11 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 Tests for device-wide GEMM interface
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*/
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#include <iostream>
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/cutlass.h"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/reduction/kernel/reduce_split_k.h"
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#include "cutlass/reduction/thread/reduction_operators.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/reference/host/gemm.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/tensor_norm.h"
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#include "cutlass/util/tensor_view_io.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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namespace test {
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namespace reduction {
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template <typename ReductionKernel>
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__global__ void kernel_reduce_splitk(typename ReductionKernel::Params params) {
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__shared__ typename ReductionKernel::SharedStorage shared_storage;
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ReductionKernel reduction_op;
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reduction_op(params, shared_storage);
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}
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template <typename ReductionKernel>
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class ReduceSplitKTestbed {
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public:
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using ElementAccumulator = typename ReductionKernel::ElementAccumulator;
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using ElementWorkspace = typename ReductionKernel::ElementWorkspace;
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using ElementOutput = typename ReductionKernel::ElementOutput;
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using Layout = cutlass::layout::RowMajor;
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public:
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cutlass::Distribution::Kind distribution_workspace;
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cutlass::Distribution::Kind distribution_source;
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uint64_t seed;
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public:
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/// Ctor
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ReduceSplitKTestbed(
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cutlass::Distribution::Kind distribution_workspace = cutlass::Distribution::Uniform,
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cutlass::Distribution::Kind distribution_source = cutlass::Distribution::Uniform,
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uint64_t seed = 2019
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):
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distribution_workspace(distribution_workspace),
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distribution_source(distribution_source),
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seed(seed) {
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}
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/// Helper to initialize a tensor view
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template <typename Element, typename Layout>
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bool initialize_tensor(cutlass::TensorView<Element, Layout> view,
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cutlass::Distribution::Kind dist_kind,
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uint64_t seed) {
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if (dist_kind == cutlass::Distribution::Uniform) {
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cutlass::reference::host::TensorFillRandomUniform(view, seed, 8, -8, 0);
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}
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else if (dist_kind == cutlass::Distribution::Gaussian) {
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cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5, -1);
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} else if (dist_kind == cutlass::Distribution::Identity) {
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cutlass::reference::host::TensorFillIdentity(view);
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} else if (dist_kind == cutlass::Distribution::Sequential) {
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cutlass::reference::host::BlockFillSequential(view.data(),
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view.capacity());
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} else {
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// TODO: Implement the rest
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EXPECT_TRUE(false) << "Not implemented";
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return false;
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}
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return true;
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}
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/// Runs a single problem size
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bool run(
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cutlass::MatrixCoord problem_size,
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int partitions,
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ElementAccumulator alpha = 1,
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ElementAccumulator beta = 0) {
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cutlass::HostTensor<ElementWorkspace, Layout> workspace({
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problem_size.row() * partitions,
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problem_size.column()
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});
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cutlass::HostTensor<ElementOutput, Layout> source(problem_size);
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cutlass::HostTensor<ElementOutput, Layout> destination(problem_size);
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cutlass::HostTensor<ElementOutput, Layout> destination_reference(problem_size, false);
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//
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// Initialize
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//
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initialize_tensor(workspace.host_view(), distribution_workspace, seed);
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initialize_tensor(source.host_view(), distribution_source, seed + 23);
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cutlass::reference::host::TensorFill(destination.host_view());
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workspace.sync_device();
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source.sync_device();
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destination.sync_device();
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//
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// Launch reduction kernel
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//
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dim3 block = ReductionKernel::block_shape();
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dim3 grid = ReductionKernel::grid_shape(problem_size);
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typename ReductionKernel::Params params(
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problem_size,
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partitions,
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problem_size.row() * problem_size.column(),
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workspace.device_ref(),
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destination.device_ref(),
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source.device_ref(),
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{alpha, beta}
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);
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test::reduction::kernel_reduce_splitk<ReductionKernel><<< grid, block >>>(params);
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cudaError_t result = cudaDeviceSynchronize();
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EXPECT_EQ(result, cudaSuccess)
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<< "CUDA error: " << cudaGetErrorString(result);
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destination.sync_host();
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//
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// Compute reference
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//
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for (int m = 0; m < problem_size.row(); ++m) {
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for (int n = 0; n < problem_size.column(); ++n) {
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ElementAccumulator accum = 0;
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for (int k = 0; k < partitions; ++k) {
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accum += ElementAccumulator(workspace.at({m + k * problem_size.row(), n}));
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}
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ElementAccumulator c = ElementAccumulator(source.at({m, n}));
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destination_reference.at({m, n}) = ElementOutput(accum * alpha + beta * c);
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}
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}
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//
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// Compare
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//
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EXPECT_GT(cutlass::reference::host::TensorNorm(destination.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(destination_reference.host_view()), 0);
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bool passed = cutlass::reference::host::TensorEquals(
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destination.host_view(), destination_reference.host_view());
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EXPECT_TRUE(passed)
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<< "Workspace =\n" << workspace.host_view() << "\n\n"
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<< "\n"
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<< "Reference =\n" << destination_reference.host_view() << "\n\n"
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<< "Computed =\n" << destination.host_view() << "\n";
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return passed;
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}
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/// Runs through a variety of test cases
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bool run_all() {
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cutlass::MatrixCoord problem_sizes[] = {
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{8, 8},
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{136, 72},
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{248, 232},
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};
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int partition_counts[] = {
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1,3,4,5,11
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};
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bool passed = false;
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for (cutlass::MatrixCoord problem : problem_sizes) {
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for (int partitions : partition_counts) {
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passed = run(problem, partitions);
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if (!passed) {
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return false;
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}
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}
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}
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return passed;
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}
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};
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} // namespace reduction
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} // namespace test
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Strictly F32 data
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//
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TEST(Reduction_ReduceSplitK, f32_f32_f32_1_1x32) {
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using ElementWorkspace = float;
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using ElementAccumulator = float;
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using ElementOutput = float;
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int const kN = 1;
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using Shape = cutlass::MatrixShape<1, 32>;
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kN,
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ElementAccumulator,
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ElementAccumulator
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>;
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using ReductionOp = cutlass::reduction::thread::ReduceAdd<
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ElementAccumulator,
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ElementWorkspace,
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kN
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>;
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using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
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Shape,
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OutputOp,
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ReductionOp
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>;
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test::reduction::ReduceSplitKTestbed<ReductionKernel> testbed;
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EXPECT_TRUE(testbed.run_all());
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Vectorized access
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//
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TEST(Reduction_ReduceSplitK, f32_f32_f32_2_4x64) {
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using ElementWorkspace = float;
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using ElementAccumulator = float;
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using ElementOutput = float;
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int const kN = 2;
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using Shape = cutlass::MatrixShape<4, 64>;
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kN,
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ElementAccumulator,
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ElementAccumulator
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>;
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using ReductionOp = cutlass::reduction::thread::ReduceAdd<
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ElementAccumulator,
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ElementWorkspace,
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kN
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>;
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using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
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Shape,
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OutputOp,
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ReductionOp
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>;
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test::reduction::ReduceSplitKTestbed<ReductionKernel> testbed;
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EXPECT_TRUE(testbed.run_all());
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Vectorized access
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//
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TEST(Reduction_ReduceSplitK, f32_f32_f16_2_4x64) {
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using ElementWorkspace = float;
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using ElementAccumulator = float;
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using ElementOutput = cutlass::half_t;
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int const kN = 2;
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using Shape = cutlass::MatrixShape<4, 64>;
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kN,
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ElementAccumulator,
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ElementAccumulator
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>;
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using ReductionOp = cutlass::reduction::thread::ReduceAdd<
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ElementAccumulator,
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ElementWorkspace,
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kN
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>;
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using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
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Shape,
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OutputOp,
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ReductionOp
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>;
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test::reduction::ReduceSplitKTestbed<ReductionKernel> testbed;
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EXPECT_TRUE(testbed.run_all());
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Vectorized access
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//
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TEST(Reduction_ReduceSplitK, f32_f32_f16_8_4x64) {
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using ElementWorkspace = float;
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using ElementAccumulator = float;
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using ElementOutput = cutlass::half_t;
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int const kN = 8;
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using Shape = cutlass::MatrixShape<4, 64>;
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using OutputOp = cutlass::epilogue::thread::LinearCombination<
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ElementOutput,
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kN,
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ElementAccumulator,
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ElementAccumulator
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>;
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using ReductionOp = cutlass::reduction::thread::ReduceAdd<
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ElementAccumulator,
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ElementWorkspace,
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kN
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>;
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using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
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Shape,
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OutputOp,
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ReductionOp
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>;
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test::reduction::ReduceSplitKTestbed<ReductionKernel> testbed;
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EXPECT_TRUE(testbed.run_all());
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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