mirror of https://github.com/google/gemma.cpp.git
122 lines
3.6 KiB
C++
122 lines
3.6 KiB
C++
// Copyright 2024 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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// https://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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#include "gemma/optimizer.h"
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#include <random>
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#include "gemma/common.h"
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#include "gemma/configs.h"
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#include "gemma/weights.h"
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#include "hwy/contrib/thread_pool/thread_pool.h"
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namespace gcpp {
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namespace {
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class WeightInitializer {
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public:
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WeightInitializer(std::mt19937& gen) : dist_(0.0f, 1.0f), gen_(gen) {}
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template <size_t N>
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void operator()(const char* name, std::array<float, N>& tensor) {
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for (size_t i = 0; i < N; ++i) {
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tensor[i] = dist_(gen_);
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}
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}
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private:
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std::normal_distribution<float> dist_;
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std::mt19937& gen_;
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};
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template <typename TConfig>
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void RandInitWeights(ByteStorageT& weights_u8, hwy::ThreadPool& pool,
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std::mt19937& gen) {
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auto& weights = *reinterpret_cast<WeightsF<TConfig>*>(weights_u8.get());
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// TODO(szabadka) Use the same weight initialization method as in the python
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// version.
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WeightInitializer init(gen);
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ForEachTensor1<float, TConfig>(init, weights);
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}
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class WeightUpdater {
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public:
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explicit WeightUpdater(float lr) : lr_(lr) {}
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template <size_t kCapacity>
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void operator()(const char* name, const std::array<float, kCapacity>& grad,
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std::array<float, kCapacity>& weights) {
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for (size_t i = 0; i < kCapacity; ++i) {
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weights[i] += lr_ * grad[i];
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}
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}
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private:
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float lr_;
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};
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template <typename TConfig>
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void UpdateWeights(const ByteStorageT& grad_u8, float scale,
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ByteStorageT& weights_u8, hwy::ThreadPool& pool) {
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const auto& grad =
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*reinterpret_cast<const WeightsF<TConfig>*>(grad_u8.get());
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auto& weights = *reinterpret_cast<WeightsF<TConfig>*>(weights_u8.get());
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WeightUpdater updater(scale);
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ForEachTensor2<float, TConfig>(updater, grad, weights);
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}
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} // namespace
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void RandInitWeights(Model model, ByteStorageT& weights_u8,
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hwy::ThreadPool& pool, std::mt19937& gen) {
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switch (model) {
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case Model::GEMMA_2B:
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RandInitWeights<ConfigGemma2B>(weights_u8, pool, gen);
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break;
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case Model::GEMMA_7B:
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RandInitWeights<ConfigGemma7B>(weights_u8, pool, gen);
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break;
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case Model::GRIFFIN_2B:
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RandInitWeights<ConfigGriffin2B>(weights_u8, pool, gen);
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break;
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case Model::GEMMA_TINY:
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RandInitWeights<ConfigGemmaTiny>(weights_u8, pool, gen);
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break;
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default:
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HWY_ABORT("Model type %d unknown.", static_cast<int>(model));
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}
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}
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void UpdateWeights(Model model, const ByteStorageT& grad, float scale,
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ByteStorageT& weights, hwy::ThreadPool& pool) {
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switch (model) {
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case Model::GEMMA_2B:
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UpdateWeights<ConfigGemma2B>(grad, scale, weights, pool);
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break;
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case Model::GEMMA_7B:
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UpdateWeights<ConfigGemma7B>(grad, scale, weights, pool);
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break;
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case Model::GRIFFIN_2B:
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UpdateWeights<ConfigGriffin2B>(grad, scale, weights, pool);
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break;
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case Model::GEMMA_TINY:
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UpdateWeights<ConfigGemmaTiny>(grad, scale, weights, pool);
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break;
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default:
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HWY_ABORT("Model type %d unknown.", static_cast<int>(model));
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}
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}
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} // namespace gcpp
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