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提交 91756a5a 编写于 作者: Q qiaolongfei
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize-opyreader

上级 c5292b18 8cd17c04
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Showing with 5645 addition and 4580 deletion
Dockerfile
浏览文件 @ 91756a5a
......@@ -24,6 +24,7 @@ COPY ./paddle/scripts/docker/root/ /root/
RUN apt-get update && \
apt-get install -y --allow-downgrades patchelf \
python3 python3-dev python3-pip \
git python-pip python-dev python-opencv openssh-server bison \
libnccl2=2.1.2-1+cuda8.0 libnccl-dev=2.1.2-1+cuda8.0 \
wget unzip unrar tar xz-utils bzip2 gzip coreutils ntp \
......@@ -70,24 +71,33 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
# specify sphinx version as 1.5.6 and remove -U option for [pip install -U
# sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
# version(1.7.1 for now), which causes building documentation failed.
RUN easy_install -U pip && \
RUN pip3 install -U wheel && \
pip3 install -U docopt PyYAML sphinx==1.5.6 && \
pip3 install sphinx-rtd-theme==0.1.9 recommonmark && \
easy_install -U pip && \
pip install -U wheel && \
pip install -U docopt PyYAML sphinx==1.5.6 && \
pip install sphinx-rtd-theme==0.1.9 recommonmark
RUN pip install pre-commit 'ipython==5.3.0' && \
RUN pip3 install pre-commit 'ipython==5.3.0' && \
pip3 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3 install opencv-python && \
pip install pre-commit 'ipython==5.3.0' && \
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip install opencv-python
#For docstring checker
RUN pip3 install pylint pytest astroid isort
RUN pip install pylint pytest astroid isort LinkChecker
COPY ./python/requirements.txt /root/
RUN pip3 install -r /root/requirements.txt
RUN pip install -r /root/requirements.txt
# To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
# the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
RUN apt-get install -y libssl-dev libffi-dev
RUN pip3 install certifi urllib3[secure]
RUN pip install certifi urllib3[secure]
......
cmake/external/anakin.cmake
浏览文件 @ 91756a5a
......@@ -52,6 +52,7 @@ ExternalProject_Add(
PREFIX ${ANAKIN_SOURCE_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS ${CMAKE_ARGS_PREFIX}
-DUSE_LOGGER=YES
-DUSE_X86_PLACE=YES
-DBUILD_WITH_UNIT_TEST=NO
-DPROTOBUF_ROOT=${THIRD_PARTY_PATH}/install/protobuf
......
paddle/fluid/API.spec
浏览文件 @ 91756a5a
此差异已折叠。
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 91756a5a
......@@ -56,9 +56,9 @@ else()
cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
endif()
if (NOT WIN32)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
else()
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto version)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto version)
endif (NOT WIN32)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
......@@ -141,20 +141,22 @@ cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
cc_library(naive_executor SRCS naive_executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
if(WITH_DISTRIBUTE)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method sendrecvop_grpc cares grpc++_unsecure grpc_unsecure gpr graph_to_program_pass)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(executor.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
else()
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass)
cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass elementwise_add_op)
endif()
if (NOT WIN32)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
graph graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fast_threaded_ssa_graph_executor fuse_elewise_add_act_pass)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
graph build_strategy
fast_threaded_ssa_graph_executor)
endif() # NOT WIN32
cc_library(prune SRCS prune.cc DEPS framework_proto)
......@@ -167,15 +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 @ c5292b18
/* 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 @ c5292b18
/* 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 @ c5292b18
此差异已折叠。
paddle/fluid/framework/concurrency_test.cc 已删除 100644 → 0
浏览文件 @ c5292b18
/* 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/details/CMakeLists.txt
浏览文件 @ 91756a5a
......@@ -54,3 +54,8 @@ cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_execu
# device_context reduce_op_handle )
cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executor.cc
DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context)
cc_library(build_strategy SRCS build_strategy.cc DEPS
graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fuse_elewise_add_act_pass)
paddle/fluid/framework/details/build_strategy.cc 0 → 100644
浏览文件 @ 91756a5a
/* 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/details/build_strategy.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
namespace paddle {
namespace framework {
namespace details {
class ParallelExecutorPassBuilder : public ir::PassBuilder {
public:
explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
: ir::PassBuilder(), strategy_(strategy) {
// Add a graph viz pass to record a graph.
if (!strategy_.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy_.debug_graphviz_path_.c_str(), "_original_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
}
// Add op fusion.
if (strategy.fuse_elewise_add_act_ops_) {
auto fuse_elewise_add_act_pass = AppendPass("fuse_elewise_add_act_pass");
// Add a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
viz_pass->Set<std::string>("graph_viz_path",
new std::string(graph_path));
}
}
// Convert graph to run on multi-devices.
auto multi_devices_pass = AppendPass("multi_devices_pass");
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
&strategy_);
// Add a graph print pass to record a graph with device info.
if (!strategy_.debug_graphviz_path_.empty()) {
auto multi_devices_print_pass = AppendPass("multi_devices_print_pass");
multi_devices_print_pass->SetNotOwned<const std::string>(
"debug_graphviz_path", &strategy_.debug_graphviz_path_);
multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
"graph_printer", new details::GraphvizSSAGraphPrinter);
}
// Verify that the graph is correct for multi-device executor.
AppendPass("multi_devices_check_pass");
}
private:
BuildStrategy strategy_;
};
std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy()
const {
pass_builder_.reset(new ParallelExecutorPassBuilder(*this));
return pass_builder_;
}
std::unique_ptr<ir::Graph> BuildStrategy::Apply(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes,
#ifdef PADDLE_WITH_CUDA
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
#else
const bool use_cuda) const {
#endif
// Create a default one if not initialized by user.
if (!pass_builder_) {
CreatePassesFromStrategy();
}
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
if (pass->Type() == "multi_devices_pass") {
pass->Erase("places");
pass->SetNotOwned<const std::vector<platform::Place>>("places", &places);
pass->Erase("loss_var_name");
pass->SetNotOwned<const std::string>("loss_var_name", &loss_var_name);
pass->Erase("params");
pass->SetNotOwned<const std::unordered_set<std::string>>("params",
&param_names);
pass->Erase("local_scopes");
pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
&local_scopes);
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
}
graph = pass->Apply(std::move(graph));
}
return graph;
}
} // namespace details
} // namespace framework
} // namespace paddle
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
USE_PASS(multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 91756a5a
......@@ -15,6 +15,17 @@
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/pass_builder.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
......@@ -57,6 +68,30 @@ struct BuildStrategy {
bool fuse_elewise_add_act_ops_{false};
bool enable_data_balance_{false};
// User normally doesn't need to call this API.
// The PassBuilder allows for more customized insert, remove of passes
// from python side.
// A new PassBuilder is created based on configs defined above and
// passes are owned by the PassBuilder.
std::shared_ptr<ir::PassBuilder> CreatePassesFromStrategy() const;
// Apply the passes built by the pass_builder_. The passes will be
// applied to the Program and output an ir::Graph.
std::unique_ptr<ir::Graph> Apply(
const ProgramDesc &main_program,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes,
#ifdef PADDLE_WITH_CUDA
const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const;
#else
const bool use_cuda) const;
#endif
private:
mutable std::shared_ptr<ir::PassBuilder> pass_builder_;
};
} // namespace details
......
paddle/fluid/framework/details/reference_count_pass.cc
浏览文件 @ 91756a5a
......@@ -80,15 +80,15 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
// This is weird but there is really some variables without var_desc
// in computation_op
if (var_desc == nullptr) {
if (compute_op->Node()->Op()->Block()->FindVar(var_name) == nullptr)
continue;
} else {
if (var_desc->Persistable()) continue;
auto var_type = var_desc->Proto()->type().type();
if (var_type != proto::VarType::LOD_TENSOR &&
var_type != proto::VarType::SELECTED_ROWS) {
continue;
}
var_desc = compute_op->Node()->Op()->Block()->FindVar(var_name);
if (var_desc == nullptr) continue;
}
if (var_desc->Persistable()) continue;
auto var_type = var_desc->Proto()->type().type();
if (var_type != proto::VarType::LOD_TENSOR &&
var_type != proto::VarType::SELECTED_ROWS) {
continue;
}
// compute op only runs in one device
......
paddle/fluid/framework/executor.cc
浏览文件 @ 91756a5a
......@@ -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 @ 91756a5a
......@@ -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 @ 91756a5a
set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
file(WRITE ${pass_file} "// Generated by the paddle/fluid/framework/ir/CMakeLists.txt. DO NOT EDIT!\n\n")
file(APPEND ${pass_file} "\#pragma once\n")
file(APPEND ${pass_file} "\#include \"paddle/fluid/framework/ir/pass.h\"\n")
......@@ -28,12 +29,13 @@ cc_library(graph_pattern_detector SRCS graph_pattern_detector.cc DEPS graph grap
pass_library(graph_to_program_pass base)
pass_library(graph_viz_pass base)
pass_library(fc_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(conv_relu_mkldnn_fuse_pass inference)
endif()
if (WITH_MKLDNN)
pass_library(conv_relu_mkldnn_fuse_pass inference)
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)
......@@ -41,12 +43,14 @@ cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass
set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
cc_library(pass_builder SRCS pass_builder.cc DEPS pass)
cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
if(WITH_MKLDNN)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
endif()
if (WITH_MKLDNN)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
endif ()
paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.cc 0 → 100644
浏览文件 @ 91756a5a
// 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/inference/api/timer.h → paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.h
浏览文件 @ 91756a5a
......@@ -11,29 +11,30 @@
// 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 <chrono> // NOLINT
#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 inference {
namespace framework {
namespace ir {
// Fusing of Embedding , FC and LSTM op
// Timer for timer
class Timer {
// Just FC without bias
class EmbeddingFCLSTMFusePass : public FusePassBase {
public:
std::chrono::high_resolution_clock::time_point start;
std::chrono::high_resolution_clock::time_point startu;
void tic() { start = std::chrono::high_resolution_clock::now(); }
double toc() {
startu = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> time_span =
std::chrono::duration_cast<std::chrono::duration<double>>(startu -
start);
double used_time_ms = static_cast<double>(time_span.count()) * 1000.0;
return used_time_ms;
}
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 inference
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_helper.cc
浏览文件 @ 91756a5a
......@@ -12,11 +12,11 @@ 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/graph_helper.h"
#include <algorithm>
#include <deque>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace paddle {
namespace framework {
namespace ir {
......@@ -113,6 +113,74 @@ std::map<ir::Node *, std::unordered_set<ir::Node *>> BuildOperationAdjList(
return adj_list;
}
size_t GraphNum(const Graph &graph) {
std::unordered_set<ir::Node *> nodes = graph.Nodes();
std::unordered_set<ir::Node *> visited_nodes;
visited_nodes.reserve(nodes.size());
std::deque<ir::Node *> q_nodes;
std::vector<std::unordered_set<ir::Node *>> graph_nodes;
std::unordered_set<ir::Node *> g_nodes;
size_t graph_count = 0;
auto traverse_nodes = [&visited_nodes,
&q_nodes](const std::vector<ir::Node *> &nodes) {
std::copy_if(
nodes.begin(), nodes.end(), std::back_inserter(q_nodes),
[&visited_nodes](Node *node) { return !visited_nodes.count(node); });
};
while (visited_nodes.size() != nodes.size()) {
if (!q_nodes.empty()) {
auto cur_node = q_nodes.front();
q_nodes.pop_front();
visited_nodes.insert(cur_node);
g_nodes.insert(cur_node);
traverse_nodes(cur_node->inputs);
traverse_nodes(cur_node->outputs);
} else {
++graph_count;
if (g_nodes.size()) {
graph_nodes.emplace_back(g_nodes);
}
g_nodes.clear();
for (auto &n : nodes) {
if (visited_nodes.count(n) == 0) {
q_nodes.push_back(n);
break;
}
}
}
}
if (g_nodes.size()) {
graph_nodes.emplace_back(g_nodes);
}
if (VLOG_IS_ON(10)) {
VLOG(10) << "graph_num: " << graph_nodes.size();
for (auto &g_n : graph_nodes) {
VLOG(10) << "graph_nodes: " << g_n.size();
if (g_n.size() < 10) {
std::stringstream out;
for (auto &node : g_n) {
out << "\nNode: " << node->Name() << " in [";
for (auto &n : node->inputs) {
out << n->Name() << ", ";
}
out << "], out[";
for (auto &n : node->outputs) {
out << n->Name() << ", ";
}
out << "]";
}
VLOG(10) << out.str();
}
}
}
return graph_count;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_helper.h
浏览文件 @ 91756a5a
......@@ -27,6 +27,8 @@ namespace ir {
// Test if the graph contains circle.
bool HasCircle(const Graph &graph);
size_t GraphNum(const Graph &graph);
// Topology Sort the operations in the graph from inputs to outputs.
// `graph` cannot contain circle.
std::vector<ir::Node *> TopologySortOperations(const Graph &graph);
......
paddle/fluid/framework/ir/graph_helper_test.cc
浏览文件 @ 91756a5a
......@@ -120,6 +120,97 @@ TEST(GraphHelperTest, Basic) {
ASSERT_EQ(node_map.at("op2"), 1UL);
ASSERT_TRUE(node_map.at("op3") < node_map.at("op5"));
}
void BuildZeroGraph(Graph* g) {}
void BuildOneGraph(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* o3 = g->CreateEmptyNode("op3", Node::Type::kOperation);
ir::Node* o4 = g->CreateEmptyNode("op4", Node::Type::kOperation);
ir::Node* o5 = g->CreateEmptyNode("op5", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
ir::Node* v3 = g->CreateEmptyNode("var3", Node::Type::kVariable);
ir::Node* v4 = g->CreateEmptyNode("var4", Node::Type::kVariable);
// o1->v1->o2
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
// o2->v2->o3
// o2->v2->o4
o2->outputs.push_back(v2);
o3->inputs.push_back(v2);
o4->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o3);
v2->outputs.push_back(o4);
// o2->v3->o5
o2->outputs.push_back(v3);
o5->inputs.push_back(v3);
v3->inputs.push_back(o2);
v3->outputs.push_back(o5);
// o3-v4->o5
o3->outputs.push_back(v4);
o5->inputs.push_back(v4);
v4->inputs.push_back(o3);
v4->outputs.push_back(o5);
}
void BuildTwoGraphs(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* o3 = g->CreateEmptyNode("op3", Node::Type::kOperation);
ir::Node* o4 = g->CreateEmptyNode("op4", Node::Type::kOperation);
ir::Node* o5 = g->CreateEmptyNode("op5", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
ir::Node* v3 = g->CreateEmptyNode("var3", Node::Type::kVariable);
ir::Node* v4 = g->CreateEmptyNode("var4", Node::Type::kVariable);
// o1->v1->o2
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
// o2->v2->o3
// o2->v2->o4
o2->outputs.push_back(v2);
o3->inputs.push_back(v2);
o4->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o3);
v2->outputs.push_back(o4);
// o2->v3->o5
// o2->outputs.push_back(v3);
o5->inputs.push_back(v3);
// v3->inputs.push_back(o2);
v3->outputs.push_back(o5);
// o3-v4->o5
o3->outputs.push_back(v4);
// o5->inputs.push_back(v4);
v4->inputs.push_back(o3);
// v4->outputs.push_back(o5);
}
TEST(GraphHelperTest, GraphNum) {
ProgramDesc prog;
Graph g(prog);
BuildZeroGraph(&g);
ASSERT_EQ(GraphNum(g), 0);
Graph g2(prog);
BuildOneGraph(&g2);
ASSERT_EQ(GraphNum(g2), 1);
Graph g3(prog);
BuildTwoGraphs(&g3);
ASSERT_EQ(GraphNum(g3), 2);
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ 91756a5a
......@@ -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 @ 91756a5a
......@@ -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/ir/pass.cc
浏览文件 @ 91756a5a
......@@ -19,7 +19,6 @@ namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<Graph> Pass::Apply(std::unique_ptr<Graph> graph) const {
PADDLE_ENFORCE(!applied_, "Pass can only Apply() once.");
PADDLE_ENFORCE(graph.get(), "graph passed to Pass::Apply() cannot be empty.");
for (const std::string& attr : required_pass_attrs_) {
PADDLE_ENFORCE(attrs_.find(attr) != attrs_.end(),
......
paddle/fluid/framework/ir/pass.h
浏览文件 @ 91756a5a
......@@ -42,6 +42,8 @@ class Pass {
attr_dels_.clear();
}
std::string Type() const { return type_; }
std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph) const;
// Get a reference to the attributed previously set.
......@@ -52,6 +54,21 @@ class Pass {
return *boost::any_cast<AttrType *>(attrs_.at(attr_name));
}
bool Has(const std::string &attr_name) const {
return attrs_.find(attr_name) != attrs_.end();
}
void Erase(const std::string &attr_name) {
if (!Has(attr_name)) {
return;
}
if (attr_dels_.find(attr_name) != attr_dels_.end()) {
attr_dels_[attr_name]();
attr_dels_.erase(attr_name);
}
attrs_.erase(attr_name);
}
// Set a pointer to the attribute. Pass takes ownership of the attribute.
template <typename AttrType>
void Set(const std::string &attr_name, AttrType *attr) {
......@@ -68,13 +85,15 @@ class Pass {
// should delete the attribute.
template <typename AttrType>
void SetNotOwned(const std::string &attr_name, AttrType *attr) {
PADDLE_ENFORCE(attrs_.count(attr_name) == 0);
PADDLE_ENFORCE(attrs_.count(attr_name) == 0, "%s already set in the pass",
attr_name);
attrs_[attr_name] = attr;
}
protected:
virtual std::unique_ptr<Graph> ApplyImpl(
std::unique_ptr<Graph> graph) const = 0;
virtual std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const {
LOG(FATAL) << "Calling virtual Pass not implemented.";
}
private:
template <typename PassType>
......@@ -89,7 +108,10 @@ class Pass {
required_graph_attrs_.insert(attrs.begin(), attrs.end());
}
void RegisterType(const std::string &type) { type_ = type; }
mutable bool applied_{false};
std::string type_;
std::unordered_set<std::string> required_pass_attrs_;
std::unordered_set<std::string> required_graph_attrs_;
std::map<std::string, boost::any> attrs_;
......@@ -143,10 +165,11 @@ struct PassRegistrar : public Registrar {
PADDLE_ENFORCE(!PassRegistry::Instance().Has(pass_type),
"'%s' is registered more than once.", pass_type);
PassRegistry::Instance().Insert(
pass_type, [this]() -> std::unique_ptr<Pass> {
pass_type, [this, pass_type]() -> std::unique_ptr<Pass> {
std::unique_ptr<Pass> pass(new PassType());
pass->RegisterRequiredPassAttrs(this->required_pass_attrs_);
pass->RegisterRequiredGraphAttrs(this->required_graph_attrs_);
pass->RegisterType(pass_type);
return pass;
});
}
......
paddle/fluid/framework/ir/pass_builder.cc 0 → 100644
浏览文件 @ 91756a5a
/* 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/pass_builder.h"
namespace paddle {
namespace framework {
namespace ir {
std::shared_ptr<Pass> PassBuilder::AppendPass(const std::string& pass_type) {
auto pass = ir::PassRegistry::Instance().Get(pass_type);
passes_.emplace_back(pass.release());
return passes_.back();
}
void PassBuilder::RemovePass(size_t idx) {
PADDLE_ENFORCE(passes_.size() > idx);
passes_.erase(passes_.begin() + idx);
}
std::shared_ptr<Pass> PassBuilder::InsertPass(size_t idx,
const std::string& pass_type) {
PADDLE_ENFORCE(passes_.size() >= idx);
std::shared_ptr<Pass> pass(
ir::PassRegistry::Instance().Get(pass_type).release());
passes_.insert(passes_.begin() + idx, std::move(pass));
return passes_[idx];
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/pass_builder.h 0 → 100644
浏览文件 @ 91756a5a
/* 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 <string>
#include <vector>
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class PassBuilder {
public:
PassBuilder() {}
virtual ~PassBuilder() {}
// Append a new pass to the end.
std::shared_ptr<Pass> AppendPass(const std::string& pass_type);
// Insert a new pass after `idx`.
std::shared_ptr<Pass> InsertPass(size_t idx, const std::string& pass_type);
// Remove a new pass at `idx`.
void RemovePass(size_t idx);
// Returns a list of all passes.
std::vector<std::shared_ptr<Pass>> AllPasses() const { return passes_; }
protected:
std::vector<std::shared_ptr<Pass>> passes_;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/pass_test.cc
浏览文件 @ 91756a5a
......@@ -82,12 +82,10 @@ TEST(PassTest, TestPassAttrCheck) {
ASSERT_EQ(graph->Get<int>("copy_test_pass_attr"), 2);
ASSERT_EQ(graph->Get<int>("copy_test_graph_attr"), 2);
try {
graph = pass->Apply(std::move(graph));
} catch (paddle::platform::EnforceNotMet e) {
exception = std::string(e.what());
}
ASSERT_TRUE(exception.find("Pass can only Apply() once") != exception.npos);
// Allow apply more than once.
graph.reset(new Graph(prog));
graph->Set<int>("test_graph_attr", new int);
graph = pass->Apply(std::move(graph));
pass = PassRegistry::Instance().Get("test_pass");
pass->SetNotOwned<int>("test_pass_attr", &val);
......
paddle/fluid/framework/naive_executor.cc 0 → 100644
浏览文件 @ 91756a5a
// 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 <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"
namespace paddle {
namespace framework {
// These code can be shared with Executor.
static void InitializeVariable(Variable *var, proto::VarType::Type var_type) {
if (var_type == proto::VarType::LOD_TENSOR) {
var->GetMutable<LoDTensor>();
} else if (var_type == proto::VarType::SELECTED_ROWS) {
var->GetMutable<SelectedRows>();
} else if (var_type == proto::VarType::FEED_MINIBATCH) {
var->GetMutable<FeedFetchList>();
} else if (var_type == proto::VarType::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else if (var_type == proto::VarType::STEP_SCOPES) {
var->GetMutable<std::vector<framework::Scope>>();
} else if (var_type == proto::VarType::LOD_RANK_TABLE) {
var->GetMutable<LoDRankTable>();
} else if (var_type == proto::VarType::LOD_TENSOR_ARRAY) {
var->GetMutable<LoDTensorArray>();
} else if (var_type == proto::VarType::PLACE_LIST) {
var->GetMutable<platform::PlaceList>();
} else if (var_type == proto::VarType::READER) {
var->GetMutable<ReaderHolder>();
} 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]",
var_type);
}
}
void NaiveExecutor::Prepare(Scope *parent_scope,
const ProgramDesc &program_desc, int block_id,
bool with_feed_fetch_ops) {
if (!parent_scope) {
scope_ = new framework::Scope;
} else {
scope_ = &parent_scope->NewScope();
}
CreateVariables(program_desc, scope_, block_id);
CreateOps(program_desc, block_id, with_feed_fetch_ops);
}
void NaiveExecutor::Run() {
for (auto &op : ops_) {
VLOG(4) << "run " << op->Type();
op->Run(*scope_, place_);
}
}
void NaiveExecutor::CreateVariables(const ProgramDesc &desc, Scope *scope,
int block_id) {
PADDLE_ENFORCE(scope);
auto &global_block = desc.Block(block_id);
const Scope *ancestor_scope = scope;
while (ancestor_scope->parent()) {
ancestor_scope = ancestor_scope->parent();
}
if (ancestor_scope != scope) {
for (auto &var : global_block.AllVars()) {
if (var->Name() == framework::kEmptyVarName) {
continue;
}
// Create persistable vars in ancestor scope.
if (var->Persistable()) {
auto *ptr = const_cast<Scope *>(ancestor_scope)->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else { // Create temporary variables in local scope.
auto *ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
} else {
for (auto &var : global_block.AllVars()) {
auto *ptr = scope->Var(var->Name());
InitializeVariable(ptr, var->GetType());
VLOG(3) << "Create variable " << var->Name() << ", which pointer is "
<< ptr;
}
}
}
void NaiveExecutor::CreateOps(const ProgramDesc &desc, int block_id,
bool with_feed_fetch_ops) {
for (const auto &op_desc : desc.Block(block_id).AllOps()) {
if (!with_feed_fetch_ops &&
(op_desc->Type() == "feed" || op_desc->Type() == "fetch")) {
string::PrettyLogEndl(string::Style::detail(), "--- skip [%s], %s -> %s",
op_desc->Input("X")[0], op_desc->Type(),
op_desc->Output("Out")[0]);
continue;
}
ops_.emplace_back(OpRegistry::CreateOp(*op_desc));
}
}
LoDTensor *NaiveExecutor::FindTensor(const std::string &name) {
PADDLE_ENFORCE(scope_, "Need to init scope first");
auto *var = scope_->FindVar(name);
PADDLE_ENFORCE(var, "No variable [%s] in the scope");
auto *tensor = const_cast<LoDTensor *>(&var->Get<LoDTensor>());
return tensor;
}
void NaiveExecutor::CleanFeedFetchOps() {
std::vector<std::unique_ptr<OperatorBase>> ops;
for (auto &op : ops_) {
if (op->Type() != "feed" && op->Type() != "fetch") {
ops.emplace_back(std::move(op));
}
}
ops_.swap(ops);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/naive_executor.h 0 → 100644
浏览文件 @ 91756a5a
// 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/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace framework {
/*
* Simple, intuitive and effective. Only single thread is supported, and
* currently designed for inference.
*/
class NaiveExecutor {
public:
explicit NaiveExecutor(const platform::Place& place) : place_(place) {}
// Create child scope.
// Create variables.
// @with_feed_fetch_ops: whether to work with the feed and fetch operators.
void Prepare(Scope* parent_scope, const ProgramDesc& program_desc,
int block_id, bool with_feed_fetch_ops);
// Run all the operators.
void Run();
// Get an tensor to operating directly, without the need for feed_ops.
LoDTensor* FindTensor(const std::string& name);
Scope* scope() { return scope_; }
void CleanFeedFetchOps();
protected:
void CreateVariables(const ProgramDesc& desc, Scope* scope, int block_id);
void CreateOps(const ProgramDesc& desc, int block_id,
bool with_feed_fetch_ops);
private:
const platform::Place place_;
// Catch the required resource to avoid recreate.
std::vector<std::unique_ptr<OperatorBase>> ops_;
Scope* scope_;
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/naive_executor_test.cc 0 → 100644
浏览文件 @ 91756a5a
// 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/naive_executor.h"
#include <gtest/gtest.h>
#include <algorithm>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace framework {
TEST(NaiveExecutor, Basic) {
ProgramDesc program;
auto* main_block = program.MutableBlock(0);
auto* a = main_block->Var("a"); // input
auto* b = main_block->Var("b"); // input
auto* c = main_block->Var("c"); // input
a->SetType(proto::VarType::LOD_TENSOR);
b->SetType(proto::VarType::LOD_TENSOR);
c->SetType(proto::VarType::LOD_TENSOR);
auto* add = main_block->AppendOp();
add->SetType("elementwise_add");
add->SetInput("X", {"a"});
add->SetInput("Y", {"b"});
add->SetOutput("Out", {"c"});
auto place = platform::CPUPlace();
NaiveExecutor exe(place);
exe.Prepare(nullptr, program, 0, false /*with feed fetch ops*/);
auto* a_tensor = exe.FindTensor("a");
auto* b_tensor = exe.FindTensor("b");
auto* c_tensor = exe.FindTensor("c");
a_tensor->Resize({1, 4});
b_tensor->Resize({1, 4});
c_tensor->Resize({1, 4});
b_tensor->mutable_data<float>(place);
a_tensor->mutable_data<float>(place);
float a_arr[] = {0, 1, 2, 3};
float b_arr[] = {0.0, .1, .2, .3};
std::copy_n(a_arr, 4, a_tensor->mutable_data<float>(place));
std::copy_n(b_arr, 4, b_tensor->mutable_data<float>(place));
exe.Run();
auto* c_data = c_tensor->mutable_data<float>(place);
for (int i = 0; i < 4; i++) {
EXPECT_NEAR(c_data[i], 1.1 * i, 1e-3);
}
}
} // namespace framework
} // namespace paddle
USE_OP(elementwise_add);
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ 91756a5a
......@@ -132,9 +132,7 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
AddAttr<std::string>(OpNamescopeAttrName(), "Operator name with namesope.")
.SetDefault("");
AddAttr<std::vector<std::string>>(OpCreationCallstackAttrName(),
"Callstack for Op Creatation.")
.SetDefault({});
Validate();
}
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ 91756a5a
......@@ -46,7 +46,6 @@ class OpProtoAndCheckerMaker {
static const char *OpRoleAttrName() { return "op_role"; }
static const char *OpRoleVarAttrName() { return "op_role_var"; }
static const char *OpNamescopeAttrName() { return "op_namescope"; }
static const char *OpCreationCallstackAttrName() { return "op_callstack"; }
void operator()(proto::OpProto *proto, OpAttrChecker *attr_checker);
......
paddle/fluid/framework/operator.cc
浏览文件 @ 91756a5a
......@@ -14,17 +14,15 @@ limitations under the License. */
#define GLOG_NO_ABBREVIATED_SEVERITIES
#define GOOGLE_GLOG_DLL_DECL
#include "paddle/fluid/framework/operator.h"
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <algorithm>
#include <sstream>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_transform.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/shape_inference.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -142,48 +140,19 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
}
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
try {
if (VLOG_IS_ON(4)) {
VLOG(4) << place << " " << DebugStringEx(&scope);
}
if (platform::is_gpu_place(place)) {
VLOG(4) << place << " " << DebugStringEx(&scope);
if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("Cannot run operator on place %s", place);
PADDLE_THROW("Cannot run operator on place %s", place);
#else
auto dev_id = boost::get<platform::CUDAPlace>(place).device;
platform::SetDeviceId(dev_id);
auto dev_id = boost::get<platform::CUDAPlace>(place).device;
platform::SetDeviceId(dev_id);
#endif
}
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
if (VLOG_IS_ON(3)) {
VLOG(3) << place << " " << DebugStringEx(&scope);
}
} catch (platform::EnforceNotMet exception) {
if (Attrs().count("sub_block") != 0) {
throw exception;
}
auto& callstack = Attr<std::vector<std::string>>(
OpProtoAndCheckerMaker::OpCreationCallstackAttrName());
if (callstack.empty()) {
throw exception;
}
std::ostringstream sout;
sout << "Invoke operator " << Type() << " error.\n";
sout << "Python Callstacks: \n";
for (auto& line : callstack) {
sout << line;
}
sout << "C++ Callstacks: \n";
sout << exception.err_str_;
exception.err_str_ = sout.str();
throw exception;
} catch (...) {
std::rethrow_exception(std::current_exception());
}
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
VLOG(3) << place << " " << DebugStringEx(&scope);
}
bool OperatorBase::HasInputs(const std::string& name) const {
......@@ -211,7 +180,7 @@ const std::vector<std::string>& OperatorBase::Inputs(
}
bool OperatorBase::HasOutputs(const std::string& name) const {
if (outputs_.end() != outputs_.find(name)) {
if (outputs_.find(name) != outputs_.end()) {
return true;
} else {
return false;
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 91756a5a
......@@ -13,21 +13,19 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/parallel_executor.h"
#include <string>
#include <tuple>
#include <vector>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -35,80 +33,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
std::unique_ptr<ir::Graph> ApplyParallelExecutorPass(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes, const bool use_cuda,
#ifdef PADDLE_WITH_CUDA
const BuildStrategy &strategy, platform::NCCLContextMap *nccl_ctxs) {
#else
const BuildStrategy &strategy) {
#endif
// Convert the program to graph.
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
// Apply a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = ir::PassRegistry::Instance().Get("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_original_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
graph = viz_pass->Apply(std::move(graph));
}
// Apply op fusion.
if (strategy.fuse_elewise_add_act_ops_) {
auto fuse_elewise_add_act_pass =
ir::PassRegistry::Instance().Get("fuse_elewise_add_act_pass");
graph = fuse_elewise_add_act_pass->Apply(std::move(graph));
// Apply a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = ir::PassRegistry::Instance().Get("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
graph = viz_pass->Apply(std::move(graph));
}
}
// Convert graph to run on multi-devices.
auto multi_devices_pass =
ir::PassRegistry::Instance().Get("multi_devices_pass");
multi_devices_pass->SetNotOwned<const std::vector<platform::Place>>("places",
&places);
multi_devices_pass->SetNotOwned<const std::string>("loss_var_name",
&loss_var_name);
multi_devices_pass->SetNotOwned<const std::unordered_set<std::string>>(
"params", &param_names);
multi_devices_pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
&local_scopes);
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy", &strategy);
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
multi_devices_pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
graph = multi_devices_pass->Apply(std::move(graph));
// Apply a graph print pass to record a graph with device info.
if (!strategy.debug_graphviz_path_.empty()) {
auto multi_devices_print_pass =
ir::PassRegistry::Instance().Get("multi_devices_print_pass");
multi_devices_print_pass->SetNotOwned<const std::string>(
"debug_graphviz_path", &strategy.debug_graphviz_path_);
multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
"graph_printer", new details::GraphvizSSAGraphPrinter);
graph = multi_devices_print_pass->Apply(std::move(graph));
}
// Verify that the graph is correct for multi-device executor.
auto multi_devices_check_pass =
ir::PassRegistry::Instance().Get("multi_devices_check_pass");
graph = multi_devices_check_pass->Apply(std::move(graph));
return graph;
}
class ParallelExecutorPrivate {
public:
explicit ParallelExecutorPrivate(const std::vector<platform::Place> &places)
......@@ -199,10 +123,9 @@ ParallelExecutor::ParallelExecutor(
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
#ifdef PADDLE_WITH_CUDA
std::unique_ptr<ir::Graph> graph = ApplyParallelExecutorPass(
std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
main_program, member_->places_, loss_var_name, params,
member_->local_scopes_, member_->use_cuda_, build_strategy,
member_->nccl_ctxs_.get());
member_->local_scopes_, member_->use_cuda_, member_->nccl_ctxs_.get());
auto max_memory_size = GetEagerDeletionThreshold();
if (max_memory_size >= 0) {
......@@ -228,11 +151,19 @@ ParallelExecutor::ParallelExecutor(
}
}
#else
std::unique_ptr<ir::Graph> graph = ApplyParallelExecutorPass(
main_program, member_->places_, loss_var_name, params,
member_->local_scopes_, member_->use_cuda_, build_strategy);
std::unique_ptr<ir::Graph> graph =
build_strategy.Apply(main_program, member_->places_, loss_var_name,
params, member_->local_scopes_, member_->use_cuda_);
#endif
if (VLOG_IS_ON(5)) {
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
}
}
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
......@@ -319,6 +250,13 @@ void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
#ifdef PADDLE_WITH_CUDA
if (!gcs_.empty()) {
ResetReferenceCount();
for (auto &pair : cur_ref_cnts_) {
auto &name_map = *(pair.second);
for (auto &fetch_name : fetch_tensors) {
name_map.erase(fetch_name);
}
name_map.erase(fetched_var_name);
}
}
#endif
auto fetch_data = member_->executor_->Run(fetch_tensors);
......@@ -373,12 +311,6 @@ ParallelExecutor::~ParallelExecutor() {
} // namespace framework
} // namespace paddle
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
USE_PASS(multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
#ifdef PADDLE_WITH_CUDA
USE_PASS(reference_count_pass);
#endif
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 91756a5a
......@@ -14,14 +14,14 @@ limitations under the License. */
#pragma once
#include <paddle/fluid/framework/details/build_strategy.h>
#include <atomic>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/program_desc.h"
......
paddle/fluid/framework/tuple.h
浏览文件 @ 91756a5a
......@@ -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 @ 91756a5a
......@@ -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 @ 91756a5a
......@@ -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 @ 91756a5a
......@@ -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/CMakeLists.txt
浏览文件 @ 91756a5a
......@@ -20,7 +20,8 @@ cc_library(paddle_fluid_origin DEPS ${fluid_modules} paddle_fluid_api)
add_subdirectory(api)
# Create static library
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api analysis_predictor)
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api
analysis_predictor zero_copy_tensor)
if(NOT APPLE)
# TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.sym")
......@@ -31,6 +32,7 @@ endif()
cc_library(paddle_fluid_shared SHARED
SRCS io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
${CMAKE_CURRENT_SOURCE_DIR}/api/analysis_predictor.cc
${CMAKE_CURRENT_SOURCE_DIR}/api/details/zero_copy_tensor.cc
DEPS ${fluid_modules} paddle_fluid_api)
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
......@@ -53,7 +55,7 @@ if(NOT APPLE)
endif()
if(WITH_TESTING)
# tests/book depends the models that generated by python/paddle/fluid/tests/book
# tests/book depends the models that generated by python/paddle/fluid/tests/book
add_subdirectory(tests/book)
if(WITH_INFERENCE_API_TEST)
add_subdirectory(tests/api)
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ 91756a5a
cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass)
set(analysis_deps
framework_proto proto_desc ir_pass_manager graph pass paddle_fluid_api executor pretty_log)
framework_proto proto_desc ir_pass_manager graph pass paddle_fluid_api executor pretty_log)
cc_library(analysis SRCS pass_manager.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc
analyzer.cc
......
paddle/fluid/inference/analysis/analysis_pass.h
浏览文件 @ 91756a5a
......@@ -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 @ 91756a5a
......@@ -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/CMakeLists.txt
浏览文件 @ 91756a5a
......@@ -18,10 +18,10 @@ if(APPLE)
endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager ${GLOB_PASS_LIB})
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB})
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
endif()
function(inference_api_test TARGET_NAME)
......@@ -43,8 +43,10 @@ function(inference_api_test TARGET_NAME)
endif(WITH_TESTING)
endfunction(inference_api_test)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)
cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
DEPS paddle_inference_api)
......@@ -52,18 +54,22 @@ cc_test(test_paddle_inference_api
inference_api_test(test_api_impl SRC api_impl_tester.cc
ARGS test_word2vec test_image_classification)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps} paddle_inference_api
ARGS --dirname=${PYTHON_TESTS_DIR}/book)
if(WITH_GPU AND TENSORRT_FOUND)
cc_library(paddle_inference_tensorrt_subgraph_engine
SRCS api_tensorrt_subgraph_engine.cc
DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter)
DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter zero_copy_tensor_dummy)
inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_engine_tester.cc ARGS test_word2vec)
endif()
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
# compile the libinference_anakin_api.a and anakin.so.
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml)
cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber)
cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml scope zero_copy_tensor_dummy)
cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber scope)
function(anakin_target target_name)
target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endfunction()
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 91756a5a
......@@ -16,9 +16,12 @@
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/utils/singleton.h"
......@@ -28,8 +31,11 @@ DECLARE_bool(profile);
namespace paddle {
using contrib::AnalysisConfig;
bool AnalysisPredictor::Init(
const std::shared_ptr<framework::Scope>& parent_scope) {
const std::shared_ptr<framework::Scope> &parent_scope,
const std::shared_ptr<framework::ProgramDesc> &program) {
VLOG(3) << "Predictor::init()";
#if !defined(_WIN32)
if (FLAGS_profile) {
......@@ -43,7 +49,8 @@ bool AnalysisPredictor::Init(
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
LOG(WARNING) << "ir optimize only supports CPU currently";
LOG(WARNING) << "ir optimize only supports CPU currently, enable_ir_optim "
"is turned false.";
config_.enable_ir_optim = false;
} else {
place_ = paddle::platform::CPUPlace();
......@@ -56,37 +63,134 @@ bool AnalysisPredictor::Init(
scope_.reset(new paddle::framework::Scope());
}
executor_.reset(new paddle::framework::Executor(place_));
executor_.reset(new paddle::framework::NaiveExecutor(place_));
// Initialize the inference program
if (!config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
inference_program_ = paddle::inference::Load(executor_.get(), scope_.get(),
config_.model_dir);
} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
inference_program_ = paddle::inference::Load(
executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
if (!program) {
if (!LoadProgramDesc()) return false;
OptimizeInferenceProgram();
} else {
LOG(ERROR) << "fail to load inference model from " << config_.model_dir;
inference_program_ = program;
}
executor_->Prepare(scope_.get(), *inference_program_, 0,
config_.use_feed_fetch_ops);
// Get the feed_target_names and fetch_target_names
PrepareFeedFetch();
return true;
}
bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data,
int batch_size) {
VLOG(3) << "Predictor::predict";
inference::Timer timer;
timer.tic();
// set feed variable
std::vector<framework::LoDTensor> feeds;
framework::Scope *scope = sub_scope_ ? sub_scope_ : scope_.get();
if (!SetFeed(inputs, scope)) {
LOG(ERROR) << "fail to set feed";
return false;
}
// Run the inference program
// if share variables, we need not create variables
executor_->Run();
OptimizeInferenceProgram();
if (config_._use_mkldnn) {
executor_->EnableMKLDNN(*inference_program_);
// get fetch variable
if (!GetFetch(output_data, scope)) {
LOG(ERROR) << "fail to get fetches";
return false;
}
ctx_ = executor_->Prepare(*inference_program_, 0);
VLOG(3) << "predict cost: " << timer.toc() << "ms";
return true;
}
VLOG(5) << "to create variables";
PADDLE_ENFORCE(scope_.get());
executor_->CreateVariables(*inference_program_,
sub_scope_ ? sub_scope_ : scope_.get(), 0);
// Get the feed_target_names and fetch_target_names
PrepareFeedFetch();
bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::set_feed";
if (inputs.size() != feeds_.size()) {
LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
<< inputs.size();
return false;
}
// Cache the inputs memory for better concurrency performance.
feed_tensors_.resize(inputs.size());
for (size_t i = 0; i < inputs.size(); ++i) {
auto &input = feed_tensors_[i];
framework::DDim ddim = framework::make_ddim(inputs[i].shape);
void *input_ptr;
if (inputs[i].dtype == PaddleDType::INT64) {
input_ptr = input.mutable_data<int64_t>(ddim, platform::CPUPlace());
} else if (inputs[i].dtype == PaddleDType::FLOAT32) {
input_ptr = input.mutable_data<float>(ddim, platform::CPUPlace());
} else {
LOG(ERROR) << "unsupported feed type " << inputs[i].dtype;
return false;
}
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
// TODO(Superjomn) Low performance, need optimization for heavy LoD copy.
framework::LoD lod;
for (auto &level : inputs[i].lod) {
lod.emplace_back(level);
}
input.set_lod(lod);
int idx = -1;
if (config_.specify_input_name) {
idx = feed_names_[inputs[i].name];
} else {
idx = boost::get<int>(feeds_[i]->GetAttr("col"));
}
framework::SetFeedVariable(scope, input, "feed", idx);
}
return true;
}
template <typename T>
void AnalysisPredictor::GetFetchOne(const framework::LoDTensor &fetch,
PaddleTensor *output) {
// set shape.
auto shape = framework::vectorize(fetch.dims());
output->shape.assign(shape.begin(), shape.end());
// set data.
const T *data = fetch.data<T>();
int num_elems = inference::VecReduceToInt(shape);
output->data.Resize(num_elems * sizeof(T));
// The fetched tensor output by fetch op, should always in CPU memory, so just
// copy.
memcpy(output->data.data(), data, num_elems * sizeof(T));
// set lod
output->lod.clear();
for (auto &level : fetch.lod()) {
output->lod.emplace_back(level.begin(), level.end());
}
}
bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
framework::Scope *scope) {
VLOG(3) << "Predictor::get_fetch";
outputs->resize(fetchs_.size());
for (size_t i = 0; i < fetchs_.size(); ++i) {
int idx = boost::get<int>(fetchs_[i]->GetAttr("col"));
PADDLE_ENFORCE((size_t)idx == i);
framework::LoDTensor &fetch =
framework::GetFetchVariable(*scope, "fetch", idx);
auto type = fetch.type();
auto output = &(outputs->at(i));
if (type == typeid(float)) {
GetFetchOne<float>(fetch, output);
output->dtype = PaddleDType::FLOAT32;
} else if (type == typeid(int64_t)) {
GetFetchOne<int64_t>(fetch, output);
output->dtype = PaddleDType::INT64;
} else {
LOG(ERROR) << "unknown type, only support float32 and int64 now.";
}
}
return true;
}
......@@ -107,6 +211,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
new std::string(config_.prog_file));
argument_.fluid_model_param_path.reset(new std::string(config_.param_file));
}
argument_.origin_program_desc.reset(
new ProgramDesc(*inference_program_->Proto()));
PADDLE_ENFORCE(
......@@ -127,9 +232,8 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
}
template <>
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<contrib::AnalysisConfig, PaddleEngineKind::kAnalysis>(
const contrib::AnalysisConfig& config) {
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) {
VLOG(3) << "create AnalysisConfig";
if (config.use_gpu) {
// 1. GPU memeroy
......@@ -150,15 +254,90 @@ CreatePaddlePredictor<contrib::AnalysisConfig, PaddleEngineKind::kAnalysis>(
}
std::unique_ptr<PaddlePredictor> predictor(new AnalysisPredictor(config));
if (!dynamic_cast<AnalysisPredictor*>(predictor.get())->Init(nullptr)) {
if (!dynamic_cast<AnalysisPredictor *>(predictor.get())->Init(nullptr)) {
return nullptr;
}
return predictor;
}
void AnalysisPredictor::PrepareFeedFetch() {
for (auto *op : inference_program_->Block(0).AllOps()) {
if (op->Type() == "feed") {
int idx = boost::get<int>(op->GetAttr("col"));
if (feeds_.size() <= static_cast<size_t>(idx)) {
feeds_.resize(idx + 1);
}
feeds_[idx] = op;
feed_names_[op->Output("Out")[0]] = idx;
} else if (op->Type() == "fetch") {
int idx = boost::get<int>(op->GetAttr("col"));
if (fetchs_.size() <= static_cast<size_t>(idx)) {
fetchs_.resize(idx + 1);
}
fetchs_[idx] = op;
}
}
}
std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetInputTensor(
const std::string &name) {
PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
std::unique_ptr<ZeroCopyTensor> res(
new ZeroCopyTensor(static_cast<void *>(executor_->scope())));
res->input_or_output_ = true;
res->SetName(name);
return res;
}
std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetOutputTensor(
const std::string &name) {
PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
std::unique_ptr<ZeroCopyTensor> res(
new ZeroCopyTensor(static_cast<void *>(executor_->scope())));
res->input_or_output_ = false;
res->SetName(name);
return res;
}
bool AnalysisPredictor::ZeroCopyRun() {
executor_->Run();
return true;
}
bool AnalysisPredictor::LoadProgramDesc() {
// Initialize the inference program
std::unique_ptr<framework::Executor> tmp_exe(
new framework::Executor(platform::CPUPlace()));
if (!config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
inference_program_ = paddle::inference::Load(
static_cast<framework::Executor *>(tmp_exe.get()), scope_.get(),
config_.model_dir);
} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
inference_program_ = paddle::inference::Load(
static_cast<framework::Executor *>(tmp_exe.get()), scope_.get(),
config_.prog_file, config_.param_file);
} else {
LOG(ERROR) << string::Sprintf(
"not valid model path '%s' or program path '%s'.", config_.model_dir,
config_.param_file);
return false;
}
return true;
}
std::unique_ptr<PaddlePredictor> AnalysisPredictor::Clone() {
auto *x = new AnalysisPredictor(config_);
x->Init(scope_, inference_program_);
return std::unique_ptr<PaddlePredictor>(x);
}
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<contrib::AnalysisConfig>(
const contrib::AnalysisConfig& config) {
const contrib::AnalysisConfig &config) {
return CreatePaddlePredictor<contrib::AnalysisConfig,
PaddleEngineKind::kAnalysis>(config);
}
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ 91756a5a
......@@ -12,42 +12,81 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
using inference::analysis::Argument;
using inference::analysis::Analyzer;
using framework::proto::ProgramDesc;
using framework::NaiveExecutor;
using contrib::AnalysisConfig;
/* This predictor is based on the original native predictor with IR and Analysis
* support. It will optimize IR and Parameters in the runtime.
* TODO(Superjomn) Replace the Navive predictor?
*/
class AnalysisPredictor : public NativePaddlePredictor {
class AnalysisPredictor : public PaddlePredictor {
public:
explicit AnalysisPredictor(const contrib::AnalysisConfig& config)
: NativePaddlePredictor(config), config_(config) {}
explicit AnalysisPredictor(const AnalysisConfig &config) : config_(config) {}
bool Init(const std::shared_ptr<framework::Scope>& parent_scope);
bool Init(const std::shared_ptr<framework::Scope> &parent_scope,
const std::shared_ptr<framework::ProgramDesc> &program = nullptr);
bool Run(const std::vector<PaddleTensor>& inputs,
std::vector<PaddleTensor>* output_data,
int batch_size = -1) override {
return NativePaddlePredictor::Run(inputs, output_data, batch_size);
}
bool Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data,
int batch_size = -1) override;
std::unique_ptr<ZeroCopyTensor> GetInputTensor(
const std::string &name) override;
std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
const std::string &name) override;
bool ZeroCopyRun() override;
void PrepareFeedFetch();
void OptimizeInferenceProgram();
Argument& analysis_argument() { return argument_; }
Argument &analysis_argument() { return argument_; }
std::unique_ptr<PaddlePredictor> Clone() override;
framework::Scope *scope() { return executor_->scope(); }
framework::ProgramDesc &program() { return *inference_program_; }
protected:
bool LoadProgramDesc();
bool SetFeed(const std::vector<PaddleTensor> &input_datas,
framework::Scope *scope);
bool GetFetch(std::vector<PaddleTensor> *output_data,
framework::Scope *scope);
template <typename T>
void GetFetchOne(const framework::LoDTensor &fetchs,
PaddleTensor *output_data);
private:
contrib::AnalysisConfig config_;
Argument argument_;
std::unique_ptr<NaiveExecutor> executor_;
platform::Place place_;
std::shared_ptr<framework::Scope> scope_;
framework::Scope *sub_scope_{nullptr};
std::shared_ptr<framework::ProgramDesc> inference_program_;
std::vector<framework::OpDesc *> feeds_;
std::map<std::string, size_t> feed_names_;
std::vector<framework::OpDesc *> fetchs_;
// Memory buffer for feed inputs. The temporary LoDTensor will cause serious
// concurrency problems, so cache them.
std::vector<framework::LoDTensor> feed_tensors_;
};
} // namespace paddle
paddle/fluid/inference/api/analysis_predictor_tester.cc 0 → 100644
浏览文件 @ 91756a5a
// 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 <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
DEFINE_string(dirname, "", "dirname to tests.");
namespace paddle {
namespace inference {
using contrib::AnalysisConfig;
TEST(AnalysisPredictor, ZeroCopy) {
AnalysisConfig config;
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
config.use_feed_fetch_ops = false;
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto w0 = predictor->GetInputTensor("firstw");
auto w1 = predictor->GetInputTensor("secondw");
auto w2 = predictor->GetInputTensor("thirdw");
auto w3 = predictor->GetInputTensor("forthw");
w0->Reshape({4, 1});
w1->Reshape({4, 1});
w2->Reshape({4, 1});
w3->Reshape({4, 1});
auto* w0_data = w0->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w1_data = w1->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w2_data = w2->mutable_data<int64_t>(PaddlePlace::kCPU);
auto* w3_data = w3->mutable_data<int64_t>(PaddlePlace::kCPU);
for (int i = 0; i < 4; i++) {
w0_data[i] = i;
w1_data[i] = i;
w2_data[i] = i;
w3_data[i] = i;
}
predictor->ZeroCopyRun();
auto out = predictor->GetOutputTensor("fc_1.tmp_2");
PaddlePlace place;
int size = 0;
auto* out_data = out->data<float>(&place, &size);
LOG(INFO) << "output size: " << size / sizeof(float);
LOG(INFO) << "output_data: " << out_data;
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/api/api.cc
浏览文件 @ 91756a5a
/* 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. */
// 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/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle_inference_api.h"
namespace paddle {
......@@ -26,7 +32,7 @@ int PaddleDtypeSize(PaddleDType dtype) {
}
}
PaddleBuf::PaddleBuf(PaddleBuf&& other)
PaddleBuf::PaddleBuf(PaddleBuf &&other)
: data_(other.data_),
length_(other.length_),
memory_owned_(other.memory_owned_) {
......@@ -35,9 +41,9 @@ PaddleBuf::PaddleBuf(PaddleBuf&& other)
other.length_ = 0;
}
PaddleBuf::PaddleBuf(const PaddleBuf& other) { *this = other; }
PaddleBuf::PaddleBuf(const PaddleBuf &other) { *this = other; }
PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
PaddleBuf &PaddleBuf::operator=(const PaddleBuf &other) {
if (!other.memory_owned_) {
data_ = other.data_;
length_ = other.length_;
......@@ -51,7 +57,7 @@ PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
return *this;
}
PaddleBuf& PaddleBuf::operator=(PaddleBuf&& other) {
PaddleBuf &PaddleBuf::operator=(PaddleBuf &&other) {
// only the buffer with external memory can be copied
data_ = other.data_;
length_ = other.length_;
......@@ -75,7 +81,7 @@ void PaddleBuf::Resize(size_t length) {
}
}
void PaddleBuf::Reset(void* data, size_t length) {
void PaddleBuf::Reset(void *data, size_t length) {
Free();
memory_owned_ = false;
data_ = data;
......@@ -85,7 +91,7 @@ void PaddleBuf::Reset(void* data, size_t length) {
void PaddleBuf::Free() {
if (memory_owned_ && data_) {
PADDLE_ENFORCE_GT(length_, 0);
free(static_cast<char*>(data_));
free(static_cast<char *>(data_));
data_ = nullptr;
length_ = 0;
}
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 91756a5a
......@@ -23,7 +23,6 @@ limitations under the License. */
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_bool(profile, false, "Turn on profiler for fluid");
......@@ -145,7 +144,7 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
VLOG(4) << "Run prepared context";
executor_->RunPreparedContext(ctx_.get(), scope,
false, /* don't create local scope each time*/
false /* don't create variable eatch time */);
false /* don't create variable each time */);
VLOG(4) << "Finish prepared context";
// get fetch variable
if (!GetFetch(output_data, scope)) {
......
paddle/fluid/inference/api/api_impl.h
浏览文件 @ 91756a5a
/* 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
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,
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. */
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
......@@ -30,6 +30,8 @@
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -52,6 +54,8 @@ class NativePaddlePredictor : public PaddlePredictor {
~NativePaddlePredictor() override;
framework::Scope *scope() { return sub_scope_ ? sub_scope_ : scope_.get(); }
protected:
bool SetFeed(const std::vector<PaddleTensor> &input_datas,
framework::Scope *scope);
......
paddle/fluid/inference/api/api_impl_tester.cc
浏览文件 @ 91756a5a
......@@ -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 {
......@@ -43,7 +49,7 @@ PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
NativeConfig GetConfig() {
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.15;
#ifdef PADDLE_WITH_CUDA
......@@ -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);
}
}
......@@ -110,7 +116,7 @@ void MainImageClassification(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
FLAGS_dirname + "/image_classification_resnet.inference.model";
const bool is_combined = false;
std::vector<std::vector<int64_t>> feed_target_shapes =
......@@ -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);
}
});
}
......@@ -214,7 +220,7 @@ void MainThreadsImageClassification(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
FLAGS_dirname + "/image_classification_resnet.inference.model";
auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
std::vector<framework::LoDTensor> jobs(num_jobs);
......@@ -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/demo_ci/run.sh
浏览文件 @ 91756a5a
......@@ -2,6 +2,9 @@ set -x
PADDLE_ROOT=$1
TURN_ON_MKL=$2 # use MKL or Openblas
TEST_GPU_CPU=$3 # test both GPU/CPU mode or only CPU mode
DATA_DIR=$4 # dataset
cd `dirname $0`
current_dir=`pwd`
if [ $2 == ON ]; then
# You can export yourself if move the install path
MKL_LIB=${PADDLE_ROOT}/build/fluid_install_dir/third_party/install/mklml/lib
......@@ -29,15 +32,15 @@ function download() {
fi
cd ..
}
mkdir -p data
cd data
mkdir -p $DATA_DIR
cd $DATA_DIR
vis_demo_list='se_resnext50 ocr mobilenet'
for vis_demo_name in $vis_demo_list; do
download $vis_demo_name
done
cd ..
# compile and test the demo
cd $current_dir
mkdir -p build
cd build
......@@ -73,9 +76,9 @@ for WITH_STATIC_LIB in ON OFF; do
for use_gpu in $use_gpu_list; do
for vis_demo_name in $vis_demo_list; do
./vis_demo \
--modeldir=../data/$vis_demo_name/model \
--data=../data/$vis_demo_name/data.txt \
--refer=../data/$vis_demo_name/result.txt \
--modeldir=$DATA_DIR/$vis_demo_name/model \
--data=$DATA_DIR/$vis_demo_name/data.txt \
--refer=$DATA_DIR/$vis_demo_name/result.txt \
--use_gpu=$use_gpu
if [ $? -ne 0 ]; then
echo "vis demo $vis_demo_name runs fail."
......
paddle/fluid/inference/api/details/zero_copy_tensor.cc 0 → 100644
浏览文件 @ 91756a5a
// 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/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
void ZeroCopyTensor::Reshape(const std::vector<int> &shape) {
PADDLE_ENFORCE(!name_.empty(),
"Need to SetName first, so that the corresponding tensor can "
"be retrieved.");
PADDLE_ENFORCE(input_or_output_,
"Can't reshape the output tensor, it is readonly");
PADDLE_ENFORCE(scope_);
auto *scope = static_cast<framework::Scope *>(scope_);
auto *var = scope->FindVar(name_);
PADDLE_ENFORCE(var, "No tensor called [%s] in the runtime scope", name_);
auto *tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(shape));
}
template <typename T>
T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
switch (static_cast<int>(place)) {
case static_cast<int>(PaddlePlace::kCPU): {
return tensor->mutable_data<T>(platform::CPUPlace());
}
case static_cast<int>(PaddlePlace::kGPU): {
return tensor->mutable_data<T>(platform::CUDAPlace());
}
default:
PADDLE_THROW("Unsupported place: %d", static_cast<int>(place));
break;
}
return nullptr;
}
template <typename T>
T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
auto *res = tensor->data<T>();
if (platform::is_cpu_place(tensor->place())) {
*place = PaddlePlace::kCPU;
} else if (platform::is_gpu_place(tensor->place())) {
*place = PaddlePlace::kGPU;
} else {
*place = PaddlePlace::kUNK;
}
*size = tensor->numel();
return res;
}
template float *ZeroCopyTensor::data<float>(PaddlePlace *place, int *size);
template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place, int *size);
template float *ZeroCopyTensor::mutable_data<float>(PaddlePlace place);
template int64_t *ZeroCopyTensor::mutable_data<int64_t>(PaddlePlace place);
void *ZeroCopyTensor::FindTensor() const {
PADDLE_ENFORCE(!name_.empty(),
"Need to SetName first, so that the corresponding tensor can "
"be retrieved.");
PADDLE_ENFORCE(scope_);
auto *scope = static_cast<framework::Scope *>(scope_);
auto *var = scope->FindVar(name_);
PADDLE_ENFORCE(var, "No tensor called [%s] in the runtime scope", name_);
auto *tensor = var->GetMutable<framework::LoDTensor>();
return tensor;
}
std::vector<int64_t> ZeroCopyTensor::shape() {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
PADDLE_ENFORCE(tensor, "not found tensor called %s in the scope", name_);
return framework::vectorize(tensor->dims());
}
void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
framework::LoD lod;
for (auto &level : x) {
lod.emplace_back(level);
}
tensor->set_lod(lod);
}
std::vector<std::vector<size_t>> ZeroCopyTensor::lod() const {
std::vector<std::vector<size_t>> res;
auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
for (auto &level : tensor->lod()) {
res.emplace_back(level);
}
return res;
}
} // namespace paddle
paddle/fluid/inference/api/details/zero_copy_tensor_dummy.cc 0 → 100644
浏览文件 @ 91756a5a
// 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/inference/api/paddle_inference_api.h"
namespace paddle {
void ZeroCopyTensor::Reshape(const std::vector<int> &shape) {}
template <typename T>
T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
return nullptr;
}
template <typename T>
T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
return nullptr;
}
template float *ZeroCopyTensor::data<float>(PaddlePlace *place, int *size);
template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place, int *size);
template float *ZeroCopyTensor::mutable_data(PaddlePlace place);
template int64_t *ZeroCopyTensor::mutable_data(PaddlePlace place);
void *ZeroCopyTensor::FindTensor() const { return nullptr; }
std::vector<int64_t> ZeroCopyTensor::shape() { return {}; }
void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {}
std::vector<std::vector<size_t>> ZeroCopyTensor::lod() const {
return std::vector<std::vector<size_t>>();
}
} // namespace paddle
paddle/fluid/inference/api/helper.h
浏览文件 @ 91756a5a
......@@ -16,17 +16,34 @@
#include <glog/logging.h>
#include <sys/time.h>
#include <algorithm>
#include <chrono> // NOLINT
#include <numeric>
#include <sstream>
#include <string>
#include <vector>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/timer.h"
#include "paddle/fluid/string/printf.h"
#include "paddle_inference_api.h"
namespace paddle {
namespace inference {
// Timer for timer
class Timer {
public:
std::chrono::high_resolution_clock::time_point start;
std::chrono::high_resolution_clock::time_point startu;
void tic() { start = std::chrono::high_resolution_clock::now(); }
double toc() {
startu = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> time_span =
std::chrono::duration_cast<std::chrono::duration<double>>(startu -
start);
double used_time_ms = static_cast<double>(time_span.count()) * 1000.0;
return used_time_ms;
}
};
static void split(const std::string &str, char sep,
std::vector<std::string> *pieces) {
pieces->clear();
......@@ -93,6 +110,20 @@ static void TensorAssignData(PaddleTensor *tensor,
}
}
template <typename T>
static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
const std::vector<std::vector<T>> &data) {
int size{0};
auto *ptr = tensor->mutable_data<T>(PaddlePlace::kCPU);
int c = 0;
for (const auto &f : data) {
for (T v : f) {
ptr[c++] = v;
}
}
return size;
}
static std::string DescribeTensor(const PaddleTensor &tensor) {
std::stringstream os;
os << "Tensor [" << tensor.name << "]\n";
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 91756a5a
......@@ -101,6 +101,40 @@ struct PaddleTensor {
std::vector<std::vector<size_t>> lod; // Tensor+LoD equals LoDTensor
};
enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
// Tensor without copy, currently only supports AnalysisPredictor.
class ZeroCopyTensor {
public:
void Reshape(const std::vector<int>& shape);
// Get the memory in CPU or GPU with specific data type, should Reshape first
// to tell the data size.
// Once can directly call this data to feed the data.
// This is for write the input tensor.
template <typename T>
T* mutable_data(PaddlePlace place);
// Get the memory directly, will return the place and memory size by pointer.
// This is for reading the output tensor.
template <typename T>
T* data(PaddlePlace* place, int* size);
std::vector<int64_t> shape();
void SetLoD(const std::vector<std::vector<size_t>>& x);
std::vector<std::vector<size_t>> lod() const;
protected:
ZeroCopyTensor(void* scope) : scope_{scope} {}
void SetName(const std::string& name) { name_ = name; }
void* FindTensor() const;
private:
std::string name_;
bool input_or_output_;
friend class AnalysisPredictor;
void* scope_{nullptr};
};
/*
* A simple Inference API for Paddle.
*/
......@@ -120,6 +154,19 @@ class PaddlePredictor {
std::vector<PaddleTensor>* output_data,
int batch_size = -1) = 0;
// Zero copy input and output optimization.
// Get the input or output tensors, and operate on their memory directly,
// without copy.
virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
const std::string& name) {
return nullptr;
}
virtual std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
const std::string& name) {
return nullptr;
}
virtual bool ZeroCopyRun() { return false; }
// Clone a predictor that share the model weights, the Cloned predictor should
// be thread-safe.
virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
......@@ -216,9 +263,13 @@ 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};
// NOTE this is just for internal development, please not use it.
// NOT stable yet.
bool _use_mkldnn{false};
};
......
paddle/fluid/inference/tests/api/anakin_rnn1_tester.cc
浏览文件 @ 91756a5a
......@@ -22,7 +22,6 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/timer.h"
#include "utils/logger/logger.h"
DEFINE_string(model, "", "Directory of the inference model.");
......
paddle/fluid/memory/malloc.cc
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paddle/fluid/operators/channel_create_op.cc 已删除 100644 → 0
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paddle/fluid/operators/channel_recv_op.cc 已删除 100644 → 0
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paddle/fluid/operators/conv_op.h
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paddle/fluid/operators/top_k_op.cc
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paddle/fluid/pybind/CMakeLists.txt
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paddle/fluid/pybind/const_value.cc
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paddle/fluid/pybind/protobuf.cc
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paddle/fluid/pybind/pybind.cc
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paddle/scripts/paddle_build.sh
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python/CMakeLists.txt
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python/paddle/fluid/clip.py
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python/paddle/fluid/framework.py
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python/paddle/fluid/layers/io.py
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