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提交 f63ab561 编写于 作者: D Dang Qingqing
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Fix conflict.

上级 8f5d918a 425a8821
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Showing with 1028 addition and 3722 deletion
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ f63ab561
......@@ -169,15 +169,8 @@ cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
cc_test(op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto)
cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
# cc_test(channel_test SRCS channel_test.cc)
cc_test(tuple_test SRCS tuple_test.cc )
if (NOT WIN32)
cc_test(rw_lock_test SRCS rw_lock_test.cc)
endif (NOT WIN32)
# disable test temporarily.
# TODO https://github.com/PaddlePaddle/Paddle/issues/11971
# cc_test(concurrency_test SRCS concurrency_test.cc DEPS go_op channel_close_op channel_create_op
# channel_send_op channel_recv_op sum_op select_op elementwise_add_op compare_op
# conditional_block_op while_op assign_op print_op executor proto_desc)
paddle/fluid/framework/channel.h 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* 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 <stddef.h> // for size_t
#include <condition_variable> // NOLINT
#include <typeindex>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
enum class ChannelAction {
SEND = 0,
RECEIVE = 1,
CLOSE = 2,
};
// Channel is the abstract class of buffered and un-buffered channels.
template <typename T>
class Channel {
public:
virtual bool CanSend() = 0;
virtual bool CanReceive() = 0;
virtual void Send(T*) = 0;
virtual bool Receive(T*) = 0;
virtual size_t Cap() = 0;
virtual void Lock() = 0;
virtual void Unlock() = 0;
virtual bool IsClosed() = 0;
virtual void Close() = 0;
virtual ~Channel() {}
virtual void AddToSendQ(const void* referrer, T* data,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(ChannelAction)> cb) = 0;
virtual void AddToReceiveQ(const void* referrer, T* data,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(ChannelAction)> cb) = 0;
virtual void RemoveFromSendQ(const void* referrer) = 0;
virtual void RemoveFromReceiveQ(const void* referrer) = 0;
};
// Forward declaration of channel implementations.
template <typename T>
class ChannelImpl;
template <typename T>
Channel<T>* MakeChannel(size_t buffer_size) {
return new ChannelImpl<T>(buffer_size);
}
template <typename T>
void CloseChannel(Channel<T>* ch) {
ch->Close();
}
/*
* The ChannelHolder class serves two main purposes:
* 1. It acts as a unified wrapper for the different kinds of
* channels, i.e. Buffered and Unbuffered channels. This is
* similar to the ReaderHolder class.
* 2. It also helps us in TypeHiding. This is similar to the
* PlaceHolder implementations in variable.h and tensor.h.
*/
class ChannelHolder {
public:
template <typename T>
void Reset(size_t buffer_size) {
holder_.reset(new PlaceholderImpl<T>(buffer_size));
}
template <typename T>
void Send(T* data) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
PADDLE_ENFORCE_EQ(
holder_->Type(), std::type_index(typeid(T)),
"Channel type is not same as the type of the data being sent");
// Static cast should be safe because we have ensured that types are same
Channel<T>* channel = static_cast<Channel<T>*>(holder_->Ptr());
PADDLE_ENFORCE_EQ(channel != nullptr, true, "Channel should not be null.");
channel->Send(data);
}
template <typename T>
bool Receive(T* data) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
PADDLE_ENFORCE_EQ(
holder_->Type(), std::type_index(typeid(T)),
"Channel type is not same as the type of the data being sent");
Channel<T>* channel = static_cast<Channel<T>*>(holder_->Ptr());
PADDLE_ENFORCE_EQ(channel != nullptr, true, "Channel should not be null.");
return channel->Receive(data);
}
bool IsClosed() {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->IsClosed();
}
bool CanSend() {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->CanSend();
}
bool CanReceive() {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->CanReceive();
}
void close() {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
holder_->Close();
}
size_t Cap() {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->Cap();
}
void Lock() {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
holder_->Lock();
}
void Unlock() {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
holder_->Unlock();
}
template <typename T>
void AddToSendQ(const void* referrer, T* data,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(ChannelAction)> cb) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
Channel<T>* channel = static_cast<Channel<T>*>(holder_->Ptr());
if (channel != nullptr) {
channel->AddToSendQ(referrer, data, cond, cb);
}
}
template <typename T>
void AddToReceiveQ(const void* referrer, T* data,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(ChannelAction)> cb) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
Channel<T>* channel = static_cast<Channel<T>*>(holder_->Ptr());
if (channel != nullptr) {
channel->AddToReceiveQ(referrer, data, cond, cb);
}
}
void RemoveFromSendQ(const void* referrer) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
holder_->RemoveFromSendQ(referrer);
}
void RemoveFromReceiveQ(const void* referrer) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
holder_->RemoveFromReceiveQ(referrer);
}
inline bool IsInitialized() const { return holder_ != nullptr; }
inline const std::type_index Type() {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->Type();
}
private:
/**
* @note Placeholder hides type T, so it doesn't appear as a template
* parameter of ChannelHolder.
*/
struct Placeholder {
virtual ~Placeholder() {}
virtual const std::type_index Type() const = 0;
virtual void* Ptr() const = 0;
virtual bool IsClosed() = 0;
virtual bool CanSend() = 0;
virtual bool CanReceive() = 0;
virtual void RemoveFromSendQ(const void* referrer) = 0;
virtual void RemoveFromReceiveQ(const void* referrer) = 0;
virtual void Close() = 0;
virtual void Lock() = 0;
virtual void Unlock() = 0;
virtual size_t Cap() = 0;
};
template <typename T>
struct PlaceholderImpl : public Placeholder {
explicit PlaceholderImpl(size_t buffer_size)
: type_(std::type_index(typeid(T))) {
channel_.reset(MakeChannel<T>(buffer_size));
}
virtual const std::type_index Type() const { return type_; }
virtual void* Ptr() const { return static_cast<void*>(channel_.get()); }
virtual bool IsClosed() {
if (channel_) {
return channel_->IsClosed();
}
return false;
}
virtual bool CanSend() {
if (channel_) {
return channel_->CanSend();
}
return false;
}
virtual bool CanReceive() {
if (channel_) {
return channel_->CanReceive();
}
return false;
}
virtual void RemoveFromSendQ(const void* referrer) {
if (channel_) {
channel_->RemoveFromSendQ(referrer);
}
}
virtual void RemoveFromReceiveQ(const void* referrer) {
if (channel_) {
channel_->RemoveFromReceiveQ(referrer);
}
}
virtual void Close() {
if (channel_) channel_->Close();
}
virtual size_t Cap() {
if (channel_)
return channel_->Cap();
else
return -1;
}
virtual void Lock() {
if (channel_) channel_->Lock();
}
virtual void Unlock() {
if (channel_) channel_->Unlock();
}
std::unique_ptr<Channel<T>> channel_;
const std::type_index type_;
};
// Pointer to a PlaceholderImpl object
std::unique_ptr<Placeholder> holder_;
};
} // namespace framework
} // namespace paddle
#include "paddle/fluid/framework/channel_impl.h"
paddle/fluid/framework/channel_impl.h 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* 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 <stddef.h> // for size_t
#include <atomic>
#include <condition_variable> // NOLINT
#include <deque>
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
template <typename T>
class ChannelImpl : 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 CanSend();
virtual bool CanReceive();
virtual void Send(T *);
virtual bool Receive(T *);
virtual size_t Cap() { return cap_; }
virtual void Lock();
virtual void Unlock();
virtual bool IsClosed();
virtual void Close();
explicit ChannelImpl(size_t);
virtual ~ChannelImpl();
virtual void AddToSendQ(const void *referrer, T *data,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(ChannelAction)> cb);
virtual void AddToReceiveQ(const void *referrer, T *data,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(ChannelAction)> cb);
virtual void RemoveFromSendQ(const void *referrer);
virtual void RemoveFromReceiveQ(const void *referrer);
private:
struct QueueMessage {
T *data;
std::shared_ptr<std::condition_variable_any> cond;
bool chan_closed = false;
bool completed = false;
const void *referrer; // TODO(thuan): figure out better way to do this
std::function<bool(ChannelAction)> callback;
explicit QueueMessage(T *item)
: data(item), cond(std::make_shared<std::condition_variable_any>()) {}
QueueMessage(T *item, std::shared_ptr<std::condition_variable_any> cond)
: data(item), cond(cond) {}
void Wait(std::unique_lock<std::recursive_mutex> &lock) {
cond->wait(lock, [this]() { return completed; });
}
void Notify() {
completed = true;
cond->notify_all();
}
};
void send_return() {
send_ctr--;
destructor_cond_.notify_all();
}
bool recv_return(bool value) {
recv_ctr--;
destructor_cond_.notify_all();
return value;
}
std::shared_ptr<QueueMessage> get_first_message(
std::deque<std::shared_ptr<QueueMessage>> *queue, ChannelAction action) {
while (!queue->empty()) {
// Check whether this message was added by Select
// If this was added by Select then execute the callback
// to check if you can execute this message. The callback
// can return false if some other case was executed in Select.
// In that case just discard this QueueMessage and process next.
std::shared_ptr<QueueMessage> m = queue->front();
queue->pop_front();
if (m->callback == nullptr || m->callback(action)) return m;
}
return nullptr;
}
size_t cap_;
std::recursive_mutex mu_;
bool closed_;
std::deque<T> buf_;
std::deque<std::shared_ptr<QueueMessage>> recvq;
std::deque<std::shared_ptr<QueueMessage>> sendq;
std::atomic<unsigned> send_ctr{0};
std::atomic<unsigned> recv_ctr{0};
std::condition_variable_any destructor_cond_;
};
template <typename T>
ChannelImpl<T>::ChannelImpl(size_t capacity)
: cap_(capacity), closed_(false), send_ctr(0), recv_ctr(0) {
PADDLE_ENFORCE_GE(capacity, 0);
}
template <typename T>
bool ChannelImpl<T>::CanSend() {
std::lock_guard<std::recursive_mutex> lock{mu_};
return !closed_ && (!recvq.empty() || buf_.size() < cap_);
}
template <typename T>
bool ChannelImpl<T>::CanReceive() {
std::lock_guard<std::recursive_mutex> lock{mu_};
return !(closed_ && buf_.empty()) && (!sendq.empty() || buf_.size() > 0);
}
template <typename T>
void ChannelImpl<T>::Send(T *item) {
send_ctr++;
std::unique_lock<std::recursive_mutex> lock{mu_};
// If channel is closed, throw exception
if (closed_) {
send_return();
lock.unlock();
PADDLE_THROW("Cannot send on closed channel");
}
// If there is a receiver, directly pass the value we want
// to send to the receiver, bypassing the channel buffer if any
if (!recvq.empty()) {
std::shared_ptr<QueueMessage> m =
get_first_message(&recvq, ChannelAction::SEND);
if (m != nullptr) {
*(m->data) = std::move(*item);
m->Notify();
send_return();
return;
} else {
Send(item);
send_return();
return;
}
}
// Unbuffered channel will always bypass this
// If buffered channel has space in buffer,
// write the element to the buffer.
if (buf_.size() < cap_) {
// Copy to buffer
buf_.push_back(std::move(*item));
send_return();
return;
}
// Block on channel, because some receiver will complete
// the operation for us
auto m = std::make_shared<QueueMessage>(item);
sendq.push_back(m);
m->Wait(lock);
if (m->chan_closed) {
send_return();
lock.unlock();
PADDLE_THROW("Cannot send on closed channel");
}
send_return();
}
template <typename T>
bool ChannelImpl<T>::Receive(T *item) {
recv_ctr++;
std::unique_lock<std::recursive_mutex> lock{mu_};
// If channel is closed and buffer is empty or
// channel is unbuffered
if (closed_ && buf_.empty()) return recv_return(false);
// If there is a sender, directly receive the value we want
// from the sender. In case of a buffered channel, read from
// buffer and move front of send queue to the buffer
if (!sendq.empty()) {
std::shared_ptr<QueueMessage> m =
get_first_message(&sendq, ChannelAction::RECEIVE);
if (buf_.size() > 0) {
// Case 1 : Channel is Buffered
// Do Data transfer from front of buffer
// and add a QueueMessage to the buffer
*item = std::move(buf_.front());
buf_.pop_front();
// If first message from sendq is not null
// add it to the buffer and notify it
if (m != nullptr) {
// Copy to buffer
buf_.push_back(std::move(*(m->data)));
m->Notify();
} // Ignore if there is no first message
} else {
// Case 2: Channel is Unbuffered
// Do data transfer from front of SendQ
// If front is nullptr, then recursively call itself
if (m != nullptr) {
*item = std::move(*(m->data));
m->Notify();
} else {
return recv_return(Receive(item));
}
}
return recv_return(true);
}
// If this is a buffered channel and there are items in buffer
if (buf_.size() > 0) {
// Directly read from buffer
*item = std::move(buf_.front());
buf_.pop_front();
// return true
return recv_return(true);
}
// No sender available, block on this channel
// Some receiver will complete the option for us
auto m = std::make_shared<QueueMessage>(item);
recvq.push_back(m);
m->Wait(lock);
return recv_return(!m->chan_closed);
}
template <typename T>
void ChannelImpl<T>::Lock() {
mu_.lock();
}
template <typename T>
void ChannelImpl<T>::Unlock() {
mu_.unlock();
}
template <typename T>
bool ChannelImpl<T>::IsClosed() {
std::lock_guard<std::recursive_mutex> lock{mu_};
return closed_;
}
template <typename T>
void ChannelImpl<T>::Close() {
std::unique_lock<std::recursive_mutex> lock{mu_};
if (closed_) {
// TODO(abhinavarora): closing an already closed channel should panic
lock.unlock();
return;
}
closed_ = true;
// Empty the readers
while (!recvq.empty()) {
std::shared_ptr<QueueMessage> m = recvq.front();
recvq.pop_front();
m->chan_closed = true;
// Execute callback function (if any)
if (m->callback != nullptr) {
m->callback(ChannelAction::CLOSE);
}
m->Notify();
}
// Empty the senders
while (!sendq.empty()) {
std::shared_ptr<QueueMessage> m = sendq.front();
sendq.pop_front();
m->chan_closed = true;
// Execute callback function (if any)
if (m->callback != nullptr) {
m->callback(ChannelAction::CLOSE);
}
m->Notify();
}
}
template <typename T>
void ChannelImpl<T>::AddToSendQ(
const void *referrer, T *data,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(ChannelAction)> cb) {
std::lock_guard<std::recursive_mutex> lock{mu_};
auto m = std::make_shared<QueueMessage>(data, cond);
m->referrer = referrer;
m->callback = cb;
sendq.push_back(m);
}
template <typename T>
void ChannelImpl<T>::AddToReceiveQ(
const void *referrer, T *data,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(ChannelAction)> cb) {
std::lock_guard<std::recursive_mutex> lock{mu_};
auto m = std::make_shared<QueueMessage>(data, cond);
m->referrer = referrer;
m->callback = cb;
recvq.push_back(m);
}
template <typename T>
void ChannelImpl<T>::RemoveFromSendQ(const void *referrer) {
std::lock_guard<std::recursive_mutex> lock{mu_};
for (auto it = sendq.begin(); it != sendq.end();) {
std::shared_ptr<QueueMessage> sendMsg = (std::shared_ptr<QueueMessage>)*it;
if (sendMsg->referrer == referrer) {
it = sendq.erase(it);
} else {
++it;
}
}
}
template <typename T>
void ChannelImpl<T>::RemoveFromReceiveQ(const void *referrer) {
std::lock_guard<std::recursive_mutex> lock{mu_};
for (auto it = recvq.begin(); it != recvq.end();) {
std::shared_ptr<QueueMessage> recvMsg = (std::shared_ptr<QueueMessage>)*it;
if (recvMsg->referrer == referrer) {
it = recvq.erase(it);
} else {
++it;
}
}
}
template <typename T>
ChannelImpl<T>::~ChannelImpl() {
Close();
// 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
std::unique_lock<std::recursive_mutex> lock{mu_};
destructor_cond_.wait(lock,
[this]() { return send_ctr == 0 && recv_ctr == 0; });
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/channel_test.cc 已删除 100644 → 0
浏览文件 @ 8f5d918a
此差异已折叠。
paddle/fluid/framework/concurrency_test.cc 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* 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. */
#include <thread> // NOLINT
#include "gtest/gtest.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_registry.h"
USE_NO_KERNEL_OP(go);
USE_NO_KERNEL_OP(channel_close);
USE_NO_KERNEL_OP(channel_create);
USE_NO_KERNEL_OP(channel_recv);
USE_NO_KERNEL_OP(channel_send);
USE_NO_KERNEL_OP(elementwise_add);
USE_NO_KERNEL_OP(select);
USE_NO_KERNEL_OP(conditional_block);
USE_NO_KERNEL_OP(equal);
USE_NO_KERNEL_OP(assign);
USE_NO_KERNEL_OP(while);
USE_NO_KERNEL_OP(print);
namespace f = paddle::framework;
namespace p = paddle::platform;
namespace paddle {
namespace framework {
template <typename T>
LoDTensor *CreateVariable(Scope *scope, const p::CPUPlace &place,
std::string name, T value) {
// Create LoDTensor<int> of dim [1]
auto var = scope->Var(name);
auto tensor = var->GetMutable<LoDTensor>();
tensor->Resize({1});
T *expect = tensor->mutable_data<T>(place);
expect[0] = value;
return tensor;
}
void AddOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, AttributeMap attrs,
BlockDesc *block) {
// insert op
auto op = block->AppendOp();
op->SetType(type);
for (auto &kv : inputs) {
op->SetInput(kv.first, kv.second);
}
for (auto &kv : outputs) {
op->SetOutput(kv.first, kv.second);
}
op->SetAttrMap(attrs);
}
void AddCase(ProgramDesc *program, Scope *scope, p::CPUPlace *place,
BlockDesc *casesBlock, int caseId, int caseType,
std::string caseChannel, std::string caseVarName,
std::function<void(BlockDesc *, Scope *)> func) {
std::string caseCondName = std::string("caseCond") + std::to_string(caseId);
std::string caseCondXVarName =
std::string("caseCondX") + std::to_string(caseId);
BlockDesc *caseBlock = program->AppendBlock(*casesBlock);
func(caseBlock, scope);
CreateVariable(scope, *place, caseCondName, false);
CreateVariable(scope, *place, caseCondXVarName, caseId);
CreateVariable(scope, *place, caseVarName, caseId);
scope->Var("step_scope");
AddOp("equal", {{"X", {caseCondXVarName}}, {"Y", {"caseToExecute"}}},
{{"Out", {caseCondName}}}, {}, casesBlock);
AddOp("conditional_block", {{"X", {caseCondName}}, {"Params", {}}},
{{"Out", {}}, {"Scope", {"step_scope"}}},
{{"sub_block", caseBlock}, {"is_scalar_condition", true}}, casesBlock);
}
void AddFibonacciSelect(Scope *scope, p::CPUPlace *place, ProgramDesc *program,
BlockDesc *parentBlock, std::string dataChanName,
std::string quitChanName) {
BlockDesc *whileBlock = program->AppendBlock(*parentBlock);
CreateVariable(scope, *place, "whileExitCond", true);
CreateVariable(scope, *place, "caseToExecute", -1);
CreateVariable(scope, *place, "case1var", 0);
CreateVariable(scope, *place, "xtemp", 0);
// TODO(thuan): Need to create fibXToSend, since channel send moves the actual
// data,
// which causes the data to be no longer accessible to do the fib calculation
// TODO(abhinav): Change channel send to do a copy instead of a move!
CreateVariable(scope, *place, "fibXToSend", 0);
CreateVariable(scope, *place, "fibX", 0);
CreateVariable(scope, *place, "fibY", 1);
CreateVariable(scope, *place, "quitVar", 0);
BlockDesc *casesBlock = program->AppendBlock(*whileBlock);
std::function<void(BlockDesc * caseBlock)> f = [](BlockDesc *caseBlock) {};
// TODO(thuan): Remove this once we change channel send to do a copy instead
// of move
AddOp("assign", {{"X", {"fibX"}}}, {{"Out", {"fibXToSend"}}}, {}, whileBlock);
// Case 0: Send to dataChanName
std::function<void(BlockDesc * caseBlock, Scope * scope)> case0Func = [&](
BlockDesc *caseBlock, Scope *scope) {
AddOp("assign", {{"X", {"fibX"}}}, {{"Out", {"xtemp"}}}, {}, caseBlock);
AddOp("assign", {{"X", {"fibY"}}}, {{"Out", {"fibX"}}}, {}, caseBlock);
AddOp("elementwise_add", {{"X", {"xtemp"}}, {"Y", {"fibY"}}},
{{"Out", {"fibY"}}}, {}, caseBlock);
};
AddCase(program, scope, place, casesBlock, 0, 1, dataChanName, "fibXToSend",
case0Func);
std::string case0Config =
std::string("0,1,") + dataChanName + std::string(",fibXToSend");
// Case 1: Receive from quitChanName
std::function<void(BlockDesc * caseBlock, Scope * scope)> case2Func = [&](
BlockDesc *caseBlock, Scope *scope) {
// Exit the while loop after we receive from quit channel.
// We assign a false to "whileExitCond" variable, which will
// break out of while_op loop
CreateVariable(scope, *place, "whileFalse", false);
AddOp("assign", {{"X", {"whileFalse"}}}, {{"Out", {"whileExitCond"}}}, {},
caseBlock);
};
AddCase(program, scope, place, casesBlock, 1, 2, quitChanName, "quitVar",
case2Func);
std::string case1Config =
std::string("1,2,") + quitChanName + std::string(",quitVar");
// Select block
AddOp("select", {{"X", {dataChanName, quitChanName}},
{"case_to_execute", {"caseToExecute"}}},
{{"Out", {}}},
{{"sub_block", casesBlock},
{"cases", std::vector<std::string>{case0Config, case1Config}}},
whileBlock);
scope->Var("stepScopes");
AddOp("while",
{{"X", {dataChanName, quitChanName}}, {"Condition", {"whileExitCond"}}},
{{"Out", {}}, {"StepScopes", {"stepScopes"}}},
{{"sub_block", whileBlock}}, parentBlock);
}
TEST(Concurrency, Go_Op) {
Scope scope;
p::CPUPlace place;
// Initialize scope variables
p::CPUDeviceContext ctx(place);
// Create channel variable
scope.Var("Channel");
// Create Variables, x0 will be put into channel,
// result will be pulled from channel
CreateVariable(&scope, place, "Status", false);
CreateVariable(&scope, place, "x0", 99);
CreateVariable(&scope, place, "result", 0);
framework::Executor executor(place);
ProgramDesc program;
BlockDesc *block = program.MutableBlock(0);
// Create channel OP
AddOp("channel_create", {}, {{"Out", {"Channel"}}},
{{"capacity", 10}, {"data_type", f::proto::VarType::LOD_TENSOR}},
block);
// Create Go Op routine
BlockDesc *goOpBlock = program.AppendBlock(program.Block(0));
AddOp("channel_send", {{"Channel", {"Channel"}}, {"X", {"x0"}}},
{{"Status", {"Status"}}}, {}, goOpBlock);
// Create Go Op
AddOp("go", {{"X", {"Channel", "x0"}}}, {}, {{"sub_block", goOpBlock}},
block);
// Create Channel Receive Op
AddOp("channel_recv", {{"Channel", {"Channel"}}},
{{"Status", {"Status"}}, {"Out", {"result"}}}, {}, block);
// Create Channel Close Op
AddOp("channel_close", {{"Channel", {"Channel"}}}, {}, {}, block);
// Check the result tensor to make sure it is set to 0
const LoDTensor &tensor = (scope.FindVar("result"))->Get<LoDTensor>();
auto *initialData = tensor.data<int>();
EXPECT_EQ(initialData[0], 0);
executor.Run(program, &scope, 0, true, true);
// After we call executor.run, the Go operator should do a channel_send to
// set the "result" variable to 99.
auto *finalData = tensor.data<int>();
EXPECT_EQ(finalData[0], 99);
}
/**
* This test implements the fibonacci function using go_op and select_op
*/
TEST(Concurrency, Select) {
Scope scope;
p::CPUPlace place;
// Initialize scope variables
p::CPUDeviceContext ctx(place);
CreateVariable(&scope, place, "Status", false);
CreateVariable(&scope, place, "result", 0);
CreateVariable(&scope, place, "currentXFib", 0);
framework::Executor executor(place);
ProgramDesc program;
BlockDesc *block = program.MutableBlock(0);
// Create channel OP
std::string dataChanName = "Channel";
scope.Var(dataChanName);
AddOp("channel_create", {}, {{"Out", {dataChanName}}},
{{"capacity", 0}, {"data_type", f::proto::VarType::LOD_TENSOR}}, block);
std::string quitChanName = "Quit";
scope.Var(quitChanName);
AddOp("channel_create", {}, {{"Out", {quitChanName}}},
{{"capacity", 0}, {"data_type", f::proto::VarType::LOD_TENSOR}}, block);
// Create Go Op routine, which loops 10 times over fibonacci sequence
CreateVariable(&scope, place, "xReceiveVar", 0);
BlockDesc *goOpBlock = program.AppendBlock(program.Block(0));
for (int i = 0; i < 10; ++i) {
AddOp("channel_recv", {{"Channel", {dataChanName}}},
{{"Status", {"Status"}}, {"Out", {"currentXFib"}}}, {}, goOpBlock);
AddOp("print", {{"In", {"currentXFib"}}}, {{"Out", {"currentXFib"}}},
{{"first_n", 100},
{"summarize", -1},
{"print_tensor_name", false},
{"print_tensor_type", true},
{"print_tensor_shape", false},
{"print_tensor_lod", false},
{"print_phase", std::string("FORWARD")},
{"message", std::string("X: ")}},
goOpBlock);
}
CreateVariable(&scope, place, "quitSignal", 0);
AddOp("channel_send", {{"Channel", {quitChanName}}, {"X", {"quitSignal"}}},
{{"Status", {"Status"}}}, {}, goOpBlock);
// Create Go Op
AddOp("go", {{"X", {dataChanName, quitChanName}}}, {},
{{"sub_block", goOpBlock}}, block);
AddFibonacciSelect(&scope, &place, &program, block, dataChanName,
quitChanName);
// Create Channel Close Op
AddOp("channel_close", {{"Channel", {dataChanName}}}, {}, {}, block);
AddOp("channel_close", {{"Channel", {quitChanName}}}, {}, {}, block);
executor.Run(program, &scope, 0, true, true);
// After we call executor.run, "result" variable should be equal to 34
// (which is 10 loops through fibonacci sequence)
const LoDTensor &tensor = (scope.FindVar("currentXFib"))->Get<LoDTensor>();
auto *finalData = tensor.data<int>();
EXPECT_EQ(finalData[0], 34);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/executor.cc
浏览文件 @ f63ab561
......@@ -14,7 +14,6 @@ limitations under the License. */
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
......@@ -76,15 +75,13 @@ void InitializeVariable(Variable* var, proto::VarType::Type var_type) {
var->GetMutable<platform::PlaceList>();
} else if (var_type == proto::VarType::READER) {
var->GetMutable<ReaderHolder>();
} else if (var_type == proto::VarType::CHANNEL) {
var->GetMutable<ChannelHolder>();
} else if (var_type == proto::VarType::RAW) {
// GetMutable will be called in operator
} else {
PADDLE_THROW(
"Variable type %d is not in "
"[LOD_TENSOR, SELECTED_ROWS, FEED_MINIBATCH, FETCH_LIST, "
"LOD_RANK_TABLE, PLACE_LIST, READER, CHANNEL, RAW]",
"LOD_RANK_TABLE, PLACE_LIST, READER, RAW]",
var_type);
}
}
......
paddle/fluid/framework/framework.proto
浏览文件 @ f63ab561
......@@ -126,7 +126,6 @@ message VarType {
LOD_TENSOR_ARRAY = 13;
PLACE_LIST = 14;
READER = 15;
CHANNEL = 16;
// Any runtime decided variable type is raw
// raw variables should manage their own allocations
// in operators like nccl_op
......@@ -158,12 +157,6 @@ message VarType {
message ReaderDesc { repeated LoDTensorDesc lod_tensor = 1; }
optional ReaderDesc reader = 5;
message ChannelDesc {
required Type data_type = 1;
required int64 capacity = 2;
}
optional ChannelDesc channel = 6;
message Tuple { repeated Type element_type = 1; }
optional Tuple tuple = 7;
}
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ f63ab561
......@@ -34,6 +34,7 @@ endif ()
pass_library(attention_lstm_fuse_pass inference)
pass_library(infer_clean_graph_pass inference)
pass_library(fc_lstm_fuse_pass inference)
pass_library(embedding_fc_lstm_fuse_pass inference)
pass_library(fc_gru_fuse_pass inference)
pass_library(seq_concat_fc_fuse_pass inference)
......
paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.cc 0 → 100644
浏览文件 @ f63ab561
// 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.
#include "paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.h"
#include <algorithm>
#include <string>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace framework {
namespace ir {
static int BuildFusion(Graph* graph, const std::string& name_scope,
Scope* scope, bool with_fc_bias) {
GraphPatternDetector gpd;
auto* pattern = gpd.mutable_pattern();
// Build pattern
PDNode* x = pattern->NewNode(patterns::PDNodeName(name_scope, "x"))
->assert_is_op_input("lookup_table")
->assert_var_not_persistable();
patterns::Embedding embedding_pattern(pattern, name_scope);
// TODO(jczaja): Intermediate can only be for val that are not used anywhere
// but lookup table output may go into other LSTM (for reverse
// direction)
auto* embedding_out = embedding_pattern(x);
patterns::FC fc_pattern(pattern, name_scope);
// fc_out is a tmp var, will be removed after fuse, so marked as intermediate.
auto* fc_out = fc_pattern(embedding_out, with_fc_bias)->AsIntermediate();
patterns::LSTM lstm_pattern(pattern, name_scope);
lstm_pattern(fc_out);
// Create New OpDesc
auto embedding_lstm_creator = [&](Node* embedding, Node* W, Node* lstm,
Node* input, Node* weight_x, Node* weight_h,
Node* bias, Node* hidden, Node* cell,
Node* xx, Node* fc_bias) {
OpDesc op_desc;
op_desc.SetType("fused_embedding_fc_lstm");
#define SET_IN(Key, node__) op_desc.SetInput(#Key, {node__->Name()});
SET_IN(Ids, input);
SET_IN(WeightH, weight_h);
// Neet to have this passed as We need Wc data for peephole connections
SET_IN(Bias, bias);
#undef SET_IN
// Multiply embeddings with Weights
PADDLE_ENFORCE(scope);
const std::string& embeddings = patterns::UniqueKey("Embeddings");
auto* embeddings_var = scope->Var(embeddings);
PADDLE_ENFORCE(embeddings_var);
auto* embeddings_tensor =
embeddings_var->GetMutable<framework::LoDTensor>();
// Get WeightX size: [single_embedding, fc_size]
// and embedding size: [dict_size, single_embedding]
// and create new size of embeddings eg. [dict_size , hidden_size]
auto* embedding_var = scope->FindVar(W->Name());
PADDLE_ENFORCE(embedding_var);
const auto& embedding_tensor = embedding_var->Get<framework::LoDTensor>();
const auto& weightx_tensor =
scope->FindVar(weight_x->Name())->Get<framework::LoDTensor>();
embeddings_tensor->Resize(
{embedding_tensor.dims()[0], weightx_tensor.dims()[1]});
// Multiplie embeddings via WeightsX and add bias
auto embedding_data = embedding_tensor.data<float>();
auto weightx_data = weightx_tensor.data<float>();
auto embeddings_data =
embeddings_tensor->mutable_data<float>(platform::CPUPlace());
// Adding biases to GEMM result to be
auto* lstm_bias_var = scope->FindVar(bias->Name());
PADDLE_ENFORCE(lstm_bias_var);
const auto& lstm_bias_tensor = lstm_bias_var->Get<framework::LoDTensor>();
auto alpha = 1.0f;
auto beta = 1.0f;
int m = embedding_tensor.dims()[0];
int n = weightx_tensor.dims()[1];
int k = embedding_tensor.dims()[1];
// Copy only gate biases values (only actual bias data, not peephole
// weights)
std::vector<float> combined_biases;
combined_biases.reserve(n);
std::copy_n(lstm_bias_tensor.data<float>(), n,
std::back_inserter(combined_biases));
if (with_fc_bias) {
// Add FC-bias with LSTM-bias (into GEMM result to be)
auto* fc_bias_var = scope->FindVar(fc_bias->Name());
const auto& fc_bias_tensor = fc_bias_var->Get<framework::LoDTensor>();
for (int i = 0; i < fc_bias_tensor.numel(); i++) {
combined_biases[i] += fc_bias_tensor.data<float>()[i];
}
}
// broadcast biases
std::vector<float> ones(m, 1.0f);
paddle::operators::math::CBlas<float>::GEMM(
CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, 1, alpha, &ones[0], 1,
&combined_biases[0], n, 0.0f, embeddings_data, n);
// Wx*embeddings + biases
paddle::operators::math::CBlas<float>::GEMM(
CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, k, alpha,
embedding_data, k, weightx_data, n, beta, embeddings_data, n);
op_desc.SetInput("Embeddings", {embeddings});
// Create temp variables.
const std::string BatchedInput = patterns::UniqueKey("BatchedInput");
const std::string BatchedCellPreAct =
patterns::UniqueKey("BatchedCellPreAct");
const std::string BatchedGate = patterns::UniqueKey("BatchedGate");
scope->Var(BatchedInput)->GetMutable<framework::LoDTensor>();
scope->Var(BatchedCellPreAct)->GetMutable<framework::LoDTensor>();
scope->Var(BatchedGate)->GetMutable<framework::LoDTensor>();
op_desc.SetInput("H0", {});
op_desc.SetInput("C0", {});
op_desc.SetOutput("Hidden", {hidden->Name()});
op_desc.SetOutput("Cell", {cell->Name()});
op_desc.SetOutput("XX", {xx->Name()});
op_desc.SetOutput("BatchedGate", {BatchedGate});
op_desc.SetOutput("BatchCellPreAct", {BatchedCellPreAct});
op_desc.SetOutput("BatchedInput", {BatchedInput});
op_desc.SetAttr("is_reverse", lstm->Op()->GetAttr("is_reverse"));
op_desc.SetAttr("use_peepholes", lstm->Op()->GetAttr("use_peepholes"));
// TODO(TJ): get from attr
op_desc.SetAttr("use_seq", true);
PADDLE_ENFORCE(graph->Has(kParamScopeAttr));
auto* scope = graph->Get<Scope*>(kParamScopeAttr);
#define OP_SET_OUT(x) \
const std::string x = patterns::UniqueKey(#x); \
op_desc.SetOutput(#x, {x}); \
scope->Var(x)->GetMutable<LoDTensor>()
OP_SET_OUT(BatchedCell);
OP_SET_OUT(BatchedHidden);
OP_SET_OUT(ReorderedH0);
OP_SET_OUT(ReorderedC0);
#undef OP_SET_OUT
auto* op = graph->CreateOpNode(&op_desc);
IR_NODE_LINK_TO(input, op);
IR_NODE_LINK_TO(weight_x, op);
IR_NODE_LINK_TO(weight_h, op);
IR_NODE_LINK_TO(bias, op);
IR_NODE_LINK_TO(op, hidden);
return op;
};
int fusion_count{0};
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
GET_IR_NODE_FROM_SUBGRAPH(lstm, lstm, lstm_pattern);
GET_IR_NODE_FROM_SUBGRAPH(Weight, Weight, lstm_pattern);
GET_IR_NODE_FROM_SUBGRAPH(Bias, Bias, lstm_pattern);
GET_IR_NODE_FROM_SUBGRAPH(Cell, Cell, lstm_pattern);
GET_IR_NODE_FROM_SUBGRAPH(Hidden, Hidden, lstm_pattern);
GET_IR_NODE_FROM_SUBGRAPH(lookup_table, lookup_table, embedding_pattern);
GET_IR_NODE_FROM_SUBGRAPH(W, W, embedding_pattern);
GET_IR_NODE_FROM_SUBGRAPH(w, w, fc_pattern);
GET_IR_NODE_FROM_SUBGRAPH(mul, mul, fc_pattern);
// TODO(jczaja): Add support for is_sparse / is_distributed
auto is_sparse = boost::get<bool>(lookup_table->Op()->GetAttr("is_sparse"));
auto is_distributed =
boost::get<bool>(lookup_table->Op()->GetAttr("is_distributed"));
if (is_sparse == true || is_distributed == true) {
return;
}
if (with_fc_bias) {
GET_IR_NODE_FROM_SUBGRAPH(fc_out, Out, fc_pattern);
GET_IR_NODE_FROM_SUBGRAPH(fc_bias, bias, fc_pattern);
GET_IR_NODE_FROM_SUBGRAPH(elementwise_add, elementwise_add, fc_pattern);
embedding_lstm_creator(lookup_table, W, lstm, subgraph.at(x), w, Weight,
Bias, Hidden, Cell, fc_out, fc_bias);
// Remove unneeded nodes.
// TODO(jczaja): Proper removing of lookup table
std::unordered_set<const Node*> marked_nodes(
//{lookup_table, mul, lstm, elementwise_add, fc_bias, W});
{mul, lstm, elementwise_add, fc_bias});
GraphSafeRemoveNodes(graph, marked_nodes);
} else {
GET_IR_NODE_FROM_SUBGRAPH(fc_out, mul_out, fc_pattern);
embedding_lstm_creator(lookup_table, W, lstm, subgraph.at(x), w, Weight,
Bias, Hidden, Cell, fc_out, nullptr);
// Remove unneeded nodes.
// TODO(jczaja): Proper removing of lookup table
// std::unordered_set<const Node*> marked_nodes({lookup_table, W, mul,
// lstm});
std::unordered_set<const Node*> marked_nodes({mul, lstm});
GraphSafeRemoveNodes(graph, marked_nodes);
}
++fusion_count;
};
gpd(graph, handler);
return fusion_count;
}
std::unique_ptr<ir::Graph> EmbeddingFCLSTMFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
FusePassBase::Init(name_scope_, graph.get());
int fusion_count = BuildFusion(graph.get(), name_scope_, param_scope(),
true /*with_fc_bias*/);
AddStatis(fusion_count);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(embedding_fc_lstm_fuse_pass,
paddle::framework::ir::EmbeddingFCLSTMFusePass);
paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.h 0 → 100644
浏览文件 @ f63ab561
// 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 "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
// Fusing of Embedding , FC and LSTM op
// Just FC without bias
class EmbeddingFCLSTMFusePass : public FusePassBase {
public:
virtual ~EmbeddingFCLSTMFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
const std::string name_scope_{"embedding_fc_lstm_fuse"};
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ f63ab561
......@@ -692,6 +692,24 @@ PDNode *patterns::FC::operator()(paddle::framework::ir::PDNode *x,
}
}
PDNode *patterns::Embedding::operator()(PDNode *x) {
x->assert_is_op_input("lookup_table", "Ids");
auto *lookup_table_op =
pattern->NewNode(lookup_table_repr())->assert_is_op("lookup_table");
#define NEW_NODE(arg__, io__) \
auto *arg__ = pattern->NewNode(arg__##_repr()) \
->assert_is_op_##io__("lookup_table", #arg__);
NEW_NODE(W, input);
NEW_NODE(Out, output);
#undef NEW_NODE
lookup_table_op->LinksFrom({x, W});
lookup_table_op->LinksTo({Out});
return Out;
}
PDNode *patterns::LSTM::operator()(PDNode *x) {
x->assert_is_op_input("lstm", "Input");
auto *lstm_op = pattern->NewNode(lstm_repr())->assert_is_op("lstm");
......
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ f63ab561
......@@ -418,6 +418,23 @@ struct FC : public PatternBase {
PATTERN_DECL_NODE(Out);
};
// Embedding
struct Embedding : public PatternBase {
Embedding(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "embedding") {}
PDNode* operator()(PDNode* x);
// declare operator node's name
PATTERN_DECL_NODE(lookup_table);
// Inputs
//
PATTERN_DECL_NODE(Ids);
PATTERN_DECL_NODE(W); // embeddings
// Outputs
PATTERN_DECL_NODE(Out);
};
struct LSTM : public PatternBase {
LSTM(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "lstm") {}
......
paddle/fluid/framework/naive_executor.cc
浏览文件 @ f63ab561
......@@ -12,11 +12,13 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/framework/channel.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/string/pretty_log.h"
......@@ -44,8 +46,6 @@ static void InitializeVariable(Variable *var, proto::VarType::Type var_type) {
var->GetMutable<platform::PlaceList>();
} else if (var_type == proto::VarType::READER) {
var->GetMutable<ReaderHolder>();
} else if (var_type == proto::VarType::CHANNEL) {
var->GetMutable<ChannelHolder>();
} else if (var_type == proto::VarType::RAW) {
// GetMutable will be called in operator
} else {
......
paddle/fluid/framework/tuple.h
浏览文件 @ f63ab561
......@@ -17,7 +17,6 @@ limitations under the License. */
#include <stdexcept>
#include <string>
#include <vector>
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/var_desc.h"
......
paddle/fluid/framework/var_desc.cc
浏览文件 @ f63ab561
......@@ -88,13 +88,7 @@ std::vector<std::vector<int64_t>> VarDesc::GetShapes() const {
}
void VarDesc::SetDataType(proto::VarType::Type data_type) {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
mutable_channel_desc()->set_data_type(data_type);
break;
default:
mutable_tensor_desc()->set_data_type(data_type);
}
mutable_tensor_desc()->set_data_type(data_type);
}
void VarDesc::SetDataTypes(
......@@ -115,13 +109,7 @@ void VarDesc::SetDataTypes(
}
proto::VarType::Type VarDesc::GetDataType() const {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return channel_desc().data_type();
break;
default:
return tensor_desc().data_type();
}
return tensor_desc().data_type();
}
std::vector<proto::VarType::Type> VarDesc::GetDataTypes() const {
......@@ -134,17 +122,6 @@ std::vector<proto::VarType::Type> VarDesc::GetDataTypes() const {
return res;
}
void VarDesc::SetCapacity(int64_t capacity) {
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
desc_.mutable_type()->mutable_channel()->set_capacity(capacity);
break;
default:
PADDLE_THROW("Setting 'capacity' is not supported by the type of var %s.",
this->Name());
}
}
void VarDesc::SetLoDLevel(int32_t lod_level) {
switch (desc_.type().type()) {
case proto::VarType::LOD_TENSOR:
......@@ -214,19 +191,6 @@ std::vector<int32_t> VarDesc::GetLoDLevels() const {
}
}
const proto::VarType::ChannelDesc &VarDesc::channel_desc() const {
PADDLE_ENFORCE(desc_.has_type(), "The var's type hasn't been set.");
PADDLE_ENFORCE(desc_.type().has_type(), "The var type hasn't been set.");
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return desc_.type().channel();
default:
PADDLE_THROW(
"Getting 'channel_desc' is not supported by the type of var %s.",
this->Name());
}
}
const proto::VarType::TensorDesc &VarDesc::tensor_desc() const {
PADDLE_ENFORCE(desc_.has_type(), "The var's type hasn't been set.");
PADDLE_ENFORCE(desc_.type().has_type(), "The var type hasn't been set.");
......@@ -262,20 +226,6 @@ std::vector<proto::VarType::TensorDesc> VarDesc::tensor_descs() const {
}
}
proto::VarType::ChannelDesc *VarDesc::mutable_channel_desc() {
PADDLE_ENFORCE(desc_.has_type(), "The var type hasn't been set.");
PADDLE_ENFORCE(desc_.type().has_type(), "The var type hasn't been set.");
switch (desc_.type().type()) {
case proto::VarType::CHANNEL:
return desc_.mutable_type()->mutable_channel();
default:
PADDLE_THROW(
"Getting 'mutable_channel_desc' is not supported by the type of var "
"%s.",
this->Name());
}
}
proto::VarType::TensorDesc *VarDesc::mutable_tensor_desc() {
PADDLE_ENFORCE(desc_.has_type(), "The var type hasn't been set.");
PADDLE_ENFORCE(desc_.type().has_type(), "The var type hasn't been set.");
......
paddle/fluid/framework/var_desc.h
浏览文件 @ f63ab561
......@@ -87,8 +87,6 @@ class VarDesc {
void SetDataTypes(
const std::vector<proto::VarType::Type> &multiple_data_type);
void SetCapacity(int64_t capacity);
proto::VarType::Type GetDataType() const;
std::vector<proto::VarType::Type> GetDataTypes() const;
......@@ -110,10 +108,8 @@ class VarDesc {
void SetPersistable(bool persistable) { desc_.set_persistable(persistable); }
private:
const proto::VarType::ChannelDesc &channel_desc() const;
const proto::VarType::TensorDesc &tensor_desc() const;
std::vector<proto::VarType::TensorDesc> tensor_descs() const;
proto::VarType::ChannelDesc *mutable_channel_desc();
proto::VarType::TensorDesc *mutable_tensor_desc();
std::vector<proto::VarType::TensorDesc *> mutable_tensor_descs();
......
paddle/fluid/framework/var_type.h
浏览文件 @ f63ab561
......@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor.h"
......@@ -41,8 +40,6 @@ inline proto::VarType::Type ToVarType(std::type_index type) {
return proto::VarType_Type_SELECTED_ROWS;
} else if (IsType<ReaderHolder>(type)) {
return proto::VarType_Type_READER;
} else if (IsType<ChannelHolder>(type)) {
return proto::VarType_Type_CHANNEL;
} else {
PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
}
......@@ -66,9 +63,6 @@ inline void VisitVarType(const framework::Variable& var, Visitor visitor) {
case proto::VarType_Type_READER:
visitor(var.Get<ReaderHolder>());
return;
case proto::VarType_Type_CHANNEL:
visitor(var.Get<ChannelHolder>());
return;
default:
PADDLE_THROW("Not supported visit type, %d", ToVarType(var.Type()));
}
......
paddle/fluid/inference/analysis/analysis_pass.h
浏览文件 @ f63ab561
......@@ -41,12 +41,6 @@ class AnalysisPass {
// all passes have run.
virtual bool Finalize() { return false; }
// Get a Pass appropriate to print the Node this pass operates on.
virtual AnalysisPass *CreatePrinterPass(std::ostream &os,
const std::string &banner) const {
return nullptr;
}
// Create a debugger Pass that draw the DFG by graphviz toolkit.
virtual AnalysisPass *CreateGraphvizDebugerPass() const { return nullptr; }
......
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @ f63ab561
......@@ -64,14 +64,15 @@ class Analyzer : public OrderedRegistry<PassManager> {
// larger fusion.
const std::vector<std::string> all_ir_passes_{{
// Manual update the passes here.
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"fc_lstm_fuse_pass", //
"mul_lstm_fuse_pass", //
"fc_gru_fuse_pass", //
"mul_gru_fuse_pass", //
"seq_concat_fc_fuse_pass", //
"fc_fuse_pass", //
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"embedding_fc_lstm_fuse_pass", //
"fc_lstm_fuse_pass", //
"mul_lstm_fuse_pass", //
"fc_gru_fuse_pass", //
"mul_gru_fuse_pass", //
"seq_concat_fc_fuse_pass", //
"fc_fuse_pass", //
#ifdef PADDLE_WITH_MKLDNN
"conv_relu_mkldnn_fuse_pass", //
#endif
......
paddle/fluid/inference/api/api_impl_tester.cc
浏览文件 @ f63ab561
......@@ -21,6 +21,12 @@ limitations under the License. */
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/tests/test_helper.h"
#ifdef __clang__
#define ACC_DIFF 4e-3
#else
#define ACC_DIFF 1e-3
#endif
DEFINE_string(dirname, "", "Directory of the inference model.");
namespace paddle {
......@@ -99,8 +105,8 @@ void MainWord2Vec(bool use_gpu) {
float* lod_data = output1.data<float>();
for (int i = 0; i < output1.numel(); ++i) {
EXPECT_LT(lod_data[i] - data[i], 1e-3);
EXPECT_GT(lod_data[i] - data[i], -1e-3);
EXPECT_LT(lod_data[i] - data[i], ACC_DIFF);
EXPECT_GT(lod_data[i] - data[i], -ACC_DIFF);
}
}
......@@ -144,7 +150,7 @@ void MainImageClassification(bool use_gpu) {
float* data = static_cast<float*>(outputs[0].data.data());
float* lod_data = output1.data<float>();
for (size_t j = 0; j < len / sizeof(float); ++j) {
EXPECT_NEAR(lod_data[j], data[j], 1e-3);
EXPECT_NEAR(lod_data[j], data[j], ACC_DIFF);
}
}
......@@ -199,7 +205,7 @@ void MainThreadsWord2Vec(bool use_gpu) {
float* ref_data = refs[tid].data<float>();
EXPECT_EQ(refs[tid].numel(), static_cast<int64_t>(len / sizeof(float)));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
EXPECT_NEAR(ref_data[i], data[i], ACC_DIFF);
}
});
}
......@@ -251,7 +257,7 @@ void MainThreadsImageClassification(bool use_gpu) {
float* ref_data = refs[tid].data<float>();
EXPECT_EQ((size_t)refs[tid].numel(), len / sizeof(float));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
EXPECT_NEAR(ref_data[i], data[i], ACC_DIFF);
}
});
}
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ f63ab561
......@@ -263,7 +263,7 @@ struct AnalysisConfig : public NativeConfig {
bool enable_ir_optim = true;
// Manually determine the IR passes to run.
IrPassMode ir_mode{IrPassMode::kExclude};
std::vector<std::string> ir_passes;
std::vector<std::string> ir_passes{"embedding_fc_lstm_fuse_pass"};
// NOT stable yet.
bool use_feed_fetch_ops{true};
......
paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc
浏览文件 @ f63ab561
......@@ -104,5 +104,18 @@ TEST(Analyzer_Text_Classification, compare) {
CompareNativeAndAnalysis(cfg, input_slots_all);
}
TEST(Analyzer_Text_Classification, compare_against_embedding_fc_lstm_fused) {
AnalysisConfig cfg;
SetConfig(&cfg);
// Enable embedding_fc_lstm_fuse_pass (disabled by default)
auto it = std::find(cfg.ir_passes.begin(), cfg.ir_passes.end(),
"embedding_fc_lstm_fuse_pass");
if (it != cfg.ir_passes.end()) cfg.ir_passes.erase(it);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(cfg, input_slots_all);
}
} // namespace inference
} // namespace paddle
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ f63ab561
......@@ -314,11 +314,6 @@ op_library(save_combine_op DEPS lod_tensor)
op_library(load_combine_op DEPS lod_tensor)
op_library(concat_op DEPS concat)
# FIXME(thuan): Move CSP operators to paddle/fluid/framework/operators/concurrency
add_subdirectory(concurrency)
op_library(channel_send_op DEPS concurrency)
op_library(channel_recv_op DEPS concurrency)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
......
paddle/fluid/operators/channel_close_op.cc 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* Copyright (c) 2016 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. */
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/op_registry.h"
namespace pf = paddle::framework;
static constexpr char kChannel[] = "Channel";
namespace paddle {
namespace operators {
class ChannelCloseOp : public framework::OperatorBase {
public:
ChannelCloseOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &inp = *scope.FindVar(Input(kChannel));
// Get the mutable version of the channel variable and closes it.
pf::ChannelHolder *ch = inp.GetMutable<framework::ChannelHolder>();
ch->close();
}
};
class ChannelCloseOpOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("Channel"),
"The input of ChannelClose op must be set");
}
};
class ChannelCloseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(kChannel,
"The Channel Variable that should be closed by"
" the ChannelClose Op.");
AddComment(R"DOC(
Channel Close Operator.
This operator closes an open channel.
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(channel_close, paddle::operators::ChannelCloseOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::ChannelCloseOpMaker);
paddle/fluid/operators/channel_create_op.cc 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* Copyright (c) 2016 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. */
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
namespace pf = paddle::framework;
static constexpr char kOutput[] = "Out";
namespace paddle {
namespace operators {
class ChannelCreateOp : public framework::OperatorBase {
public:
ChannelCreateOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &out = *scope.FindVar(Output(kOutput));
// Determine the datatype and capacity of the channel to be created
// from the attributes provided.
auto dtype =
static_cast<framework::proto::VarType::Type>(Attr<int>("data_type"));
auto capacity = Attr<int>("capacity");
// Based on the datatype, create a new channel holder initialized with
// the given capacity. When capacity is 0, an unbuffered channel is
// created.
pf::ChannelHolder *ch = out.GetMutable<framework::ChannelHolder>();
if (dtype == framework::proto::VarType::LOD_TENSOR) {
ch->Reset<pf::LoDTensor>(capacity);
} else if (dtype == framework::proto::VarType::SELECTED_ROWS) {
ch->Reset<pf::SelectedRows>(capacity);
} else if (dtype == framework::proto::VarType::LOD_RANK_TABLE) {
ch->Reset<pf::LoDRankTable>(capacity);
} else if (dtype == framework::proto::VarType::LOD_TENSOR_ARRAY) {
ch->Reset<pf::LoDTensorArray>(capacity);
} else if (dtype == framework::proto::VarType::READER) {
ch->Reset<pf::ReaderHolder>(capacity);
} else if (dtype == framework::proto::VarType::CHANNEL) {
ch->Reset<pf::ChannelHolder>(capacity);
} else if (dtype == framework::proto::VarType::BOOL) {
ch->Reset<bool>(capacity);
} else if (dtype == framework::proto::VarType::INT32) {
ch->Reset<int>(capacity);
} else if (dtype == framework::proto::VarType::INT64) {
ch->Reset<int64_t>(capacity);
} else if (dtype == framework::proto::VarType::FP32) {
ch->Reset<float>(capacity);
} else if (dtype == framework::proto::VarType::FP64) {
ch->Reset<double>(capacity);
} else {
PADDLE_THROW(
"Data type %d is not in "
"[LOD_TENSOR, SELECTED_ROWS, LOD_RANK_TABLE, LOD_TENSOR_ARRAY, "
"READER, CHANNEL, BOOL, INT32, INT64, FP32, FP64]",
dtype);
}
}
};
class ChannelCreateOpOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasOutput(kOutput),
"The output of ChannelCreate op must be set");
context->SetOutputDim(kOutput, {1});
}
};
class ChannelCreateOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput(kOutput,
"The object of a Channel type created by ChannelCreate Op.");
AddAttr<int>("capacity", "The size of the buffer of Channel.")
.SetDefault(0);
AddAttr<int>("data_type", "The data type of elements inside the Channel.");
AddComment(R"DOC(
Channel Create Operator.
This operator creates an object of the VarType Channel and returns it.
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(channel_create, paddle::operators::ChannelCreateOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::ChannelCreateOpMaker);
paddle/fluid/operators/channel_recv_op.cc 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* Copyright (c) 2016 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. */
#include "paddle/fluid/framework/channel.h"
#include <paddle/fluid/framework/lod_rank_table.h>
#include <paddle/fluid/framework/lod_tensor_array.h>
#include <paddle/fluid/framework/reader.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/concurrency/channel_util.h"
#include "paddle/fluid/operators/math/math_function.h"
static constexpr char Channel[] = "Channel";
static constexpr char Status[] = "Status";
static constexpr char Out[] = "Out";
namespace paddle {
namespace operators {
void SetReceiveStatus(const platform::Place &dev_place,
framework::Variable *status_var, bool status) {
auto cpu = platform::CPUPlace();
auto status_tensor =
status_var->GetMutable<framework::LoDTensor>()->mutable_data<bool>({1},
cpu);
status_tensor[0] = status;
}
class ChannelRecvOp : public framework::OperatorBase {
public:
ChannelRecvOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const {
PADDLE_ENFORCE(ctx->HasInput(Channel),
"Input(Channel) of ChannelRecvOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(Out),
"Input(Channel) of ChannelRecvOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(Status),
"Output(Status) of ChannelRecvOp should not be null.");
ctx->SetOutputDim("Status", {1});
}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// Get the channel holder created by channel_create op, passed as input.
framework::ChannelHolder *ch =
scope.FindVar(Input(Channel))->GetMutable<framework::ChannelHolder>();
auto output_var = scope.FindVar(Output(Out));
// Receive the data from the channel.
bool ok = concurrency::ChannelReceive(ch, output_var);
// Set the status output of the `ChannelReceive` call.
SetReceiveStatus(dev_place, scope.FindVar(Output(Status)), ok);
}
};
class ChannelRecvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(Channel,
"(Channel) A variable which \"receives\" the a value sent"
"to it by a channel_send op.")
.AsDuplicable();
AddOutput(Out,
"(Variable) Output Variable that will hold the data received"
" from the Channel")
.AsDuplicable();
AddOutput(Status,
"(Tensor) An LoD Tensor that returns a boolean status of the"
"result of the receive operation.")
.AsDuplicable();
AddComment(R"DOC(
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(channel_recv, paddle::operators::ChannelRecvOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::ChannelRecvOpMaker);
paddle/fluid/operators/channel_send_op.cc 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* Copyright (c) 2016 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. */
#include "paddle/fluid/framework/channel.h"
#include <paddle/fluid/framework/lod_rank_table.h>
#include <paddle/fluid/framework/lod_tensor_array.h>
#include <paddle/fluid/framework/reader.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/concurrency/channel_util.h"
#include "paddle/fluid/operators/math/math_function.h"
static constexpr char Channel[] = "Channel";
static constexpr char X[] = "X";
namespace paddle {
namespace operators {
class ChannelSendOp : public framework::OperatorBase {
public:
ChannelSendOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const {
PADDLE_ENFORCE(ctx->HasInput(Channel),
"Input(Channel) of ChannelSendOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput(X),
"Input(X) of ChannelSendOp should not be null.");
}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// Get the channel holder created by channel_create op, passed as input.
framework::ChannelHolder *ch =
scope.FindVar(Input(Channel))->GetMutable<framework::ChannelHolder>();
auto input_var = scope.FindVar(Input(X));
// Send the input data through the channel.
concurrency::ChannelSend(ch, input_var);
}
};
class ChannelSendOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(Channel,
"(Channel) A variable which \"sends\" the passed in value to "
"a listening receiver.")
.AsDuplicable();
AddInput(X, "(Variable) The value which gets sent by the channel.")
.AsDuplicable();
AddComment(R"DOC(
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(channel_send, paddle::operators::ChannelSendOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::ChannelSendOpMaker);
paddle/fluid/operators/concurrency/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ 8f5d918a
cc_library(concurrency SRCS channel_util.cc DEPS device_context framework_proto boost eigen3)
paddle/fluid/operators/concurrency/channel_util.cc 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* Copyright (c) 2016 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. */
#include "paddle/fluid/operators/concurrency/channel_util.h"
#include "paddle/fluid/framework/var_type.h"
namespace poc = paddle::operators::concurrency;
void poc::ChannelSend(framework::ChannelHolder *ch, framework::Variable *var) {
auto type = framework::ToVarType(var->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR)
ch->Send(var->GetMutable<framework::LoDTensor>());
else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE)
ch->Send(var->GetMutable<framework::LoDRankTable>());
else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY)
ch->Send(var->GetMutable<framework::LoDTensorArray>());
else if (type == framework::proto::VarType_Type_SELECTED_ROWS)
ch->Send(var->GetMutable<framework::SelectedRows>());
else if (type == framework::proto::VarType_Type_READER)
ch->Send(var->GetMutable<framework::ReaderHolder>());
else if (type == framework::proto::VarType_Type_CHANNEL)
ch->Send(var->GetMutable<framework::ChannelHolder>());
else
PADDLE_THROW("ChannelSend:Unsupported type");
}
bool poc::ChannelReceive(framework::ChannelHolder *ch,
framework::Variable *var) {
// Get type of channel and use that to call mutable data for Variable
auto type = framework::ToVarType(ch->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR)
return ch->Receive(var->GetMutable<framework::LoDTensor>());
else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE)
return ch->Receive(var->GetMutable<framework::LoDRankTable>());
else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY)
return ch->Receive(var->GetMutable<framework::LoDTensorArray>());
else if (type == framework::proto::VarType_Type_SELECTED_ROWS)
return ch->Receive(var->GetMutable<framework::SelectedRows>());
else if (type == framework::proto::VarType_Type_READER)
return ch->Receive(var->GetMutable<framework::ReaderHolder>());
else if (type == framework::proto::VarType_Type_CHANNEL)
return ch->Receive(var->GetMutable<framework::ChannelHolder>());
else
PADDLE_THROW("ChannelReceive:Unsupported type");
}
void poc::ChannelAddToSendQ(framework::ChannelHolder *ch, const void *referrer,
framework::Variable *var,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(framework::ChannelAction)> cb) {
auto type = framework::ToVarType(var->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR) {
ch->AddToSendQ(referrer, var->GetMutable<framework::LoDTensor>(), cond, cb);
} else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE) {
ch->AddToSendQ(referrer, var->GetMutable<framework::LoDRankTable>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY) {
ch->AddToSendQ(referrer, var->GetMutable<framework::LoDTensorArray>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_SELECTED_ROWS) {
ch->AddToSendQ(referrer, var->GetMutable<framework::SelectedRows>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_READER) {
ch->AddToSendQ(referrer, var->GetMutable<framework::ReaderHolder>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_CHANNEL) {
ch->AddToSendQ(referrer, var->GetMutable<framework::ChannelHolder>(), cond,
cb);
} else {
PADDLE_THROW("ChannelAddToSendQ:Unsupported type");
}
}
void poc::ChannelAddToReceiveQ(
framework::ChannelHolder *ch, const void *referrer,
framework::Variable *var, std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(framework::ChannelAction)> cb) {
auto type = framework::ToVarType(var->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDTensor>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDRankTable>(),
cond, cb);
} else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDTensorArray>(),
cond, cb);
} else if (type == framework::proto::VarType_Type_SELECTED_ROWS) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::SelectedRows>(),
cond, cb);
} else if (type == framework::proto::VarType_Type_READER) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::ReaderHolder>(),
cond, cb);
} else if (type == framework::proto::VarType_Type_CHANNEL) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::ChannelHolder>(),
cond, cb);
} else {
PADDLE_THROW("ChannelAddToReceiveQ:Unsupported type");
}
}
paddle/fluid/operators/distributed/grpc_client.h
浏览文件 @ f63ab561
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <time.h>
#include <atomic>
#include <chrono> // NOLINT
#include <condition_variable> // NOLINT
......
paddle/fluid/operators/distributed/request_handler.h
浏览文件 @ f63ab561
......@@ -15,6 +15,7 @@
#pragma once
#include <time.h>
#include <condition_variable> // NOLINT
#include <functional>
#include <string>
......
paddle/fluid/operators/distributed/rpc_server.h
浏览文件 @ f63ab561
......@@ -14,6 +14,7 @@
#pragma once
#include <atomic>
#include <set>
#include <string>
#include <thread> // NOLINT
......
paddle/fluid/operators/fused_embedding_fc_lstm_op.cc 0 → 100644
浏览文件 @ f63ab561
此差异已折叠。
paddle/fluid/operators/concurrency/channel_util.h → paddle/fluid/operators/fused_embedding_fc_lstm_op.h
浏览文件 @ f63ab561
......@@ -4,7 +4,7 @@ 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
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,
......@@ -13,26 +13,29 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
namespace concurrency {
void ChannelSend(framework::ChannelHolder *ch, framework::Variable *var);
bool ChannelReceive(framework::ChannelHolder *ch, framework::Variable *var);
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
class FusedEmbeddingFCLSTMOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
void ChannelAddToSendQ(framework::ChannelHolder *ch, const void *referrer,
framework::Variable *var,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(framework::ChannelAction)> cb);
void ChannelAddToReceiveQ(framework::ChannelHolder *ch, const void *referrer,
framework::Variable *var,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(framework::ChannelAction)> cb);
class FusedEmbeddingFCLSTMOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace concurrency
} // namespace operators
} // namespace paddle
paddle/fluid/operators/select_op.cc 已删除 100644 → 0
浏览文件 @ 8f5d918a
/* Copyright (c) 2016 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. */
#include <memory>
#include <thread> // NOLINT
#include <vector>
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/concurrency/channel_util.h"
#include <boost/tokenizer.hpp>
namespace paddle {
namespace operators {
static constexpr char kX[] = "X";
static constexpr char kCaseToExecute[] = "case_to_execute";
static constexpr char kOutputs[] = "Out";
static constexpr char kCases[] = "cases";
static constexpr char kCasesBlock[] = "sub_block";
class SelectOp : public framework::OperatorBase {
public:
SelectOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
enum class SelectOpCaseType {
DEFAULT = 0,
SEND = 1,
RECEIVE = 2,
};
struct SelectOpCase {
int caseIndex;
SelectOpCaseType caseType;
std::string channelName;
std::string varName;
SelectOpCase() {}
SelectOpCase(int caseIndex, SelectOpCaseType caseType,
std::string channelName, std::string varName)
: caseIndex(caseIndex),
caseType(caseType),
channelName(channelName),
varName(varName) {}
};
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
std::vector<std::string> casesConfigs =
Attr<std::vector<std::string>>(kCases);
framework::BlockDesc *casesBlock =
Attr<framework::BlockDesc *>(kCasesBlock);
framework::Scope &casesBlockScope = scope.NewScope();
std::string caseToExecuteVarName = Input(kCaseToExecute);
framework::Variable *caseToExecuteVar =
casesBlockScope.FindVar(caseToExecuteVarName);
// Construct cases from "conditional_block_op"(s) in the casesBlock
std::vector<std::shared_ptr<SelectOpCase>> cases =
ParseAndShuffleCases(&casesConfigs);
// Get all unique channels involved in select
std::set<framework::ChannelHolder *> channelsSet;
for (auto c : cases) {
if (!c->channelName.empty()) {
auto channelVar = scope.FindVar(c->channelName);
framework::ChannelHolder *ch =
channelVar->GetMutable<framework::ChannelHolder>();
if (channelsSet.find(ch) == channelsSet.end()) {
channelsSet.insert(ch);
}
}
}
// Order all channels by their pointer address
std::vector<framework::ChannelHolder *> channels(channelsSet.begin(),
channelsSet.end());
std::sort(channels.begin(), channels.end());
// Poll all cases
int32_t caseToExecute = pollCases(&scope, &cases, channels);
// At this point, the case to execute has already been determined,
// so we can proceed with executing the cases block
framework::LoDTensor *caseToExecuteTensor =
caseToExecuteVar->GetMutable<framework::LoDTensor>();
caseToExecuteTensor->data<int32_t>()[0] = caseToExecute;
// Execute the cases block, only one case will be executed since we set the
// case_to_execute value to the index of the case we want to execute
framework::Executor executor(dev_place);
framework::ProgramDesc *program = casesBlock->Program();
executor.Run(*program, &casesBlockScope, casesBlock->ID(),
false /*create_local_scope*/);
}
/**
* Goes through all operators in the casesConfigs and processes
* "conditional_block" operators. These operators are mapped to our
* SelectOpCase objects. We randomize the case orders, and set the
* default case (if any exists) as the last case)
* @param casesBlock
* @return
*/
std::vector<std::shared_ptr<SelectOpCase>> ParseAndShuffleCases(
std::vector<std::string> *casesConfigs) const {
std::vector<std::shared_ptr<SelectOpCase>> cases;
std::shared_ptr<SelectOpCase> defaultCase;
if (casesConfigs != nullptr) {
boost::char_delimiters_separator<char> sep(false, ",", "");
for (std::vector<std::string>::iterator itr = casesConfigs->begin();
itr < casesConfigs->end(); ++itr) {
std::string caseConfig = *itr;
boost::tokenizer<> tokens(caseConfig, sep);
boost::tokenizer<>::iterator tok_iter = tokens.begin();
PADDLE_ENFORCE(tok_iter != tokens.end(), "Cannot get case index");
std::string caseIndexString = *tok_iter;
int caseIndex = std::stoi(caseIndexString);
++tok_iter;
PADDLE_ENFORCE(tok_iter != tokens.end(), "Cannot get case type");
std::string caseTypeString = *tok_iter;
SelectOpCaseType caseType = (SelectOpCaseType)std::stoi(caseTypeString);
std::string caseChannel;
std::string caseChannelVar;
++tok_iter;
if (caseType != SelectOpCaseType::DEFAULT) {
PADDLE_ENFORCE(tok_iter != tokens.end(), "Cannot get case channel");
caseChannel = *tok_iter;
++tok_iter;
PADDLE_ENFORCE(tok_iter != tokens.end(),
"Cannot get case channel variable");
caseChannelVar = *tok_iter;
}
auto c = std::make_shared<SelectOpCase>(caseIndex, caseType,
caseChannel, caseChannelVar);
if (caseType == SelectOpCaseType::DEFAULT) {
PADDLE_ENFORCE(defaultCase == nullptr,
"Select can only contain one default case.");
defaultCase = c;
} else {
cases.push_back(c);
}
}
}
// Randomly sort cases, with default case being last
std::random_shuffle(cases.begin(), cases.end());
if (defaultCase != nullptr) {
cases.push_back(defaultCase);
}
return cases;
}
/**
* This method will recursively poll the cases and determines if any case
* condition is true.
* If none of the cases conditions are true (and there is no default case),
* then block
* the thread. The thread may be woken up by a channel operation, at which
* point we
* execute the case.
* @param scope
* @param cases
* @param channels
* @return
*/
int32_t pollCases(const framework::Scope *scope,
std::vector<std::shared_ptr<SelectOpCase>> *cases,
std::vector<framework::ChannelHolder *> channels) const {
// Lock all involved channels
lockChannels(channels);
std::atomic<int> caseToExecute(-1);
std::vector<std::shared_ptr<SelectOpCase>>::iterator it = cases->begin();
while (it != cases->end()) {
std::shared_ptr<SelectOpCase> c = *it;
auto chVar = scope->FindVar(c->channelName);
framework::ChannelHolder *ch =
chVar->GetMutable<framework::ChannelHolder>();
switch (c->caseType) {
case SelectOpCaseType::SEND:
PADDLE_ENFORCE(!ch->IsClosed(), "Cannot send to a closed channel");
if (ch->CanSend()) {
// We can send to channel directly, send the data to channel
// and execute case
auto chVar = scope->FindVar(c->varName);
concurrency::ChannelSend(ch, chVar);
caseToExecute = c->caseIndex;
}
break;
case SelectOpCaseType::RECEIVE:
if (ch->CanReceive()) {
// We can receive from channel directly, send the data to channel
// and execute case
auto chVar = scope->FindVar(c->varName);
concurrency::ChannelReceive(ch, chVar);
caseToExecute = c->caseIndex;
}
break;
case SelectOpCaseType::DEFAULT:
caseToExecute = c->caseIndex;
break;
}
if (caseToExecute != -1) {
// We found a case to execute, stop looking at other case statements
break;
}
++it;
}
if (caseToExecute == -1) {
// None of the cases are eligible to execute, enqueue current thread
// into all the sending/receiving queue of each involved channel
std::atomic<bool> completed(false);
std::recursive_mutex mutex;
std::unique_lock<std::recursive_mutex> lock{mutex};
// std::condition_variable_any selectCond;
auto selectCond = std::make_shared<std::condition_variable_any>();
std::recursive_mutex callbackMutex;
pushThreadOnChannelQueues(scope, cases, selectCond, &caseToExecute,
&completed, &callbackMutex);
// TODO(thuan): Atomically unlock all channels and sleep current thread
unlockChannels(channels);
selectCond->wait(lock, [&completed]() { return completed.load(); });
// Select has been woken up by case operation
lockChannels(channels);
removeThreadOnChannelQueues(scope, cases);
if (caseToExecute == -1) {
// Recursively poll cases, since we were woken up by a channel close
// TODO(thuan): Need to test if this is a valid case
unlockChannels(channels);
return pollCases(scope, cases, channels);
}
}
// At this point, caseToExecute != -1, and we can proceed with executing
// the case block
unlockChannels(channels);
return caseToExecute;
}
void lockChannels(std::vector<framework::ChannelHolder *> chs) const {
std::vector<framework::ChannelHolder *>::iterator it = chs.begin();
while (it != chs.end()) {
framework::ChannelHolder *ch = *it;
ch->Lock();
++it;
}
}
void unlockChannels(std::vector<framework::ChannelHolder *> chs) const {
std::vector<framework::ChannelHolder *>::reverse_iterator it = chs.rbegin();
while (it != chs.rend()) {
framework::ChannelHolder *ch = *it;
ch->Unlock();
++it;
}
}
void pushThreadOnChannelQueues(
const framework::Scope *scope,
std::vector<std::shared_ptr<SelectOpCase>> *cases,
std::shared_ptr<std::condition_variable_any> rCond,
std::atomic<int> *caseToExecute, std::atomic<bool> *completed,
std::recursive_mutex *callbackMutex) const {
std::vector<std::shared_ptr<SelectOpCase>>::iterator it = cases->begin();
while (it != cases->end()) {
std::shared_ptr<SelectOpCase> c = *it;
auto chVar = scope->FindVar(c->channelName);
framework::ChannelHolder *ch =
chVar->GetMutable<framework::ChannelHolder>();
std::function<bool(framework::ChannelAction channelAction)> cb =
[&caseToExecute, &completed, &callbackMutex,
c](framework::ChannelAction channelAction) {
std::lock_guard<std::recursive_mutex> lock{*callbackMutex};
bool canProcess = false;
if (!(*completed)) {
// If the channel wasn't closed, we set the caseToExecute index
// as this current case
if (channelAction != framework::ChannelAction::CLOSE) {
*caseToExecute = c->caseIndex;
}
// This will allow our conditional variable to break out of wait
*completed = true;
canProcess = true;
}
return canProcess;
};
switch (c->caseType) {
case SelectOpCaseType::SEND: {
auto chOutputVar = scope->FindVar(c->varName);
concurrency::ChannelAddToSendQ(ch, this, chOutputVar, rCond, cb);
break;
}
case SelectOpCaseType::RECEIVE: {
auto chOutputVar = scope->FindVar(c->varName);
concurrency::ChannelAddToReceiveQ(ch, this, chOutputVar, rCond, cb);
break;
}
default:
break;
}
++it;
}
}
void removeThreadOnChannelQueues(
const framework::Scope *scope,
std::vector<std::shared_ptr<SelectOpCase>> *cases) const {
std::vector<std::shared_ptr<SelectOpCase>>::iterator it = cases->begin();
while (it != cases->end()) {
std::shared_ptr<SelectOpCase> c = *it;
auto chVar = scope->FindVar(c->channelName);
framework::ChannelHolder *ch =
chVar->GetMutable<framework::ChannelHolder>();
switch (c->caseType) {
case SelectOpCaseType::SEND: {
ch->RemoveFromSendQ(this);
break;
}
case SelectOpCaseType::RECEIVE: {
ch->RemoveFromReceiveQ(this);
break;
}
default:
break;
}
++it;
}
}
};
class SelectOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(kX,
"A set of variables, which are required by operators inside the "
"cases of Select Op")
.AsDuplicable();
AddInput(kCaseToExecute,
"(Int) The variable the sets the index of the case to execute, "
"after evaluating the channels being sent to and received from")
.AsDuplicable();
AddOutput(kOutputs,
"A set of variables, which will be assigned with values "
"generated by the operators inside the cases of Select Op.")
.AsDuplicable();
AddAttr<std::vector<std::string>>(kCases,
"(String vector) Serialized list of"
"all cases in the select op. Each"
"case is serialized as: "
"'<index>,<type>,<channel>,<value>'"
"where type is 0 for default, 1 for"
"send, and 2 for receive"
"No channel and values are needed for"
"default cases.");
AddAttr<framework::BlockDesc *>(kCasesBlock,
"The cases block inside select_op");
AddComment(R"DOC(
)DOC");
}
};
// TODO(thuan): Implement Gradient Operator for SELECT_OP
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(select, paddle::operators::SelectOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::SelectOpMaker);
paddle/fluid/pybind/protobuf.cc
浏览文件 @ f63ab561
......@@ -214,7 +214,6 @@ void BindVarDsec(pybind11::module *m) {
.def("set_shapes", &pd::VarDesc::SetShapes)
.def("set_dtype", &pd::VarDesc::SetDataType)
.def("set_dtypes", &pd::VarDesc::SetDataTypes)
.def("set_capacity", &pd::VarDesc::SetCapacity)
.def("shape", &pd::VarDesc::GetShape,
pybind11::return_value_policy::reference)
.def("shapes", &pd::VarDesc::GetShapes,
......@@ -251,7 +250,6 @@ void BindVarDsec(pybind11::module *m) {
.value("STEP_SCOPES", pd::proto::VarType::STEP_SCOPES)
.value("LOD_RANK_TABLE", pd::proto::VarType::LOD_RANK_TABLE)
.value("LOD_TENSOR_ARRAY", pd::proto::VarType::LOD_TENSOR_ARRAY)
.value("CHANNEL", pd::proto::VarType::CHANNEL)
.value("PLACE_LIST", pd::proto::VarType::PLACE_LIST)
.value("READER", pd::proto::VarType::READER)
.value("RAW", pd::proto::VarType::RAW);
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ f63ab561
......@@ -21,7 +21,6 @@ limitations under the License. */
#include <utility>
#include <vector>
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/framework.pb.h"
......
paddle/legacy/trainer/tests/CMakeLists.txt
浏览文件 @ f63ab561
......@@ -16,7 +16,11 @@ endfunction()
trainer_test(test_Compare)
trainer_test(test_PyDataProviderWrapper)
trainer_test(test_recurrent_machine_generation)
trainer_test(test_Trainer)
if(NOT APPLE)
trainer_test(test_Trainer)
else()
message(WARNING "These tests has been disabled in OSX for random fail: \n test_Trainer")
endif()
############### test_TrainerOnePass ##########################
if(WITH_PYTHON)
......
python/paddle/fluid/concurrency.py 已删除 100644 → 0
浏览文件 @ 8f5d918a
# 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.
from __future__ import print_function
from .layers.control_flow import BlockGuard, equal
from .framework import Operator
from .layer_helper import LayerHelper, unique_name
from .layers import fill_constant
from . import core
__all__ = [
'make_channel', 'channel_send', 'channel_recv', 'channel_close', 'Select'
]
class Go(BlockGuard):
def __init__(self, name=None):
self.helper = LayerHelper("go", name=name)
super(Go, self).__init__(self.helper.main_program)
def __enter__(self):
super(Go, self).__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
self._construct_go_op()
return super(Go, self).__exit__(exc_type, exc_val, exc_tb)
def _construct_go_op(self):
main_program = self.helper.main_program
go_block = main_program.current_block()
parent_block = main_program.block(main_program.current_block()
.parent_idx)
inner_outputs = set()
x_name_list = set()
for op in go_block.ops:
# Iterate over all operators, get all the inputs
# and add as input to the Go operator.
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in inner_outputs:
x_name_list.add(in_var_name)
for oname in op.output_names:
for out_var_name in op.output(oname):
inner_outputs.add(out_var_name)
# Iterate over all operators , get all the outputs
# add to the output list of Go operator only if
# they exist in the parent block.
out_vars = []
for inner_out_name in inner_outputs:
if inner_out_name in parent_block.vars:
out_vars.append(parent_block.var(inner_out_name))
parent_block.append_op(
type='go',
inputs={
'X': [
parent_block._var_recursive(x_name)
for x_name in x_name_list
]
},
outputs={},
attrs={'sub_block': go_block})
class SelectCase(object):
DEFAULT = 0
SEND = 1
RECEIVE = 2
def __init__(self,
select,
case_idx,
case_to_execute,
channel_action_fn=None,
channel=None,
value=None,
is_copy=False):
self.select = select
self.helper = LayerHelper('conditional_block')
self.main_program = self.helper.main_program
self.is_scalar_condition = True
self.case_to_execute = case_to_execute
self.idx = case_idx
# Since we aren't going to use the `channel_send` or `channel_recv`
# functions directly, we just need to capture the name.
self.action = (self.SEND
if channel_action_fn.__name__ == ('channel_send') else
self.RECEIVE) if channel_action_fn else self.DEFAULT
X = value
if self.action == self.SEND and is_copy:
# We create of copy of the data we want to send
copied_X = self.select.parent_block.create_var(
name=unique_name.generate(value.name + '_copy'),
type=value.type,
dtype=value.dtype,
shape=value.shape,
lod_level=value.lod_level,
capacity=value.capacity
if hasattr(value, 'capacity') else None, )
self.select.parent_block.append_op(
type="assign", inputs={"X": value}, outputs={"Out": copied_X})
X = copied_X
self.value = X
self.channel = channel
def __enter__(self):
self.block = self.main_program._create_block()
def construct_op(self):
main_program = self.helper.main_program
cases_block = main_program.current_block()
inner_outputs = set()
input_set = set()
params = set()
for op in self.block.ops:
# Iterate over all operators, get all the inputs
# and add as input to the SelectCase operator.
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in inner_outputs:
input_set.add(in_var_name)
for oname in op.output_names:
for out_var_name in op.output(oname):
inner_outputs.add(out_var_name)
param_list = [
cases_block.var(each_name) for each_name in params
if each_name not in input_set
]
# Iterate over all operators, get all the outputs
# add to the output list of SelectCase operator only if
# they exist in the parent block.
out_vars = []
for inner_out_name in inner_outputs:
if inner_out_name in cases_block.vars:
out_vars.append(cases_block.var(inner_out_name))
# First, create an op that will determine whether or not this is the
# conditional variable to execute.
should_execute_block = equal(
fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=self.idx),
self.case_to_execute)
step_scope = cases_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
cases_block.append_op(
type='conditional_block',
inputs={'X': [should_execute_block],
'Params': param_list},
outputs={'Out': out_vars,
'Scope': [step_scope]},
attrs={
'sub_block': self.block,
'is_scalar_condition': self.is_scalar_condition
})
return '%s,%s,%s,%s' % (self.idx, self.action, self.channel.name
if self.channel else '', self.value.name
if self.value else '')
def __exit__(self, exc_type, exc_val, exc_tb):
self.main_program._rollback()
if exc_type is not None:
return False # re-raise exception
return True
class Select(BlockGuard):
def __init__(self, name=None):
self.helper = LayerHelper('select', name=name)
self.parent_block = self.helper.main_program.current_block()
self.cases = []
super(Select, self).__init__(self.helper.main_program)
self.case_to_execute = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=-1)
def __enter__(self):
super(Select, self).__enter__()
return self
def case(self, channel_action_fn, channel, value, is_copy=False):
"""Create a new block for this condition.
"""
select_case = SelectCase(self,
len(self.cases), self.case_to_execute,
channel_action_fn, channel, value, is_copy)
self.cases.append(select_case)
return select_case
def default(self):
"""Create a default case block for this condition.
"""
default_case = SelectCase(self, len(self.cases), self.case_to_execute)
self.cases.append(default_case)
return default_case
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
# Create a select op and another block to wrap its
# case blocks.
select_block = self.helper.main_program.current_block()
parent_block = self.helper.main_program.block(select_block.parent_idx)
# Construct each case op, inside the newly created select block.
serialized_cases = []
for case in self.cases:
serialized_cases.append(case.construct_op())
intermediate = set()
params = set()
for case_block in select_block.ops:
if case_block.attrs and 'sub_block' in case_block.attrs:
for each_op in case_block.attrs['sub_block'].ops:
assert isinstance(each_op, Operator)
for iname in each_op.input_names:
for in_var_name in each_op.input(iname):
if in_var_name not in intermediate:
params.add(in_var_name)
for oname in each_op.output_names:
for out_var_name in each_op.output(oname):
intermediate.add(out_var_name)
out_list = [
parent_block.var(var_name) for var_name in parent_block.vars
if var_name in intermediate
]
X = [select_block._var_recursive(x_name) for x_name in params]
# Needs to be used by `equal` inside the cases block.
X.append(self.case_to_execute)
# Construct the select op.
parent_block.append_op(
type='select',
inputs={'X': X,
'case_to_execute': self.case_to_execute},
attrs={'sub_block': select_block,
'cases': serialized_cases},
outputs={'Out': out_list})
return super(Select, self).__exit__(exc_type, exc_val, exc_tb)
def make_channel(dtype, capacity=0):
"""
Helps implementation of a concurrent program by creating a "channel" of
a defined data type. Channels allow for the passing of data in
concurrent scenarios - such as when using threads to divide computation.
Channels can be used to "send" and "receive" such data concurrently.
There are two kinds of channels: unbuffered and buffered. Unbuffered
channels have no capacity - and thus, block on send and only unblock only
once what they have sent has been received.
On the other hand, buffered channels are initialized with a capacity -
and do not block on sends.
Use this method in combination with `channel_send`, `channel_recv`,
`channel_close`, and `Go` to design a concurrent Paddle program.
Args:
dtype (ParamAttr|string): Data type of the data sent in the channel.
This data type should be the string name of a numpy data type.
capacity (ParamAttr|int): Size of the channel. Defaults to 0 for
to create an unbuffered channel.
Returns:
Variable: The channel variable that can be used to send an receive data
of the defined dtype.
Examples:
.. code-block:: python
ch = fluid.make_channel(dtype='int32', capacity=10)
...
# Code to execute in a Go block, which receives the channel data.
fluid.channel_send(ch, 100)
fluid.channel_close(ch)
"""
helper = LayerHelper('channel_create', **locals())
main_program = helper.main_program
make_channel_block = main_program.current_block()
# Make a channel variable (using the channel data type) and make sure it
# persists into the global scope.
channel = helper.create_variable(
name=unique_name.generate('channel'),
type=core.VarDesc.VarType.CHANNEL,
persistable=True)
create_channel_op = make_channel_block.append_op(
type="channel_create",
outputs={"Out": channel},
attrs={"data_type": dtype,
"capacity": capacity})
return channel
def channel_send(channel, value, is_copy=False):
"""
Sends a value through a channel variable. Used by an unbuffered or buffered
channel to pass data from within or to a concurrent Go block, where
`channel_recv` to used to get the passed value.
Args:
channel (Variable|Channel): Channel variable created using
`make_channel`.
value (Variable): Value to send to channel
is_copy (bool): Copy data while channel send. If False, then data
is moved. The input cannot be used after move. (default False)
Returns:
Variable: The boolean status on whether or not the channel
successfully sent the passed value.
Examples:
.. code-block:: python
ch = fluid.make_channel(dtype='int32', capacity=10)
...
# Code to execute in a Go block, which receives the channel data.
fluid.channel_send(ch, 100)
"""
helper = LayerHelper('channel_send', **locals())
main_program = helper.main_program
channel_send_block = main_program.current_block()
X = value
if is_copy:
copied_X = helper.create_variable(
name=unique_name.generate(value.name + '_copy'),
type=value.type,
dtype=value.dtype,
shape=value.shape,
lod_level=value.lod_level,
capacity=value.capacity if hasattr(value, 'capacity') else None)
assign_op = channel_send_block.append_op(
type="assign", inputs={"X": value}, outputs={"Out": copied_X})
X = copied_X
channel_send_block.append_op(
type="channel_send", inputs={
"Channel": channel,
"X": X,
})
def channel_recv(channel, return_value):
"""
Receives a value through a channel variable. Used by an unbuffered or
buffered channel within a concurrent Go block to get data from originally
sent using `channel_send`, or from outside such a block where
`channel_send` is used to send the value.
Args:
channel (Variable|Channel): Channel variable created using
`make_channel`.
return_value (Variable): Variable to set as a result of running channel_recv_op
Returns:
Variable: The received value from the channel.
Variable: The boolean status on whether or not the channel
successfully received the passed value.
Examples:
.. code-block:: python
ch = fluid.make_channel(dtype='int32', capacity=10)
with fluid.Go():
returned_value, return_status = fluid.channel_recv(ch, 'int32')
# Code to send data through the channel.
"""
helper = LayerHelper('channel_recv', **locals())
main_program = helper.main_program
channel_recv_block = main_program.current_block()
status = helper.create_variable(
name=unique_name.generate('status'),
type=core.VarDesc.VarType.LOD_TENSOR,
dtype=core.VarDesc.VarType.BOOL)
channel_recv_op = channel_recv_block.append_op(
type="channel_recv",
inputs={"Channel": channel},
outputs={"Out": return_value,
"Status": status})
return return_value, status
def channel_close(channel):
"""
Closes a channel created using `make_channel`.
Args:
channel (Variable|Channel): Channel variable created using
`make_channel`.
Examples:
.. code-block:: python
ch = fluid.make_channel(dtype='int32', capacity=10)
...
# Code to receive and send data through a channel
...
fluid.channel_close(ch)
"""
helper = LayerHelper('channel_close', **locals())
main_program = helper.main_program
channel_close_block = main_program.current_block()
channel_close_op = channel_close_block.append_op(
type="channel_close", inputs={"Channel": channel})
python/paddle/fluid/contrib/tests/test_quantize_transpiler.py
浏览文件 @ f63ab561
......@@ -244,6 +244,7 @@ class TestQuantizeTranspiler(unittest.TestCase):
test_loss2, = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
w_freeze = np.array(fluid.global_scope().find_var('conv2d_1.w_0')
.get_tensor())
# fail: -432.0 != -433.0, this is due to the calculation precision
......
python/paddle/fluid/framework.py
浏览文件 @ f63ab561
......@@ -541,8 +541,7 @@ class Operator(object):
'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine',
'ncclInit', 'channel_create', 'channel_close', 'channel_send',
'channel_recv', 'select', 'checkpoint_notify', 'gen_nccl_id'
'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
}
def __init__(self,
......
python/paddle/fluid/tests/book/high-level-api/recognize_digits/CMakeLists.txt
浏览文件 @ f63ab561
......@@ -2,6 +2,16 @@ file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
if(NOT APPLE)
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
else()
foreach(src ${TEST_OPS})
if(${src} STREQUAL "test_recognize_digits_conv")
message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
else()
py_test(${src} SRCS ${src}.py)
endif()
endforeach()
endif()
python/paddle/fluid/tests/no_test_concurrency.py 已删除 100644 → 0
浏览文件 @ 8f5d918a
# 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.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import framework, unique_name, layer_helper
from paddle.fluid.executor import Executor
from paddle.fluid.layers import fill_constant, assign, While, elementwise_add, Print
class TestRoutineOp(unittest.TestCase):
def test_simple_routine(self):
ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
# Create LOD_TENSOR<INT64> and put it into the scope. This placeholder
# variable will be filled in and returned by fluid.channel_recv
result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
with fluid.Go():
input_value = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.FP64, value=1234)
fluid.channel_send(ch, input_value)
result, status = fluid.channel_recv(ch, result)
fluid.channel_close(ch)
cpu = core.CPUPlace()
exe = Executor(cpu)
outs = exe.run(fetch_list=[result])
self.assertEqual(outs[0], 1234)
def test_daisy_chain(self):
'''
Mimics classic Daisy-chain test: https://talks.golang.org/2012/concurrency.slide#39
'''
n = 100
leftmost = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
left = leftmost
# TODO(thuan): Use fluid.While() after scope capture is implemented.
# https://github.com/PaddlePaddle/Paddle/issues/8502
for i in range(n):
right = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
one_tensor = self._create_one_dim_tensor(1)
result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
result, status = fluid.channel_recv(right, result)
one_added = fluid.layers.elementwise_add(x=one_tensor, y=result)
fluid.channel_send(left, one_added)
left = right
# Trigger the channel propagation by sending a "1" to rightmost channel
with fluid.Go():
one_tensor = self._create_one_dim_tensor(1)
fluid.channel_send(right, one_tensor)
leftmost_result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT64)
leftmost_result, status = fluid.channel_recv(leftmost, leftmost_result)
cpu = core.CPUPlace()
exe = Executor(cpu)
leftmost_data = exe.run(fetch_list=[leftmost_result])
# The leftmost_data should be equal to the number of channels + 1
self.assertEqual(leftmost_data[0][0], n + 1)
def _create_one_dim_tensor(self, value):
one_dim_tensor = fill_constant(shape=[1], dtype='int', value=value)
one_dim_tensor.stop_gradient = True
return one_dim_tensor
def _create_tensor(self, name, type, dtype):
return framework.default_main_program().current_block().create_var(
name=unique_name.generate(name), type=type, dtype=dtype)
def _create_persistable_tensor(self, name, type, dtype):
return framework.default_main_program().current_block().create_var(
name=unique_name.generate(name),
type=type,
dtype=dtype,
persistable=True)
def test_select(self):
with framework.program_guard(framework.Program()):
ch1 = fluid.make_channel(
dtype=core.VarDesc.VarType.LOD_TENSOR, capacity=1)
result1 = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.FP64)
input_value = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.FP64, value=10)
with fluid.Select() as select:
with select.case(fluid.channel_send, ch1, input_value):
# Execute something.
pass
with select.default():
pass
# This should not block because we are using a buffered channel.
result1, status = fluid.channel_recv(ch1, result1)
fluid.channel_close(ch1)
cpu = core.CPUPlace()
exe = Executor(cpu)
result = exe.run(fetch_list=[result1])
self.assertEqual(result[0][0], 10)
def test_fibonacci(self):
"""
Mimics Fibonacci Go example: https://tour.golang.org/concurrency/5
"""
with framework.program_guard(framework.Program()):
quit_ch_input_var = self._create_persistable_tensor(
'quit_ch_input', core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT32)
quit_ch_input = fill_constant(
shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0,
out=quit_ch_input_var)
result = self._create_persistable_tensor(
'result', core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.INT32)
fill_constant(
shape=[1],
dtype=core.VarDesc.VarType.INT32,
value=0,
out=result)
x = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
y = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=1)
while_cond = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.BOOL, value=True)
while_false = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.BOOL, value=False)
x_tmp = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
def fibonacci(channel, quit_channel):
while_op = While(cond=while_cond)
with while_op.block():
result2 = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
with fluid.Select() as select:
with select.case(
fluid.channel_send, channel, x, is_copy=True):
assign(input=x, output=x_tmp)
assign(input=y, output=x)
assign(elementwise_add(x=x_tmp, y=y), output=y)
with select.case(fluid.channel_recv, quit_channel,
result2):
# Quit
helper = layer_helper.LayerHelper('assign')
helper.append_op(
type='assign',
inputs={'X': [while_false]},
outputs={'Out': [while_cond]})
ch1 = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
for i in range(10):
fluid.channel_recv(ch1, result)
Print(result)
fluid.channel_send(quit_ch, quit_ch_input)
fibonacci(ch1, quit_ch)
fluid.channel_close(ch1)
fluid.channel_close(quit_ch)
cpu = core.CPUPlace()
exe = Executor(cpu)
exe_result = exe.run(fetch_list=[result])
self.assertEqual(exe_result[0][0], 34)
def test_ping_pong(self):
"""
Mimics Ping Pong example: https://gobyexample.com/channel-directions
"""
with framework.program_guard(framework.Program()):
result = self._create_tensor('return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.FP64)
ping_result = self._create_tensor('ping_return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.FP64)
def ping(ch, message):
fluid.channel_send(ch, message, is_copy=True)
def pong(ch1, ch2):
fluid.channel_recv(ch1, ping_result)
fluid.channel_send(ch2, ping_result, is_copy=True)
pings = fluid.make_channel(
dtype=core.VarDesc.VarType.LOD_TENSOR, capacity=1)
pongs = fluid.make_channel(
dtype=core.VarDesc.VarType.LOD_TENSOR, capacity=1)
msg = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.FP64, value=9)
ping(pings, msg)
pong(pings, pongs)
fluid.channel_recv(pongs, result)
fluid.channel_close(pings)
fluid.channel_close(pongs)
cpu = core.CPUPlace()
exe = Executor(cpu)
exe_result = exe.run(fetch_list=[result])
self.assertEqual(exe_result[0][0], 9)
if __name__ == '__main__':
unittest.main()
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