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提交 9f488783 编写于 作者: Q qiaolongfei
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into change-mklml-download-url

上级 72913dc2 7bf82f82
隐藏空白更改
内联 并排
Showing with 265 addition and 164 deletion
paddle/fluid/framework/block_desc.h
浏览文件 @ 9f488783
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <deque>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <vector>
......@@ -96,6 +97,8 @@ class BlockDesc {
*/
void RemoveOp(size_t s, size_t e);
void RemoveVar(const std::string &name) { vars_.erase(name); }
std::vector<OpDesc *> AllOps() const;
size_t OpSize() const { return ops_.size(); }
......
paddle/fluid/framework/channel.h
浏览文件 @ 9f488783
......@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once
#include <stddef.h> // for size_t
#include <condition_variable>
#include <stddef.h> // for size_t
#include <condition_variable> // NOLINT
#include <typeindex>
#include "paddle/fluid/platform/enforce.h"
......@@ -216,7 +216,8 @@ class ChannelHolder {
template <typename T>
struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(size_t buffer_size) : type_(std::type_index(typeid(T))) {
explicit PlaceholderImpl(size_t buffer_size)
: type_(std::type_index(typeid(T))) {
channel_.reset(MakeChannel<T>(buffer_size));
}
......
paddle/fluid/framework/channel_impl.h
浏览文件 @ 9f488783
......@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once
#include <stddef.h> // for size_t
#include <atomic>
#include <condition_variable>
#include <condition_variable> // NOLINT
#include <deque>
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/platform/enforce.h"
......@@ -38,7 +38,7 @@ class ChannelImpl : public paddle::framework::Channel<T> {
virtual void Unlock();
virtual bool IsClosed();
virtual void Close();
ChannelImpl(size_t);
explicit ChannelImpl(size_t);
virtual ~ChannelImpl();
virtual void AddToSendQ(const void *referrer, T *data,
......@@ -60,7 +60,7 @@ class ChannelImpl : public paddle::framework::Channel<T> {
const void *referrer; // TODO(thuan): figure out better way to do this
std::function<bool(ChannelAction)> callback;
QueueMessage(T *item)
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)
......@@ -88,15 +88,15 @@ class ChannelImpl : public paddle::framework::Channel<T> {
}
std::shared_ptr<QueueMessage> get_first_message(
std::deque<std::shared_ptr<QueueMessage>> &queue, ChannelAction action) {
while (!queue.empty()) {
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();
std::shared_ptr<QueueMessage> m = queue->front();
queue->pop_front();
if (m->callback == nullptr || m->callback(action)) return m;
}
return nullptr;
......@@ -147,7 +147,7 @@ void ChannelImpl<T>::Send(T *item) {
// 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);
get_first_message(&recvq, ChannelAction::SEND);
if (m != nullptr) {
*(m->data) = std::move(*item);
......@@ -198,7 +198,7 @@ bool ChannelImpl<T>::Receive(T *item) {
// buffer and move front of send queue to the buffer
if (!sendq.empty()) {
std::shared_ptr<QueueMessage> m =
get_first_message(sendq, ChannelAction::RECEIVE);
get_first_message(&sendq, ChannelAction::RECEIVE);
if (buf_.size() > 0) {
// Case 1 : Channel is Buffered
// Do Data transfer from front of buffer
......@@ -219,8 +219,9 @@ bool ChannelImpl<T>::Receive(T *item) {
if (m != nullptr) {
*item = std::move(*(m->data));
m->Notify();
} else
} else {
return recv_return(Receive(item));
}
}
return recv_return(true);
}
......
paddle/fluid/framework/channel_test.cc
浏览文件 @ 9f488783
......@@ -14,8 +14,8 @@ limitations under the License. */
#include "paddle/fluid/framework/channel.h"
#include <chrono>
#include <thread>
#include <chrono> // NOLINT
#include <thread> // NOLINT
#include "gtest/gtest.h"
using paddle::framework::Channel;
......@@ -166,9 +166,9 @@ TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
std::thread t([&]() {
// Try to write more than buffer size.
for (size_t i = 0; i < 2 * buffer_size; ++i) {
if (i < buffer_size)
if (i < buffer_size) {
ch->Send(&i); // should block after 10 iterations
else {
} else {
bool is_exception = false;
try {
ch->Send(&i);
......@@ -212,12 +212,12 @@ TEST(Channel, RecevingOrderEqualToSendingOrderWithBufferedChannel3) {
}
void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to read and are blocked because of no writers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -230,7 +230,7 @@ void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
// Verify that all the threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
......@@ -241,21 +241,21 @@ void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
void ChannelCloseUnblocksSendersTest(Channel<int> *ch, bool isBuffered) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
bool send_success[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
bool send_success[kNumThreads];
// Launches threads that try to write and are blocked because of no readers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
send_success[i] = false;
t[i] = std::thread(
......@@ -277,13 +277,13 @@ void ChannelCloseUnblocksSendersTest(Channel<int> *ch, bool isBuffered) {
if (isBuffered) {
// If ch is Buffered, atleast 4 threads must be blocked.
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (!thread_ended[i]) ct++;
}
EXPECT_GE(ct, 4);
} else {
// If ch is UnBuffered, all the threads should be blocked.
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
}
......@@ -294,21 +294,21 @@ void ChannelCloseUnblocksSendersTest(Channel<int> *ch, bool isBuffered) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
if (isBuffered) {
// Verify that only 1 send was successful
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (send_success[i]) ct++;
}
// Only 1 send must be successful
EXPECT_EQ(ct, 1);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
// This tests that closing a buffered channel also unblocks
......@@ -409,13 +409,13 @@ TEST(Channel, UnbufferedMoreReceiveLessSendTest) {
// This tests that destroying a channel unblocks
// any senders waiting for channel to have write space
void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
bool send_success[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
bool send_success[kNumThreads];
// Launches threads that try to write and are blocked because of no readers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
send_success[i] = false;
t[i] = std::thread(
......@@ -438,14 +438,14 @@ void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
if (isBuffered) {
// If channel is buffered, verify that atleast 4 threads are blocked
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (thread_ended[i] == false) ct++;
}
// Atleast 4 threads must be blocked
EXPECT_GE(ct, 4);
} else {
// Verify that all the threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
}
......@@ -454,13 +454,13 @@ void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
// Count number of successful sends
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (send_success[i]) ct++;
}
......@@ -473,18 +473,18 @@ void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
}
// Join all threads
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
// This tests that destroying a channel also unblocks
// any receivers waiting on the channel
void ChannelDestroyUnblockReceivers(Channel<int> *ch) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to read and are blocked because of no writers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -498,18 +498,18 @@ void ChannelDestroyUnblockReceivers(Channel<int> *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(100)); // wait
// Verify that all threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
// delete the channel
delete ch;
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
TEST(Channel, BufferedChannelDestroyUnblocksReceiversTest) {
......@@ -679,12 +679,12 @@ TEST(ChannelHolder, TypeMismatchReceiveTest) {
}
void ChannelHolderCloseUnblocksReceiversTest(ChannelHolder *ch) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to read and are blocked because of no writers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -697,7 +697,7 @@ void ChannelHolderCloseUnblocksReceiversTest(ChannelHolder *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
// Verify that all the threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
......@@ -708,21 +708,21 @@ void ChannelHolderCloseUnblocksReceiversTest(ChannelHolder *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
void ChannelHolderCloseUnblocksSendersTest(ChannelHolder *ch, bool isBuffered) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
bool send_success[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
bool send_success[kNumThreads];
// Launches threads that try to write and are blocked because of no readers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
send_success[i] = false;
t[i] = std::thread(
......@@ -744,13 +744,13 @@ void ChannelHolderCloseUnblocksSendersTest(ChannelHolder *ch, bool isBuffered) {
if (isBuffered) {
// If ch is Buffered, atleast 4 threads must be blocked.
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (!thread_ended[i]) ct++;
}
EXPECT_GE(ct, 4);
} else {
// If ch is UnBuffered, all the threads should be blocked.
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
}
......@@ -761,21 +761,21 @@ void ChannelHolderCloseUnblocksSendersTest(ChannelHolder *ch, bool isBuffered) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
if (isBuffered) {
// Verify that only 1 send was successful
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (send_success[i]) ct++;
}
// Only 1 send must be successful
EXPECT_EQ(ct, 1);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
// This tests that closing a channelholder unblocks
......@@ -813,13 +813,13 @@ TEST(Channel, ChannelHolderCloseUnblocksSendersTest) {
// This tests that destroying a channelholder unblocks
// any senders waiting for channel
void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
bool send_success[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
bool send_success[kNumThreads];
// Launches threads that try to write and are blocked because of no readers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
send_success[i] = false;
t[i] = std::thread(
......@@ -841,14 +841,14 @@ void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
if (isBuffered) {
// If channel is buffered, verify that atleast 4 threads are blocked
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (thread_ended[i] == false) ct++;
}
// Atleast 4 threads must be blocked
EXPECT_GE(ct, 4);
} else {
// Verify that all the threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
}
......@@ -857,13 +857,13 @@ void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
// Count number of successfuld sends
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (send_success[i]) ct++;
}
......@@ -876,18 +876,18 @@ void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
}
// Join all threads
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
// This tests that destroying a channelholder also unblocks
// any receivers waiting on the channel
void ChannelHolderDestroyUnblockReceivers(ChannelHolder *ch) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to read and are blocked because of no writers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -901,18 +901,18 @@ void ChannelHolderDestroyUnblockReceivers(ChannelHolder *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
// delete the channel
delete ch;
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
TEST(ChannelHolder, ChannelHolderDestroyUnblocksReceiversTest) {
......@@ -945,12 +945,12 @@ TEST(ChannelHolder, ChannelHolderDestroyUnblocksSendersTest) {
// This tests that closing a channelholder many times.
void ChannelHolderManyTimesClose(ChannelHolder *ch) {
const int num_threads = 15;
std::thread t[num_threads];
bool thread_ended[num_threads];
const int kNumThreads = 15;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to send data to channel.
for (size_t i = 0; i < num_threads / 3; i++) {
for (size_t i = 0; i < kNumThreads / 3; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *ended) {
......@@ -962,7 +962,7 @@ void ChannelHolderManyTimesClose(ChannelHolder *ch) {
}
// Launches threads that try to receive data to channel.
for (size_t i = num_threads / 3; i < 2 * num_threads / 3; i++) {
for (size_t i = kNumThreads / 3; i < 2 * kNumThreads / 3; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -976,7 +976,7 @@ void ChannelHolderManyTimesClose(ChannelHolder *ch) {
}
// Launches threads that try to close the channel.
for (size_t i = 2 * num_threads / 3; i < num_threads; i++) {
for (size_t i = 2 * kNumThreads / 3; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -991,13 +991,13 @@ void ChannelHolderManyTimesClose(ChannelHolder *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(100)); // wait
// Verify that all threads are unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
EXPECT_TRUE(ch->IsClosed());
// delete the channel
delete ch;
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
TEST(ChannelHolder, ChannelHolderManyTimesCloseTest) {
......
paddle/fluid/framework/lod_tensor.h
浏览文件 @ 9f488783
......@@ -142,6 +142,7 @@ class LoDTensor : public Tensor {
return (lod_)[level].size() - 1;
}
// Split LoDTensor and copy to each place specified in places.
std::vector<LoDTensor> SplitLoDTensor(
const std::vector<platform::Place> places) const;
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 9f488783
......@@ -150,13 +150,30 @@ void ParallelExecutor::BCastParamsToGPUs(
#endif
}
void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
const std::string &fetched_var_name) {
void ParallelExecutor::Run(
const std::vector<std::string> &fetch_tensors,
const std::string &fetched_var_name,
const std::unordered_map<std::string, LoDTensor> &feed_tensors) {
platform::RecordBlock b(0);
SplitTensorToPlaces(feed_tensors);
auto fetch_data = member_->executor_->Run(fetch_tensors);
*member_->global_scope_->Var(fetched_var_name)->GetMutable<FeedFetchList>() =
fetch_data;
}
void ParallelExecutor::SplitTensorToPlaces(
const std::unordered_map<std::string, LoDTensor> &feed_tensors) {
for (auto it : feed_tensors) {
auto lod_tensors = it.second.SplitLoDTensor(member_->places_);
for (size_t j = 0; j < member_->places_.size(); ++j) {
// TODO(panxy0718): Do I need to delete this var?
member_->local_scopes_[j]
->Var(it.first)
->GetMutable<LoDTensor>()
->ShareDataWith(lod_tensors[j]);
}
}
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 9f488783
......@@ -42,9 +42,13 @@ class ParallelExecutor {
bool allow_op_delay);
void Run(const std::vector<std::string>& fetch_tensors,
const std::string& fetched_var_name = "fetched_var");
const std::string& fetched_var_name,
const std::unordered_map<std::string, LoDTensor>& feed_tensors);
private:
void SplitTensorToPlaces(
const std::unordered_map<std::string, LoDTensor>& feed_tensors);
ParallelExecutorPrivate* member_;
void BCastParamsToGPUs(const ProgramDesc& startup_program) const;
......
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ 9f488783
......@@ -128,10 +128,32 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
// Tensor core is supported since the volta GPU and
// is only enabled when input and filter data are float16
if (dev_ctx.GetComputeCapability() >= 70 &&
std::type_index(typeid(T)) ==
std::type_index(typeid(platform::float16))) {
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
// Currently tensor core is only enabled using this algo
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
} else {
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
}
#endif
// get workspace size able to allocate
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
// It is possible for float16 on Volta GPU to allocate more memory than
// the limit because the algo is overrided to use tensor core.
PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
"workspace_size to be allocated exceeds the limit");
// Allocate on GPU memory
platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
......
paddle/fluid/operators/fc_mkldnn_op.cc
浏览文件 @ 9f488783
......@@ -27,8 +27,8 @@ template <typename T>
class MKLDNNMD {
public:
explicit MKLDNNMD(const T* in, const T* w, bool bias)
: in{paddle::framework::vectorize2int(in->dims())},
w{paddle::framework::vectorize2int(w->dims())} {
: in(paddle::framework::vectorize2int(in->dims())),
w(paddle::framework::vectorize2int(w->dims())) {
with_bias_ = bias;
}
......@@ -78,7 +78,7 @@ class MKLDNNMD {
class MKLDNNMemory {
public:
MKLDNNMemory(MKLDNNMD<Tensor>* t, const mkldnn::engine& e)
: md_{t}, engine_{e} {}
: md_(t), engine_(e) {}
virtual ~MKLDNNMemory() = default;
template <typename Output>
......
paddle/fluid/platform/cudnn_helper.h
浏览文件 @ 9f488783
......@@ -257,9 +257,11 @@ class ScopedConvolutionDescriptor {
}
#endif
cudnnDataType_t compute_type =
(type == CUDNN_DATA_DOUBLE) ? CUDNN_DATA_DOUBLE : CUDNN_DATA_FLOAT;
PADDLE_ENFORCE(dynload::cudnnSetConvolutionNdDescriptor(
desc_, pads.size(), pads.data(), strides.data(), dilations.data(),
CUDNN_CROSS_CORRELATION, type));
CUDNN_CROSS_CORRELATION, compute_type));
return desc_;
}
......
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @ 9f488783
......@@ -16,7 +16,7 @@ limitations under the License. */
#include <cudnn.h>
#include <dlfcn.h>
#include <mutex>
#include <mutex> // NOLINT
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
namespace paddle {
......@@ -140,7 +140,8 @@ CUDNN_DNN_ROUTINE_EACH_R5(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#if CUDNN_VERSION >= 7001
#define CUDNN_DNN_ROUTINE_EACH_R7(__macro) \
__macro(cudnnSetConvolutionGroupCount);
__macro(cudnnSetConvolutionGroupCount); \
__macro(cudnnSetConvolutionMathType);
CUDNN_DNN_ROUTINE_EACH_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#endif
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 9f488783
......@@ -15,6 +15,8 @@ limitations under the License. */
#include "paddle/fluid/pybind/protobuf.h"
#include <deque>
#include <iostream>
#include <string>
#include <tuple>
#include "paddle/fluid/framework/backward.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_desc.h"
......@@ -98,7 +100,7 @@ namespace pybind {
using namespace paddle::framework; // NOLINT
template <typename T>
static py::bytes SerializeMessage(T &self) {
static py::bytes SerializeMessage(T &self) { // NOLINT
// Check IsInitialized in Python
std::string retv;
PADDLE_ENFORCE(self.Proto()->SerializePartialToString(&retv),
......@@ -107,7 +109,7 @@ static py::bytes SerializeMessage(T &self) {
}
// Bind Methods
void BindProgramDesc(py::module &m) {
void BindProgramDesc(py::module &m) { // NOLINT
py::class_<ProgramDesc>(m, "ProgramDesc", "")
.def(py::init<>())
.def("__init__",
......@@ -151,7 +153,7 @@ void BindProgramDesc(py::module &m) {
});
}
void BindBlockDesc(py::module &m) {
void BindBlockDesc(py::module &m) { // NOLINT
py::class_<BlockDesc>(m, "BlockDesc", "")
.def_property_readonly("id", &BlockDesc::ID)
.def_property_readonly("parent", &BlockDesc::Parent)
......@@ -200,13 +202,19 @@ void BindBlockDesc(py::module &m) {
return self.FindVarRecursive(name);
},
py::return_value_policy::reference)
.def("remove_var",
[](BlockDesc &self, py::bytes byte_name) {
std::string name = byte_name;
return self.RemoveVar(name);
},
py::return_value_policy::reference)
.def("all_vars", &BlockDesc::AllVars, py::return_value_policy::reference)
.def("op_size", &BlockDesc::OpSize)
.def("op", &BlockDesc::Op, py::return_value_policy::reference)
.def("serialize_to_string", SerializeMessage<BlockDesc>);
}
void BindVarDsec(py::module &m) {
void BindVarDsec(py::module &m) { // NOLINT
py::class_<VarDesc> var_desc(m, "VarDesc", "");
var_desc
.def("name",
......@@ -257,7 +265,7 @@ void BindVarDsec(py::module &m) {
.value("RAW", proto::VarType::RAW);
}
void BindOpDesc(py::module &m) {
void BindOpDesc(py::module &m) { // NOLINT
py::enum_<proto::AttrType>(m, "AttrType", "")
.value("INT", proto::AttrType::INT)
.value("INTS", proto::AttrType::INTS)
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 9f488783
......@@ -26,25 +26,29 @@ class ParallelExecutor(object):
use_cuda,
num_threads=None,
allow_op_delay=False):
places = []
self._places = []
self._act_places = []
if use_cuda:
for i in xrange(core.get_cuda_device_count()):
p = core.Place()
p.set_place(core.CUDAPlace(i))
places.append(p)
self._act_places.append(core.CUDAPlace(i))
p.set_place(self._act_places[-1])
self._places.append(p)
else:
for i in xrange(multiprocessing.cpu_count()):
p = core.Place()
p.set_place(core.CPUPlace())
places.append(p)
self._act_places.append(core.CPUPlace(i))
p.set_place(self._act_places[-1])
self._places.append(p)
assert self._places, "no place for execution"
if num_threads is None:
if use_cuda:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
num_threads = len(places)
num_threads = len(self._places)
else:
min(len(places) * 2, multiprocessing.cpu_count())
min(len(self._places) * 2, multiprocessing.cpu_count())
startup = framework.default_startup_program()
main = framework.default_main_program()
......@@ -53,7 +57,7 @@ class ParallelExecutor(object):
self.executor = core.ParallelExecutor(
num_threads,
True if use_cuda else False, # use_event
places,
self._places,
set([
p.name for p in main.global_block().iter_parameters()
if not p.stop_gradient
......@@ -65,8 +69,25 @@ class ParallelExecutor(object):
allow_op_delay)
self.scope = scope
def run(self, fetch_list):
def run(self, fetch_list, feed_dict={}):
"""
:param fetch_list: A list of variable names that will be fetched.
:param feed_dict: A dict mapping for feed variable name to LoDTensor
or numpy array.
:return: fetched value list.
"""
if not isinstance(feed_dict, dict):
raise TypeError("feed_dict should be a dict")
feed_tensor_dict = {}
for i, feed_name in enumerate(feed_dict):
feed_tensor = feed_dict[feed_name]
if not isinstance(feed_tensor, core.LoDTensor):
feed_tensor = core.LoDTensor()
feed_tensor.set(feed_dict[feed_name], self._act_places[0])
feed_tensor_dict[feed_name] = feed_tensor
fetch_var_name = '@FETCHED_VAR_NAME@'
self.executor.run(fetch_list, fetch_var_name)
self.executor.run(fetch_list, fetch_var_name, feed_tensor_dict)
arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
return [arr[i] for i in range(len(arr))]
python/paddle/fluid/tests/unittests/test_parallel_executor.py
浏览文件 @ 9f488783
......@@ -21,13 +21,17 @@ import paddle.dataset.mnist as mnist
import paddle.dataset.wmt16 as wmt16
def simple_fc_net():
reader = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(reader)
def simple_fc_net(use_feed):
if use_feed:
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
else:
reader = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(reader)
hidden = img
for _ in xrange(4):
hidden = fluid.layers.fc(
......@@ -42,13 +46,18 @@ def simple_fc_net():
return loss
def fc_with_batchnorm():
reader = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(reader)
def fc_with_batchnorm(use_feed):
if use_feed:
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
else:
reader = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(reader)
hidden = img
for _ in xrange(1):
hidden = fluid.layers.fc(
......@@ -135,7 +144,9 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
def SE_ResNeXt152Small(batch_size=2):
def SE_ResNeXt152Small(batch_size=2, use_feed=False):
assert not use_feed, "SE_ResNeXt doesn't support feed yet"
img = fluid.layers.fill_constant(
shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0)
label = fluid.layers.fill_constant(
......@@ -185,30 +196,28 @@ class TestParallelExecutorBase(unittest.TestCase):
memory_opt=True,
iter=10,
batch_size=None,
allow_op_delay=False):
allow_op_delay=False,
feed_dict={}):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = method()
loss = method(use_feed=len(feed_dict) > 0)
adam = fluid.optimizer.Adam()
adam.minimize(loss)
if memory_opt:
fluid.memory_optimize(main)
exe = fluid.ParallelExecutor(
loss_name=loss.name,
use_cuda=True,
allow_op_delay=allow_op_delay)
exe = fluid.ParallelExecutor(loss_name=loss.name, use_cuda=True)
if batch_size is not None:
batch_size *= fluid.core.get_cuda_device_count()
begin = time.time()
first_loss, = exe.run([loss.name])
first_loss, = exe.run([loss.name], feed_dict=feed_dict)
first_loss = numpy.array(first_loss)
for i in xrange(iter):
exe.run([])
exe.run([], feed_dict=feed_dict)
last_loss, = exe.run([loss.name])
last_loss, = exe.run([loss.name], feed_dict=feed_dict)
end = time.time()
if batch_size is not None:
......@@ -242,9 +251,19 @@ class TestMNIST(TestParallelExecutorBase):
self.check_network_convergence(simple_fc_net)
self.check_network_convergence(simple_fc_net, allow_op_delay=True)
img = numpy.zeros(shape=[32, 784], dtype='float32')
label = numpy.ones(shape=[32, 1], dtype='int64')
self.check_network_convergence(
simple_fc_net, feed_dict={"image": img,
"label": label})
def test_batchnorm_fc(self):
self.check_network_convergence(fc_with_batchnorm)
self.check_network_convergence(fc_with_batchnorm, allow_op_delay=True)
img = numpy.zeros(shape=[32, 784], dtype='float32')
label = numpy.ones(shape=[32, 1], dtype='int64')
self.check_network_convergence(
fc_with_batchnorm, feed_dict={"image": img,
"label": label})
class TestResnet(TestParallelExecutorBase):
......@@ -400,7 +419,8 @@ def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
import transformer_model
def transformer():
def transformer(use_feed):
assert not use_feed, "transfomer doesn't support feed yet"
return transformer_model.transformer(
ModelHyperParams.src_vocab_size + 1,
ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1,
......
python/paddle/fluid/tests/unittests/test_protobuf_descs.py
浏览文件 @ 9f488783
......@@ -19,9 +19,9 @@ from paddle.fluid.framework import Program
class TestOpDesc(unittest.TestCase):
def test_op_desc(self):
prog = core.ProgramDesc()
self.assertIsNotNone(prog)
block = prog.block(0)
program_desc = core.ProgramDesc()
self.assertIsNotNone(program_desc)
block = program_desc.block(0)
self.assertIsNotNone(block)
op = block.append_op()
self.assertIsNotNone(op)
......@@ -67,7 +67,7 @@ class TestOpDesc(unittest.TestCase):
self.assertEqual(8, len(op.attr_names()))
op.set_block_attr("block_attr", prog.block(0))
op.set_block_attr("block_attr", program_desc.block(0))
self.assertEqual(0, op.block_attr("block_attr"))
mul_op = block.append_op()
......@@ -88,20 +88,20 @@ class TestProgramDesc(unittest.TestCase):
del program_desc
def test_append_block(self):
prog_desc = core.ProgramDesc()
self.assertIsNotNone(prog_desc)
block_root = prog_desc.block(0)
program_desc = core.ProgramDesc()
self.assertIsNotNone(program_desc)
block_root = program_desc.block(0)
self.assertIsNotNone(block_root)
self.assertEqual(block_root.id, 0)
block1 = prog_desc.append_block(block_root)
block2 = prog_desc.append_block(block1)
block1 = program_desc.append_block(block_root)
block2 = program_desc.append_block(block1)
self.assertIsNotNone(block1)
self.assertEqual(block1.id, block2.parent)
self.assertEqual(block_root.id, block1.parent)
block3 = prog_desc.append_block(block_root)
block3 = program_desc.append_block(block_root)
self.assertEqual(block3.parent, block_root.id)
self.assertEqual(prog_desc.block(1).id, 1)
self.assertEqual(4, prog_desc.num_blocks())
self.assertEqual(program_desc.block(1).id, 1)
self.assertEqual(4, program_desc.num_blocks())
class TestVarDesc(unittest.TestCase):
......@@ -162,9 +162,9 @@ class TestVarDesc(unittest.TestCase):
class TestBlockDesc(unittest.TestCase):
def test_add_var(self):
prog = core.ProgramDesc()
self.assertIsNotNone(prog)
block = prog.block(0)
program_desc = core.ProgramDesc()
self.assertIsNotNone(program_desc)
block = program_desc.block(0)
self.assertIsNotNone(block)
var1 = block.var("var1")
var2 = block.var("var2")
......@@ -175,9 +175,9 @@ class TestBlockDesc(unittest.TestCase):
self.assertEqual(var2_re, var2)
def test_add_op(self):
prog = core.ProgramDesc()
self.assertIsNotNone(prog)
block = prog.block(0)
program_desc = core.ProgramDesc()
self.assertIsNotNone(program_desc)
block = program_desc.block(0)
self.assertIsNotNone(block)
op1 = block.append_op()
op2 = block.append_op()
......@@ -189,9 +189,9 @@ class TestBlockDesc(unittest.TestCase):
def test_remove_op(self):
program = Program()
prog = program.desc
self.assertIsNotNone(prog)
block = prog.block(0)
program_desc = program.desc
self.assertIsNotNone(program_desc)
block = program_desc.block(0)
self.assertIsNotNone(block)
op0 = block.append_op()
......
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