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提交 cbe128bb 编写于 作者: Q Qiao Longfei
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize-sum-seq-pooling-op

上级 de539d72 5ed3e6f3
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Showing with 1070 addition and 328 deletion
.gitignore
浏览文件 @ cbe128bb
......@@ -28,3 +28,4 @@ third_party/
build_*
# clion workspace.
cmake-build-*
model_test
Dockerfile
浏览文件 @ cbe128bb
......@@ -75,14 +75,14 @@ RUN pip3 install -U wheel && \
pip3 install -U docopt PyYAML sphinx==1.5.6 && \
pip3 install sphinx-rtd-theme==0.1.9 recommonmark && \
easy_install -U pip && \
pip install -U wheel && \
pip install -U pip setuptools wheel && \
pip install -U docopt PyYAML sphinx==1.5.6 && \
pip install sphinx-rtd-theme==0.1.9 recommonmark
RUN pip3 install pre-commit 'ipython==5.3.0' && \
RUN pip3 install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip3 install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip3 install opencv-python && \
pip install pre-commit 'ipython==5.3.0' && \
pip install 'pre-commit==1.10.4' 'ipython==5.3.0' && \
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip install opencv-python
......
paddle/fluid/API.spec
浏览文件 @ cbe128bb
......@@ -86,7 +86,7 @@ paddle.fluid.layers.reduce_prod ArgSpec(args=['input', 'dim', 'keep_dim', 'name'
paddle.fluid.layers.sequence_first_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_last_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_slice ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer'))
paddle.fluid.layers.split ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.ctc_greedy_decoder ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.edit_distance ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None))
......@@ -107,7 +107,7 @@ paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label',
paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))
paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, True, None))
paddle.fluid.layers.reshape ArgSpec(args=['x', 'shape', 'actual_shape', 'act', 'inplace', 'name'], varargs=None, keywords=None, defaults=(None, None, False, None))
paddle.fluid.layers.squeeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.unsqueeze ArgSpec(args=['input', 'axes', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lod_reset ArgSpec(args=['x', 'y', 'target_lod'], varargs=None, keywords=None, defaults=(None, None))
......@@ -174,6 +174,7 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ cbe128bb
......@@ -252,9 +252,9 @@ std::vector<ir::Node *> SortOpsAndDelayOptimizeOp(const ir::Graph &graph) {
std::vector<ir::Node *> sorted_ret;
for (size_t i = 0; i < ret.size(); ++i) {
if (i < last_backward) {
if (boost::get<int>(ret[i]->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) ==
static_cast<int>(OpRole::kOptimize)) {
if (static_cast<bool>(boost::get<int>(ret[i]->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) &
static_cast<int>(OpRole::kOptimize))) {
optimize_ops.push_back(ret[i]);
} else {
sorted_ret.push_back(ret[i]);
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ cbe128bb
......@@ -542,6 +542,33 @@ class CPUVector : public std::vector<T, std::allocator<T>> {
this->reserve(this->size() + size_t(end - begin));
this->insert(this->end(), begin, end);
}
const T *CUDAData(platform::Place place) const {
PADDLE_THROW(
"Vector::CUDAData() method is not supported in CPU-only version");
}
T *CUDAMutableData(platform::Place place) {
PADDLE_THROW(
"Vector::CUDAMutableData() method is not supported in CPU-only "
"version");
}
const T *Data(platform::Place place) const {
PADDLE_ENFORCE(
platform::is_cpu_place(place),
"Vector::Data() method is not supported when not in CPUPlace");
return this->data();
}
T *MutableData(platform::Place place) {
PADDLE_ENFORCE(
platform::is_cpu_place(place),
"Vector::MutableData() method is not supported when not in CPUPlace");
return this->data();
}
const void *Handle() const { return static_cast<const void *>(this); }
};
template <typename T>
......
paddle/fluid/framework/naive_executor.cc
浏览文件 @ cbe128bb
......@@ -146,22 +146,5 @@ void NaiveExecutor::CleanFeedFetchOps() {
ops_.swap(ops);
}
void NaiveExecutor::EnableMKLDNN(const ProgramDesc &program) {
#ifdef PADDLE_WITH_MKLDNN
VLOG(3) << "use_mkldnn=True";
for (size_t block_id = 0; block_id < program.Size(); ++block_id) {
auto *block = const_cast<ProgramDesc &>(program).MutableBlock(block_id);
for (auto *op : block->AllOps()) {
if (op->HasAttr("use_mkldnn")) {
op->SetAttr("use_mkldnn", true);
}
}
}
#else
LOG(WARNING)
<< "'MKLDNN' is not supported, Please re-compile with WITH_MKLDNN option";
#endif
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/naive_executor.h
浏览文件 @ cbe128bb
......@@ -48,8 +48,6 @@ class NaiveExecutor {
void CleanFeedFetchOps();
void EnableMKLDNN(const ProgramDesc& program);
protected:
void CreateVariables(const ProgramDesc& desc, Scope* scope, int block_id);
......
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ cbe128bb
......@@ -71,6 +71,8 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
static_cast<int>(OpRole::kLoss) | static_cast<int>(OpRole::kForward),
static_cast<int>(OpRole::kLoss) |
static_cast<int>(OpRole::kBackward),
static_cast<int>(OpRole::kOptimize) |
static_cast<int>(OpRole::kLRSched),
static_cast<int>(OpRole::kNotSpecified)})
.SetDefault(static_cast<int>(OpRole::kNotSpecified));
AddAttr<std::vector<std::string>>(OpRoleVarAttrName(),
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ cbe128bb
......@@ -20,6 +20,9 @@ limitations under the License. */
namespace paddle {
namespace framework {
//////////////////////////
// Don't add more roles to make this too complicated!
//////////////////////////
enum class OpRole {
kForward = 0x0000,
kBackward = 0x0001,
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ cbe128bb
......@@ -156,12 +156,6 @@ ParallelExecutor::ParallelExecutor(
params, member_->local_scopes_, member_->use_cuda_);
#endif
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
}
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
......
paddle/fluid/framework/threadpool.cc
浏览文件 @ cbe128bb
......@@ -25,7 +25,6 @@ DEFINE_int32(dist_threadpool_size, 0,
namespace paddle {
namespace framework {
std::unique_ptr<ThreadPool> ThreadPool::threadpool_(nullptr);
std::once_flag ThreadPool::init_flag_;
......@@ -47,8 +46,7 @@ void ThreadPool::Init() {
}
}
ThreadPool::ThreadPool(int num_threads)
: total_threads_(num_threads), idle_threads_(num_threads), running_(true) {
ThreadPool::ThreadPool(int num_threads) : running_(true) {
threads_.resize(num_threads);
for (auto& thread : threads_) {
// TODO(Yancey1989): binding the thread on the specify CPU number
......@@ -59,6 +57,7 @@ ThreadPool::ThreadPool(int num_threads)
ThreadPool::~ThreadPool() {
{
// notify all threads to stop running
std::lock_guard<std::mutex> l(mutex_);
running_ = false;
scheduled_.notify_all();
}
......@@ -69,36 +68,24 @@ ThreadPool::~ThreadPool() {
}
}
void ThreadPool::Wait() {
std::unique_lock<std::mutex> lock(mutex_);
completed_.wait(lock, [=] { return Done() == true; });
}
void ThreadPool::TaskLoop() {
while (running_) {
while (true) {
std::unique_lock<std::mutex> lock(mutex_);
scheduled_.wait(lock, [=] { return !tasks_.empty() || !running_; });
if (!running_) {
break;
scheduled_.wait(
lock, [this] { return !this->tasks_.empty() || !this->running_; });
if (!running_ || tasks_.empty()) {
return;
}
// pop a task from the task queue
auto task = std::move(tasks_.front());
tasks_.pop();
--idle_threads_;
lock.unlock();
// run the task
task();
{
std::unique_lock<std::mutex> lock(mutex_);
++idle_threads_;
if (Done()) {
completed_.notify_all();
}
}
}
}
......
paddle/fluid/framework/threadpool.h
浏览文件 @ cbe128bb
......@@ -57,15 +57,6 @@ class ThreadPool {
~ThreadPool();
// Returns the number of threads created by the constructor.
size_t Threads() const { return total_threads_; }
// Returns the number of currently idle threads.
size_t IdleThreads() {
std::unique_lock<std::mutex> lock(mutex_);
return idle_threads_;
}
// Run pushes a function to the task queue and returns a std::future
// object. To wait for the completion of the task, call
// std::future::wait().
......@@ -94,25 +85,13 @@ class ThreadPool {
});
std::future<std::unique_ptr<platform::EnforceNotMet>> f = task.get_future();
tasks_.push(std::move(task));
lock.unlock();
scheduled_.notify_one();
return f;
}
// Wait until all the tasks are completed.
void Wait();
private:
DISABLE_COPY_AND_ASSIGN(ThreadPool);
// If the task queue is empty and avaialbe is equal to the number of
// threads, means that all tasks are completed. Note: this function
// is not thread-safe. Returns true if all tasks are completed.
// Note: don't delete the data member total_threads_ and use
// threads_.size() instead; because you'd need to lock the mutex
// before accessing threads_.
bool Done() { return tasks_.empty() && idle_threads_ == total_threads_; }
// The constructor starts threads to run TaskLoop, which retrieves
// and runs tasks from the queue.
void TaskLoop();
......@@ -125,14 +104,11 @@ class ThreadPool {
static std::once_flag init_flag_;
std::vector<std::unique_ptr<std::thread>> threads_;
const size_t total_threads_;
size_t idle_threads_;
std::queue<Task> tasks_;
std::mutex mutex_;
bool running_;
std::condition_variable scheduled_;
std::condition_variable completed_;
};
class ThreadPoolIO : ThreadPool {
......
paddle/fluid/framework/threadpool_test.cc
浏览文件 @ cbe128bb
......@@ -19,10 +19,11 @@ limitations under the License. */
namespace framework = paddle::framework;
void do_sum(framework::ThreadPool* pool, std::atomic<int>* sum, int cnt) {
std::vector<std::future<void>> fs;
void do_sum(std::vector<std::future<void>>* fs, std::mutex* mu,
std::atomic<int>* sum, int cnt) {
for (int i = 0; i < cnt; ++i) {
fs.push_back(framework::Async([sum]() { sum->fetch_add(1); }));
std::lock_guard<std::mutex> l(*mu);
fs->push_back(framework::Async([sum]() { sum->fetch_add(1); }));
}
}
......@@ -40,18 +41,21 @@ TEST(ThreadPool, ConcurrentInit) {
}
TEST(ThreadPool, ConcurrentRun) {
framework::ThreadPool* pool = framework::ThreadPool::GetInstance();
std::atomic<int> sum(0);
std::vector<std::thread> threads;
std::vector<std::future<void>> fs;
std::mutex fs_mu;
int n = 50;
// sum = (n * (n + 1)) / 2
for (int i = 1; i <= n; ++i) {
std::thread t(do_sum, pool, &sum, i);
std::thread t(do_sum, &fs, &fs_mu, &sum, i);
threads.push_back(std::move(t));
}
for (auto& t : threads) {
t.join();
}
pool->Wait();
for (auto& t : fs) {
t.wait();
}
EXPECT_EQ(sum, ((n + 1) * n) / 2);
}
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ cbe128bb
......@@ -107,6 +107,9 @@ void Analyzer::Run(Argument* argument) {
passes.push_back("mkldnn_placement_pass");
}
#endif
// infer_clean_graph_pass should be the first default pass
// after mkldnn_placement_pass.
passes.push_back("infer_clean_graph_pass");
for (auto& pass : ir_passes_) {
if (!disabled_ir_passes_.count(pass)) {
passes.push_back(pass);
......
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @ cbe128bb
......@@ -67,7 +67,6 @@ class Analyzer : public OrderedRegistry<PassManager> {
// larger fusion.
const std::vector<std::string> all_ir_passes_{{
// Manual update the passes here.
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"seqconv_eltadd_relu_fuse_pass", //
"embedding_fc_lstm_fuse_pass", //
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ cbe128bb
......@@ -124,7 +124,7 @@ class ZeroCopyTensor {
std::vector<std::vector<size_t>> lod() const;
protected:
ZeroCopyTensor(void* scope) : scope_{scope} {}
explicit ZeroCopyTensor(void* scope) : scope_{scope} {}
void SetName(const std::string& name) { name_ = name; }
void* FindTensor() const;
......@@ -259,12 +259,6 @@ struct AnalysisConfig : public NativeConfig {
kExclude // Specify the disabled passes in `ir_passes`.
};
void SetIncludeMode() {
ir_mode = IrPassMode::kInclude;
// this pass has to be run at the beginning of all fuse passes
ir_passes = {"infer_clean_graph_pass"};
}
// Determine whether to perform graph optimization.
bool enable_ir_optim = true;
// Manually determine the IR passes to run.
......
paddle/fluid/operators/detection/generate_proposals_op.cc
浏览文件 @ cbe128bb
......@@ -284,7 +284,7 @@ static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox,
selected_indices.push_back(idx);
++selected_num;
}
sorted_indices.erase(sorted_indices.end());
sorted_indices.erase(sorted_indices.end() - 1);
if (flag && eta < 1 && adaptive_threshold > 0.5) {
adaptive_threshold *= eta;
}
......
paddle/fluid/operators/dropout_op.cc
浏览文件 @ cbe128bb
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/dropout_op.h"
#include <string>
namespace paddle {
namespace operators {
......@@ -57,6 +58,29 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
"will be dropped.")
.SetDefault(false);
AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
AddAttr<std::string>(
"dropout_implementation",
"[\"downgrade_in_infer\"|\"upscale_in_train\"]"
"There are two kinds of ways to implement dropout"
"(the mask below is a tensor have the same shape with input"
"the value of mask is 0 or 1, the ratio of 0 is dropout_prob)"
"1. downgrade_in_infer(default), downgrade the outcome at inference "
"time"
" train: out = input * mask"
" inference: out = input * dropout_prob"
"2. upscale_in_train, upscale the outcome at training time, do nothing "
"in inference"
" train: out = input * mask / ( 1.0 - dropout_prob )"
" inference: out = input"
" dropout op can be removed from the program. the program will be "
"efficient")
.SetDefault("downgrade_in_infer")
.AddCustomChecker([](const std::string& type) {
PADDLE_ENFORCE(
type == "downgrade_in_infer" || type == "upscale_in_train",
"dropout_implementation can only be downgrade_in_infer or "
"upscale_in_train");
});
AddComment(R"DOC(
Dropout Operator.
......@@ -104,7 +128,9 @@ REGISTER_OPERATOR(dropout, ops::DropoutOp, ops::DropoutOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(dropout_grad, ops::DropoutOpGrad);
REGISTER_OP_CPU_KERNEL(
dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>);
dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>,
ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
dropout_grad,
ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>);
ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::DropoutGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/dropout_op.cu
浏览文件 @ cbe128bb
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <thrust/iterator/counting_iterator.h>
#include <thrust/random.h>
#include <thrust/transform.h>
#include <string>
#include "paddle/fluid/operators/dropout_op.h"
#include "paddle/fluid/platform/float16.h"
......@@ -26,7 +27,8 @@ namespace operators {
template <typename T>
__global__ void RandomGenerator(const size_t n, const int seed,
const float dropout_prob, const T* src,
T* mask_data, T* dst) {
T* mask_data, T* dst,
bool is_upscale_in_train) {
thrust::minstd_rand rng;
rng.seed(seed);
thrust::uniform_real_distribution<float> dist(0, 1);
......@@ -47,7 +49,11 @@ __global__ void RandomGenerator(const size_t n, const int seed,
if (dist(rng) < dropout_prob) {
mask = static_cast<T>(0);
} else {
mask = static_cast<T>(1);
if (is_upscale_in_train) {
mask = static_cast<T>(1.0f / (1.0f - dropout_prob));
} else {
mask = static_cast<T>(1);
}
}
dest = s * mask;
mask_data[idx] = mask;
......@@ -67,6 +73,8 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
y->mutable_data<T>(context.GetPlace());
float dropout_prob = context.Attr<float>("dropout_prob");
auto dropout_implementation =
context.Attr<std::string>("dropout_implementation");
auto& place = *context.template device_context<Place>().eigen_device();
if (!context.Attr<bool>("is_test")) {
auto* mask = context.Output<Tensor>("Mask");
......@@ -83,11 +91,16 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
int grid = (x->numel() + threads - 1) / threads;
RandomGenerator<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, seed, dropout_prob, x_data, mask_data, y_data);
size, seed, dropout_prob, x_data, mask_data, y_data,
(dropout_implementation == "upscale_in_train"));
} 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);
if (dropout_implementation == "upscale_in_train") {
Y.device(place) = X;
} else {
Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
}
}
}
};
......@@ -99,6 +112,8 @@ namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
dropout, ops::GPUDropoutKernel<plat::CUDADeviceContext, float>,
ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(dropout_grad,
ops::DropoutGradKernel<plat::CUDADeviceContext, float>);
ops::GPUDropoutKernel<plat::CUDADeviceContext, plat::float16>,
ops::GPUDropoutKernel<plat::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
dropout_grad, ops::DropoutGradKernel<plat::CUDADeviceContext, float>,
ops::DropoutGradKernel<plat::CUDADeviceContext, double>);
paddle/fluid/operators/dropout_op.h
浏览文件 @ cbe128bb
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <random>
#include <string>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -36,6 +37,8 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
auto* y_data = y->mutable_data<T>(context.GetPlace());
float dropout_prob = context.Attr<float>("dropout_prob");
auto dropout_implementation =
context.Attr<std::string>("dropout_implementation");
if (!context.Attr<bool>("is_test")) {
auto* mask = context.Output<Tensor>("Mask");
auto* mask_data = mask->mutable_data<T>(context.GetPlace());
......@@ -49,14 +52,20 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
engine.seed(seed);
std::uniform_real_distribution<float> dist(0, 1);
size_t size = framework::product(mask->dims());
for (size_t i = 0; i < size; ++i) {
if (dist(engine) < dropout_prob) {
mask_data[i] = 0;
y_data[i] = 0;
} else {
mask_data[i] = 1;
y_data[i] = x_data[i];
if (dropout_implementation == "upscale_in_train") {
mask_data[i] = 1.0f / static_cast<T>(1.0f - dropout_prob);
y_data[i] = x_data[i] / static_cast<T>(1.0f - dropout_prob);
} else {
mask_data[i] = 1;
y_data[i] = x_data[i];
}
}
}
} else {
......@@ -64,7 +73,11 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
auto Y = EigenMatrix<T>::Reshape(*y, 1);
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
Y.device(place) = X * (1.0f - dropout_prob);
if (dropout_implementation == "upscale_in_train") {
Y.device(place) = X;
} else {
Y.device(place) = X * static_cast<T>(1.0f - dropout_prob);
}
}
}
};
......
paddle/fluid/operators/fusion_gru_op.cc
浏览文件 @ cbe128bb
......@@ -16,10 +16,9 @@ limitations under the License. */
#include <cstring> // for memcpy
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
......@@ -174,58 +173,44 @@ class FusionGRUKernel : public framework::OpKernel<T> {
}
}
#define INIT_VEC_FUNC \
std::function<void(const int, const T *, T *)> act_gate, act_state; \
std::function<void(const int, const T*, const T*, const T*, T*)> cross; \
auto& act_gate_str = ctx.Attr<std::string>("gate_activation"); \
auto& act_state_str = ctx.Attr<std::string>("activation"); \
if (platform::jit::MayIUse(platform::jit::avx)) { \
math::VecActivations<T, platform::jit::avx> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::avx>; \
} else { \
math::VecActivations<T, platform::jit::isa_any> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::isa_any>; \
}
#define INIT_BASE_INPUT_OUTPUT \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse");
#define INIT_BASE_SIZES \
auto x_dims = x->dims(); /* T x M*/ \
auto wh_dims = wh->dims(); /* D x 3D*/ \
const int total_T = x_dims[0]; \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D3 = wh_dims[1]; \
const int D2 = D * 2;
#define INIT_BASE_DEFINES \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto x_lod = x->lod(); \
auto x_dims = x->dims(); /* T x M*/ \
auto wh_dims = wh->dims(); /* D x 3D*/ \
const int total_T = x_dims[0]; \
const int D3 = wh_dims[1]
#define INIT_OTHER_DEFINES \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D2 = D * 2; \
const auto& ker = math::jitkernel::KernelPool::Instance() \
.template Get<math::jitkernel::GRUKernel<T>, \
const std::string&, const std::string&>( \
ctx.Attr<std::string>("gate_activation"), \
ctx.Attr<std::string>("activation"), D); \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
auto place = ctx.GetPlace(); \
T* xx_data = xx->mutable_data<T>(place)
void SeqCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_VEC_FUNC
auto x_lod = x->lod();
INIT_BASE_DEFINES;
INIT_OTHER_DEFINES;
const int N = x_lod[0].size() - 1;
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : nullptr;
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
const T* wh_state_data = wh_data + D * D2;
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(place);
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D3, M, x_data, wx_data,
xx_data,
......@@ -252,14 +237,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
if (h0_data) {
prev_hidden_data = h0_data + bid * D;
} else {
// W: {W_update, W_reset; W_state}
// update gate
act_gate(D, xx_data, xx_data);
// state gate
act_state(D, xx_data + D2, xx_data + D2);
// out = a*b
blas.VMUL(D, xx_data, xx_data + D2, hidden_out_data);
// save prev
ker->ComputeH1(xx_data, hidden_out_data);
prev_hidden_data = hidden_out_data;
tstart = 1;
move_step();
......@@ -269,17 +247,12 @@ class FusionGRUKernel : public framework::OpKernel<T> {
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D2, D, static_cast<T>(1),
prev_hidden_data, D, wh_data, D2, static_cast<T>(1), xx_data,
D3);
act_gate(D2, xx_data, xx_data);
// rt = rt*ht_1 inplace result
blas.VMUL(D, prev_hidden_data, xx_data + D, hidden_out_data);
ker->ComputeHtPart1(xx_data, prev_hidden_data, hidden_out_data);
// gemm rt * Ws
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D, D, static_cast<T>(1),
hidden_out_data, D, wh_state_data, D, static_cast<T>(1),
xx_data + D2, D3);
act_state(D, xx_data + D2, xx_data + D2);
// out = zt*ht~ + (1-zt)*ht_1
cross(D, xx_data, xx_data + D2, prev_hidden_data, hidden_out_data);
ker->ComputeHtPart2(xx_data, prev_hidden_data, hidden_out_data);
// save prev
prev_hidden_data = hidden_out_data;
move_step();
......@@ -289,28 +262,19 @@ class FusionGRUKernel : public framework::OpKernel<T> {
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
if (x->lod()[0].size() == 2) {
INIT_BASE_DEFINES;
if (x_lod[0].size() == 2) {
xx->Resize({total_T, D3});
SeqCompute(ctx);
return;
}
INIT_VEC_FUNC
INIT_OTHER_DEFINES;
auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
auto* batched_out = ctx.Output<LoDTensor>("BatchedOut");
const T* x_data = x->data<T>();
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* batched_input_data = batched_input->mutable_data<T>(ctx.GetPlace());
T* batched_out_data = batched_out->mutable_data<T>(ctx.GetPlace());
hidden_out->mutable_data<T>(ctx.GetPlace());
T* batched_input_data = batched_input->mutable_data<T>(place);
T* batched_out_data = batched_out->mutable_data<T>(place);
hidden_out->mutable_data<T>(place);
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
......@@ -336,7 +300,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* prev_hidden_data = nullptr;
if (h0) {
// reorder h0
T* reordered_h0_data = reordered_h0->mutable_data<T>(ctx.GetPlace());
T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
const T* h0_data = h0->data<T>();
prev_hidden_data = reordered_h0_data;
size_t sz = sizeof(T) * D;
......@@ -350,12 +314,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* cur_out_data = batched_out_data;
// W: {W_update, W_reset; W_state}
for (int i = 0; i < max_bs; ++i) {
// update gate
act_gate(D, cur_in_data, cur_in_data);
// state gate
act_state(D, cur_in_data + D2, cur_in_data + D2);
// out = a*b
blas.VMUL(D, cur_in_data, cur_in_data + D2, cur_out_data);
ker->ComputeH1(cur_in_data, cur_out_data);
// add offset
cur_in_data += D3;
cur_out_data += D;
......@@ -380,10 +339,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* cur_out_data = batched_out_data;
T* cur_prev_hidden_data = prev_hidden_data;
for (int i = 0; i < cur_bs; ++i) {
act_gate(D2, cur_batched_data, cur_batched_data);
// rt = rt*ht_1 inplace result
blas.VMUL(D, cur_prev_hidden_data, cur_batched_data + D, cur_out_data);
ker->ComputeHtPart1(cur_batched_data, cur_prev_hidden_data,
cur_out_data);
cur_batched_data += D3;
cur_prev_hidden_data += D;
cur_out_data += D;
......@@ -397,12 +354,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
cur_prev_hidden_data = prev_hidden_data;
for (int i = 0; i < cur_bs; ++i) {
// ht~ = act_state(...)
act_state(D, cur_batched_data + D2, cur_batched_data + D2);
// out = zt*ht~ + (1-zt)*ht_1
cross(D, cur_batched_data, cur_batched_data + D2, cur_prev_hidden_data,
cur_out_data);
ker->ComputeHtPart2(cur_batched_data, cur_prev_hidden_data,
cur_out_data);
cur_batched_data += D3;
cur_prev_hidden_data += D;
cur_out_data += D;
......@@ -416,9 +369,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
batched_out->set_lod(batched_lod);
to_seq(dev_ctx, *batched_out, hidden_out);
}
#undef INIT_VEC_FUNC
#undef INIT_BASE_SIZES
#undef INIT_BASE_INPUT_OUTPUT
#undef INIT_OTHER_DEFINES
#undef INIT_BASE_DEFINES
};
} // namespace operators
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ cbe128bb
......@@ -68,6 +68,7 @@ cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selec
cc_test(im2col_test SRCS im2col_test.cc DEPS im2col)
cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col)
cc_test(sequence_padding_test SRCS sequence_padding_test.cc DEPS sequence_padding)
cc_test(sequence_pooling_test SRCS sequence_pooling_test.cc DEPS sequence_pooling)
if(WITH_GPU)
nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function)
nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
......@@ -75,6 +76,6 @@ endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
cc_library(jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc
DEPS cpu_info cblas)
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
paddle/fluid/operators/math/algorithm.h
浏览文件 @ cbe128bb
......@@ -39,6 +39,52 @@ HOSTDEVICE inline int64_t BinarySearch(const T *x, int64_t num, const T &val) {
return -1;
}
template <typename T>
HOSTDEVICE inline size_t LowerBound(const T *x, size_t num, const T &val) {
#ifdef __CUDA_ARCH__
// The following code is from
// https://en.cppreference.com/w/cpp/algorithm/lower_bound
auto *first = x;
int64_t count = static_cast<int64_t>(num);
while (count > 0) {
int64_t step = (count >> 1);
auto *it = first + step;
if (*it < val) {
first = ++it;
count -= (step + 1);
} else {
count = step;
}
}
return static_cast<size_t>(first - x);
#else
return static_cast<size_t>(std::lower_bound(x, x + num, val) - x);
#endif
}
template <typename T>
HOSTDEVICE inline size_t UpperBound(const T *x, size_t num, const T &val) {
#ifdef __CUDA_ARCH__
// The following code is from
// https://en.cppreference.com/w/cpp/algorithm/upper_bound
auto *first = x;
int64_t count = static_cast<int64_t>(num);
while (count > 0) {
auto step = (count >> 1);
auto *it = first + step;
if (val < *it) {
count = step;
} else {
first = ++it;
count -= (step + 1);
}
}
return static_cast<size_t>(first - x);
#else
return static_cast<size_t>(std::upper_bound(x, x + num, val) - x);
#endif
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @ cbe128bb
......@@ -142,6 +142,15 @@ class LSTMKernel : public Kernel {
const T *wp_data = nullptr) const = 0;
};
template <typename T>
class GRUKernel : public Kernel {
public:
// compute h1 without h0
virtual void ComputeH1(T *gates, T *ht) const = 0;
virtual void ComputeHtPart1(T *gates, const T *ht_1, T *ht) const = 0;
virtual void ComputeHtPart2(T *gates, const T *ht_1, T *ht) const = 0;
};
} // namespace jitkernel
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/jit_kernel_lstm.cc → paddle/fluid/operators/math/jit_kernel_rnn.cc
浏览文件 @ cbe128bb
......@@ -136,6 +136,23 @@ static std::shared_ptr<const VActKernel<T>> GetActKernel(
return nullptr;
}
#ifdef __AVX__
template <jit::cpu_isa_t isa>
static std::unique_ptr<AVXAct> GetAVXAct(const std::string& type) {
if (type == "sigmoid") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>());
} else if (type == "relu") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>());
} else if (type == "tanh") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>());
} else if (type == "identity" || type == "") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>());
}
PADDLE_THROW("Not support type: %s", type);
return nullptr;
}
#endif
/* LSTM JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class LSTMKernelImpl : public LSTMKernel<T> {
......@@ -192,61 +209,49 @@ class LSTMKernelImpl : public LSTMKernel<T> {
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
if (type == "sigmoid") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>()); \
} else if (type == "relu") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>()); \
} else if (type == "tanh") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>()); \
} else if (type == "identity" || type == "") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>()); \
} \
PADDLE_THROW("Not support type: %s", type); \
}; \
avx_act_gate_ = GetAVXAct(act_gate); \
avx_act_cand_ = GetAVXAct(act_cand); \
avx_act_cell_ = GetAVXAct(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */ \
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1( \
float* gates, float* ct, float* ht, const float* wp_data) const { \
__m256 c, i, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = igated * cgated*/ \
c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
_mm256_storeu_ps(ct, c); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
#define INTRI8_FLOAT(isa) \
template <> \
LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_cand_ = GetAVXAct<isa>(act_cand); \
avx_act_cell_ = GetAVXAct<isa>(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */ \
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1( \
float* gates, float* ct, float* ht, const float* wp_data) const { \
__m256 c, i, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = igated * cgated*/ \
c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
_mm256_storeu_ps(ct, c); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
}
// TODO(TJ): optimize keq16
......@@ -354,6 +359,126 @@ REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
#undef JITKERNEL_DECLARE_LSTM
#undef JITKERNEL_KEY_LSTM
#undef JITKERNEL_NEW_LSTM_IMPL
/* GRU JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class GRUKernelImpl : public GRUKernel<T> {
public:
explicit GRUKernelImpl(const std::string& act_gate,
const std::string& act_state, int d)
: GRUKernel<T>() {
d_ = d;
d2_ = d * 2;
act_gate_d2_ = GetActKernel<T>(act_gate, d2_);
act_gate_d_ = GetActKernel<T>(act_gate, d);
act_state_d_ = GetActKernel<T>(act_state, d);
vmul_d_ = KernelPool::Instance().template Get<VMulKernel<T>>(d);
}
void ComputeH1(T* gates, T* ht) const override {
act_gate_d_->Compute(gates, gates);
act_state_d_->Compute(gates + d2_, gates + d2_);
vmul_d_->Compute(gates, gates + d2_, ht);
}
void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
// W: {W_update, W_reset; W_state}
act_gate_d2_->Compute(gates, gates);
vmul_d_->Compute(ht_1, gates + d_, ht);
}
void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
T* y = gates + d2_;
act_state_d_->Compute(y, y);
// out = zt*ht~ + (1-zt)*ht_1
for (int i = 0; i < d_; ++i) {
ht[i] = gates[i] * y[i] + (static_cast<T>(1) - gates[i]) * ht_1[i];
}
}
private:
int d_, d2_;
std::shared_ptr<const VActKernel<T>> act_gate_d2_, act_gate_d_, act_state_d_;
std::shared_ptr<const VMulKernel<T>> vmul_d_;
#ifdef __AVX__
std::unique_ptr<const AVXAct> avx_act_gate_, avx_act_state_;
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
GRUKernelImpl<float, isa, kEQ8>::GRUKernelImpl( \
const std::string& act_gate, const std::string& act_state, int d) \
: GRUKernel<float>() { \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_state_ = GetAVXAct<isa>(act_state); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeH1(float* gates, float* ht) \
const { \
__m256 u, s; \
/* W: {W_update, W_reset; W_state} */ \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
s = _mm256_mul_ps(avx_act_gate_->Compute(u), avx_act_state_->Compute(s)); \
_mm256_storeu_ps(ht, s); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart1( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */ \
__m256 r, ht0; \
r = _mm256_loadu_ps(gates + 8); \
ht0 = _mm256_loadu_ps(ht_1); \
r = _mm256_mul_ps(avx_act_gate_->Compute(r), ht0); \
_mm256_storeu_ps(ht, r); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart2( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */ \
__m256 u, s, ht0; \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
ht0 = _mm256_loadu_ps(ht_1); \
u = avx_act_gate_->Compute(u); \
s = _mm256_mul_ps(u, avx_act_state_->Compute(s)); \
u = _mm256_sub_ps(_mm256_set1_ps(1.f), u); \
u = _mm256_mul_ps(u, ht0); \
u = _mm256_add_ps(s, u); \
_mm256_storeu_ps(ht, u); \
}
#ifdef __AVX__
INTRI8_FLOAT(jit::avx);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f);
#endif
#define JITKERNEL_DECLARE_GRU(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const GRUKernel<ker_dtype>> KernelPool::Get< \
GRUKernel<ker_dtype>, const std::string&, const std::string&, int>( \
const std::string& act_gate, const std::string& act_state, int d)
#define JITKERNEL_KEY_GRU(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d) + act_gate + act_state
#define JITKERNEL_NEW_GRU_IMPL(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
REGISTER_JITKERNEL_ARGS(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
#undef INTRI8_FLOAT
#undef JITKERNEL_NEW_GRU_IMPL
#undef JITKERNEL_KEY_GRU
#undef JITKERNEL_DECLARE_GRU
} // namespace jitkernel
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @ cbe128bb
......@@ -157,6 +157,31 @@ class FirstSeqPoolFunctor {
}
};
template <typename T>
class SumSeqPoolGradFunctor {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& out_grad,
framework::LoDTensor* in_grad) {
auto lod = in_grad->lod()[0];
int64_t out_w = out_grad.numel() / out_grad.dims()[0];
int64_t in_w = in_grad->numel() / in_grad->dims()[0];
PADDLE_ENFORCE(in_w == out_w);
const T* out_g_data = out_grad.data<T>();
T* in_g_data = in_grad->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
int64_t in_offset = lod[i] * in_w;
const T* out_pos = out_g_data + i * out_w;
T* in_pos = in_g_data + in_offset;
for (int r = 0; r != h; ++r) {
blas.VCOPY(in_w, out_pos, in_pos + r * in_w);
}
}
}
};
template <typename T>
class SequencePoolFunctor<platform::CPUDeviceContext, T> {
public:
......@@ -231,9 +256,15 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
math::SetConstant<platform::CPUDeviceContext, T> functor;
functor(context, in_grad, 0);
}
if (pooltype == "SUM") {
math::SumSeqPoolGradFunctor<T> sum_pool_grad;
sum_pool_grad(context, out_grad, in_grad);
return;
}
auto lod = in_grad->lod()[0];
auto& place = *context.eigen_device();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
static_cast<int>(lod[i + 1]));
......@@ -247,12 +278,6 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
if (pooltype == "AVERAGE") {
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
} else if (pooltype == "SUM") {
const T* out_g_data = out_g_t.data<T>();
T* in_g_data = in_g_t.mutable_data<T>(context.GetPlace());
for (int r = 0; r != h; ++r) {
blas.VCOPY(w, out_g_data, in_g_data + r * w);
}
} else if (pooltype == "SQRT") {
in_g_e.device(place) =
(out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
......
paddle/fluid/operators/math/sequence_pooling_test.cc 0 → 100644
浏览文件 @ cbe128bb
/* 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/math/sequence_pooling.h"
#include <gtest/gtest.h>
#include <vector>
template <typename DeviceContext, typename Place, typename T>
void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
paddle::framework::LoDTensor cpu_out_grad;
paddle::framework::LoDTensor cpu_in_grad;
paddle::framework::LoDTensor out_grad;
paddle::framework::LoDTensor in_grad;
const size_t second_dim = 128u;
// construct out_grad's tensor in cpu
const size_t out_first_dim = lod[0].size() - 1;
auto out_dims = paddle::framework::make_ddim(
{static_cast<int64_t>(out_first_dim), static_cast<int64_t>(second_dim)});
cpu_out_grad.mutable_data<T>(out_dims, paddle::platform::CPUPlace());
for (int64_t i = 0; i < cpu_out_grad.numel(); ++i) {
cpu_out_grad.data<T>()[i] = static_cast<T>(i);
}
// copy to dst out_grad
auto* place = new Place();
DeviceContext* context = new DeviceContext(*place);
if (paddle::platform::is_cpu_place(*place)) {
out_grad = cpu_out_grad;
} else {
TensorCopySync(cpu_out_grad, *place, &out_grad);
}
// construct in_grad
in_grad.set_lod(lod);
auto in_dims = paddle::framework::make_ddim(
{static_cast<int64_t>(lod[0].back()), static_cast<int64_t>(second_dim)});
in_grad.mutable_data<T>(in_dims, context->GetPlace());
// check tensor contruction result
PADDLE_ENFORCE_EQ(in_grad.dims().size(), out_grad.dims().size());
for (int64_t i = 1; i < out_grad.dims().size(); ++i) {
PADDLE_ENFORCE_EQ(in_grad.dims()[i], out_grad.dims()[i]);
}
// call functor
paddle::operators::math::SequencePoolGradFunctor<DeviceContext, T>()(
*context, "SUM", out_grad, &in_grad);
if (paddle::platform::is_cpu_place(*place)) {
cpu_in_grad = in_grad;
} else {
TensorCopySync(in_grad, paddle::platform::CPUPlace(), &cpu_in_grad);
cpu_in_grad.set_lod(in_grad.lod());
}
EXPECT_EQ(in_grad.numel(), lod[0].back() * second_dim);
EXPECT_EQ(in_grad.lod(), lod);
if (paddle::platform::is_cpu_place(*place)) {
for (int64_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
int64_t begin = in_grad.lod()[0][i];
int64_t end = in_grad.lod()[0][i + 1];
paddle::framework::Tensor tmp = in_grad.Slice(begin, end);
for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
for (int64_t m = 0; m != second_dim; ++m) {
EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
out_grad.data<T>()[m + i * second_dim]);
}
}
}
} else {
for (int64_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
int64_t begin = cpu_in_grad.lod()[0][i];
int64_t end = cpu_in_grad.lod()[0][i + 1];
paddle::framework::Tensor tmp = cpu_in_grad.Slice(begin, end);
for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
for (int64_t m = 0; m != second_dim; ++m) {
EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
cpu_out_grad.data<T>()[m + i * second_dim]);
}
}
}
}
delete place;
delete context;
}
TEST(SequencePoolingGrad, CPU_SUM) {
paddle::framework::LoD lod1;
lod1.push_back(std::vector<size_t>{0, 10});
TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
paddle::platform::CPUPlace, float>(lod1);
paddle::framework::LoD lod2;
lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
paddle::platform::CPUPlace, float>(lod2);
}
#ifdef PADDLE_WITH_CUDA
TEST(SequencePoolingGrad, CUDA_SUM) {
paddle::framework::LoD lod1;
lod1.push_back(std::vector<size_t>{0, 10});
TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace, float>(lod1);
paddle::framework::LoD lod2;
lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace, float>(lod2);
}
#endif
paddle/fluid/operators/sequence_reverse_op.cc 0 → 100644
浏览文件 @ cbe128bb
// 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/sequence_reverse_op.h"
namespace ops = paddle::operators;
REGISTER_OPERATOR(sequence_reverse, ops::SequenceReverseOp,
ops::SequenceReverseOpMaker,
ops::SequenceReverseGradOpDescMaker);
REGISTER_OP_CPU_KERNEL(
sequence_reverse,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, uint8_t>,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/sequence_reverse_op.cu 0 → 100644
浏览文件 @ cbe128bb
// 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/sequence_reverse_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
sequence_reverse,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, uint8_t>,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/sequence_reverse_op.h 0 → 100644
浏览文件 @ cbe128bb
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/algorithm.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
class SequenceReverseOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
auto x_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_GE(x_dim.size(), 2,
"Rank of Input(X) must be not less than 2.");
ctx->SetOutputDim("Y", x_dim);
ctx->ShareLoD("X", "Y");
}
};
class SequenceReverseOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input LoDTensor of sequence_reverse op.");
AddOutput("Y", "The output LoDTensor of sequence_reverse op.");
AddComment(R"DOC(
SequenceReverse Operator.
Reverse each sequence in input X along dim 0.
Assuming X is a LoDTensor with dims [5, 4] and lod [[0, 2, 5]], where:
X.data() = [
[1, 2, 3, 4],
[5, 6, 7, 8], # the 0-th sequence with length 2
[9, 10, 11, 12],
[13, 14, 15, 16],
[17, 18, 19, 20] # the 1-st sequence with length 3
]
The output Y would be a LoDTensor sharing the same dims and lod with input X,
and:
Y.data() = [
[5, 6, 7, 8],
[1, 2, 3, 4], # the reversed 0-th sequence with length 2
[17, 18, 19, 20],
[13, 14, 15, 16],
[9, 10, 11, 12] # the reversed 1-st sequence with length 3
]
This Operator is useful to build a reverse dynamic RNN network.
This Operator only supports one-level lod currently.
)DOC");
}
};
template <typename T>
struct SequenceReverseFunctor {
SequenceReverseFunctor(const T *x, T *y, const size_t *lod, size_t lod_count,
size_t row_numel)
: x_(x), y_(y), lod_(lod), lod_count_(lod_count), row_numel_(row_numel) {}
HOSTDEVICE void operator()(size_t idx_x) const {
auto row_idx_x = idx_x / row_numel_;
auto lod_idx = math::UpperBound(lod_, lod_count_, row_idx_x);
auto row_idx_y = lod_[lod_idx - 1] + (lod_[lod_idx] - 1 - row_idx_x);
auto idx_y = row_idx_y * row_numel_ + idx_x % row_numel_;
y_[idx_y] = x_[idx_x];
}
const T *x_;
T *y_;
const size_t *lod_;
size_t lod_count_;
size_t row_numel_;
};
template <typename DeviceContext, typename T>
class SequenceReverseOpKernel : public framework::OpKernel<T> {
using LoDTensor = framework::LoDTensor;
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto &x = *ctx.Input<LoDTensor>("X");
auto *y = ctx.Output<LoDTensor>("Y");
PADDLE_ENFORCE_EQ(x.lod().size(), 1,
"SequenceReverse Op only support one level lod.");
auto &dev_ctx = ctx.template device_context<DeviceContext>();
const size_t *lod;
size_t lod_count = x.lod()[0].size();
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
lod = x.lod()[0].CUDAData(ctx.GetPlace());
} else {
#endif
lod = x.lod()[0].data();
#ifdef PADDLE_WITH_CUDA
}
#endif
size_t limit = static_cast<size_t>(x.numel());
size_t row_numel = static_cast<size_t>(limit / x.dims()[0]);
auto *x_data = x.data<T>();
auto *y_data = y->mutable_data<T>(ctx.GetPlace());
PADDLE_ENFORCE_NE(x_data, y_data,
"SequenceReverse Op does not support in-place operation");
SequenceReverseFunctor<T> functor(x_data, y_data, lod, lod_count,
row_numel);
platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
for_range(functor);
}
};
class SequenceReverseGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("sequence_reverse");
op->SetInput("X", OutputGrad("Y"));
op->SetOutput("Y", InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/softmax_cudnn_op.cu.cc
浏览文件 @ cbe128bb
......@@ -76,6 +76,8 @@ namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_KERNEL(softmax, CUDNN, plat::CUDAPlace,
ops::SoftmaxCUDNNKernel<float>,
ops::SoftmaxCUDNNKernel<double>,
ops::SoftmaxCUDNNKernel<plat::float16>);
REGISTER_OP_KERNEL(softmax_grad, CUDNN, plat::CUDAPlace,
ops::SoftmaxGradCUDNNKernel<float>);
ops::SoftmaxGradCUDNNKernel<float>,
ops::SoftmaxGradCUDNNKernel<double>);
paddle/fluid/operators/transpose_op.cc
浏览文件 @ cbe128bb
......@@ -210,18 +210,21 @@ REGISTER_OPERATOR(transpose, ops::TransposeOp, ops::TransposeOpMaker,
REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
ops::Transpose2GradMaker);
REGISTER_OPERATOR(transpose2_grad, ops::Transpose2OpGrad);
REGISTER_OP_CPU_KERNEL(
transpose2,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>);
transpose2, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>);
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/transpose_op.cu.cc
浏览文件 @ cbe128bb
......@@ -16,15 +16,18 @@ limitations under the License. */
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
transpose,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
transpose, ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
transpose2,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>);
ops::TransposeKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>);
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CUDADeviceContext, double>);
python/paddle/dataset/wmt16.py
浏览文件 @ cbe128bb
......@@ -78,7 +78,7 @@ def __build_dict(tar_file, dict_size, save_path, lang):
six.iteritems(word_dict), key=lambda x: x[1],
reverse=True)):
if idx + 3 == dict_size: break
fout.write("%s\n" % (word[0]))
fout.write("%s\n" % (cpt.to_bytes(word[0])))
def __load_dict(tar_file, dict_size, lang, reverse=False):
......
python/paddle/fluid/clip.py
浏览文件 @ cbe128bb
......@@ -272,7 +272,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
)
square = grad * grad
local_norm_var = layers.cast(layers.reduce_sum(input=square), 'float64')
local_norm_var = layers.reduce_sum(input=square)
context[self.group_name].append(local_norm_var)
self.context = context
......@@ -282,7 +282,6 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
if group_scale_name not in self.context:
group_norm_var = layers.sums(input=self.context[self.group_name])
group_norm_var = layers.sqrt(x=group_norm_var)
group_norm_var = layers.cast(group_norm_var, 'float32')
clip_var = self.context[self.group_name + "_clip"]
group_scale_var = layers.elementwise_div(
x=clip_var,
......@@ -333,7 +332,8 @@ def append_gradient_clip_ops(param_grads):
for p, g in param_grads:
if g is None:
continue
with p.block.program._optimized_guard([p, g]):
with p.block.program._optimized_guard(
[p, g]), framework.name_scope('append_clip'):
clip_attr = getattr(p, 'gradient_clip_attr', NullGradientClipAttr())
if clip_attr is None:
clip_attr = NullGradientClipAttr()
......@@ -348,7 +348,8 @@ def append_gradient_clip_ops(param_grads):
for p, g in param_grads:
if g is None:
continue
with p.block.program._optimized_guard([p, g]):
with p.block.program._optimized_guard(
[p, g]), framework.name_scope('append_graident_clip'):
res.append(clip_attr._create_operators(param=p, grad=g))
return res
......
python/paddle/fluid/framework.py
浏览文件 @ cbe128bb
......@@ -1496,6 +1496,9 @@ class Program(object):
>>> with program._optimized_guard([p,g]):
>>> p = p - 0.001 * g
"""
tmp_role = self._current_role
tmp_var = self._op_role_var
OpRole = core.op_proto_and_checker_maker.OpRole
self._current_role = OpRole.Optimize
self._op_role_var = [
......@@ -1503,11 +1506,11 @@ class Program(object):
for var in param_and_grads
]
yield
self._op_role_var = []
self._current_role = OpRole.Forward
self._op_role_var = tmp_var
self._current_role = tmp_role
@contextlib.contextmanager
def _lr_schedule_guard(self):
def _lr_schedule_guard(self, is_with_opt=False):
"""
A with guard to set :code:`LRSched` :code:`OpRole` and
:code:`OpRoleVar` automatically. The :code:`OpRoleVar` is
......@@ -1515,6 +1518,10 @@ class Program(object):
Notes: This is a very low level API. Users should not use it directly.
Args:
is_with_opt: Only set to true if these ops a in the middle
of a bunch of optimize ops so that it can be treated
correctly. For example, sgd->lr_op->sgd->lr_op->sgd.
Examples:
......@@ -1528,6 +1535,8 @@ class Program(object):
OpRole = core.op_proto_and_checker_maker.OpRole
self._current_role = OpRole.LRSched
if is_with_opt:
self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize)
# TODO(typhoonzero): how to set target learning rate var
self._op_role_var = []
yield
......
python/paddle/fluid/layers/nn.py
浏览文件 @ cbe128bb
......@@ -154,6 +154,7 @@ __all__ = [
'mul',
'sigmoid_cross_entropy_with_logits',
'maxout',
'sequence_reverse',
'affine_channel',
]
......@@ -980,7 +981,12 @@ def cos_sim(X, Y):
return out
def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
def dropout(x,
dropout_prob,
is_test=False,
seed=None,
name=None,
dropout_implementation="downgrade_in_infer"):
"""
Computes dropout.
......@@ -1000,6 +1006,21 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
units will be dropped. DO NOT use a fixed seed in training.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
dropout_implementation(string): ['downgrade_in_infer'(defauld)|'upscale_in_train']
1. downgrade_in_infer(default), downgrade the outcome at inference
train: out = input * mask
inference: out = input * dropout_prob
(make is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
2. upscale_in_train, upscale the outcome at training time
train: out = input * mask / ( 1.0 - dropout_prob )
inference: out = input
(make is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
dropout op can be removed from the program.
the program will be efficient
Returns:
Variable: A tensor variable is the shape with `x`.
......@@ -1029,7 +1050,8 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
'dropout_prob': dropout_prob,
'is_test': is_test,
'fix_seed': seed is not None,
'seed': seed if seed is not None else 0
'seed': seed if seed is not None else 0,
'dropout_implementation': dropout_implementation,
})
return out
......@@ -4844,7 +4866,7 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
return counter
def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
"""
Gives a new shape to the input Tensor without changing its data.
......@@ -4892,15 +4914,22 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
:attr:`shape` specifying shape. That is to
say :attr:`actual_shape` has a higher priority
than :attr:`shape`.
act (str): The non-linear activation to be applied to output variable.
inplace(bool): If this flag is set true, the output
shares data with input without copying, otherwise
a new output tensor is created
whose data is copied from input x.
act (str): The non-linear activation to be applied to the reshaped tensor
variable.
inplace(bool): Must use :attr:`False` if :attr:`x` is used in multiple
operators. If this flag is set :attr:`True`, reuse input
:attr:`x` to reshape, which will change the shape of
tensor variable :attr:`x` and might cause errors when
:attr:`x` is used in multiple operators. If :attr:`False`,
preserve the shape :attr:`x` and create a new output tensor
variable whose data is copied from input x but reshaped.
name (str): The name of this layer. It is optional.
Returns:
Variable: The output tensor.
Variable: The reshaped tensor variable if :attr:`act` is None. It is a \
new tensor variable if :attr:`inplace` is :attr:`False`, \
otherwise it is :attr:`x`. If :attr:`act` is not None, return \
the activated tensor variable.
Raises:
TypeError: if actual_shape is neither Variable nor None.
......@@ -4911,7 +4940,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
data = fluid.layers.data(
name='data', shape=[2, 4, 6], dtype='float32')
reshaped = fluid.layers.reshape(
x=data, shape=[-1, 0, 3, 2], act='tanh', inplace=True)
x=data, shape=[-1, 0, 3, 2], inplace=True)
"""
if not (isinstance(shape, list) or isinstance(shape, tuple)):
......@@ -4938,7 +4967,8 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"except one unknown dimension.")
helper = LayerHelper("reshape2", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
out = x if inplace else helper.create_variable_for_type_inference(
dtype=x.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="reshape2",
......@@ -7455,6 +7485,33 @@ def maxout(x, groups, name=None):
return out
@templatedoc()
def sequence_reverse(x, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
Returns:
out(${y_type}): ${y_comment}
"""
helper = LayerHelper("sequence_reverse", **locals())
if name is None:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="sequence_reverse",
inputs={"X": x},
outputs={"Y": out},
attrs=dict())
return out
def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
"""
Applies a separate affine transformation to each channel of the input.
......
python/paddle/fluid/optimizer.py
浏览文件 @ cbe128bb
......@@ -111,7 +111,9 @@ class Optimizer(object):
if param_lr == 1.0:
return self._global_learning_rate()
else:
with default_main_program()._lr_schedule_guard():
with default_main_program()._lr_schedule_guard(
is_with_opt=True), framework.name_scope(
'scale_with_param_lr'):
return self._global_learning_rate() * param_lr
def _create_accumulators(self, block, parameters):
......@@ -602,7 +604,8 @@ class AdamOptimizer(Optimizer):
for param, grad in param_and_grads:
if grad is None:
continue
with param.block.program._optimized_guard([param, grad]):
with param.block.program._optimized_guard(
[param, grad]), name_scope("optimizer"):
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
param)
beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
......@@ -740,7 +743,8 @@ class AdamaxOptimizer(Optimizer):
for param, grad in parameters_and_grads:
if grad is None:
continue
with param.block.program._optimized_guard([param, grad]):
with param.block.program._optimized_guard(
[param, grad]), name_scope('adamx'):
beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
param)
main_block.append_op(
......@@ -1279,7 +1283,8 @@ class ModelAverage(Optimizer):
for param, grad in self.params_grads:
if grad is None:
continue
with param.block.program._optimized_guard([param, grad]):
with param.block.program._optimized_guard(
[param, grad]), name_scope('move_average'):
self._append_average_accumulate_op(param)
self.apply_program = Program()
......
python/paddle/fluid/regularizer.py
浏览文件 @ cbe128bb
......@@ -47,7 +47,8 @@ def append_regularization_ops(parameters_and_grads, regularization=None):
if grad is None:
params_and_grads.append((param, grad))
continue
with param.block.program._optimized_guard([param, grad]):
with param.block.program._optimized_guard(
[param, grad]), framework.name_scope('regularization'):
regularization_term = None
if param.regularizer is not None:
# Add variable for regularization term in grad block
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ cbe128bb
......@@ -78,9 +78,9 @@ if(WITH_DISTRIBUTE)
set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext)
set_tests_properties(test_dist_se_resnext PROPERTIES TIMEOUT 1000)
# TODO: fix this test
#py_test_modules(test_dist_transformer MODULES test_dist_transformer)
#set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
# FIXME(typhoonzero): add this back
#py_test_modules(test_dist_transformer MODULES test_dist_transformer)
#set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
endif(NOT APPLE)
py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
endif()
......
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ cbe128bb
......@@ -35,7 +35,7 @@ import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid import core
from test_dist_base import TestDistRunnerBase, runtime_main
from test_dist_base import TestDistRunnerBase, runtime_main, RUN_STEP
import paddle.compat as cpt
from paddle.compat import long_type
......@@ -562,18 +562,12 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
if batch_id >= 5:
if batch_id >= RUN_STEP:
break
feed_list = []
total_num_token = 0
#if TrainTaskConfig.local:
# lr_rate = lr_scheduler.update_learning_rate()
#for place_id, data_buffer in enumerate(
# split_data(
# data, num_part=dev_count)):
if TrainTaskConfig.local:
lr_rate = lr_scheduler.update_learning_rate()
......@@ -619,12 +613,11 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
init = True
# Validate and save the model for inference.
if batch_id == 0 or batch_id == 4:
if TrainTaskConfig.val_file_pattern is not None:
val_avg_cost, val_ppl = test()
print("[%f]" % val_avg_cost)
else:
assert (False)
if TrainTaskConfig.val_file_pattern is not None:
val_avg_cost, val_ppl = test()
print("[%f]" % val_avg_cost)
else:
assert (False)
#import transformer_reader as reader
......@@ -1701,7 +1694,7 @@ class DistTransformer2x2(TestDistRunnerBase):
def run_trainer(self, args):
TrainTaskConfig.use_gpu = args.use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
args.is_dist, not args.sync_mode)
if args.is_dist:
......
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @ cbe128bb
......@@ -40,7 +40,8 @@ class TestDistMnistAsync(TestDistBase):
self._sync_mode = False
self._use_reduce = False
def test_dist_train(self):
# FIXME(typhoonzero): fix async mode test later
def no_test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=200)
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ cbe128bb
......@@ -40,7 +40,8 @@ class TestDistSeResneXt2x2Async(TestDistBase):
self._sync_mode = False
self._use_reader_alloc = False
def test_dist_train(self):
#FIXME(typhoonzero): fix async mode later
def no_test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py
浏览文件 @ cbe128bb
......@@ -42,7 +42,8 @@ class TestDistSimnetBow2x2DenseAsync(TestDistBase):
self._sync_mode = False
self._enforce_place = "CPU"
def test_simnet_bow(self):
#FIXME(typhoonzero): fix async tests later
def no_test_simnet_bow(self):
need_envs = {
"IS_DISTRIBUTED": '0',
"IS_SPARSE": '0',
......@@ -78,7 +79,8 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
self._sync_mode = False
self._enforce_place = "CPU"
def test_simnet_bow(self):
#FIXME(typhoonzero): fix async tests later
def no_test_simnet_bow(self):
need_envs = {
"IS_DISTRIBUTED": '0',
"IS_SPARSE": '1',
......
python/paddle/fluid/tests/unittests/test_dist_transformer.py
浏览文件 @ cbe128bb
......@@ -61,7 +61,8 @@ class TestDistTransformer2x2Sync(TestDistBase):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1e-5)
self.check_with_place(
"dist_transformer.py", delta=1e-5, check_error_log=False)
class TestDistTransformer2x2Async(TestDistBase):
......@@ -70,7 +71,8 @@ class TestDistTransformer2x2Async(TestDistBase):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1.0)
self.check_with_place(
"dist_transformer.py", delta=1.0, check_error_log=False)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_dropout_op.py
浏览文件 @ cbe128bb
......@@ -85,6 +85,69 @@ class TestDropoutOp5(OpTest):
self.check_output()
class TestDropoutOp6(TestDropoutOp):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {
'dropout_prob': 1.0,
'fix_seed': True,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': np.zeros((32, 64)).astype('float32'),
'Mask': np.zeros((32, 64)).astype('float32')
}
class TestDropoutOp7(TestDropoutOp):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
self.attrs = {
'dropout_prob': 0.0,
'fix_seed': True,
'is_test': False,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {
'Out': self.inputs['X'],
'Mask': np.ones((32, 64, 2)).astype('float32')
}
class TestDropoutOp8(OpTest):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
self.attrs = {
'dropout_prob': 0.35,
'fix_seed': True,
'is_test': True,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {'Out': self.inputs['X']}
def test_check_output(self):
self.check_output()
class TestDropoutOp9(OpTest):
def setUp(self):
self.op_type = "dropout"
self.inputs = {'X': np.random.random((32, 64, 3)).astype("float32")}
self.attrs = {
'dropout_prob': 0.75,
'is_test': True,
'dropout_implementation': 'upscale_in_train'
}
self.outputs = {'Out': self.inputs['X']}
def test_check_output(self):
self.check_output()
class TestFP16DropoutOp(OpTest):
def setUp(self):
self.op_type = "dropout"
......
python/paddle/fluid/tests/unittests/test_fusion_gru_op.py
浏览文件 @ cbe128bb
......@@ -125,6 +125,12 @@ class TestFusionGRUOpMD2(TestFusionGRUOp):
self.D = 8
class TestFusionGRUOpMD3(TestFusionGRUOp):
def set_confs(self):
self.M = 17
self.D = 15
class TestFusionGRUOpBS1(TestFusionGRUOp):
def set_confs(self):
self.lod = [[3]]
......
python/paddle/fluid/tests/unittests/test_sequence_reverse.py 0 → 100644
浏览文件 @ cbe128bb
# 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 paddle.fluid as fluid
import paddle.fluid.core as core
from op_test import OpTest
import numpy as np
class TestSequenceReverseBase(OpTest):
def initParameters(self):
pass
def setUp(self):
self.size = (10, 3, 4)
self.lod = [2, 3, 5]
self.dtype = 'float32'
self.initParameters()
self.op_type = 'sequence_reverse'
self.x = np.random.random(self.size).astype(self.dtype)
self.y = self.get_output()
self.inputs = {'X': (self.x, [self.lod, ]), }
self.outputs = {'Y': (self.y, [self.lod, ]), }
def get_output(self):
tmp_x = np.reshape(self.x, newshape=[self.x.shape[0], -1])
tmp_y = np.ndarray(tmp_x.shape).astype(self.dtype)
prev_idx = 0
for cur_len in self.lod:
idx_range = range(prev_idx, prev_idx + cur_len)
tmp_y[idx_range, :] = np.flip(tmp_x[idx_range, :], 0)
prev_idx += cur_len
return np.reshape(tmp_y, newshape=self.x.shape).astype(self.dtype)
def test_output(self):
self.check_output(0)
def test_grad(self):
self.check_grad(['X'], 'Y')
class TestSequenceReserve1(TestSequenceReverseBase):
def initParameters(self):
self.size = (12, 10)
self.lod = [4, 5, 3]
class TestSequenceReverse2(TestSequenceReverseBase):
def initParameters(self):
self.size = (12, 10)
self.lod = [12]
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ cbe128bb
......@@ -49,6 +49,7 @@ LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
)
OPT_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Optimize
RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
DIST_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.Dist
LR_SCHED_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.LRSched
......@@ -1717,8 +1718,10 @@ to transpile() call.")
lr_ops = []
block = self.origin_program.global_block()
for op in block.ops:
if int(op.attr(RPC_OP_ROLE_ATTR_NAME)) == int(
LR_SCHED_OP_ROLE_ATTR_VALUE):
role_id = int(op.attr(RPC_OP_ROLE_ATTR_NAME))
if role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) or \
role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) | \
int(OPT_OP_ROLE_ATTR_VALUE):
lr_ops.append(op)
log("append lr op: ", op.type)
return lr_ops
......
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