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binary-elementwise-operator-tester.cc
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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.
#include "binary-elementwise-operator-tester.h"
#include <stdint.h>
#include <algorithm>
#include <array>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <cstdlib>
#include <functional>
#include <limits>
#include <memory>
#include <numeric>
#include <random>
#include <vector>
#include <gtest/gtest.h>
#include "xnnpack.h"
#include "xnnpack/math.h"
#include "replicable_random_device.h"
#include <climits>
void BinaryElementwiseOperatorTester::TestQS8() const {
ASSERT_NE(operation_type(), OperationType::Unknown);
ASSERT_GE(input1_zero_point(), std::numeric_limits<int8_t>::min());
ASSERT_LE(input1_zero_point(), std::numeric_limits<int8_t>::max());
ASSERT_GE(input2_zero_point(), std::numeric_limits<int8_t>::min());
ASSERT_LE(input2_zero_point(), std::numeric_limits<int8_t>::max());
ASSERT_GE(output_zero_point(), std::numeric_limits<int8_t>::min());
ASSERT_LE(output_zero_point(), std::numeric_limits<int8_t>::max());
ASSERT_GE(qmin(), std::numeric_limits<int8_t>::min());
ASSERT_LE(qmax(), std::numeric_limits<int8_t>::max());
ASSERT_LT(qmin(), qmax());
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<int32_t> i8dist(
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
// Compute generalized shapes.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
std::fill(input1_dims.begin(), input1_dims.end(), 1);
std::fill(input2_dims.begin(), input2_dims.end(), 1);
std::fill(output_dims.begin(), output_dims.end(), 1);
std::copy(input1_shape().cbegin(), input1_shape().cend(),
input1_dims.end() - num_input1_dims());
std::copy(input2_shape().cbegin(), input2_shape().cend(),
input2_dims.end() - num_input2_dims());
for (size_t i = 0; i < XNN_MAX_TENSOR_DIMS; i++) {
if (input1_dims[i] != 1 && input2_dims[i] != 1) {
ASSERT_EQ(input1_dims[i], input2_dims[i]);
}
output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
}
const size_t num_output_elements =
std::accumulate(output_dims.begin(), output_dims.end(), size_t(1),
std::multiplies<size_t>());
// Compute generalized strides.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides;
size_t input1_stride = 1, input2_stride = 1, output_stride = 1;
for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) {
input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride;
input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride;
output_strides[i - 1] = output_stride;
input1_stride *= input1_dims[i - 1];
input2_stride *= input2_dims[i - 1];
output_stride *= output_dims[i - 1];
}
std::vector<int8_t> input1(XNN_EXTRA_BYTES / sizeof(xnn_float16) +
num_input1_elements());
std::vector<int8_t> input2(XNN_EXTRA_BYTES / sizeof(xnn_float16) +
num_input2_elements());
std::vector<int8_t> output(num_output_elements);
std::vector<float> output_ref(num_output_elements);
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input1.begin(), input1.end(), [&]() { return i8dist(rng); });
std::generate(input2.begin(), input2.end(), [&]() { return i8dist(rng); });
std::fill(output.begin(), output.end(), 0xAA);
// Compute reference 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++) {
output_ref[i * output_strides[0] + j * output_strides[1] +
k * output_strides[2] + l * output_strides[3] +
m * output_strides[4] + n * output_strides[5]] =
Compute(
input1_scale() * (static_cast<int32_t>(
input1[i * input1_strides[0] +
j * input1_strides[1] +
k * input1_strides[2] +
l * input1_strides[3] +
m * input1_strides[4] +
n * input1_strides[5]]) -
input1_zero_point()),
input2_scale() * (static_cast<int32_t>(
input2[i * input2_strides[0] +
j * input2_strides[1] +
k * input2_strides[2] +
l * input2_strides[3] +
m * input2_strides[4] +
n * input2_strides[5]]) -
input2_zero_point())) /
output_scale() +
static_cast<float>(output_zero_point());
}
}
}
}
}
}
for (float& output_value : output_ref) {
output_value = std::max<float>(output_value, static_cast<float>(qmin()));
output_value = std::min<float>(output_value, static_cast<float>(qmax()));
}
// Create, setup, run, and destroy a binary elementwise operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t binary_elementwise_op = nullptr;
xnn_status status = xnn_status_unsupported_parameter;
switch (operation_type()) {
case OperationType::Add:
status = xnn_create_add_nd_qs8(
input1_zero_point(), input1_scale(), input2_zero_point(),
input2_scale(), output_zero_point(), output_scale(),
static_cast<int8_t>(qmin()), static_cast<int8_t>(qmax()), 0,
&binary_elementwise_op);
break;
case OperationType::Multiply:
status = xnn_create_multiply_nd_qs8(
input1_zero_point(), input1_scale(), input2_zero_point(),
input2_scale(), output_zero_point(), output_scale(),
static_cast<int8_t>(qmin()), static_cast<int8_t>(qmax()), 0,
&binary_elementwise_op);
break;
case OperationType::Subtract:
status = xnn_create_subtract_nd_qs8(
input1_zero_point(), input1_scale(), input2_zero_point(),
input2_scale(), output_zero_point(), output_scale(),
static_cast<int8_t>(qmin()), static_cast<int8_t>(qmax()), 0,
&binary_elementwise_op);
break;
default:
FAIL() << "Unsupported operation type";
}
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, binary_elementwise_op);
// Smart pointer to automatically delete binary_elementwise_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)>
auto_binary_elementwise_op(binary_elementwise_op, xnn_delete_operator);
switch (operation_type()) {
case OperationType::Add:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_add_nd_qs8(binary_elementwise_op, num_input1_dims(),
input1_shape().data(), num_input2_dims(),
input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_add_nd_qs8(binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Multiply:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_multiply_nd_qs8(
binary_elementwise_op, num_input1_dims(), input1_shape().data(),
num_input2_dims(), input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_multiply_nd_qs8(
binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Subtract:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_subtract_nd_qs8(
binary_elementwise_op, num_input1_dims(), input1_shape().data(),
num_input2_dims(), input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_subtract_nd_qs8(
binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
default:
FAIL() << "Unsupported operation type";
}
ASSERT_EQ(xnn_status_success,
xnn_run_operator(binary_elementwise_op, /*threadpool=*/nullptr));
// 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_NEAR(static_cast<float>(output[index]),
output_ref[index], 0.6f)
<< "(i, j, k, l, m, n) = (" << i << ", " << j << ", " << k
<< ", " << l << ", " << m << ", " << n << ")"
<< ", input1 zero point = " << input1_zero_point()
<< ", input1 scale = " << input1_scale()
<< ", input2 zero point = " << input2_zero_point()
<< ", input2 scale = " << input2_scale()
<< ", output zero point = " << output_zero_point()
<< ", output scale = " << output_scale();
}
}
}
}
}
}
}
}
void BinaryElementwiseOperatorTester::TestQU8() const {
ASSERT_NE(operation_type(), OperationType::Unknown);
ASSERT_GE(input1_zero_point(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(input1_zero_point(), std::numeric_limits<uint8_t>::max());
ASSERT_GE(input2_zero_point(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(input2_zero_point(), std::numeric_limits<uint8_t>::max());
ASSERT_GE(output_zero_point(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(output_zero_point(), std::numeric_limits<uint8_t>::max());
ASSERT_GE(qmin(), std::numeric_limits<uint8_t>::min());
ASSERT_LE(qmax(), std::numeric_limits<uint8_t>::max());
ASSERT_LT(qmin(), qmax());
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<int32_t> u8dist(
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
// Compute generalized shapes.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
std::fill(input1_dims.begin(), input1_dims.end(), 1);
std::fill(input2_dims.begin(), input2_dims.end(), 1);
std::fill(output_dims.begin(), output_dims.end(), 1);
std::copy(input1_shape().cbegin(), input1_shape().cend(),
input1_dims.end() - num_input1_dims());
std::copy(input2_shape().cbegin(), input2_shape().cend(),
input2_dims.end() - num_input2_dims());
for (size_t i = 0; i < XNN_MAX_TENSOR_DIMS; i++) {
if (input1_dims[i] != 1 && input2_dims[i] != 1) {
ASSERT_EQ(input1_dims[i], input2_dims[i]);
}
output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
}
const size_t num_output_elements =
std::accumulate(output_dims.begin(), output_dims.end(), size_t(1),
std::multiplies<size_t>());
// Compute generalized strides.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides;
size_t input1_stride = 1, input2_stride = 1, output_stride = 1;
for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) {
input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride;
input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride;
output_strides[i - 1] = output_stride;
input1_stride *= input1_dims[i - 1];
input2_stride *= input2_dims[i - 1];
output_stride *= output_dims[i - 1];
}
std::vector<uint8_t> input1(XNN_EXTRA_BYTES / sizeof(xnn_float16) +
num_input1_elements());
std::vector<uint8_t> input2(XNN_EXTRA_BYTES / sizeof(xnn_float16) +
num_input2_elements());
std::vector<uint8_t> output(num_output_elements);
std::vector<float> output_ref(num_output_elements);
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input1.begin(), input1.end(), [&]() { return u8dist(rng); });
std::generate(input2.begin(), input2.end(), [&]() { return u8dist(rng); });
std::fill(output.begin(), output.end(), 0xAA);
// Compute reference 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++) {
output_ref[i * output_strides[0] + j * output_strides[1] +
k * output_strides[2] + l * output_strides[3] +
m * output_strides[4] + n * output_strides[5]] =
Compute(
input1_scale() * (static_cast<int32_t>(
input1[i * input1_strides[0] +
j * input1_strides[1] +
k * input1_strides[2] +
l * input1_strides[3] +
m * input1_strides[4] +
n * input1_strides[5]]) -
input1_zero_point()),
input2_scale() * (static_cast<int32_t>(
input2[i * input2_strides[0] +
j * input2_strides[1] +
k * input2_strides[2] +
l * input2_strides[3] +
m * input2_strides[4] +
n * input2_strides[5]]) -
input2_zero_point())) /
output_scale() +
static_cast<float>(output_zero_point());
}
}
}
}
}
}
for (float& output_value : output_ref) {
output_value = std::max<float>(output_value, static_cast<float>(qmin()));
output_value = std::min<float>(output_value, static_cast<float>(qmax()));
}
// Create, setup, run, and destroy a binary elementwise operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t binary_elementwise_op = nullptr;
xnn_status status = xnn_status_unsupported_parameter;
switch (operation_type()) {
case OperationType::Add:
status = xnn_create_add_nd_qu8(
input1_zero_point(), input1_scale(), input2_zero_point(),
input2_scale(), output_zero_point(), output_scale(),
static_cast<uint8_t>(qmin()), static_cast<uint8_t>(qmax()), 0,
&binary_elementwise_op);
break;
case OperationType::Multiply:
status = xnn_create_multiply_nd_qu8(
input1_zero_point(), input1_scale(), input2_zero_point(),
input2_scale(), output_zero_point(), output_scale(),
static_cast<uint8_t>(qmin()), static_cast<uint8_t>(qmax()), 0,
&binary_elementwise_op);
break;
case OperationType::Subtract:
status = xnn_create_subtract_nd_qu8(
input1_zero_point(), input1_scale(), input2_zero_point(),
input2_scale(), output_zero_point(), output_scale(),
static_cast<uint8_t>(qmin()), static_cast<uint8_t>(qmax()), 0,
&binary_elementwise_op);
break;
default:
FAIL() << "Unsupported operation type";
}
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, binary_elementwise_op);
// Smart pointer to automatically delete binary_elementwise_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)>
auto_binary_elementwise_op(binary_elementwise_op, xnn_delete_operator);
switch (operation_type()) {
case OperationType::Add:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_add_nd_qu8(binary_elementwise_op, num_input1_dims(),
input1_shape().data(), num_input2_dims(),
input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_add_nd_qu8(binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Multiply:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_multiply_nd_qu8(
binary_elementwise_op, num_input1_dims(), input1_shape().data(),
num_input2_dims(), input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_multiply_nd_qu8(
binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Subtract:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_subtract_nd_qu8(
binary_elementwise_op, num_input1_dims(), input1_shape().data(),
num_input2_dims(), input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_subtract_nd_qu8(
binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
default:
FAIL() << "Unsupported operation type";
}
ASSERT_EQ(xnn_status_success,
xnn_run_operator(binary_elementwise_op, /*threadpool=*/nullptr));
// 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_NEAR(
static_cast<float>(static_cast<int32_t>(output[index])),
output_ref[index], 0.6f)
<< "(i, j, k, l, m, n) = (" << i << ", " << j << ", " << k
<< ", " << l << ", " << m << ", " << n << ")"
<< ", input1 zero point = " << input1_zero_point()
<< ", input1 scale = " << input1_scale()
<< ", input2 zero point = " << input2_zero_point()
<< ", input2 scale = " << input2_scale()
<< ", output zero point = " << output_zero_point()
<< ", output scale = " << output_scale();
}
}
}
}
}
}
}
}
void BinaryElementwiseOperatorTester::TestF16() const {
ASSERT_NE(operation_type(), OperationType::Unknown);
ASSERT_LT(qmin(), qmax());
xnnpack::ReplicableRandomDevice rng;
std::uniform_real_distribution<float> f32dist(0.01f, 1.0f);
// Compute generalized shapes.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
std::fill(input1_dims.begin(), input1_dims.end(), 1);
std::fill(input2_dims.begin(), input2_dims.end(), 1);
std::fill(output_dims.begin(), output_dims.end(), 1);
std::copy(input1_shape().cbegin(), input1_shape().cend(),
input1_dims.end() - num_input1_dims());
std::copy(input2_shape().cbegin(), input2_shape().cend(),
input2_dims.end() - num_input2_dims());
for (size_t i = 0; i < XNN_MAX_TENSOR_DIMS; i++) {
if (input1_dims[i] != 1 && input2_dims[i] != 1) {
ASSERT_EQ(input1_dims[i], input2_dims[i]);
}
output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
}
const size_t num_output_elements =
std::accumulate(output_dims.begin(), output_dims.end(), size_t(1),
std::multiplies<size_t>());
// Compute generalized strides.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides;
size_t input1_stride = 1, input2_stride = 1, output_stride = 1;
for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) {
input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride;
input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride;
output_strides[i - 1] = output_stride;
input1_stride *= input1_dims[i - 1];
input2_stride *= input2_dims[i - 1];
output_stride *= output_dims[i - 1];
}
std::vector<xnn_float16> input1(XNN_EXTRA_BYTES / sizeof(xnn_float16) +
num_input1_elements());
std::vector<xnn_float16> input2(XNN_EXTRA_BYTES / sizeof(xnn_float16) +
num_input2_elements());
std::vector<xnn_float16> output(num_output_elements);
std::vector<float> output_ref(num_output_elements);
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input1.begin(), input1.end(),
[&]() { return f32dist(rng); });
std::generate(input2.begin(), input2.end(),
[&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), std::nanf(""));
// Compute reference 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++) {
output_ref[i * output_strides[0] + j * output_strides[1] +
k * output_strides[2] + l * output_strides[3] +
m * output_strides[4] + n * output_strides[5]] =
Compute(
input1[i * input1_strides[0] +
j * input1_strides[1] +
k * input1_strides[2] +
l * input1_strides[3] +
m * input1_strides[4] +
n * input1_strides[5]],
input2[i * input2_strides[0] +
j * input2_strides[1] +
k * input2_strides[2] +
l * input2_strides[3] +
m * input2_strides[4] +
n * input2_strides[5]]);
}
}
}
}
}
}
// Compute clamping parameters.
const float accumulated_min =
*std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max =
*std::max_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
float output_min =
accumulated_min +
accumulated_range *
(static_cast<float>(qmin() - std::numeric_limits<int16_t>::min()) /
static_cast<float>(std::numeric_limits<int16_t>::max() -
std::numeric_limits<int16_t>::min()));
if (qmin() == std::numeric_limits<int16_t>::min()) {
output_min = -std::numeric_limits<float>::infinity();
}
float output_max =
accumulated_max -
accumulated_range *
(static_cast<float>(std::numeric_limits<int16_t>::max() - qmax()) /
static_cast<float>(std::numeric_limits<int16_t>::max() -
std::numeric_limits<int16_t>::min()));
if (qmax() == std::numeric_limits<int16_t>::max()) {
output_max = +std::numeric_limits<float>::infinity();
}
output_min = xnn_float16(output_min);
output_max = xnn_float16(output_max);
for (float& output_value : output_ref) {
output_value = std::max(output_value, output_min);
output_value = std::min(output_value, output_max);
}
// Create, setup, run, and destroy a binary elementwise operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t binary_elementwise_op = nullptr;
xnn_status status = xnn_status_unsupported_parameter;
switch (operation_type()) {
case OperationType::Add:
status = xnn_create_add_nd_f16(output_min, output_max, 0,
&binary_elementwise_op);
break;
case OperationType::Divide:
status = xnn_create_divide_nd_f16(output_min, output_max, 0,
&binary_elementwise_op);
break;
case OperationType::Maximum:
status = xnn_create_maximum_nd_f16(0, &binary_elementwise_op);
break;
case OperationType::Minimum:
status = xnn_create_minimum_nd_f16(0, &binary_elementwise_op);
break;
case OperationType::Multiply:
status = xnn_create_multiply_nd_f16(output_min, output_max, 0,
&binary_elementwise_op);
break;
case OperationType::SquaredDifference:
status =
xnn_create_squared_difference_nd_f16(0, &binary_elementwise_op);
break;
case OperationType::Subtract:
status = xnn_create_subtract_nd_f16(output_min, output_max, 0,
&binary_elementwise_op);
break;
default:
FAIL() << "Unsupported operation type";
}
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, binary_elementwise_op);
// Smart pointer to automatically delete binary_elementwise_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)>
auto_binary_elementwise_op(binary_elementwise_op, xnn_delete_operator);
switch (operation_type()) {
case OperationType::Add:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_add_nd_f16(binary_elementwise_op, num_input1_dims(),
input1_shape().data(), num_input2_dims(),
input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_add_nd_f16(binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Divide:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_divide_nd_f16(binary_elementwise_op, num_input1_dims(),
input1_shape().data(), num_input2_dims(),
input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_divide_nd_f16(binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Maximum:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_maximum_nd_f16(binary_elementwise_op, num_input1_dims(),
input1_shape().data(), num_input2_dims(),
input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_maximum_nd_f16(binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Minimum:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_minimum_nd_f16(binary_elementwise_op, num_input1_dims(),
input1_shape().data(), num_input2_dims(),
input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_minimum_nd_f16(binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Multiply:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_multiply_nd_f16(
binary_elementwise_op, num_input1_dims(), input1_shape().data(),
num_input2_dims(), input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_multiply_nd_f16(
binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::SquaredDifference:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_squared_difference_nd_f16(
binary_elementwise_op, num_input1_dims(), input1_shape().data(),
num_input2_dims(), input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_squared_difference_nd_f16(
binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
case OperationType::Subtract:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_subtract_nd_f16(
binary_elementwise_op, num_input1_dims(), input1_shape().data(),
num_input2_dims(), input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_subtract_nd_f16(
binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
default:
FAIL() << "Unsupported operation type";
}
ASSERT_EQ(xnn_status_success,
xnn_run_operator(binary_elementwise_op, /*threadpool=*/nullptr));
// 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_NEAR(
output[index], output_ref[index],
std::max(1.0e-4f, std::abs(output_ref[index]) * 1.0e-2f))
<< "(i, j, k, l, m, n) = (" << i << ", " << j << ", " << k
<< ", " << l << ", " << m << ", " << n << ")";
}
}
}
}
}
}
}
}
void BinaryElementwiseOperatorTester::TestS32() const {
ASSERT_NE(operation_type(), OperationType::Unknown);
ASSERT_LT(qmin(), qmax());
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<int32_t> s32dist(-10000, 10000);
// Compute generalized shapes.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
std::fill(input1_dims.begin(), input1_dims.end(), 1);
std::fill(input2_dims.begin(), input2_dims.end(), 1);
std::fill(output_dims.begin(), output_dims.end(), 1);
std::copy(input1_shape().cbegin(), input1_shape().cend(),
input1_dims.end() - num_input1_dims());
std::copy(input2_shape().cbegin(), input2_shape().cend(),
input2_dims.end() - num_input2_dims());
for (size_t i = 0; i < XNN_MAX_TENSOR_DIMS; i++) {
if (input1_dims[i] != 1 && input2_dims[i] != 1) {
ASSERT_EQ(input1_dims[i], input2_dims[i]);
}
output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
}
const size_t num_output_elements =
std::accumulate(output_dims.begin(), output_dims.end(), size_t(1),
std::multiplies<size_t>());
// Compute generalized strides.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides;
size_t input1_stride = 1, input2_stride = 1, output_stride = 1;
for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) {
input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride;
input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride;
output_strides[i - 1] = output_stride;
input1_stride *= input1_dims[i - 1];
input2_stride *= input2_dims[i - 1];
output_stride *= output_dims[i - 1];
}
std::vector<int32_t> input1(XNN_EXTRA_BYTES / sizeof(int32_t) +
num_input1_elements());
std::vector<int32_t> input2(XNN_EXTRA_BYTES / sizeof(int32_t) +
num_input2_elements());
std::vector<int32_t> output(num_output_elements);
std::vector<int32_t> output_ref(num_output_elements);
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input1.begin(), input1.end(), [&]() { return s32dist(rng); });
std::generate(input2.begin(), input2.end(), [&]() { return s32dist(rng); });
std::fill(output.begin(), output.end(), INT_MAX);
// Compute reference 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++) {
output_ref[i * output_strides[0] + j * output_strides[1] +
k * output_strides[2] + l * output_strides[3] +
m * output_strides[4] + n * output_strides[5]] =
Compute(
input1[i * input1_strides[0] + j * input1_strides[1] +
k * input1_strides[2] + l * input1_strides[3] +
m * input1_strides[4] + n * input1_strides[5]],
input2[i * input2_strides[0] + j * input2_strides[1] +
k * input2_strides[2] + l * input2_strides[3] +
m * input2_strides[4] + n * input2_strides[5]]);
}
}
}
}
}
}
// Create, setup, run, and destroy a binary elementwise operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t binary_elementwise_op = nullptr;
switch (operation_type()) {
case OperationType::Multiply:
ASSERT_EQ(xnn_status_success,
xnn_create_multiply_nd_s32(0, &binary_elementwise_op));
break;
default:
FAIL() << "Unsupported operation type";
}
ASSERT_NE(nullptr, binary_elementwise_op);
// Smart pointer to automatically delete binary_elementwise_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)>
auto_binary_elementwise_op(binary_elementwise_op, xnn_delete_operator);
switch (operation_type()) {
case OperationType::Multiply:
ASSERT_EQ(
xnn_status_success,
xnn_reshape_multiply_nd_s32(
binary_elementwise_op, num_input1_dims(), input1_shape().data(),
num_input2_dims(), input2_shape().data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_multiply_nd_s32(
binary_elementwise_op, input1.data(),
input2.data(), output.data()));
break;
default:
FAIL() << "Unsupported operation type";
}
ASSERT_EQ(xnn_status_success,
xnn_run_operator(binary_elementwise_op, /*threadpool=*/nullptr));
// 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]);
}
}
}
}
}
}
}
}
void BinaryElementwiseOperatorTester::TestF32() const {
ASSERT_NE(operation_type(), OperationType::Unknown);
ASSERT_LT(qmin(), qmax());
xnnpack::ReplicableRandomDevice rng;
std::uniform_real_distribution<float> f32dist(0.01f, 1.0f);
// Compute generalized shapes.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_dims;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_dims;
std::fill(input1_dims.begin(), input1_dims.end(), 1);
std::fill(input2_dims.begin(), input2_dims.end(), 1);
std::fill(output_dims.begin(), output_dims.end(), 1);
std::copy(input1_shape().cbegin(), input1_shape().cend(),
input1_dims.end() - num_input1_dims());
std::copy(input2_shape().cbegin(), input2_shape().cend(),
input2_dims.end() - num_input2_dims());
for (size_t i = 0; i < XNN_MAX_TENSOR_DIMS; i++) {
if (input1_dims[i] != 1 && input2_dims[i] != 1) {
ASSERT_EQ(input1_dims[i], input2_dims[i]);
}
output_dims[i] = std::max(input1_dims[i], input2_dims[i]);
}
const size_t num_output_elements =
std::accumulate(output_dims.begin(), output_dims.end(), size_t(1),
std::multiplies<size_t>());
// Compute generalized strides.
std::array<size_t, XNN_MAX_TENSOR_DIMS> input1_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> input2_strides;
std::array<size_t, XNN_MAX_TENSOR_DIMS> output_strides;
size_t input1_stride = 1, input2_stride = 1, output_stride = 1;
for (size_t i = XNN_MAX_TENSOR_DIMS; i != 0; i--) {
input1_strides[i - 1] = input1_dims[i - 1] == 1 ? 0 : input1_stride;
input2_strides[i - 1] = input2_dims[i - 1] == 1 ? 0 : input2_stride;
output_strides[i - 1] = output_stride;
input1_stride *= input1_dims[i - 1];
input2_stride *= input2_dims[i - 1];
output_stride *= output_dims[i - 1];
}
std::vector<float> input1(XNN_EXTRA_BYTES / sizeof(float) +
num_input1_elements());
std::vector<float> input2(XNN_EXTRA_BYTES / sizeof(float) +
num_input2_elements());
std::vector<float> output(num_output_elements);
std::vector<float> output_ref(num_output_elements);
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input1.begin(), input1.end(), [&]() { return f32dist(rng); });
std::generate(input2.begin(), input2.end(), [&]() { return f32dist(rng); });
std::fill(output.begin(), output.end(), nanf(""));
// Compute reference 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++) {
output_ref[i * output_strides[0] + j * output_strides[1] +
k * output_strides[2] + l * output_strides[3] +
m * output_strides[4] + n * output_strides[5]] =
Compute(
input1[i * input1_strides[0] + j * input1_strides[1] +
k * input1_strides[2] + l * input1_strides[3] +
m * input1_strides[4] + n * input1_strides[5]],
input2[i * input2_strides[0] + j * input2_strides[1] +
k * input2_strides[2] + l * input2_strides[3] +
m * input2_strides[4] + n * input2_strides[5]]);
}
}
}
}
}
}
const float accumulated_min =
*std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max =
*std::max_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_range = accumulated_max - accumulated_min;
float output_min =
accumulated_min +
accumulated_range *
(static_cast<float>(qmin() - std::numeric_limits<int16_t>::min()) /
static_cast<float>(std::numeric_limits<int16_t>::max() -
std::numeric_limits<int16_t>::min()));
if (qmin() == std::numeric_limits<int16_t>::min()) {
output_min = -std::numeric_limits<float>::infinity();
}
float output_max =
accumulated_max -
accumulated_range *
(static_cast<float>(std::numeric_limits<int16_t>::max() - qmax()) /
static_cast<float>(std::numeric_limits<int16_t>::max() -
std::numeric_limits<int16_t>::min()));
if (qmax() == std::numeric_limits<int16_t>::max()) {
output_max = +std::numeric_limits<float>::infinity();
}
for (float& output_value : output_ref) {
output_value = std::max(output_value, output_min);
output_value = std::min(output_value, output_max);
}
// Create, setup, run, and destroy a binary elementwise operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t binary_elementwise_op = nullptr;
switch (operation_type()) {
case OperationType::Add:
ASSERT_EQ(xnn_status_success,
xnn_create_add_nd_f32(output_min, output_max, 0,
&binary_elementwise_op));
break;
case OperationType::CopySign:
ASSERT_EQ(xnn_status_success,
xnn_create_copysign_nd_f32(/*flags=*/0, &binary_elementwise_op));
break;
case OperationType::Divide:
ASSERT_EQ(xnn_status_success,
xnn_create_divide_nd_f32(output_min, output_max, 0,
&binary_elementwise_op));
break;
case OperationType::Maximum:
ASSERT_EQ(xnn_status_success,
xnn_create_maximum_nd_f32(0, &binary_elementwise_op));
break;
case OperationType::Minimum:
ASSERT_EQ(xnn_status_success,
xnn_create_minimum_nd_f32(0, &binary_elementwise_op));
break;
case OperationType::Multiply:
ASSERT_EQ(xnn_status_success,
xnn_create_multiply_nd_f32(output_min, output_max, 0,
&binary_elementwise_op));
break;
case OperationType::Subtract:
ASSERT_EQ(xnn_status_success,
xnn_create_subtract_nd_f32(output_min, output_max, 0,