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depthwise-convolution-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>
#include <array>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <limits>
#include <memory>
#include <random>
#include <type_traits>
#include <vector>
#include <gtest/gtest.h>
#include <fp16/fp16.h>
#include "xnnpack.h"
#include "xnnpack/aligned-allocator.h"
#include "xnnpack/common.h"
#include "xnnpack/node-type.h"
#include "xnnpack/operator-utils.h"
#include "xnnpack/operator.h"
#include "xnnpack/requantization.h"
#include "xnnpack/subgraph.h"
#include "convolution-test-helpers.h"
#include "replicable_random_device.h"
namespace xnnpack {
template <class T, class BiasType = T> class DepthwiseConvolutionTestBase : public ::testing::Test {
protected:
DepthwiseConvolutionTestBase() {
input_size_dist = std::uniform_int_distribution<uint32_t>(10, 15);
kernel_size_dist = std::uniform_int_distribution<uint32_t>(1, 7);
stride_dist = std::uniform_int_distribution<uint32_t>(1, 2);
f32dist = std::uniform_real_distribution<float>(0.1f, 1.0f);
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);
input_channels = input_size_dist(rng);
kernel_height = kernel_size_dist(rng);
kernel_width = kernel_size_dist(rng);
subsampling_height = stride_dist(rng);
subsampling_width = stride_dist(rng);
depth_multiplier = kernel_size_dist(rng);
dilation_height = stride_dist(rng);
dilation_width = stride_dist(rng);
input_padding_top = kernel_size_dist(rng);
input_padding_right = kernel_size_dist(rng);
input_padding_bottom = kernel_size_dist(rng);
input_padding_left = kernel_size_dist(rng);
output_height = xnn_compute_convolution_output_dimension(
input_padding_top + input_height + input_padding_bottom, kernel_height,
dilation_height, subsampling_height);
output_width = xnn_compute_convolution_output_dimension(
input_padding_left + input_width + input_padding_right, kernel_width,
dilation_width, subsampling_width);
output_channels = input_channels * depth_multiplier;
output_min = -std::numeric_limits<float>::infinity();
output_max = std::numeric_limits<float>::infinity();
input_dims = {{batch_size, input_height, input_width, input_channels}};
filter_dims = {{1, kernel_height, kernel_width, output_channels}};
bias_dims = {{output_channels}};
output_dims = {{batch_size, output_height, output_width, output_channels}};
input = std::vector<T>(XNN_EXTRA_BYTES / sizeof(T) +
batch_size * input_height * input_width *
input_channels);
filter = std::vector<T>(batch_size * kernel_height * kernel_width *
output_channels);
bias = std::vector<BiasType>(output_channels);
operator_output = std::vector<T>(batch_size * output_height * output_width *
output_channels);
subgraph_output = std::vector<T>(batch_size * output_height * output_width *
output_channels);
}
xnnpack::ReplicableRandomDevice 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;
uint32_t input_padding_top;
uint32_t input_padding_right;
uint32_t input_padding_bottom;
uint32_t input_padding_left;
uint32_t batch_size;
uint32_t input_height;
uint32_t input_width;
uint32_t kernel_height;
uint32_t kernel_width;
uint32_t subsampling_height;
uint32_t subsampling_width;
uint32_t dilation_height;
uint32_t dilation_width;
uint32_t depth_multiplier;
uint32_t input_channels;
uint32_t 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> filter_dims;
std::array<size_t, 1> bias_dims;
std::array<size_t, 4> output_dims;
std::vector<T> input;
std::vector<T> filter;
std::vector<BiasType> bias;
std::vector<T> operator_output;
std::vector<T> subgraph_output;
};
template <class T> class QuantizedDepthwiseConvolutionTestBase : public DepthwiseConvolutionTestBase<T, int32_t> {
protected:
QuantizedDepthwiseConvolutionTestBase()
{
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());
u8dist = std::uniform_int_distribution<int32_t>(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->input_channels * this->depth_multiplier);
scale_dist = std::uniform_real_distribution<float>(1.0f, 5.0f);
input_scale = scale_dist(this->rng);
kernel_scale = scale_dist(this->rng);
kernel_zero_point = 0;
if (std::is_same<T, int8_t>::value) {
input_zero_point = i8dist(this->rng);
}
else {
input_zero_point = u8dist(this->rng);
}
}
std::uniform_int_distribution<int32_t> i8dist;
std::uniform_int_distribution<int32_t> u8dist;
std::uniform_int_distribution<int32_t> w8dist;
std::uniform_real_distribution<float> scale_dist;
std::vector<int32_t> accumulators;
float input_scale;
float kernel_scale;
float output_scale = 1.0f;
typedef typename std::conditional<std::is_same<T, uint8_t>::value, uint8_t, int8_t>::type ZeroPointType;
ZeroPointType input_zero_point;
ZeroPointType kernel_zero_point;
ZeroPointType output_zero_point = 0;
};
using DepthwiseConvolutionTestQC8 = QuantizedDepthwiseConvolutionTestBase<int8_t>;
using DepthwiseConvolutionTestQS8 = QuantizedDepthwiseConvolutionTestBase<int8_t>;
using DepthwiseConvolutionTestQU8 = QuantizedDepthwiseConvolutionTestBase<uint8_t>;
using DepthwiseConvolutionTestF16 = DepthwiseConvolutionTestBase<xnn_float16, float>;
using DepthwiseConvolutionTestF32 = DepthwiseConvolutionTestBase<float>;
TEST_F(DepthwiseConvolutionTestQC8, define)
{
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
std::vector<float> requantization_scales(input_channels * depth_multiplier, 1.0f);
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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint8, requantization_scales.data(), filter_dims.size(), 3,
filter_dims.data(), filter.data(), /*external_id=*/1,
/*flags=*/0, &filter_id));
uint32_t bias_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success,
xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint32, requantization_scales.data(), bias_dims.size(), 0, 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_depthwise_convolution_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, depth_multiplier,
input_channels, output_min, output_max, input_id, filter_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_depthwise_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qc8);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.depth_multiplier, depth_multiplier);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_channels, input_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], filter_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(DepthwiseConvolutionTestQS8, 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, 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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, 0, kernel_scale, filter_dims.size(), filter_dims.data(),
filter.data(), /*external_id=*/1,
/*flags=*/0, &filter_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_depthwise_convolution_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, depth_multiplier,
input_channels, output_min, output_max, input_id, filter_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_depthwise_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qs8);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.depth_multiplier, depth_multiplier);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_channels, input_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], filter_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(DepthwiseConvolutionTestQU8, 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, 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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, 0, kernel_scale, filter_dims.size(), filter_dims.data(),
filter.data(), /*external_id=*/1,
/*flags=*/0, &filter_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_depthwise_convolution_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, depth_multiplier,
input_channels, output_min, output_max, input_id, filter_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_depthwise_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qu8);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.depth_multiplier, depth_multiplier);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_channels, input_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], filter_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(DepthwiseConvolutionTestF16, 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_fp16, 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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success,
xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, filter_dims.size(), filter_dims.data(), filter.data(), /*external_id=*/1,
/*flags=*/0, &filter_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_fp16, 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_depthwise_convolution_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, depth_multiplier,
input_channels, output_min, output_max, input_id, filter_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_depthwise_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_fp16);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.depth_multiplier, depth_multiplier);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_channels, input_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], filter_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(DepthwiseConvolutionTestF32, 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, XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success,
xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, filter_dims.size(), filter_dims.data(), filter.data(), /*external_id=*/1,
/*flags=*/0, &filter_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_depthwise_convolution_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, depth_multiplier,
input_channels, output_min, output_max, input_id, filter_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_depthwise_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_fp32);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.depthwise_convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.depthwise_convolution_2d.depth_multiplier, depth_multiplier);
ASSERT_EQ(node->params.depthwise_convolution_2d.input_channels, input_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], filter_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(DepthwiseConvolutionTestQC8, matches_operator_api)
{
std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });
std::generate(filter.begin(), filter.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));
std::vector<float> requantization_scales(input_channels * depth_multiplier);
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);
// Compute reference results, without renormalization.
compute_depthwise_convolution_qs8_reference_results(
batch_size,
output_height,
output_width,
input_height,
input_width,
input_padding_top,
input_padding_right,
input_padding_bottom,
input_padding_left,
kernel_height,
kernel_width,
subsampling_height,
subsampling_width,
dilation_height,
dilation_width,
input_channels,
depth_multiplier,
input_zero_point,
input,
filter,
accumulators,
/*has_bias=*/true,
bias);
// Compute renormalization parameters.
for (size_t c = 0; c < input_channels * depth_multiplier; 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 * input_channels * depth_multiplier + c]);
accumulated_max = std::max(accumulated_max, accumulators[px * input_channels * depth_multiplier + 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;
}
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
xnn_operator_t op = nullptr;
// Call operator API.
const xnn_status status = xnn_create_convolution2d_nhwc_qs8_qc8w(
input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height, kernel_width,
subsampling_height, subsampling_width, dilation_height, dilation_width,
/*groups=*/input_channels, /*group_input_channels=*/1,
/*group_output_channels=*/depth_multiplier, input_channels, input_channels * depth_multiplier, input_zero_point,
input_scale, requantization_scales.data(), filter.data(), bias.data(), output_zero_point, output_scale,
quantized_output_min, quantized_output_max,
/*flags=*/XNN_FLAG_DEPTHWISE_CONVOLUTION, nullptr, 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);
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
ASSERT_EQ(
xnn_status_success, xnn_reshape_convolution2d_nhwc_qs8_qc8w(
op, batch_size, input_height, input_width,
&workspace_size, &workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(workspace_size, 0);
ASSERT_EQ(workspace_alignment, 1);
std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> workspace(workspace_size);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qs8_qc8w(op, workspace.data(), input.data(), operator_output.data()));
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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint8, requantization_scales.data(), filter_dims.size(), 3,
filter_dims.data(), filter.data(), /*external_id=*/1,
/*flags=*/0, &filter_id));
uint32_t bias_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success,
xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint32, requantization_scales.data(), bias_dims.size(), 0, 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_depthwise_convolution_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, depth_multiplier,
input_channels, output_min, output_max, input_id, filter_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));
ASSERT_EQ(subgraph_output, operator_output);
}
TEST_F(DepthwiseConvolutionTestQS8, 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(filter.begin(), filter.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));
compute_convolution_qs8_reference_results(
batch_size,
output_height,
output_width,
input_height,
input_width,
input_padding_top,
input_padding_right,
input_padding_bottom,
input_padding_left,
kernel_height,
kernel_width,
subsampling_height,
subsampling_width,
dilation_height,
dilation_width,
/*groups=*/input_channels,
/*group_input_channels=*/1,
/*group_output_channels=*/depth_multiplier,
input_zero_point,
input,
filter,
accumulators,
/*has_bias=*/true,
bias);
// 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_convolution2d_nhwc_qs8(
input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height, kernel_width,
subsampling_height, subsampling_width, dilation_height, dilation_width,
/*groups=*/input_channels, /*group_input_channels=*/1,
/*group_output_channels=*/depth_multiplier, input_channels, input_channels * depth_multiplier, input_zero_point,
input_scale, kernel_scale, filter.data(), bias.data(), output_zero_point, output_scale, quantized_output_min,
quantized_output_max,
/*flags=*/XNN_FLAG_DEPTHWISE_CONVOLUTION, nullptr, 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);
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
ASSERT_EQ(
xnn_status_success, xnn_reshape_convolution2d_nhwc_qs8(
op, batch_size, input_height, input_width,
&workspace_size, &workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(workspace_size, 0);
ASSERT_EQ(workspace_alignment, 1);
std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> workspace(workspace_size);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qs8(op, workspace.data(), input.data(), operator_output.data()));
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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, kernel_zero_point, kernel_scale, filter_dims.size(),
filter_dims.data(), filter.data(), /*external_id=*/1,
/*flags=*/0, &filter_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_depthwise_convolution_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, depth_multiplier,
input_channels, output_min, output_max, input_id, filter_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));
ASSERT_EQ(subgraph_output, operator_output);
}
TEST_F(DepthwiseConvolutionTestQU8, 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(filter.begin(), filter.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));
// Compute reference results, without renormalization.
compute_convolution_qu8_reference_results(
batch_size,
output_height,
output_width,
input_height,
input_width,
input_padding_top,
input_padding_right,
input_padding_bottom,
input_padding_left,
kernel_height,
kernel_width,
subsampling_height,
subsampling_width,
dilation_height,
dilation_width,
/*groups=*/input_channels,
/*group_input_channels=*/1,
/*group_output_channels=*/depth_multiplier,
input_zero_point,
kernel_zero_point,
input,
filter,
accumulators,
/*has_bias=*/true,
bias);
// 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_convolution2d_nhwc_qu8(
input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height, kernel_width,
subsampling_height, subsampling_width, dilation_height, dilation_width,
/*groups=*/input_channels, /*group_input_channels=*/1,
/*group_output_channels=*/depth_multiplier, input_channels, input_channels * depth_multiplier, input_zero_point,
input_scale, kernel_zero_point, kernel_scale, filter.data(), bias.data(), output_zero_point, output_scale,
quantized_output_min, quantized_output_max,
/*flags=*/XNN_FLAG_DEPTHWISE_CONVOLUTION, nullptr, 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);
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
ASSERT_EQ(
xnn_status_success, xnn_reshape_convolution2d_nhwc_qu8(
op, batch_size, input_height, input_width,
&workspace_size, &workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(workspace_size, 0);
ASSERT_EQ(workspace_alignment, 1);
std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> workspace(workspace_size);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qu8(op, workspace.data(), input.data(), operator_output.data()));
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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, 0, kernel_scale, filter_dims.size(), filter_dims.data(),
filter.data(), /*external_id=*/1,
/*flags=*/0, &filter_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_depthwise_convolution_2d(
subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, depth_multiplier,
input_channels, output_min, output_max, input_id, filter_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));
ASSERT_EQ(subgraph_output, operator_output);
}
TEST_F(DepthwiseConvolutionTestF16, 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(filter.begin(), filter.end(), [&]() { return f32dist(rng); });
std::generate(bias.begin(), bias.end(), [&]() { return f32dist(rng); });
std::fill(operator_output.begin(), operator_output.end(), std::nanf(""));
std::fill(subgraph_output.begin(), subgraph_output.end(), std::nanf(""));
// Call operator API.
const xnn_status status = xnn_create_convolution2d_nhwc_f16(
input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height, kernel_width,
subsampling_height, subsampling_width, dilation_height, dilation_width,
/*groups=*/input_channels, /*group_input_channels=*/1,
/*group_output_channels=*/depth_multiplier, input_channels, input_channels * depth_multiplier, filter.data(),
bias.data(), output_min, output_max,
/*flags=*/XNN_FLAG_DEPTHWISE_CONVOLUTION | XNN_FLAG_FP32_STATIC_WEIGHTS, nullptr, 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);
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
ASSERT_EQ(xnn_status_success,
xnn_reshape_convolution2d_nhwc_f16(
op, batch_size, input_height, input_width, &workspace_size,
&workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_NE(workspace_size, SIZE_MAX);
ASSERT_NE(workspace_alignment, SIZE_MAX);
std::vector<char, AlignedAllocator<char, XNN_ALLOCATION_ALIGNMENT>> workspace(workspace_size);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_f16(op, workspace.data(), input.data(), operator_output.data()));
ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));
// Call subgraph API.