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494 lines
20 KiB
C++
494 lines
20 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define EIGEN_USE_THREADS
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#include "main.h"
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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using Eigen::RowMajor;
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using Eigen::ColMajor;
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// A set of tests to verify that different TensorExecutor strategies yields the
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// same results for all the ops, supporting tiled evaluation.
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template <int NumDims>
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static array<Index, NumDims> RandomDims(int min_dim = 1, int max_dim = 20) {
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array<Index, NumDims> dims;
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for (int i = 0; i < NumDims; ++i) {
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dims[i] = internal::random<int>(min_dim, max_dim);
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}
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return dims;
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_binary_expr(Device d)
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{
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static constexpr int Options = 0 | Layout;
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// Pick a large enough tensor size to bypass small tensor block evaluation
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// optimization.
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auto dims = RandomDims<NumDims>(50 / NumDims, 100 / NumDims);
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Tensor<T, NumDims, Options, Index> lhs(dims);
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Tensor<T, NumDims, Options, Index> rhs(dims);
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Tensor<T, NumDims, Options, Index> dst(dims);
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lhs.setRandom();
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rhs.setRandom();
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const auto expr = lhs + rhs;
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
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using Executor =
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internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
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Executor::run(Assign(dst, expr), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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T sum = lhs.coeff(i) + rhs.coeff(i);
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VERIFY_IS_EQUAL(sum, dst.coeff(i));
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}
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_broadcasting(Device d)
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{
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(1, 10);
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Tensor<T, NumDims, Options, Index> src(dims);
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src.setRandom();
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const auto broadcasts = RandomDims<NumDims>(1, 7);
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const auto expr = src.broadcast(broadcasts);
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// We assume that broadcasting on a default device is tested and correct, so
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// we can rely on it to verify correctness of tensor executor and tiling.
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Tensor<T, NumDims, Options, Index> golden;
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golden = expr;
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// Now do the broadcasting using configured tensor executor.
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Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
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using Executor =
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internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
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Executor::run(Assign(dst, expr), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_chipping_rvalue(Device d) {
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auto dims = RandomDims<NumDims>(1, 10);
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Tensor<T, NumDims, Layout, Index> src(dims);
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src.setRandom();
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#define TEST_CHIPPING(CHIP_DIM) \
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if (NumDims > (CHIP_DIM)) { \
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const auto offset = internal::random<Index>(0, dims[(CHIP_DIM)] - 1); \
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const auto expr = src.template chip<(CHIP_DIM)>(offset); \
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\
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Tensor<T, NumDims - 1, Layout, Index> golden; \
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golden = expr; \
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\
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Tensor<T, NumDims - 1, Layout, Index> dst(golden.dimensions()); \
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\
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; \
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using Executor = internal::TensorExecutor<const Assign, Device, \
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Vectorizable, Tileable>; \
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\
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Executor::run(Assign(dst, expr), d); \
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\
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { \
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); \
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} \
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}
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TEST_CHIPPING(0)
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TEST_CHIPPING(1)
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TEST_CHIPPING(2)
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TEST_CHIPPING(3)
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TEST_CHIPPING(4)
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TEST_CHIPPING(5)
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#undef TEST_CHIPPING
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_chipping_lvalue(Device d) {
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auto dims = RandomDims<NumDims>(1, 10);
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#define TEST_CHIPPING(CHIP_DIM) \
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if (NumDims > (CHIP_DIM)) { \
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/* Generate random data that we'll assign to the chipped tensor dim. */ \
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array<Index, NumDims - 1> src_dims; \
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for (int i = 0; i < NumDims - 1; ++i) { \
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int dim = i < (CHIP_DIM) ? i : i + 1; \
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src_dims[i] = dims[dim]; \
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} \
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\
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Tensor<T, NumDims - 1, Layout, Index> src(src_dims); \
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src.setRandom(); \
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\
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const auto offset = internal::random<Index>(0, dims[(CHIP_DIM)] - 1); \
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\
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/* Generate random data to fill non-chipped dimensions*/ \
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Tensor<T, NumDims, Layout, Index> random(dims); \
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random.setRandom(); \
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\
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Tensor<T, NumDims, Layout, Index> golden(dims); \
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golden = random; \
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golden.template chip<(CHIP_DIM)>(offset) = src; \
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\
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Tensor<T, NumDims, Layout, Index> dst(dims); \
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dst = random; \
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auto expr = dst.template chip<(CHIP_DIM)>(offset); \
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\
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using Assign = TensorAssignOp<decltype(expr), const decltype(src)>; \
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using Executor = internal::TensorExecutor<const Assign, Device, \
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Vectorizable, Tileable>; \
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\
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Executor::run(Assign(expr, src), d); \
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\
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { \
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); \
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} \
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}
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TEST_CHIPPING(0)
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TEST_CHIPPING(1)
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TEST_CHIPPING(2)
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TEST_CHIPPING(3)
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TEST_CHIPPING(4)
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TEST_CHIPPING(5)
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#undef TEST_CHIPPING
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_shuffle_rvalue(Device d) {
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(1, 10);
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Tensor<T, NumDims, Options, Index> src(dims);
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src.setRandom();
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// Create a random dimension re-ordering/shuffle.
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std::vector<Index> shuffle;
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for (int i = 0; i < NumDims; ++i) shuffle.push_back(i);
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std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937());
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const auto expr = src.shuffle(shuffle);
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// We assume that shuffling on a default device is tested and correct, so
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// we can rely on it to verify correctness of tensor executor and tiling.
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Tensor<T, NumDims, Options, Index> golden;
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golden = expr;
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// Now do the shuffling using configured tensor executor.
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Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
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using Executor =
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internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
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Executor::run(Assign(dst, expr), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_shuffle_lvalue(Device d) {
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(5, 10);
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Tensor<T, NumDims, Options, Index> src(dims);
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src.setRandom();
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// Create a random dimension re-ordering/shuffle.
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std::vector<Index> shuffle;
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for (int i = 0; i < NumDims; ++i) shuffle.push_back(i);
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std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937());
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array<Index, NumDims> shuffled_dims;
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for (int i = 0; i < NumDims; ++i) shuffled_dims[shuffle[i]] = dims[i];
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// We assume that shuffling on a default device is tested and correct, so
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// we can rely on it to verify correctness of tensor executor and tiling.
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Tensor<T, NumDims, Options, Index> golden(shuffled_dims);
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golden.shuffle(shuffle) = src;
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// Now do the shuffling using configured tensor executor.
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Tensor<T, NumDims, Options, Index> dst(shuffled_dims);
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auto expr = dst.shuffle(shuffle);
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using Assign = TensorAssignOp<decltype(expr), const decltype(src)>;
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using Executor =
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internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
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Executor::run(Assign(expr, src), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_reduction(Device d)
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{
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static_assert(NumDims >= 2);
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static constexpr int ReducedDims = NumDims - 2;
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(5, 10);
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Tensor<T, NumDims, Options, Index> src(dims);
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src.setRandom();
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// Pick two random and unique reduction dimensions.
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int reduction0 = internal::random<int>(0, NumDims - 1);
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int reduction1 = internal::random<int>(0, NumDims - 1);
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while (reduction0 == reduction1) {
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reduction1 = internal::random<int>(0, NumDims - 1);
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}
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DSizes<Index, 2> reduction_axis;
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reduction_axis[0] = reduction0;
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reduction_axis[1] = reduction1;
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Tensor<T, ReducedDims, Options, Index> golden = src.sum(reduction_axis);
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// Now do the reduction using configured tensor executor.
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Tensor<T, ReducedDims, Options, Index> dst(golden.dimensions());
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auto expr = src.sum(reduction_axis);
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
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using Executor =
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internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
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Executor::run(Assign(dst, expr), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_reshape(Device d)
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{
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static_assert(NumDims >= 2);
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static constexpr int ReshapedDims = NumDims - 1;
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(5, 10);
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Tensor<T, NumDims, Options, Index> src(dims);
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src.setRandom();
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// Multiple 0th dimension and then shuffle.
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std::vector<Index> shuffle;
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for (int i = 0; i < ReshapedDims; ++i) shuffle.push_back(i);
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std::shuffle(shuffle.begin(), shuffle.end(), std::mt19937());
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DSizes<Index, ReshapedDims> reshaped_dims;
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reshaped_dims[shuffle[0]] = dims[0] * dims[1];
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for (int i = 2; i < NumDims; ++i) reshaped_dims[shuffle[i]] = dims[i];
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Tensor<T, ReshapedDims, Options, Index> golden = src.reshape(reshaped_dims);
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// Now reshape using configured tensor executor.
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Tensor<T, ReshapedDims, Options, Index> dst(golden.dimensions());
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auto expr = src.reshape(reshaped_dims);
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
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using Executor =
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internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
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Executor::run(Assign(dst, expr), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_slice_rvalue(Device d)
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{
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static_assert(NumDims >= 2);
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(5, 10);
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Tensor<T, NumDims, Options, Index> src(dims);
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src.setRandom();
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// Pick a random slice of src tensor.
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auto slice_start = DSizes<Index, NumDims>(RandomDims<NumDims>());
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auto slice_size = DSizes<Index, NumDims>(RandomDims<NumDims>());
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// Make sure that slice start + size do not overflow tensor dims.
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for (int i = 0; i < NumDims; ++i) {
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slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]);
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slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]);
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}
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Tensor<T, NumDims, Options, Index> golden =
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src.slice(slice_start, slice_size);
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// Now reshape using configured tensor executor.
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Tensor<T, NumDims, Options, Index> dst(golden.dimensions());
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auto expr = src.slice(slice_start, slice_size);
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
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using Executor =
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internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
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Executor::run(Assign(dst, expr), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable,
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bool Tileable, int Layout>
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static void test_execute_slice_lvalue(Device d)
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{
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static_assert(NumDims >= 2);
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(5, 10);
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Tensor<T, NumDims, Options, Index> src(dims);
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src.setRandom();
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// Pick a random slice of src tensor.
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auto slice_start = DSizes<Index, NumDims>(RandomDims<NumDims>(1, 10));
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auto slice_size = DSizes<Index, NumDims>(RandomDims<NumDims>(1, 10));
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// Make sure that slice start + size do not overflow tensor dims.
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for (int i = 0; i < NumDims; ++i) {
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slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]);
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slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]);
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}
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Tensor<T, NumDims, Options, Index> slice(slice_size);
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slice.setRandom();
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// Asign a slice using default executor.
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Tensor<T, NumDims, Options, Index> golden = src;
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golden.slice(slice_start, slice_size) = slice;
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// And using configured execution strategy.
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Tensor<T, NumDims, Options, Index> dst = src;
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auto expr = dst.slice(slice_start, slice_size);
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using Assign = TensorAssignOp<decltype(expr), const decltype(slice)>;
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using Executor =
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internal::TensorExecutor<const Assign, Device, Vectorizable, Tileable>;
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Executor::run(Assign(expr, slice), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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}
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#define CALL_SUBTEST_COMBINATIONS(NAME, T, NUM_DIMS) \
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CALL_SUBTEST((NAME<T, NUM_DIMS, DefaultDevice, false, false, ColMajor>(default_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, DefaultDevice, false, true, ColMajor>(default_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, DefaultDevice, true, false, ColMajor>(default_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, DefaultDevice, true, true, ColMajor>(default_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, DefaultDevice, false, false, RowMajor>(default_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, DefaultDevice, false, true, RowMajor>(default_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, DefaultDevice, true, false, RowMajor>(default_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, DefaultDevice, true, true, RowMajor>(default_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, ThreadPoolDevice, false, false, ColMajor>(tp_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, ThreadPoolDevice, false, true, ColMajor>(tp_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, ThreadPoolDevice, true, false, ColMajor>(tp_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, ThreadPoolDevice, true, true, ColMajor>(tp_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, ThreadPoolDevice, false, false, RowMajor>(tp_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, ThreadPoolDevice, false, true, RowMajor>(tp_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, ThreadPoolDevice, true, false, RowMajor>(tp_device))); \
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CALL_SUBTEST((NAME<T, NUM_DIMS, ThreadPoolDevice, true, true, RowMajor>(tp_device)))
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EIGEN_DECLARE_TEST(cxx11_tensor_executor) {
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Eigen::DefaultDevice default_device;
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const auto num_threads = internal::random<int>(1, 24);
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Eigen::ThreadPool tp(num_threads);
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Eigen::ThreadPoolDevice tp_device(&tp, num_threads);
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CALL_SUBTEST_COMBINATIONS(test_execute_binary_expr, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_binary_expr, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_binary_expr, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_broadcasting, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_broadcasting, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_broadcasting, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_chipping_rvalue, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_chipping_rvalue, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_chipping_rvalue, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_chipping_lvalue, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_chipping_lvalue, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_chipping_lvalue, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_shuffle_rvalue, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_shuffle_rvalue, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_shuffle_rvalue, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_shuffle_lvalue, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_shuffle_lvalue, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_shuffle_lvalue, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_reduction, float, 2);
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CALL_SUBTEST_COMBINATIONS(test_execute_reduction, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_reduction, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_reduction, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_reshape, float, 2);
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CALL_SUBTEST_COMBINATIONS(test_execute_reshape, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_reshape, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_reshape, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_slice_rvalue, float, 2);
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CALL_SUBTEST_COMBINATIONS(test_execute_slice_rvalue, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_slice_rvalue, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_slice_rvalue, float, 5);
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CALL_SUBTEST_COMBINATIONS(test_execute_slice_lvalue, float, 2);
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CALL_SUBTEST_COMBINATIONS(test_execute_slice_lvalue, float, 3);
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CALL_SUBTEST_COMBINATIONS(test_execute_slice_lvalue, float, 4);
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CALL_SUBTEST_COMBINATIONS(test_execute_slice_lvalue, float, 5);
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}
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#undef CALL_SUBTEST_COMBINATIONS
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