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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> // 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::uniform_real_distribution.
#include <vector> // For std::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 InputType, class KernelType = InputType, class BiasType = InputType, class OutputType = InputType> class ConvolutionTestBase : public ::testing::Test {
protected:
ConvolutionTestBase() {
input_size_dist = std::uniform_int_distribution<uint32_t>(10, 15);
kernel_size_dist = std::uniform_int_distribution<uint32_t>(1, 5);
subsampling_dist = std::uniform_int_distribution<uint32_t>(1, 5);
// max value of (dilation * kernel size) must be smaller than input size.
dilation_dist = std::uniform_int_distribution<uint32_t>(1, 2);
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);
subsampling_height = subsampling_dist(rng);
subsampling_width = subsampling_dist(rng);
dilation_height = dilation_dist(rng);
dilation_width = dilation_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();
output_height = xnn_compute_convolution_output_dimension(
input_height, kernel_height, dilation_height, subsampling_height);
output_width = xnn_compute_convolution_output_dimension(
input_width, kernel_width, dilation_width, subsampling_width);
input_dims = {
{batch_size, input_height, input_width, groups * group_input_channels}};
filter_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<InputType>(XNN_EXTRA_BYTES / sizeof(InputType) +
batch_size * input_height * input_width *
groups * group_input_channels);
filter =
std::vector<KernelType>(groups * group_output_channels * kernel_height *
kernel_width * group_input_channels);
bias = std::vector<BiasType>(groups * group_output_channels);
operator_output =
std::vector<OutputType>(batch_size * output_height * output_width *
groups * group_output_channels);
subgraph_output =
std::vector<OutputType>(batch_size * output_height * output_width *
groups * group_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> subsampling_dist;
std::uniform_int_distribution<uint32_t> dilation_dist;
std::uniform_int_distribution<int32_t> i32dist;
std::uniform_real_distribution<float> f32dist;
std::uniform_real_distribution<float> scale_dist;
const uint32_t input_padding_top = 0;
const uint32_t input_padding_right = 0;
const uint32_t input_padding_bottom = 0;
const uint32_t input_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 subsampling_height;
uint32_t subsampling_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> filter_dims;
std::array<size_t, 1> bias_dims;
std::array<size_t, 4> output_dims;
std::vector<InputType> input;
std::vector<KernelType> filter;
std::vector<BiasType> bias;
std::vector<OutputType> operator_output;
std::vector<OutputType> subgraph_output;
};
template <class InputType, class KernelType = InputType, class BiasType = InputType, class OutputType = InputType> class QuantizedConvolutionTestBase : public ConvolutionTestBase<InputType, KernelType, BiasType, OutputType> {
protected:
QuantizedConvolutionTestBase()
{
i8dist =
std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
w8dist = std::uniform_int_distribution<int32_t>(-std::numeric_limits<KernelType>::max(), std::numeric_limits<KernelType>::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);
}
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 ConvolutionTestQC8 = QuantizedConvolutionTestBase<int8_t, int8_t, int32_t,int8_t>;
using ConvolutionTestQD8F16QC8W = QuantizedConvolutionTestBase<float, int8_t, float, xnn_float16>;
using ConvolutionTestQD8F32QC8W = QuantizedConvolutionTestBase<float, int8_t, float, float>;
using ConvolutionTestQS8 = QuantizedConvolutionTestBase<int8_t, int8_t, int32_t,int8_t>;
using ConvolutionTestQU8 = QuantizedConvolutionTestBase<uint8_t, uint8_t, int32_t,uint8_t>;
using ConvolutionTestF16 = ConvolutionTestBase<xnn_float16, float, float>;
using ConvolutionTestF32 = ConvolutionTestBase<float>;
TEST_F(ConvolutionTestQC8, 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);
std::vector<float> scale(groups * group_output_channels, 1.0f);
uint32_t filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success,
xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint8, scale.data(), filter_dims.size(), 0, 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, scale.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, 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_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, groups,
group_input_channels, group_output_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_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qc8);
ASSERT_EQ(node->params.convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.convolution_2d.groups, groups);
ASSERT_EQ(node->params.convolution_2d.group_input_channels, group_input_channels);
ASSERT_EQ(node->params.convolution_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], 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(ConvolutionTestQD8F16QC8W, define)
{
std::vector<float> requantization_scales(group_output_channels * groups, 1.0f);
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_VALUE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_dynamically_quantized_tensor_value(
subgraph, xnn_datatype_qdint8, input_dims.size(), /*num_nonbatch_dims=*/1, input_dims.data(),
/*external_id=*/0, /*flags=*/0, &input_id));
ASSERT_NE(input_id, XNN_INVALID_VALUE_ID);
uint32_t kernel_id = XNN_INVALID_VALUE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint8, requantization_scales.data(), filter_dims.size(), /*channel_dim=*/0,
filter_dims.data(), filter.data(), /*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_VALUE_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_VALUE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp16, output_dims.size(), output_dims.data(), nullptr,
/*external_id=*/3, /*flags=*/0, &output_id));
ASSERT_NE(output_id, XNN_INVALID_VALUE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_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, 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_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qd8_to_fp16);
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(ConvolutionTestQD8F16QC8W, internally_allocated_dynamic_quantization_parameters)
{
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/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;
std::vector<xnn_float16> convert_input(batch_size * input_height * input_width * groups * group_input_channels + XNN_EXTRA_BYTES / sizeof(xnn_float16));
std::vector<int8_t> operator_dq_data(batch_size * input_height * input_width * groups * group_input_channels + XNN_EXTRA_BYTES);
std::fill(operator_output.begin(), operator_output.end(), UINT16_C(0xDEAD));
std::fill(subgraph_output.begin(), subgraph_output.end(), UINT16_C(0xDEAD));
std::vector<xnn_dynamic_quantization_params> quantization_params(batch_size + XNN_EXTRA_QUANTIZATION_PARAMS);
std::vector<float> kernel_scale(group_output_channels * groups);
std::generate(kernel_scale.begin(), kernel_scale.end(), [&]() { return scale_dist(rng); });
std::generate(filter.begin(), filter.end(), [&]() { return w8dist(rng); });
std::generate(bias.begin(), bias.end(), [&]() { return f32dist(rng); });
std::generate(convert_input.begin(), convert_input.end(), [&]() { return f32dist(rng); });
const float output_min = -std::numeric_limits<float>::infinity();
const float output_max = std::numeric_limits<float>::infinity();
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
// Call operator API.
xnn_operator_t convert_op = nullptr;
xnn_operator_t convolution_op = nullptr;
const size_t quantized_batch_size = input_height * input_width * group_input_channels * groups;
xnn_status status = xnn_create_convert_nc_f16_qd8(
/*flags=*/0, &convert_op);
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, convert_op);
ASSERT_EQ(xnn_status_success, xnn_reshape_convert_nc_f16_qd8(convert_op, batch_size, quantized_batch_size,
quantized_batch_size, quantized_batch_size, /*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_convert_nc_f16_qd8(convert_op, convert_input.data(),
operator_dq_data.data(), quantization_params.data()));
ASSERT_EQ(xnn_status_success, xnn_run_operator(convert_op, /*threadpool=*/nullptr));
status = xnn_create_convolution2d_nhwc_qd8_f16_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, group_input_channels,
group_output_channels, groups * group_input_channels, groups * group_output_channels,
kernel_scale.data(), filter.data(), bias.data(), output_min, output_max,
/*flags=*/0, nullptr, nullptr, &convolution_op);
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, convolution_op);
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,
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nhwc_qd8_f16_qc8w(convolution_op, /*workspace=*/nullptr, operator_dq_data.data(), operator_output.data(),
quantization_params.data()));
ASSERT_EQ(xnn_status_success, xnn_run_operator(convolution_op, /*threadpool=*/nullptr));
// Call subgraph API.
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp16, input_dims.size(), input_dims.data(), nullptr, /*external_id=*/0,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t dq_quantized_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_dynamically_quantized_tensor_value(
subgraph, xnn_datatype_qdint8, input_dims.size(), /*num_nonbatch_dims=*/3, input_dims.data(),
XNN_INVALID_VALUE_ID, /*flags=*/0, &dq_quantized_id));
ASSERT_NE(dq_quantized_id, XNN_INVALID_NODE_ID);
uint32_t kernel_id = XNN_INVALID_VALUE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint8, kernel_scale.data(), filter_dims.size(), /*channel_dim=*/0,
filter_dims.data(), filter.data(), /*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_VALUE_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, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
xnn_runtime_t runtime = nullptr;
ASSERT_EQ(xnn_status_success, xnn_define_convert(subgraph, input_id, dq_quantized_id, /*flags=*/0));
ASSERT_EQ(xnn_status_success,
xnn_define_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, groups,
group_input_channels, group_output_channels, output_min, output_max, dq_quantized_id, kernel_id, bias_id, output_id,
/*flags=*/0));
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, convert_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(ConvolutionTestQD8F32QC8W, define)
{
std::vector<float> requantization_scales(group_output_channels * groups, 1.0f);
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_VALUE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_dynamically_quantized_tensor_value(
subgraph, xnn_datatype_qdint8, input_dims.size(), /*num_nonbatch_dims=*/1, input_dims.data(),
/*external_id=*/0, /*flags=*/0, &input_id));
ASSERT_NE(input_id, XNN_INVALID_VALUE_ID);
uint32_t kernel_id = XNN_INVALID_VALUE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint8, requantization_scales.data(), filter_dims.size(), /*channel_dim=*/0,
filter_dims.data(), filter.data(), /*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_VALUE_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_VALUE_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_VALUE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_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, 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_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qd8_to_fp32);
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(ConvolutionTestQD8F32QC8W, internally_allocated_dynamic_quantization_parameters)
{
xnn_subgraph_t subgraph = nullptr;
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/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;
std::vector<float> convert_input(batch_size * input_height * input_width * groups * group_input_channels + XNN_EXTRA_BYTES / sizeof(float));
std::vector<int8_t> operator_dq_data(batch_size * input_height * input_width * groups * group_input_channels + XNN_EXTRA_BYTES);
std::fill(operator_output.begin(), operator_output.end(), nanf(""));
std::fill(subgraph_output.begin(), subgraph_output.end(), nanf(""));
std::vector<xnn_dynamic_quantization_params> quantization_params(batch_size + XNN_EXTRA_QUANTIZATION_PARAMS);
std::vector<float> kernel_scale(group_output_channels * groups);
std::generate(kernel_scale.begin(), kernel_scale.end(), [&]() { return scale_dist(rng); });
std::generate(filter.begin(), filter.end(), [&]() { return w8dist(rng); });
std::generate(bias.begin(), bias.end(), [&]() { return f32dist(rng); });
std::generate(convert_input.begin(), convert_input.end(), [&]() { return f32dist(rng); });
const float output_min = -std::numeric_limits<float>::infinity();
const float output_max = std::numeric_limits<float>::infinity();
size_t workspace_size = SIZE_MAX;
size_t workspace_alignment = SIZE_MAX;
// Call operator API.
xnn_operator_t convert_op = nullptr;
xnn_operator_t convolution_op = nullptr;
const size_t quantized_batch_size = input_height * input_width * group_input_channels * groups;
xnn_status status = xnn_create_convert_nc_f32_qd8(
/*flags=*/0, &convert_op);
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, convert_op);
ASSERT_EQ(xnn_status_success, xnn_reshape_convert_nc_f32_qd8(convert_op, batch_size, quantized_batch_size,
quantized_batch_size, quantized_batch_size, /*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success, xnn_setup_convert_nc_f32_qd8(convert_op, convert_input.data(),
operator_dq_data.data(), quantization_params.data()));
ASSERT_EQ(xnn_status_success, xnn_run_operator(convert_op, /*threadpool=*/nullptr));
status = xnn_create_convolution2d_nhwc_qd8_f32_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, group_input_channels,
group_output_channels, groups * group_input_channels, groups * group_output_channels,
kernel_scale.data(), filter.data(), bias.data(), output_min, output_max,
/*flags=*/0, nullptr, nullptr, &convolution_op);
std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
if (status == xnn_status_unsupported_hardware) {
GTEST_SKIP();
}
ASSERT_EQ(xnn_status_success, status);
ASSERT_NE(nullptr, convolution_op);
ASSERT_EQ( xnn_status_success, xnn_reshape_convolution2d_nhwc_qd8_f32_qc8w(
convolution_op, batch_size, input_height, input_width,
&workspace_size, &workspace_alignment,
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
/*threadpool=*/nullptr));
ASSERT_EQ(xnn_status_success,
xnn_setup_convolution2d_nhwc_qd8_f32_qc8w(convolution_op, /*workspace=*/nullptr, operator_dq_data.data(), operator_output.data(),
quantization_params.data()));
ASSERT_EQ(xnn_status_success, xnn_run_operator(convolution_op, /*threadpool=*/nullptr));
// Call subgraph API.
ASSERT_EQ(
xnn_status_success, xnn_define_tensor_value(
subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr, /*external_id=*/0,
/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
uint32_t dq_quantized_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_dynamically_quantized_tensor_value(
subgraph, xnn_datatype_qdint8, input_dims.size(), /*num_nonbatch_dims=*/3, input_dims.data(),
XNN_INVALID_VALUE_ID, /*flags=*/0, &dq_quantized_id));
ASSERT_NE(dq_quantized_id, XNN_INVALID_NODE_ID);
uint32_t kernel_id = XNN_INVALID_VALUE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_channelwise_quantized_tensor_value(
subgraph, xnn_datatype_qcint8, kernel_scale.data(), filter_dims.size(), /*channel_dim=*/0,
filter_dims.data(), filter.data(), /*external_id=*/1, /*flags=*/0, &kernel_id));
uint32_t bias_id = XNN_INVALID_VALUE_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=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
xnn_runtime_t runtime = nullptr;
ASSERT_EQ(xnn_status_success, xnn_define_convert(subgraph, input_id, dq_quantized_id, /*flags=*/0));
ASSERT_EQ(xnn_status_success,
xnn_define_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, groups,
group_input_channels, group_output_channels, output_min, output_max, dq_quantized_id, kernel_id, bias_id, output_id,
/*flags=*/0));
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, convert_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(ConvolutionTestQS8, 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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_qint8, 0, 1.0f, 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, 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_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, groups,
group_input_channels, group_output_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_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qs8);
ASSERT_EQ(node->params.convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.convolution_2d.groups, groups);
ASSERT_EQ(node->params.convolution_2d.group_input_channels, group_input_channels);
ASSERT_EQ(node->params.convolution_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], 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(ConvolutionTestQU8, 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 filter_id = XNN_INVALID_NODE_ID;
ASSERT_EQ(
xnn_status_success, xnn_define_quantized_tensor_value(
subgraph, xnn_datatype_quint8, 0, 1.0f, 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, 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_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, groups,
group_input_channels, group_output_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_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_qu8);
ASSERT_EQ(node->params.convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.convolution_2d.groups, groups);
ASSERT_EQ(node->params.convolution_2d.group_input_channels, group_input_channels);
ASSERT_EQ(node->params.convolution_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], 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(ConvolutionTestF16, 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, /*flags=*/0, &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, /*flags=*/0, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_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, groups,
group_input_channels, group_output_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_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_fp16);
ASSERT_EQ(node->params.convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.convolution_2d.groups, groups);
ASSERT_EQ(node->params.convolution_2d.group_input_channels, group_input_channels);
ASSERT_EQ(node->params.convolution_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], 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(ConvolutionTestF32, 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 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, /*flags=*/0, &output_id));
ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
ASSERT_EQ(
xnn_status_success,
xnn_define_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, groups,
group_input_channels, group_output_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_convolution_2d);
ASSERT_EQ(node->compute_type, xnn_compute_type_fp32);
ASSERT_EQ(node->params.convolution_2d.input_padding_top, input_padding_top);
ASSERT_EQ(node->params.convolution_2d.input_padding_right, input_padding_right);
ASSERT_EQ(node->params.convolution_2d.input_padding_bottom, input_padding_bottom);
ASSERT_EQ(node->params.convolution_2d.input_padding_left, input_padding_left);
ASSERT_EQ(node->params.convolution_2d.kernel_height, kernel_height);
ASSERT_EQ(node->params.convolution_2d.kernel_width, kernel_width);
ASSERT_EQ(node->params.convolution_2d.subsampling_height, subsampling_height);
ASSERT_EQ(node->params.convolution_2d.subsampling_width, subsampling_width);
ASSERT_EQ(node->params.convolution_2d.dilation_height, dilation_height);
ASSERT_EQ(node->params.convolution_2d.dilation_width, dilation_width);
ASSERT_EQ(node->params.convolution_2d.groups, groups);
ASSERT_EQ(node->params.convolution_2d.group_input_channels, group_input_channels);
ASSERT_EQ(node->params.convolution_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], 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(ConvolutionTestQC8, 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));
std::vector<float> requantization_scales(groups * group_output_channels);
const int8_t input_zero_point = i8dist(rng);
const int8_t output_zero_point = i8dist(rng);
const float input_scale = scale_dist(rng);
const float output_scale = scale_dist(rng);
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_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,
group_input_channels,
group_output_channels,
input_zero_point,
input,
filter,
accumulators,
/*has_bias=*/true,
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;
}
// 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, group_input_channels,
group_output_channels, groups * group_input_channels, groups * group_output_channels, input_zero_point, input_scale,
requantization_scales.data(), filter.data(), bias.data(), output_zero_point, output_scale, quantized_output_min,
quantized_output_max, /*flags=*/0, 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);
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qs8_qc8w(op, /*workspace=*/nullptr, 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(), 0,
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_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, groups,
group_input_channels, group_output_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));