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提交 903e3f3f 编写于 作者: S shippingwang
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Merge branch 'release/1.0.0' of https://github.com/PaddlePaddle/Paddle into release/1.0.0 

Fix import paddle.v2.plot to import paddle.utils
上级 77c8ddb5 3b7e20b0
展开全部 隐藏空白更改
内联 并排
Showing with 3185 addition and 4522 deletion
Dockerfile
浏览文件 @ 903e3f3f
......@@ -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/cblas.cmake
浏览文件 @ 903e3f3f
......@@ -40,7 +40,7 @@ set(OPENBLAS_LIB_SEARCH_PATHS
/usr/local/opt/openblas/lib)
find_path(OPENBLAS_INC_DIR NAMES cblas.h
PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS})
PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS} NO_DEFAULT_PATH)
find_path(OPENBLAS_LAPACKE_INC_DIR NAMES lapacke.h
PATHS ${OPENBLAS_INCLUDE_SEARCH_PATHS})
find_library(OPENBLAS_LIB NAMES openblas
......
cmake/external/openblas.cmake
浏览文件 @ 903e3f3f
......@@ -27,7 +27,7 @@ IF(NOT ${CBLAS_FOUND})
SET(CBLAS_SOURCES_DIR ${THIRD_PARTY_PATH}/openblas)
SET(CBLAS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/openblas)
SET(CBLAS_INCLUDE_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
SET(CBLAS_INC_DIR "${CBLAS_INSTALL_DIR}/include" CACHE PATH "openblas include directory." FORCE)
SET(CBLAS_LIBRARIES
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
......@@ -96,7 +96,7 @@ IF(NOT ${CBLAS_FOUND})
ENDIF(NOT WIN32)
SET(CBLAS_PROVIDER openblas)
IF(WITH_C_API)
INSTALL(DIRECTORY ${CBLAS_INCLUDE_DIR} DESTINATION third_party/openblas)
INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
# Because libopenblas.a is a symbolic link of another library, thus need to
# install the whole directory.
IF(ANDROID)
......@@ -117,8 +117,8 @@ IF(NOT ${CBLAS_FOUND})
ENDIF(NOT ${CBLAS_FOUND})
MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
MESSAGE(STATUS "BLAS Include: ${CBLAS_INCLUDE_DIR}")
INCLUDE_DIRECTORIES(${CBLAS_INCLUDE_DIR})
MESSAGE(STATUS "BLAS Include: ${CBLAS_INC_DIR}")
INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
# FIXME(gangliao): generate cblas target to track all high performance
# linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
......
paddle/contrib/float16/float16_transpiler.py
浏览文件 @ 903e3f3f
......@@ -102,8 +102,8 @@ class Float16Transpiler:
continue
for input_arg in current_op.input_arg_names:
if input_arg in self.input_map:
current_op.rename_input(input_arg,
self.input_map[input_arg])
current_op._rename_input(input_arg,
self.input_map[input_arg])
def _remove_unused_var(self):
'''
......@@ -187,7 +187,7 @@ class Float16Transpiler:
shape=var.shape,
persistable=var.persistable)
find_op(var)
var.op.rename_output(var_name, tmp_var_name)
var.op._rename_output(var_name, tmp_var_name)
self.block._insert_op(
i,
type="cast",
......
paddle/fluid/API.spec
浏览文件 @ 903e3f3f
此差异已折叠。
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 903e3f3f
......@@ -167,15 +167,8 @@ 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 @ 77c8ddb5
/* 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 @ 77c8ddb5
/* 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 @ 77c8ddb5
此差异已折叠。
paddle/fluid/framework/concurrency_test.cc 已删除 100644 → 0
浏览文件 @ 77c8ddb5
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <thread> // NOLINT
#include "gtest/gtest.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_registry.h"
USE_NO_KERNEL_OP(go);
USE_NO_KERNEL_OP(channel_close);
USE_NO_KERNEL_OP(channel_create);
USE_NO_KERNEL_OP(channel_recv);
USE_NO_KERNEL_OP(channel_send);
USE_NO_KERNEL_OP(elementwise_add);
USE_NO_KERNEL_OP(select);
USE_NO_KERNEL_OP(conditional_block);
USE_NO_KERNEL_OP(equal);
USE_NO_KERNEL_OP(assign);
USE_NO_KERNEL_OP(while);
USE_NO_KERNEL_OP(print);
namespace f = paddle::framework;
namespace p = paddle::platform;
namespace paddle {
namespace framework {
template <typename T>
LoDTensor *CreateVariable(Scope *scope, const p::CPUPlace &place,
std::string name, T value) {
// Create LoDTensor<int> of dim [1]
auto var = scope->Var(name);
auto tensor = var->GetMutable<LoDTensor>();
tensor->Resize({1});
T *expect = tensor->mutable_data<T>(place);
expect[0] = value;
return tensor;
}
void AddOp(const std::string &type, const VariableNameMap &inputs,
const VariableNameMap &outputs, AttributeMap attrs,
BlockDesc *block) {
// insert op
auto op = block->AppendOp();
op->SetType(type);
for (auto &kv : inputs) {
op->SetInput(kv.first, kv.second);
}
for (auto &kv : outputs) {
op->SetOutput(kv.first, kv.second);
}
op->SetAttrMap(attrs);
}
void AddCase(ProgramDesc *program, Scope *scope, p::CPUPlace *place,
BlockDesc *casesBlock, int caseId, int caseType,
std::string caseChannel, std::string caseVarName,
std::function<void(BlockDesc *, Scope *)> func) {
std::string caseCondName = std::string("caseCond") + std::to_string(caseId);
std::string caseCondXVarName =
std::string("caseCondX") + std::to_string(caseId);
BlockDesc *caseBlock = program->AppendBlock(*casesBlock);
func(caseBlock, scope);
CreateVariable(scope, *place, caseCondName, false);
CreateVariable(scope, *place, caseCondXVarName, caseId);
CreateVariable(scope, *place, caseVarName, caseId);
scope->Var("step_scope");
AddOp("equal", {{"X", {caseCondXVarName}}, {"Y", {"caseToExecute"}}},
{{"Out", {caseCondName}}}, {}, casesBlock);
AddOp("conditional_block", {{"X", {caseCondName}}, {"Params", {}}},
{{"Out", {}}, {"Scope", {"step_scope"}}},
{{"sub_block", caseBlock}, {"is_scalar_condition", true}}, casesBlock);
}
void AddFibonacciSelect(Scope *scope, p::CPUPlace *place, ProgramDesc *program,
BlockDesc *parentBlock, std::string dataChanName,
std::string quitChanName) {
BlockDesc *whileBlock = program->AppendBlock(*parentBlock);
CreateVariable(scope, *place, "whileExitCond", true);
CreateVariable(scope, *place, "caseToExecute", -1);
CreateVariable(scope, *place, "case1var", 0);
CreateVariable(scope, *place, "xtemp", 0);
// TODO(thuan): Need to create fibXToSend, since channel send moves the actual
// data,
// which causes the data to be no longer accessible to do the fib calculation
// TODO(abhinav): Change channel send to do a copy instead of a move!
CreateVariable(scope, *place, "fibXToSend", 0);
CreateVariable(scope, *place, "fibX", 0);
CreateVariable(scope, *place, "fibY", 1);
CreateVariable(scope, *place, "quitVar", 0);
BlockDesc *casesBlock = program->AppendBlock(*whileBlock);
std::function<void(BlockDesc * caseBlock)> f = [](BlockDesc *caseBlock) {};
// TODO(thuan): Remove this once we change channel send to do a copy instead
// of move
AddOp("assign", {{"X", {"fibX"}}}, {{"Out", {"fibXToSend"}}}, {}, whileBlock);
// Case 0: Send to dataChanName
std::function<void(BlockDesc * caseBlock, Scope * scope)> case0Func = [&](
BlockDesc *caseBlock, Scope *scope) {
AddOp("assign", {{"X", {"fibX"}}}, {{"Out", {"xtemp"}}}, {}, caseBlock);
AddOp("assign", {{"X", {"fibY"}}}, {{"Out", {"fibX"}}}, {}, caseBlock);
AddOp("elementwise_add", {{"X", {"xtemp"}}, {"Y", {"fibY"}}},
{{"Out", {"fibY"}}}, {}, caseBlock);
};
AddCase(program, scope, place, casesBlock, 0, 1, dataChanName, "fibXToSend",
case0Func);
std::string case0Config =
std::string("0,1,") + dataChanName + std::string(",fibXToSend");
// Case 1: Receive from quitChanName
std::function<void(BlockDesc * caseBlock, Scope * scope)> case2Func = [&](
BlockDesc *caseBlock, Scope *scope) {
// Exit the while loop after we receive from quit channel.
// We assign a false to "whileExitCond" variable, which will
// break out of while_op loop
CreateVariable(scope, *place, "whileFalse", false);
AddOp("assign", {{"X", {"whileFalse"}}}, {{"Out", {"whileExitCond"}}}, {},
caseBlock);
};
AddCase(program, scope, place, casesBlock, 1, 2, quitChanName, "quitVar",
case2Func);
std::string case1Config =
std::string("1,2,") + quitChanName + std::string(",quitVar");
// Select block
AddOp("select", {{"X", {dataChanName, quitChanName}},
{"case_to_execute", {"caseToExecute"}}},
{{"Out", {}}},
{{"sub_block", casesBlock},
{"cases", std::vector<std::string>{case0Config, case1Config}}},
whileBlock);
scope->Var("stepScopes");
AddOp("while",
{{"X", {dataChanName, quitChanName}}, {"Condition", {"whileExitCond"}}},
{{"Out", {}}, {"StepScopes", {"stepScopes"}}},
{{"sub_block", whileBlock}}, parentBlock);
}
TEST(Concurrency, Go_Op) {
Scope scope;
p::CPUPlace place;
// Initialize scope variables
p::CPUDeviceContext ctx(place);
// Create channel variable
scope.Var("Channel");
// Create Variables, x0 will be put into channel,
// result will be pulled from channel
CreateVariable(&scope, place, "Status", false);
CreateVariable(&scope, place, "x0", 99);
CreateVariable(&scope, place, "result", 0);
framework::Executor executor(place);
ProgramDesc program;
BlockDesc *block = program.MutableBlock(0);
// Create channel OP
AddOp("channel_create", {}, {{"Out", {"Channel"}}},
{{"capacity", 10}, {"data_type", f::proto::VarType::LOD_TENSOR}},
block);
// Create Go Op routine
BlockDesc *goOpBlock = program.AppendBlock(program.Block(0));
AddOp("channel_send", {{"Channel", {"Channel"}}, {"X", {"x0"}}},
{{"Status", {"Status"}}}, {}, goOpBlock);
// Create Go Op
AddOp("go", {{"X", {"Channel", "x0"}}}, {}, {{"sub_block", goOpBlock}},
block);
// Create Channel Receive Op
AddOp("channel_recv", {{"Channel", {"Channel"}}},
{{"Status", {"Status"}}, {"Out", {"result"}}}, {}, block);
// Create Channel Close Op
AddOp("channel_close", {{"Channel", {"Channel"}}}, {}, {}, block);
// Check the result tensor to make sure it is set to 0
const LoDTensor &tensor = (scope.FindVar("result"))->Get<LoDTensor>();
auto *initialData = tensor.data<int>();
EXPECT_EQ(initialData[0], 0);
executor.Run(program, &scope, 0, true, true);
// After we call executor.run, the Go operator should do a channel_send to
// set the "result" variable to 99.
auto *finalData = tensor.data<int>();
EXPECT_EQ(finalData[0], 99);
}
/**
* This test implements the fibonacci function using go_op and select_op
*/
TEST(Concurrency, Select) {
Scope scope;
p::CPUPlace place;
// Initialize scope variables
p::CPUDeviceContext ctx(place);
CreateVariable(&scope, place, "Status", false);
CreateVariable(&scope, place, "result", 0);
CreateVariable(&scope, place, "currentXFib", 0);
framework::Executor executor(place);
ProgramDesc program;
BlockDesc *block = program.MutableBlock(0);
// Create channel OP
std::string dataChanName = "Channel";
scope.Var(dataChanName);
AddOp("channel_create", {}, {{"Out", {dataChanName}}},
{{"capacity", 0}, {"data_type", f::proto::VarType::LOD_TENSOR}}, block);
std::string quitChanName = "Quit";
scope.Var(quitChanName);
AddOp("channel_create", {}, {{"Out", {quitChanName}}},
{{"capacity", 0}, {"data_type", f::proto::VarType::LOD_TENSOR}}, block);
// Create Go Op routine, which loops 10 times over fibonacci sequence
CreateVariable(&scope, place, "xReceiveVar", 0);
BlockDesc *goOpBlock = program.AppendBlock(program.Block(0));
for (int i = 0; i < 10; ++i) {
AddOp("channel_recv", {{"Channel", {dataChanName}}},
{{"Status", {"Status"}}, {"Out", {"currentXFib"}}}, {}, goOpBlock);
AddOp("print", {{"In", {"currentXFib"}}}, {{"Out", {"currentXFib"}}},
{{"first_n", 100},
{"summarize", -1},
{"print_tensor_name", false},
{"print_tensor_type", true},
{"print_tensor_shape", false},
{"print_tensor_lod", false},
{"print_phase", std::string("FORWARD")},
{"message", std::string("X: ")}},
goOpBlock);
}
CreateVariable(&scope, place, "quitSignal", 0);
AddOp("channel_send", {{"Channel", {quitChanName}}, {"X", {"quitSignal"}}},
{{"Status", {"Status"}}}, {}, goOpBlock);
// Create Go Op
AddOp("go", {{"X", {dataChanName, quitChanName}}}, {},
{{"sub_block", goOpBlock}}, block);
AddFibonacciSelect(&scope, &place, &program, block, dataChanName,
quitChanName);
// Create Channel Close Op
AddOp("channel_close", {{"Channel", {dataChanName}}}, {}, {}, block);
AddOp("channel_close", {{"Channel", {quitChanName}}}, {}, {}, block);
executor.Run(program, &scope, 0, true, true);
// After we call executor.run, "result" variable should be equal to 34
// (which is 10 loops through fibonacci sequence)
const LoDTensor &tensor = (scope.FindVar("currentXFib"))->Get<LoDTensor>();
auto *finalData = tensor.data<int>();
EXPECT_EQ(finalData[0], 34);
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/executor.cc
浏览文件 @ 903e3f3f
......@@ -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 @ 903e3f3f
......@@ -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/graph_traits.cc
浏览文件 @ 903e3f3f
......@@ -14,6 +14,8 @@
#include "paddle/fluid/framework/ir/graph_traits.h"
#include <vector>
namespace paddle {
namespace framework {
namespace ir {
......
paddle/fluid/framework/selected_rows_test.cc
浏览文件 @ 903e3f3f
......@@ -27,8 +27,11 @@ class SelectedRowsTester : public ::testing::Test {
selected_rows_.reset(new SelectedRows(rows, height));
Tensor* value = selected_rows_->mutable_value();
value->mutable_data<float>(
auto* data = value->mutable_data<float>(
make_ddim({static_cast<int64_t>(rows.size()), row_numel}), place_);
for (int64_t i = 0; i < value->numel(); ++i) {
data[i] = static_cast<float>(i);
}
}
protected:
......@@ -60,6 +63,10 @@ TEST_F(SelectedRowsTester, SerializeAndDeseralize) {
ASSERT_EQ(selected_rows_->height(), dst_tensor.height());
ASSERT_EQ(selected_rows_->value().dims(), dst_tensor.value().dims());
ASSERT_EQ(selected_rows_->GetCompleteDims(), dst_tensor.GetCompleteDims());
auto* dst_data = dst_tensor.value().data<float>();
for (int64_t i = 0; i < dst_tensor.value().numel(); ++i) {
ASSERT_EQ(dst_data[i], static_cast<float>(i));
}
}
TEST(SelectedRows, SparseTable) {
......
paddle/fluid/framework/tuple.h
浏览文件 @ 903e3f3f
......@@ -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 @ 903e3f3f
......@@ -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 @ 903e3f3f
......@@ -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 @ 903e3f3f
......@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor.h"
......@@ -41,8 +40,6 @@ inline proto::VarType::Type ToVarType(std::type_index type) {
return proto::VarType_Type_SELECTED_ROWS;
} else if (IsType<ReaderHolder>(type)) {
return proto::VarType_Type_READER;
} else if (IsType<ChannelHolder>(type)) {
return proto::VarType_Type_CHANNEL;
} else {
PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
}
......@@ -66,9 +63,6 @@ inline void VisitVarType(const framework::Variable& var, Visitor visitor) {
case proto::VarType_Type_READER:
visitor(var.Get<ReaderHolder>());
return;
case proto::VarType_Type_CHANNEL:
visitor(var.Get<ChannelHolder>());
return;
default:
PADDLE_THROW("Not supported visit type, %d", ToVarType(var.Type()));
}
......
paddle/fluid/inference/analysis/analysis_pass.h
浏览文件 @ 903e3f3f
......@@ -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_tester.cc
浏览文件 @ 903e3f3f
......@@ -37,12 +37,16 @@ TEST(Analyzer, analysis_without_tensorrt) {
TEST(Analyzer, analysis_with_tensorrt) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
Argument argument;
argument.Set<int>("minimum_subgraph_size", new int(0));
argument.Set<int>("max_batch_size", new int(3));
argument.Set<int>("workspace_size", new int(1 << 20));
argument.Set<std::string>("precision_mode", new std::string("FP32"));
argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
Analyzer analyser;
analyser.Run(&argument);
}
void TestWord2vecPrediction(const std::string &model_path) {
void TestWord2vecPrediction(const std::string& model_path) {
NativeConfig config;
config.model_dir = model_path;
config.use_gpu = false;
......@@ -73,8 +77,8 @@ void TestWord2vecPrediction(const std::string &model_path) {
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << "data: "
<< static_cast<float *>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float *>(outputs.front().data.data())[i],
<< static_cast<float*>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
}
}
......
paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc
浏览文件 @ 903e3f3f
......@@ -97,8 +97,10 @@ void DataFlowGraphToFluidPass::AddFluidOp(Node *node) {
}
}
void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
void CreateTrtEngineOp(Node *node, Argument *argument,
framework::proto::BlockDesc *block) {
PADDLE_ENFORCE(argument->main_dfg.get());
const DataFlowGraph &graph = *(argument->main_dfg);
static int counter{0};
PADDLE_ENFORCE(node->IsFunctionBlock());
framework::OpDesc desc;
......@@ -204,7 +206,10 @@ void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
PADDLE_ENFORCE(!block->vars().empty(), "the block has no var-desc");
// Set attrs
SetAttr(desc.Proto(), "subgraph", block->SerializeAsString());
SetAttr(desc.Proto(), "max_batch_size", argument->Get<int>("max_batch_size"));
SetAttr(desc.Proto(), "workspace_size", argument->Get<int>("workspace_size"));
SetAttr(desc.Proto(), "engine_uniq_key", "trt-" + std::to_string(counter++));
SetAttr(desc.Proto(), "parameters", ExtractParameters(graph.nodes.nodes()));
SetAttr(desc.Proto(), "output_name_mapping", output_mapping);
......@@ -248,7 +253,7 @@ void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
*block_desc.Proto()->mutable_vars() =
argument_->origin_program_desc->blocks(0).vars();
PADDLE_ENFORCE(!block_desc.Proto()->vars().empty());
CreateTrtEngineOp(node, *argument_->main_dfg, block_desc.Proto());
CreateTrtEngineOp(node, argument_, block_desc.Proto());
auto *main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
auto *op = main_block->add_ops();
PADDLE_ENFORCE(!node->pb_msg().empty(), "failed to set desc for block");
......
paddle/fluid/inference/analysis/subgraph_splitter.cc
浏览文件 @ 903e3f3f
......@@ -309,6 +309,8 @@ void SubGraphFuse::operator()() { ReplaceNodesWithSubGraphs(); }
void SubGraphFuse::ReplaceNodesWithSubGraphs() {
auto subgraphs = SubGraphSplitter(graph_, node_inside_subgraph_teller_)();
for (auto &subgraph : subgraphs) {
if (subgraph.size() <= argument_->Get<int>("minimum_subgraph_size"))
continue;
std::unordered_set<Node *> subgraph_uniq(subgraph.begin(), subgraph.end());
// replace this sub-graph with the first node. Two steps: 1. Create a Block
// Node that contains this subgraph 2. Mark the nodes inside the sub-graph
......
paddle/fluid/inference/analysis/subgraph_splitter.h
浏览文件 @ 903e3f3f
......@@ -20,6 +20,7 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/inference/analysis/argument.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/node.h"
......@@ -63,8 +64,11 @@ class SubGraphFuse {
public:
using NodeInsideSubgraphTeller = SubGraphSplitter::NodeInsideSubgraphTeller;
SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller)
: graph_(graph), node_inside_subgraph_teller_(teller) {}
SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller,
Argument *argument)
: graph_(graph),
node_inside_subgraph_teller_(teller),
argument_(argument) {}
// The main method which run all the logic.
void operator()();
......@@ -76,6 +80,7 @@ class SubGraphFuse {
private:
DataFlowGraph *graph_;
NodeInsideSubgraphTeller node_inside_subgraph_teller_;
Argument *argument_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/subgraph_splitter_tester.cc
浏览文件 @ 903e3f3f
......@@ -66,10 +66,12 @@ TEST(SubGraphSplitter, Split) {
TEST(SubGraphSplitter, Fuse) {
auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
auto dfg = ProgramDescToDFG(desc);
Argument argument;
argument.Set<int>("minimum_subgraph_size", new int(3));
size_t count0 = dfg.nodes.size();
SubGraphFuse fuse(&dfg, teller);
SubGraphFuse fuse(&dfg, teller, &argument);
fuse();
int count1 = 0;
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc
浏览文件 @ 903e3f3f
......@@ -24,7 +24,7 @@ TensorRTSubGraphPass::TensorRTSubGraphPass(
: node_inside_subgraph_teller_(teller) {}
void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
SubGraphFuse(graph, node_inside_subgraph_teller_)();
SubGraphFuse(graph, node_inside_subgraph_teller_, argument_)();
VLOG(4) << "debug info "
<< graph->HumanReadableInfo(false /*show_values*/,
true /*show_functions*/);
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h
浏览文件 @ 903e3f3f
......@@ -33,7 +33,10 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
explicit TensorRTSubGraphPass(const NodeInsideSubgraphTeller& teller);
bool Initialize(Argument* argument) override { return true; }
bool Initialize(Argument* argument) override {
argument_ = argument;
return true;
}
// This class get a sub-graph as input and determine whether to transform this
// sub-graph into TensorRT.
......@@ -46,6 +49,7 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
private:
NodeInsideSubgraphTeller node_inside_subgraph_teller_;
Argument* argument_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass_tester.cc
浏览文件 @ 903e3f3f
......@@ -36,6 +36,10 @@ TEST(TensorRTSubGraphPass, main) {
};
Argument argument(FLAGS_inference_model_dir);
argument.Set<int>("minimum_subgraph_size", new int(0));
argument.Set<int>("max_batch_size", new int(3));
argument.Set<int>("workspace_size", new int(1 << 20));
argument.Set<std::string>("precision_mode", new std::string("FP32"));
DFG_GraphvizDrawPass::Config config{FLAGS_dot_dir, "origin"};
DFG_GraphvizDrawPass::Config config1{FLAGS_dot_dir, "fusion"};
......
paddle/fluid/inference/api/api_impl_tester.cc
浏览文件 @ 903e3f3f
......@@ -21,6 +21,12 @@ limitations under the License. */
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/tests/test_helper.h"
#ifdef __clang__
#define ACC_DIFF 4e-3
#else
#define ACC_DIFF 1e-3
#endif
DEFINE_string(dirname, "", "Directory of the inference model.");
namespace paddle {
......@@ -99,8 +105,8 @@ void MainWord2Vec(bool use_gpu) {
float* lod_data = output1.data<float>();
for (int i = 0; i < output1.numel(); ++i) {
EXPECT_LT(lod_data[i] - data[i], 1e-3);
EXPECT_GT(lod_data[i] - data[i], -1e-3);
EXPECT_LT(lod_data[i] - data[i], ACC_DIFF);
EXPECT_GT(lod_data[i] - data[i], -ACC_DIFF);
}
}
......@@ -144,7 +150,7 @@ void MainImageClassification(bool use_gpu) {
float* data = static_cast<float*>(outputs[0].data.data());
float* lod_data = output1.data<float>();
for (size_t j = 0; j < len / sizeof(float); ++j) {
EXPECT_NEAR(lod_data[j], data[j], 1e-3);
EXPECT_NEAR(lod_data[j], data[j], ACC_DIFF);
}
}
......@@ -199,7 +205,7 @@ void MainThreadsWord2Vec(bool use_gpu) {
float* ref_data = refs[tid].data<float>();
EXPECT_EQ(refs[tid].numel(), static_cast<int64_t>(len / sizeof(float)));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
EXPECT_NEAR(ref_data[i], data[i], ACC_DIFF);
}
});
}
......@@ -251,7 +257,7 @@ void MainThreadsImageClassification(bool use_gpu) {
float* ref_data = refs[tid].data<float>();
EXPECT_EQ((size_t)refs[tid].numel(), len / sizeof(float));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
EXPECT_NEAR(ref_data[i], data[i], ACC_DIFF);
}
});
}
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
浏览文件 @ 903e3f3f
......@@ -35,8 +35,6 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
FLAGS_IA_enable_tensorrt_subgraph_engine = true;
VLOG(3) << "Predictor::init()";
FLAGS_tensorrt_max_batch_size = config_.max_batch_size;
FLAGS_tensorrt_workspace_size = config_.workspace_size;
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
} else {
......@@ -92,6 +90,14 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
void OptimizeInferenceProgram() {
// Analyze inference_program
Argument argument;
argument.Set<int>("minimum_subgraph_size",
new int(config_.minimum_subgraph_size));
argument.Set<int>("max_batch_size", new int(config_.max_batch_size));
argument.Set<int>("workspace_size", new int(config_.workspace_size));
argument.Set<std::string>("precision_mode",
new std::string(config_.precision_mode));
if (!config_.model_dir.empty()) {
argument.fluid_model_dir.reset(new std::string(config_.model_dir));
} else {
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 903e3f3f
......@@ -194,6 +194,14 @@ struct MixedRTConfig : public NativeConfig {
// For workspace_size, refer it from here:
// https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
int workspace_size{1 << 30};
// We transform the Ops that can be converted into TRT layer in the model,
// and aggregate these Ops into subgraphs for TRT execution.
// We set this variable to control the minimum number of nodes in the
// subgraph, 3 as default value.
int minimum_subgraph_size = 3;
// Reserved configuration
// We just support "FP32" now, "FP16" and "INT8" will be supported.
std::string precision_mode = "FP32";
};
// NOTE WIP, not stable yet.
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 903e3f3f
......@@ -85,3 +85,13 @@ if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
DEPS inference_anakin_api_shared dynload_cuda SERIAL)
endif()
endif()
if(WITH_GPU AND TENSORRT_FOUND)
set(TRT_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/trt")
if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR})
inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
endif()
cc_test(test_trt_models SRCS trt_models_tester.cc
ARGS --dirname=${TRT_MODEL_INSTALL_DIR}/trt_test_models
DEPS paddle_inference_tensorrt_subgraph_engine)
endif()
paddle/fluid/inference/tests/api/trt_models_tester.cc 0 → 100644
浏览文件 @ 903e3f3f
// 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 <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
using paddle::contrib::MixedRTConfig;
DEFINE_string(dirname, "", "Directory of the inference model.");
NativeConfig GetConfigNative() {
NativeConfig config;
config.model_dir = FLAGS_dirname;
// LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.45;
config.use_gpu = true;
config.device = 0;
return config;
}
MixedRTConfig GetConfigTRT() {
MixedRTConfig config;
config.model_dir = FLAGS_dirname;
config.use_gpu = true;
config.fraction_of_gpu_memory = 0.2;
config.device = 0;
config.max_batch_size = 3;
return config;
}
void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
NativeConfig config0 = GetConfigNative();
config0.model_dir = model_dirname;
MixedRTConfig config1 = GetConfigTRT();
config1.model_dir = model_dirname;
config1.max_batch_size = batch_size;
auto predictor0 =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
auto predictor1 =
CreatePaddlePredictor<MixedRTConfig,
PaddleEngineKind::kAutoMixedTensorRT>(config1);
// Prepare inputs
int height = 224;
int width = 224;
float *data = new float[batch_size * 3 * height * width];
memset(data, 0, sizeof(float) * (batch_size * 3 * height * width));
data[0] = 1.0f;
// Prepare inputs
PaddleTensor tensor;
tensor.name = "input_0";
tensor.shape = std::vector<int>({batch_size, 3, height, width});
tensor.data = PaddleBuf(static_cast<void *>(data),
sizeof(float) * (batch_size * 3 * height * width));
tensor.dtype = PaddleDType::FLOAT32;
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
// Prepare outputs
std::vector<PaddleTensor> outputs0;
std::vector<PaddleTensor> outputs1;
CHECK(predictor0->Run(paddle_tensor_feeds, &outputs0));
CHECK(predictor1->Run(paddle_tensor_feeds, &outputs1, batch_size));
// Get output.
ASSERT_EQ(outputs0.size(), 1UL);
ASSERT_EQ(outputs1.size(), 1UL);
const size_t num_elements = outputs0.front().data.length() / sizeof(float);
const size_t num_elements1 = outputs1.front().data.length() / sizeof(float);
EXPECT_EQ(num_elements, num_elements1);
auto *data0 = static_cast<float *>(outputs0.front().data.data());
auto *data1 = static_cast<float *>(outputs1.front().data.data());
ASSERT_GT(num_elements, 0UL);
for (size_t i = 0; i < std::min(num_elements, num_elements1); i++) {
EXPECT_NEAR(data0[i], data1[i], 1e-3);
}
}
TEST(trt_models_test, main) {
std::vector<std::string> infer_models = {"mobilenet", "resnet50",
"resnext50"};
for (auto &model_dir : infer_models) {
CompareTensorRTWithFluid(1, FLAGS_dirname + "/" + model_dir);
}
}
} // namespace paddle
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 903e3f3f
......@@ -301,6 +301,7 @@ op_library(fusion_lstm_op DEPS cpu_lstm_compute)
if (WITH_GPU)
op_library(conv_op DEPS vol2col depthwise_conv im2col)
op_library(layer_norm_op DEPS cub)
op_library(reduce_mean_op DEPS cub)
else()
op_library(conv_op DEPS vol2col im2col)
endif()
......@@ -313,11 +314,6 @@ op_library(save_combine_op DEPS lod_tensor)
op_library(load_combine_op DEPS lod_tensor)
op_library(concat_op DEPS concat)
# FIXME(thuan): Move CSP operators to paddle/fluid/framework/operators/concurrency
add_subdirectory(concurrency)
op_library(channel_send_op DEPS concurrency)
op_library(channel_recv_op DEPS concurrency)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
......
paddle/fluid/operators/auc_op.cc
浏览文件 @ 903e3f3f
......@@ -36,11 +36,16 @@ class AucOp : public framework::OperatorWithKernel {
"Out and Label should have same height.");
int num_pred_buckets = ctx->Attrs().Get<int>("num_thresholds") + 1;
int slide_steps = ctx->Attrs().Get<int>("slide_steps");
PADDLE_ENFORCE_GE(num_pred_buckets, 1, "num_thresholds must larger than 1");
PADDLE_ENFORCE_GE(slide_steps, 0, "slide_steps must be natural number");
ctx->SetOutputDim("AUC", {1});
ctx->SetOutputDim("BatchAUC", {1});
ctx->SetOutputDim("StatPosOut", {num_pred_buckets});
ctx->SetOutputDim("StatNegOut", {num_pred_buckets});
slide_steps = slide_steps == 0 ? 1 : slide_steps;
ctx->SetOutputDim("StatPosOut", {slide_steps, num_pred_buckets});
ctx->SetOutputDim("StatNegOut", {slide_steps, num_pred_buckets});
}
protected:
......@@ -62,6 +67,7 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Label",
"A 2D int tensor indicating the label of the training data. "
"shape: [batch_size, 1]");
// TODO(typhoonzero): support weight input
AddInput("StatPos", "Statistic value when label = 1");
AddInput("StatNeg", "Statistic value when label = 0");
......@@ -69,18 +75,19 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("AUC",
"A scalar representing the "
"current area-under-the-curve.");
AddOutput("BatchAUC", "The AUC for current batch");
AddOutput("StatPosOut", "Statistic value when label = 1");
AddOutput("StatNegOut", "Statistic value when label = 0");
AddAttr<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
.SetDefault("ROC");
AddAttr<int>("num_thresholds",
"The number of thresholds to use when discretizing the"
" roc curve.")
AddAttr<int>(
"num_thresholds",
"The number of thresholds to use when discretizing the roc curve.")
.SetDefault((2 << 12) - 1);
AddAttr<int>("slide_steps", "Use slide steps to calc batch auc.")
.SetDefault(1);
AddComment(R"DOC(
Area Under The Curve (AUC) Operator.
......
paddle/fluid/operators/auc_op.h
浏览文件 @ 903e3f3f
......@@ -32,7 +32,9 @@ class AucKernel : public framework::OpKernel<T> {
std::string curve = ctx.Attr<std::string>("curve");
int num_thresholds = ctx.Attr<int>("num_thresholds");
// buckets contain numbers from 0 to num_thresholds
int num_pred_buckets = num_thresholds + 1;
int slide_steps = ctx.Attr<int>("slide_steps");
// Only use output var for now, make sure it's persistable and
// not cleaned up for each batch.
......@@ -40,16 +42,19 @@ class AucKernel : public framework::OpKernel<T> {
auto *stat_pos = ctx.Output<Tensor>("StatPosOut");
auto *stat_neg = ctx.Output<Tensor>("StatNegOut");
auto *stat_pos_data = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
auto *stat_neg_data = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
calcAuc(ctx, label, predict, stat_pos_data, stat_neg_data, num_thresholds,
auc);
auto *origin_stat_pos = stat_pos->mutable_data<int64_t>(ctx.GetPlace());
auto *origin_stat_neg = stat_neg->mutable_data<int64_t>(ctx.GetPlace());
auto *batch_auc = ctx.Output<Tensor>("BatchAUC");
std::vector<int64_t> stat_pos_batch(num_pred_buckets, 0);
std::vector<int64_t> stat_neg_batch(num_pred_buckets, 0);
calcAuc(ctx, label, predict, stat_pos_batch.data(), stat_neg_batch.data(),
num_thresholds, batch_auc);
std::vector<int64_t> stat_pos_data(num_pred_buckets, 0);
std::vector<int64_t> stat_neg_data(num_pred_buckets, 0);
auto stat_pos_calc = stat_pos_data.data();
auto stat_neg_calc = stat_neg_data.data();
statAuc(label, predict, num_pred_buckets, num_thresholds, slide_steps,
origin_stat_pos, origin_stat_neg, &stat_pos_calc, &stat_neg_calc);
calcAuc(ctx, stat_pos_calc, stat_neg_calc, num_thresholds, auc);
}
private:
......@@ -58,29 +63,76 @@ class AucKernel : public framework::OpKernel<T> {
return (X1 > X2 ? (X1 - X2) : (X2 - X1)) * (Y1 + Y2) / 2.0;
}
inline static void calcAuc(const framework::ExecutionContext &ctx,
const framework::Tensor *label,
inline static void statAuc(const framework::Tensor *label,
const framework::Tensor *predict,
int64_t *stat_pos, int64_t *stat_neg,
int num_thresholds,
framework::Tensor *auc_tensor) {
const int num_pred_buckets,
const int num_thresholds, const int slide_steps,
int64_t *origin_stat_pos, int64_t *origin_stat_neg,
int64_t **stat_pos, int64_t **stat_neg) {
size_t batch_size = predict->dims()[0];
size_t inference_width = predict->dims()[1];
const T *inference_data = predict->data<T>();
const auto *label_data = label->data<int64_t>();
auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
for (size_t i = 0; i < batch_size; i++) {
uint32_t binIdx = static_cast<uint32_t>(
inference_data[i * inference_width + 1] * num_thresholds);
if (label_data[i]) {
stat_pos[binIdx] += 1.0;
(*stat_pos)[binIdx] += 1.0;
} else {
stat_neg[binIdx] += 1.0;
(*stat_neg)[binIdx] += 1.0;
}
}
int bucket_length = num_pred_buckets * sizeof(int64_t);
// will stat auc unlimited.
if (slide_steps == 0) {
for (int slide = 0; slide < num_pred_buckets; ++slide) {
origin_stat_pos[slide] += (*stat_pos)[slide];
origin_stat_neg[slide] += (*stat_neg)[slide];
}
*stat_pos = origin_stat_pos;
*stat_neg = origin_stat_neg;
} else {
for (int slide = 1; slide < slide_steps; ++slide) {
int dst_idx = (slide - 1) * num_pred_buckets;
int src_inx = slide * num_pred_buckets;
std::memcpy(origin_stat_pos + dst_idx, origin_stat_pos + src_inx,
bucket_length);
std::memcpy(origin_stat_neg + dst_idx, origin_stat_neg + src_inx,
bucket_length);
}
std::memcpy(origin_stat_pos + (slide_steps - 1) * num_pred_buckets,
*stat_pos, bucket_length);
std::memcpy(origin_stat_neg + (slide_steps - 1) * num_pred_buckets,
*stat_neg, bucket_length);
std::memset(*stat_pos, 0, bucket_length);
std::memset(*stat_neg, 0, bucket_length);
for (int slide = 0; slide < num_pred_buckets; ++slide) {
int stat_pos_steps = 0;
int stat_neg_steps = 0;
for (int step = 0; step < slide_steps; ++step) {
stat_pos_steps += origin_stat_pos[slide + step * num_pred_buckets];
stat_neg_steps += origin_stat_neg[slide + step * num_pred_buckets];
}
(*stat_pos)[slide] += stat_pos_steps;
(*stat_neg)[slide] += stat_neg_steps;
}
}
}
inline static void calcAuc(const framework::ExecutionContext &ctx,
int64_t *stat_pos, int64_t *stat_neg,
int num_thresholds,
framework::Tensor *auc_tensor) {
auto *auc = auc_tensor->mutable_data<double>(ctx.GetPlace());
*auc = 0.0f;
double totPos = 0.0;
......@@ -96,7 +148,6 @@ class AucKernel : public framework::OpKernel<T> {
totPos += stat_pos[idx];
totNeg += stat_neg[idx];
*auc += trapezoidArea(totNeg, totNegPrev, totPos, totPosPrev);
--idx;
}
......
paddle/fluid/operators/channel_close_op.cc 已删除 100644 → 0
浏览文件 @ 77c8ddb5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/op_registry.h"
namespace pf = paddle::framework;
static constexpr char kChannel[] = "Channel";
namespace paddle {
namespace operators {
class ChannelCloseOp : public framework::OperatorBase {
public:
ChannelCloseOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &inp = *scope.FindVar(Input(kChannel));
// Get the mutable version of the channel variable and closes it.
pf::ChannelHolder *ch = inp.GetMutable<framework::ChannelHolder>();
ch->close();
}
};
class ChannelCloseOpOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("Channel"),
"The input of ChannelClose op must be set");
}
};
class ChannelCloseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(kChannel,
"The Channel Variable that should be closed by"
" the ChannelClose Op.");
AddComment(R"DOC(
Channel Close Operator.
This operator closes an open channel.
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(channel_close, paddle::operators::ChannelCloseOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::ChannelCloseOpMaker);
paddle/fluid/operators/channel_create_op.cc 已删除 100644 → 0
浏览文件 @ 77c8ddb5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/reader.h"
namespace pf = paddle::framework;
static constexpr char kOutput[] = "Out";
namespace paddle {
namespace operators {
class ChannelCreateOp : public framework::OperatorBase {
public:
ChannelCreateOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
auto &out = *scope.FindVar(Output(kOutput));
// Determine the datatype and capacity of the channel to be created
// from the attributes provided.
auto dtype =
static_cast<framework::proto::VarType::Type>(Attr<int>("data_type"));
auto capacity = Attr<int>("capacity");
// Based on the datatype, create a new channel holder initialized with
// the given capacity. When capacity is 0, an unbuffered channel is
// created.
pf::ChannelHolder *ch = out.GetMutable<framework::ChannelHolder>();
if (dtype == framework::proto::VarType::LOD_TENSOR) {
ch->Reset<pf::LoDTensor>(capacity);
} else if (dtype == framework::proto::VarType::SELECTED_ROWS) {
ch->Reset<pf::SelectedRows>(capacity);
} else if (dtype == framework::proto::VarType::LOD_RANK_TABLE) {
ch->Reset<pf::LoDRankTable>(capacity);
} else if (dtype == framework::proto::VarType::LOD_TENSOR_ARRAY) {
ch->Reset<pf::LoDTensorArray>(capacity);
} else if (dtype == framework::proto::VarType::READER) {
ch->Reset<pf::ReaderHolder>(capacity);
} else if (dtype == framework::proto::VarType::CHANNEL) {
ch->Reset<pf::ChannelHolder>(capacity);
} else if (dtype == framework::proto::VarType::BOOL) {
ch->Reset<bool>(capacity);
} else if (dtype == framework::proto::VarType::INT32) {
ch->Reset<int>(capacity);
} else if (dtype == framework::proto::VarType::INT64) {
ch->Reset<int64_t>(capacity);
} else if (dtype == framework::proto::VarType::FP32) {
ch->Reset<float>(capacity);
} else if (dtype == framework::proto::VarType::FP64) {
ch->Reset<double>(capacity);
} else {
PADDLE_THROW(
"Data type %d is not in "
"[LOD_TENSOR, SELECTED_ROWS, LOD_RANK_TABLE, LOD_TENSOR_ARRAY, "
"READER, CHANNEL, BOOL, INT32, INT64, FP32, FP64]",
dtype);
}
}
};
class ChannelCreateOpOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasOutput(kOutput),
"The output of ChannelCreate op must be set");
context->SetOutputDim(kOutput, {1});
}
};
class ChannelCreateOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput(kOutput,
"The object of a Channel type created by ChannelCreate Op.");
AddAttr<int>("capacity", "The size of the buffer of Channel.")
.SetDefault(0);
AddAttr<int>("data_type", "The data type of elements inside the Channel.");
AddComment(R"DOC(
Channel Create Operator.
This operator creates an object of the VarType Channel and returns it.
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(channel_create, paddle::operators::ChannelCreateOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::ChannelCreateOpMaker);
paddle/fluid/operators/channel_recv_op.cc 已删除 100644 → 0
浏览文件 @ 77c8ddb5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/channel.h"
#include <paddle/fluid/framework/lod_rank_table.h>
#include <paddle/fluid/framework/lod_tensor_array.h>
#include <paddle/fluid/framework/reader.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/concurrency/channel_util.h"
#include "paddle/fluid/operators/math/math_function.h"
static constexpr char Channel[] = "Channel";
static constexpr char Status[] = "Status";
static constexpr char Out[] = "Out";
namespace paddle {
namespace operators {
void SetReceiveStatus(const platform::Place &dev_place,
framework::Variable *status_var, bool status) {
auto cpu = platform::CPUPlace();
auto status_tensor =
status_var->GetMutable<framework::LoDTensor>()->mutable_data<bool>({1},
cpu);
status_tensor[0] = status;
}
class ChannelRecvOp : public framework::OperatorBase {
public:
ChannelRecvOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const {
PADDLE_ENFORCE(ctx->HasInput(Channel),
"Input(Channel) of ChannelRecvOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(Out),
"Input(Channel) of ChannelRecvOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(Status),
"Output(Status) of ChannelRecvOp should not be null.");
ctx->SetOutputDim("Status", {1});
}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// Get the channel holder created by channel_create op, passed as input.
framework::ChannelHolder *ch =
scope.FindVar(Input(Channel))->GetMutable<framework::ChannelHolder>();
auto output_var = scope.FindVar(Output(Out));
// Receive the data from the channel.
bool ok = concurrency::ChannelReceive(ch, output_var);
// Set the status output of the `ChannelReceive` call.
SetReceiveStatus(dev_place, scope.FindVar(Output(Status)), ok);
}
};
class ChannelRecvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(Channel,
"(Channel) A variable which \"receives\" the a value sent"
"to it by a channel_send op.")
.AsDuplicable();
AddOutput(Out,
"(Variable) Output Variable that will hold the data received"
" from the Channel")
.AsDuplicable();
AddOutput(Status,
"(Tensor) An LoD Tensor that returns a boolean status of the"
"result of the receive operation.")
.AsDuplicable();
AddComment(R"DOC(
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(channel_recv, paddle::operators::ChannelRecvOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::ChannelRecvOpMaker);
paddle/fluid/operators/channel_send_op.cc 已删除 100644 → 0
浏览文件 @ 77c8ddb5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/channel.h"
#include <paddle/fluid/framework/lod_rank_table.h>
#include <paddle/fluid/framework/lod_tensor_array.h>
#include <paddle/fluid/framework/reader.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/concurrency/channel_util.h"
#include "paddle/fluid/operators/math/math_function.h"
static constexpr char Channel[] = "Channel";
static constexpr char X[] = "X";
namespace paddle {
namespace operators {
class ChannelSendOp : public framework::OperatorBase {
public:
ChannelSendOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const {
PADDLE_ENFORCE(ctx->HasInput(Channel),
"Input(Channel) of ChannelSendOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput(X),
"Input(X) of ChannelSendOp should not be null.");
}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
// Get the channel holder created by channel_create op, passed as input.
framework::ChannelHolder *ch =
scope.FindVar(Input(Channel))->GetMutable<framework::ChannelHolder>();
auto input_var = scope.FindVar(Input(X));
// Send the input data through the channel.
concurrency::ChannelSend(ch, input_var);
}
};
class ChannelSendOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(Channel,
"(Channel) A variable which \"sends\" the passed in value to "
"a listening receiver.")
.AsDuplicable();
AddInput(X, "(Variable) The value which gets sent by the channel.")
.AsDuplicable();
AddComment(R"DOC(
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(channel_send, paddle::operators::ChannelSendOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::ChannelSendOpMaker);
paddle/fluid/operators/concurrency/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ 77c8ddb5
cc_library(concurrency SRCS channel_util.cc DEPS device_context framework_proto boost eigen3)
paddle/fluid/operators/concurrency/channel_util.cc 已删除 100644 → 0
浏览文件 @ 77c8ddb5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/concurrency/channel_util.h"
#include "paddle/fluid/framework/var_type.h"
namespace poc = paddle::operators::concurrency;
void poc::ChannelSend(framework::ChannelHolder *ch, framework::Variable *var) {
auto type = framework::ToVarType(var->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR)
ch->Send(var->GetMutable<framework::LoDTensor>());
else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE)
ch->Send(var->GetMutable<framework::LoDRankTable>());
else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY)
ch->Send(var->GetMutable<framework::LoDTensorArray>());
else if (type == framework::proto::VarType_Type_SELECTED_ROWS)
ch->Send(var->GetMutable<framework::SelectedRows>());
else if (type == framework::proto::VarType_Type_READER)
ch->Send(var->GetMutable<framework::ReaderHolder>());
else if (type == framework::proto::VarType_Type_CHANNEL)
ch->Send(var->GetMutable<framework::ChannelHolder>());
else
PADDLE_THROW("ChannelSend:Unsupported type");
}
bool poc::ChannelReceive(framework::ChannelHolder *ch,
framework::Variable *var) {
// Get type of channel and use that to call mutable data for Variable
auto type = framework::ToVarType(ch->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR)
return ch->Receive(var->GetMutable<framework::LoDTensor>());
else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE)
return ch->Receive(var->GetMutable<framework::LoDRankTable>());
else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY)
return ch->Receive(var->GetMutable<framework::LoDTensorArray>());
else if (type == framework::proto::VarType_Type_SELECTED_ROWS)
return ch->Receive(var->GetMutable<framework::SelectedRows>());
else if (type == framework::proto::VarType_Type_READER)
return ch->Receive(var->GetMutable<framework::ReaderHolder>());
else if (type == framework::proto::VarType_Type_CHANNEL)
return ch->Receive(var->GetMutable<framework::ChannelHolder>());
else
PADDLE_THROW("ChannelReceive:Unsupported type");
}
void poc::ChannelAddToSendQ(framework::ChannelHolder *ch, const void *referrer,
framework::Variable *var,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(framework::ChannelAction)> cb) {
auto type = framework::ToVarType(var->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR) {
ch->AddToSendQ(referrer, var->GetMutable<framework::LoDTensor>(), cond, cb);
} else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE) {
ch->AddToSendQ(referrer, var->GetMutable<framework::LoDRankTable>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY) {
ch->AddToSendQ(referrer, var->GetMutable<framework::LoDTensorArray>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_SELECTED_ROWS) {
ch->AddToSendQ(referrer, var->GetMutable<framework::SelectedRows>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_READER) {
ch->AddToSendQ(referrer, var->GetMutable<framework::ReaderHolder>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_CHANNEL) {
ch->AddToSendQ(referrer, var->GetMutable<framework::ChannelHolder>(), cond,
cb);
} else {
PADDLE_THROW("ChannelAddToSendQ:Unsupported type");
}
}
void poc::ChannelAddToReceiveQ(
framework::ChannelHolder *ch, const void *referrer,
framework::Variable *var, std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(framework::ChannelAction)> cb) {
auto type = framework::ToVarType(var->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDTensor>(), cond,
cb);
} else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDRankTable>(),
cond, cb);
} else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::LoDTensorArray>(),
cond, cb);
} else if (type == framework::proto::VarType_Type_SELECTED_ROWS) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::SelectedRows>(),
cond, cb);
} else if (type == framework::proto::VarType_Type_READER) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::ReaderHolder>(),
cond, cb);
} else if (type == framework::proto::VarType_Type_CHANNEL) {
ch->AddToReceiveQ(referrer, var->GetMutable<framework::ChannelHolder>(),
cond, cb);
} else {
PADDLE_THROW("ChannelAddToReceiveQ:Unsupported type");
}
}
paddle/fluid/operators/concurrency/channel_util.h 已删除 100644 → 0
浏览文件 @ 77c8ddb5
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/variable.h"
namespace paddle {
namespace operators {
namespace concurrency {
void ChannelSend(framework::ChannelHolder *ch, framework::Variable *var);
bool ChannelReceive(framework::ChannelHolder *ch, framework::Variable *var);
void ChannelAddToSendQ(framework::ChannelHolder *ch, const void *referrer,
framework::Variable *var,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(framework::ChannelAction)> cb);
void ChannelAddToReceiveQ(framework::ChannelHolder *ch, const void *referrer,
framework::Variable *var,
std::shared_ptr<std::condition_variable_any> cond,
std::function<bool(framework::ChannelAction)> cb);
} // namespace concurrency
} // namespace operators
} // namespace paddle
paddle/fluid/operators/conv_op.h
浏览文件 @ 903e3f3f
......@@ -380,7 +380,8 @@ class DepthwiseConvKernel : public framework::OpKernel<T> {
math::DepthwiseConvFunctor<DeviceContext, T> depthwiseConv;
auto& dev_ctx = context.template device_context<DeviceContext>();
depthwiseConv(dev_ctx, *input, filter, strides, paddings, output);
depthwiseConv(dev_ctx, *input, filter, strides, paddings, dilations,
output);
}
};
......@@ -415,14 +416,14 @@ class DepthwiseConvGradKernel : public framework::OpKernel<T> {
input_grad->mutable_data<T>(context.GetPlace());
set_zero(dev_ctx, input_grad, static_cast<T>(0));
depthwiseConvInputGrad(dev_ctx, *input, filter, *output_grad, strides,
paddings, input_grad);
paddings, dilations, input_grad);
}
if (filter_grad) {
filter_grad->mutable_data<T>(context.GetPlace());
set_zero(dev_ctx, filter_grad, static_cast<T>(0));
depthwiseConvFilterGrad(dev_ctx, *input, *output_grad, strides, paddings,
filter_grad);
dilations, filter_grad);
}
}
};
......
paddle/fluid/operators/conv_transpose_op.h
浏览文件 @ 903e3f3f
......@@ -345,7 +345,7 @@ class DepthwiseConvTransposeKernel : public framework::OpKernel<T> {
math::DepthwiseConvInputGradFunctor<DeviceContext, T>
depthwiseConvInputGrad;
depthwiseConvInputGrad(dev_ctx, *output, filter, *input, strides, paddings,
output);
dilations, output);
}
};
......@@ -367,10 +367,11 @@ class DepthwiseConvTransposeGradKernel : public framework::OpKernel<T> {
auto& dev_ctx = context.template device_context<DeviceContext>();
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
if (input_grad) {
math::DepthwiseConvFunctor<DeviceContext, T> depthwiseConv;
depthwiseConv(dev_ctx, *output_grad, filter, strides, paddings,
depthwiseConv(dev_ctx, *output_grad, filter, strides, paddings, dilations,
input_grad);
}
......@@ -382,7 +383,7 @@ class DepthwiseConvTransposeGradKernel : public framework::OpKernel<T> {
math::DepthwiseConvFilterGradFunctor<DeviceContext, T>
depthwiseConvFilterGrad;
depthwiseConvFilterGrad(dev_ctx, *output_grad, *input, strides, paddings,
filter_grad);
dilations, filter_grad);
}
}
};
......
paddle/fluid/operators/cub_reduce.h 0 → 100644
浏览文件 @ 903e3f3f
// 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 <algorithm>
#include <cmath>
#include <numeric>
#include <set>
#include <vector>
#include <cub/cub.cuh> // NOLINT
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace operators {
namespace detail {
template <typename T, size_t ElementCount>
struct Array {
public:
HOSTDEVICE inline Array() {}
HOSTDEVICE inline T& operator[](size_t index) { return data_[index]; }
HOSTDEVICE inline const T& operator[](size_t index) const {
return data_[index];
}
HOSTDEVICE constexpr inline size_t size() const { return ElementCount; }
template <typename VectorLikeType>
static inline Array<T, ElementCount> From(const VectorLikeType& vec) {
PADDLE_ENFORCE_EQ(vec.size(), ElementCount, "size not match");
size_t n = static_cast<size_t>(vec.size());
Array<T, ElementCount> ret;
for (size_t i = 0; i < n; ++i) ret[i] = vec[i];
return ret;
}
private:
T data_[ElementCount];
};
// reduce the last axis of 2d array
template <typename Tx, typename Ty, typename ReduceOp, typename TransformOp,
int BlockDim>
__global__ void ReduceKernel2D(const Tx* x, Ty* y, ReduceOp reducer,
TransformOp transformer, Ty init,
int reduce_num) {
__shared__ typename cub::BlockReduce<Ty, BlockDim>::TempStorage temp_storage;
int idx_x = blockIdx.x * reduce_num;
int idx_y = threadIdx.x;
Ty reduce_var = init;
for (int idx_y = threadIdx.x; idx_y < reduce_num; idx_y += BlockDim)
reduce_var = reducer(reduce_var, transformer(x[idx_x + idx_y]));
reduce_var =
cub::BlockReduce<Ty, BlockDim>(temp_storage).Reduce(reduce_var, reducer);
if (threadIdx.x == 0) {
y[blockIdx.x] = reduce_var;
}
}
template <typename Tx, typename Ty, typename ReduceOp, typename TransformOp,
int BlockDim, int Rank, int ReduceRank>
__global__ void ReduceKernel(const Tx* x, Ty* y, ReduceOp reducer,
TransformOp transformer, Ty init, int reduce_num,
Array<int, Rank> x_strides,
Array<int, ReduceRank> reduce_dim,
Array<int, ReduceRank> reduce_strides,
Array<int, Rank - ReduceRank> left_dim,
Array<int, Rank - ReduceRank> left_strides) {
__shared__ typename cub::BlockReduce<Ty, BlockDim>::TempStorage temp_storage;
Array<int, Rank> sub_index;
int left_idx = blockIdx.x;
for (int i = 0; i < Rank - ReduceRank; ++i) {
sub_index[left_dim[i]] = left_idx / left_strides[i];
left_idx %= left_strides[i];
}
int reduce_idx = threadIdx.x;
for (int j = 0; j < ReduceRank; ++j) {
sub_index[reduce_dim[j]] = reduce_idx / reduce_strides[j];
reduce_idx %= reduce_strides[j];
}
int idx_x = 0;
for (int k = 0; k < Rank; ++k) idx_x += (sub_index[k] * x_strides[k]);
Ty reduce_var = static_cast<Ty>(transformer(x[idx_x]));
for (int i = threadIdx.x + BlockDim; i < reduce_num; i += BlockDim) {
int reduce_idx = i;
for (int j = 0; j < ReduceRank; ++j) {
sub_index[reduce_dim[j]] = reduce_idx / reduce_strides[j];
reduce_idx %= reduce_strides[j];
}
int idx_x = 0;
for (int k = 0; k < Rank; ++k) idx_x += (sub_index[k] * x_strides[k]);
reduce_var = static_cast<Ty>(reducer(reduce_var, transformer(x[idx_x])));
}
reduce_var =
cub::BlockReduce<Ty, BlockDim>(temp_storage).Reduce(reduce_var, reducer);
if (threadIdx.x == 0) {
y[blockIdx.x] = reduce_var;
}
}
static inline std::vector<int> GetStrides(const std::vector<int>& dims) {
int n = static_cast<int>(dims.size());
if (n == 0) return std::vector<int>();
std::vector<int> strides(n);
strides.back() = 1;
for (int i = n - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * dims[i + 1];
}
return strides;
}
static inline std::vector<int> GetStrides(const std::vector<int>& dims,
const std::vector<int>& idx) {
int n = static_cast<int>(idx.size());
if (n == 0) return std::vector<int>();
std::vector<int> strides(n);
strides.back() = 1;
for (int i = n - 2; i >= 0; --i) {
strides[i] = strides[i + 1] * dims[idx[i + 1]];
}
return strides;
}
constexpr int kMaxBlockDim = 512;
static inline int GetDesiredBlockDim(int block_dim) {
return block_dim >= kMaxBlockDim
? kMaxBlockDim
: (1 << static_cast<int>(std::log2(block_dim)));
}
template <typename Tx, typename Ty, int BlockDim, typename ReduceOp,
typename TransformOp>
static void TensorReduceImpl(
const Tx* x_data, Ty* y_data, const platform::Place& place,
const ReduceOp& reducer, const TransformOp& transformer, const Ty& init,
int left_num, int reduce_num, const std::vector<int>& x_strides,
const std::vector<int>& reduce_dim, const std::vector<int>& reduce_strides,
const std::vector<int>& left_dim, const std::vector<int>& left_strides,
cudaStream_t stream) {
#define CUB_RANK_CASE(i, ...) \
case i: { \
constexpr auto kRank = i; \
switch (reduce_rank) { __VA_ARGS__; } \
} break
#define CUB_REDUCE_RANK_CASE(i, ...) \
case i: { \
constexpr auto kReduceRank = i; \
ReduceKernel<Tx, Ty, ReduceOp, TransformOp, BlockDim, kRank, \
kReduceRank><<<left_num, BlockDim, 0, stream>>>( \
x_data, y_data, reducer, transformer, init, reduce_num, \
Array<int, kRank>::From(x_strides), \
Array<int, kReduceRank>::From(reduce_dim), \
Array<int, kReduceRank>::From(reduce_strides), \
Array<int, kRank - kReduceRank>::From(left_dim), \
Array<int, kRank - kReduceRank>::From(left_strides)); \
} break
int rank = x_strides.size();
int reduce_rank = reduce_strides.size();
if (rank == reduce_rank) {
cub::TransformInputIterator<Ty, TransformOp, const Tx*> trans_x(
x_data, transformer);
size_t temp_storage_bytes = 0;
cub::DeviceReduce::Reduce(nullptr, temp_storage_bytes, trans_x, y_data,
reduce_num, reducer, init, stream);
framework::Tensor tmp;
auto* temp_storage = tmp.mutable_data<uint8_t>(
framework::make_ddim({static_cast<int64_t>(temp_storage_bytes)}),
place);
cub::DeviceReduce::Reduce(temp_storage, temp_storage_bytes, trans_x, y_data,
reduce_num, reducer, init, stream);
return;
}
if (rank == 2 && reduce_rank == 1 && reduce_dim[0] == 1) {
ReduceKernel2D<Tx, Ty, ReduceOp, TransformOp,
BlockDim><<<left_num, BlockDim, 0, stream>>>(
x_data, y_data, reducer, transformer, init, reduce_num);
return;
}
/*
if (rank == 3 && reduce_rank == 1 && reduce_dim[0] == 1) {
// TODO(liangdun): we can optimize 3d case which the 2nd axis is reduced.
// Currently, it is handled by code below, but inefficient
return;
}
*/
switch (rank) {
CUB_RANK_CASE(2, CUB_REDUCE_RANK_CASE(1););
CUB_RANK_CASE(3, CUB_REDUCE_RANK_CASE(1); CUB_REDUCE_RANK_CASE(2););
CUB_RANK_CASE(4, CUB_REDUCE_RANK_CASE(1); CUB_REDUCE_RANK_CASE(2);
CUB_REDUCE_RANK_CASE(3););
CUB_RANK_CASE(5, CUB_REDUCE_RANK_CASE(1); CUB_REDUCE_RANK_CASE(2);
CUB_REDUCE_RANK_CASE(3); CUB_REDUCE_RANK_CASE(4););
CUB_RANK_CASE(6, CUB_REDUCE_RANK_CASE(1); CUB_REDUCE_RANK_CASE(2);
CUB_REDUCE_RANK_CASE(3); CUB_REDUCE_RANK_CASE(4);
CUB_REDUCE_RANK_CASE(5););
CUB_RANK_CASE(7, CUB_REDUCE_RANK_CASE(1); CUB_REDUCE_RANK_CASE(2);
CUB_REDUCE_RANK_CASE(3); CUB_REDUCE_RANK_CASE(4);
CUB_REDUCE_RANK_CASE(5); CUB_REDUCE_RANK_CASE(6););
CUB_RANK_CASE(8, CUB_REDUCE_RANK_CASE(1); CUB_REDUCE_RANK_CASE(2);
CUB_REDUCE_RANK_CASE(3); CUB_REDUCE_RANK_CASE(4);
CUB_REDUCE_RANK_CASE(5); CUB_REDUCE_RANK_CASE(6););
CUB_RANK_CASE(9, CUB_REDUCE_RANK_CASE(1); CUB_REDUCE_RANK_CASE(2);
CUB_REDUCE_RANK_CASE(3); CUB_REDUCE_RANK_CASE(4);
CUB_REDUCE_RANK_CASE(5); CUB_REDUCE_RANK_CASE(6);
CUB_REDUCE_RANK_CASE(7); CUB_REDUCE_RANK_CASE(8););
}
#undef CUB_REDUCE_RANK_CASE
#undef CUB_RANK_CASE
}
} // namespace detail
template <typename Tx, typename Ty, typename ReduceOp, typename TransformOp>
void TensorReduce(const framework::Tensor& x, framework::Tensor* y,
std::vector<int> origin_reduce_dims, const Ty& init,
const ReduceOp& reducer, const TransformOp& transformer,
cudaStream_t stream) {
auto x_dim = framework::vectorize2int(x.dims());
std::vector<int> new_x_dim, new_reduce_dims;
int is_reduced = 0;
for (auto e : origin_reduce_dims) {
auto pos = e >= 0 ? e : e + x_dim.size();
is_reduced |= 1 << e;
}
for (int i = 0; i < x_dim.size(); i++) {
if ((i == 0) || (((is_reduced >> i) ^ (is_reduced >> (i - 1))) & 1)) {
new_x_dim.push_back(x_dim[i]);
if ((is_reduced >> i) & 1)
new_reduce_dims.push_back(new_x_dim.size() - 1);
} else {
new_x_dim[new_x_dim.size() - 1] *= x_dim[i];
}
}
x_dim = new_x_dim;
origin_reduce_dims = new_reduce_dims;
int x_rank = static_cast<int>(x_dim.size());
std::set<int> left_set, reduce_set;
for (int i = 0; i < x_rank; ++i) left_set.insert(i);
for (auto e : origin_reduce_dims) {
left_set.erase(e);
reduce_set.insert(e);
}
std::vector<int> reduce_dim(reduce_set.begin(), reduce_set.end());
std::vector<int> left_dim(left_set.begin(), left_set.end());
std::vector<int> x_strides = detail::GetStrides(x_dim);
std::vector<int> reduce_strides = detail::GetStrides(x_dim, reduce_dim);
std::vector<int> left_strides = detail::GetStrides(x_dim, left_dim);
int reduce_num = reduce_strides[0] * x_dim[reduce_dim[0]];
int left_num = 1;
if (left_dim.size()) left_num = left_strides[0] * x_dim[left_dim[0]];
std::vector<int> y_dim(left_dim.size());
for (int i = 0; i < left_dim.size(); ++i) {
y_dim[i] = x_dim[left_dim[i]];
}
auto x_data = x.data<Tx>();
auto y_data = y->mutable_data<Ty>(x.place());
if (reduce_num == 1) return;
#define CUB_BLOCK_DIM_CASE(block_dim) \
case block_dim: { \
constexpr auto kBlockDim = block_dim; \
detail::TensorReduceImpl<Tx, Ty, block_dim, ReduceOp, TransformOp>( \
x_data, y_data, x.place(), reducer, transformer, init, left_num, \
reduce_num, x_strides, reduce_dim, reduce_strides, left_dim, \
left_strides, stream); \
} break
switch (detail::GetDesiredBlockDim(reduce_num)) {
CUB_BLOCK_DIM_CASE(512);
CUB_BLOCK_DIM_CASE(256);
CUB_BLOCK_DIM_CASE(128);
CUB_BLOCK_DIM_CASE(64);
CUB_BLOCK_DIM_CASE(32);
CUB_BLOCK_DIM_CASE(16);
CUB_BLOCK_DIM_CASE(8);
CUB_BLOCK_DIM_CASE(4);
CUB_BLOCK_DIM_CASE(2);
}
#undef CUB_BLOCK_DIM_CASE
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/distributed/grpc_client.h
浏览文件 @ 903e3f3f
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <time.h>
#include <atomic>
#include <chrono> // NOLINT
#include <condition_variable> // NOLINT
......
paddle/fluid/operators/distributed/request_handler.h
浏览文件 @ 903e3f3f
......@@ -15,6 +15,7 @@
#pragma once
#include <time.h>
#include <condition_variable> // NOLINT
#include <functional>
#include <string>
......
paddle/fluid/operators/distributed/rpc_server.h
浏览文件 @ 903e3f3f
......@@ -14,6 +14,7 @@
#pragma once
#include <atomic>
#include <set>
#include <string>
#include <thread> // NOLINT
......
paddle/fluid/operators/elementwise_op.h
浏览文件 @ 903e3f3f
......@@ -89,7 +89,7 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<bool>("use_mkldnn", "(bool, default false). Used by MKLDNN.")
.SetDefault(false);
AddComment(string::Sprintf(R"DOC(
Limited Elementwise %s Operator
Elementwise %s Operator
The equation is:
......
paddle/fluid/operators/fusion_gru_op.cc
浏览文件 @ 903e3f3f
......@@ -290,12 +290,13 @@ class FusionGRUKernel : public framework::OpKernel<T> {
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
if (x->lod()[0].size() == 2) {
xx->Resize({total_T, D3});
SeqCompute(ctx);
return;
}
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_VEC_FUNC
auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
......
paddle/fluid/operators/fusion_lstm_op.cc
浏览文件 @ 903e3f3f
......@@ -424,11 +424,12 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = platform::CPUDeviceContext;
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
if (x->lod()[0].size() == 2) {
xx->Resize({x_dims[0], D4});
SeqCompute(ctx);
return;
}
INIT_BASE_SIZES
INIT_VEC_FUNC
INIT_BASE_INPUT_DATAS
......
paddle/fluid/operators/math/depthwise_conv.h
浏览文件 @ 903e3f3f
......@@ -32,7 +32,8 @@ class DepthwiseConvFunctor {
void operator()(const DeviceContext& context, const framework::Tensor& input,
const framework::Tensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* output);
const std::vector<int>& paddings,
const std::vector<int>& dilations, framework::Tensor* output);
};
template <typename DeviceContext, typename T>
......@@ -43,6 +44,7 @@ class DepthwiseConvInputGradFunctor {
const framework::Tensor& output_grad,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& dilations,
framework::Tensor* input_grad);
};
......@@ -53,6 +55,7 @@ class DepthwiseConvFilterGradFunctor {
const framework::Tensor& output_grad,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& dilations,
framework::Tensor* filter_grad);
};
......
paddle/fluid/operators/reduce_mean_op.cu
浏览文件 @ 903e3f3f
......@@ -12,17 +12,64 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <vector>
#include "paddle/fluid/operators/cub_reduce.h"
#include "paddle/fluid/operators/reduce_mean_op.h"
REGISTER_OP_CUDA_KERNEL(reduce_mean,
ops::ReduceKernel<paddle::platform::CUDADeviceContext,
float, ops::MeanFunctor>,
ops::ReduceKernel<paddle::platform::CUDADeviceContext,
double, ops::MeanFunctor>,
ops::ReduceKernel<paddle::platform::CUDADeviceContext,
int, ops::MeanFunctor>,
ops::ReduceKernel<paddle::platform::CUDADeviceContext,
int64_t, ops::MeanFunctor>);
namespace paddle {
namespace operators {
template <typename T>
struct DivideFunctor {
HOSTDEVICE explicit inline DivideFunctor(int n) : n_inv((T)(1.0 / n)) {}
HOSTDEVICE inline T operator()(const T& x) const { return x * n_inv; }
private:
T n_inv;
};
template <typename T>
class ReduceMeanKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
bool reduce_all = context.Attr<bool>("reduce_all");
auto* input = context.Input<Tensor>("X");
auto* output = context.Output<Tensor>("Out");
auto dims = context.Attr<std::vector<int>>("dim");
bool keep_dim = context.Attr<bool>("keep_dim");
std::vector<int> reduce_dims;
if (reduce_all) {
reduce_dims.resize(input->dims().size());
for (int i = 0; i < reduce_dims.size(); ++i) reduce_dims[i] = i;
} else {
for (auto e : dims) {
reduce_dims.push_back(e >= 0 ? e : e + input->dims().size());
}
}
int reduce_num = 1;
for (int i = 0; i < reduce_dims.size(); ++i) {
reduce_num *= input->dims()[reduce_dims[i]];
}
auto stream = context.cuda_device_context().stream();
TensorReduce<T, T, cub::Sum, DivideFunctor<T>>(
*input, output, reduce_dims, static_cast<T>(0), cub::Sum(),
DivideFunctor<T>(reduce_num), stream);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_CUDA_KERNEL(reduce_mean, ops::ReduceMeanKernel<float>,
ops::ReduceMeanKernel<double>,
ops::ReduceMeanKernel<int>,
ops::ReduceMeanKernel<int64_t>);
REGISTER_OP_CUDA_KERNEL(
reduce_mean_grad, ops::ReduceGradKernel<paddle::platform::CUDADeviceContext,
float, ops::MeanGradFunctor>,
......
paddle/fluid/operators/reduce_sum_op.cu
浏览文件 @ 903e3f3f
......@@ -12,17 +12,59 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/cub_reduce.h"
#include "paddle/fluid/operators/reduce_sum_op.h"
REGISTER_OP_CUDA_KERNEL(reduce_sum,
ops::ReduceKernel<paddle::platform::CUDADeviceContext,
float, ops::SumFunctor>,
ops::ReduceKernel<paddle::platform::CUDADeviceContext,
double, ops::SumFunctor>,
ops::ReduceKernel<paddle::platform::CUDADeviceContext,
int, ops::SumFunctor>,
ops::ReduceKernel<paddle::platform::CUDADeviceContext,
int64_t, ops::SumFunctor>);
namespace paddle {
namespace operators {
template <typename T>
struct IdentityFunctor {
HOSTDEVICE explicit inline IdentityFunctor() {}
HOSTDEVICE inline T operator()(const T& x) const { return x; }
};
template <typename T>
class ReduceSumKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
bool reduce_all = context.Attr<bool>("reduce_all");
auto* input = context.Input<Tensor>("X");
auto* output = context.Output<Tensor>("Out");
auto dims = context.Attr<std::vector<int>>("dim");
bool keep_dim = context.Attr<bool>("keep_dim");
std::vector<int> reduce_dims;
if (reduce_all) {
reduce_dims.resize(input->dims().size());
for (int i = 0; i < reduce_dims.size(); ++i) reduce_dims[i] = i;
} else {
for (auto e : dims) {
reduce_dims.push_back(e >= 0 ? e : e + input->dims().size());
}
}
int reduce_num = 1;
for (int i = 0; i < reduce_dims.size(); ++i) {
reduce_num *= input->dims()[reduce_dims[i]];
}
auto stream = context.cuda_device_context().stream();
TensorReduce<T, T, cub::Sum, IdentityFunctor<T>>(
*input, output, reduce_dims, static_cast<T>(0), cub::Sum(),
IdentityFunctor<T>(), stream);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_CUDA_KERNEL(reduce_sum, ops::ReduceSumKernel<float>,
ops::ReduceSumKernel<double>, ops::ReduceSumKernel<int>,
ops::ReduceSumKernel<int64_t>);
REGISTER_OP_CUDA_KERNEL(
reduce_sum_grad, ops::ReduceGradKernel<paddle::platform::CUDADeviceContext,
float, ops::SumGradFunctor>,
......
paddle/fluid/operators/scale_op.cc
浏览文件 @ 903e3f3f
......@@ -77,8 +77,10 @@ class ScaleOpVarTypeInference : public framework::VarTypeInference {
auto out_var_name = op_desc.Output("Out").front();
auto *out_var = block->FindVarRecursive(out_var_name);
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
if (in_var_name != out_var_name) {
out_var->SetType(in_var.GetType());
out_var->SetDataType(in_var.GetDataType());
}
}
};
......
paddle/fluid/operators/select_op.cc 已删除 100644 → 0
浏览文件 @ 77c8ddb5
此差异已折叠。
paddle/fluid/operators/sequence_slice_op.h
浏览文件 @ 903e3f3f
......@@ -75,11 +75,11 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
}
for (size_t i = 0; i < n; ++i) {
PADDLE_ENFORCE_LT(0, offset_data[i],
PADDLE_ENFORCE_LE(0, offset_data[i],
"The offset[%d] must greater than zero.", i);
PADDLE_ENFORCE_LT(0, length_data[i],
"The length[%d] must greater than zero.", i);
PADDLE_ENFORCE_LT(lod[0][i] + offset_data[i] + length_data[i],
PADDLE_ENFORCE_LE(lod[0][i] + offset_data[i] + length_data[i],
lod[0][i + 1], "The target tensor's length overflow.");
}
......
paddle/fluid/operators/sum_op.h
浏览文件 @ 903e3f3f
......@@ -32,7 +32,7 @@ class SumKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto in_vars = context.MultiInputVar("X");
int N = in_vars.size();
size_t in_num = in_vars.size();
auto out_var = context.OutputVar("Out");
bool in_place = out_var == in_vars[0];
......@@ -53,7 +53,7 @@ class SumKernel : public framework::OpKernel<T> {
auto &place =
*context.template device_context<DeviceContext>().eigen_device();
// If in_place, just skip the first tensor
for (int i = in_place ? 1 : 0; i < N; i++) {
for (size_t i = in_place ? 1 : 0; i < in_num; i++) {
if (in_vars[i]->IsType<framework::LoDTensor>()) {
auto &in_t = in_vars[i]->Get<framework::LoDTensor>();
if (in_t.numel() == 0) {
......@@ -101,13 +101,13 @@ class SumKernel : public framework::OpKernel<T> {
// Runtime InferShape
size_t first_dim = 0;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
first_dim += sel_row.rows().size();
}
std::vector<int64_t> in_dim;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() > 0) {
in_dim = framework::vectorize(sel_row.value().dims());
......@@ -116,7 +116,8 @@ class SumKernel : public framework::OpKernel<T> {
}
if (in_dim.empty()) {
VLOG(3) << "WARNING: all the inputs are empty";
in_dim = framework::vectorize(get_selected_row(N - 1).value().dims());
in_dim =
framework::vectorize(get_selected_row(in_num - 1).value().dims());
} else {
in_dim[0] = static_cast<int64_t>(first_dim);
}
......@@ -133,7 +134,7 @@ class SumKernel : public framework::OpKernel<T> {
math::SelectedRowsAddTo<DeviceContext, T> functor;
int64_t offset = 0;
for (int i = 0; i < N; i++) {
for (size_t i = 0; i < in_num; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() == 0) {
continue;
......
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ 903e3f3f
......@@ -22,8 +22,6 @@
namespace paddle {
DEFINE_int32(tensorrt_engine_batch_size, 1, "the batch_size of TensorRT");
DEFINE_int32(tensorrt_max_batch_size, 1, "TensorRT maximum batch size");
DEFINE_int32(tensorrt_workspace_size, 16 << 20, "TensorRT workspace size");
namespace operators {
......@@ -34,6 +32,8 @@ class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Ys", "A list of outputs").AsDuplicable();
AddAttr<std::string>("subgraph", "the subgraph.");
AddAttr<std::string>("engine_uniq_key", "unique key for the TRT engine.");
AddAttr<int>("max_batch_size", "the maximum batch size.");
AddAttr<int>("workspace_size", "the workspace size.");
AddComment("TensorRT engine operator.");
}
};
......
paddle/fluid/operators/tensorrt_engine_op.h
浏览文件 @ 903e3f3f
......@@ -28,8 +28,6 @@
namespace paddle {
DECLARE_int32(tensorrt_engine_batch_size);
DECLARE_int32(tensorrt_max_batch_size);
DECLARE_int32(tensorrt_workspace_size);
namespace operators {
......@@ -92,14 +90,14 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto engine_name = context.Attr<std::string>("engine_uniq_key");
int max_batch_size = context.Attr<int>("max_batch_size");
if (!Singleton<TRT_EngineManager>::Global().HasEngine(engine_name)) {
Prepare(context);
}
auto* engine = Singleton<TRT_EngineManager>::Global().Get(engine_name);
auto input_names = context.op().Inputs("Xs");
PADDLE_ENFORCE(!input_names.empty(), "should pass more than one inputs");
PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size,
FLAGS_tensorrt_max_batch_size);
PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size, max_batch_size);
std::vector<std::string> output_maps =
context.Attr<std::vector<std::string>>("output_name_mapping");
......@@ -173,8 +171,9 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
// Get the ProgramDesc and pass to convert.
framework::proto::BlockDesc block_desc;
block_desc.ParseFromString(context.Attr<std::string>("subgraph"));
int max_batch = FLAGS_tensorrt_max_batch_size;
auto max_workspace = FLAGS_tensorrt_workspace_size;
int max_batch_size = context.Attr<int>("max_batch_size");
int workspace_size = context.Attr<int>("workspace_size");
auto params = context.Attr<std::vector<std::string>>("parameters");
std::unordered_set<std::string> parameters;
for (const auto& param : params) {
......@@ -186,7 +185,7 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
// TODO(Superjomn) replace this with a different stream
auto* engine = Singleton<TRT_EngineManager>::Global().Create(
max_batch, max_workspace, nullptr /*engine hold its own stream*/,
max_batch_size, workspace_size, nullptr /*engine hold its own stream*/,
context.Attr<std::string>("engine_uniq_key"),
boost::get<platform::CUDAPlace>(context.GetPlace()).device);
......
paddle/fluid/operators/tensorrt_engine_op_test.cc
浏览文件 @ 903e3f3f
......@@ -58,8 +58,6 @@ void AddTensorToBlockDesc(framework::proto::BlockDesc* block,
using inference::analysis::SetAttr;
TEST(TensorRTEngineOp, manual) {
FLAGS_tensorrt_engine_batch_size = 2;
FLAGS_tensorrt_max_batch_size = 2;
framework::ProgramDesc program;
auto* block_ = program.Proto()->add_blocks();
block_->set_idx(0);
......@@ -101,6 +99,8 @@ TEST(TensorRTEngineOp, manual) {
engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z0"}));
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", 2);
SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 2 << 10);
SetAttr<std::string>(engine_op_desc.Proto(), "engine_uniq_key", "a_engine");
SetAttr<std::vector<std::string>>(engine_op_desc.Proto(), "parameters",
std::vector<std::string>({}));
......@@ -129,8 +129,6 @@ TEST(TensorRTEngineOp, manual) {
}
void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
FLAGS_tensorrt_engine_batch_size = batch_size;
FLAGS_tensorrt_max_batch_size = batch_size;
framework::ProgramDesc program;
framework::Scope scope;
platform::CUDAPlace place;
......@@ -195,8 +193,8 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
SetAttr<int>(engine_op_desc.Proto(), "max_batch", batch_size);
SetAttr<int>(engine_op_desc.Proto(), "max_workspace", 2 << 10);
SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", batch_size);
SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 2 << 10);
SetAttr<std::vector<std::string>>(
engine_op_desc.Proto(), "parameters",
std::vector<std::string>({"y0", "y1", "y2", "y3"}));
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 903e3f3f
......@@ -214,7 +214,6 @@ void BindVarDsec(pybind11::module *m) {
.def("set_shapes", &pd::VarDesc::SetShapes)
.def("set_dtype", &pd::VarDesc::SetDataType)
.def("set_dtypes", &pd::VarDesc::SetDataTypes)
.def("set_capacity", &pd::VarDesc::SetCapacity)
.def("shape", &pd::VarDesc::GetShape,
pybind11::return_value_policy::reference)
.def("shapes", &pd::VarDesc::GetShapes,
......@@ -251,7 +250,6 @@ void BindVarDsec(pybind11::module *m) {
.value("STEP_SCOPES", pd::proto::VarType::STEP_SCOPES)
.value("LOD_RANK_TABLE", pd::proto::VarType::LOD_RANK_TABLE)
.value("LOD_TENSOR_ARRAY", pd::proto::VarType::LOD_TENSOR_ARRAY)
.value("CHANNEL", pd::proto::VarType::CHANNEL)
.value("PLACE_LIST", pd::proto::VarType::PLACE_LIST)
.value("READER", pd::proto::VarType::READER)
.value("RAW", pd::proto::VarType::RAW);
......@@ -285,12 +283,12 @@ void BindOpDesc(pybind11::module *m) {
.def("set_output", &pd::OpDesc::SetOutput)
.def("input_arg_names", &pd::OpDesc::InputArgumentNames)
.def("output_arg_names", &pd::OpDesc::OutputArgumentNames)
.def("rename_input", &pd::OpDesc::RenameInput)
.def("rename_output", &pd::OpDesc::RenameOutput)
.def("_rename_input", &pd::OpDesc::RenameInput)
.def("_rename_output", &pd::OpDesc::RenameOutput)
.def("has_attr", &pd::OpDesc::HasAttr)
.def("attr_type", &pd::OpDesc::GetAttrType)
.def("attr_names", &pd::OpDesc::AttrNames)
.def("set_attr", &pd::OpDesc::SetAttr)
.def("_set_attr", &pd::OpDesc::SetAttr)
.def("attr", &pd::OpDesc::GetAttr)
.def("set_block_attr", &pd::OpDesc::SetBlockAttr)
.def("set_blocks_attr", &pd::OpDesc::SetBlocksAttr)
......@@ -300,8 +298,8 @@ void BindOpDesc(pybind11::module *m) {
std::string ser(seriralized);
self.SetAttr(name, ser);
})
.def("block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("_block_attr_id", &pd::OpDesc::GetBlockAttrId)
.def("_blocks_attr_ids", &pd::OpDesc::GetBlocksAttrIds)
.def("check_attrs", &pd::OpDesc::CheckAttrs)
.def("infer_shape", &pd::OpDesc::InferShape)
.def("infer_var_type", &pd::OpDesc::InferVarType)
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 903e3f3f
......@@ -21,7 +21,6 @@ limitations under the License. */
#include <utility>
#include <vector>
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/framework.pb.h"
......
paddle/legacy/trainer/tests/CMakeLists.txt
浏览文件 @ 903e3f3f
......@@ -16,7 +16,11 @@ endfunction()
trainer_test(test_Compare)
trainer_test(test_PyDataProviderWrapper)
trainer_test(test_recurrent_machine_generation)
trainer_test(test_Trainer)
if(NOT APPLE)
trainer_test(test_Trainer)
else()
message(WARNING "These tests has been disabled in OSX for random fail: \n test_Trainer")
endif()
############### test_TrainerOnePass ##########################
if(WITH_PYTHON)
......
paddle/scripts/paddle_build.sh
浏览文件 @ 903e3f3f
......@@ -70,8 +70,8 @@ function cmake_gen() {
PYTHON_FLAGS=""
SYSTEM=`uname -s`
if [ "$SYSTEM" == "Darwin" ]; then
echo "using python abi: $1"
if [[ "$1" == "cp27-cp27m" ]] || [[ "$1" == "" ]]; then
echo "using python abi: $1"
if [ -d "/Library/Frameworks/Python.framework/Versions/2.7" ]; then
export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/2.7
export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/2.7
......@@ -82,7 +82,18 @@ function cmake_gen() {
else
exit 1
fi
# TODO: qiyang add python3 part here
elif [ "$1" == "cp35-cp35m" ]; then
if [ -d "/Library/Frameworks/Python.framework/Versions/3.5" ]; then
export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/
export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/
export PATH=/Library/Frameworks/Python.framework/Versions/3.5/bin/:${PATH}
PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/bin/python3
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.5/include/python3.5m/
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/libpython3.5m.dylib"
WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
else
exit 1
fi
fi
else
if [ "$1" != "" ]; then
......@@ -384,10 +395,11 @@ EOF
ctest --output-on-failure -j $1
# make install should also be test when unittest
make install -j 8
pip install /usr/local/opt/paddle/share/wheels/*.whl
pip install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then
paddle version
fi
pip uninstall -y paddlepaddle
fi
}
......@@ -586,7 +598,7 @@ EOF
EOF
if [[ ${WITH_GPU} == "ON" ]]; then
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} &&"
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} || true"
else
NCCL_DEPS=""
fi
......@@ -602,9 +614,8 @@ EOF
cat >> ${PADDLE_ROOT}/build/Dockerfile <<EOF
ADD python/dist/*.whl /
# run paddle version to install python packages first
RUN apt-get update &&\
${NCCL_DEPS}\
apt-get install -y wget python-pip python-opencv libgtk2.0-dev dmidecode python-tk && easy_install -U pip && \
RUN apt-get update && ${NCCL_DEPS}
RUN apt-get install -y wget python-pip python-opencv libgtk2.0-dev dmidecode python-tk && easy_install -U pip && \
pip install /*.whl; apt-get install -f -y && \
apt-get clean -y && \
rm -f /*.whl && \
......@@ -735,7 +746,7 @@ function main() {
cmake_gen ${PYTHON_ABI:-""}
build
run_test
assert_api_not_changed
assert_api_not_changed ${PYTHON_ABI:-""}
;;
*)
print_usage
......
python/paddle/dataset/common.py
浏览文件 @ 903e3f3f
......@@ -77,13 +77,14 @@ def download(url, module_name, md5sum, save_name=None):
retry_limit = 3
while not (os.path.exists(filename) and md5file(filename) == md5sum):
if os.path.exists(filename):
print("file md5", md5file(filename), md5sum)
sys.stderr.write("file %s md5 %s" % (md5file(filename), md5sum))
if retry < retry_limit:
retry += 1
else:
raise RuntimeError("Cannot download {0} within retry limit {1}".
format(url, retry_limit))
print("Cache file %s not found, downloading %s" % (filename, url))
sys.stderr.write("Cache file %s not found, downloading %s" %
(filename, url))
r = requests.get(url, stream=True)
total_length = r.headers.get('content-length')
......@@ -100,10 +101,11 @@ def download(url, module_name, md5sum, save_name=None):
dl += len(data)
f.write(data)
done = int(50 * dl / total_length)
sys.stdout.write("\r[%s%s]" % ('=' * done,
sys.stderr.write("\r[%s%s]" % ('=' * done,
' ' * (50 - done)))
sys.stdout.flush()
sys.stderr.write("\n")
sys.stdout.flush()
return filename
......
python/paddle/fluid/backward.py
浏览文件 @ 903e3f3f
此差异已折叠。
python/paddle/fluid/clip.py
浏览文件 @ 903e3f3f
......@@ -75,8 +75,8 @@ class ErrorClipByValue(BaseErrorClipAttr):
clip_op_desc.set_type("clip")
clip_op_desc.set_input("X", [grad_name])
clip_op_desc.set_output("Out", [grad_name])
clip_op_desc.set_attr("min", self.min)
clip_op_desc.set_attr("max", self.max)
clip_op_desc._set_attr("min", self.min)
clip_op_desc._set_attr("max", self.max)
def error_clip_callback(block, context):
......@@ -271,7 +271,8 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
"All parameters' 'clip_norm' of a same group should be the same"
)
local_norm_var = layers.reduce_sum(input=layers.pow(x=grad, factor=2.0))
square = grad * grad
local_norm_var = layers.cast(layers.reduce_sum(input=square), 'float64')
context[self.group_name].append(local_norm_var)
self.context = context
......@@ -281,6 +282,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
if group_scale_name not in self.context:
group_norm_var = layers.sums(input=self.context[self.group_name])
group_norm_var = layers.sqrt(x=group_norm_var)
group_norm_var = layers.cast(group_norm_var, 'float32')
clip_var = self.context[self.group_name + "_clip"]
group_scale_var = layers.elementwise_div(
x=clip_var,
......
python/paddle/fluid/concurrency.py 已删除 100644 → 0
浏览文件 @ 77c8ddb5
此差异已折叠。
python/paddle/fluid/framework.py
浏览文件 @ 903e3f3f
此差异已折叠。
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 903e3f3f
......@@ -21,7 +21,7 @@ from .. import core
from ..framework import Program, Variable, Operator
from ..layer_helper import LayerHelper, unique_name
from ..initializer import force_init_on_cpu
from .ops import logical_and, logical_not, logical_or
from .nn import logical_and, logical_not, logical_or
import numpy
import warnings
import six
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 903e3f3f
此差异已折叠。
python/paddle/fluid/layers/ops.py
浏览文件 @ 903e3f3f
此差异已折叠。
python/paddle/fluid/nets.py
浏览文件 @ 903e3f3f
此差异已折叠。
python/paddle/fluid/optimizer.py
浏览文件 @ 903e3f3f
此差异已折叠。
python/paddle/fluid/tests/unittests/dist_ctr.py 0 → 100644
浏览文件 @ 903e3f3f
此差异已折叠。
python/paddle/fluid/tests/unittests/dist_ctr_reader.py 0 → 100644
浏览文件 @ 903e3f3f
此差异已折叠。
python/paddle/fluid/tests/unittests/dist_simnet_bow.py 0 → 100644
浏览文件 @ 903e3f3f
此差异已折叠。
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