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Paddle/paddle/pserver/ParameterServer2.cpp

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "ParameterServer2.h"
#include <algorithm>
#include <fstream>
#include "paddle/math/SIMDFunctions.h"
#include "paddle/parameter/AverageOptimizer.h"
#include "paddle/parameter/FirstOrderOptimizer.h"
#include "paddle/parameter/OptimizerFunctions.h"
#include "paddle/parameter/OptimizerWithRegularizer.h"
#include "paddle/parameter/ParameterOptimizer.h"
#include "paddle/parameter/ParameterUpdateFunctions.h"
#include "paddle/parameter/Regularizer.h"
#include "paddle/utils/Flags.h"
#include "paddle/utils/GlobalConstants.h"
#include "paddle/utils/Stat.h"
DEFINE_int32(pserver_num_threads, 1, "number of threads for sync op exec");
DEFINE_double(async_lagged_ratio_min,
1.0,
"control config_.async_lagged_grad_discard_ratio() min value");
DEFINE_double(
async_lagged_ratio_default,
1.5,
"if async_lagged_grad_discard_ratio is not set in trainer_config.conf"
"use it as defalut value");
namespace paddle {
const std::string ParameterServer2::kRetMsgInvalidMatrixHandle =
"Invalid matrix handle";
const std::string ParameterServer2::kRetMsgInvalidVectorHandle =
"Invalid vector handle";
const std::string ParameterServer2::kRetMsgUnknownOperation =
"Unknown operation";
ParameterServer2::ParameterServer2(const std::string& addr,
int port,
int rdmaCpu)
: ProtoServer(addr, port, rdmaCpu),
dataSize_(0),
size_(0),
gradientReadyBarrier_(FLAGS_num_gradient_servers + 1),
parameterReadyBarrier_(FLAGS_num_gradient_servers + 1),
passBarrier_(FLAGS_num_gradient_servers + 1),
numPassFinishClients_(0),
allClientPassFinish_(false),
serverId_(-1),
batchId_(-1) {
/**
* register function for remote client calling, these functions
* will be mapped to a data structure for quick looking up. each
* request from trainer can contains one function name to indicate
* remote action. this architecture looks like rpc style for pserver.
*/
REGISTER_SERVICE_FUNCTION_EX(ParameterServer2, sendParameter);
REGISTER_SERVICE_FUNCTION_EX(ParameterServer2, sendData);
REGISTER_SERVICE_FUNCTION(ParameterServer2, setConfig);
REGISTER_SERVICE_FUNCTION(ParameterServer2, setStatus);
REGISTER_SERVICE_FUNCTION(ParameterServer2, getStatus);
REGISTER_SERVICE_FUNCTION(ParameterServer2, doOperation);
REGISTER_SERVICE_FUNCTION(ParameterServer2, createVector);
REGISTER_SERVICE_FUNCTION(ParameterServer2, releaseVector);
REGISTER_SERVICE_FUNCTION(ParameterServer2, createMatrix);
REGISTER_SERVICE_FUNCTION(ParameterServer2, releaseMatrix);
REGISTER_SERVICE_FUNCTION(ParameterServer2, waitPassStart);
REGISTER_SERVICE_FUNCTION(ParameterServer2, waitPassFinish);
REGISTER_SERVICE_FUNCTION(ParameterServer2, synchronize);
REGISTER_SERVICE_FUNCTION(ParameterServer2, asyncFinishPass);
REGISTER_SERVICE_FUNCTION(ParameterServer2, loadValueVector);
REGISTER_SERVICE_FUNCTION(ParameterServer2, saveValueVector);
/// thread pool for parallelizing some computations
if (FLAGS_pserver_num_threads > 1) {
syncThreadPool_.reset(new SyncThreadPool(FLAGS_pserver_num_threads, false));
}
}
bool ParameterServer2::init() {
vectors_.resize(NUM_PARAMETER_TYPES);
configMap_.clear();
numSamplesProcessed_ = 0;
cost_ = 0;
char* mpienv = getenv("OMPI_COMM_WORLD_SIZE");
if (mpienv != NULL) {
mpiSize_ = atoi(mpienv);
} else {
mpiSize_ = 1;
}
status_ = PSERVER_STATUS_NOT_SET;
dataMems_.resize(FLAGS_num_gradient_servers);
synchronizeBarriers_.resize(SyncObject_ARRAYSIZE);
for (auto& barrier : synchronizeBarriers_) {
barrier.reset(new ThreadBarrier(FLAGS_num_gradient_servers));
}
// initialization for dicarding lagging gradient
asyncUpdateSteps_ = 0;
asyncTrainerSteps_.resize(FLAGS_num_gradient_servers);
asyncTrainerSteps_.assign(asyncTrainerSteps_.size(), 0);
asyncLaggedGradientsNum_ = 0;
asyncUpdateStat_.resize(static_cast<int>(FLAGS_num_gradient_servers *
FLAGS_async_lagged_ratio_default));
asyncUpdateStat_.assign(asyncUpdateStat_.size(), 0);
asyncTrainerDiscardStat_.resize(FLAGS_num_gradient_servers);
asyncTrainerDiscardStat_.assign(asyncTrainerDiscardStat_.size(), 0);
asyncTrainerCommitStat_.resize(FLAGS_num_gradient_servers);
asyncTrainerCommitStat_.assign(asyncTrainerCommitStat_.size(), 0);
return true;
}
void ParameterServer2::getStatus(const GetStatusRequest& request,
ProtoResponseCallback callback) {
(void)request;
GetStatusResponse response;
response.set_status(status_);
callback(response);
}
void ParameterServer2::setStatus(const SetStatusRequest& request,
ProtoResponseCallback callback) {
status_ = request.status();
SetStatusResponse response;
callback(response);
}
void ParameterServer2::setConfig(const SetConfigRequest& request,
ProtoResponseCallback callback) {
{
std::lock_guard<RWLock> guard(parameterMutex_);
serverId_ = request.server_id();
isSparseServer_ = request.is_sparse_server();
if (!request.save_dir().empty()) {
mkDir(request.save_dir().c_str());
}
for (const auto& config : request.param_configs()) {
CHECK(!configMap_.count(config.para_id()))
<< "Duplicated parameter name: " << config.name();
configMap_[config.para_id()] = config;
CHECK_EQ(config.sparse_remote_update(), isSparseServer_);
}
config_ = request.opt_config();
if (config_.algorithm() == TrainAlgorithm::AsyncSGD) {
auto asyncLaggedRatio = config_.async_lagged_grad_discard_ratio();
if (asyncLaggedRatio <= FLAGS_async_lagged_ratio_min) {
LOG(INFO) << "WARNING: async_lagged_grad_discard_ratio is too small"
<< "reset to default, async_lagged_grad_discard_ratio = "
<< FLAGS_async_lagged_ratio_default;
asyncLaggedRatio = FLAGS_async_lagged_ratio_default;
}
asyncLaggedThreshold_ =
static_cast<int64_t>(FLAGS_num_gradient_servers * asyncLaggedRatio);
LOG(INFO) << "discard lagged async gradient ratio: " << asyncLaggedRatio
<< " asyncLaggedhreshold: " << asyncLaggedThreshold_;
}
if (isSparseServer_ && config_.num_batches_per_send_parameter() > 1) {
/// sparse server must NOT use local update mode
config_.set_num_batches_per_send_parameter(1);
}
if (config_.num_batches_per_send_parameter() > 1 &&
config_.center_parameter_update_method() == "average") {
/// scaling L1/L2 decay rate as large as L1/L2 apply in trainer
/// if parameter regularization in pserver
for (auto& pair : configMap_) {
ParameterConfig& config = pair.second;
if (config_.num_batches_per_send_parameter() ==
config.num_batches_regularization()) {
real scale =
config_.delta_add_rate() * config.num_batches_regularization();
if (config_.algorithm() == "sgd") {
scale *= FLAGS_num_gradient_servers;
}
config.set_decay_rate(config.decay_rate() * scale);
if (config.decay_rate() > 0.1f) {
LOG(FATAL) << "L2 decay=" << config.decay_rate()
<< " for parameter:" << config.name()
<< " is too large after scale in pserver!";
}
config.set_decay_rate_l1(config.decay_rate_l1() * scale);
if (config.decay_rate_l1() > 0.1f) {
LOG(FATAL) << "L1 decay=" << config.decay_rate_l1()
<< " for parameter:" << config.name()
<< " is too large after scale in pserver!";
}
LOG(INFO) << "parameter:" << config.name()
<< " decay apply in pserver,"
<< " L1 decay=" << config.decay_rate_l1()
<< " L2 decay=" << config.decay_rate();
}
}
}
}
SetConfigResponse response;
callback(response);
/// always defined, barrier slowest node function need it.
statSet_.reset(new StatSet("ParameterServer" + std::to_string(serverId_)));
}
real bufferSum(const std::vector<ParameterServer2::Buffer>& buffers) {
real sum = 0;
for (const auto buffer : buffers) {
for (size_t i = 0; i < buffer.size; ++i) {
sum += buffer.base[i];
}
}
return sum;
}
void ParameterServer2::mergeSegments(BlockSegments* segments) {
if (segments->empty()) {
return;
}
std::sort(segments->begin(), segments->end());
auto curr = segments->begin();
for (auto it = segments->begin(); it != segments->end(); ++it) {
if (it->first <= curr->second) {
curr->second = std::max(curr->second, it->second);
} else {
++curr;
*curr = *it;
}
}
++curr;
segments->erase(curr, segments->end());
}
void ParameterServer2::setParameter(const SendParameterRequest& request,
std::vector<Buffer>& inputBuffers,
SendParameterResponse* response,
std::vector<Buffer>* outputBuffers) {
(void)response;
(void)outputBuffers;
LOG(INFO) << "pserver: setParameter";
std::lock_guard<RWLock> guard(parameterMutex_);
int64_t numBlocks = blockIdMap_.size();
CHECK_EQ(blockIdMap_.size(), blockOffsetMap_.size());
/// total bytes for all the added blocks
int64_t totalSize = size_;
std::vector<int64_t> offsets;
offsets.reserve(request.blocks_size());
std::vector<int64_t> blockIds;
blockIds.reserve(request.blocks_size());
int bufferIndex = 0;
if (!request.blocks().size()) {
LOG(WARNING)
<< "--ports_num or --ports_num_for_sparse might be too large, "
<< "or total dense parameter size or sparse parameters size "
<< "might be too small, this psever doesn't store any parameter.";
return;
}
for (const auto& block : request.blocks()) {
/// block size for parameter(e.g. 128 for sparse row, 1K for dense)
uint64_t blockSize = getParameterConfig(block).parameter_block_size();
BlockKey key(block.para_id(), block.block_id());
if (inputBuffers.size()) { // if !=PSERVER_UPDATE_MODE_SET_PARAM_ZERO
Buffer buffer = inputBuffers[bufferIndex];
++bufferIndex;
CHECK_EQ(buffer.size, block.block_size())
<< "data size is too big:"
<< " block_size=" << block.block_size()
<< " data_size=" << buffer.size;
}
/// add a new block
if (blockIdMap_.count(key) == 0) {
blockOffsetMap_[key] = totalSize;
blockIdMap_[key] = numBlocks;
++numBlocks;
totalSize += blockSize;
}
offsets.push_back(blockOffsetMap_[key]);
blockIds.push_back(blockIdMap_[key]);
}
size_ = totalSize;
LOG(INFO) << "pserver: new cpuvector: size=" << size_;
if (!vectors_[PARAMETER_VALUE]) {
/// vectors_
const auto types = sgdOptimizerGetTypes(config_, true /*inPserver*/);
for (const auto type : types) {
vectors_[type].reset(new CpuVector(size_));
vectors_[type]->zeroMem();
}
blockInfos_.resize(numBlocks);
for (auto& info : blockInfos_) {
info.lock.reset(new std::mutex());
}
} else {
CHECK_EQ((size_t)size_, vectors_[PARAMETER_VALUE]->getSize())
<< "Currently adding new blocks is not supported. "
<< "All blocks must be added in one setParameter call";
}
VectorPtr buf = vectors_[PARAMETER_VALUE];
usedSegments_.reserve(offsets.size());
/// if offsets is empty, means parameter_block_size is too big or too many
/// nodes.
if (offsets.empty()) {
LOG(WARNING) << "in setParameter: offsets is empty";
}
for (size_t i = 0; i < offsets.size(); ++i) {
size_t blockId = blockIds[i];
BlockInfo& info = blockInfos_[blockId];
const ParameterConfig& config = getParameterConfig(request.blocks(i));
info.config = &config;
info.offset = offsets[i];
info.optimizer.reset(sgdOptimizerCreate(
config_, config, config.sparse_remote_update(), true /*inPserver*/));
if (config.sparse_remote_update()) {
size_t width = config.dims(1);
CHECK_EQ(config.parameter_block_size(), width)
<< "block size: " << config.parameter_block_size()
<< "width : " << width;
}
info.optimizer->init(1, info.config);
usedSegments_.push_back(std::make_pair(
offsets[i], offsets[i] + request.blocks(i).block_size()));
}
mergeSegments(&usedSegments_);
if (request.update_mode() == PSERVER_UPDATE_MODE_SET_PARAM) {
/// copy param from trainer
for (size_t i = 0; i < offsets.size(); ++i) {
Buffer buffer = inputBuffers[i];
real* start = buf->getPoint(offsets[i]);
CHECK_LE(offsets[i] + buffer.size, buf->getSize());
memcpy(start, buffer.base, sizeof(real) * buffer.size);
}
} else {
CHECK(request.update_mode() == PSERVER_UPDATE_MODE_SET_PARAM_ZERO);
/// nothing to do, value vector zero mem already
}
}
void ParameterServer2::addGradient(const SendParameterRequest& request,
std::vector<Buffer>& inputBuffers,
SendParameterResponse* response,
std::vector<Buffer>* outputBuffers) {
VLOG(1) << "pserver: addGradient";
/// forwardbackward delta from all trainers
/// indicate the fluctuation caused by forwardbackward.
#ifndef PADDLE_METRIC_LEARNING
// @TODO(yanfei):
// add support tuning forwardbackward balance for metric learning
if (!numPassFinishClients_) {
REGISTER_BARRIER_DELTA_SERVER_SET(
*statSet_,
"forwardbackwardDelta",
FLAGS_num_gradient_servers,
request.trainer_id(),
request.forwardbackward_time(),
isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
}
#endif
{
/// approximately pure network overhead
REGISTER_TIMER_DYNAMIC_SET(
"pushRecv", timeToMicroSecond(*handleRequestBegin_), -1, *statSet_);
}
#ifndef PADDLE_DISABLE_TIMER
gettimeofday(&(*addGradBegin_), nullptr);
#endif
/// barrier fluctuation caused by network and previous forwardbackward
if (!numPassFinishClients_) {
REGISTER_BARRIER_TIMER_SERVER_SET(
*statSet_,
"handleReqBegin",
FLAGS_num_gradient_servers,
request.trainer_id(),
(*handleRequestBegin_),
isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
}
if (!numPassFinishClients_) {
REGISTER_BARRIER_TIMER_SERVER(
*statSet_,
"addGradBegin",
FLAGS_num_gradient_servers,
request.trainer_id(),
isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
}
{
REGISTER_TIMER_DYNAMIC("addGradCore", -1, *statSet_);
ReadLockGuard guard(parameterMutex_);
int bufferIndex = 0;
for (const auto& block : request.blocks()) {
int64_t offset = getBlockOffset(block);
CHECK_GE(offset, 0) << "Only existing parameter block is allowed: "
<< " id=" << block.para_id()
<< " block id=" << block.block_id();
int64_t blockId = getBlockId(block);
CHECK_GE(blockId, 0) << "Only existing parameter block is allowed: "
<< " id=" << block.para_id()
<< " block id=" << block.block_id();
Buffer buffer = inputBuffers[bufferIndex];
++bufferIndex;
const real* gradientBuffer = buffer.base;
real* gradientSumBuffer = vectors_[PARAMETER_GRADIENT]->getPoint(offset);
size_t size = buffer.size;
BlockInfo& info = blockInfos_[blockId];
const ParameterConfig& config = getParameterConfig(blockId);
if (config.sparse_remote_update()) {
CHECK_EQ(size, config.parameter_block_size());
} else { // dense
CHECK_LE(size, config.parameter_block_size());
}
std::lock_guard<std::mutex> guard(*info.lock);
simd::addTo(gradientSumBuffer, gradientBuffer, size);
}
if (!numPassFinishClients_) {
REGISTER_BARRIER_TIMER_SERVER(
*statSet_,
"addGradCoreFinish",
FLAGS_num_gradient_servers,
request.trainer_id(),
isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
}
}
if (request.batch_status() == BATCH_FINISH ||
request.batch_status() == BATCH_START_AND_FINISH) {
numSamplesProcessed_ += request.num_samples();
cost_ += request.cost();
VLOG(1) << "num samples: " << numSamplesProcessed_
<< ", new cost:" << cost_;
/// numPassFinishClients_ means some trainer has entered finishPass
if (!numPassFinishClients_) {
REGISTER_SLOW_NODES_PROBE(
*statSet_,
"SLOW_NODES",
FLAGS_num_gradient_servers,
request.trainer_id(),
isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
}
/// notify doOperation gradient ready
gradientReadyBarrier_.wait();
/// if wait pass finish does not start, do check
if (!numPassFinishClients_) {
CHECK_BARRIER_TIMER(*statSet_,
"SLOW_NODES",
FLAGS_num_gradient_servers,
isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
}
/// barrier performance while all parameter add is finished
/// can indicate the fluctation caused by computation at pserver.
if (!numPassFinishClients_) {
REGISTER_BARRIER_TIMER_SERVER(
*statSet_,
"paraReady",
FLAGS_num_gradient_servers,
request.trainer_id(),
isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
}
/// wait doOperation finish
parameterReadyBarrier_.wait();
VLOG(1) << "start send back";
{
/// total time except overhead of network.
REGISTER_TIMER_DYNAMIC_SET("sendParaNoRecvNoSend",
timeToMicroSecond(*addGradBegin_),
-1,
*statSet_);
}
}
}
bool ParameterServer2::asyncGrdientCommitCheckAndStat(
const SendParameterRequest& request) {
const auto trainerId = request.trainer_id();
int64_t trainerSteps = asyncTrainerSteps_[trainerId];
CHECK_GE(asyncUpdateSteps_, trainerSteps)
<< " async update steps overflows "
<< " trainer id: " << trainerId
<< " async update steps in pserver: " << asyncUpdateSteps_
<< " async update steps in request: " << trainerSteps;
asyncUpdateSteps_++;
bool commitGradient = true;
int64_t delta = asyncUpdateSteps_ - trainerSteps;
if (delta >= asyncLaggedThreshold_) {
VLOG(1) << "discard Async Update: "
<< " trainer id: " << trainerId
<< " pserver steps: " << asyncUpdateSteps_
<< " request steps: " << trainerSteps;
asyncLaggedGradientsNum_++;
commitGradient = false;
}
/// stat on lagged steps, to get total discard distribution
if (static_cast<size_t>(delta) < asyncUpdateStat_.size()) {
asyncUpdateStat_[delta]++;
} else {
asyncUpdateStat_[asyncUpdateStat_.size() - 1]++;
}
/// stat on trainerId and discard, to get trainer condition
if (commitGradient) {
asyncTrainerCommitStat_[trainerId]++;
} else {
asyncTrainerDiscardStat_[trainerId]++;
}
return commitGradient;
}
void ParameterServer2::printAsyncGradientCommitStatAndReset() {
std::stringstream statFormat;
if (asyncUpdateSteps_) {
statFormat << "async discard gradients stat: " << std::endl;
statFormat << "serverId: " << serverId_
<< " serverType: " << isSparseServer_
<< " total updates: " << asyncUpdateSteps_
<< " discard updates: " << asyncLaggedGradientsNum_
<< " discard ratio: "
<< (real)asyncLaggedGradientsNum_ / (real)asyncUpdateSteps_;
statFormat << std::endl;
statFormat << std::endl;
statFormat << "Async Gradient Update Steps distribution: " << std::endl
<< "Sample: 1:1912(0.00284449) means "
<< "the updates step=1 count 1912 times "
<< "and account for 0.284449% of total updates" << std::endl;
size_t index = 0;
for (const auto& stat : asyncUpdateStat_) {
statFormat << index << ":" << stat << "("
<< (real)stat / (real)asyncUpdateSteps_ << ") ";
index++;
}
statFormat << std::endl;
statFormat << std::endl;
statFormat << "Async Gradient Discard based on trainer_id: " << std::endl
<< "Sample: 2:22(0.0016363) means "
<< "total discarded updates from trainer_id=2 count 22 "
<< "and account for 0.16363% of all updates from trainer_id=2"
<< std::endl;
for (auto i = 0; i < FLAGS_num_gradient_servers; i++) {
real ratio =
(real)asyncTrainerDiscardStat_[i] /
(real)(asyncTrainerCommitStat_[i] + asyncTrainerDiscardStat_[i]);
statFormat << i << ":" << asyncTrainerDiscardStat_[i] << "(" << ratio
<< ")"
<< " ";
}
LOG(INFO) << statFormat.str();
/// reset stat
asyncUpdateSteps_ = 0;
asyncTrainerSteps_.assign(asyncTrainerSteps_.size(), 0);
asyncLaggedGradientsNum_ = 0;
asyncUpdateStat_.assign(asyncUpdateStat_.size(), 0);
asyncTrainerDiscardStat_.assign(asyncTrainerDiscardStat_.size(), 0);
asyncTrainerCommitStat_.assign(asyncTrainerCommitStat_.size(), 0);
}
}
static ThreadLocal<std::vector<bool>> localBlockBitset_;
void ParameterServer2::asyncSGD(const SendParameterRequest& request,
std::vector<Buffer>& inputBuffers,
SendParameterResponse* response,
std::vector<Buffer>* outputBuffers) {
int64_t numBlocks = blockIdMap_.size();
auto& localBlockBitset = *localBlockBitset_;
if (isSparseServer_) {
if (localBlockBitset.empty()) {
localBlockBitset.resize(numBlocks);
}
localBlockBitset.assign(numBlocks, false);
}
ReadLockGuard guard(parameterMutex_);
if (request.send_back_parameter()) {
outputBuffers->reserve(request.blocks_size());
}
bool commitGradient = asyncGrdientCommitCheckAndStat(request);
VectorPtr* vecs = Parameter::getTlsTempBufs();
size_t bufferIndex = 0;
for (const auto& block : request.blocks()) {
int64_t offset = getBlockOffset(block);
CHECK_GE(offset, 0) << "Only existing parameter block is allowed: "
<< " id=" << block.para_id()
<< " block id=" << block.block_id();
int64_t blockId = getBlockId(block);
CHECK_GE(blockId, 0) << "Only existing parameter block is allowed: "
<< " id=" << block.para_id()
<< " block id=" << block.block_id();
Buffer buffer = inputBuffers[bufferIndex];
++bufferIndex;
size_t size = buffer.size;
BlockInfo& info = blockInfos_[blockId];
const ParameterConfig& config = getParameterConfig(blockId);
std::lock_guard<std::mutex> guard(*info.lock);
/// gradients are too obsolete, will be discarded
if (commitGradient) {
info.optimizer->startBatch(numSamplesProcessed_);
for (const auto type : info.optimizer->getParameterTypes()) {
vecs[type]->subVecFrom(*vectors_[type], offset, size);
}
vecs[PARAMETER_GRADIENT]->subVecFrom(buffer.base, 0, size);
info.optimizer->update(vecs, config, isSparseServer_ ? 0 : -1);
if (auto callback = info.optimizer->needSpecialTraversal(config)) {
blockTraverse(info, config, offset, size, vecs, callback);
}
info.optimizer->finishBatch();
}
if (commitGradient && isSparseServer_) {
localBlockBitset[blockId] = true;
}
if (!isSparseServer_ && request.send_back_parameter()) { // dense
int type = request.send_back_parameter_type();
sendBackParameter(block, type, response, &buffer, outputBuffers);
}
} /// foreach block
asyncTrainerSteps_[request.trainer_id()] = asyncUpdateSteps_;
if (commitGradient && isSparseServer_) {
/// find blocks that trainer do not request update
for (int64_t blockId = 0; blockId < numBlocks; ++blockId) {
if (localBlockBitset[blockId]) {
continue;
}
BlockInfo& info = blockInfos_[blockId];
const ParameterConfig& config = *info.config;
size_t size = config.parameter_block_size();
std::lock_guard<std::mutex> guard(*info.lock);
info.optimizer->startBatch(numSamplesProcessed_);
if (auto callback = info.optimizer->needSpecialTraversal(config)) {
blockTraverse(info, config, info.offset, size, vecs, callback);
}
info.optimizer->finishBatch();
}
}
if (commitGradient && (request.batch_status() == BATCH_FINISH ||
request.batch_status() == BATCH_START_AND_FINISH)) {
numSamplesProcessed_ += request.num_samples();
}
/// show some performance log if needed
if (request.trainer_id() == 0) {
/// batchId_ is approximately equal to "real batchId_"
batchId_++;
tuningAsyncsgdMidOutput();
}
}
void ParameterServer2::getParameter(const SendParameterRequest& request,
std::vector<Buffer>& inputBuffers,
SendParameterResponse* response,
std::vector<Buffer>* outputBuffers) {
(void)inputBuffers;
LOG(INFO) << "pserver: getParameter";
ReadLockGuard guard(parameterMutex_);
for (const auto& block : request.blocks()) {
int type = request.send_back_parameter_type();
sendBackParameter(block, type, response, outputBuffers);
}
}
void ParameterServer2::getParameterSparse(const SendParameterRequest& request,
std::vector<Buffer>& inputBuffers,
SendParameterResponse* response,
std::vector<Buffer>* outputBuffers) {
(void)inputBuffers;
auto& buffer = *readWriteBuffer_;
size_t numReals = 0;
for (const auto& block : request.blocks()) {
numReals += getParameterConfig(block).dims(1);
}
buffer.resize(numReals);
VLOG(3) << "pserver: getParameterSparse, numReals=" << numReals;
ReadLockGuard guard(parameterMutex_);
size_t offset = 0;
for (const auto& block : request.blocks()) {
size_t width = getParameterConfig(block).dims(1);
Buffer buf = {buffer.data() + offset, width};
int type = request.send_back_parameter_type();
sendBackParameterSparse(block, type, response, &buf, width, outputBuffers);
offset += width;
}
}
void ParameterServer2::sendBackParameter(const ParameterBlock& block,
int parameterType,
SendParameterResponse* response,
std::vector<Buffer>* outputBuffers) {
ParameterBlock* returnBlock = response->add_blocks();
returnBlock->set_para_id(block.para_id());
returnBlock->set_block_id(block.block_id());
returnBlock->set_begin_pos(block.begin_pos());
returnBlock->set_block_size(block.block_size());
int64_t offset = getBlockOffset(block);
CHECK_GE(offset, 0) << "Only existing parameter block is allowed: "
<< " id=" << block.para_id()
<< " block id=" << block.block_id();
real* valueBuffer = vectors_[parameterType]->getPoint(offset);
outputBuffers->push_back({valueBuffer, (size_t)block.block_size()});
}
void ParameterServer2::sendBackParameter(const ParameterBlock& block,
int parameterType,
SendParameterResponse* response,
Buffer* buffer,
std::vector<Buffer>* outputBuffers) {
ParameterBlock* returnBlock = response->add_blocks();
returnBlock->set_para_id(block.para_id());
returnBlock->set_block_id(block.block_id());
returnBlock->set_begin_pos(block.begin_pos());
returnBlock->set_block_size(block.block_size());
int64_t offset = getBlockOffset(block);
CHECK_GE(offset, 0) << "Only existing parameter block is allowed: "
<< " id=" << block.para_id()
<< " block id=" << block.block_id();
size_t size = buffer->size;
real* valueBuffer = vectors_[parameterType]->getPoint(offset);
/// copy to second buffer to avoid to be polluted by other request
memcpy(buffer->base, valueBuffer, sizeof(real) * size);
outputBuffers->push_back({buffer->base, size});
}
void ParameterServer2::sendBackParameterSparse(
const ParameterBlock& block,
int parameterType,
SendParameterResponse* response,
Buffer* buffer,
size_t width,
std::vector<Buffer>* outputBuffers) {
ParameterBlock* returnBlock = response->add_blocks();
returnBlock->set_para_id(block.para_id());
returnBlock->set_block_id(block.block_id());
returnBlock->set_begin_pos(block.begin_pos());
returnBlock->set_block_size(block.block_size());
int64_t offset = getBlockOffset(block);
CHECK_GE(offset, 0) << "Only existing parameter block is allowed: "
<< " id=" << block.para_id()
<< " block id=" << block.block_id();
real* valueBuffer = vectors_[parameterType]->getPoint(offset);
CHECK_EQ(buffer->size, width);
memcpy(buffer->base, valueBuffer, width * sizeof(real));
outputBuffers->push_back(*buffer);
}
void ParameterServer2::readAllBlocks(
MsgReader* msgReader, std::vector<ParameterServer2::Buffer>* buffers) {
auto& buffer = *readWriteBuffer_;
size_t numBlocks = msgReader->getNumBlocks();
buffer.resizeWithAlignHints(msgReader->getTotalLength() / sizeof(real),
numBlocks);
std::vector<void*> bufs(numBlocks);
buffers->clear();
buffers->reserve(numBlocks);
buffer.resetAlignAlloc();
for (size_t i = 0; i < numBlocks; ++i) {
size_t len = msgReader->getBlockLength(i);
CHECK_EQ(len % sizeof(real), (size_t)0);
size_t size = len / sizeof(real);
bufs[i] = buffer.nextBlock(size);
buffers->push_back({(real*)bufs[i], size});
}
msgReader->readBlocks(bufs);
}
void ParameterServer2::sendParameter(const SendParameterRequest& request,
std::unique_ptr<MsgReader> msgReader,
ProtoResponseCallbackEx callback) {
SendParameterResponse response;
std::vector<Buffer> inputBuffers;
std::vector<Buffer> outputBuffers;
readAllBlocks(msgReader.get(), &inputBuffers);
msgReader.reset();
switch (request.update_mode()) {
case PSERVER_UPDATE_MODE_SET_PARAM:
case PSERVER_UPDATE_MODE_SET_PARAM_ZERO:
setParameter(request, inputBuffers, &response, &outputBuffers);
break;
case PSERVER_UPDATE_MODE_GET_PARAM:
getParameter(request, inputBuffers, &response, &outputBuffers);
break;
case PSERVER_UPDATE_MODE_GET_PARAM_SPARSE:
getParameterSparse(request, inputBuffers, &response, &outputBuffers);
break;
case PSERVER_UPDATE_MODE_ASYNC_SGD:
asyncSGD(request, inputBuffers, &response, &outputBuffers);
break;
case PSERVER_UPDATE_MODE_ADD_GRADIENT:
addGradient(request, inputBuffers, &response, &outputBuffers);
break;
case PSERVER_UPDATE_MODE_AVERAGE_PARAMETER:
break;
}
switch (request.update_mode()) {
case PSERVER_UPDATE_MODE_ADD_GRADIENT:
(*requestVec_).push_back(request);
(*callbackVec_).push_back(callback);
if (request.batch_status() == BATCH_FINISH ||
request.batch_status() == BATCH_START_AND_FINISH) {
for (size_t i = 0; i < (*requestVec_).size(); i++) {
ReadLockGuard guard(parameterMutex_);
SendParameterRequest& request = (*requestVec_)[i];
SendParameterResponse responseTemp;
std::vector<iovec> outputIovs;
if (request.send_back_parameter()) {
CHECK(!isSparseServer_);
std::vector<Buffer> outputBuffersTemp;
for (const auto& block : request.blocks()) {
int type = request.send_back_parameter_type();
sendBackParameter(block, type, &responseTemp, &outputBuffersTemp);
}
outputIovs.reserve(outputBuffersTemp.size());
for (auto buffer : outputBuffersTemp) {
outputIovs.push_back({buffer.base, buffer.size * sizeof(real)});
}
}
ProtoResponseCallbackEx& callbackTemp = (*callbackVec_)[i];
callbackTemp(responseTemp, outputIovs);
}
(*requestVec_).clear();
(*callbackVec_).clear();
/// barrier perfromance while all data are send finished.
/// indicates network flucatuation for big message.
if (!numPassFinishClients_) {
REGISTER_BARRIER_TIMER_SERVER(
*statSet_,
"sendParamFinish",
FLAGS_num_gradient_servers,
request.trainer_id(),
isSparseServer_ ? "_sparseUpdater" : "_denseUpdater");
}
/// all time exhausted in parameterServer for big message.
/// it contains network and computation at pserver.
{
/// total time including overhead of network.
REGISTER_TIMER_DYNAMIC_SET("sendParaTotal",
timeToMicroSecond(*handleRequestBegin_),
-1,
*statSet_);
}
/// all time exhausted in pserverServer except recieve network.
{
/// total time except overhead of network receive
REGISTER_TIMER_DYNAMIC_SET("sendParaNoRecv",
timeToMicroSecond(*addGradBegin_),
-1,
*statSet_);
}
}
break;
case PSERVER_UPDATE_MODE_SET_PARAM:
case PSERVER_UPDATE_MODE_SET_PARAM_ZERO:
case PSERVER_UPDATE_MODE_GET_PARAM:
case PSERVER_UPDATE_MODE_GET_PARAM_SPARSE:
case PSERVER_UPDATE_MODE_ASYNC_SGD:
case PSERVER_UPDATE_MODE_AVERAGE_PARAMETER:
std::vector<iovec> outputIovs;
outputIovs.reserve(outputBuffers.size());
for (auto buffer : outputBuffers) {
outputIovs.push_back({buffer.base, buffer.size * sizeof(real)});
}
callback(response, outputIovs);
break;
}
}
template <typename Dtype>
void ParameterServer2::reduceAndSendData(const SendDataRequest& request,
std::unique_ptr<MsgReader>& msgReader,
ProtoResponseCallbackEx& callback) {
SendDataResponse response;
response.set_type(request.type());
response.set_server_id(serverId_);
auto sendData = reinterpret_cast<Dtype*>(dataMems_[0].get()->getBuf());
size_t rawMemSize = dataMems_[0].get()->getSize();
CHECK_EQ(rawMemSize % sizeof(Dtype), 0U);
size_t dataMemSize = rawMemSize / sizeof(Dtype);
for (size_t i = 1; i < dataMems_.size(); ++i) {
CHECK_EQ(dataMems_[i].get()->getSize(), rawMemSize);
auto data = reinterpret_cast<Dtype*>(dataMems_[i].get()->getBuf());
for (size_t j = 0; j < dataMemSize; ++j) {
sendData[j] += data[j];
}
}
std::vector<iovec> outputIovs;
auto block = response.add_blocks();
outputIovs.push_back({sendData, rawMemSize});
block->set_total_size(rawMemSize);
block->set_data_size(sizeof(Dtype));
callback(response, outputIovs);
}
void ParameterServer2::templateReduceSum(const SendDataRequest& request,
std::unique_ptr<MsgReader>& msgReader,
ProtoResponseCallbackEx& callback) {
const auto& block = request.blocks(0);
switch (block.data_type()) {
case TRANS_FLOAT:
reduceAndSendData<float>(request, msgReader, callback);
break;
case TRANS_DOUBLE:
reduceAndSendData<double>(request, msgReader, callback);
break;
case TRANS_INT32:
reduceAndSendData<int>(request, msgReader, callback);
break;
case TRANS_UINT32_T:
reduceAndSendData<uint32_t>(request, msgReader, callback);
break;
case TRANS_INT64_T:
reduceAndSendData<int64_t>(request, msgReader, callback);
break;
case TRANS_UINT64_T:
reduceAndSendData<uint64_t>(request, msgReader, callback);
break;
default:
LOG(FATAL) << "not supported";
break;
}
}
void ParameterServer2::sendData(const SendDataRequest& request,
std::unique_ptr<MsgReader> msgReader,
ProtoResponseCallbackEx callback) {
SendDataResponse response;
response.set_type(request.type());
response.set_server_id(serverId_);
switch (request.update_mode()) {
case DATA_UPDATE_MODE_SET_OWN: {
CHECK_EQ(msgReader->getNumBlocks(), (size_t)(request.blocks_size()));
size_t totalLen = msgReader->getTotalLength();
if (totalLen > 0) {
CHECK_EQ(msgReader->getNumBlocks(), 1U)
<< "Only one block currently support now!";
const auto& block = request.blocks(0);
if (0 == dataSize_) {
dataSize_ = block.data_size();
} else {
CHECK_EQ(dataSize_, block.data_size());
}
int64_t serverId = request.server_id();
if (serverId_ < 0) {
serverId_ = serverId;
} else {
CHECK_EQ(serverId_, serverId);
}
int64_t clientId = request.client_id();
dataMems_[clientId] = std::make_shared<CpuMemoryHandle>(totalLen);
CHECK_EQ(totalLen % sizeof(block.data_size()), 0U);
msgReader->readNextBlock(dataMems_[clientId].get()->getBuf());
}
msgReader.reset();
std::vector<iovec> outputIovs;
callback(response, outputIovs);
break;
}
case DATA_UPDATE_MODE_GET_ALL: {
/// Currently only support DATA_REDUCE_SUM
/// And their Operations are just add
CHECK(DATA_REDUCE_SUM == request.type());
templateReduceSum(request, msgReader, callback);
break;
}
default: { LOG(FATAL) << "not supported"; }
}
}
void ParameterServer2::clearUnusedSegments(CpuVector* vec) {
real* data = vec->getData();
if (usedSegments_.empty()) {
return;
}
memset(data, 0, sizeof(real) * usedSegments_[0].first);
memset(data + usedSegments_.back().second,
0,
sizeof(real) * (size_ - usedSegments_.back().second));
size_t n = size_ - usedSegments_.back().second;
for (size_t i = 1; i < usedSegments_.size(); ++i) {
memset(
data + usedSegments_[i - 1].second,
0,
sizeof(real) * (usedSegments_[i].first - usedSegments_[i - 1].second));
n += usedSegments_[i].first - usedSegments_[i - 1].second;
}
}
void ParameterServer2::parallelExecForEachBlock(ExecFunc func) {
SyncThreadPool::execHelper(syncThreadPool_.get(),
[&](int tid, size_t numThreads) {
int64_t numBlocks = blockIdMap_.size();
VectorPtr* vecs = Parameter::getTlsTempBufs();
for (int64_t blockId = tid; blockId < numBlocks;
blockId += numThreads) {
func(blockId, vecs);
}
});
}
void ParameterServer2::blockTraverse(
BlockInfo& info,
const ParameterConfig& config,
int64_t offset,
size_t size,
const VectorPtr vecs[],
const ParameterOptimizer::TraverseCallback& callback) {
/// setup sub bufs
for (const auto type : info.optimizer->getParameterTypes()) {
vecs[type]->subVecFrom(*vectors_[type], offset, size);
}
callback(vecs, config, config.sparse_remote_update() ? 0 : -1LU);
}
void ParameterServer2::op_SGD(const Operation& operation,
OperationResult* result) {
(void)operation;
(void)result;
if (allClientPassFinish_) {
/// when all clients signal pass finished, the update
/// is empty.
return;
}
{
REGISTER_TIMER_DYNAMIC("op_SGD", -1, *statSet_);
parallelExecForEachBlock([&](int64_t blockId, const VectorPtr vecs[]) {
BlockInfo& info = blockInfos_[blockId];
const ParameterConfig& config = getParameterConfig(blockId);
int64_t offset = info.offset;
size_t size = config.parameter_block_size();
info.optimizer->startBatch(numSamplesProcessed_);
for (const auto type : info.optimizer->getParameterTypes()) {
vecs[type]->subVecFrom(*vectors_[type], offset, size);
}
info.optimizer->update(
vecs, config, config.sparse_remote_update() ? 0 : -1LU);
vecs[PARAMETER_GRADIENT]->zeroMem();
if (auto callback = info.optimizer->needSpecialTraversal(config)) {
blockTraverse(info, config, offset, size, vecs, callback);
}
info.optimizer->finishBatch();
});
}
batchId_++;
tuningSgdMidOutput();
}
void ParameterServer2::op_start_pass(const Operation& operation,
OperationResult* result) {
(void)operation;
(void)result;
parallelExecForEachBlock([&](int64_t blockId, const VectorPtr vecs[]) {
BlockInfo& info = blockInfos_[blockId];
info.optimizer->startPass();
});
}
void ParameterServer2::op_finish_pass(const Operation& operation,
OperationResult* result) {
(void)operation;
(void)result;
parallelExecForEachBlock([&](int64_t blockId, const VectorPtr vecs[]) {
BlockInfo& info = blockInfos_[blockId];
const ParameterConfig& config = getParameterConfig(blockId);
size_t size = config.parameter_block_size();
/// catch up with
if (auto callback = info.optimizer->startCatchUpWith()) {
blockTraverse(info, config, info.offset, size, vecs, callback);
info.optimizer->finishCatchUpWith();
}
/// finish pass
info.optimizer->finishPass();
});
tuningSgdFinished();
batchId_ = 0;
}
void ParameterServer2::op_apply(const Operation& operation,
OperationResult* result) {
(void)operation;
(void)result;
parallelExecForEachBlock([&](int64_t blockId, const VectorPtr vecs[]) {
BlockInfo& info = blockInfos_[blockId];
const ParameterConfig& config = getParameterConfig(blockId);
int64_t offset = info.offset;
size_t size = config.parameter_block_size();
// catch up with
if (auto callback = info.optimizer->startCatchUpWith()) {
blockTraverse(info, config, offset, size, vecs, callback);
info.optimizer->finishCatchUpWith();
}
// apply to PARAMETER_APPLY
if (auto callback = info.optimizer->apply()) {
blockTraverse(info, config, offset, size, vecs, callback);
}
});
}
void ParameterServer2::op_randomize(const Operation& operation,
OperationResult* result) {
LOG(INFO) << "ParameterServer2::op_randomize: serverId=" << serverId_;
CpuVector& valueVec = *vectors_[PARAMETER_VALUE];
parallelExecForEachBlock([&](int64_t blockId, const VectorPtr vecs[]) {
BlockInfo& info = blockInfos_[blockId];
const ParameterConfig& config = getParameterConfig(blockId);
size_t size = config.parameter_block_size();
vecs[PARAMETER_VALUE]->subVecFrom(valueVec, info.offset, size);
Parameter::randomize(vecs[PARAMETER_VALUE], config);
});
}
void ParameterServer2::loadValueVector(const LoadValueRequest& request,
ProtoResponseCallback callback) {
LoadValueResponse response;
LOG(INFO) << "ParameterServer2::loadValueVector: serverId=" << serverId_;
constexpr int kBufLen = 100;
char buf[kBufLen];
snprintf(buf, kBufLen, "/pserver.%04d", static_cast<int>(serverId_));
std::string filename = request.dir_name() + buf;
std::ifstream fs(filename, std::ios_base::binary);
CHECK(fs) << "Fail to open " << filename;
CpuVector& vec = *vectors_[PARAMETER_VALUE];
Parameter::Header header;
CHECK(fs.read(reinterpret_cast<char*>(&header), sizeof(header)))
<< "Fail to read parameters in pserver";
CHECK_EQ(header.version, Parameter::kFormatVersion)
<< "Incorrect format version: " << header.version;
CHECK_EQ(header.size, (size_t)size_)
<< "The size (" << header.size << ") in the file does not match the size "
<< "(" << size_ << ") of the pserver: " << serverId_;
CHECK_EQ(header.valueSize, sizeof(real)) << "Unsupported valueSize "
<< header.valueSize;
CHECK(fs.read(reinterpret_cast<char*>(vec.getData()),
header.size * sizeof(real)));
callback(response);
}
void ParameterServer2::saveValueVector(const SaveValueRequest& request,
ProtoResponseCallback callback) {
SaveValueResponse response;
LOG(INFO) << "ParameterServer2::SaveValueVector: serverId=" << serverId_;
mkDir(request.dir_name().c_str());
constexpr int kBufLen = 100;
char buf[kBufLen];
snprintf(buf, kBufLen, "/pserver.%04d", static_cast<int>(serverId_));
std::string filename = request.dir_name() + buf;
std::ofstream fs(filename, std::ios_base::binary);
CHECK(fs) << "Fail to open " << filename;
CpuVector& vec = vectors_[PARAMETER_APPLY] ? *vectors_[PARAMETER_APPLY]
: *vectors_[PARAMETER_VALUE];
Parameter::Header header;
header.version = Parameter::kFormatVersion;
header.valueSize = sizeof(real);
header.size = size_;
CHECK_EQ(header.size, vec.getSize());
CHECK(fs.write(reinterpret_cast<char*>(&header), sizeof(header)))
<< "Fail to write parameter in pserver: " << serverId_;
CHECK(fs.write(reinterpret_cast<char*>(vec.getData()),
header.size * sizeof(real)))
<< "Fail to write parameter in pserver: " << serverId_;
callback(response);
}
void ParameterServer2::op_RESET(const Operation& operation,
OperationResult* result) {
(void)result;
CpuVector* u = vectors_[operation.pvectors(0)].get();
u->reset(operation.scalars(0));
clearUnusedSegments(u);
}
void ParameterServer2::op_utv(const Operation& operation,
OperationResult* result) {
real* u = vectors_[operation.pvectors(0)]->getData();
real* v = vectors_[operation.pvectors(1)]->getData();
int64_t size = size_;
double sum = 0;
for (int64_t i = 0; i < size; ++i) {
sum += (double)u[i] * (double)v[i];
}
result->add_scalars(sum);
}
void ParameterServer2::op_au_bv(const Operation& operation,
OperationResult* result) {
(void)result;
real* u = vectors_[operation.pvectors(0)]->getData();
real* v = vectors_[operation.pvectors(1)]->getData();
int64_t size = size_;
real a = operation.scalars(0);
real b = operation.scalars(1);
for (int64_t i = 0; i < size; ++i) {
v[i] = a * u[i] + b * v[i];
}
}
void ParameterServer2::op_COPY(const Operation& operation,
OperationResult* result) {
(void)result;
real* u = vectors_[operation.pvectors(0)]->getData();
real* v = vectors_[operation.pvectors(1)]->getData();
int64_t size = size_;
for (int64_t i = 0; i < size; ++i) {
v[i] = u[i];
}
}
void ParameterServer2::op_au(const Operation& operation,
OperationResult* result) {
(void)result;
real* u = vectors_[operation.pvectors(0)]->getData();
int64_t size = size_;
real a = operation.scalars(0);
for (int64_t i = 0; i < size; ++i) {
u[i] *= a;
}
}
void ParameterServer2::op_au_bv_cw(const Operation& operation,
OperationResult* result) {
(void)result;
real* u = vectors_[operation.pvectors(0)]->getData();
real* v = vectors_[operation.pvectors(1)]->getData();
real* w = vectors_[operation.pvectors(2)]->getData();
int64_t size = size_;
real a = operation.scalars(0);
real b = operation.scalars(1);
real c = operation.scalars(2);
for (int64_t i = 0; i < size; ++i) {
w[i] = a * u[i] + b * v[i] + c * w[i];
}
}
void ParameterServer2::op_make_steepest_desc_dir(const Operation& operation,
OperationResult* result) {
(void)result;
real* dir = vectors_[operation.pvectors(0)]->getData();
real* grad = vectors_[operation.pvectors(1)]->getData();
real* x = vectors_[operation.pvectors(2)]->getData();
int64_t size = size_;
real l1weight = operation.scalars(0);
for (int64_t i = 0; i < size; ++i) {
if (x[i] < 0) {
dir[i] = -grad[i] + l1weight;
} else if (x[i] > 0) {
dir[i] = -grad[i] - l1weight;
} else {
if (grad[i] < -l1weight) {
dir[i] = -grad[i] - l1weight;
} else if (grad[i] > l1weight) {
dir[i] = -grad[i] + l1weight;
} else {
dir[i] = 0;
}
}
}
}
void ParameterServer2::op_fix_dir_signs(const Operation& operation,
OperationResult* result) {
(void)result;
real* dir = vectors_[operation.pvectors(0)]->getData();
real* steepestDescDir = vectors_[operation.pvectors(1)]->getData();
int64_t size = size_;
for (int64_t i = 0; i < size; ++i) {
if (dir[i] * steepestDescDir[i] <= 0) {
dir[i] = 0;
}
}
}
void ParameterServer2::op_fix_omega_signs(const Operation& operation,
OperationResult* result) {
(void)result;
real* x = vectors_[operation.pvectors(0)]->getData();
real* newx = vectors_[operation.pvectors(1)]->getData();
int64_t size = size_;
for (int64_t i = 0; i < size; ++i) {
if (x[i] * newx[i] < 0) {
newx[i] = 0;
}
}
}
void ParameterServer2::op_dir_deriv(const Operation& operation,
OperationResult* result) {
real* dir = vectors_[operation.pvectors(0)]->getData();
real* grad = vectors_[operation.pvectors(1)]->getData();
real* x = vectors_[operation.pvectors(2)]->getData();
int64_t size = size_;
real l1weight = operation.scalars(0);
double sum = 0;
for (int64_t i = 0; i < size; ++i) {
if (dir[i] != 0) {
if (x[i] < 0) {
sum += dir[i] * (grad[i] - l1weight);
} else if (x[i] > 0) {
sum += dir[i] * (grad[i] + l1weight);
} else if (dir[i] < 0) {
sum += dir[i] * (grad[i] - l1weight);
} else if (dir[i] > 0) {
sum += dir[i] * (grad[i] + l1weight);
}
}
}
result->add_scalars(sum);
}
void ParameterServer2::op_cost(const Operation& operation,
OperationResult* result) {
real* x = vectors_[operation.pvectors(0)]->getData();
real* newgrad = vectors_[operation.pvectors(1)]->getData();
int64_t size = size_;
real l1weight = operation.scalars(0);
real l2weight = operation.scalars(1);
double cost_real = cost_ / mpiSize_;
double sum_weight_l1 = 0;
double sum_weight_l2 = 0;
for (int64_t i = 0; i < size; ++i) {
sum_weight_l1 += std::abs(x[i]);
sum_weight_l2 += x[i] * x[i];
newgrad[i] += 2.0 * l2weight * x[i];
}
cost_real += l1weight * sum_weight_l1 + l2weight * sum_weight_l2;
result->add_scalars(cost_real);
}
ParameterServer2::OperatorFunction ParameterServer2::opFuncs[] = {
nullptr, // PSERVER_OP_utu = 0;
&ParameterServer2::op_utv, // PSERVER_OP_utv = 1;
&ParameterServer2::op_au, // PSERVER_OP_au = 2;
&ParameterServer2::op_au_bv, // PSERVER_OP_au_bv = 3;
nullptr, // PSERVER_OP_aAx_bu = 4;
&ParameterServer2::op_SGD, // PSERVER_OP_SGD = 5;
&ParameterServer2::op_RESET, // PSERVER_OP_RESET = 6;
&ParameterServer2::op_COPY, // PSERVER_OP_COPY = 7;
&ParameterServer2::op_au_bv_cw, // PSERVER_OP_au_bv_cw = 8;
&ParameterServer2::op_make_steepest_desc_dir,
/// PSERVER_OP_MAKE_STEEPEST_DESC_DIR = 9;
&ParameterServer2::op_fix_dir_signs, // PSERVER_OP_FIX_SIGNS = 10;
&ParameterServer2::op_dir_deriv, // PSERVER_OP_DIR_DERIV = 11;
&ParameterServer2::op_fix_omega_signs, // PSERVER_OP_FIX_OMEGA_SIGNS = 12;
&ParameterServer2::op_cost, // PSERVER_OP_COST = 13
&ParameterServer2::op_start_pass, // PSERVER_OP_START_PASS = 14
&ParameterServer2::op_finish_pass, // PSERVER_OP_FINISH_PASS = 15
&ParameterServer2::op_randomize, // PSERVER_OP_RANDOMIZE = 16
&ParameterServer2::op_apply, // PSERVER_OP_APPLY = 17
};
void ParameterServer2::doOperation(const DoOperationRequest& request,
ProtoResponseCallback callback) {
if (request.wait_for_gradient()) {
/// wait gradient update
gradientReadyBarrier_.wait();
allClientPassFinish_ = numPassFinishClients_ == FLAGS_num_gradient_servers;
}
DoOperationResponse response;
response.set_pass_finish(allClientPassFinish_);
for (const auto& op : request.operations()) {
OperationResult* opResult = response.add_results();
if (op.operation() >= ARRAYSIZE(opFuncs)) {
LOG(ERROR) << "Unknown operation " << op.operation();
response.set_return_message(kRetMsgUnknownOperation);
}
OperatorFunction opFunc = opFuncs[op.operation()];
if (!opFunc) {
LOG(ERROR) << "Operation not implemented: " << op.operation();
response.set_return_message(kRetMsgUnknownOperation);
}
(this->*opFunc)(op, opResult);
}
if (request.send_back_parameter()) {
/// clean current cost
cost_ = 0;
if (allClientPassFinish_ && request.release_pass()) {
/// This signals that all clients finish one pass, so waitPassFinish()
/// will stop waiting.
numPassFinishClients_ = 0;
}
/// notify addGradient() to send back parameter
parameterReadyBarrier_.wait();
}
callback(response);
}
void ParameterServer2::waitPassStart(const WaitPassStartRequest& request,
ProtoResponseCallback callback) {
passBarrier_.wait();
callback(WaitPassStartResponse());
}
void ParameterServer2::waitPassFinish(const WaitPassFinishRequest& request,
ProtoResponseCallback callback) {
numPassFinishClients_ += 1;
while (numPassFinishClients_ != 0) {
/// notify doOperation gradient ready
gradientReadyBarrier_.wait();
/// wait doOperation finish
parameterReadyBarrier_.wait();
}
callback(WaitPassFinishResponse());
}
void ParameterServer2::synchronize(const SynchronizeRequest& request,
ProtoResponseCallback callback) {
synchronizeBarriers_[request.sync_object_id()]->wait();
dataSize_ = 0;
callback(SynchronizeResponse());
}
void ParameterServer2::asyncFinishPass(const SynchronizeRequest& request,
ProtoResponseCallback callback) {
synchronizeBarriers_[request.sync_object_id()]->wait();
callback(SynchronizeResponse());
if (request.trainer_id() == 0) {
tuningAsyncsgdFinished();
batchId_ = 0;
}
}
void ParameterServer2::createVector(const CreateVectorRequest& request,
ProtoResponseCallback callback) {
(void)request;
CreateVectorResponse response;
LOG(INFO) << "ParameterServer2::createVector: size=" << size_;
CpuVectorPtr vec = std::make_shared<CpuVector>(size_);
int64_t handle = -1;
{
std::lock_guard<RWLock> guard(parameterMutex_);
handle = vectors_.size();
vectors_.push_back(vec);
}
response.set_handle(handle);
callback(response);
}
void ParameterServer2::releaseVector(const ReleaseVectorRequest& request,
ProtoResponseCallback callback) {
ReleaseVectorResponse response;
CpuVectorPtr vec;
{
std::lock_guard<RWLock> guard(parameterMutex_);
vec.swap(vectors_[request.handle()]);
}
callback(response);
}
void ParameterServer2::createMatrix(const CreateMatrixRequest& request,
ProtoResponseCallback callback) {
CreateMatrixResponse response;
/// We need to create column major matrix of size_ * num_cols
/// Matrix is row majoar. Need to tranpose when use it.
CpuMatrixPtr mat = std::make_shared<CpuMatrix>(request.num_cols(), size_);
int64_t handle = -1;
{
std::lock_guard<RWLock> guard(parameterMutex_);
handle = matrices_.size();
matrices_.push_back(mat);
}
response.set_handle(handle);
callback(response);
}
void ParameterServer2::releaseMatrix(const ReleaseMatrixRequest& request,
ProtoResponseCallback callback) {
ReleaseMatrixResponse response;
CpuMatrixPtr mat;
{
std::lock_guard<RWLock> guard(parameterMutex_);
mat.swap(matrices_[request.handle()]);
}
callback(response);
}
void ParameterServer2::tuningSgdMidOutput() {
if (batchId_ && batchId_ % FLAGS_log_period_server == 0) {
LOG(INFO) << "======== Batch=" << batchId_ << "=======";
statSet_->setThreadInfo(true);
statSet_->printAllStatus();
/// not reset raw data for reducing the overhead of performance tuning
statSet_->reset(false);
}
}
void ParameterServer2::tuningSgdFinished() {
LOG(INFO) << "======== Batch=" << batchId_ << " pass END"
<< "=======";
statSet_->setThreadInfo(true);
statSet_->printAllStatus();
/**
* reset raw data at end of pass since some raw data could be not
* complete. Otherwise the raw data will pollute next pass performance
* tuning
*/
statSet_->reset();
}
void ParameterServer2::tuningAsyncsgdMidOutput() {
#ifndef PADDLE_DISABLE_TIMER
if (batchId_ && batchId_ % FLAGS_log_period_server == 0) {
LOG(INFO) << "======== [not accurate] Batch=" << batchId_ << "=======";
printAsyncGradientCommitStatAndReset();
}
#endif
}
void ParameterServer2::tuningAsyncsgdFinished() {
LOG(INFO) << "======== [not accurate] Batch=" << batchId_ << " pass END"
<< "=======";
printAsyncGradientCommitStatAndReset();
}
} // namespace paddle
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