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android13/external/XNNPACK/test/constant-pad-operator-tester.h

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// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#pragma once
#include <gtest/gtest.h>
#include <algorithm>
#include <array>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <initializer_list>
#include <numeric>
#include <random>
#include <vector>
#include <xnnpack.h>
class ConstantPadOperatorTester {
public:
inline ConstantPadOperatorTester& input_shape(std::initializer_list<size_t> input_shape) {
assert(input_shape.size() <= XNN_MAX_TENSOR_DIMS);
input_shape_ = std::vector<size_t>(input_shape);
return *this;
}
inline const std::vector<size_t>& input_shape() const {
return input_shape_;
}
inline size_t input_dim(size_t i) const {
return i < input_shape_.size() ? input_shape_[i] : 1;
}
inline size_t num_dims() const {
return input_shape_.size();
}
inline size_t num_input_elements() const {
return std::accumulate(
input_shape_.cbegin(), input_shape_.cend(), size_t(1), std::multiplies<size_t>());
}
inline ConstantPadOperatorTester& pre_paddings(std::initializer_list<size_t> pre_paddings) {
assert(pre_paddings.size() <= XNN_MAX_TENSOR_DIMS);
pre_paddings_ = std::vector<size_t>(pre_paddings);
return *this;
}
inline const std::vector<size_t>& pre_paddings() const {
return pre_paddings_;
}
inline size_t pre_padding(size_t i) const {
return i < pre_paddings_.size() ? pre_paddings_[i] : 0;
}
inline size_t num_pre_paddings() const {
return pre_paddings_.size();
}
inline ConstantPadOperatorTester& post_paddings(std::initializer_list<size_t> post_paddings) {
assert(post_paddings.size() <= XNN_MAX_TENSOR_DIMS);
post_paddings_ = std::vector<size_t>(post_paddings);
return *this;
}
inline const std::vector<size_t>& post_paddings() const {
return post_paddings_;
}
inline size_t post_padding(size_t i) const {
return i < post_paddings_.size() ? post_paddings_[i] : 0;
}
inline size_t num_post_paddings() const {
return post_paddings_.size();
}
inline size_t output_dim(size_t i) const {
return pre_padding(i) + input_dim(i) + post_padding(i);
}
inline size_t num_output_elements() const {
size_t elements = 1;
for (size_t i = 0; i < num_dims(); i++) {
elements *= output_dim(i);
}
return elements;
}
inline ConstantPadOperatorTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void TestX8() const {
ASSERT_EQ(num_dims(), num_pre_paddings());
ASSERT_EQ(num_dims(), num_post_paddings());
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), rng);
// Compute generalized shapes.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_pre_paddings;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_post_paddings;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
std::fill(input_dims.begin(), input_dims.end(), 1);
std::fill(input_pre_paddings.begin(), input_pre_paddings.end(), 0);
std::fill(input_post_paddings.begin(), input_post_paddings.end(), 0);
std::fill(output_dims.begin(), output_dims.end(), 1);
for (size_t i = 0; i < num_dims(); i++) {
input_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = input_dim(i);
input_pre_paddings[XNN_MAX_TENSOR_DIMS - num_dims() + i] = pre_padding(i);
input_post_paddings[XNN_MAX_TENSOR_DIMS - num_dims() + i] = post_padding(i);
output_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = output_dim(i);
}
// Compute generalized strides.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides;
size_t input_stride = 1, output_stride = 1;
for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) {
input_strides[i - 1] = input_stride;
output_strides[i - 1] = output_stride;
input_stride *= input_dims[i - 1];
output_stride *= output_dims[i - 1];
}
std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + num_input_elements());
std::vector<uint8_t> output(num_output_elements());
std::vector<uint8_t> output_ref(num_output_elements());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(u8rng));
std::fill(output.begin(), output.end(), UINT32_C(0xAA));
const uint8_t padding_value = u8rng();
// Compute reference results.
std::fill(output_ref.begin(), output_ref.end(), padding_value);
for (size_t i = 0; i < input_dims[0]; i++) {
for (size_t j = 0; j < input_dims[1]; j++) {
for (size_t k = 0; k < input_dims[2]; k++) {
for (size_t l = 0; l < input_dims[3]; l++) {
for (size_t m = 0; m < input_dims[4]; m++) {
for (size_t n = 0; n < input_dims[5]; n++) {
const size_t output_index =
(i + input_pre_paddings[0]) * output_strides[0] +
(j + input_pre_paddings[1]) * output_strides[1] +
(k + input_pre_paddings[2]) * output_strides[2] +
(l + input_pre_paddings[3]) * output_strides[3] +
(m + input_pre_paddings[4]) * output_strides[4] +
(n + input_pre_paddings[5]) * output_strides[5];
const size_t input_index =
i * input_strides[0] + j * input_strides[1] + k * input_strides[2] +
l * input_strides[3] + m * input_strides[4] + n * input_strides[5];
output_ref[output_index] = input[input_index];
}
}
}
}
}
}
// Create, setup, run, and destroy a binary elementwise operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t pad_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_constant_pad_nd_x8(
&padding_value, 0, &pad_op));
ASSERT_NE(nullptr, pad_op);
// Smart pointer to automatically delete pad_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_pad_op(pad_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_constant_pad_nd_x8(
pad_op,
num_dims(),
input_shape().data(), pre_paddings().data(), post_paddings().data(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(pad_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < output_dims[0]; i++) {
for (size_t j = 0; j < output_dims[1]; j++) {
for (size_t k = 0; k < output_dims[2]; k++) {
for (size_t l = 0; l < output_dims[3]; l++) {
for (size_t m = 0; m < output_dims[4]; m++) {
for (size_t n = 0; n < output_dims[5]; n++) {
const size_t index =
i * output_strides[0] + j * output_strides[1] + k * output_strides[2] +
l * output_strides[3] + m * output_strides[4] + n * output_strides[5];
ASSERT_EQ(output[index], output_ref[index])
<< "(i, j, k, l, m, n) = ("
<< i << ", " << j << ", " << k << ", " << l << ", " << m << ", " << n << ")"
<< ", padding value = " << padding_value;
}
}
}
}
}
}
}
}
void TestX16() const {
ASSERT_EQ(num_dims(), num_pre_paddings());
ASSERT_EQ(num_dims(), num_post_paddings());
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u16rng = std::bind(std::uniform_int_distribution<uint16_t>(), rng);
// Compute generalized shapes.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_pre_paddings;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_post_paddings;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
std::fill(input_dims.begin(), input_dims.end(), 1);
std::fill(input_pre_paddings.begin(), input_pre_paddings.end(), 0);
std::fill(input_post_paddings.begin(), input_post_paddings.end(), 0);
std::fill(output_dims.begin(), output_dims.end(), 1);
for (size_t i = 0; i < num_dims(); i++) {
input_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = input_dim(i);
input_pre_paddings[XNN_MAX_TENSOR_DIMS - num_dims() + i] = pre_padding(i);
input_post_paddings[XNN_MAX_TENSOR_DIMS - num_dims() + i] = post_padding(i);
output_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = output_dim(i);
}
// Compute generalized strides.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides;
size_t input_stride = 1, output_stride = 1;
for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) {
input_strides[i - 1] = input_stride;
output_strides[i - 1] = output_stride;
input_stride *= input_dims[i - 1];
output_stride *= output_dims[i - 1];
}
std::vector<uint16_t> input(XNN_EXTRA_BYTES / sizeof(uint16_t) + num_input_elements());
std::vector<uint16_t> output(num_output_elements());
std::vector<uint16_t> output_ref(num_output_elements());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(u16rng));
std::fill(output.begin(), output.end(), UINT16_C(0xDEAD));
const uint16_t padding_value = u16rng();
// Compute reference results.
std::fill(output_ref.begin(), output_ref.end(), padding_value);
for (size_t i = 0; i < input_dims[0]; i++) {
for (size_t j = 0; j < input_dims[1]; j++) {
for (size_t k = 0; k < input_dims[2]; k++) {
for (size_t l = 0; l < input_dims[3]; l++) {
for (size_t m = 0; m < input_dims[4]; m++) {
for (size_t n = 0; n < input_dims[5]; n++) {
const size_t output_index =
(i + input_pre_paddings[0]) * output_strides[0] +
(j + input_pre_paddings[1]) * output_strides[1] +
(k + input_pre_paddings[2]) * output_strides[2] +
(l + input_pre_paddings[3]) * output_strides[3] +
(m + input_pre_paddings[4]) * output_strides[4] +
(n + input_pre_paddings[5]) * output_strides[5];
const size_t input_index =
i * input_strides[0] + j * input_strides[1] + k * input_strides[2] +
l * input_strides[3] + m * input_strides[4] + n * input_strides[5];
output_ref[output_index] = input[input_index];
}
}
}
}
}
}
// Create, setup, run, and destroy a binary elementwise operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t pad_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_constant_pad_nd_x16(
&padding_value, 0, &pad_op));
ASSERT_NE(nullptr, pad_op);
// Smart pointer to automatically delete pad_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_pad_op(pad_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_constant_pad_nd_x16(
pad_op,
num_dims(),
input_shape().data(), pre_paddings().data(), post_paddings().data(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(pad_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < output_dims[0]; i++) {
for (size_t j = 0; j < output_dims[1]; j++) {
for (size_t k = 0; k < output_dims[2]; k++) {
for (size_t l = 0; l < output_dims[3]; l++) {
for (size_t m = 0; m < output_dims[4]; m++) {
for (size_t n = 0; n < output_dims[5]; n++) {
const size_t index =
i * output_strides[0] + j * output_strides[1] + k * output_strides[2] +
l * output_strides[3] + m * output_strides[4] + n * output_strides[5];
ASSERT_EQ(output[index], output_ref[index])
<< "(i, j, k, l, m, n) = ("
<< i << ", " << j << ", " << k << ", " << l << ", " << m << ", " << n << ")"
<< ", padding value = " << padding_value;
}
}
}
}
}
}
}
}
void TestX32() const {
ASSERT_EQ(num_dims(), num_pre_paddings());
ASSERT_EQ(num_dims(), num_post_paddings());
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto u32rng = std::bind(std::uniform_int_distribution<uint32_t>(), rng);
// Compute generalized shapes.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_pre_paddings;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_post_paddings;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
std::fill(input_dims.begin(), input_dims.end(), 1);
std::fill(input_pre_paddings.begin(), input_pre_paddings.end(), 0);
std::fill(input_post_paddings.begin(), input_post_paddings.end(), 0);
std::fill(output_dims.begin(), output_dims.end(), 1);
for (size_t i = 0; i < num_dims(); i++) {
input_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = input_dim(i);
input_pre_paddings[XNN_MAX_TENSOR_DIMS - num_dims() + i] = pre_padding(i);
input_post_paddings[XNN_MAX_TENSOR_DIMS - num_dims() + i] = post_padding(i);
output_dims[XNN_MAX_TENSOR_DIMS - num_dims() + i] = output_dim(i);
}
// Compute generalized strides.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides;
size_t input_stride = 1, output_stride = 1;
for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) {
input_strides[i - 1] = input_stride;
output_strides[i - 1] = output_stride;
input_stride *= input_dims[i - 1];
output_stride *= output_dims[i - 1];
}
std::vector<uint32_t> input(XNN_EXTRA_BYTES / sizeof(uint32_t) + num_input_elements());
std::vector<uint32_t> output(num_output_elements());
std::vector<uint32_t> output_ref(num_output_elements());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(u32rng));
std::fill(output.begin(), output.end(), UINT32_C(0xDEADBEEF));
const uint32_t padding_value = u32rng();
// Compute reference results.
std::fill(output_ref.begin(), output_ref.end(), padding_value);
for (size_t i = 0; i < input_dims[0]; i++) {
for (size_t j = 0; j < input_dims[1]; j++) {
for (size_t k = 0; k < input_dims[2]; k++) {
for (size_t l = 0; l < input_dims[3]; l++) {
for (size_t m = 0; m < input_dims[4]; m++) {
for (size_t n = 0; n < input_dims[5]; n++) {
const size_t output_index =
(i + input_pre_paddings[0]) * output_strides[0] +
(j + input_pre_paddings[1]) * output_strides[1] +
(k + input_pre_paddings[2]) * output_strides[2] +
(l + input_pre_paddings[3]) * output_strides[3] +
(m + input_pre_paddings[4]) * output_strides[4] +
(n + input_pre_paddings[5]) * output_strides[5];
const size_t input_index =
i * input_strides[0] + j * input_strides[1] + k * input_strides[2] +
l * input_strides[3] + m * input_strides[4] + n * input_strides[5];
output_ref[output_index] = input[input_index];
}
}
}
}
}
}
// Create, setup, run, and destroy a binary elementwise operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t pad_op = nullptr;
ASSERT_EQ(xnn_status_success,
xnn_create_constant_pad_nd_x32(
&padding_value, 0, &pad_op));
ASSERT_NE(nullptr, pad_op);
// Smart pointer to automatically delete pad_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_pad_op(pad_op, xnn_delete_operator);
ASSERT_EQ(xnn_status_success,
xnn_setup_constant_pad_nd_x32(
pad_op,
num_dims(),
input_shape().data(), pre_paddings().data(), post_paddings().data(),
input.data(), output.data(),
nullptr /* thread pool */));
ASSERT_EQ(xnn_status_success,
xnn_run_operator(pad_op, nullptr /* thread pool */));
// Verify results.
for (size_t i = 0; i < output_dims[0]; i++) {
for (size_t j = 0; j < output_dims[1]; j++) {
for (size_t k = 0; k < output_dims[2]; k++) {
for (size_t l = 0; l < output_dims[3]; l++) {
for (size_t m = 0; m < output_dims[4]; m++) {
for (size_t n = 0; n < output_dims[5]; n++) {
const size_t index =
i * output_strides[0] + j * output_strides[1] + k * output_strides[2] +
l * output_strides[3] + m * output_strides[4] + n * output_strides[5];
ASSERT_EQ(output[index], output_ref[index])
<< "(i, j, k, l, m, n) = ("
<< i << ", " << j << ", " << k << ", " << l << ", " << m << ", " << n << ")"
<< ", padding value = " << padding_value;
}
}
}
}
}
}
}
}
private:
std::vector<size_t> input_shape_;
std::vector<size_t> pre_paddings_;
std::vector<size_t> post_paddings_;
size_t iterations_{3};
};
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