Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into doc
ranqiu
7 years ago
commit
231d3a88f8
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## how to use timeline tool to do profile
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1. Add `with profiler.profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number.
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```python
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with profiler.profiler('All', 'total', '/tmp/profile') as prof:
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for pass_id in range(pass_num):
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for batch_id, data in enumerate(train_reader()):
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exe.run(fluid.default_main_program(),
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feed=feeder.feed(data),
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fetch_list=[],
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use_program_cache=True)
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...
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```
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1. Run `python paddle/tools/timeline.py` to process `/tmp/profile`, it will generate another
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file `/tmp/timeline` by default. You can change the path by cmd parameter, please take a look at
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[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py) for details.
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1. Open chrome and visit <chrome://tracing/>, use `load` button to load the generated `timeline` file.
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1. The resulting timeline should be like:
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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,
|
||||
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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#pragma once
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#include <stddef.h> // for size_t
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#include <atomic>
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#include <condition_variable>
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#include <deque>
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#include "paddle/fluid/framework/channel.h"
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace framework {
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template <typename T>
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class ChannelImpl : public paddle::framework::Channel<T> {
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friend Channel<T> *paddle::framework::MakeChannel<T>(size_t);
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friend void paddle::framework::CloseChannel<T>(Channel<T> *);
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public:
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virtual bool Send(T *);
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virtual bool Receive(T *);
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virtual size_t Cap() { return cap_; }
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virtual void Lock();
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virtual void Unlock();
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virtual void Close();
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ChannelImpl(size_t);
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virtual ~ChannelImpl();
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private:
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struct QueueMessage {
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T *data;
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std::condition_variable_any cond;
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bool chan_closed = false;
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bool completed = false;
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QueueMessage(T *item) : data(item) {}
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void Wait(std::unique_lock<std::recursive_mutex> &lock) {
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cond.wait(lock, [this]() { return completed; });
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}
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void Notify() {
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completed = true;
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cond.notify_all();
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}
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};
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bool send_return(bool value) {
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send_ctr--;
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destructor_cond_.notify_all();
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return value;
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}
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bool recv_return(bool value) {
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recv_ctr--;
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destructor_cond_.notify_all();
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return value;
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}
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size_t cap_;
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std::recursive_mutex mu_;
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bool closed_;
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std::deque<T> buf_;
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std::deque<std::shared_ptr<QueueMessage>> recvq;
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std::deque<std::shared_ptr<QueueMessage>> sendq;
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std::atomic<unsigned> send_ctr{0};
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std::atomic<unsigned> recv_ctr{0};
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std::condition_variable_any destructor_cond_;
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};
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template <typename T>
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ChannelImpl<T>::ChannelImpl(size_t capacity)
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: cap_(capacity), closed_(false), send_ctr(0), recv_ctr(0) {
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PADDLE_ENFORCE_GE(capacity, 0);
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}
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template <typename T>
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bool ChannelImpl<T>::Send(T *item) {
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send_ctr++;
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std::unique_lock<std::recursive_mutex> lock{mu_};
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// If channel is closed, do nothing
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if (closed_) {
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lock.unlock();
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// TODO(abhinavarora) Should panic on closed channel
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return send_return(false);
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}
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// If there is a receiver, directly pass the value we want
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// to send to the receiver, bypassing the channel buffer if any
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if (!recvq.empty()) {
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std::shared_ptr<QueueMessage> m = recvq.front();
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recvq.pop_front();
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// Do the data transfer
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*(m->data) = std::move(*item);
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// Wake up the blocked process and unlock
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m->Notify();
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lock.unlock();
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return send_return(true);
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}
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// Unbuffered channel will always bypass this
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// If buffered channel has space in buffer,
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// write the element to the buffer.
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if (buf_.size() < cap_) {
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// Copy to buffer
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buf_.push_back(std::move(*item));
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// Release lock and return true
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lock.unlock();
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return send_return(true);
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}
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// Block on channel, because some receiver will complete
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// the operation for us
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auto m = std::make_shared<QueueMessage>(item);
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sendq.push_back(m);
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m->Wait(lock);
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// TODO(abhinavarora) Should panic on closed channel
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return send_return(!m->chan_closed);
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}
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template <typename T>
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bool ChannelImpl<T>::Receive(T *item) {
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recv_ctr++;
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std::unique_lock<std::recursive_mutex> lock{mu_};
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// If channel is closed and buffer is empty or
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// channel is unbuffered
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if (closed_ && buf_.empty()) {
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lock.unlock();
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return recv_return(false);
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}
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// If there is a sender, directly receive the value we want
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// from the sender, bypassing the channel buffer if any
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if (!sendq.empty()) {
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std::shared_ptr<QueueMessage> m = sendq.front();
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sendq.pop_front();
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// Do the data transfer
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*item = std::move(*(m->data));
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// Wake up the blocked process and unlock
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m->Notify();
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lock.unlock();
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return recv_return(true);
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}
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// If this is a buffered channel and there are items in buffer
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if (buf_.size() > 0) {
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// Directly read from buffer
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*item = std::move(buf_.front());
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buf_.pop_front();
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// Release lock and return true
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lock.unlock();
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return recv_return(true);
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}
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// No sender available, block on this channel
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// Some receiver will complete the option for us
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auto m = std::make_shared<QueueMessage>(item);
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recvq.push_back(m);
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m->Wait(lock);
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return recv_return(!m->chan_closed);
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}
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template <typename T>
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void ChannelImpl<T>::Lock() {
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mu_.lock();
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}
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template <typename T>
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void ChannelImpl<T>::Unlock() {
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mu_.unlock();
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}
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template <typename T>
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void ChannelImpl<T>::Close() {
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std::unique_lock<std::recursive_mutex> lock{mu_};
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if (closed_) {
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// TODO(abhinavarora): closing an already closed channel should panic
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lock.unlock();
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return;
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}
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closed_ = true;
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// Empty the readers
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while (!recvq.empty()) {
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std::shared_ptr<QueueMessage> m = recvq.front();
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recvq.pop_front();
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m->chan_closed = true;
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m->Notify();
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}
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// Empty the senders
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while (!sendq.empty()) {
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std::shared_ptr<QueueMessage> m = sendq.front();
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sendq.pop_front();
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m->chan_closed = true;
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m->Notify();
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}
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}
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template <typename T>
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ChannelImpl<T>::~ChannelImpl() {
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Close();
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// The destructor must wait for all readers and writers to complete their task
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// The channel has been closed, so we will not accept new readers and writers
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std::unique_lock<std::recursive_mutex> lock{mu_};
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destructor_cond_.wait(lock,
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[this]() { return send_ctr == 0 && recv_ctr == 0; });
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}
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} // namespace framework
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} // namespace paddle
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File diff suppressed because it is too large
Load Diff
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data_type_transform.cc
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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.
|
||||
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. */
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#include "paddle/fluid/framework/data_type_transform.h"
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#include "paddle/fluid/framework/tensor_util.h"
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#include "gtest/gtest.h"
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TEST(DataTypeTransform, GPUTransform) {
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using namespace paddle::framework;
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using namespace paddle::platform;
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auto cpu_place = CPUPlace();
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auto gpu_place = CUDAPlace(0);
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CUDADeviceContext context(gpu_place);
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auto kernel_fp16 = OpKernelType(proto::VarType::FP16, gpu_place,
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DataLayout::kAnyLayout, LibraryType::kPlain);
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auto kernel_fp32 = OpKernelType(proto::VarType::FP32, gpu_place,
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DataLayout::kAnyLayout, LibraryType::kPlain);
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auto kernel_fp64 = OpKernelType(proto::VarType::FP64, gpu_place,
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DataLayout::kAnyLayout, LibraryType::kPlain);
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auto kernel_int32 = OpKernelType(proto::VarType::INT32, gpu_place,
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DataLayout::kAnyLayout, LibraryType::kPlain);
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auto kernel_int64 = OpKernelType(proto::VarType::INT64, gpu_place,
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DataLayout::kAnyLayout, LibraryType::kPlain);
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auto kernel_bool = OpKernelType(proto::VarType::BOOL, gpu_place,
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DataLayout::kAnyLayout, LibraryType::kPlain);
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// data type transform from float32
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{
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Tensor in;
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Tensor in_gpu;
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Tensor out_gpu;
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Tensor out;
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float* in_ptr = in.mutable_data<float>(make_ddim({2, 3}), cpu_place);
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float arr[6] = {0, 1, 2, 3, 4, 5};
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int data_number = sizeof(arr) / sizeof(arr[0]);
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memcpy(in_ptr, arr, sizeof(arr));
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TensorCopy(in, gpu_place, context, &in_gpu);
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context.Wait();
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TransDataType(kernel_fp32, kernel_fp64, in_gpu, &out_gpu);
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TensorCopy(out_gpu, cpu_place, context, &out);
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context.Wait();
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double* out_data_double = out.data<double>();
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for (int i = 0; i < data_number; ++i) {
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EXPECT_EQ(out_data_double[i], static_cast<double>(arr[i]));
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}
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TransDataType(kernel_fp32, kernel_int32, in_gpu, &out_gpu);
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TensorCopy(out_gpu, cpu_place, context, &out);
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context.Wait();
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int* out_data_int = out.data<int>();
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for (int i = 0; i < data_number; ++i) {
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EXPECT_EQ(out_data_int[i], static_cast<int>(arr[i]));
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}
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}
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// data type transform from/to float16
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{
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Tensor in;
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Tensor in_gpu;
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Tensor out_gpu;
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Tensor out;
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float16* ptr = in.mutable_data<float16>(make_ddim({2, 3}), cpu_place);
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float16 arr[6] = {float16(0), float16(1), float16(2),
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float16(3), float16(4), float16(5)};
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int data_number = sizeof(arr) / sizeof(arr[0]);
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memcpy(ptr, arr, sizeof(arr));
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TensorCopy(in, gpu_place, context, &in_gpu);
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context.Wait();
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// transform from float16 to other data types
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TransDataType(kernel_fp16, kernel_fp32, in_gpu, &out_gpu);
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TensorCopy(out_gpu, cpu_place, context, &out);
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context.Wait();
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float* out_data_float = out.data<float>();
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for (int i = 0; i < data_number; ++i) {
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EXPECT_EQ(out_data_float[i], static_cast<float>(ptr[i]));
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}
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TransDataType(kernel_fp16, kernel_fp64, in_gpu, &out_gpu);
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TensorCopy(out_gpu, cpu_place, context, &out);
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context.Wait();
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double* out_data_double = out.data<double>();
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for (int i = 0; i < data_number; ++i) {
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EXPECT_EQ(out_data_double[i], static_cast<double>(ptr[i]));
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}
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TransDataType(kernel_fp16, kernel_int32, in_gpu, &out_gpu);
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TensorCopy(out_gpu, cpu_place, context, &out);
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context.Wait();
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int* out_data_int = out.data<int>();
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for (int i = 0; i < data_number; ++i) {
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EXPECT_EQ(out_data_int[i], static_cast<int>(ptr[i]));
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}
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TransDataType(kernel_fp16, kernel_int64, in_gpu, &out_gpu);
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TensorCopy(out_gpu, cpu_place, context, &out);
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context.Wait();
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int64_t* out_data_int64 = out.data<int64_t>();
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for (int i = 0; i < data_number; ++i) {
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EXPECT_EQ(out_data_int64[i], static_cast<int64_t>(ptr[i]));
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}
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TransDataType(kernel_fp16, kernel_bool, in_gpu, &out_gpu);
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TensorCopy(out_gpu, cpu_place, context, &out);
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context.Wait();
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bool* out_data_bool = out.data<bool>();
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for (int i = 0; i < data_number; ++i) {
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EXPECT_EQ(out_data_bool[i], static_cast<bool>(ptr[i]));
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}
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// transform float to float16
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float* in_data_float = in.mutable_data<float>(make_ddim({2, 3}), cpu_place);
|
||||
for (int i = 0; i < data_number; ++i) {
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in_data_float[i] = i;
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}
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||||
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TensorCopy(in, gpu_place, context, &in_gpu);
|
||||
context.Wait();
|
||||
TransDataType(kernel_fp32, kernel_fp16, in_gpu, &out_gpu);
|
||||
TensorCopy(out_gpu, cpu_place, context, &out);
|
||||
context.Wait();
|
||||
|
||||
ptr = out.data<float16>();
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||||
for (int i = 0; i < data_number; ++i) {
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||||
EXPECT_EQ(ptr[i].x, static_cast<float16>(in_data_float[i]).x);
|
||||
}
|
||||
|
||||
// transform double to float16
|
||||
double* in_data_double =
|
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in.mutable_data<double>(make_ddim({2, 3}), cpu_place);
|
||||
for (int i = 0; i < data_number; ++i) {
|
||||
in_data_double[i] = i;
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||||
}
|
||||
|
||||
TensorCopy(in, gpu_place, context, &in_gpu);
|
||||
context.Wait();
|
||||
TransDataType(kernel_fp64, kernel_fp16, in_gpu, &out_gpu);
|
||||
TensorCopy(out_gpu, cpu_place, context, &out);
|
||||
context.Wait();
|
||||
|
||||
ptr = out.data<float16>();
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||||
for (int i = 0; i < data_number; ++i) {
|
||||
EXPECT_EQ(ptr[i].x, static_cast<float16>(in_data_double[i]).x);
|
||||
}
|
||||
|
||||
// transform int to float16
|
||||
int* in_data_int = in.mutable_data<int>(make_ddim({2, 3}), cpu_place);
|
||||
for (int i = 0; i < data_number; ++i) {
|
||||
in_data_int[i] = i;
|
||||
}
|
||||
|
||||
TensorCopy(in, gpu_place, context, &in_gpu);
|
||||
context.Wait();
|
||||
TransDataType(kernel_int32, kernel_fp16, in_gpu, &out_gpu);
|
||||
TensorCopy(out_gpu, cpu_place, context, &out);
|
||||
context.Wait();
|
||||
|
||||
ptr = out.data<float16>();
|
||||
for (int i = 0; i < data_number; ++i) {
|
||||
EXPECT_EQ(ptr[i].x, static_cast<float16>(in_data_int[i]).x);
|
||||
}
|
||||
|
||||
// transform int64 to float16
|
||||
int64_t* in_data_int64 =
|
||||
in.mutable_data<int64_t>(make_ddim({2, 3}), cpu_place);
|
||||
for (int i = 0; i < data_number; ++i) {
|
||||
in_data_int64[i] = i;
|
||||
}
|
||||
|
||||
TensorCopy(in, gpu_place, context, &in_gpu);
|
||||
context.Wait();
|
||||
TransDataType(kernel_int64, kernel_fp16, in_gpu, &out_gpu);
|
||||
TensorCopy(out_gpu, cpu_place, context, &out);
|
||||
context.Wait();
|
||||
|
||||
ptr = out.data<float16>();
|
||||
for (int i = 0; i < data_number; ++i) {
|
||||
EXPECT_EQ(ptr[i].x, static_cast<float16>(in_data_int64[i]).x);
|
||||
}
|
||||
|
||||
// transform bool to float16
|
||||
bool* in_data_bool = in.mutable_data<bool>(make_ddim({2, 3}), cpu_place);
|
||||
for (int i = 0; i < data_number; ++i) {
|
||||
in_data_bool[i] = i;
|
||||
}
|
||||
|
||||
TensorCopy(in, gpu_place, context, &in_gpu);
|
||||
context.Wait();
|
||||
TransDataType(kernel_bool, kernel_fp16, in_gpu, &out_gpu);
|
||||
TensorCopy(out_gpu, cpu_place, context, &out);
|
||||
context.Wait();
|
||||
|
||||
ptr = out.data<float16>();
|
||||
for (int i = 0; i < data_number; ++i) {
|
||||
EXPECT_EQ(ptr[i].x, static_cast<float16>(in_data_bool[i]).x);
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -1,142 +0,0 @@
|
||||
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
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. */
|
||||
|
||||
#pragma once
|
||||
#include <atomic>
|
||||
#include <condition_variable>
|
||||
#include <deque>
|
||||
#include <mutex>
|
||||
|
||||
#include "paddle/fluid/framework/channel.h"
|
||||
#include "paddle/fluid/platform/enforce.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace details {
|
||||
|
||||
// Four of the properties of Buffered Channel:
|
||||
// - A send to a full channel blocks temporarily until a receive from the
|
||||
// channel or the channel is closed.
|
||||
// - A receive from an empty channel blocks temporarily until a send to the
|
||||
// channel or the channel is closed.
|
||||
// - A send to a closed channel returns false immediately.
|
||||
// - A receive from a closed channel returns false immediately.
|
||||
|
||||
template <typename T>
|
||||
class Buffered : public paddle::framework::Channel<T> {
|
||||
friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);
|
||||
friend void paddle::framework::CloseChannel<T>(Channel<T>*);
|
||||
|
||||
public:
|
||||
virtual bool Send(T*);
|
||||
virtual bool Receive(T*);
|
||||
virtual size_t Cap() { return cap_; }
|
||||
virtual void Close();
|
||||
virtual ~Buffered();
|
||||
|
||||
private:
|
||||
size_t cap_;
|
||||
std::mutex mu_;
|
||||
std::condition_variable empty_cond_var_;
|
||||
std::condition_variable full_cond_var_;
|
||||
std::condition_variable destructor_cond_var_;
|
||||
std::deque<T> channel_;
|
||||
std::atomic<bool> closed_{false};
|
||||
std::atomic<unsigned> send_ctr{0};
|
||||
std::atomic<unsigned> recv_ctr{0};
|
||||
|
||||
Buffered(size_t cap) : cap_(cap), closed_(false) {
|
||||
PADDLE_ENFORCE_GT(cap, 0);
|
||||
}
|
||||
|
||||
void NotifyAllParticipants(std::unique_lock<std::mutex>*);
|
||||
};
|
||||
|
||||
template <typename T>
|
||||
bool Buffered<T>::Send(T* item) {
|
||||
bool ret = false;
|
||||
if (closed_) {
|
||||
return ret;
|
||||
}
|
||||
send_ctr++;
|
||||
std::unique_lock<std::mutex> lock(mu_);
|
||||
full_cond_var_.wait(lock,
|
||||
[this]() { return channel_.size() < cap_ || closed_; });
|
||||
if (!closed_) {
|
||||
channel_.push_back(std::move(*item));
|
||||
lock.unlock();
|
||||
empty_cond_var_.notify_one();
|
||||
ret = true;
|
||||
}
|
||||
send_ctr--;
|
||||
destructor_cond_var_.notify_one();
|
||||
return ret;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
bool Buffered<T>::Receive(T* item) {
|
||||
bool ret = false;
|
||||
// Once the channel has been closed and all data has been consumed,
|
||||
// just return false. Don't even try acquiring the mutex.
|
||||
if (closed_ && channel_.empty()) {
|
||||
return false;
|
||||
}
|
||||
recv_ctr++;
|
||||
std::unique_lock<std::mutex> lock(mu_);
|
||||
empty_cond_var_.wait(lock, [this]() { return !channel_.empty() || closed_; });
|
||||
if (!channel_.empty()) {
|
||||
*item = std::move(channel_.front());
|
||||
channel_.pop_front();
|
||||
full_cond_var_.notify_one();
|
||||
ret = true;
|
||||
}
|
||||
recv_ctr--;
|
||||
destructor_cond_var_.notify_one();
|
||||
return ret;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void Buffered<T>::Close() {
|
||||
if (closed_) {
|
||||
return;
|
||||
}
|
||||
std::unique_lock<std::mutex> lock(mu_);
|
||||
closed_ = true;
|
||||
NotifyAllParticipants(&lock);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
Buffered<T>::~Buffered() {
|
||||
std::unique_lock<std::mutex> lock(mu_);
|
||||
closed_ = true;
|
||||
channel_.clear();
|
||||
NotifyAllParticipants(&lock);
|
||||
|
||||
// The destructor must wait for all readers and writers to complete their task
|
||||
// The channel has been closed, so we will not accept new readers and writers
|
||||
lock.lock();
|
||||
destructor_cond_var_.wait(
|
||||
lock, [this]() { return send_ctr == 0 && recv_ctr == 0; });
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void Buffered<T>::NotifyAllParticipants(std::unique_lock<std::mutex>* lock) {
|
||||
lock->unlock();
|
||||
full_cond_var_.notify_all();
|
||||
empty_cond_var_.notify_all();
|
||||
}
|
||||
|
||||
} // namespace details
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
@ -1,174 +0,0 @@
|
||||
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
|
||||
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. */
|
||||
|
||||
#pragma once
|
||||
#include <atomic>
|
||||
#include <condition_variable>
|
||||
#include <mutex>
|
||||
|
||||
#include "paddle/fluid/framework/channel.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace framework {
|
||||
namespace details {
|
||||
|
||||
// Four of the properties of UnBuffered Channel:
|
||||
// - A send to a channel blocks temporarily until a receive from the
|
||||
// channel or the channel is closed.
|
||||
// - A receive from a channel blocks temporarily until a send to the
|
||||
// channel or the channel is closed.
|
||||
// - A send to a closed channel returns false immediately.
|
||||
// - A receive from a closed channel returns false immediately.
|
||||
template <typename T>
|
||||
class UnBuffered : public paddle::framework::Channel<T> {
|
||||
friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);
|
||||
friend void paddle::framework::CloseChannel<T>(Channel<T>*);
|
||||
|
||||
public:
|
||||
virtual bool Send(T*);
|
||||
virtual bool Receive(T*);
|
||||
virtual size_t Cap() { return 0; }
|
||||
virtual void Close();
|
||||
virtual ~UnBuffered();
|
||||
|
||||
private:
|
||||
std::mutex mu_ch_;
|
||||
// Mutex for readers and writers who are waiting for other reader
|
||||
// and writer to complete execution
|
||||
std::recursive_mutex mu_read_, mu_write_;
|
||||
// reader_found_ is set true when a reader is ready to accept data
|
||||
// writer_found_ is set true when a writer is ready to send data
|
||||
// A transaction occurs only when both are true
|
||||
std::atomic<bool> reader_found_{false}, writer_found_{false};
|
||||
std::condition_variable cv_channel_;
|
||||
std::condition_variable_any cv_reader_, cv_writer_, cv_destructor_;
|
||||
T* item{nullptr};
|
||||
std::atomic<bool> closed_{false};
|
||||
std::atomic<unsigned> send_ctr{0};
|
||||
std::atomic<unsigned> recv_ctr{0};
|
||||
|
||||
UnBuffered() : closed_(false) {}
|
||||
|
||||
void NotifyAllParticipants(std::unique_lock<std::mutex>*);
|
||||
};
|
||||
|
||||
// This function implements the concept of how data should
|
||||
// be sent from a writer to a reader.
|
||||
template <typename T>
|
||||
bool UnBuffered<T>::Send(T* data) {
|
||||
bool ret = false;
|
||||
if (closed_) {
|
||||
return ret;
|
||||
}
|
||||
send_ctr++;
|
||||
// Prevent other writers from entering
|
||||
std::unique_lock<std::recursive_mutex> writer_lock(mu_write_);
|
||||
writer_found_ = true;
|
||||
std::unique_lock<std::recursive_mutex> cv_lock(mu_write_);
|
||||
// If writer comes first, it should wait till a reader arrives
|
||||
cv_writer_.wait(cv_lock,
|
||||
[this]() { return reader_found_ == true || closed_; });
|
||||
cv_reader_.notify_one();
|
||||
if (!closed_) {
|
||||
std::unique_lock<std::mutex> channel_lock(mu_ch_);
|
||||
item = data;
|
||||
channel_lock.unlock();
|
||||
cv_channel_.notify_one();
|
||||
channel_lock.lock();
|
||||
cv_channel_.wait(channel_lock,
|
||||
[this]() { return item == nullptr || closed_; });
|
||||
ret = true;
|
||||
}
|
||||
writer_found_ = false;
|
||||
send_ctr--;
|
||||
cv_destructor_.notify_one();
|
||||
return ret;
|
||||
}
|
||||
|
||||
// This function implements the concept of how
|
||||
// data that was sent by a writer is read from a reader.
|
||||
template <typename T>
|
||||
bool UnBuffered<T>::Receive(T* data) {
|
||||
bool ret = false;
|
||||
// If channel is closed, we don't even want any reader to enter.
|
||||
// Unlike a buffered channel, an unbuffered channel does not allow
|
||||
// readers to read after closing because there is no buffer to be consumed.
|
||||
if (closed_) return ret;
|
||||
recv_ctr++;
|
||||
// Prevent other readers from entering
|
||||
std::unique_lock<std::recursive_mutex> read_lock{mu_read_};
|
||||
reader_found_ = true;
|
||||
std::unique_lock<std::recursive_mutex> cv_lock{mu_read_};
|
||||
// If reader comes first, it should wait till a writer arrives
|
||||
cv_reader_.wait(cv_lock,
|
||||
[this]() { return writer_found_ == true || closed_; });
|
||||
cv_writer_.notify_one();
|
||||
if (!closed_) {
|
||||
std::unique_lock<std::mutex> lock_ch{mu_ch_};
|
||||
// Reader should wait for the writer to first write its data
|
||||
cv_channel_.wait(lock_ch, [this]() { return item != nullptr || closed_; });
|
||||
if (!closed_) {
|
||||
*data = std::move(*item);
|
||||
item = nullptr;
|
||||
lock_ch.unlock();
|
||||
ret = true;
|
||||
}
|
||||
cv_channel_.notify_one();
|
||||
}
|
||||
reader_found_ = false;
|
||||
recv_ctr--;
|
||||
cv_destructor_.notify_one();
|
||||
return ret;
|
||||
}
|
||||
|
||||
// This function implements the sequence of events
|
||||
// that take place once the channel is closed.
|
||||
template <typename T>
|
||||
void UnBuffered<T>::Close() {
|
||||
if (closed_) {
|
||||
return;
|
||||
}
|
||||
std::unique_lock<std::mutex> lock(mu_ch_);
|
||||
item = nullptr;
|
||||
closed_ = true;
|
||||
NotifyAllParticipants(&lock);
|
||||
}
|
||||
|
||||
// This function implements the sequence of events
|
||||
// that are executed once the object of an UnBuffered
|
||||
// channel is destroyed.
|
||||
template <typename T>
|
||||
UnBuffered<T>::~UnBuffered() {
|
||||
std::unique_lock<std::mutex> lock(mu_ch_);
|
||||
item = nullptr;
|
||||
closed_ = true;
|
||||
NotifyAllParticipants(&lock);
|
||||
lock.lock();
|
||||
cv_destructor_.wait(lock,
|
||||
[this]() { return send_ctr == 0 && recv_ctr == 0; });
|
||||
}
|
||||
|
||||
// This function notifies all the readers, writers and
|
||||
// the channel condition variables.
|
||||
template <typename T>
|
||||
void UnBuffered<T>::NotifyAllParticipants(std::unique_lock<std::mutex>* lock) {
|
||||
lock->unlock();
|
||||
cv_writer_.notify_all();
|
||||
cv_channel_.notify_all();
|
||||
cv_reader_.notify_all();
|
||||
}
|
||||
|
||||
} // namespace details
|
||||
} // namespace framework
|
||||
} // namespace paddle
|
||||
Some files were not shown because too many files have changed in this diff Show More
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Reference in new issue