| // Copyright 2019 Google LLC |
| // |
| // This source code is licensed under the BSD-style license found in the |
| // LICENSE file in the root directory of this source tree. |
| |
| #pragma once |
| |
| #include <gtest/gtest.h> |
| |
| #include <algorithm> |
| #include <cassert> |
| #include <cstddef> |
| #include <cstdlib> |
| #include <functional> |
| #include <random> |
| #include <vector> |
| |
| #include <fp16.h> |
| |
| #include <xnnpack.h> |
| #include <xnnpack/params-init.h> |
| #include <xnnpack/params.h> |
| |
| |
| class VUnaryMicrokernelTester { |
| public: |
| enum class OpType { |
| ReLU, |
| RoundToNearestEven, |
| RoundTowardsZero, |
| RoundUp, |
| RoundDown, |
| }; |
| |
| enum class Variant { |
| Native, |
| Scalar, |
| }; |
| |
| inline VUnaryMicrokernelTester& 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 VUnaryMicrokernelTester& inplace(bool inplace) { |
| this->inplace_ = inplace; |
| return *this; |
| } |
| |
| inline bool inplace() const { |
| return this->inplace_; |
| } |
| |
| inline VUnaryMicrokernelTester& slope(float slope) { |
| this->slope_ = slope; |
| return *this; |
| } |
| |
| inline float slope() const { |
| return this->slope_; |
| } |
| |
| inline VUnaryMicrokernelTester& prescale(float prescale) { |
| this->prescale_ = prescale; |
| return *this; |
| } |
| |
| inline float prescale() const { |
| return this->prescale_; |
| } |
| |
| inline VUnaryMicrokernelTester& alpha(float alpha) { |
| this->alpha_ = alpha; |
| return *this; |
| } |
| |
| inline float alpha() const { |
| return this->alpha_; |
| } |
| |
| inline VUnaryMicrokernelTester& beta(float beta) { |
| this->beta_ = beta; |
| return *this; |
| } |
| |
| inline float beta() const { |
| return this->beta_; |
| } |
| |
| inline VUnaryMicrokernelTester& qmin(uint8_t qmin) { |
| this->qmin_ = qmin; |
| return *this; |
| } |
| |
| inline uint8_t qmin() const { |
| return this->qmin_; |
| } |
| |
| inline VUnaryMicrokernelTester& qmax(uint8_t qmax) { |
| this->qmax_ = qmax; |
| return *this; |
| } |
| |
| inline uint8_t qmax() const { |
| return this->qmax_; |
| } |
| |
| inline VUnaryMicrokernelTester& iterations(size_t iterations) { |
| this->iterations_ = iterations; |
| return *this; |
| } |
| |
| inline size_t iterations() const { |
| return this->iterations_; |
| } |
| |
| void Test(xnn_f32_vunary_ukernel_function vunary, OpType op_type, Variant variant = Variant::Native) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = std::uniform_real_distribution<float>(-125.0f, 125.0f); |
| auto f32rng = std::bind(distribution, std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<double> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| switch (op_type) { |
| case OpType::ReLU: |
| y_ref[i] = std::max(x_data[i], 0.0f); |
| break; |
| default: |
| GTEST_FAIL() << "Unexpected operation type"; |
| return; |
| } |
| } |
| |
| // Call optimized micro-kernel. |
| vunary(batch_size() * sizeof(float), x_data, y.data(), nullptr); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_NEAR(y[i], y_ref[i], std::max(5.0e-6, std::abs(y_ref[i]) * 1.0e-5)) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vabs_ukernel_function vabs, xnn_init_f32_abs_params_fn init_params = nullptr) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<float> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = std::abs(x_data[i]); |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_abs_params params; |
| if (init_params != nullptr) { |
| init_params(¶ms); |
| } |
| |
| // Call optimized micro-kernel. |
| vabs(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(y[i], y_ref[i]) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vclamp_ukernel_function vclamp, xnn_init_f32_minmax_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 255.0f), std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<float> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = std::max(std::min(x_data[i], float(qmax())), float(qmin())); |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_minmax_params params; |
| init_params(¶ms, float(qmin()), float(qmax())); |
| |
| // Call optimized micro-kernel. |
| vclamp(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(y[i], y_ref[i]) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_velu_ukernel_function velu, xnn_init_f32_elu_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(-20.0f, 20.0f), std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<double> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = std::signbit(x_data[i]) ? alpha() * std::expm1(double(x_data[i]) * prescale()) : double(x_data[i]) * beta(); |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_elu_params params; |
| init_params(¶ms, prescale(), alpha(), beta()); |
| |
| // Call optimized micro-kernel. |
| velu(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_NEAR(y[i], y_ref[i], std::max(5.0e-6, std::abs(y_ref[i]) * 1.0e-5)) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vhswish_ukernel_function vhswish, xnn_init_f32_hswish_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(-4.0f, 4.0f), std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<double> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = (x_data[i] / 6.0f) * std::max(std::min(x_data[i] + 3.0f, 6.0f), 0.0f); |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_hswish_params params; |
| init_params(¶ms); |
| |
| // Call optimized micro-kernel. |
| vhswish(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_NEAR(y[i], y_ref[i], std::max(5.0e-6, std::abs(y_ref[i]) * 1.0e-5)) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vlrelu_ukernel_function vlrelu, xnn_init_f32_lrelu_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(-125.0f, 125.0f), std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<double> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = std::signbit(x_data[i]) ? x_data[i] * slope() : x_data[i]; |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_lrelu_params params; |
| init_params(¶ms, slope()); |
| |
| // Call optimized micro-kernel. |
| vlrelu(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(y[i], y_ref[i]) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vneg_ukernel_function vneg, xnn_init_f32_neg_params_fn init_params = nullptr) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(-1.0f, 1.0f), std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<float> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = -x_data[i]; |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_neg_params params; |
| if (init_params != nullptr) { |
| init_params(¶ms); |
| } |
| |
| // Call optimized micro-kernel. |
| vneg(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(y[i], y_ref[i]) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vround_ukernel_function vrnd, OpType op_type, xnn_init_f32_rnd_params_fn init_params = nullptr) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = std::uniform_real_distribution<float>(-5.0f, 5.0f); |
| auto f32rng = std::bind(distribution, std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<float> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| switch (op_type) { |
| case OpType::RoundToNearestEven: |
| y_ref[i] = std::nearbyint(double(x_data[i])); |
| break; |
| case OpType::RoundTowardsZero: |
| y_ref[i] = std::trunc(double(x_data[i])); |
| break; |
| case OpType::RoundUp: |
| y_ref[i] = std::ceil(double(x_data[i])); |
| break; |
| case OpType::RoundDown: |
| y_ref[i] = std::floor(double(x_data[i])); |
| break; |
| default: |
| GTEST_FAIL() << "Unexpected operation type"; |
| return; |
| } |
| } |
| |
| // Prepare parameters. |
| xnn_f32_rnd_params params; |
| if (init_params != nullptr) { |
| init_params(¶ms); |
| } |
| |
| // Call optimized micro-kernel. |
| vrnd(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(y[i], y_ref[i]) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vsigmoid_ukernel_function vsigmoid, xnn_init_f32_sigmoid_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto distribution = std::uniform_real_distribution<float>(-125.0f, 125.0f); |
| auto f32rng = std::bind(distribution, std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<double> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| const double e = std::exp(double(x_data[i])); |
| y_ref[i] = e / (1.0 + e); |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_sigmoid_params params; |
| init_params(¶ms); |
| |
| // Call optimized micro-kernel. |
| vsigmoid(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_NEAR(y[i], y_ref[i], std::max(5.0e-6, std::abs(y_ref[i]) * 1.0e-5)) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vsqr_ukernel_function vsqr, xnn_init_f32_default_params_fn init_params = nullptr) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(-10.0f, 10.0f), std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<float> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = x_data[i] * x_data[i]; |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_default_params params; |
| if (init_params != nullptr) { |
| init_params(¶ms); |
| } |
| |
| // Call optimized micro-kernel. |
| vsqr(batch_size() * sizeof(float), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(y[i], y_ref[i]) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| void Test(xnn_f32_vsqrt_ukernel_function vsqrt, xnn_init_f32_sqrt_params_fn init_params = nullptr) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 10.0f), std::ref(rng)); |
| |
| std::vector<float> x(batch_size() + XNN_EXTRA_BYTES / sizeof(float)); |
| std::vector<float> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(float) : 0)); |
| std::vector<float> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f32rng)); |
| } else { |
| std::generate(x.begin(), x.end(), std::ref(f32rng)); |
| std::fill(y.begin(), y.end(), nanf("")); |
| } |
| const float* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = std::sqrt(x_data[i]); |
| } |
| |
| // Prepare parameters. |
| union xnn_f32_sqrt_params params; |
| if (init_params != nullptr) { |
| init_params(¶ms); |
| } |
| |
| // Call optimized micro-kernel. |
| vsqrt(batch_size() * sizeof(float), x_data, y.data(), init_params != nullptr ? ¶ms : nullptr); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(y[i], y_ref[i]) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << x[i]; |
| } |
| } |
| } |
| |
| inline void Test(xnn_f32_vabs_ukernel_function vunary, OpType op_type, Variant variant = Variant::Native) const { |
| Test(xnn_f32_vunary_ukernel_function(vunary), op_type, variant); |
| } |
| |
| inline void Test(xnn_f32_velu_ukernel_function vunary, OpType op_type, Variant variant = Variant::Native) const { |
| Test(xnn_f32_vunary_ukernel_function(vunary), op_type, variant); |
| } |
| |
| inline void Test(xnn_f32_vneg_ukernel_function vunary, OpType op_type, Variant variant = Variant::Native) const { |
| Test(xnn_f32_vunary_ukernel_function(vunary), op_type, variant); |
| } |
| |
| inline void Test(xnn_f32_vrelu_ukernel_function vunary, OpType op_type, Variant variant = Variant::Native) const { |
| Test(xnn_f32_vunary_ukernel_function(vunary), op_type, variant); |
| } |
| |
| void Test(xnn_f16_vclamp_ukernel_function vclamp, xnn_init_f16_minmax_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 255.0f), std::ref(rng)); |
| auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| |
| std::vector<uint16_t> x(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
| std::vector<uint16_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0)); |
| std::vector<float> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(x.begin(), x.end(), std::ref(f16rng)); |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f16rng)); |
| } else { |
| std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */); |
| } |
| const uint16_t* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = std::max(std::min(fp16_ieee_to_fp32_value(x_data[i]), float(qmax())), float(qmin())); |
| } |
| |
| // Prepare parameters. |
| union xnn_f16_minmax_params params; |
| init_params(¶ms, fp16_ieee_from_fp32_value(float(qmin())), fp16_ieee_from_fp32_value(float(qmax()))); |
| |
| // Call optimized micro-kernel. |
| vclamp(batch_size() * sizeof(uint16_t), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_NEAR(y_ref[i], fp16_ieee_to_fp32_value(y[i]), std::max(1.0e-3f, std::abs(y_ref[i]) * 1.0e-2f)) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << fp16_ieee_to_fp32_value(x[i]); |
| } |
| } |
| } |
| |
| void Test(xnn_f16_vhswish_ukernel_function vhswish, xnn_init_f16_hswish_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto f32rng = std::bind(std::uniform_real_distribution<float>(-4.0f, 4.0f), std::ref(rng)); |
| auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| |
| std::vector<uint16_t> x(batch_size() + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
| std::vector<uint16_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint16_t) : 0)); |
| std::vector<float> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(x.begin(), x.end(), std::ref(f16rng)); |
| if (inplace()) { |
| std::generate(y.begin(), y.end(), std::ref(f16rng)); |
| } else { |
| std::fill(y.begin(), y.end(), UINT16_C(0x7E00) /* NaN */); |
| } |
| const uint16_t* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| const float x_value = fp16_ieee_to_fp32_value(x_data[i]); |
| y_ref[i] = (x_value / 6.0f) * std::max(std::min(x_value + 3.0f, 6.0f), 0.0f); |
| } |
| |
| // Prepare parameters. |
| union xnn_f16_hswish_params params; |
| init_params(¶ms); |
| |
| // Call optimized micro-kernel. |
| vhswish(batch_size() * sizeof(uint16_t), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_NEAR(y_ref[i], fp16_ieee_to_fp32_value(y[i]), std::max(1.0e-3f, std::abs(y_ref[i]) * 1.0e-2f)) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << fp16_ieee_to_fp32_value(x[i]); |
| } |
| } |
| } |
| |
| void Test(xnn_s8_vclamp_ukernel_function vclamp, xnn_init_s8_minmax_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto i8rng = std::bind( |
| std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), |
| std::ref(rng)); |
| |
| std::vector<int8_t> x(batch_size() + XNN_EXTRA_BYTES / sizeof(int8_t)); |
| std::vector<int8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(int8_t) : 0)); |
| std::vector<int8_t> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(x.begin(), x.end(), std::ref(i8rng)); |
| if (inplace()) { |
| std::copy(x.cbegin(), x.cend(), y.begin()); |
| } else { |
| std::fill(y.begin(), y.end(), INT8_C(0xA5)); |
| } |
| const int8_t* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = std::min(std::max(x_data[i], int8_t(qmin() - 0x80)), int8_t(qmax() - 0x80)); |
| } |
| |
| // Prepare parameters. |
| union xnn_s8_minmax_params params; |
| init_params(¶ms, int8_t(qmin() - 0x80), int8_t(qmax() - 0x80)); |
| |
| // Call optimized micro-kernel. |
| vclamp(batch_size() * sizeof(int8_t), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(int32_t(y_ref[i]), int32_t(y[i])) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << int32_t(x[i]); |
| } |
| } |
| } |
| |
| void Test(xnn_u8_vclamp_ukernel_function vclamp, xnn_init_u8_minmax_params_fn init_params) const { |
| std::random_device random_device; |
| auto rng = std::mt19937(random_device()); |
| auto u8rng = std::bind( |
| std::uniform_int_distribution<int32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng)); |
| |
| std::vector<uint8_t> x(batch_size() + XNN_EXTRA_BYTES / sizeof(uint8_t)); |
| std::vector<uint8_t> y(batch_size() + (inplace() ? XNN_EXTRA_BYTES / sizeof(uint8_t) : 0)); |
| std::vector<uint8_t> y_ref(batch_size()); |
| for (size_t iteration = 0; iteration < iterations(); iteration++) { |
| std::generate(x.begin(), x.end(), std::ref(u8rng)); |
| if (inplace()) { |
| std::copy(x.cbegin(), x.cend(), y.begin()); |
| } else { |
| std::fill(y.begin(), y.end(), UINT8_C(0xA5)); |
| } |
| const uint8_t* x_data = inplace() ? y.data() : x.data(); |
| |
| // Compute reference results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| y_ref[i] = std::min(std::max(x_data[i], qmin()), qmax()); |
| } |
| |
| // Prepare parameters. |
| union xnn_u8_minmax_params params; |
| init_params(¶ms, qmin(), qmax()); |
| |
| // Call optimized micro-kernel. |
| vclamp(batch_size() * sizeof(uint8_t), x_data, y.data(), ¶ms); |
| |
| // Verify results. |
| for (size_t i = 0; i < batch_size(); i++) { |
| ASSERT_EQ(uint32_t(y_ref[i]), uint32_t(y[i])) |
| << "at " << i << " / " << batch_size() << ", x[" << i << "] = " << uint32_t(x[i]); |
| } |
| } |
| } |
| |
| private: |
| size_t batch_size_ = 1; |
| bool inplace_ = false; |
| float slope_ = 0.5f; |
| float prescale_ = 1.0f; |
| float alpha_ = 1.0f; |
| float beta_ = 1.0f; |
| uint8_t qmin_ = 0; |
| uint8_t qmax_ = 255; |
| size_t iterations_ = 15; |
| }; |