| // Copyright 2021 Google LLC |
| // |
| // This source code is licensed under the BSD-style license found in the |
| // LICENSE file in the root directory of this source tree. |
| |
| #pragma once |
| |
| #include <gtest/gtest.h> |
| |
| #include <algorithm> |
| #include <cassert> |
| #include <cmath> |
| #include <cstddef> |
| #include <cstdlib> |
| #include <functional> |
| #include <limits> |
| #include <random> |
| #include <vector> |
| |
| #include <fp16.h> |
| |
| #include <xnnpack.h> |
| #include <xnnpack/params.h> |
| #include <xnnpack/params-init.h> |
| |
| |
| class VCvtMicrokernelTester { |
| public: |
| inline VCvtMicrokernelTester& batch_size(size_t batch_size) { |
| assert(batch_size != 0); |
| this->batch_size_ = batch_size; |
| return *this; |
| } |
| |
| inline size_t batch_size() const { |
| return this->batch_size_; |
| } |
| |
| inline VCvtMicrokernelTester& scale(float scale) { |
| assert(scale > 0.0f); |
| assert(std::isnormal(scale)); |
| this->scale_ = scale; |
| return *this; |
| } |
| |
| inline float scale() const { |
| return this->scale_; |
| } |
| |
| inline VCvtMicrokernelTester& zero_point(int16_t zero_point) { |
| this->zero_point_ = zero_point; |
| return *this; |
| } |
| |
| inline int16_t zero_point() const { |
| return this->zero_point_; |
| } |
| |
| inline VCvtMicrokernelTester& qmin(int16_t qmin) { |
| this->qmin_ = qmin; |
| return *this; |
| } |
| |
| inline int16_t qmin() const { |
| return this->qmin_; |
| } |
| |
| inline VCvtMicrokernelTester& qmax(int16_t qmax) { |
| this->qmax_ = qmax; |
| return *this; |
| } |
| |
| inline int16_t qmax() const { |
| return this->qmax_; |
| } |
| |
| inline VCvtMicrokernelTester& iterations(size_t iterations) { |
| this->iterations_ = iterations; |
| return *this; |
| } |
| |
| inline size_t iterations() const { |
| return this->iterations_; |
| } |
| |
| void Test(xnn_f16_f32_vcvt_ukernel_function vcvt) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = std::uniform_real_distribution<float>(-100.0f, 100.0f); |
| auto f32rng = std::bind(distribution, std::ref(rng)); |
| auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| |
| std::vector<uint16_t> input(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
| std::vector<float> output(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f16rng)); |
| std::fill(output.begin(), output.end(), nanf("")); |
| |
| // Call optimized micro-kernel. |
| vcvt(batch_size() * sizeof(uint16_t), input.data(), output.data(), nullptr /* params */); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(fp32_to_bits(output[i]), fp32_to_bits(fp16_ieee_to_fp32_value(input[i]))) |
| << "at " << i << " / " << batch_size() |
| << ", x[" << i << "] = 0x" << std::hex << std::setw(4) << std::setfill('0') << input[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_f16_vcvt_ukernel_function vcvt) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = std::uniform_real_distribution<float>(-100.0f, 100.0f); |
| auto f32rng = std::bind(distribution, std::ref(rng)); |
| |
| std::vector<float> input(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<uint16_t> output(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), UINT16_C(0x7E)); |
| |
| // Call optimized micro-kernel. |
| vcvt(batch_size() * sizeof(float), input.data(), output.data(), nullptr /* params */); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(output[i], fp16_ieee_from_fp32_value(input[i])) |
| << "at " << i << " / " << batch_size() |
| << ", x[" << i << "] = 0x" << std::hex << std::setw(8) << std::setfill('0') << fp32_to_bits(input[i]) |
| << " (" << input[i] << ")"; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_qs8_vcvt_ukernel_function vcvt, xnn_init_f32_qs8_cvt_params_fn init_params = nullptr) const { |
| ASSERT_GE(qmin(), std::numeric_limits<int8_t>::min()); |
| ASSERT_LE(qmax(), std::numeric_limits<int8_t>::max()); |
| ASSERT_LT(qmin(), qmax()); |
| |
| ASSERT_GE(zero_point(), std::numeric_limits<int8_t>::min()); |
| ASSERT_LE(zero_point(), std::numeric_limits<int8_t>::max()); |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = std::uniform_real_distribution<float>(-1.0f, 1.0f); |
| auto f32rng = std::bind(distribution, std::ref(rng)); |
| |
| std::vector<float> input(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<int8_t> output(batch_size()); |
| std::vector<int8_t> output_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), INT8_C(0xA5)); |
| |
| union xnn_f32_qs8_cvt_params params; |
| if (init_params) { |
| init_params(¶ms, scale(), zero_point(), qmin(), qmax()); |
| } |
| |
| // Call optimized micro-kernel. |
| vcvt(batch_size() * sizeof(float), input.data(), output.data(), ¶ms); |
| |
| // Compute reference results |
| for (size_t i = 0; i < batch_size(); i++) { |
| float scaled_input = input[i] * scale(); |
| scaled_input = std::min<float>(scaled_input, float(qmax() - zero_point())); |
| scaled_input = std::max<float>(scaled_input, float(qmin() - zero_point())); |
| output_ref[i] = int8_t(std::lrintf(scaled_input) + long(zero_point())); |
| } |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(int32_t(output[i]), int32_t(output_ref[i])) |
| << "at " << i << " / " << batch_size() |
| << ", x[" << i << "] = 0x" << std::hex << std::setw(8) << std::setfill('0') << fp32_to_bits(input[i]) |
| << " (" << input[i] << ")"; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_qu8_vcvt_ukernel_function vcvt, xnn_init_f32_qu8_cvt_params_fn init_params = nullptr) const { |
| ASSERT_GE(qmin(), std::numeric_limits<uint8_t>::min()); |
| ASSERT_LE(qmax(), std::numeric_limits<uint8_t>::max()); |
| ASSERT_LT(qmin(), qmax()); |
| |
| ASSERT_GE(zero_point(), std::numeric_limits<uint8_t>::min()); |
| ASSERT_LE(zero_point(), std::numeric_limits<uint8_t>::max()); |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = std::uniform_real_distribution<float>(-1.0f, 1.0f); |
| auto f32rng = std::bind(distribution, std::ref(rng)); |
| |
| std::vector<float> input(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<uint8_t> output(batch_size()); |
| std::vector<uint8_t> output_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| std::fill(output.begin(), output.end(), UINT8_C(0xA5)); |
| |
| union xnn_f32_qu8_cvt_params params; |
| if (init_params) { |
| init_params(¶ms, scale(), zero_point(), qmin(), qmax()); |
| } |
| |
| // Call optimized micro-kernel. |
| vcvt(batch_size() * sizeof(float), input.data(), output.data(), ¶ms); |
| |
| // Compute reference results |
| for (size_t i = 0; i < batch_size(); i++) { |
| float scaled_input = input[i] * scale(); |
| scaled_input = std::min<float>(scaled_input, float(qmax() - zero_point())); |
| scaled_input = std::max<float>(scaled_input, float(qmin() - zero_point())); |
| output_ref[i] = uint8_t(std::lrintf(scaled_input) + long(zero_point())); |
| } |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(int32_t(output[i]), int32_t(output_ref[i])) |
| << "at " << i << " / " << batch_size() |
| << ", x[" << i << "] = 0x" << std::hex << std::setw(8) << std::setfill('0') << fp32_to_bits(input[i]) |
| << " (" << input[i] << ")"; |
| } |
| } |
| } |
| |
| void Test(xnn_qs8_f32_vcvt_ukernel_function vcvt, xnn_init_qs8_f32_cvt_params_fn init_params = nullptr) const { |
| ASSERT_GE(zero_point(), std::numeric_limits<int8_t>::min()); |
| ASSERT_LE(zero_point(), std::numeric_limits<int8_t>::max()); |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = |
| std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()); |
| auto i8rng = std::bind(distribution, std::ref(rng)); |
| |
| std::vector<int8_t> input(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t)); |
| std::vector<float> output(batch_size()); |
| std::vector<float> output_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(i8rng)); |
| std::fill(output.begin(), output.end(), std::nanf("")); |
| |
| union xnn_qs8_f32_cvt_params params; |
| if (init_params) { |
| init_params(¶ms, scale(), zero_point()); |
| } |
| |
| // Call optimized micro-kernel. |
| vcvt(batch_size() * sizeof(int8_t), input.data(), output.data(), ¶ms); |
| |
| // Compute reference results |
| for (size_t i = 0; i < batch_size(); i++) { |
| output_ref[i] = float(int16_t(input[i]) - zero_point()) * scale(); |
| } |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(output[i], output_ref[i]) |
| << "at " << i << " / " << batch_size() |
| << ", x[" << i << "] = " << int32_t(input[i]); |
| } |
| } |
| } |
| |
| void Test(xnn_qu8_f32_vcvt_ukernel_function vcvt, xnn_init_qu8_f32_cvt_params_fn init_params = nullptr) const { |
| ASSERT_GE(zero_point(), std::numeric_limits<uint8_t>::min()); |
| ASSERT_LE(zero_point(), std::numeric_limits<uint8_t>::max()); |
| |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = |
| std::uniform_int_distribution<int32_t>(std::numeric_limits<uint8_t>::min(), std::numeric_limits<uint8_t>::max()); |
| auto u8rng = std::bind(distribution, std::ref(rng)); |
| |
| std::vector<uint8_t> input(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| std::vector<float> output(batch_size()); |
| std::vector<float> output_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| std::fill(output.begin(), output.end(), std::nanf("")); |
| |
| union xnn_qu8_f32_cvt_params params; |
| if (init_params) { |
| init_params(¶ms, scale(), zero_point()); |
| } |
| |
| // Call optimized micro-kernel. |
| vcvt(batch_size() * sizeof(uint8_t), input.data(), output.data(), ¶ms); |
| |
| // Compute reference results |
| for (size_t i = 0; i < batch_size(); i++) { |
| output_ref[i] = float(int16_t(input[i]) - zero_point()) * scale(); |
| } |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(output[i], output_ref[i]) |
| << "at " << i << " / " << batch_size() |
| << ", x[" << i << "] = " << int32_t(input[i]); |
| } |
| } |
| } |
| |
| private: |
| float scale_ = 1.75f; |
| int16_t zero_point_ = 1; |
| int16_t qmin_ = std::numeric_limits<int16_t>::min(); |
| int16_t qmax_ = std::numeric_limits<int16_t>::max(); |
| size_t batch_size_ = 1; |
| size_t iterations_ = 15; |
| }; |