121 lines
4.5 KiB
C++
121 lines
4.5 KiB
C++
// Copyright 2022 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#pragma once
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#include <algorithm>
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#include <array>
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#include <functional>
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#include <limits>
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#include <memory>
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#include <numeric>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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#include <xnnpack/node-type.h>
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#include <xnnpack/operator.h>
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#include <xnnpack/requantization.h>
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#include <xnnpack/subgraph.h>
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#include <gtest/gtest.h>
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template <typename T> class BinaryTest : public ::testing::Test {
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protected:
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BinaryTest()
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{
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random_device = std::unique_ptr<std::random_device>(new std::random_device());
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rng = std::mt19937((*random_device)());
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shape_dist = std::uniform_int_distribution<size_t>(0, XNN_MAX_TENSOR_DIMS);
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dim_dist = std::uniform_int_distribution<size_t>(1, 9);
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f32dist = std::uniform_real_distribution<float>(0.01f, 1.0f);
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i8dist =
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
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u8dist =
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std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
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scale_dist = std::uniform_real_distribution<float>(0.1f, 5.0f);
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}
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void SetUp() override
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{
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std::vector<size_t> input1_shape = RandomShape();
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std::vector<size_t> input2_shape;
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std::vector<size_t> output_shape;
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// Create input dimensions.
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// Create input 2 with an equal or larger number of dimensions.
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const size_t input2_num_dims = std::uniform_int_distribution<size_t>(input1_shape.size(), XNN_MAX_TENSOR_DIMS)(rng);
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input2_shape = RandomShape(input2_num_dims);
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// Ensure that the inputs dimensions match.
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std::copy_backward(input1_shape.begin(), input1_shape.end(), input2_shape.end());
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// Choose a random dimension to broadcast for each input.
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const size_t input1_broadcast_dim = std::uniform_int_distribution<size_t>(0, input1_shape.size())(rng);
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if (input1_broadcast_dim < input1_shape.size()) {
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input1_shape[input1_broadcast_dim] = 1;
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}
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const size_t input2_broadcast_dim = std::uniform_int_distribution<size_t>(0, input2_shape.size())(rng);
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if (input2_broadcast_dim < input2_shape.size()) {
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input2_shape[input2_broadcast_dim] = 1;
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}
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// Calculate generalized shapes.
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std::fill(input1_dims.begin(), input1_dims.end(), 1);
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std::fill(input2_dims.begin(), input2_dims.end(), 1);
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std::fill(output_dims.begin(), output_dims.end(), 1);
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std::copy_backward(input1_shape.cbegin(), input1_shape.cend(), input1_dims.end());
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std::copy_backward(input2_shape.cbegin(), input2_shape.cend(), input2_dims.end());
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for (size_t i = 0; i < XNN_MAX_TENSOR_DIMS; i++) {
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if (input1_dims[i] != 1 && input2_dims[i] != 1) {
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ASSERT_EQ(input1_dims[i], input2_dims[i]) << "i: " << i;
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}
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output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
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}
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input1 = std::vector<T>(XNN_EXTRA_BYTES / sizeof(T) + NumElements(input1_shape));
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input2 = std::vector<T>(XNN_EXTRA_BYTES / sizeof(T) + NumElements(input2_shape));
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operator_output = std::vector<T>(NumElements(output_dims));
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subgraph_output = std::vector<T>(operator_output.size());
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}
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std::vector<size_t> RandomShape(size_t num_dims)
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{
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std::vector<size_t> dims(num_dims);
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std::generate(dims.begin(), dims.end(), [&] { return dim_dist(rng); });
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return dims;
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}
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std::vector<size_t> RandomShape() { return RandomShape(shape_dist(rng)); }
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size_t NumElements(std::vector<size_t>& dims)
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{
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return std::accumulate(dims.begin(), dims.end(), size_t(1), std::multiplies<size_t>());
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}
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size_t NumElements(std::array<size_t, XNN_MAX_TENSOR_DIMS>& dims)
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{
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return std::accumulate(dims.begin(), dims.end(), size_t(1), std::multiplies<size_t>());
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}
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std::unique_ptr<std::random_device> random_device;
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std::mt19937 rng;
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std::uniform_int_distribution<size_t> shape_dist;
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std::uniform_int_distribution<size_t> dim_dist;
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std::uniform_real_distribution<float> f32dist;
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std::uniform_real_distribution<float> scale_dist;
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std::uniform_int_distribution<int32_t> i8dist;
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std::uniform_int_distribution<int32_t> u8dist;
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float output_min = -std::numeric_limits<float>::infinity();
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float output_max = std::numeric_limits<float>::infinity();
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std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_dims;
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std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_dims;
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std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
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std::vector<T> input1;
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std::vector<T> input2;
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std::vector<T> operator_output;
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std::vector<T> subgraph_output;
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};
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