mirror of https://github.com/google/gemma.cpp.git
127 lines
4.0 KiB
C++
127 lines
4.0 KiB
C++
// Copyright 2023 Google LLC
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// SPDX-License-Identifier: Apache-2.0
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#ifndef THIRD_PARTY_GEMMA_CPP_UTIL_TEST_UTIL_H_
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#define THIRD_PARTY_GEMMA_CPP_UTIL_TEST_UTIL_H_
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#include <stddef.h>
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#include <stdint.h>
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#include <algorithm> // std::sort
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#include <cmath>
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#include <iostream>
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#include "util/basics.h" // RngStream
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#include "util/mat.h"
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#include "hwy/base.h"
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// IWYU pragma: begin_exports
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#include "hwy/nanobenchmark.h"
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#include "hwy/stats.h"
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#include "hwy/tests/test_util.h" // RandomState
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// IWYU pragma: end_exports
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namespace gcpp {
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// Excludes outliers; we might not have enough samples for a reliable mode.
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HWY_INLINE double TrimmedMean(double* seconds, size_t num) {
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std::sort(seconds, seconds + num);
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double sum = 0;
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int count = 0;
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for (size_t i = num / 4; i < num / 2; ++i) {
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sum += seconds[i];
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count += 1;
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}
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HWY_DASSERT(num != 0);
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return sum / count;
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}
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// Returns normalized value in [-1, 1).
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HWY_INLINE float RandomFloat(RngStream& rng) {
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const uint32_t exp = hwy::BitCastScalar<uint32_t>(1.0f);
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const uint32_t mantissa_mask = hwy::MantissaMask<float>();
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const uint32_t representation = exp | (rng() & mantissa_mask);
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const float f12 = hwy::BitCastScalar<float>(representation);
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HWY_DASSERT(1.0f <= f12 && f12 < 2.0f); // exponent is 2^0, only mantissa
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const float f = (2.0f * (f12 - 1.0f)) - 1.0f;
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HWY_DASSERT(-1.0f <= f && f < 1.0f);
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return f;
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}
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// Returns random Gaussian (mean=0, stddev=1/3 similar to expected weights)
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// using the central limit theorem. Avoid std::normal_distribution for
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// consistent cross-platform output.
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// TODO: use RngStream instead of RandomState.
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HWY_INLINE double RandomGaussian(hwy::RandomState& rng) {
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uint64_t sum = 0;
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constexpr int kReps = 40;
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for (int rep = 0; rep < kReps; ++rep) {
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sum += hwy::Random32(&rng) & 0xFFFFF;
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}
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const double sum_f =
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static_cast<double>(sum) / static_cast<double>(0xFFFFF * kReps);
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HWY_ASSERT(0.0 <= sum_f && sum_f <= 1.0);
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const double plus_minus_1 = 2.0 * sum_f - 1.0;
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HWY_ASSERT(-1.0 <= plus_minus_1 && plus_minus_1 <= 1.0);
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// Normalize by stddev of sum of uniform random scaled to [-1, 1].
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return plus_minus_1 * std::sqrt(kReps / 3.0);
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};
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// Returns true if val is inside [min, max].
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template <typename T>
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static inline bool IsInside(T expected_min, T expected_max, T val) {
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HWY_DASSERT(expected_min <= expected_max);
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return expected_min <= val && val <= expected_max;
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}
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template <typename T>
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static inline bool IsNear(T expected, T val, T epsilon = T{1E-6}) {
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return IsInside(expected - epsilon, expected + epsilon, val);
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}
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HWY_INLINE void VerifyGaussian(hwy::Stats& stats) {
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// Inputs are roughly [-1, 1] and symmetric about zero.
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HWY_ASSERT(IsNear(-1.0f, stats.Min(), 0.10f));
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HWY_ASSERT(IsNear(+1.0f, stats.Max(), 0.10f));
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HWY_ASSERT(IsInside(-2E-3, 2E-3, stats.Mean()));
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HWY_ASSERT(IsInside(-0.15, 0.15, stats.Skewness()));
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// Near-Gaussian.
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HWY_ASSERT(IsInside(0.30, 0.35, stats.StandardDeviation()));
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HWY_ASSERT(IsNear(3.0, stats.Kurtosis(), 0.3));
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}
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template <typename T>
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void FillMatPtrT(MatPtrT<T>& mat) {
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for (int i = 0; i < mat.Rows(); ++i) {
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for (int j = 0; j < mat.Cols(); ++j) {
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mat.Row(i)[j] = hwy::Unpredictable1() * 0.01f * (i + j + 1);
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}
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}
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}
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template <typename T>
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void PrintMatPtr(MatPtrT<T> mat) {
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for (int i = 0; i < mat.Rows(); ++i) {
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for (int j = 0; j < mat.Cols(); ++j) {
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std::cerr << mat.Row(i)[j] << " ,";
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}
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std::cerr << std::endl;
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}
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};
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} // namespace gcpp
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#endif // THIRD_PARTY_GEMMA_CPP_UTIL_TEST_UTIL_H_
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