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Paddle
“8d4af9c50cad4d233ad12e13ad08881d22fc782c”上不存在“data_backup/1.leetcode/122-买卖股票的最佳时机 II/solution.cpp”
提交 f1f508fc 编写于 作者: _青葱's avatar _青葱
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into build_manually 

Merge branch 'develop'
上级 86626a74 eb62a778
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Showing with 1180 addition and 263 deletion
cmake/external/mklml.cmake
浏览文件 @ f1f508fc
......@@ -34,7 +34,7 @@ SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
SET(MKLML_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(MKLML_INSTALL_DIR ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR}/${MKLML_VER})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR})
SET(MKLML_INC_DIR ${MKLML_ROOT}/include)
SET(MKLML_LIB_DIR ${MKLML_ROOT}/lib)
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
......@@ -46,7 +46,7 @@ INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
FILE(WRITE ${MKLML_DOWNLOAD_DIR}/CMakeLists.txt
"PROJECT(MKLML)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${MKLML_VER}\n"
"install(DIRECTORY ${MKLML_VER}/include ${MKLML_VER}/lib \n"
" DESTINATION ${MKLML_DST_DIR})\n")
ExternalProject_Add(
......
cmake/inference_lib.cmake
浏览文件 @ f1f508fc
......@@ -69,6 +69,12 @@ if(NOT CBLAS_FOUND)
SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
DSTS ${dst_dir} ${dst_dir}
)
elseif (WITH_MKLML)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/mklml")
copy(mklml_lib
SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
)
endif()
# paddle fluid module
......
doc/fluid/design/concurrent/go_op.md 0 → 100644
浏览文件 @ f1f508fc
# go_op Design
## Introduction
The **go_op** allows user's of PaddlePaddle to run program blocks on a detached
thread. It works in conjuction with CSP operators (channel_send,
channel_receive, channel_open, channel_close, and select) to allow users to
concurrently process data and communicate easily between different threads.
## How to use it
```
channel = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
# Send a tensor of value 99 to "channel" on a detached thread
tensor = fill_constant(shape=[1], dtype='int', value=99)
tensor.stop_gradient = True
fluid.channel_send(channel, tensor)
# Receive sent tensor from "channel" on the main thread
result = fill_constant(shape=[1], dtype='int', value=-1)
fluid.channel_recv(ch, result)
```
The go operator can be accessed by using the fluid.Go() control flow. This
will create a new sub block, where the user can add additional operators
to be ran on the thread.
**Note:** Since back propegation is currently not support in the go_op, users
should ensure that operators in the go block does not require gradient
calculations.
## How it Works
Similar to other control blocks, go_op will create a sub block and add it
as a child to the current block. Operators and variables defined in this
block will be added to the go sub_block.
In addition, the go operator will create a new child scope whose parent is
the global scope. Please refer to [block captures](#block-captures) for more
information.
When Paddle executor runs go_op, go_op will take the sub_block and pass it to
the executor.run method (along with a newly created local scope) on a detached
thread.
An example of the generated program description is shown below. Take note of
the **go_op** in particular. It is added as an operator in the current
block (in this example, block0). The **go_op** contains a `sub_block`
attribute, which points to the id of the block that will be executed in a
detached thread.
```
blocks {
idx: 0
parent_idx: -1
vars {
name: "return_value"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: INT64
}
}
}
}
vars {
name: "status_recv"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: BOOL
}
}
}
}
...
ops {
outputs {
parameter: "Out"
arguments: "channel"
}
type: "channel_create"
attrs {
name: "data_type"
type: INT
i: 7
}
attrs {
name: "capacity"
type: INT
i: 0
}
}
ops {
inputs {
parameter: "X"
arguments: "channel"
}
type: "go"
attrs {
name: "sub_block"
type: BLOCK
block_idx: 1
}
}
ops {
inputs {
parameter: "Channel"
arguments: "channel"
}
outputs {
parameter: "Out"
arguments: "return_value"
}
outputs {
parameter: "Status"
arguments: "status_recv"
}
type: "channel_recv"
}
...
}
blocks {
idx: 1
parent_idx: 0
vars {
name: "status"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: BOOL
}
}
}
}
...
ops {
outputs {
parameter: "Out"
arguments: "fill_constant_1.tmp_0"
}
type: "fill_constant"
attrs {
name: "force_cpu"
type: BOOLEAN
b: false
}
attrs {
name: "value"
type: FLOAT
f: 99.0
}
attrs {
name: "shape"
type: INTS
ints: 1
}
attrs {
name: "dtype"
type: INT
i: 3
}
}
ops {
inputs {
parameter: "Channel"
arguments: "channel"
}
inputs {
parameter: "X"
arguments: "fill_constant_1.tmp_0"
}
outputs {
parameter: "Status"
arguments: "status"
}
type: "channel_send"
attrs {
name: "copy"
type: BOOLEAN
b: false
}
}
```
## Current Limitations
#### <a name="block-captures"></a>Scopes and block captures:
Paddle utilizes [scopes](./../concepts/scope.md) to store variables used in a
block. When a block is executed, a new local scope is created from the parent
scope (ie: scope derived from the parent block) and associated with the new
child block. After the block finishes executing, then the local scope and
all associated variables in the scope is deleted.
This works well in a single threaded scenario, however with introduction of
go_op, a child block may continue to execute even after the parent block has
exited. If the go_op tries to access variables located in the parent block's
scope, it may receive a segmentation fault because the parent scope may have
been deleted.
We need to implement block closures in order to prevent access to parent
scope variables from causing a segmentation fault. As a temporary workaround,
please ensure that all variables accessed in the go block is not destructed
before it is being accessed. Currently, the go_op will explicitly enforce
this requirement and raise an exception if a variable could not be found in
the scope.
Please refer to [Closure issue](https://github.com/PaddlePaddle/Paddle/issues/8502)
for more details.
#### Green Threads
Golang utilizes `green threads`, which is a mechnism for the runtime library to
manage multiple threads (instead of natively by the OS). Green threads usually
allows for faster thread creation and switching, as there is less overhead
when spawning these threads. For the first version of CSP, we only support
OS threads.
#### Backward Propegation:
go_op currently does not support backwards propagation. Please use go_op with
non training operators.
doc/v2/dev/index_en.rst
浏览文件 @ f1f508fc
Development
------------
PaddlePaddle adheres to the following three sections of code and document specifications.
PaddlePaddle uses git for version control and Docker is used for building and testing environment. The code includes Cuda, C++, Python, Shell and other programming languages，which comply with Google C++ Style, Pep-8, and the code base includes style checking by an automatic inspection tool. Code comments need to follow the Doxygen specification. The code that does not meet the style requirements will fail to compile. We provide the following guidelines for the use of Git, build tests and code development.
.. toctree::
:maxdepth: 1
contribute_to_paddle_en.md
PaddlePaddle is well documented in English and Chinese. We recommend using the English version of the documents and problem description. The design documents focus on problem descriptions, backgrounds, and are followed by solutions. As documents are generated by Sphinx, code comments should comply with the Sphinx documentation standard. We recommend to use the paddlepaddle.org tool to compile and generate and preview documents locally. Please refer to:
.. toctree::
:maxdepth: 1
write_docs_en.rst
PaddlePaddle V2 defines new operations by adding new Layers. You can implement various complex layers by combining basic APIs to satisfy most applications. If you want to customize layer, please refer to the following, and welcome to propose patch.
.. toctree::
:maxdepth: 1
new_layer_en.rst
doc/v2/faq/cluster/index_en.rst
浏览文件 @ f1f508fc
......@@ -2,4 +2,15 @@
Cluster Training and Prediction
###############################
TBD
.. contents::
1. Network connection errors in the log during multi-node cluster training
------------------------------------------------
There are maybe some errors in the log belonging to network connection problem during multi-node cluster training, for example, :code:`Connection reset by peer`.
This kind of error is usually caused by the abnormal exit of a training process in some node, and the other nodes cannot connect with this node any longer. Steps to troubleshoot the problem are as follows:
* Find the first error in the :code:`train.log`, :code:`server.log`, check whether other fault casued the problem, such as FPE, lacking of memory or disk.
* If the first error in server.log says "Address already used", this may be caused by the port conflict of the non-exclusive execution. Connect the sys-admin to check if the current MPI cluster supports jobs submitted with parameter :code:`resource=full`. If the current MPI cluster does not support this parameter, change the server port and try agian.
* If the current MPI cluster does not support exclusive pattern which allows a process to occupy the whole node, ask the administrator to replace or update the this cluster.
paddle/fluid/framework/channel.h
浏览文件 @ f1f508fc
......@@ -34,7 +34,7 @@ class Channel {
public:
virtual bool CanSend() = 0;
virtual bool CanReceive() = 0;
virtual bool Send(T*) = 0;
virtual void Send(T*) = 0;
virtual bool Receive(T*) = 0;
virtual size_t Cap() = 0;
virtual void Lock() = 0;
......@@ -84,96 +84,113 @@ class ChannelHolder {
}
template <typename T>
bool Send(T* data) {
if (!IsInitialized()) return false;
PADDLE_ENFORCE_EQ(holder_->Type(), std::type_index(typeid(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());
return channel != nullptr ? channel->Send(data) : false;
PADDLE_ENFORCE_EQ(channel != nullptr, true, "Channel should not be null.");
channel->Send(data);
}
template <typename T>
bool Receive(T* data) {
if (!IsInitialized()) return false;
PADDLE_ENFORCE_EQ(holder_->Type(), std::type_index(typeid(T)));
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());
return channel != nullptr ? channel->Receive(data) : false;
PADDLE_ENFORCE_EQ(channel != nullptr, true, "Channel should not be null.");
return channel->Receive(data);
}
bool IsClosed() {
if (IsInitialized()) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->IsClosed();
}
return false;
}
bool CanSend() {
if (IsInitialized()) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->CanSend();
}
return false;
}
bool CanReceive() {
if (IsInitialized()) {
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->CanReceive();
}
return false;
}
void close() {
if (IsInitialized()) holder_->Close();
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
holder_->Close();
}
size_t Cap() {
if (IsInitialized()) return holder_->Cap();
return -1;
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->Cap();
}
void Lock() {
if (IsInitialized()) holder_->Lock();
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
holder_->Lock();
}
void Unlock() {
if (IsInitialized()) holder_->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) {
if (IsInitialized()) {
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) {
if (IsInitialized()) {
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) {
if (IsInitialized()) holder_->RemoveFromSendQ(referrer);
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
holder_->RemoveFromSendQ(referrer);
}
void RemoveFromReceiveQ(const void* referrer) {
if (IsInitialized()) holder_->RemoveFromReceiveQ(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);
PADDLE_ENFORCE_EQ(IsInitialized(), true,
"The Channel hasn't been initialized");
return holder_->Type();
}
......
paddle/fluid/framework/channel_impl.h
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......@@ -31,7 +31,7 @@ class ChannelImpl : public paddle::framework::Channel<T> {
public:
virtual bool CanSend();
virtual bool CanReceive();
virtual bool Send(T *);
virtual void Send(T *);
virtual bool Receive(T *);
virtual size_t Cap() { return cap_; }
virtual void Lock();
......@@ -76,10 +76,9 @@ class ChannelImpl : public paddle::framework::Channel<T> {
}
};
bool send_return(bool value) {
void send_return() {
send_ctr--;
destructor_cond_.notify_all();
return value;
}
bool recv_return(bool value) {
......@@ -118,15 +117,15 @@ bool ChannelImpl<T>::CanReceive() {
}
template <typename T>
bool ChannelImpl<T>::Send(T *item) {
void ChannelImpl<T>::Send(T *item) {
send_ctr++;
std::unique_lock<std::recursive_mutex> lock{mu_};
// If channel is closed, do nothing
// If channel is closed, throw exception
if (closed_) {
lock.unlock();
// TODO(abhinavarora) Should panic on closed channel
return send_return(false);
send_return();
PADDLE_THROW("Cannot send on closed channel");
}
// If there is a receiver, directly pass the value we want
......@@ -143,7 +142,7 @@ bool ChannelImpl<T>::Send(T *item) {
if (m->callback != nullptr) do_send = m->callback(ChannelAction::SEND);
if (do_send)
*(m->data) = std::move(*item);
else
else {
// We cannot do the data transfer because
// this QueueMessage was added by Select
// and some other case was executed.
......@@ -151,12 +150,17 @@ bool ChannelImpl<T>::Send(T *item) {
// We do not care about notifying other
// because they would have been notified
// by the executed select case.
return send_return(Send(item));
lock.unlock();
Send(item);
send_return();
return;
}
// Wake up the blocked process and unlock
m->Notify();
lock.unlock();
return send_return(true);
send_return();
return;
}
// Unbuffered channel will always bypass this
......@@ -167,7 +171,8 @@ bool ChannelImpl<T>::Send(T *item) {
buf_.push_back(std::move(*item));
// Release lock and return true
lock.unlock();
return send_return(true);
send_return();
return;
}
// Block on channel, because some receiver will complete
......@@ -175,8 +180,12 @@ bool ChannelImpl<T>::Send(T *item) {
auto m = std::make_shared<QueueMessage>(item);
sendq.push_back(m);
m->Wait(lock);
// TODO(abhinavarora) Should panic on closed channel
return send_return(!m->chan_closed);
if (m->chan_closed) {
lock.unlock();
send_return();
PADDLE_THROW("Cannot send on closed channel");
}
send_return();
}
template <typename T>
......
paddle/fluid/framework/channel_test.cc
浏览文件 @ f1f508fc
......@@ -16,7 +16,6 @@ limitations under the License. */
#include <chrono>
#include <thread>
#include "gtest/gtest.h"
using paddle::framework::Channel;
......@@ -41,7 +40,7 @@ void RecevingOrderEqualToSendingOrder(Channel<int> *ch) {
unsigned sum_send = 0;
std::thread t([&]() {
for (int i = 0; i < 5; i++) {
EXPECT_EQ(ch->Send(&i), true);
ch->Send(&i);
sum_send += i;
}
});
......@@ -61,7 +60,7 @@ TEST(Channel, SufficientBufferSizeDoesntBlock) {
const size_t buffer_size = 10;
auto ch = MakeChannel<size_t>(buffer_size);
for (size_t i = 0; i < buffer_size; ++i) {
EXPECT_EQ(ch->Send(&i), true); // should not block
ch->Send(&i);
}
size_t out;
......@@ -82,7 +81,7 @@ void SendReceiveWithACloseChannelShouldPanic(Channel<size_t> *ch) {
const size_t data = 5;
std::thread send_thread{[&]() {
size_t i = data;
EXPECT_EQ(ch->Send(&i), true); // should not block
ch->Send(&i); // should not block
}};
std::thread recv_thread{[&]() {
......@@ -94,12 +93,18 @@ void SendReceiveWithACloseChannelShouldPanic(Channel<size_t> *ch) {
send_thread.join();
recv_thread.join();
// After closing send should return false. Receive should
// also return false as there is no data in queue.
// After closing send should panic. Receive should
// also false as there is no data in queue.
CloseChannel(ch);
send_thread = std::thread{[&]() {
size_t i = data;
EXPECT_EQ(ch->Send(&i), false); // should return false
bool is_exception = false;
try {
ch->Send(&i);
} catch (paddle::platform::EnforceNotMet e) {
is_exception = true;
}
EXPECT_EQ(is_exception, true);
}};
recv_thread = std::thread{[&]() {
size_t i;
......@@ -129,7 +134,7 @@ TEST(Channel, ReceiveFromBufferedChannelReturnResidualValuesTest) {
auto ch = MakeChannel<size_t>(buffer_size);
for (size_t i = 0; i < buffer_size; ++i) {
EXPECT_EQ(ch->Send(&i), true); // sending should not block
ch->Send(&i); // sending should not block
}
size_t out;
......@@ -160,9 +165,16 @@ TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
// Try to write more than buffer size.
for (size_t i = 0; i < 2 * buffer_size; ++i) {
if (i < buffer_size)
EXPECT_EQ(ch->Send(&i), true); // should block after 10 iterations
else
EXPECT_EQ(ch->Send(&i), false);
ch->Send(&i); // should block after 10 iterations
else {
bool is_exception = false;
try {
ch->Send(&i);
} catch (paddle::platform::EnforceNotMet e) {
is_exception = true;
}
EXPECT_EQ(is_exception, true);
}
}
});
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
......@@ -231,7 +243,13 @@ void ChannelCloseUnblocksSendersTest(Channel<int> *ch, bool isBuffered) {
t[i] = std::thread(
[&](bool *ended, bool *success) {
int data = 10;
*success = ch->Send(&data);
bool is_exception = false;
try {
ch->Send(&data);
} catch (paddle::platform::EnforceNotMet e) {
is_exception = true;
}
*success = !is_exception;
*ended = true;
},
&thread_ended[i], &send_success[i]);
......@@ -316,8 +334,11 @@ TEST(Channel, UnbufferedLessReceiveMoreSendTest) {
// Try to send more number of times
// than receivers
for (int i = 0; i < 4; i++) {
try {
ch->Send(&i);
sum_send += i;
} catch (paddle::platform::EnforceNotMet e) {
}
}
});
for (int i = 0; i < 3; i++) {
......@@ -382,7 +403,13 @@ void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
t[i] = std::thread(
[&](bool *ended, bool *success) {
int data = 10;
*success = ch->Send(&data);
bool is_exception = false;
try {
ch->Send(&data);
} catch (paddle::platform::EnforceNotMet e) {
is_exception = true;
}
*success = !is_exception;
*ended = true;
},
&thread_ended[i], &send_success[i]);
......@@ -508,7 +535,7 @@ void ChannelHolderSendReceive(ChannelHolder *ch) {
unsigned sum_send = 0;
std::thread t([&]() {
for (int i = 0; i < 5; i++) {
EXPECT_EQ(ch->Send(&i), true);
ch->Send(&i);
sum_send += i;
}
});
......@@ -541,8 +568,22 @@ TEST(ChannelHolder, ChannelUninitializedTest) {
ChannelHolder *ch = new ChannelHolder();
EXPECT_EQ(ch->IsInitialized(), false);
int i = 10;
EXPECT_EQ(ch->Send(&i), false);
EXPECT_EQ(ch->Receive(&i), false);
bool send_exception = false;
try {
ch->Send(&i);
} catch (paddle::platform::EnforceNotMet e) {
send_exception = true;
}
EXPECT_EQ(send_exception, true);
bool recv_exception = false;
try {
ch->Receive(&i);
} catch (paddle::platform::EnforceNotMet e) {
recv_exception = true;
}
EXPECT_EQ(recv_exception, true);
bool is_exception = false;
try {
ch->Type();
......@@ -669,7 +710,13 @@ void ChannelHolderCloseUnblocksSendersTest(ChannelHolder *ch, bool isBuffered) {
t[i] = std::thread(
[&](bool *ended, bool *success) {
int data = 10;
*success = ch->Send(&data);
bool is_exception = false;
try {
ch->Send(&data);
} catch (paddle::platform::EnforceNotMet e) {
is_exception = true;
}
*success = !is_exception;
*ended = true;
},
&thread_ended[i], &send_success[i]);
......@@ -760,7 +807,13 @@ void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
t[i] = std::thread(
[&](bool *ended, bool *success) {
int data = 10;
*success = ch->Send(&data);
bool is_exception = false;
try {
ch->Send(&data);
} catch (paddle::platform::EnforceNotMet e) {
is_exception = true;
}
*success = !is_exception;
*ended = true;
},
&thread_ended[i], &send_success[i]);
......
paddle/fluid/framework/tensor.h
浏览文件 @ f1f508fc
......@@ -45,10 +45,11 @@ class Tensor {
friend struct EigenVector;
public:
Tensor() : offset_(0) {}
Tensor() : offset_(0), is_pinned_(false) {}
/*! Constructor with place should only be used in pybind. */
explicit Tensor(const platform::Place& place) : offset_(0) {
explicit Tensor(const platform::Place& place)
: offset_(0), is_pinned_(false) {
holder_->set_place(place);
}
......@@ -69,11 +70,12 @@ class Tensor {
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(platform::Place place);
inline T* mutable_data(platform::Place place, bool is_pinned = false);
inline void* mutable_data(platform::Place place, std::type_index type);
inline void* mutable_data(platform::Place place, std::type_index type,
bool is_pinned = false);
inline void* mutable_data(platform::Place place);
inline void* mutable_data(platform::Place place, bool is_pinned = false);
/**
* @brief Return a pointer to mutable memory block.
......@@ -84,7 +86,8 @@ class Tensor {
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(DDim dims, platform::Place place);
inline T* mutable_data(DDim dims, platform::Place place,
bool is_pinned = false);
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
......@@ -92,6 +95,9 @@ class Tensor {
/*! Return the numel of the memory block. */
inline int64_t numel() const;
/*! Return the numel of the memory block. */
inline bool isPinned() const;
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
......@@ -146,12 +152,14 @@ class Tensor {
template <typename Place>
struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(Place place, size_t size, std::type_index type)
: ptr_(static_cast<uint8_t*>(memory::Alloc(place, size)),
memory::PODDeleter<uint8_t, Place>(place)),
PlaceholderImpl(Place place, size_t size, std::type_index type,
bool is_pinned = false)
: ptr_(static_cast<uint8_t*>(memory::Alloc(place, size, is_pinned)),
memory::PODDeleter<uint8_t, Place>(place, is_pinned)),
place_(place),
size_(size),
type_(type) {
type_(type),
is_pinned_(is_pinned) {
PADDLE_ENFORCE_NOT_NULL(ptr_, "Insufficient %s memory to allocation.",
(is_cpu_place(place_) ? "CPU" : "GPU"));
}
......@@ -174,6 +182,9 @@ class Tensor {
/* the current type of memory */
std::type_index type_;
/*! use pinned memory or not. */
bool is_pinned_;
};
/*! holds the memory block if allocated. */
......@@ -208,6 +219,7 @@ class Tensor {
* PlaceHolder::ptr_ and where the tensor data really begins.
*/
size_t offset_;
bool is_pinned_;
};
inline void Tensor::switch_place(platform::Place new_place) {
......
paddle/fluid/framework/tensor_impl.h
浏览文件 @ f1f508fc
......@@ -101,19 +101,21 @@ inline T* Tensor::data() {
}
template <typename T>
inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
inline T* Tensor::mutable_data(DDim dims, platform::Place place,
bool is_pinned) {
static_assert(std::is_pod<T>::value, "T must be POD");
Resize(dims);
return mutable_data<T>(place);
return mutable_data<T>(place, is_pinned);
}
template <typename T>
inline T* Tensor::mutable_data(platform::Place place) {
inline T* Tensor::mutable_data(platform::Place place, bool is_pinned) {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T*>(mutable_data(place, typeid(T)));
return reinterpret_cast<T*>(mutable_data(place, typeid(T), is_pinned));
}
inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
inline void* Tensor::mutable_data(platform::Place place, std::type_index type,
bool is_pinned) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
......@@ -127,26 +129,27 @@ inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CPUPlace>(
boost::get<platform::CPUPlace>(place), size, type));
boost::get<platform::CPUPlace>(place), size, type, is_pinned));
} else if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
}
#else
holder_.reset(new PlaceholderImpl<platform::CUDAPlace>(
boost::get<platform::CUDAPlace>(place), size, type));
boost::get<platform::CUDAPlace>(place), size, type, is_pinned));
}
#endif
offset_ = 0;
is_pinned_ = is_pinned;
}
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
}
inline void* Tensor::mutable_data(platform::Place place) {
inline void* Tensor::mutable_data(platform::Place place, bool is_pinned) {
PADDLE_ENFORCE(this->holder_ != nullptr,
"Cannot invoke mutable data if current hold nothing");
return mutable_data(place, holder_->type());
return mutable_data(place, holder_->type(), is_pinned);
}
inline Tensor& Tensor::ShareDataWith(const Tensor& src) {
......@@ -188,6 +191,8 @@ inline const DDim& Tensor::dims() const { return dims_; }
inline int64_t Tensor::numel() const { return product(dims_); }
inline bool Tensor::isPinned() const { return is_pinned_; }
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
Tensor res;
res.ShareDataWith(src);
......
paddle/fluid/memory/detail/system_allocator.cc
浏览文件 @ f1f508fc
......@@ -119,6 +119,50 @@ void GPUAllocator::Free(void* p, size_t size, size_t index) {
bool GPUAllocator::UseGpu() const { return true; }
// PINNED memory allows direct DMA transfers by the GPU to and from system
// memory. It’s locked to a physical address.
void* CUDAPinnedAllocator::Alloc(size_t& index, size_t size) {
if (size <= 0) return nullptr;
void* p;
// NOTE: here, we use GpuMaxAllocSize() as the maximum memory size
// of host pinned allocation. Allocates too much would reduce
// the amount of memory available to the underlying system for paging.
size_t usable = paddle::platform::GpuMaxAllocSize() - fallback_alloc_size_;
if (size > usable) return nullptr;
// PINNED memory is visible to all CUDA contexts.
cudaError_t result = cudaMallocHost(&p, size);
if (result == cudaSuccess) {
index = 1;
fallback_alloc_size_ += size;
return p;
}
return nullptr;
}
void CUDAPinnedAllocator::Free(void* p, size_t size, size_t index) {
cudaError_t err;
PADDLE_ASSERT(index == 1);
PADDLE_ASSERT(fallback_alloc_size_ >= size);
fallback_alloc_size_ -= size;
err = cudaFreeHost(p);
// Purposefully allow cudaErrorCudartUnloading, because
// that is returned if you ever call cudaFreeHost after the
// driver has already shutdown. This happens only if the
// process is terminating, in which case we don't care if
// cudaFreeHost succeeds.
if (err != cudaErrorCudartUnloading) {
PADDLE_ENFORCE(err, "cudaFreeHost failed in GPUPinnedAllocator::Free.");
}
}
bool CUDAPinnedAllocator::UseGpu() const { return true; }
#endif
} // namespace detail
......
paddle/fluid/memory/detail/system_allocator.h
浏览文件 @ f1f508fc
......@@ -51,6 +51,18 @@ class GPUAllocator : public SystemAllocator {
size_t gpu_alloc_size_ = 0;
size_t fallback_alloc_size_ = 0;
};
class CUDAPinnedAllocator : public SystemAllocator {
public:
virtual void* Alloc(size_t& index, size_t size);
virtual void Free(void* p, size_t size, size_t index);
virtual bool UseGpu() const;
private:
size_t gpu_alloc_size_ =
0; // TODO(zcd): how to define the upper limit of CUDAPinnedMemory?
size_t fallback_alloc_size_ = 0;
};
#endif
} // namespace detail
......
paddle/fluid/memory/memory.cc
浏览文件 @ f1f508fc
......@@ -38,7 +38,8 @@ BuddyAllocator* GetCPUBuddyAllocator() {
}
template <>
void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size) {
void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size,
bool is_pinned) {
VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place);
void* p = GetCPUBuddyAllocator()->Alloc(size);
VLOG(10) << " pointer=" << p;
......@@ -46,7 +47,8 @@ void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size) {
}
template <>
void Free<platform::CPUPlace>(platform::CPUPlace place, void* p) {
void Free<platform::CPUPlace>(platform::CPUPlace place, void* p,
bool is_pinned) {
VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place);
GetCPUBuddyAllocator()->Free(p);
}
......@@ -82,15 +84,47 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
return as[gpu_id];
}
BuddyAllocator* GetCUDAPinnedBuddyAllocator(int gpu_id) {
static BuddyAllocator** as = NULL;
if (as == NULL) {
int gpu_num = platform::GetCUDADeviceCount();
as = new BuddyAllocator*[gpu_num];
for (int gpu = 0; gpu < gpu_num; gpu++) {
as[gpu] = nullptr;
}
}
platform::SetDeviceId(gpu_id);
if (!as[gpu_id]) {
as[gpu_id] = new BuddyAllocator(new detail::CUDAPinnedAllocator,
platform::GpuMinChunkSize(),
platform::GpuMaxChunkSize());
VLOG(10) << "\n\nNOTE: each GPU device use "
<< FLAGS_fraction_of_gpu_memory_to_use * 100
<< "% of GPU memory.\n"
<< "You can set GFlags environment variable '"
<< "FLAGS_fraction_of_gpu_memory_to_use"
<< "' to change the fraction of GPU usage.\n\n";
}
return as[gpu_id];
}
template <>
size_t Used<platform::CUDAPlace>(platform::CUDAPlace place) {
return GetGPUBuddyAllocator(place.device)->Used();
}
template <>
void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size) {
void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size,
bool is_pinned) {
void* ptr;
if (is_pinned) {
auto* buddy_allocator = GetCUDAPinnedBuddyAllocator(place.device);
ptr = buddy_allocator->Alloc(size);
} else {
auto* buddy_allocator = GetGPUBuddyAllocator(place.device);
auto* ptr = buddy_allocator->Alloc(size);
ptr = buddy_allocator->Alloc(size);
}
if (ptr == nullptr) {
int cur_dev = platform::GetCurrentDeviceId();
platform::SetDeviceId(place.device);
......@@ -108,8 +142,13 @@ void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size) {
}
template <>
void Free<platform::CUDAPlace>(platform::CUDAPlace place, void* p) {
void Free<platform::CUDAPlace>(platform::CUDAPlace place, void* p,
bool is_pinned) {
if (is_pinned) {
GetCUDAPinnedBuddyAllocator(place.device)->Free(p);
} else {
GetGPUBuddyAllocator(place.device)->Free(p);
}
}
#endif
......
paddle/fluid/memory/memory.h
浏览文件 @ f1f508fc
......@@ -33,7 +33,7 @@ namespace memory {
* address is valid or not.
*/
template <typename Place>
void* Alloc(Place place, size_t size);
void* Alloc(Place place, size_t size, bool is_pinned = false);
/**
* \brief Free memory block in one place.
......@@ -43,7 +43,7 @@ void* Alloc(Place place, size_t size);
*
*/
template <typename Place>
void Free(Place place, void* ptr);
void Free(Place place, void* ptr, bool is_pinned = false);
/**
* \brief Total size of used memory in one place.
......@@ -74,11 +74,13 @@ class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
public:
explicit PODDeleter(Place place) : place_(place) {}
void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr)); }
explicit PODDeleter(Place place, bool is_pinned = false)
: place_(place), is_pinned_(is_pinned) {}
void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr), is_pinned_); }
private:
Place place_;
bool is_pinned_;
};
/**
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ f1f508fc
......@@ -264,3 +264,4 @@ cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memor
cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
nv_test(dropout_op_test SRCS dropout_op_test.cc DEPS dropout_op tensor)
paddle/fluid/operators/channel_send_op.cc
浏览文件 @ f1f508fc
......@@ -23,21 +23,10 @@ limitations under the License. */
static constexpr char Channel[] = "Channel";
static constexpr char X[] = "X";
static constexpr char Status[] = "Status";
static constexpr char copy[] = "copy";
namespace paddle {
namespace operators {
void SetSendStatus(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 ChannelSendOp : public framework::OperatorBase {
public:
ChannelSendOp(const std::string &type,
......@@ -51,9 +40,6 @@ class ChannelSendOp : public framework::OperatorBase {
"Input(Channel) of ChannelSendOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput(X),
"Input(X) of ChannelSendOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(Status),
"Output(Status) of ChannelSendOp should not be null.");
ctx->SetOutputDim("Status", {1});
}
private:
......@@ -65,10 +51,7 @@ class ChannelSendOp : public framework::OperatorBase {
auto input_var = scope.FindVar(Input(X));
// Send the input data through the channel.
bool ok = concurrency::ChannelSend(ch, input_var);
// Set the status output of the `ChannelSend` call.
SetSendStatus(dev_place, *scope.FindVar(Output(Status)), ok);
concurrency::ChannelSend(ch, input_var);
}
};
......@@ -82,12 +65,6 @@ class ChannelSendOpMaker : public framework::OpProtoAndCheckerMaker {
.AsDuplicable();
AddInput(X, "(Variable) The value which gets sent by the channel.")
.AsDuplicable();
AddOutput(Status,
"(Tensor) An LoD Tensor that returns a boolean status of the"
"result of the send operation.")
.AsDuplicable();
AddAttr<bool>(copy, "(bool, default false) Should copy before send")
.SetDefault(false);
AddComment(R"DOC(
)DOC");
}
......
paddle/fluid/operators/concurrency/channel_util.cc
浏览文件 @ f1f508fc
......@@ -17,20 +17,20 @@ limitations under the License. */
namespace poc = paddle::operators::concurrency;
bool poc::ChannelSend(framework::ChannelHolder *ch, framework::Variable *var) {
void poc::ChannelSend(framework::ChannelHolder *ch, framework::Variable *var) {
auto type = framework::ToVarType(var->Type());
if (type == framework::proto::VarType_Type_LOD_TENSOR)
return ch->Send(var->GetMutable<framework::LoDTensor>());
ch->Send(var->GetMutable<framework::LoDTensor>());
else if (type == framework::proto::VarType_Type_LOD_RANK_TABLE)
return ch->Send(var->GetMutable<framework::LoDRankTable>());
ch->Send(var->GetMutable<framework::LoDRankTable>());
else if (type == framework::proto::VarType_Type_LOD_TENSOR_ARRAY)
return ch->Send(var->GetMutable<framework::LoDTensorArray>());
ch->Send(var->GetMutable<framework::LoDTensorArray>());
else if (type == framework::proto::VarType_Type_SELECTED_ROWS)
return ch->Send(var->GetMutable<framework::SelectedRows>());
ch->Send(var->GetMutable<framework::SelectedRows>());
else if (type == framework::proto::VarType_Type_READER)
return ch->Send(var->GetMutable<framework::ReaderHolder>());
ch->Send(var->GetMutable<framework::ReaderHolder>());
else if (type == framework::proto::VarType_Type_CHANNEL)
return ch->Send(var->GetMutable<framework::ChannelHolder>());
ch->Send(var->GetMutable<framework::ChannelHolder>());
else
PADDLE_THROW("ChannelSend:Unsupported type");
}
......
paddle/fluid/operators/concurrency/channel_util.h
浏览文件 @ f1f508fc
......@@ -21,7 +21,7 @@ namespace paddle {
namespace operators {
namespace concurrency {
bool ChannelSend(framework::ChannelHolder *ch, framework::Variable *var);
void ChannelSend(framework::ChannelHolder *ch, framework::Variable *var);
bool ChannelReceive(framework::ChannelHolder *ch, framework::Variable *var);
void ChannelAddToSendQ(framework::ChannelHolder *ch, const void *referrer,
......
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @ f1f508fc
......@@ -49,9 +49,8 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
s->Prepare(var_h, time_out);
s->response_call_back_ = NULL;
auto call = std::move(s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req,
&cq_));
auto call = s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, (void*)s);
});
......@@ -107,8 +106,8 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
::grpc::ByteBuffer buf;
RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
auto call = std::move(s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/GetVariable", buf, &cq_));
auto call = s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/GetVariable", buf, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, (void*)s);
});
......
paddle/fluid/operators/detail/variable_response.cc
浏览文件 @ f1f508fc
......@@ -48,6 +48,8 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
void* dest, int size) {
const void* data = NULL;
int size_to_write = 0;
int length = size;
int total_written = 0;
if (platform::is_gpu_place(place)) {
#ifdef PADDLE_WITH_CUDA
......@@ -56,16 +58,21 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
platform::CPUPlace cpu;
char* p = reinterpret_cast<char*>(dest);
while (size > 0) {
while (total_written < length) {
if (!input->GetDirectBufferPointer(&data, &size_to_write)) {
return false;
}
// NOTE: if raw buffer is large and have two neighbor fields of raw
// buffers GetDirectBufferPointer can get all of them, use length to
// truncate it.
if (total_written + size_to_write > length) {
size_to_write = length - total_written;
}
memory::Copy(boost::get<platform::CUDAPlace>(place),
reinterpret_cast<void*>(p), cpu, data, size_to_write,
gpu_dev_ctx.stream());
p += size_to_write;
size -= size_to_write;
total_written += size_to_write;
input->Skip(size_to_write);
}
......@@ -77,16 +84,21 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
}
char* p = reinterpret_cast<char*>(dest);
while (size > 0) {
while (total_written < length) {
if (!input->GetDirectBufferPointer(&data, &size_to_write)) {
return false;
}
// NOTE: if raw buffer is large and have two neighbor fields of raw buffers
// GetDirectBufferPointer can get all of them, use length to truncate it.
if (total_written + size_to_write > length) {
size_to_write = length - total_written;
}
// TODO(gongwb): can we avoid copy?
platform::CPUPlace cpu;
memory::Copy(cpu, reinterpret_cast<void*>(p), cpu, data, size_to_write);
p += size_to_write;
size -= size_to_write;
total_written += size_to_write;
input->Skip(size_to_write);
}
......@@ -153,6 +165,7 @@ bool VariableResponse::CopySelectRowsData(
const platform::DeviceContext& ctx, int length) {
auto var = scope_->FindVar(meta_.varname());
auto* slr = var->GetMutable<framework::SelectedRows>();
slr->mutable_rows()->resize(length / 8); // int64
int64_t* rows_data = slr->mutable_rows()->data();
// copy rows CPU data, GPU data will be copied lazily.
......@@ -233,7 +246,6 @@ int VariableResponse::Parse(Source* source) {
if (tag != 0) {
return -1;
}
return 0;
}
......
paddle/fluid/operators/dropout_op.cu
浏览文件 @ f1f508fc
......@@ -55,9 +55,6 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
y->mutable_data<T>(context.GetPlace());
float dropout_prob = context.Attr<float>("dropout_prob");
auto X = EigenMatrix<T>::Reshape(*x, 1);
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto& place = *context.template device_context<Place>().eigen_device();
if (!context.Attr<bool>("is_test")) {
auto* mask = context.Output<Tensor>("Mask");
......@@ -76,6 +73,8 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, seed, dropout_prob, x_data, mask_data, y_data);
} else {
auto X = EigenMatrix<T>::Reshape(*x, 1);
auto Y = EigenMatrix<T>::Reshape(*y, 1);
Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
}
}
......
paddle/fluid/operators/dropout_op_test.cc 0 → 100644
浏览文件 @ f1f508fc
/* 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 <unistd.h>
#include <string>
#include <thread>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/operators/dropout_op.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/string/printf.h"
namespace f = paddle::framework;
namespace p = paddle::platform;
namespace m = paddle::operators::math;
USE_OP(dropout);
void Compare(f::Scope& scope, p::DeviceContext& ctx) {
// init
auto var = scope.Var("X");
auto tensor = var->GetMutable<f::LoDTensor>();
tensor->Resize({10, 10});
std::vector<float> init;
for (int64_t i = 0; i < 10 * 10; ++i) {
init.push_back(1.0);
}
TensorFromVector(init, ctx, tensor);
auto place = ctx.GetPlace();
auto out_var = scope.Var("Out");
auto out_tensor = out_var->GetMutable<f::LoDTensor>();
out_tensor->Resize({10, 10});
out_tensor->mutable_data<float>(place); // allocate
auto mask_var = scope.Var("Mask");
auto mask_tensor = mask_var->GetMutable<f::LoDTensor>();
mask_tensor->Resize({10, 10});
mask_tensor->mutable_data<float>(place); // allocate
// run
f::AttributeMap attrs;
float dropout_prob = 0.5;
attrs.insert({"fix_seed", 1});
attrs.insert({"seed", 3});
attrs.insert({"dropout_prob", dropout_prob});
auto dropout_op = f::OpRegistry::CreateOp(
"dropout", {{"X", {"X"}}}, {{"Out", {"Out"}}, {"Mask", {"Mask"}}}, attrs);
dropout_op->Run(scope, place);
std::vector<float> out_vec;
TensorToVector(*out_tensor, ctx, &out_vec);
std::vector<float> std_out = {
0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1,
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,
1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,
1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1};
EXPECT_EQ(out_vec.size(), std_out.size());
for (uint32_t i = 0; i < out_vec.size(); i++) {
EXPECT_EQ(out_vec[i], std_out[i]);
}
}
TEST(Dropout, CPUDense) {
f::Scope scope;
p::CPUPlace place;
p::CPUDeviceContext ctx(place);
Compare(scope, ctx);
}
TEST(Dropout, GPUDense) {
f::Scope scope;
p::CUDAPlace place;
p::CUDADeviceContext ctx(place);
Compare(scope, ctx);
}
paddle/fluid/operators/layer_norm_op.h
浏览文件 @ f1f508fc
......@@ -22,6 +22,103 @@ limitations under the License. */
namespace paddle {
namespace operators {
// Wrap RowwiseMean and ColwiseMean.
// Reuse the cpu codes and replace the gpu codes with cublas_gemv, which is
// significantly faster. Unlike the RowwiseMean and ColwiseMean, the
// implementation only considers 2D.
template <typename DeviceContext, typename T>
struct RowwiseMean2D {
RowwiseMean2D(int left, int right, const platform::DeviceContext& dev_ctx);
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor* vec);
};
#ifdef PADDLE_WITH_CUDA
template <typename T>
class RowwiseMean2D<platform::CUDADeviceContext, T> {
public:
RowwiseMean2D(int left, int right, const platform::DeviceContext& dev_ctx)
: left_(left), right_(right) {
framework::DDim ones_dim({right_});
divisor_.mutable_data<T>(ones_dim, dev_ctx.GetPlace());
math::set_constant(dev_ctx, &divisor_, 1.0 / right);
}
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input, framework::Tensor* out) {
math::gemv<platform::CUDADeviceContext, T>(
context, false, left_, right_, 1., input.data<T>(), divisor_.data<T>(),
0., out->data<T>());
}
private:
int left_;
int right_;
framework::Tensor divisor_;
};
#endif
template <typename T>
class RowwiseMean2D<platform::CPUDeviceContext, T> {
public:
RowwiseMean2D(int left, int right, const platform::DeviceContext& dev_ctx) {}
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, framework::Tensor* out) {
row_mean_(context, input, out);
}
private:
math::RowwiseMean<platform::CPUDeviceContext, T> row_mean_;
};
template <typename DeviceContext, typename T>
struct ColwiseSum2D {
ColwiseSum2D(int left, int right, const platform::DeviceContext& dev_ctx);
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor* vec);
};
#ifdef PADDLE_WITH_CUDA
template <typename T>
class ColwiseSum2D<platform::CUDADeviceContext, T> {
public:
ColwiseSum2D(int left, int right, const platform::DeviceContext& dev_ctx)
: left_(left), right_(right) {
framework::DDim ones_dim({left_});
divisor_.mutable_data<T>(ones_dim, dev_ctx.GetPlace());
math::set_constant(dev_ctx, &divisor_, 1.0);
}
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input, framework::Tensor* out) {
math::gemv<platform::CUDADeviceContext, T>(
context, true, left_, right_, 1., input.data<T>(), divisor_.data<T>(),
0., out->data<T>());
}
private:
int left_;
int right_;
framework::Tensor divisor_;
};
#endif
template <typename T>
class ColwiseSum2D<platform::CPUDeviceContext, T> {
public:
ColwiseSum2D(int left, int right, const platform::DeviceContext& dev_ctx) {}
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, framework::Tensor* out) {
col_wise_(context, input, out);
}
private:
math::ColwiseSum<platform::CPUDeviceContext, T> col_wise_;
};
template <typename T>
struct SubAndSquareFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return (a - b) * (a - b); }
......@@ -67,15 +164,15 @@ using DataLayout = framework::DataLayout;
template <typename DeviceContext, typename T>
class LayerNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto *scale = ctx.Input<Tensor>("Scale");
auto *bias = ctx.Input<Tensor>("Bias");
auto* scale = ctx.Input<Tensor>("Scale");
auto* bias = ctx.Input<Tensor>("Bias");
auto x = *ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Y");
auto *mean = ctx.Output<Tensor>("Mean");
auto *var = ctx.Output<Tensor>("Variance");
auto* y = ctx.Output<Tensor>("Y");
auto* mean = ctx.Output<Tensor>("Mean");
auto* var = ctx.Output<Tensor>("Variance");
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
const auto x_dims = x.dims();
......@@ -94,8 +191,8 @@ class LayerNormKernel : public framework::OpKernel<T> {
out.ShareDataWith(*y);
out.Resize(matrix_shape);
auto &dev_ctx = ctx.template device_context<DeviceContext>();
math::RowwiseMean<DeviceContext, T> row_mean;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
RowwiseMean2D<DeviceContext, T> row_mean(left, right, ctx.device_context());
// get mean
row_mean(dev_ctx, x, mean);
......@@ -126,31 +223,32 @@ class LayerNormKernel : public framework::OpKernel<T> {
template <typename DeviceContext, typename T>
class LayerNormGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto x = *ctx.Input<Tensor>("X");
auto *y = ctx.Input<Tensor>("Y");
auto *mean = ctx.Input<Tensor>("Mean");
auto *var = ctx.Input<Tensor>("Variance");
auto *scale = ctx.Input<Tensor>("Scale");
auto *bias = ctx.Input<Tensor>("Bias");
auto* y = ctx.Input<Tensor>("Y");
auto* mean = ctx.Input<Tensor>("Mean");
auto* var = ctx.Input<Tensor>("Variance");
auto* scale = ctx.Input<Tensor>("Scale");
auto* bias = ctx.Input<Tensor>("Bias");
auto d_y = *ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
// init output
auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const auto &x_dims = x.dims();
const auto& x_dims = x.dims();
auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
framework::DDim matrix_shape({left, right});
d_y.Resize(matrix_shape);
auto &dev_ctx = ctx.template device_context<DeviceContext>();
math::ColwiseSum<DeviceContext, T> colwise_sum;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
ColwiseSum2D<DeviceContext, T> colwise_sum(left, right,
ctx.device_context());
Tensor temp;
Tensor temp_norm;
......@@ -190,7 +288,8 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
Tensor temp_vec;
temp_vec.mutable_data<T>(vec_shape, ctx.GetPlace());
math::RowwiseMean<DeviceContext, T> row_mean;
RowwiseMean2D<DeviceContext, T> row_mean(left, right,
ctx.device_context());
if (d_scale) {
// dy_dx
......
paddle/fluid/operators/lrn_mkldnn_op.cc
浏览文件 @ f1f508fc
......@@ -36,6 +36,14 @@ std::shared_ptr<T> insert_to_context(const std::string& key,
return p;
}
template <typename... Args>
void run_primitive(Args&&... args) {
auto forward_op = mkldnn::lrn_forward{args...};
std::vector<mkldnn::primitive> pipeline = {forward_op};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
} // namespace
template <typename T>
......@@ -87,8 +95,6 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto dst_memory = mkldnn::memory{{dst_md, mkldnn_engine},
static_cast<void*>(output_data)};
std::unique_ptr<mkldnn::lrn_forward> forward_op = nullptr;
if (!is_test) {
const std::string key = ctx.op().Output("Out");
const std::string key_src_memory = key + "@lrn_src_memory";
......@@ -108,9 +114,7 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
key_workspace_memory, dev_ctx,
forward_pd->workspace_primitive_desc());
forward_op.reset(new mkldnn::lrn_forward{*forward_pd, *src_memory,
*workspace_memory, dst_memory});
run_primitive(*forward_pd, *src_memory, *workspace_memory, dst_memory);
} else {
auto forward_pd =
mkldnn::lrn_forward::primitive_desc{forward_desc, mkldnn_engine};
......@@ -119,12 +123,8 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto workspace_memory =
mkldnn::memory{forward_pd.workspace_primitive_desc()};
forward_op.reset(new mkldnn::lrn_forward{forward_pd, src_memory,
workspace_memory, dst_memory});
run_primitive(forward_pd, src_memory, workspace_memory, dst_memory);
}
std::vector<mkldnn::primitive> pipeline = {*forward_op};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
};
......@@ -136,6 +136,9 @@ class LRNMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
"MKLDNN LRN must use float data.");
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"MKLDNN LRN must use CPUPlace.");
PADDLE_ENFORCE(
!ctx.Attr<bool>("is_test"),
"is_test attribute should be set to False in training phase.");
auto x = ctx.Input<Tensor>("X");
......
paddle/fluid/operators/lrn_op.cc
浏览文件 @ f1f508fc
......@@ -155,8 +155,8 @@ class LRNOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(x_dim.size(), 4, "Input(X)'rank of LRNOp should be 4.");
ctx->SetOutputDim("Out", x_dim);
ctx->SetOutputDim("MidOut", x_dim);
ctx->ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("MidOut", x_dim);
}
framework::OpKernelType GetExpectedKernelType(
......
paddle/fluid/operators/math/math_function.h
浏览文件 @ f1f508fc
......@@ -19,13 +19,6 @@ limitations under the License. */
#include <mkl_vml_functions.h>
#endif
#ifdef PADDLE_USE_ATLAS
extern "C" {
#include <cblas.h>
#include <clapack.h>
}
#endif
#ifdef PADDLE_USE_OPENBLAS
#include <cblas.h>
#include <lapacke.h>
......
paddle/fluid/operators/parallel_do_op.cc
浏览文件 @ f1f508fc
......@@ -144,7 +144,12 @@ class ParallelDoOp : public framework::OperatorBase {
PADDLE_ENFORCE(scope.FindVar(param)->IsType<LoDTensor>(),
"Only support parameter type as LoDTensor");
auto &src = scope.FindVar(param)->Get<LoDTensor>();
for (size_t i = 0; i < sub_scopes.size(); ++i) {
auto *sub_scope0 = sub_scopes[0];
auto *dst0 = sub_scope0->Var(param)->GetMutable<LoDTensor>();
dst0->ShareDataWith(src);
for (size_t i = 1; i < sub_scopes.size(); ++i) {
auto &place = places[i];
auto *sub_scope = sub_scopes[i];
auto *dst = sub_scope->Var(param)->GetMutable<LoDTensor>();
......
paddle/fluid/operators/reader/create_double_buffer_reader_op.cc
浏览文件 @ f1f508fc
......@@ -166,7 +166,9 @@ void DoubleBufferReader::PrefetchThreadFunc() {
std::swap(gpu_batch, batch.payloads_);
}
if (!buffer_->Send(&batch)) {
try {
buffer_->Send(&batch);
} catch (paddle::platform::EnforceNotMet e) {
VLOG(5) << "WARNING: The double buffer channel has been closed. The "
"prefetch thread will terminate.";
break;
......
paddle/fluid/operators/reader/open_files_op.cc
浏览文件 @ f1f508fc
......@@ -146,14 +146,19 @@ void MultipleReader::PrefetchThreadFunc(std::string file_name,
while (reader->HasNext()) {
std::vector<framework::LoDTensor> ins;
reader->ReadNext(&ins);
if (!buffer_->Send(&ins)) {
try {
buffer_->Send(&ins);
} catch (paddle::platform::EnforceNotMet e) {
VLOG(5) << "WARNING: The buffer channel has been closed. The prefetch "
"thread of file '"
<< file_name << "' will terminate.";
break;
}
}
if (!available_thread_idx_->Send(&thread_idx)) {
try {
available_thread_idx_->Send(&thread_idx);
} catch (paddle::platform::EnforceNotMet e) {
VLOG(5) << "WARNING: The available_thread_idx_ channel has been closed. "
"Fail to send thread_idx.";
}
......
paddle/fluid/operators/split_ids_op.cc 0 → 100644
浏览文件 @ f1f508fc
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/split_ids_op.h"
namespace paddle {
namespace operators {
class SplitIdsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SplitIdsOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Ids", "(LoDTensor) the input ids with shape{batch_num, 1}");
AddOutput("Out", "(LoDTensor) The outputs of the input Ids.")
.AsDuplicable();
AddComment(R"DOC(
Split a LoDTensor of Ids into multi LoDTensors, the number is pserver's number
Example:
Input:
X = [1,2,3,4,5,6]
Out(3 output):
out0 = [3, 6]
out1 = [1, 4]
out2 = [2, 5]
)DOC");
}
};
class SplitIdsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Ids"), "SplitIdsOp must has input Ids.");
PADDLE_ENFORCE(ctx->HasOutputs("Out"), "SplitIdsOp must has output Out.");
auto ids_var_type = ctx->GetInputsVarType("Ids").front();
PADDLE_ENFORCE_EQ(ids_var_type, framework::proto::VarType::LOD_TENSOR);
auto ids_dims = ctx->GetInputDim("Ids");
PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
PADDLE_ENFORCE_EQ(ids_dims[1], 1);
}
};
class SplitIdsOpInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
for (auto &out_var : op_desc.Output("Out")) {
block->Var(out_var)->SetType(framework::proto::VarType::LOD_TENSOR);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(split_ids, ops::SplitIdsOp, ops::SplitIdsOpMaker,
ops::SplitIdsOpInferVarType);
REGISTER_OP_CPU_KERNEL(
split_ids, ops::SplitIdsOpKernel<paddle::platform::CPUPlace, int64_t>);
paddle/fluid/operators/split_ids_op.h 0 → 100644
浏览文件 @ f1f508fc
/* 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 <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class SplitIdsOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto place = ctx.GetPlace();
if (!platform::is_cpu_place(place)) {
PADDLE_THROW("SplitIds do not support GPU kernel");
}
const auto* ids_t = ctx.Input<framework::LoDTensor>("Ids");
auto& ids_dims = ids_t->dims();
auto outs = ctx.MultiOutput<framework::LoDTensor>("Out");
const T* ids = ids_t->data<T>();
const size_t shard_num = outs.size();
std::vector<std::vector<T>> out_ids;
out_ids.resize(outs.size());
// split id by their shard_num.
for (size_t i = 0; i < ids_dims[0]; ++i) {
T id = ids[i];
size_t shard_id = static_cast<size_t>(id) % shard_num;
out_ids[shard_id].push_back(id);
}
// create tensor for each shard and send to parameter server
for (size_t i = 0; i < out_ids.size(); ++i) {
auto* shard_t = outs[i];
std::vector<T> ids = out_ids[i];
auto* shard_data = shard_t->mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(ids.size()), 1}), place);
for (size_t i = 0; i < ids.size(); ++i) {
shard_data[i] = ids[i];
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/math/MathFunctions.cpp
浏览文件 @ f1f508fc
......@@ -59,17 +59,10 @@ void* lapack_dso_handle = nullptr;
} __name; // struct DynLoad__##__name
#endif
#ifdef PADDLE_USE_ATLAS
#define PADDLE_SGETRF clapack_sgetrf
#define PADDLE_DGETRF clapack_dgetrf
#define PADDLE_SGETRI clapack_sgetri
#define PADDLE_DGETRI clapack_dgetri
#else
#define PADDLE_SGETRF LAPACKE_sgetrf
#define PADDLE_DGETRF LAPACKE_dgetrf
#define PADDLE_SGETRI LAPACKE_sgetri
#define PADDLE_DGETRI LAPACKE_dgetri
#endif
#define PADDLE_SGETRF LAPACKE_sgetrf
#define PADDLE_DGETRF LAPACKE_dgetrf
#define PADDLE_SGETRI LAPACKE_sgetri
#define PADDLE_DGETRI LAPACKE_dgetri
#define LAPACK_ROUTINE_EACH(__macro) \
__macro(PADDLE_SGETRF) \
......
paddle/math/MathFunctions.h
浏览文件 @ f1f508fc
......@@ -21,7 +21,7 @@ limitations under the License. */
#include <mkl_vml_functions.h>
#endif
#if defined(PADDLE_USE_ATLAS) || defined(PADDLE_USE_VECLIB)
#if defined(PADDLE_USE_VECLIB)
extern "C" {
#include <cblas.h>
#include <clapack.h>
......
python/paddle/fluid/concurrency.py
浏览文件 @ f1f508fc
......@@ -82,11 +82,14 @@ class SelectCase(object):
RECEIVE = 2
def __init__(self,
select,
case_idx,
case_to_execute,
channel_action_fn=None,
channel=None,
value=None):
value=None,
is_copy=False):
self.select = select
self.helper = LayerHelper('conditional_block')
self.main_program = self.helper.main_program
self.is_scalar_condition = True
......@@ -99,7 +102,24 @@ class SelectCase(object):
self.action = (self.SEND
if channel_action_fn.__name__ == ('channel_send') else
self.RECEIVE) if channel_action_fn else self.DEFAULT
self.value = value
X = value
if self.action == self.SEND and is_copy:
# We create of copy of the data we want to send
copied_X = self.select.parent_block.create_var(
name=unique_name.generate(value.name + '_copy'),
type=value.type,
dtype=value.dtype,
shape=value.shape,
lod_level=value.lod_level,
capacity=value.capacity
if hasattr(value, 'capacity') else None, )
self.select.parent_block.append_op(
type="assign", inputs={"X": value}, outputs={"Out": copied_X})
X = copied_X
self.value = X
self.channel = channel
def __enter__(self):
......@@ -173,6 +193,7 @@ class SelectCase(object):
class Select(BlockGuard):
def __init__(self, name=None):
self.helper = LayerHelper('select', name=name)
self.parent_block = self.helper.main_program.current_block()
self.cases = []
super(Select, self).__init__(self.helper.main_program)
......@@ -183,12 +204,12 @@ class Select(BlockGuard):
super(Select, self).__enter__()
return self
def case(self, channel_action_fn, channel, value):
def case(self, channel_action_fn, channel, value, is_copy=False):
"""Create a new block for this condition.
"""
select_case = SelectCase(
len(self.cases), self.case_to_execute, channel_action_fn, channel,
value)
select_case = SelectCase(self,
len(self.cases), self.case_to_execute,
channel_action_fn, channel, value, is_copy)
self.cases.append(select_case)
......@@ -197,7 +218,7 @@ class Select(BlockGuard):
def default(self):
"""Create a default case block for this condition.
"""
default_case = SelectCase(len(self.cases), self.case_to_execute)
default_case = SelectCase(self, len(self.cases), self.case_to_execute)
self.cases.append(default_case)
......@@ -339,35 +360,26 @@ def channel_send(channel, value, is_copy=False):
main_program = helper.main_program
channel_send_block = main_program.current_block()
status = helper.create_variable(
name=unique_name.generate('status'),
type=core.VarDesc.VarType.LOD_TENSOR,
dtype=core.VarDesc.VarType.BOOL)
X = value
if is_copy is True:
if is_copy:
copied_X = helper.create_variable(
name=unique_name.generate(value.name + '_copy'),
type=value.type,
dtype=value.dtype,
shape=value.shape,
lod_level=value.lod_level,
capacity=value.capacity)
capacity=value.capacity if hasattr(value, 'capacity') else None)
assign_op = channel_send_block.append_op(
type="assign_op", inputs={"X": value}, outputs={"Out": copied_X})
type="assign", inputs={"X": value}, outputs={"Out": copied_X})
X = copied_X
channel_send_op = channel_send_block.append_op(
type="channel_send",
inputs={
channel_send_block.append_op(
type="channel_send", inputs={
"Channel": channel,
"X": X,
},
outputs={"Status": status})
return status
})
def channel_recv(channel, return_value):
......
python/paddle/fluid/layers/detection.py
浏览文件 @ f1f508fc
......@@ -134,6 +134,7 @@ def detection_output(loc,
scores = nn.softmax(input=scores)
scores = ops.reshape(x=scores, shape=old_shape)
scores = nn.transpose(scores, perm=[0, 2, 1])
scores.stop_gradient = True
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
helper.append_op(
type="multiclass_nms",
......@@ -148,6 +149,7 @@ def detection_output(loc,
'score_threshold': score_threshold,
'nms_eta': 1.0
})
nmsed_outs.stop_gradient = True
return nmsed_outs
......@@ -837,4 +839,6 @@ def multi_box_head(inputs,
mbox_locs_concat = tensor.concat(mbox_locs, axis=1)
mbox_confs_concat = tensor.concat(mbox_confs, axis=1)
box.stop_gradient = True
var.stop_gradient = True
return mbox_locs_concat, mbox_confs_concat, box, var
python/paddle/fluid/layers/io.py
浏览文件 @ f1f508fc
......@@ -113,9 +113,9 @@ class ListenAndServ(object):
which can receive variables from clients and run a block.
"""
def __init__(self, endpoint, fan_in=1, optimizer_mode=True):
def __init__(self, endpoint, inputs, fan_in=1, optimizer_mode=True):
self.helper = LayerHelper("listen_and_serv")
self.inputs = []
self.inputs = inputs
self.outputs = []
self.endpoint = endpoint
self.fan_in = fan_in
......@@ -160,18 +160,13 @@ class ListenAndServ(object):
current_block = main_program.current_block()
parent_block = self.parent_block()
params, grads = self.get_params_and_grads()
param_names = [p.name for p in params]
grad_names = [g.name for g in grads]
parent_block.append_op(
type='listen_and_serv',
inputs={},
inputs={"X": self.inputs},
outputs={},
attrs={
'endpoint': self.endpoint,
'Fanin': self.fan_in,
'ParamList': param_names,
'GradList': grad_names,
'OptimizeBlock': current_block
})
......@@ -196,10 +191,14 @@ def Send(endpoints, send_vars, get_vars):
endpoints = list(set(epmap))
helper = LayerHelper("Send", **locals())
rpc_client_var = default_main_program().global_block().create_var(
name="RPC_CLIENT_VAR", persistable=True, type=core.VarDesc.VarType.RAW)
helper.append_op(
type="send",
inputs={"X": send_vars},
outputs={"Out": get_vars},
outputs={"Out": get_vars,
"RPCClient": rpc_client_var},
attrs={"endpoints": endpoints,
"epmap": epmap})
......
python/paddle/fluid/layers/nn.py
浏览文件 @ f1f508fc
......@@ -74,6 +74,7 @@ __all__ = [
'one_hot',
'autoincreased_step_counter',
'lod_reset',
'lrn',
]
......@@ -3410,3 +3411,73 @@ def lod_reset(x, y=None, target_lod=None):
raise ValueError("y and target_lod should not be both None.")
return out
def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
"""
Local Response Normalization Layer. This layer performs a type of
"lateral inhibition" by normalizing over local input regions.
The formula is as follows:
.. math::
Output(i, x, y) = Input(i, x, y) / \left(
k + \alpha \sum\limits^{\min(C, c + n/2)}_{j = \max(0, c - n/2)}
(Input(j, x, y))^2 \right)^{\beta}
In the above equation:
* :math:`n`: The number of channels to sum over.
* :math:`k`: The offset (avoid being divided by 0).
* :math:`alpha`: The scaling parameter.
* :math:`beta`: The exponent parameter.
Refer to `ImageNet Classification with Deep Convolutional Neural Networks
<https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_
Args:
input (Variable): The input tensor of this layer, and the dimension of input tensor must be 4.
n (int, default 5): The number of channels to sum over.
k (float, default 1.0): An offset (usually positive to avoid dividing by 0).
alpha (float, default 1e-4): The scaling parameter.
beta (float, default 0.75): The exponent.
name (str, default None): A name for this operation.
Raises:
ValueError: If rank of the input tensor is not 4.
Returns:
A tensor variable storing the transformation result.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[3, 112, 112], dtype="float32")
lrn = fluid.layers.lrn(input=data)
"""
helper = LayerHelper('lrn', **locals())
dtype = helper.input_dtype()
input_shape = input.shape
dims = len(input_shape)
if dims != 4:
raise ValueError(
"dims of input must be 4(not %d), and it's order must be NCHW" %
(dims))
mid_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
lrn_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="lrn",
inputs={"X": input},
outputs={
"Out": lrn_out,
"MidOut": mid_out,
},
attrs={"n": n,
"k": k,
"alpha": alpha,
"beta": beta})
return lrn_out
python/paddle/fluid/tests/test_concurrency.py
浏览文件 @ f1f508fc
......@@ -173,16 +173,10 @@ class TestRoutineOp(unittest.TestCase):
with while_op.block():
result2 = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
x_to_send_tmp = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
# TODO(abhinav): Need to perform copy when doing a channel send.
# Once this is complete, we can remove these lines
assign(input=x, output=x_to_send_tmp)
with fluid.Select() as select:
with select.case(fluid.channel_send, channel,
x_to_send_tmp):
with select.case(
fluid.channel_send, channel, x, is_copy=True):
assign(input=x, output=x_tmp)
assign(input=y, output=x)
assign(elementwise_add(x=x_tmp, y=y), output=y)
......@@ -230,21 +224,12 @@ class TestRoutineOp(unittest.TestCase):
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.FP64)
pong_result = self._create_tensor('pong_return_value',
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.FP64)
def ping(ch, message):
message_to_send_tmp = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.FP64, value=0)
assign(input=message, output=message_to_send_tmp)
fluid.channel_send(ch, message_to_send_tmp)
fluid.channel_send(ch, message, is_copy=True)
def pong(ch1, ch2):
fluid.channel_recv(ch1, ping_result)
assign(input=ping_result, output=pong_result)
fluid.channel_send(ch2, pong_result)
fluid.channel_send(ch2, ping_result, is_copy=True)
pings = fluid.make_channel(
dtype=core.VarDesc.VarType.LOD_TENSOR, capacity=1)
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ f1f508fc
......@@ -231,6 +231,13 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(layers.softmax(hid))
print(str(program))
def test_lrn(self):
program = Program()
with program_guard(program):
data = layers.data(name='data', shape=[6, 2, 2], dtype='float32')
self.assertIsNotNone(layers.lrn(data))
print(str(program))
def test_get_places(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/tests/unittests/test_lrn_op.py
浏览文件 @ f1f508fc
......@@ -97,5 +97,24 @@ class TestLRNMKLDNNOp(TestLRNOp):
self.check_output(atol=0.002)
class TestLRNMKLDNNOpWithIsTest(TestLRNMKLDNNOp):
def get_attrs(self):
attrs = TestLRNMKLDNNOp.get_attrs(self)
attrs['is_test'] = True
return attrs
def test_check_grad_normal(self):
def check_raise_is_test():
try:
self.check_grad(['X'], 'Out', max_relative_error=0.01)
except Exception as e:
t = \
"is_test attribute should be set to False in training phase."
if t in str(e):
raise AttributeError
self.assertRaises(AttributeError, check_raise_is_test)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_recv_op.py
浏览文件 @ f1f508fc
......@@ -38,14 +38,15 @@ class TestRecvOp(unittest.TestCase):
def init_serv(self, place):
main = fluid.Program()
with fluid.program_guard(main):
serv = layers.ListenAndServ(
"127.0.0.1:6174", ["X"], optimizer_mode=False)
with serv.do():
x = layers.data(
shape=[32, 32],
dtype='float32',
name="X",
append_batch_size=False)
fluid.initializer.Constant(value=1.0)(x, main.global_block())
serv = layers.ListenAndServ("127.0.0.1:6174", optimizer_mode=False)
with serv.do():
o = layers.scale(x=x, scale=10.0)
main.global_block().create_var(
name=o.name, psersistable=False, dtype=o.dtype, shape=o.shape)
......
python/paddle/fluid/tests/unittests/test_split_ids_op.py 0 → 100644
浏览文件 @ f1f508fc
# 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.
import unittest
import numpy as np
from op_test import OpTest
class TestSplitIdsOp(OpTest):
def setUp(self):
self.op_type = "split_ids"
ids = np.array([[0], [2], [2], [3], [5], [5], [6]]).astype('int64')
out0 = np.array([[0], [3], [6]]).astype('int64')
out1 = np.array([[]]).astype('int64')
out2 = np.array([[2], [2], [5], [5]]).astype('int64')
self.inputs = {'Ids': ids}
self.outputs = {'Out': [('out0', out0), ('out1', out1), ('out2', out2)]}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
python/paddle/trainer_config_helpers/activations.py
浏览文件 @ f1f508fc
......@@ -77,7 +77,7 @@ class SoftmaxActivation(BaseActivation):
.. math::
P(y=j|x) = \\frac{e^{x_j}} {\\sum^K_{k=1} e^{x_j} }
P(y=j|x) = \\frac{e^{x_j}} {\\sum^K_{k=1} e^{x_k} }
"""
def __init__(self):
......
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