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convolution-operator-tester.h
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// Copyright (c) Facebook, Inc. and its affiliates.
// All rights reserved.
//
// 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 <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <cstdlib>
#include <limits>
#include <memory>
#include <numeric>
#include <random>
#include <vector>
#include <gtest/gtest.h>
#include <fp16/fp16.h>
#include "xnnpack.h"
#include "xnnpack/aligned-allocator.h"
#include "xnnpack/cache.h"
#include "xnnpack/common.h"
#include "xnnpack/microparams.h"
#include "convolution-test-helpers.h"
#include "replicable_random_device.h"
#include "pthreadpool.h"
class ConvolutionOperatorTester {
public:
enum class WeightsType {
Default,
FP32,
};
ConvolutionOperatorTester& padding_tf_same(bool padding_same) {
if (padding_same) {
assert(padding_top() == 0);
assert(padding_left() == 0);
assert(padding_bottom() == 0);
assert(padding_right() == 0);
}
this->padding_tf_same_ = padding_same;
return *this;
}
bool padding_tf_same() const {
return this->padding_tf_same_;
}
ConvolutionOperatorTester& padding(uint32_t padding) {
assert(!padding_tf_same());
this->padding_top_ = padding;
this->padding_right_ = padding;
this->padding_bottom_ = padding;
this->padding_left_ = padding;
return *this;
}
ConvolutionOperatorTester& padding(uint32_t padding_height, uint32_t padding_width) {
assert(!padding_tf_same());
this->padding_top_ = padding_height;
this->padding_right_ = padding_width;
this->padding_bottom_ = padding_height;
this->padding_left_ = padding_width;
return *this;
}
ConvolutionOperatorTester& padding_height(uint32_t padding_height) {
assert(!padding_tf_same());
this->padding_top_ = padding_height;
this->padding_bottom_ = padding_height;
return *this;
}
ConvolutionOperatorTester& padding_width(uint32_t padding_width) {
assert(!padding_tf_same());
this->padding_right_ = padding_width;
this->padding_left_ = padding_width;
return *this;
}
ConvolutionOperatorTester& padding_top(uint32_t padding_top) {
assert(!padding_tf_same());
this->padding_top_ = padding_top;
return *this;
}
uint32_t padding_top() const {
if (padding_tf_same()) {
const uint32_t total_padding_height =
(output_height() - 1) * subsampling_height() + dilated_kernel_height() - input_height();
return total_padding_height / 2;
} else {
return this->padding_top_;
}
}
ConvolutionOperatorTester& padding_left(uint32_t padding_left) {
assert(!padding_tf_same());
this->padding_left_ = padding_left;
return *this;
}
uint32_t padding_left() const {
if (padding_tf_same()) {
const uint32_t total_padding_width =
(output_width() - 1) * subsampling_width() + dilated_kernel_width() - input_width();
return total_padding_width / 2;
} else {
return this->padding_left_;
}
}
ConvolutionOperatorTester& padding_bottom(uint32_t padding_bottom) {
assert(!padding_tf_same());
this->padding_bottom_ = padding_bottom;
return *this;
}
uint32_t padding_bottom() const {
if (padding_tf_same()) {
const uint32_t total_padding_height =
(output_height() - 1) * subsampling_height() + dilated_kernel_height() - input_height();
return total_padding_height - total_padding_height / 2;
} else {
return this->padding_bottom_;
}
}
ConvolutionOperatorTester& padding_right(uint32_t padding_right) {
assert(!padding_tf_same());
this->padding_right_ = padding_right;
return *this;
}
uint32_t padding_right() const {
if (padding_tf_same()) {
const uint32_t total_padding_width =
(output_width() - 1) * subsampling_width() + dilated_kernel_width() - input_width();
return total_padding_width - total_padding_width / 2;
} else {
return this->padding_right_;
}
}
ConvolutionOperatorTester& input_size(uint32_t input_height, uint32_t input_width) {
assert(input_height >= 1);
assert(input_width >= 1);
this->input_height_ = input_height;
this->input_width_ = input_width;
return *this;
}
ConvolutionOperatorTester& input_height(uint32_t input_height) {
assert(input_height >= 1);
this->input_height_ = input_height;
return *this;
}
uint32_t input_height() const {
return this->input_height_;
}
ConvolutionOperatorTester& input_width(uint32_t input_width) {
assert(input_width >= 1);
this->input_width_ = input_width;
return *this;
}
uint32_t input_width() const {
return this->input_width_;
}
ConvolutionOperatorTester& groups(uint32_t groups) {
assert(groups >= 1);
this->groups_ = groups;
return *this;
}
uint32_t groups() const {
return this->groups_;
}
ConvolutionOperatorTester& group_input_channels(size_t group_input_channels) {
assert(group_input_channels >= 1);
this->group_input_channels_ = group_input_channels;
return *this;
}
size_t group_input_channels() const {
return this->group_input_channels_;
}
ConvolutionOperatorTester& group_output_channels(size_t group_output_channels) {
assert(group_output_channels >= 1);
this->group_output_channels_ = group_output_channels;
return *this;
}
size_t group_output_channels() const {
return this->group_output_channels_;
}
ConvolutionOperatorTester& batch_size(size_t batch_size) {
assert(batch_size >= 1);
this->batch_size_ = batch_size;
return *this;
}
size_t batch_size() const {
return this->batch_size_;
}
ConvolutionOperatorTester& kernel_size(uint32_t kernel_size) {
assert(kernel_size >= 1);
this->kernel_height_ = kernel_size;
this->kernel_width_ = kernel_size;
return *this;
}
ConvolutionOperatorTester& kernel_size(uint32_t kernel_height, uint32_t kernel_width) {
assert(kernel_height >= 1);
assert(kernel_width >= 1);
this->kernel_height_ = kernel_height;
this->kernel_width_ = kernel_width;
return *this;
}
ConvolutionOperatorTester& transient_indirection_buffer(bool use_transient_buffer) {
this->transient_indirection_buffer_ = use_transient_buffer;
return *this;
}
bool transient_indirection_buffer() const {
return this->transient_indirection_buffer_;
}
ConvolutionOperatorTester& kernel_height(uint32_t kernel_height) {
assert(kernel_height >= 1);
this->kernel_height_ = kernel_height;
return *this;
}
uint32_t kernel_height() const {
return this->kernel_height_;
}
ConvolutionOperatorTester& kernel_width(uint32_t kernel_width) {
assert(kernel_width >= 1);
this->kernel_width_ = kernel_width;
return *this;
}
uint32_t kernel_width() const {
return this->kernel_width_;
}
ConvolutionOperatorTester& dilation(uint32_t dilation) {
assert(dilation >= 1);
this->dilation_height_ = dilation;
this->dilation_width_ = dilation;
return *this;
}
ConvolutionOperatorTester& dilation(uint32_t dilation_height, uint32_t dilation_width) {
assert(dilation_height >= 1);
assert(dilation_width >= 1);
this->dilation_height_ = dilation_height;
this->dilation_width_ = dilation_width;
return *this;
}
ConvolutionOperatorTester& dilation_height(uint32_t dilation_height) {
assert(dilation_height >= 1);
this->dilation_height_ = dilation_height;
return *this;
}
uint32_t dilation_height() const {
return this->dilation_height_;
}
ConvolutionOperatorTester& dilation_width(uint32_t dilation_width) {
assert(dilation_width >= 1);
this->dilation_width_ = dilation_width;
return *this;
}
uint32_t dilation_width() const {
return this->dilation_width_;
}
ConvolutionOperatorTester& subsampling(uint32_t subsampling) {
assert(subsampling >= 1);
this->subsampling_height_ = subsampling;
this->subsampling_width_ = subsampling;
return *this;
}
ConvolutionOperatorTester& subsampling(uint32_t subsampling_height, uint32_t subsampling_width) {
assert(subsampling_height >= 1);
assert(subsampling_width >= 1);
this->subsampling_height_ = subsampling_height;
this->subsampling_width_ = subsampling_width;
return *this;
}
ConvolutionOperatorTester& subsampling_height(uint32_t subsampling_height) {
assert(subsampling_height >= 1);
this->subsampling_height_ = subsampling_height;
return *this;
}
uint32_t subsampling_height() const {
return this->subsampling_height_;
}
ConvolutionOperatorTester& subsampling_width(uint32_t subsampling_width) {
assert(subsampling_width >= 1);
this->subsampling_width_ = subsampling_width;
return *this;
}
uint32_t subsampling_width() const {
return this->subsampling_width_;
}
ConvolutionOperatorTester& input_channel_stride(size_t input_channel_stride) {
assert(input_channel_stride >= 1);
this->input_channel_stride_ = input_channel_stride;
return *this;
}
size_t input_channel_stride() const {
if (this->input_channel_stride_ == 0) {
return group_input_channels() * groups();
} else {
assert(this->input_channel_stride_ >= group_input_channels() * groups());
return this->input_channel_stride_;
}
}
ConvolutionOperatorTester& output_channel_stride(size_t output_channel_stride) {
assert(output_channel_stride >= 1);
this->output_channel_stride_ = output_channel_stride;
return *this;
}
size_t output_channel_stride() const {
if (this->output_channel_stride_ == 0) {
return group_output_channels() * groups();
} else {
assert(this->output_channel_stride_ >= group_output_channels() * groups());
return this->output_channel_stride_;
}
}
uint32_t dilated_kernel_height() const {
return (kernel_height() - 1) * dilation_height() + 1;
}
uint32_t dilated_kernel_width() const {
return (kernel_width() - 1) * dilation_width() + 1;
}
size_t output_height() const {
if (padding_tf_same()) {
return (input_height() + subsampling_height() - 1) / subsampling_height();
} else {
const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
if (padded_input_height <= dilated_kernel_height()) {
return 1;
} else {
return (padded_input_height - dilated_kernel_height()) / subsampling_height() + 1;
}
}
}
size_t output_width() const {
if (padding_tf_same()) {
return (input_width() + subsampling_width() - 1) / subsampling_width();
} else {
const size_t padded_input_width = padding_left() + input_width() + padding_right();
if (padded_input_width <= dilated_kernel_width()) {
return 1;
} else {
return (padded_input_width - dilated_kernel_width()) / subsampling_width() + 1;
}
}
}
ConvolutionOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) {
assert(next_input_height >= 1);
assert(next_input_width >= 1);
this->next_input_height_ = next_input_height;
this->next_input_width_ = next_input_width;
return *this;
}
ConvolutionOperatorTester& next_input_height(uint32_t next_input_height) {
assert(next_input_height >= 1);
this->next_input_height_ = next_input_height;
return *this;
}
uint32_t next_input_height() const {
if (this->next_input_height_ == 0) {
return input_height();
} else {
return this->next_input_height_;
}
}
ConvolutionOperatorTester& next_input_width(uint32_t next_input_width) {
assert(next_input_width >= 1);
this->next_input_width_ = next_input_width;
return *this;
}
uint32_t next_input_width() const {
if (this->next_input_width_ == 0) {
return input_width();
} else {
return this->next_input_width_;
}
}
size_t next_output_height() const {
const size_t padded_input_height = padding_top() + next_input_height() + padding_bottom();
if (padded_input_height <= dilated_kernel_height()) {
return 1;
} else {
return (padded_input_height - dilated_kernel_height()) / subsampling_height() + 1;
}
}
size_t next_output_width() const {
const size_t padded_input_width = padding_left() + next_input_width() + padding_right();
if (padded_input_width <= dilated_kernel_width()) {
return 1;
} else {
return (padded_input_width - dilated_kernel_width()) / subsampling_width() + 1;
}
}
ConvolutionOperatorTester& next_batch_size(size_t next_batch_size) {
assert(next_batch_size >= 1);
this->next_batch_size_ = next_batch_size;
return *this;
}
size_t next_batch_size() const {
if (this->next_batch_size_ == 0) {
return batch_size();
} else {
return this->next_batch_size_;
}
}
ConvolutionOperatorTester& sparsity(float sparsity) {
this->sparsity_ = sparsity;
return *this;
}
float sparsity() const {
return this->sparsity_;
}
ConvolutionOperatorTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
uint8_t qmin() const {
return this->qmin_;
}
ConvolutionOperatorTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
uint8_t qmax() const {
return this->qmax_;
}
ConvolutionOperatorTester& force_nhwc_input(bool force_nhwc_input) {
this->force_nhwc_input_ = force_nhwc_input;
return *this;
}
bool force_nhwc_input() const {
return this->force_nhwc_input_;
}
ConvolutionOperatorTester& depthwise_layout(bool depthwise_layout) {
this->depthwise_layout_ = depthwise_layout;
return *this;
}
bool depthwise_layout() const {
return this->depthwise_layout_;
}
ConvolutionOperatorTester& has_bias(bool has_bias) {
this->has_bias_ = has_bias;
return *this;
}
bool has_bias() const {
return this->has_bias_;
}
ConvolutionOperatorTester& weights_type(WeightsType weights_type) {
this->weights_type_ = weights_type;
return *this;
}
WeightsType weights_type() const {
return this->weights_type_;
}
ConvolutionOperatorTester& multithreaded(size_t multithreaded) {
this->multithreaded_ = multithreaded;
return *this;
}
size_t multithreaded() const {
return this->multithreaded_;
}
size_t num_threads() const {
// Do not spin up excessive number of threads for tests.
return multithreaded() ? 5 : 1;
}
ConvolutionOperatorTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
size_t iterations() const {
return this->iterations_;
}
ConvolutionOperatorTester& use_weights_cache(bool use_weights_cache) {
this->use_weights_cache_ = use_weights_cache;
return *this;
}
bool use_weights_cache() const {
return this->use_weights_cache_;
}
void TestNHWCxQC8() const {
ASSERT_EQ(weights_type(), WeightsType::Default);
xnnpack::ReplicableRandomDevice rng;
std::uniform_int_distribution<int32_t> i32dist(-10000, 10000);
std::uniform_int_distribution<int32_t> i8dist(
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
std::uniform_int_distribution<int32_t> w8dist(
-std::numeric_limits<int8_t>::max(), std::numeric_limits<int8_t>::max());
std::vector<int8_t> input(XNN_EXTRA_BYTES / sizeof(int8_t) +
batch_size() * ((input_height() * input_width() - 1) * input_channel_stride() + groups() * group_input_channels()));
std::vector<int8_t> kernel(groups() * group_output_channels() * kernel_height() * kernel_width() * group_input_channels());
std::vector<int32_t> bias(groups() * group_output_channels());
std::vector<int8_t> output(batch_size() * ((output_height() * output_width() - 1) * output_channel_stride() + groups() * group_output_channels()));
std::vector<int32_t> accumulators(batch_size() * output_height() * output_width() * groups() * group_output_channels());
std::vector<double> output_ref(batch_size() * output_height() * output_width() * groups() * group_output_channels());
std::vector<float> requantization_scales(groups() * group_output_channels());
const int8_t input_zero_point = -1;
const int8_t output_zero_point = -1;
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::unique_ptr<pthreadpool, decltype(&pthreadpool_destroy)> auto_threadpool{nullptr, pthreadpool_destroy};
if (multithreaded()) {
const pthreadpool_t threadpool = pthreadpool_create(num_threads());
if (pthreadpool_get_threads_count(threadpool) <= 1) {
GTEST_SKIP();
} else {
auto_threadpool.reset(threadpool);
}
}
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(output.begin(), output.end(), INT8_C(0xA5));
// Compute reference results, without renormalization.
if (depthwise_layout()) {
ASSERT_EQ(group_input_channels(), 1);
xnnpack::compute_depthwise_convolution_qs8_reference_results(
batch_size(),
output_height(),
output_width(),
input_height(),
input_width(),
padding_top(),
padding_right(),
padding_bottom(),
padding_left(),
kernel_height(),
kernel_width(),
subsampling_height(),
subsampling_width(),
dilation_height(),
dilation_width(),
groups(),
group_output_channels(),
input_channel_stride(),
input_zero_point,
input,
kernel,
accumulators,
has_bias(),
bias);
} else {
xnnpack::compute_convolution_qs8_reference_results(
batch_size(),
output_height(),
output_width(),
input_height(),
input_width(),
padding_top(),
padding_right(),
padding_bottom(),
padding_left(),
kernel_height(),
kernel_width(),
subsampling_height(),
subsampling_width(),
dilation_height(),
dilation_width(),
groups(),
group_input_channels(),
group_output_channels(),
input_channel_stride(),
input_zero_point,
input,
kernel,
accumulators,
has_bias(),
bias);
}
// Compute renormalization parameters.
for (size_t c = 0; c < groups() * group_output_channels(); c++) {
int32_t accumulated_min = accumulators[c];
int32_t accumulated_max = accumulators[c];
for (size_t px = 0; px < batch_size() * output_height() * output_width(); px++) {
accumulated_min = std::min(accumulated_min, accumulators[px * groups() * group_output_channels() + c]);
accumulated_max = std::max(accumulated_max, accumulators[px * groups() * group_output_channels() + c]);
}
float requantization_scale = 0x1.0p-32f;
if (accumulated_max != 0) {
requantization_scale = std::max(requantization_scale,
float(int32_t(std::numeric_limits<int8_t>::max()) - int32_t(output_zero_point)) / float(accumulated_max));
}
if (accumulated_min != 0) {
requantization_scale = std::max(requantization_scale,
float(int32_t(std::numeric_limits<int8_t>::min()) - int32_t(output_zero_point)) / float(accumulated_min));
}
requantization_scale = std::min(requantization_scale, 0x1.FFFFFEp-1f);
requantization_scales[c] = requantization_scale;
}
// Renormalize reference results.
for (size_t c = 0; c < groups() * group_output_channels(); c++) {
for (size_t px = 0; px < batch_size() * output_height() * output_width(); px++) {
output_ref[px * groups() * group_output_channels() + c] = double(int32_t(output_zero_point)) +
double(accumulators[px * groups() * group_output_channels() + c]) * double(requantization_scales[c]);
}
}
std::transform(output_ref.cbegin(), output_ref.cend(), output_ref.begin(),
[this](double x) -> double {
return std::max<double>(std::min<double>(x, double(qmax() - 0x80)), double(qmin() - 0x80));
});
// Create, setup, run, and destroy Convolution operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t convolution_op = nullptr;
struct xnn_internal_weights_cache* internal_weights_cache = nullptr;
std::unique_ptr<xnn_weights_cache_provider, decltype(&xnn_delete_weights_cache)> auto_weights_cache(
nullptr, xnn_delete_weights_cache);
if (use_weights_cache()) {
xnn_weights_cache_t weights_cache = nullptr;
xnn_create_weights_cache(&weights_cache);
auto_weights_cache.reset(weights_cache);
if (weights_cache) {
internal_weights_cache = (struct xnn_internal_weights_cache*) weights_cache->context;
}
}
uint32_t flags = 0;
if (depthwise_layout()) {
flags |= XNN_FLAG_DEPTHWISE_CONVOLUTION;
}
if (padding_tf_same()) {
flags |= XNN_FLAG_TENSORFLOW_SAME_PADDING;
}
if (transient_indirection_buffer()) {
flags |= XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER;
}
xnn_status status = xnn_create_convolution2d_nhwc_qs8_qc8w(
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
kernel_height(), kernel_width(),
subsampling_height(), subsampling_width(),
dilation_height(), dilation_width(),
groups(), group_input_channels(), group_output_channels(),
input_channel_stride(), output_channel_stride(),
input_zero_point, 1.0f /* input scale */, requantization_scales.data(),
kernel.data(), has_bias() ? bias.data() : nullptr,
output_zero_point, 1.0f /* output scale */, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
flags,
/*code_cache=*/nullptr,
auto_weights_cache.get(),
&convolution_op);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, convolution_op);
if (use_weights_cache()) {
ASSERT_EQ(xnn_status_success,
xnn_finalize_weights_cache(auto_weights_cache.get(), xnn_weights_cache_finalization_kind_soft));
}
// Smart pointer to automatically delete convolution_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
ASSERT_EQ(
xnn_status_success, xnn_reshape_convolution2d_nhwc_qs8_qc8w(
convolution_op, batch_size(), input_height(), input_width(),
&workspace_size, &workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
auto_threadpool.get()));
std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> workspace(workspace_size);
std::iota(workspace.begin(), workspace.end(), 0);
if (transient_indirection_buffer()) {
ASSERT_NE(workspace_size, 0);
ASSERT_NE(workspace_size, SIZE_MAX);
ASSERT_EQ(workspace_alignment, XNN_ALLOCATION_ALIGNMENT);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qs8_qc8w(convolution_op, workspace.data(), input.data(), output.data()));
} else {
ASSERT_NE(workspace_size, SIZE_MAX);
ASSERT_NE(workspace_alignment, SIZE_MAX);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qs8_qc8w(convolution_op, workspace.data(), input.data(), output.data()));
}
ASSERT_EQ(xnn_status_success, xnn_run_operator(convolution_op, auto_threadpool.get()));
// Verify results.
VerifyNHWCxQC8(output, output_ref);
if (use_weights_cache()) {
xnn_operator_t convolution_op2 = nullptr;
size_t old_weights_cache_size = internal_weights_cache->cache.weights.size;
xnn_status status = xnn_create_convolution2d_nhwc_qs8_qc8w(
padding_tf_same() ? 0 : padding_top(), padding_tf_same() ? 0 : padding_right(),
padding_tf_same() ? 0 : padding_bottom(), padding_tf_same() ? 0 : padding_left(),
kernel_height(), kernel_width(),
subsampling_height(), subsampling_width(),
dilation_height(), dilation_width(),
groups(), group_input_channels(), group_output_channels(),
input_channel_stride(), output_channel_stride(),
input_zero_point, 1.0f /* input scale */, requantization_scales.data(),
kernel.data(), has_bias() ? bias.data() : nullptr,
output_zero_point, 1.0f /* output scale */, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80),
flags,
/*code_cache=*/nullptr,
auto_weights_cache.get(),
&convolution_op2);
(void) status;
ASSERT_NE(nullptr, convolution_op2);
// Smart pointer to automatically delete convolution_op2.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op2, xnn_delete_operator);
std::vector<int8_t> output2(output.size(), INT8_C(0xA5));
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
ASSERT_EQ(
xnn_status_success,
xnn_reshape_convolution2d_nhwc_qs8_qc8w(
convolution_op2, batch_size(), input_height(), input_width(),
&workspace_size, &workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
auto_threadpool.get()));
std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> workspace(workspace_size);
std::iota(workspace.begin(), workspace.end(), 0);
if (transient_indirection_buffer()) {
ASSERT_NE(workspace_size, 0);
ASSERT_NE(workspace_size, SIZE_MAX);
ASSERT_EQ(workspace_alignment, XNN_ALLOCATION_ALIGNMENT);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qs8_qc8w(convolution_op2, workspace.data(), input.data(), output2.data()));
} else {
ASSERT_NE(workspace_size, SIZE_MAX);
ASSERT_NE(workspace_alignment, SIZE_MAX);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qs8_qc8w(convolution_op2, workspace.data(), input.data(), output2.data()));
}
ASSERT_EQ(xnn_status_success, xnn_run_operator(convolution_op2, auto_threadpool.get()));
VerifyNHWCxQC8(output2, output_ref);
VerifyWeightsCache(*internal_weights_cache, old_weights_cache_size);
}
}
}
void VerifyNHWCxQC8(const std::vector<int8_t> &output,
const std::vector<double> &output_ref) const {
for (size_t i = 0; i < batch_size(); i++) {
for (size_t y = 0; y < output_height(); y++) {
for (size_t x = 0; x < output_width(); x++) {
for (size_t g = 0; g < groups(); g++) {
for (size_t c = 0; c < group_output_channels(); c++) {
EXPECT_LE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmax() - 0x80))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
EXPECT_GE(int32_t(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]), int32_t(qmin() - 0x80))
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
EXPECT_NEAR(
output_ref[(((i * output_height() + y) * output_width() + x) * groups() + g) * group_output_channels() + c],
double(output[((i * output_height() + y) * output_width() + x) * output_channel_stride() + g * group_output_channels() + c]),
0.9)
<< "(x, y) = (" << x << ", " << y << "), group = " << g << ", channel = " << c;
}
}
}
}
}
}
void TestNHWCxQD8F16QC8W() const {
ASSERT_EQ(weights_type(), WeightsType::Default);
xnnpack::ReplicableRandomDevice rng;
std::uniform_real_distribution<float> f32dist(-1.f, 1.f);
std::uniform_real_distribution<float> f32idist(0.1f, 1.0f);
std::uniform_int_distribution<int32_t> w8dist(
std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
// Weights typically have a Gaussian distrubution centred on zero. A
// standard deviation of 40 means that > 99.99% of values fall in the
// -127->127 range. However, the reduce the chance of overflow, we constrain
// the weights further.
std::normal_distribution<float> normal_dist{0.f, 10.f};
std::vector<int8_t> input(
XNN_EXTRA_BYTES / sizeof(int8_t) +
batch_size() *
((input_height() * input_width() - 1) * input_channel_stride() +
groups() * group_input_channels()));
std::vector<int8_t> kernel(groups() * group_output_channels() *
kernel_height() * kernel_width() *
group_input_channels());
std::vector<float> bias(groups() * group_output_channels());
std::vector<xnn_float16> output(
batch_size() *
((output_height() * output_width() - 1) * output_channel_stride() +
groups() * group_output_channels()));
std::vector<float> output_ref(batch_size() * output_height() *
output_width() * groups() *
group_output_channels());
std::vector<xnn_qd8_quantization_params> quantization_params(batch_size());
std::vector<float> kernel_scale(groups() * group_output_channels());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::unique_ptr<pthreadpool, decltype(&pthreadpool_destroy)>
auto_threadpool{nullptr, pthreadpool_destroy};
if (multithreaded()) {
const pthreadpool_t threadpool = pthreadpool_create(num_threads());
if (pthreadpool_get_threads_count(threadpool) <= 1) {
GTEST_SKIP();
} else {
auto_threadpool.reset(threadpool);
}
}
std::generate(input.begin(), input.end(), [&]() { return w8dist(rng); });
std::generate(kernel.begin(), kernel.end(), [&]() { return (int8_t) normal_dist(rng); });
std::generate(bias.begin(), bias.end(), [&]() { return f32dist(rng); });
std::generate(kernel_scale.begin(), kernel_scale.end(),
[&]() { return f32idist(rng); });
std::generate(
quantization_params.begin(), quantization_params.end(), [&]() {
return xnn_qd8_quantization_params{w8dist(rng), f32idist(rng)};
});
std::fill(output.begin(), output.end(), UINT16_C(0xDEAD));
// Compute reference results.
xnnpack::compute_convolution_qd8_f32_qc8w_reference_results(
batch_size(), output_height(), output_width(), input_height(),
input_width(), padding_top(), padding_right(), padding_bottom(),
padding_left(), kernel_height(), kernel_width(), subsampling_height(),
subsampling_width(), dilation_height(), dilation_width(), groups(),
group_input_channels(), group_output_channels(),
input_channel_stride(), input, kernel, kernel_scale,
quantization_params, output_ref, has_bias(), bias);
const float output_min = -std::numeric_limits<float>::infinity();
const float output_max = std::numeric_limits<float>::infinity();
// Clamp reference results.
for (size_t i = 0; i < output_ref.size(); ++i) {
output_ref[i] = std::max(std::min(output_ref[i], output_max), output_min);
output_ref[i] = xnn_float16(output_ref[i]);
}
// Clamp reference results.
for (float& value : output_ref) {
value = std::max(std::min(value, output_max), output_min);
}
// Create, setup, run, and destroy Convolution operator.
ASSERT_EQ(xnn_status_success, xnn_initialize(nullptr /* allocator */));
xnn_operator_t convolution_op = nullptr;
struct xnn_internal_weights_cache* internal_weights_cache = nullptr;
std::unique_ptr<xnn_weights_cache_provider, decltype(&xnn_delete_weights_cache)> auto_weights_cache(
nullptr, xnn_delete_weights_cache);
if (use_weights_cache()) {
xnn_weights_cache_t weights_cache = nullptr;
xnn_create_weights_cache(&weights_cache);
auto_weights_cache.reset(weights_cache);
if (weights_cache) {
internal_weights_cache = (struct xnn_internal_weights_cache*) weights_cache->context;
}
}
uint32_t flags = 0;
if (padding_tf_same()) {
flags |= XNN_FLAG_TENSORFLOW_SAME_PADDING;
}
if (transient_indirection_buffer()) {
flags |= XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER;
}
xnn_status status = xnn_create_convolution2d_nhwc_qd8_f16_qc8w(
padding_tf_same() ? 0 : padding_top(),
padding_tf_same() ? 0 : padding_right(),
padding_tf_same() ? 0 : padding_bottom(),
padding_tf_same() ? 0 : padding_left(), kernel_height(),
kernel_width(), subsampling_height(), subsampling_width(),
dilation_height(), dilation_width(), groups(), group_input_channels(),
group_output_channels(), input_channel_stride(),
output_channel_stride(), kernel_scale.data(), kernel.data(),
has_bias() ? bias.data() : nullptr, output_min, output_max, flags,
/*code_cache=*/nullptr, auto_weights_cache.get(), &convolution_op);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, convolution_op);
if (use_weights_cache()) {
ASSERT_EQ(
xnn_status_success,
xnn_finalize_weights_cache(
auto_weights_cache.get(), xnn_weights_cache_finalization_kind_soft));
}
// Smart pointer to automatically delete convolution_op.
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)>
auto_convolution_op(convolution_op, xnn_delete_operator);
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
ASSERT_EQ(xnn_status_success,
xnn_reshape_convolution2d_nhwc_qd8_f16_qc8w(
convolution_op, batch_size(), input_height(), input_width(),
&workspace_size, &workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
auto_threadpool.get()));
std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>>
workspace(workspace_size);
std::iota(workspace.begin(), workspace.end(), 0);
if (transient_indirection_buffer()) {
ASSERT_NE(workspace_size, 0);
ASSERT_NE(workspace_size, SIZE_MAX);
ASSERT_EQ(workspace_alignment, XNN_ALLOCATION_ALIGNMENT);
ASSERT_EQ(
xnn_status_success,
xnn_setup_convolution2d_nhwc_qd8_f16_qc8w(
convolution_op, workspace.data(), input.data(), output.data(),
reinterpret_cast<const struct xnn_dynamic_quantization_params*>(
quantization_params.data())));
} else {
ASSERT_NE(workspace_size, SIZE_MAX);
ASSERT_NE(workspace_alignment, SIZE_MAX);
ASSERT_EQ(
xnn_status_success,
xnn_setup_convolution2d_nhwc_qd8_f16_qc8w(
convolution_op, workspace.data(), input.data(), output.data(),