1621 lines
79 KiB
C++
1621 lines
79 KiB
C++
// Copyright 2022 Google LLC
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//
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree.
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#include <algorithm> // For std::generate, std::min.
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#include <array> // For std::array.
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#include <cmath> // For std::lrintf.
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#include <cstddef> // For size_t.
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#include <cstdint> // For uint32_t.
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#include <limits> // For std::numeric_limits.
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#include <memory> // For std::unique_ptr.
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#include <random> // For std::uniform_real_distribution.
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#include <vector> // For std::vector.
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#include <gtest/gtest.h>
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#include "xnnpack.h"
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#include "xnnpack/buffer.h"
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#include "xnnpack/common.h"
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#include "xnnpack/math.h"
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#include "xnnpack/node-type.h"
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#include "xnnpack/operator-utils.h"
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#include "xnnpack/operator.h"
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#include "xnnpack/requantization.h"
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#include "xnnpack/subgraph.h"
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#include "convolution-test-helpers.h"
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#include "replicable_random_device.h"
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namespace xnnpack {
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template <class InputType, class KernelType = InputType, class BiasType = InputType, class OutputType = InputType> class ConvolutionTestBase : public ::testing::Test {
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protected:
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ConvolutionTestBase() {
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input_size_dist = std::uniform_int_distribution<uint32_t>(10, 15);
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kernel_size_dist = std::uniform_int_distribution<uint32_t>(1, 5);
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subsampling_dist = std::uniform_int_distribution<uint32_t>(1, 5);
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// max value of (dilation * kernel size) must be smaller than input size.
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dilation_dist = std::uniform_int_distribution<uint32_t>(1, 2);
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f32dist = std::uniform_real_distribution<float>(0.1f, 1.0f);
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scale_dist = std::uniform_real_distribution<float>(1.0f, 5.0f);
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i32dist = std::uniform_int_distribution<int32_t>(-10000, 10000);
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batch_size = input_size_dist(rng);
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input_height = input_size_dist(rng);
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input_width = input_size_dist(rng);
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kernel_height = kernel_size_dist(rng);
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kernel_width = kernel_size_dist(rng);
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subsampling_height = subsampling_dist(rng);
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subsampling_width = subsampling_dist(rng);
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dilation_height = dilation_dist(rng);
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dilation_width = dilation_dist(rng);
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groups = input_size_dist(rng);
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group_input_channels = input_size_dist(rng);
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group_output_channels = input_size_dist(rng);
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output_min = -std::numeric_limits<float>::infinity();
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output_max = std::numeric_limits<float>::infinity();
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output_height = xnn_compute_convolution_output_dimension(
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input_height, kernel_height, dilation_height, subsampling_height);
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output_width = xnn_compute_convolution_output_dimension(
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input_width, kernel_width, dilation_width, subsampling_width);
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input_dims = {
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{batch_size, input_height, input_width, groups * group_input_channels}};
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filter_dims = {{groups * group_output_channels, kernel_height, kernel_width,
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group_input_channels}};
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bias_dims = {{groups * group_output_channels}};
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output_dims = {{batch_size, output_height, output_width,
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groups * group_output_channels}};
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input = xnnpack::Buffer<InputType>(XNN_EXTRA_BYTES / sizeof(InputType) +
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batch_size * input_height * input_width *
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groups * group_input_channels);
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filter =
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xnnpack::Buffer<KernelType>(groups * group_output_channels * kernel_height *
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kernel_width * group_input_channels);
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bias = xnnpack::Buffer<BiasType>(groups * group_output_channels);
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operator_output =
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xnnpack::Buffer<OutputType>(batch_size * output_height * output_width *
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groups * group_output_channels);
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subgraph_output =
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xnnpack::Buffer<OutputType>(batch_size * output_height * output_width *
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groups * group_output_channels);
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}
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xnnpack::ReplicableRandomDevice rng;
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std::uniform_int_distribution<uint32_t> input_size_dist;
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std::uniform_int_distribution<uint32_t> kernel_size_dist;
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std::uniform_int_distribution<uint32_t> subsampling_dist;
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std::uniform_int_distribution<uint32_t> dilation_dist;
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std::uniform_int_distribution<int32_t> i32dist;
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std::uniform_real_distribution<float> f32dist;
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std::uniform_real_distribution<float> scale_dist;
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const uint32_t input_padding_top = 0;
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const uint32_t input_padding_right = 0;
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const uint32_t input_padding_bottom = 0;
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const uint32_t input_padding_left = 0;
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uint32_t batch_size;
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uint32_t input_height;
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uint32_t input_width;
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uint32_t kernel_height;
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uint32_t kernel_width;
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uint32_t subsampling_height;
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uint32_t subsampling_width;
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uint32_t dilation_height;
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uint32_t dilation_width;
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uint32_t groups;
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uint32_t group_input_channels;
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uint32_t group_output_channels;
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float output_min;
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float output_max;
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uint32_t output_height;
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uint32_t output_width;
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std::array<size_t, 4> input_dims;
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std::array<size_t, 4> filter_dims;
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std::array<size_t, 1> bias_dims;
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std::array<size_t, 4> output_dims;
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xnnpack::Buffer<InputType> input;
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xnnpack::Buffer<KernelType> filter;
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xnnpack::Buffer<BiasType> bias;
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xnnpack::Buffer<OutputType> operator_output;
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xnnpack::Buffer<OutputType> subgraph_output;
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};
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template <class InputType, class KernelType = InputType, class BiasType = InputType, class OutputType = InputType> class QuantizedConvolutionTestBase : public ConvolutionTestBase<InputType, KernelType, BiasType, OutputType> {
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protected:
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QuantizedConvolutionTestBase()
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{
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i8dist =
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std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max());
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w8dist = std::uniform_int_distribution<int32_t>(-std::numeric_limits<KernelType>::max(), std::numeric_limits<KernelType>::max());
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std::uniform_int_distribution<int32_t> u8dist(
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std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max());
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accumulators = xnnpack::Buffer<int32_t>(
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this->batch_size * this->output_height * this->output_width * this->groups * this->group_output_channels);
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}
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std::uniform_int_distribution<int32_t> i8dist;
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std::uniform_int_distribution<int32_t> u8dist;
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std::uniform_int_distribution<int32_t> w8dist;
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xnnpack::Buffer<int32_t> accumulators;
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};
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using ConvolutionTestQC8 = QuantizedConvolutionTestBase<int8_t, int8_t, int32_t,int8_t>;
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using ConvolutionTestQD8F16QC8W = QuantizedConvolutionTestBase<float, int8_t, float, xnn_float16>;
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using ConvolutionTestQD8F32QC8W = QuantizedConvolutionTestBase<float, int8_t, float, float>;
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using ConvolutionTestQS8 = QuantizedConvolutionTestBase<int8_t, int8_t, int32_t,int8_t>;
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using ConvolutionTestQU8 = QuantizedConvolutionTestBase<uint8_t, uint8_t, int32_t,uint8_t>;
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using ConvolutionTestF16 = ConvolutionTestBase<xnn_float16, float, float>;
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using ConvolutionTestF32 = ConvolutionTestBase<float>;
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TEST_F(ConvolutionTestQC8, define)
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{
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ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
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xnn_subgraph_t subgraph = nullptr;
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ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
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std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
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uint32_t input_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_quantized_tensor_value(
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subgraph, xnn_datatype_qint8, 0, 1.0f, input_dims.size(), input_dims.data(), nullptr,
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/*external_id=*/0, /*flags=*/0, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
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xnnpack::Buffer<float> scale(groups * group_output_channels, 1.0f);
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uint32_t filter_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_channelwise_quantized_tensor_value(
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subgraph, xnn_datatype_qcint8, scale.data(), filter_dims.size(), 0, filter_dims.data(), filter.data(),
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/*external_id=*/1, /*flags=*/0, &filter_id));
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uint32_t bias_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_channelwise_quantized_tensor_value(
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subgraph, xnn_datatype_qcint32, scale.data(), bias_dims.size(), 0, bias_dims.data(), bias.data(),
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/*external_id=*/2, /*flags=*/0, &bias_id));
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uint32_t output_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_quantized_tensor_value(
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subgraph, xnn_datatype_qint8, 0, 1.0f, output_dims.size(), output_dims.data(), nullptr,
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/*external_id=*/3, /*flags=*/0, &output_id));
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ASSERT_NE(output_id, XNN_INVALID_NODE_ID);
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_convolution_2d(
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subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
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kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, groups,
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group_input_channels, group_output_channels, output_min, output_max, input_id, filter_id, bias_id, output_id,
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/*flags=*/0));
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ASSERT_EQ(subgraph->num_nodes, 1);
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const struct xnn_node* node = &subgraph->nodes[0];
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ASSERT_EQ(node->type, xnn_node_type_convolution_2d);
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ASSERT_EQ(node->params.convolution_2d.input_padding_top, input_padding_top);
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ASSERT_EQ(node->params.convolution_2d.input_padding_right, input_padding_right);
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ASSERT_EQ(node->params.convolution_2d.input_padding_bottom, input_padding_bottom);
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ASSERT_EQ(node->params.convolution_2d.input_padding_left, input_padding_left);
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ASSERT_EQ(node->params.convolution_2d.kernel_height, kernel_height);
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ASSERT_EQ(node->params.convolution_2d.kernel_width, kernel_width);
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ASSERT_EQ(node->params.convolution_2d.subsampling_height, subsampling_height);
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ASSERT_EQ(node->params.convolution_2d.subsampling_width, subsampling_width);
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ASSERT_EQ(node->params.convolution_2d.dilation_height, dilation_height);
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ASSERT_EQ(node->params.convolution_2d.dilation_width, dilation_width);
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ASSERT_EQ(node->params.convolution_2d.groups, groups);
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ASSERT_EQ(node->params.convolution_2d.group_input_channels, group_input_channels);
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ASSERT_EQ(node->params.convolution_2d.group_output_channels, group_output_channels);
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ASSERT_EQ(node->activation.output_min, output_min);
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ASSERT_EQ(node->activation.output_max, output_max);
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ASSERT_EQ(node->num_inputs, 3);
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ASSERT_EQ(node->inputs[0], input_id);
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ASSERT_EQ(node->inputs[1], filter_id);
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ASSERT_EQ(node->inputs[2], bias_id);
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ASSERT_EQ(node->num_outputs, 1);
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ASSERT_EQ(node->outputs[0], output_id);
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ASSERT_EQ(node->flags, 0);
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}
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TEST_F(ConvolutionTestQD8F16QC8W, define)
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{
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xnnpack::Buffer<float> requantization_scales(group_output_channels * groups, 1.0f);
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ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
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xnn_subgraph_t subgraph = nullptr;
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ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
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std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
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uint32_t input_id = XNN_INVALID_VALUE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_dynamically_quantized_tensor_value(
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subgraph, xnn_datatype_qdint8, input_dims.size(), /*num_nonbatch_dims=*/1, input_dims.data(),
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/*external_id=*/0, /*flags=*/0, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_VALUE_ID);
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uint32_t kernel_id = XNN_INVALID_VALUE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_channelwise_quantized_tensor_value(
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subgraph, xnn_datatype_qcint8, requantization_scales.data(), filter_dims.size(), /*channel_dim=*/0,
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filter_dims.data(), filter.data(), /*external_id=*/1, /*flags=*/0, &kernel_id));
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uint32_t bias_id = XNN_INVALID_VALUE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_tensor_value(
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subgraph, xnn_datatype_fp32, bias_dims.size(), bias_dims.data(), bias.data(),
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/*external_id=*/2, /*flags=*/0, &bias_id));
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uint32_t output_id = XNN_INVALID_VALUE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_tensor_value(
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subgraph, xnn_datatype_fp16, output_dims.size(), output_dims.data(), nullptr,
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/*external_id=*/3, /*flags=*/0, &output_id));
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ASSERT_NE(output_id, XNN_INVALID_VALUE_ID);
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ASSERT_EQ(
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xnn_status_success,
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xnn_define_convolution_2d(
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subgraph, input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height,
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kernel_width, subsampling_height, subsampling_width, dilation_height, dilation_width, groups,
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group_input_channels, group_output_channels, output_min, output_max, input_id, kernel_id, bias_id, output_id,
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/*flags=*/0));
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ASSERT_EQ(subgraph->num_nodes, 1);
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const struct xnn_node* node = &subgraph->nodes[0];
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ASSERT_EQ(node->type, xnn_node_type_convolution_2d);
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ASSERT_EQ(node->activation.output_min, output_min);
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ASSERT_EQ(node->activation.output_max, output_max);
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ASSERT_EQ(node->num_inputs, 3);
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ASSERT_EQ(node->inputs[0], input_id);
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ASSERT_EQ(node->inputs[1], kernel_id);
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ASSERT_EQ(node->inputs[2], bias_id);
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ASSERT_EQ(node->num_outputs, 1);
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ASSERT_EQ(node->outputs[0], output_id);
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ASSERT_EQ(node->flags, 0);
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}
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TEST_F(ConvolutionTestQD8F16QC8W, internally_allocated_dynamic_quantization_parameters)
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{
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xnn_subgraph_t subgraph = nullptr;
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ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/4, /*flags=*/0, &subgraph));
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std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
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uint32_t input_id = XNN_INVALID_NODE_ID;
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xnnpack::Buffer<xnn_float16> convert_input(batch_size * input_height * input_width * groups * group_input_channels + XNN_EXTRA_BYTES / sizeof(xnn_float16));
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xnnpack::Buffer<int8_t> operator_dq_data(batch_size * input_height * input_width * groups * group_input_channels + XNN_EXTRA_BYTES);
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xnnpack::Buffer<xnn_quantization_params> quantization_params(batch_size + XNN_EXTRA_QUANTIZATION_PARAMS);
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xnnpack::Buffer<float> kernel_scale(group_output_channels * groups);
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std::generate(kernel_scale.begin(), kernel_scale.end(), [&]() { return scale_dist(rng); });
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std::generate(filter.begin(), filter.end(), [&]() { return w8dist(rng); });
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std::generate(bias.begin(), bias.end(), [&]() { return f32dist(rng); });
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std::generate(convert_input.begin(), convert_input.end(), [&]() { return f32dist(rng); });
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const float output_min = -std::numeric_limits<float>::infinity();
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const float output_max = std::numeric_limits<float>::infinity();
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size_t workspace_size = SIZE_MAX;
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size_t workspace_alignment = SIZE_MAX;
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// Call operator API.
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xnn_operator_t convert_op = nullptr;
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xnn_operator_t convolution_op = nullptr;
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const size_t quantized_batch_size = input_height * input_width * group_input_channels * groups;
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xnn_status status = xnn_create_convert_nc_f16_qd8(
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/*flags=*/0, &convert_op);
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convert_op(convert_op, xnn_delete_operator);
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if (status == xnn_status_unsupported_hardware) {
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GTEST_SKIP();
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}
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ASSERT_EQ(xnn_status_success, status);
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ASSERT_NE(nullptr, convert_op);
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ASSERT_EQ(xnn_status_success, xnn_reshape_convert_nc_f16_qd8(convert_op, batch_size, quantized_batch_size,
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quantized_batch_size, quantized_batch_size, /*threadpool=*/nullptr));
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ASSERT_EQ(xnn_status_success, xnn_setup_convert_nc_f16_qd8(convert_op, convert_input.data(),
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operator_dq_data.data(), quantization_params.data()));
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ASSERT_EQ(xnn_status_success, xnn_run_operator(convert_op, /*threadpool=*/nullptr));
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status = xnn_create_convolution2d_nhwc_qd8_f16_qc8w(
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input_padding_top, input_padding_right, input_padding_bottom, input_padding_left, kernel_height, kernel_width,
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subsampling_height, subsampling_width, dilation_height, dilation_width, groups, group_input_channels,
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group_output_channels, groups * group_input_channels, groups * group_output_channels,
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kernel_scale.data(), filter.data(), bias.data(), output_min, output_max,
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/*flags=*/0, nullptr, nullptr, &convolution_op);
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std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_convolution_op(convolution_op, xnn_delete_operator);
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if (status == xnn_status_unsupported_hardware) {
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GTEST_SKIP();
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}
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ASSERT_EQ(xnn_status_success, status);
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ASSERT_NE(nullptr, convolution_op);
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ASSERT_EQ( xnn_status_success, xnn_reshape_convolution2d_nhwc_qd8_f16_qc8w(
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convolution_op, batch_size, input_height, input_width, &workspace_size, &workspace_alignment,
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/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
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/*threadpool=*/nullptr));
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ASSERT_EQ(xnn_status_success,
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xnn_setup_convolution2d_nhwc_qd8_f16_qc8w(convolution_op, /*workspace=*/nullptr, operator_dq_data.data(), operator_output.data(),
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quantization_params.data()));
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ASSERT_EQ(xnn_status_success, xnn_run_operator(convolution_op, /*threadpool=*/nullptr));
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// Call subgraph API.
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ASSERT_EQ(
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xnn_status_success, xnn_define_tensor_value(
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subgraph, xnn_datatype_fp16, input_dims.size(), input_dims.data(), nullptr, /*external_id=*/0,
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/*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
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ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
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uint32_t dq_quantized_id = XNN_INVALID_NODE_ID;
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ASSERT_EQ(
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xnn_status_success, xnn_define_dynamically_quantized_tensor_value(
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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_unary(subgraph, xnn_unary_convert, /*params=*/nullptr, 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)
|
|
{
|
|
xnnpack::Buffer<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->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)
|
|
{
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
|
|
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;
|
|
xnnpack::Buffer<float> convert_input(batch_size * input_height * input_width * groups * group_input_channels + XNN_EXTRA_BYTES / sizeof(float));
|
|
xnnpack::Buffer<int8_t> operator_dq_data(batch_size * input_height * input_width * groups * group_input_channels + XNN_EXTRA_BYTES);
|
|
xnnpack::Buffer<xnn_quantization_params> quantization_params(batch_size + XNN_EXTRA_QUANTIZATION_PARAMS);
|
|
xnnpack::Buffer<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_unary(subgraph, xnn_unary_convert, /*params=*/nullptr, 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->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->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->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->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); });
|
|
xnnpack::Buffer<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));
|
|
|
|
// Check outputs match.
|
|
for (size_t i = 0; i < operator_output.size(); i++) {
|
|
ASSERT_EQ(subgraph_output[i], operator_output[i]);
|
|
}
|
|
}
|
|
|
|
TEST_F(ConvolutionTestQS8, 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); });
|
|
const int8_t input_zero_point = -1;
|
|
const float input_scale = scale_dist(rng);
|
|
const float kernel_scale = scale_dist(rng);
|
|
|
|
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.
|
|
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, group_input_channels,
|
|
group_output_channels, groups * group_input_channels, groups * group_output_channels, input_zero_point, input_scale,
|
|
kernel_scale, 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(
|
|
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(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_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_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));
|
|
|
|
// Check outputs match.
|
|
for (size_t i = 0; i < operator_output.size(); i++) {
|
|
ASSERT_EQ(subgraph_output[i], operator_output[i]);
|
|
}
|
|
}
|
|
|
|
TEST_F(ConvolutionTestQU8, 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); });
|
|
const uint8_t input_zero_point = u8dist(rng);
|
|
const uint8_t kernel_zero_point = 0;
|
|
const float input_scale = scale_dist(rng);
|
|
const float kernel_scale = scale_dist(rng);
|
|
|
|
// Compute reference results, without renormalization.
|
|
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,
|
|
group_input_channels,
|
|
group_output_channels,
|
|
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, group_input_channels,
|
|
group_output_channels, groups * group_input_channels, groups * group_output_channels, 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=*/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_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);
|
|
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_qu8(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_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_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));
|
|
|
|
// Check outputs match.
|
|
for (size_t i = 0; i < operator_output.size(); i++) {
|
|
ASSERT_EQ(subgraph_output[i], operator_output[i]);
|
|
}
|
|
}
|
|
|
|
TEST_F(ConvolutionTestF16, 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); });
|
|
|
|
// 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, group_input_channels,
|
|
group_output_channels, groups * group_input_channels, groups * group_output_channels, filter.data(), bias.data(),
|
|
output_min, output_max,
|
|
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_EQ(workspace_size, 0);
|
|
ASSERT_EQ(workspace_alignment, 1);
|
|
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_f16(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_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_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));
|
|
|
|
// Check outputs match.
|
|
for (size_t i = 0; i < operator_output.size(); i++) {
|
|
ASSERT_EQ(subgraph_output[i], operator_output[i]);
|
|
}
|
|
}
|
|
|
|
TEST_F(ConvolutionTestF32, 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); });
|
|
|
|
// Call operator API.
|
|
const xnn_status status = xnn_create_convolution2d_nhwc_f32(
|
|
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, filter.data(), bias.data(),
|
|
output_min, 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_f32(
|
|
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_f32(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_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_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));
|
|
|
|
// Check outputs match.
|
|
for (size_t i = 0; i < operator_output.size(); i++) {
|
|
ASSERT_EQ(subgraph_output[i], operator_output[i]);
|
|
}
|
|
}
|
|
|
|
TEST_F(ConvolutionTestF32, transient_indirection_buffer)
|
|
{
|
|
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); });
|
|
|
|
// Call operator API.
|
|
const xnn_status status = xnn_create_convolution2d_nhwc_f32(
|
|
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, filter.data(), bias.data(),
|
|
output_min, output_max,
|
|
/*flags=*/XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER, 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_f32(
|
|
op, batch_size, input_height, input_width,
|
|
&workspace_size, &workspace_alignment,
|
|
/*output_height_out=*/nullptr, /*output_width_out=*/nullptr,
|
|
/*threadpool=*/nullptr));
|
|
// workspace_size might be 0 if we hit the vmulcaddc path which does not require any indirection buffers.
|
|
ASSERT_NE(workspace_size, SIZE_MAX);
|
|
ASSERT_NE(workspace_alignment, SIZE_MAX);
|
|
xnnpack::Buffer<char, XNN_ALLOCATION_ALIGNMENT> workspace(workspace_size);
|
|
ASSERT_EQ(xnn_status_success, xnn_setup_convolution2d_nhwc_f32(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_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_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=*/XNN_FLAG_TRANSIENT_INDIRECTION_BUFFER));
|
|
|
|
xnn_runtime_t runtime = nullptr;
|
|
ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
|
|
ASSERT_NE(nullptr, runtime);
|
|
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);
|
|
std::array<xnn_external_value, 2> external = {
|
|
xnn_external_value{input_id, input.data()}, xnn_external_value{output_id, subgraph_output.data()}};
|
|
ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
|
|
ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));
|
|
|
|
// Check outputs match.
|
|
for (size_t i = 0; i < operator_output.size(); i++) {
|
|
ASSERT_EQ(subgraph_output[i], operator_output[i]);
|
|
}
|
|
}
|
|
|
|
TEST_F(ConvolutionTestF32, reshape_output)
|
|
{
|
|
ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));
|
|
|
|
// Call subgraph API.
|
|
xnn_subgraph_t subgraph = nullptr;
|
|
ASSERT_EQ(xnn_status_success, xnn_create_subgraph(4, /*flags=*/0, &subgraph));
|
|
std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
|
|
|
|
uint32_t input_id = XNN_INVALID_NODE_ID;
|
|
ASSERT_EQ(
|
|
xnn_status_success, xnn_define_tensor_value(
|
|
subgraph, xnn_datatype_fp32, input_dims.size(), input_dims.data(), nullptr,
|
|
/*external_id=*/0, XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
|
|
ASSERT_NE(input_id, XNN_INVALID_NODE_ID);
|
|
|
|
uint32_t 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_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));
|
|
|
|
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); });
|
|
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));
|
|
|
|
input_dims[0] += 2;
|
|
input_dims[1] += 3;
|
|
input_dims[2] += 4;
|
|
ASSERT_EQ(xnn_status_success, xnn_reshape_external_value(runtime, input_id, input_dims.size(), input_dims.data()));
|
|
const struct xnn_node* node = &subgraph->nodes[0];
|
|
ASSERT_EQ(node->reshape(&runtime->opdata[0], runtime->values, runtime->num_values, /*threadpool=*/nullptr), xnn_status_reallocation_required);
|
|
const xnn_shape* output_shape = &runtime->values[node->outputs[0]].shape;
|
|
ASSERT_EQ(output_shape->dim[0], input_dims[0]);
|
|
ASSERT_EQ(output_shape->dim[1], runtime->opdata[0].operator_objects[0]->output_height);
|
|
ASSERT_EQ(output_shape->dim[2], runtime->opdata[0].operator_objects[0]->output_width);
|
|
ASSERT_EQ(output_shape->dim[3], output_dims[3]);
|
|
|
|
input_dims[0] -= 1;
|
|
ASSERT_EQ(xnn_status_success, xnn_reshape_external_value(runtime, input_id, input_dims.size(), input_dims.data()));
|
|
ASSERT_EQ(node->reshape(&runtime->opdata[0], runtime->values, runtime->num_values, /*threadpool=*/nullptr), xnn_status_success);
|
|
ASSERT_EQ(output_shape->dim[0], input_dims[0]);
|
|
ASSERT_EQ(output_shape->dim[1], runtime->opdata[0].operator_objects[0]->output_height);
|
|
ASSERT_EQ(output_shape->dim[2], runtime->opdata[0].operator_objects[0]->output_width);
|
|
ASSERT_EQ(output_shape->dim[3], output_dims[3]);
|
|
|
|
}
|
|
} // namespace xnnpack
|