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unplugged-system/external/XNNPACK/test/deconvolution-2d.cc

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// Copyright 2022 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 <algorithm> // For std::generate, std::min.
#include <array> // For std::array.
#include <cmath> // For std::lrintf.
#include <cstddef> // For size_t.
#include <cstdint> // For uint32_t.
#include <limits> // For std::numeric_limits.
#include <memory> // For std::unique_ptr.
#include <random> // For std::random_device, std::mt19937, std::uniform_real_distribution.
#include <vector> // For std::vector.
#include <xnnpack.h>
#include <xnnpack/operator.h>
#include <xnnpack/requantization.h>
#include <xnnpack/subgraph.h>
#include <gtest/gtest.h>
template <class T, class BiasType = T> class DeconvolutionTestBase : public ::testing::Test {
protected:
DeconvolutionTestBase()
{
random_device = std::unique_ptr<std::random_device>(new std::random_device());
rng = std::mt19937((*random_device)());
input_size_dist = std::uniform_int_distribution<uint32_t>(10, 15);
kernel_size_dist = std::uniform_int_distribution<uint32_t>(1, 5);
stride_dist = std::uniform_int_distribution<uint32_t>(1, 3);
f32dist = std::uniform_real_distribution<float>(0.1f, 1.0f);
scale_dist = std::uniform_real_distribution<float>(1.0f, 5.0f);
i32dist = std::uniform_int_distribution<int32_t>(-10000, 10000);
batch_size = input_size_dist(rng);
input_height = input_size_dist(rng);
input_width = input_size_dist(rng);
kernel_height = kernel_size_dist(rng);
kernel_width = kernel_size_dist(rng);
upsampling_height = stride_dist(rng);
upsampling_width = stride_dist(rng);
dilation_height = stride_dist(rng);
dilation_width = stride_dist(rng);
groups = input_size_dist(rng);
group_input_channels = input_size_dist(rng);
group_output_channels = input_size_dist(rng);
output_min = -std::numeric_limits<float>::infinity();
output_max = std::numeric_limits<float>::infinity();
adjustment_height = 0;
adjustment_width = 0;
output_height = xnn_compute_deconvolution_output_dimension(
input_height, padding_top + padding_bottom, adjustment_height, kernel_height, dilation_height, upsampling_height);
output_width = xnn_compute_deconvolution_output_dimension(
input_width, padding_left + padding_right, adjustment_width, kernel_width, dilation_width, upsampling_width);
input_dims = {{batch_size, input_height, input_width, group_input_channels}};
kernel_dims = {{groups * group_output_channels, kernel_height, kernel_width, group_input_channels}};
bias_dims = {{groups * group_output_channels}};
output_dims = {{batch_size, output_height, output_width, groups * group_output_channels}};
input = std::vector<T>(
XNN_EXTRA_BYTES / sizeof(T) + batch_size * input_height * input_width * groups * group_input_channels);
kernel = std::vector<T>(groups * group_output_channels * kernel_height * kernel_width * group_input_channels);
bias = std::vector<BiasType>(groups * group_output_channels);
operator_output = std::vector<T>(batch_size * output_height * output_width * groups * group_output_channels);
subgraph_output = std::vector<T>(batch_size * output_height * output_width * groups * group_output_channels);
}
std::unique_ptr<std::random_device> random_device;
std::mt19937 rng;
std::uniform_int_distribution<uint32_t> input_size_dist;
std::uniform_int_distribution<uint32_t> kernel_size_dist;
std::uniform_int_distribution<uint32_t> stride_dist;
std::uniform_int_distribution<int32_t> i32dist;
std::uniform_real_distribution<float> f32dist;
std::uniform_real_distribution<float> scale_dist;
const uint32_t padding_top = 0;
const uint32_t padding_right = 0;
const uint32_t padding_bottom = 0;
const uint32_t padding_left = 0;
uint32_t batch_size;
uint32_t input_height;
uint32_t input_width;
uint32_t kernel_height;
uint32_t kernel_width;
uint32_t upsampling_height;
uint32_t upsampling_width;
uint32_t adjustment_height;
uint32_t adjustment_width;
uint32_t dilation_height;
uint32_t dilation_width;
uint32_t groups;
uint32_t group_input_channels;
uint32_t group_output_channels;
float output_min;
float output_max;
uint32_t output_height;
uint32_t output_width;
std::array<size_t, 4> input_dims;
std::array<size_t, 4> kernel_dims;
std::array<size_t, 1> bias_dims;
std::array<size_t, 4> output_dims;
std::vector<T> input;
std::vector<T> kernel;
std::vector<BiasType> bias;
std::vector<T> operator_output;
std::vector<T> subgraph_output;
};
template <class T> class QuantizedDeconvolutionTestBase : public DeconvolutionTestBase<T, int32_t> {
protected:
QuantizedDeconvolutionTestBase()
{
i8dist = std::uniform_int_distribution<int32_t>(std::numeric_limits<T>::min(), std::numeric_limits<T>::max());
w8dist = std::uniform_int_distribution<int32_t>(-std::numeric_limits<T>::max(), std::numeric_limits<T>::max());
std::uniform_int_distribution<int32_t> u8dist(
std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
accumulators = std::vector<int32_t>(
this->batch_size * this->output_height * this->output_width * this->groups * this->group_output_channels);
}
void initialize_accumulators_from_bias()
{
for (size_t i = 0; i < this->batch_size; i++) {
for (size_t oy = 0; oy < this->output_height; oy++) {
for (size_t ox = 0; ox < this->output_width; ox++) {
for (size_t g = 0; g < this->groups; g++) {
for (size_t oc = 0; oc < this->group_output_channels; oc++) {
accumulators
[(((i * this->output_height + oy) * this->output_width + ox) * this->groups + g) *
this->group_output_channels +
oc] = this->bias[g * this->group_output_channels + oc];
}
}
}
}
}
}
std::uniform_int_distribution<int32_t> i8dist;
std::uniform_int_distribution<int32_t> u8dist;
std::uniform_int_distribution<int32_t> w8dist;
std::vector<int32_t> accumulators;
};
using DeconvolutionTestQS8 = QuantizedDeconvolutionTestBase<int8_t>;
using DeconvolutionTestQU8 = QuantizedDeconvolutionTestBase<uint8_t>;
using DeconvolutionTestF32 = DeconvolutionTestBase<float>;
TEST_F(DeconvolutionTestQS8, define)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, 0, 1.0f, input_dims.size(), input_dims.data(), nullptr,
/*external_id=*/0, /*flags=*/0, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t kernel_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, 0, 1.0f, kernel_dims.size(), kernel_dims.data(), kernel.data(),
/*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint32, 0, 1.0f, bias_dims.size(), bias_dims.data(), bias.data(),
/*external_id=*/2, /*flags=*/0, &bias_id));
uint32_t output_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, 0, 1.0f, output_dims.size(), output_dims.data(), nullptr,
/*external_id=*/3, /*flags=*/0, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_deconvolution_2d(
subgraph, padding_top, padding_right, padding_bottom, padding_left, adjustment_height, adjustment_width,
kernel_height, kernel_width, upsampling_height, upsampling_width, dilation_height, dilation_width, groups,
group_input_channels, group_output_channels, output_min, output_max, input_id, kernel_id, bias_id, output_id,
/*flags=*/0));
ASSERT_EQ(subgraph->num_nodes, 1);
const struct xnn_node* node = &subgraph->nodes[0];
ASSERT_EQ(node->type, xnn_node_type_deconvolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qs8);
ASSERT_EQ(node->params.deconvolution_2d.padding_top, padding_top);
ASSERT_EQ(node->params.deconvolution_2d.padding_right, padding_right);
ASSERT_EQ(node->params.deconvolution_2d.padding_bottom, padding_bottom);
ASSERT_EQ(node->params.deconvolution_2d.padding_left, padding_left);
ASSERT_EQ(node->params.deconvolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.deconvolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.deconvolution_2d.upsampling_height, upsampling_height);
ASSERT_EQ(node->params.deconvolution_2d.upsampling_width, upsampling_width);
ASSERT_EQ(node->params.deconvolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.deconvolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.deconvolution_2d.adjustment_height, adjustment_height);
ASSERT_EQ(node->params.deconvolution_2d.adjustment_width, adjustment_width);
ASSERT_EQ(node->params.deconvolution_2d.groups, groups);
ASSERT_EQ(node->params.deconvolution_2d.group_input_channels, group_input_channels);
ASSERT_EQ(node->params.deconvolution_2d.group_output_channels, group_output_channels);
ASSERT_EQ(node->activation.output_min, output_min);
ASSERT_EQ(node->activation.output_max, output_max);
ASSERT_EQ(node->num_inputs, 3);
ASSERT_EQ(node->inputs[0], input_id);
ASSERT_EQ(node->inputs[1], kernel_id);
ASSERT_EQ(node->inputs[2], bias_id);
ASSERT_EQ(node->num_outputs, 1);
ASSERT_EQ(node->outputs[0], output_id);
ASSERT_EQ(node->flags, 0);
}
TEST_F(DeconvolutionTestQU8, define)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, 0, 1.0f, input_dims.size(), input_dims.data(), nullptr,
/*external_id=*/0, /*flags=*/0, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t kernel_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, 0, 1.0f, kernel_dims.size(), kernel_dims.data(), kernel.data(),
/*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint32, 0, 1.0f, bias_dims.size(), bias_dims.data(), bias.data(),
/*external_id=*/2, /*flags=*/0, &bias_id));
uint32_t output_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, 0, 1.0f, output_dims.size(), output_dims.data(), nullptr,
/*external_id=*/3, /*flags=*/0, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_deconvolution_2d(
subgraph, padding_top, padding_right, padding_bottom, padding_left, adjustment_height, adjustment_width,
kernel_height, kernel_width, upsampling_height, upsampling_width, dilation_height, dilation_width, groups,
group_input_channels, group_output_channels, output_min, output_max, input_id, kernel_id, bias_id, output_id,
/*flags=*/0));
ASSERT_EQ(subgraph->num_nodes, 1);
const struct xnn_node* node = &subgraph->nodes[0];
ASSERT_EQ(node->type, xnn_node_type_deconvolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qu8);
ASSERT_EQ(node->params.deconvolution_2d.padding_top, padding_top);
ASSERT_EQ(node->params.deconvolution_2d.padding_right, padding_right);
ASSERT_EQ(node->params.deconvolution_2d.padding_bottom, padding_bottom);
ASSERT_EQ(node->params.deconvolution_2d.padding_left, padding_left);
ASSERT_EQ(node->params.deconvolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.deconvolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.deconvolution_2d.upsampling_height, upsampling_height);
ASSERT_EQ(node->params.deconvolution_2d.upsampling_width, upsampling_width);
ASSERT_EQ(node->params.deconvolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.deconvolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.deconvolution_2d.adjustment_height, adjustment_height);
ASSERT_EQ(node->params.deconvolution_2d.adjustment_width, adjustment_width);
ASSERT_EQ(node->params.deconvolution_2d.groups, groups);
ASSERT_EQ(node->params.deconvolution_2d.group_input_channels, group_input_channels);
ASSERT_EQ(node->params.deconvolution_2d.group_output_channels, group_output_channels);
ASSERT_EQ(node->activation.output_min, output_min);
ASSERT_EQ(node->activation.output_max, output_max);
ASSERT_EQ(node->num_inputs, 3);
ASSERT_EQ(node->inputs[0], input_id);
ASSERT_EQ(node->inputs[1], kernel_id);
ASSERT_EQ(node->inputs[2], bias_id);
ASSERT_EQ(node->num_outputs, 1);
ASSERT_EQ(node->outputs[0], output_id);
ASSERT_EQ(node->flags, 0);
}
TEST_F(DeconvolutionTestF32, define)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr,
/*external_id=*/0, /*flags=*/0, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t kernel_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success,
xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, kernel_dims.size(), kernel_dims.data(), kernel.data(), /*external_id=*/1,
/*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, bias_dims.size(), bias_dims.data(), bias.data(),
/*external_id=*/2, /*flags=*/0, &bias_id));
uint32_t output_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr,
/*external_id=*/3, /*flags=*/0, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_deconvolution_2d(
subgraph, padding_top, padding_right, padding_bottom, padding_left, adjustment_height, adjustment_width,
kernel_height, kernel_width, upsampling_height, upsampling_width, dilation_height, dilation_width, groups,
group_input_channels, group_output_channels, output_min, output_max, input_id, kernel_id, bias_id, output_id,
/*flags=*/0));
ASSERT_EQ(subgraph->num_nodes, 1);
const struct xnn_node* node = &subgraph->nodes[0];
ASSERT_EQ(node->type, xnn_node_type_deconvolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_fp32);
ASSERT_EQ(node->params.deconvolution_2d.padding_top, padding_top);
ASSERT_EQ(node->params.deconvolution_2d.padding_right, padding_right);
ASSERT_EQ(node->params.deconvolution_2d.padding_bottom, padding_bottom);
ASSERT_EQ(node->params.deconvolution_2d.padding_left, padding_left);
ASSERT_EQ(node->params.deconvolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.deconvolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.deconvolution_2d.upsampling_height, upsampling_height);
ASSERT_EQ(node->params.deconvolution_2d.upsampling_width, upsampling_width);
ASSERT_EQ(node->params.deconvolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.deconvolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.deconvolution_2d.adjustment_height, adjustment_height);
ASSERT_EQ(node->params.deconvolution_2d.adjustment_width, adjustment_width);
ASSERT_EQ(node->params.deconvolution_2d.groups, groups);
ASSERT_EQ(node->params.deconvolution_2d.group_input_channels, group_input_channels);
ASSERT_EQ(node->params.deconvolution_2d.group_output_channels, group_output_channels);
ASSERT_EQ(node->activation.output_min, output_min);
ASSERT_EQ(node->activation.output_max, output_max);
ASSERT_EQ(node->num_inputs, 3);
ASSERT_EQ(node->inputs[0], input_id);
ASSERT_EQ(node->inputs[1], kernel_id);
ASSERT_EQ(node->inputs[2], bias_id);
ASSERT_EQ(node->num_outputs, 1);
ASSERT_EQ(node->outputs[0], output_id);
ASSERT_EQ(node->flags, 0);
}
TEST_F(DeconvolutionTestQS8, matches_operator_api)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_operator_t op = nullptr;
std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });
std::generate(kernel.begin(), kernel.end(), [&]() { return w8dist(rng); });
std::generate(bias.begin(), bias.end(), [&]() { return i32dist(rng); });
std::fill(operator_output.begin(), operator_output.end(), INT8_C(0xA5));
std::fill(subgraph_output.begin(), subgraph_output.end(), INT8_C(0xA5));
const int8_t input_zero_point = 1;
const float input_scale = scale_dist(rng);
const float kernel_scale = scale_dist(rng);
for (size_t i = 0; i < batch_size; i++) {
for (size_t oy = 0; oy < output_height; oy++) {
for (size_t ox = 0; ox < output_width; ox++) {
for (size_t ky = 0; ky < kernel_height; ky++) {
const size_t y = oy + padding_top - ky * dilation_height;
const size_t iy = y / upsampling_height;
if (iy * upsampling_height == y && iy < input_height) {
for (size_t kx = 0; kx < kernel_width; kx++) {
const size_t x = ox + padding_left - kx * dilation_width;
const size_t ix = x / upsampling_width;
if (ix * upsampling_width == x && ix < input_width) {
for (size_t g = 0; g < groups; g++) {
for (size_t oc = 0; oc < group_output_channels; oc++) {
for (size_t ic = 0; ic < group_input_channels; ic++) {
accumulators
[(((i * output_height + oy) * output_width + ox) * groups + g) * group_output_channels + oc] +=
(int32_t(input[((i * input_height + iy) * input_width + ix) * g * group_input_channels + ic]) -
int32_t(input_zero_point)) *
int32_t(kernel
[(((g * group_output_channels + oc) * kernel_height + ky) * kernel_width + kx) *
group_input_channels +
ic]);
}
}
}
}
}
}
}
}
}
}
// Compute renormalization parameters.
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
float output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
int8_t output_zero_point = int8_t(std::max(
std::min(
lrint(-0.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
long(std::numeric_limits<int8_t>::max())),
long(std::numeric_limits<int8_t>::min())));
const int8_t quantized_output_min = xnn_qs8_quantize(output_min, output_scale, output_zero_point);
const int8_t quantized_output_max = xnn_qs8_quantize(output_max, output_scale, output_zero_point);
// Call operator API.
const xnn_status status = xnn_create_deconvolution2d_nhwc_qs8(
padding_top, padding_right, padding_bottom, padding_left, kernel_height, kernel_width, upsampling_height,
upsampling_width, dilation_height, dilation_width, groups, group_input_channels, group_output_channels,
groups * group_input_channels, groups * group_output_channels, input_zero_point, input_scale, kernel_scale,
kernel.data(), bias.data(), output_zero_point, output_scale, quantized_output_min, quantized_output_max,
/*flags=*/0, nullptr, &op);
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, op);
ASSERT_EQ(
xnn_status_success, xnn_setup_deconvolution2d_nhwc_qs8(
op, batch_size, input_height, input_width, adjustment_height, adjustment_width, input.data(),
operator_output.data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));
// Call subgraph API.
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, input_zero_point, input_scale, input_dims.size(),
input_dims.data(), nullptr, /*external_id=*/0, XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t kernel_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, 0, kernel_scale, kernel_dims.size(), kernel_dims.data(),
kernel.data(), /*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint32, 0, kernel_scale, bias_dims.size(), bias_dims.data(),
bias.data(), /*external_id=*/2, /*flags=*/0, &bias_id));
uint32_t output_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, output_zero_point, output_scale, output_dims.size(),
output_dims.data(), nullptr, /*external_id=*/3, XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_deconvolution_2d(
subgraph, padding_top, padding_right, padding_bottom, padding_left, adjustment_height, adjustment_width,
kernel_height, kernel_width, upsampling_height, upsampling_width, dilation_height, dilation_width, groups,
group_input_channels, group_output_channels, output_min, output_max, input_id, kernel_id, bias_id, output_id,
/*flags=*/0));
xnn_runtime_t runtime = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
ASSERT_NE(nullptr, runtime);
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
std::array<xnn_external_value, 2> external = {
xnn_external_value{input_id, input.data()}, xnn_external_value{output_id, subgraph_output.data()}};
ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));
// Check outputs match.
for (size_t i = 0; i < operator_output.size(); i++) {
ASSERT_EQ(subgraph_output[i], operator_output[i]);
}
}
TEST_F(DeconvolutionTestQU8, matches_operator_api)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_operator_t op = nullptr;
std::generate(input.begin(), input.end(), [&]() { return u8dist(rng); });
std::generate(kernel.begin(), kernel.end(), [&]() { return u8dist(rng); });
std::generate(bias.begin(), bias.end(), [&]() { return i32dist(rng); });
std::fill(operator_output.begin(), operator_output.end(), UINT8_C(0xA5));
std::fill(subgraph_output.begin(), subgraph_output.end(), UINT8_C(0xA5));
const uint8_t input_zero_point = u8dist(rng);
const uint8_t kernel_zero_point = 0;
const float input_scale = scale_dist(rng);
const float kernel_scale = scale_dist(rng);
// Compute reference results, without renormalization.
initialize_accumulators_from_bias();
for (size_t i = 0; i < batch_size; i++) {
for (size_t oy = 0; oy < output_height; oy++) {
for (size_t ox = 0; ox < output_width; ox++) {
for (size_t ky = 0; ky < kernel_height; ky++) {
const size_t y = oy + padding_top - ky * dilation_height;
const size_t iy = y / upsampling_height;
if (iy * upsampling_height == y && iy < input_height) {
for (size_t kx = 0; kx < kernel_width; kx++) {
const size_t x = ox + padding_left - kx * dilation_width;
const size_t ix = x / upsampling_width;
if (ix * upsampling_width == x && ix < input_width) {
for (size_t g = 0; g < groups; g++) {
for (size_t oc = 0; oc < group_output_channels; oc++) {
for (size_t ic = 0; ic < group_input_channels; ic++) {
accumulators
[(((i * output_height + oy) * output_width + ox) * groups + g) * group_output_channels + oc] +=
(int32_t(input[((i * input_height + iy) * input_width + ix) * g * group_input_channels + ic]) -
int32_t(input_zero_point)) *
(int32_t(kernel
[(((g * group_output_channels + oc) * kernel_height + ky) * kernel_width + kx) *
group_input_channels +
ic]) -
int32_t(kernel_zero_point));
}
}
}
}
}
}
}
}
}
}
// Compute renormalization parameters.
const int32_t accumulated_min = *std::min_element(accumulators.cbegin(), accumulators.cend());
const int32_t accumulated_max = *std::max_element(accumulators.cbegin(), accumulators.cend());
const double output_scale = double(uint32_t(accumulated_max - accumulated_min)) / 255.0;
const uint8_t output_zero_point = uint8_t(std::max(
std::min(
lrint(127.5 - 0.5 * double(accumulated_min + accumulated_max) / output_scale),
long(std::numeric_limits<uint8_t>::max())),
long(std::numeric_limits<uint8_t>::min())));
const uint8_t quantized_output_min = xnn_qu8_quantize(output_min, output_scale, output_zero_point);
const uint8_t quantized_output_max = xnn_qu8_quantize(output_max, output_scale, output_zero_point);
// Call operator API.
const xnn_status status = xnn_create_deconvolution2d_nhwc_qu8(
padding_top, padding_right, padding_bottom, padding_left, kernel_height, kernel_width, upsampling_height,
upsampling_width, dilation_height, dilation_width, groups, group_input_channels, group_output_channels,
groups * group_input_channels, groups * group_output_channels, input_zero_point, input_scale, kernel_zero_point,
kernel_scale, kernel.data(), bias.data(), output_zero_point, output_scale, quantized_output_min,
quantized_output_max, /*flags=*/0, nullptr, &op);
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, op);
ASSERT_EQ(
xnn_status_success, xnn_setup_deconvolution2d_nhwc_qu8(
op, batch_size, input_height, input_width, adjustment_height, adjustment_width, input.data(),
operator_output.data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));
// Call subgraph API.
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, input_zero_point, input_scale, input_dims.size(),
input_dims.data(), nullptr, /*external_id=*/0, XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t kernel_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, 0, kernel_scale, kernel_dims.size(), kernel_dims.data(),
kernel.data(), /*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint32, 0, kernel_scale, bias_dims.size(), bias_dims.data(),
bias.data(), /*external_id=*/2, /*flags=*/0, &bias_id));
uint32_t output_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, output_zero_point, output_scale, output_dims.size(),
output_dims.data(), nullptr, /*external_id=*/3, XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_deconvolution_2d(
subgraph, padding_top, padding_right, padding_bottom, padding_left, adjustment_height, adjustment_width,
kernel_height, kernel_width, upsampling_height, upsampling_width, dilation_height, dilation_width, groups,
group_input_channels, group_output_channels, output_min, output_max, input_id, kernel_id, bias_id, output_id,
/*flags=*/0));
xnn_runtime_t runtime = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
ASSERT_NE(nullptr, runtime);
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
std::array<xnn_external_value, 2> external = {
xnn_external_value{input_id, input.data()}, xnn_external_value{output_id, subgraph_output.data()}};
ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));
// Check outputs match.
for (size_t i = 0; i < operator_output.size(); i++) {
ASSERT_EQ(subgraph_output[i], operator_output[i]);
}
}
TEST_F(DeconvolutionTestF32, matches_operator_api)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_operator_t op = nullptr;
std::generate(input.begin(), input.end(), [&]() { return f32dist(rng); });
std::generate(kernel.begin(), kernel.end(), [&]() { return f32dist(rng); });
std::generate(bias.begin(), bias.end(), [&]() { return f32dist(rng); });
std::fill(operator_output.begin(), operator_output.end(), nanf(""));
std::fill(subgraph_output.begin(), subgraph_output.end(), nanf(""));
// Call operator API.
const xnn_status status = xnn_create_deconvolution2d_nhwc_f32(
padding_top, padding_right, padding_bottom, padding_left, kernel_height, kernel_width, upsampling_height,
upsampling_width, dilation_height, dilation_width, groups, group_input_channels, group_output_channels,
groups * group_input_channels, groups * group_output_channels, kernel.data(), bias.data(), output_min, output_max,
/*flags=*/0, nullptr, &op);
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, op);
ASSERT_EQ(
xnn_status_success, xnn_setup_deconvolution2d_nhwc_f32(
op, batch_size, input_height, input_width, adjustment_height, adjustment_width, input.data(),
operator_output.data(),
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));
// Call subgraph API.
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
uint32_t input_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr,
/*external_id=*/0, XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t kernel_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, kernel_dims.size(), kernel_dims.data(), kernel.data(),
/*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, bias_dims.size(), bias_dims.data(), bias.data(),
/*external_id=*/2, /*flags=*/0, &bias_id));
uint32_t output_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, output_dims.size(), output_dims.data(), nullptr,
/*external_id=*/3, XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_deconvolution_2d(
subgraph, padding_top, padding_right, padding_bottom, padding_left, adjustment_height, adjustment_width,
kernel_height, kernel_width, upsampling_height, upsampling_width, dilation_height, dilation_width, groups,
group_input_channels, group_output_channels, output_min, output_max, input_id, kernel_id, bias_id, output_id,
/*flags=*/0));
xnn_runtime_t runtime = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
ASSERT_NE(nullptr, runtime);
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
std::array<xnn_external_value, 2> external = {
xnn_external_value{input_id, input.data()}, xnn_external_value{output_id, subgraph_output.data()}};
ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));
// Check outputs match.
for (size_t i = 0; i < operator_output.size(); i++) {
ASSERT_EQ(subgraph_output[i], operator_output[i]);
}
}
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