73 lines
1.9 KiB
C++
73 lines
1.9 KiB
C++
// Copyright 2019 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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#pragma once
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#include <gtest/gtest.h>
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#include <algorithm>
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#include <cassert>
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#include <cstddef>
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#include <cstdlib>
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#include <functional>
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#include <random>
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#include <vector>
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#include <xnnpack.h>
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#include <xnnpack/params.h>
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class RAddExtExpMicrokernelTester {
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public:
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inline RAddExtExpMicrokernelTester& elements(size_t elements) {
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assert(elements != 0);
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this->elements_ = elements;
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return *this;
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}
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inline size_t elements() const {
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return this->elements_;
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}
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inline RAddExtExpMicrokernelTester& iterations(size_t iterations) {
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this->iterations_ = iterations;
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return *this;
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}
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inline size_t iterations() const {
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return this->iterations_;
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}
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void Test(xnn_f32_raddextexp_ukernel_function raddextexp) const {
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std::random_device random_device;
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auto rng = std::mt19937(random_device());
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// Choose such range that expf(x[i]) overflows, but double-precision exp doesn't overflow.
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auto f32rng = std::bind(std::uniform_real_distribution<float>(90.0f, 100.0f), rng);
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std::vector<float> x(elements() + XNN_EXTRA_BYTES / sizeof(float));
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for (size_t iteration = 0; iteration < iterations(); iteration++) {
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std::generate(x.begin(), x.end(), std::ref(f32rng));
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// Compute reference results.
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double sum_ref = 0.0f;
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for (size_t i = 0; i < elements(); i++) {
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sum_ref += exp(double(x[i]));
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}
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// Call optimized micro-kernel.
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float sum[2] = { nanf(""), nanf("") };
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raddextexp(elements() * sizeof(float), x.data(), sum);
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// Verify results.
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ASSERT_NEAR(sum_ref, exp2(double(sum[1])) * double(sum[0]), std::abs(sum_ref) * 1.0e-6)
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<< "elements = " << elements() << ", y:value = " << sum[0] << ", y:exponent = " << sum[1];
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}
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}
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private:
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size_t elements_{1};
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size_t iterations_{15};
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};
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