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283 lines
8.9 KiB
283 lines
8.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 "SparseRowMatrix.h"
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#include "CpuSparseMatrix.h"
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#include <algorithm>
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#include "paddle/utils/Logging.h"
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#include "SIMDFunctions.h"
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#include "paddle/utils/Thread.h"
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#include "paddle/utils/Util.h"
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namespace paddle {
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const unsigned int SparseRowCpuMatrix::kUnusedId_ = -1U;
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void SparseRowCpuMatrix::init(size_t height, size_t width) {
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height_ = height;
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if (!indexDictHandle_) {
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indexDictHandle_.reset(new IndexDict);
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indexDictHandle_->globalIndices.assign(height, kUnusedId_);
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}
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localIndices_ = &indexDictHandle_->localIndices;
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globalIndices_ = indexDictHandle_->globalIndices.data();
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}
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void SparseRowCpuMatrix::mul(CpuSparseMatrix* a,
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CpuMatrix* b,
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real scaleAB,
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real scaleT) {
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CpuMatrix::mul<CpuMatrix, SparseRowCpuMatrix>(a, b, this, scaleAB, scaleT);
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}
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void SparseRowCpuMatrix::copyFrom(const real* src, size_t size) {
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LOG(FATAL) << "This should not be called";
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}
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void SparseRowCpuMatrix::zeroMem() {
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apply([](real* buf, size_t len) { memset(buf, 0, sizeof(real) * len); });
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clearRows();
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}
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void SparseRowCpuMatrix::applyL1(real learningRate, real decayRate) {
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apply([=](real* buf, size_t len) {
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CpuVector value(0, nullptr);
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value.subVecFrom(buf, 0, len);
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value.applyL1(learningRate, decayRate);
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});
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}
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void SparseRowCpuMatrix::sgdUpdate(BaseMatrix& value,
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IVector& t0,
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real learningRate,
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int currentTime,
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real decayRate,
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bool useL1,
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bool fini) {
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std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
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// t0 and value are vectors
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CHECK_EQ(t0.getSize(), this->height_);
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CHECK_EQ(value.width_, this->height_ * this->width_);
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if (decayRate == 0.0f) {
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if (fini) {
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return;
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}
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for (size_t i = 0; i < localIndices.size(); ++i) {
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real* g = getLocalRow(i);
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real* v = value.rowBuf(localIndices[i]);
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for (size_t j = 0; j < this->width_; ++j) {
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v[j] -= learningRate * g[j];
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}
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}
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return;
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} // else
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if (useL1) { // L1 decay
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if (fini) {
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for (size_t i = 0; i < this->height_; ++i) {
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real* v = value.rowBuf(i);
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int* t = t0.getData() + i;
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if (t[0] < currentTime) {
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// W(t0) -> W(t+1)
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int tDiff = currentTime - t[0];
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real delta = tDiff * learningRate * decayRate;
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simd::decayL1(v, v, delta, this->width_);
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}
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}
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return;
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} // else
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for (size_t i = 0; i < localIndices.size(); ++i) {
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real* g = getLocalRow(i);
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real* v = value.rowBuf(localIndices[i]);
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int* t = t0.getData() + localIndices[i];
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if (t[0] < currentTime) {
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// W(t0) -> W(t)
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int tDiff = currentTime - t[0];
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real delta = tDiff * learningRate * decayRate;
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simd::decayL1(v, v, delta, this->width_);
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}
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// W(t) -> W(t+1)
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for (size_t j = 0; j < this->width_; ++j) {
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v[j] -= learningRate * g[j];
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}
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simd::decayL1(v, v, learningRate * decayRate, this->width_);
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// state update to t+1
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t[0] = currentTime + 1;
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}
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} else { // L2 decay
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if (fini) {
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for (size_t i = 0; i < this->height_; ++i) {
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real* v = value.rowBuf(i);
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int* t = t0.getData() + i;
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if (t[0] < currentTime) {
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// W(t0) -> W(t+1)
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int tDiff = currentTime - t[0];
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real recip = 1.0f / (1.0f + tDiff * learningRate * decayRate);
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for (size_t j = 0; j < this->width_; ++j) {
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v[j] *= recip;
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}
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}
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}
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return;
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} // else
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real recipDecay = 1.0f / (1.0f + learningRate * decayRate);
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for (size_t i = 0; i < localIndices.size(); ++i) {
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real* g = getLocalRow(i);
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real* v = value.rowBuf(localIndices[i]);
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int* t = t0.getData() + localIndices[i];
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if (t[0] < currentTime) {
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// W(t0) -> W(t)
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int tDiff = currentTime - t[0];
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real recip = 1.0f / (1.0f + tDiff * learningRate * decayRate);
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for (size_t j = 0; j < this->width_; ++j) {
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v[j] *= recip;
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}
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}
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// W(t) -> W(t+1)
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for (size_t j = 0; j < this->width_; ++j) {
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v[j] = recipDecay * (v[j] - learningRate * g[j]);
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}
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// state update to t+1
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t[0] = currentTime + 1;
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}
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}
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}
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void SparseRowCpuMatrix::addTo(BaseMatrix& dest,
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std::vector<uint32_t>& ids,
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size_t tid,
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size_t numThreads) {
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CHECK(!dest.useGpu_);
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CHECK_EQ(dest.height_ * dest.width_, this->height_ * this->width_);
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std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
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for (size_t i = 0; i < localIndices.size(); ++i) {
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uint32_t id = localIndices[i];
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if (id % numThreads == tid) {
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simd::addTo(dest.rowBuf(id), getLocalRow(i), this->width_);
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ids.push_back(id);
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}
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}
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}
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void SparseRowCpuMatrix::addTo(SparseRowCpuMatrix& dest,
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size_t tid,
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size_t numThreads) {
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CHECK(!dest.useGpu_);
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CHECK_EQ(dest.height_ * dest.width_, this->height_ * this->width_);
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std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
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for (size_t i = 0; i < localIndices.size(); ++i) {
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uint32_t id = localIndices[i];
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if (id % numThreads == tid) {
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dest.checkIndex(id);
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simd::addTo(dest.getRow(id), getLocalRow(i), this->width_);
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}
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}
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}
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void SparseRowCpuMatrix::zeroMemThread(size_t tid, size_t numThreads) {
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std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
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for (size_t i = 0; i < localIndices.size(); ++i) {
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uint32_t id = localIndices[i];
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if (id % numThreads == tid) {
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memset(this->getLocalRow(i), 0, this->width_ * sizeof(real));
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}
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}
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}
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void SparseAutoGrowRowCpuMatrix::mul(CpuSparseMatrix* a,
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CpuMatrix* b,
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real scaleAB,
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real scaleT) {
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CpuMatrix::mul<CpuMatrix, SparseAutoGrowRowCpuMatrix>(
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a, b, this, scaleAB, scaleT);
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}
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void CacheRowCpuMatrix::mul(CpuSparseMatrix* a,
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CpuMatrix* b,
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real scaleAB,
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real scaleT) {
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CpuMatrix::mul<CpuMatrix, CacheRowCpuMatrix>(a, b, this, scaleAB, scaleT);
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}
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void SparsePrefetchRowCpuMatrix::addRows(const unsigned int* ids, size_t len) {
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std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
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for (size_t i = 0; i < len; i++) {
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CHECK_LT(*(ids + i), this->getHeight())
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<< "id:" << *(ids + i) << "Height:" << this->getHeight()
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<< "sparse id value exceeds the max input dimension, "
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<< "it could be caused invalid input data samples";
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}
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localIndices.insert(localIndices.end(), ids, ids + len);
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}
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void SparsePrefetchRowCpuMatrix::addRows(MatrixPtr input) {
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CpuSparseMatrix* mat = dynamic_cast<CpuSparseMatrix*>(input.get());
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CHECK(mat) << "only support sparse matrix";
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addRows(reinterpret_cast<const unsigned int*>(mat->getCols()),
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mat->getElementCnt());
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}
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void SparsePrefetchRowCpuMatrix::addRows(IVectorPtr ids) {
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std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
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size_t numSamples = ids->getSize();
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int* index = ids->getData();
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for (size_t i = 0; i < numSamples; ++i) {
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if (index[i] == -1) continue;
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unsigned int id = (unsigned int)index[i];
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CHECK_LT(id, this->getHeight())
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<< "id:" << id << "Height:" << this->getHeight()
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<< "sparse id value exceeds the max input dimension, "
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<< "it could be caused invalid input data samples";
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localIndices.push_back(id);
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}
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}
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void SparsePrefetchRowCpuMatrix::setupIndices() {
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auto& localIndices = indexDictHandle_->localIndices;
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uniqueIds(localIndices);
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// for each sparse row
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for (size_t id = 0; id < localIndices.size(); ++id) {
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globalIndices_[localIndices[id]] = id; // sparse row -> local id
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}
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checkStoreSize();
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}
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void SparseRowCpuMatrix::checkIndices() {
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std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
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for (size_t i = 0; i < localIndices.size(); ++i) {
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CHECK_EQ(globalIndices_[localIndices[i]], i);
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}
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checkStoreSize();
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}
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} // namespace paddle
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