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“80c68d38ff3c59e12f48b4e4e88c24c89568fc0a”上不存在“paddle/legacy/gserver/layers/GatedRecurrentLayer.cpp”
提交 cf0511f2 编写于 作者: Q Qiao Longfei
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add-async-ssa-graph-executor 

test=develop
上级 43c82376 660e4106
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Showing with 3883 addition and 1148 deletion
AUTHORS.md
浏览文件 @ cf0511f2
......@@ -44,6 +44,7 @@
| qingqing01 | Qing-Qing Dang |
| reyoung | Yang Yu |
| Sand3r- | Michal Gallus |
| sfraczek | Sylwester Fraczek |
| Superjom | Chun-Wei Yan |
| tensor-tang | Jian Tang |
| tianbingsz | Tian-Bing Xu |
......@@ -54,6 +55,7 @@
| wangyang59 | Yang Wang |
| wangzhen-nlp | Zhen Wang |
| wen-bo-yang | Wen-Bo Yang |
| wojtuss | Wojciech Uss |
| wwhu | Wei-Wei Hu |
| xinghai-sun | Xing-Hai Sun |
| Xreki | Yi-Qun Liu |
......
README.md
浏览文件 @ cf0511f2
......@@ -3,8 +3,8 @@
English | [简体中文](./README_cn.md)
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -18,7 +18,7 @@ learning to many products at Baidu.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
### Latest PaddlePaddle Release: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
### Latest PaddlePaddle Release: [Fluid 1.3.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.3)
### Install Latest Stable Release:
```
# Linux CPU
......@@ -26,9 +26,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.2.0.post87
pip install paddlepaddle-gpu==1.3.0.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.2.0.post85
pip install paddlepaddle-gpu==1.3.0.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
......@@ -75,26 +75,26 @@ pip install paddlepaddle-gpu==1.2.0.post85
## Installation
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html) on our website.
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html) on our website.
## Documentation
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html) documentation.
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html) documentation.
- [Deep Learning 101](https://github.com/PaddlePaddle/book)
You might want to start from this online interactive book that can run in a Jupyter Notebook.
- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html)
- [Distributed Training](http://paddlepaddle.org/documentation/docs/en/1.3/user_guides/howto/training/multi_node_en.html)
You can run distributed training jobs on MPI clusters.
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.2/api_cn/index_cn.html)
- [Python API](http://paddlepaddle.org/documentation/docs/en/1.3/api/index_en.html)
Our new API enables much shorter programs.
- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.2/advanced_usage/development/contribute_to_paddle/index_cn.html)
- [How to Contribute](http://paddlepaddle.org/documentation/docs/en/1.3/advanced_usage/development/contribute_to_paddle/index_en.html)
We appreciate your contributions!
......
README_cn.md
浏览文件 @ cf0511f2
......@@ -3,8 +3,8 @@
[English](./README.md) | 简体中文
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -16,7 +16,7 @@ PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效
跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
### PaddlePaddle最新版本: [Fluid 1.2.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.2)
### PaddlePaddle最新版本: [Fluid 1.3.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.3)
### 安装最新稳定版本:
```
# Linux CPU
......@@ -24,9 +24,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.2.0.post87
pip install paddlepaddle-gpu==1.3.0.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.2.0.post85
pip install paddlepaddle-gpu==1.3.0.post85
# 其他平台上的安装指引请参考 http://paddlepaddle.org/
```
......@@ -57,26 +57,26 @@ pip install paddlepaddle-gpu==1.2.0.post85
## 安装
推荐阅读官网上的[安装说明](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/install/index_cn.html)
推荐阅读官网上的[安装说明](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/install/index_cn.html)
## 文档
我们提供[英文](http://paddlepaddle.org/documentation/docs/en/1.2/getstarted/index_en.html)和
[中文](http://paddlepaddle.org/documentation/docs/zh/1.2/beginners_guide/index.html) 文档
我们提供[英文](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)和
[中文](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html) 文档
- [深度学习101](https://github.com/PaddlePaddle/book)
或许您想从这个在线交互式书籍开始，可以在Jupyter Notebook中运行
- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.2/user_guides/howto/training/cluster_howto.html)
- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.3/user_guides/howto/training/multi_node.html)
可以在MPI集群上运行分布式训练任务
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.2/api_cn/index_cn.html)
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.3/api_cn/index_cn.html)
新的API支持代码更少更简洁的程序
- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.2/advanced_usage/development/contribute_to_paddle/index_cn.html)
- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.3/advanced_usage/development/contribute_to_paddle/index_cn.html)
欢迎您的贡献!
......
paddle/fluid/API.spec
浏览文件 @ cf0511f2
......@@ -43,7 +43,7 @@ paddle.fluid.AsyncExecutor.init_worker ArgSpec(args=['self', 'dist_desc', 'start
paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'filelist', 'thread_num', 'fetch', 'mode', 'debug'], varargs=None, keywords=None, defaults=('', False))
paddle.fluid.AsyncExecutor.save_model ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.CompiledProgram.__init__ ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.CompiledProgram.__init__ ArgSpec(args=['self', 'program_or_graph'], varargs=None, keywords=None, defaults=None)
paddle.fluid.CompiledProgram.with_data_parallel ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.CompiledProgram.with_inference_optimize ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=None)
paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.ExecutionStrategy) -> None
......@@ -71,7 +71,7 @@ paddle.fluid.initializer.NumpyArrayInitializer.__init__ ArgSpec(args=['self', 'v
paddle.fluid.layers.fc ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None))
paddle.fluid.layers.embedding ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32'))
paddle.fluid.layers.dynamic_lstm ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None))
paddle.fluid.layers.dynamic_lstmp ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None))
paddle.fluid.layers.dynamic_gru ArgSpec(args=['input', 'size', 'param_attr', 'bias_attr', 'is_reverse', 'gate_activation', 'candidate_activation', 'h_0', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, False, 'sigmoid', 'tanh', None, False))
paddle.fluid.layers.gru_unit ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False))
paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr'], varargs=None, keywords=None, defaults=(None,))
......@@ -121,6 +121,7 @@ paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=
paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 'uniform', None, 0, False))
paddle.fluid.layers.sampled_softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'num_samples', 'num_true', 'remove_accidental_hits', 'use_customized_samples', 'customized_samples', 'customized_probabilities', 'seed'], varargs=None, keywords=None, defaults=(1, True, False, None, None, 0))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False))
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
......@@ -303,7 +304,7 @@ paddle.fluid.layers.reciprocal ArgSpec(args=['x', 'name'], varargs=None, keyword
paddle.fluid.layers.square ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.softplus ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.softsign ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0))
paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
......
paddle/fluid/framework/block_desc.cc
浏览文件 @ cf0511f2
......@@ -163,6 +163,20 @@ std::vector<OpDesc *> BlockDesc::AllOps() const {
return res;
}
void BlockDesc::Clear() {
// clear all ops
ops_.clear();
// clear all vars which are not persistable
for (auto it = vars_.begin(); it != vars_.end();) {
if (it->second->Persistable()) {
++it;
} else {
vars_.erase(it++);
}
}
}
void BlockDesc::Flush() {
for (auto &op_desc : ops_) {
op_desc->Flush();
......
paddle/fluid/framework/block_desc.h
浏览文件 @ cf0511f2
......@@ -97,6 +97,8 @@ class BlockDesc {
std::vector<OpDesc *> AllOps() const;
void Clear();
size_t OpSize() const { return ops_.size(); }
OpDesc *Op(int idx) const { return ops_.at(idx).get(); }
......
paddle/fluid/framework/data_layout_transform.cc
浏览文件 @ cf0511f2
......@@ -134,11 +134,6 @@ void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
out_layout =
out_layout == DataLayout::kAnyLayout ? DataLayout::kNCHW : out_layout;
auto& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(
pool.Get(expected_kernel_type.place_));
auto& cpu_engine = dev_ctx->GetEngine();
std::vector<int> in_tz = paddle::framework::vectorize2int(in.dims());
std::vector<int> out_tz = in_tz;
......@@ -147,29 +142,25 @@ void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
"Input tensor type is not supported: %s", in.type());
memory::data_type out_type = in_type;
auto in_format = platform::MKLDNNFormatForSize(in_tz.size(), in.format());
auto out_format =
platform::MKLDNNFormatForSize(in_tz.size(), ToMKLDNNFormat(out_layout));
// output tensor has the same dims as input. Reorder don't change dims
out->Resize(in.dims());
if (in_format != out_format) {
// tempory mem pd fr out , to make reorder
auto out_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(out->dims()),
mkldnn::memory::format::blocked, out_type);
if (in.get_mkldnn_prim_desc() != out_mem_pd) {
void* in_data = GetDataFromTensor(in, in_type);
auto out_data = out->mutable_data(expected_kernel_type.place_, in.type());
auto in_memory =
memory({{{in_tz}, in_type, in_format}, cpu_engine}, in_data);
auto out_memory =
memory({{{out_tz}, out_type, out_format}, cpu_engine}, out_data);
auto in_memory = memory(in.get_mkldnn_prim_desc(), in_data);
auto out_memory = memory(out_mem_pd, out_data);
platform::Reorder(in_memory, out_memory);
} else {
out->ShareDataWith(in);
}
out->set_layout(out_layout);
// reset format since the out tensor will be feed to non-MKLDNN OPkernel
out->set_format(memory::format::format_undef);
#endif
}
......
paddle/fluid/framework/data_transform.cc
浏览文件 @ cf0511f2
......@@ -51,13 +51,31 @@ void TransformData(const OpKernelType &expected_kernel_type,
#ifdef PADDLE_WITH_MKLDNN
// Case1 - transform from Non-MKLDNN OPKernel to MKLDNN OPKernel
// Just set layout/format. No real transform occur
auto out_format = platform::MKLDNNFormatForSize(in.dims().size(),
ToMKLDNNFormat(lin));
out.ShareDataWith(input_tensor);
out.set_layout(DataLayout::kMKLDNN);
out.set_format(out_format);
// TODO(jczaja): Remove that once all mkldnn ops
// are modified to work with mkldnn_blocked
auto mkldnn_fmt = [&](int rank) {
switch (rank) {
case 5:
return mkldnn::memory::format::ncdhw;
case 4:
return mkldnn::memory::format::nchw;
case 3:
return mkldnn::memory::format::ncw;
case 2:
return mkldnn::memory::format::nc;
case 1:
return mkldnn::memory::format::x;
default:
return mkldnn::memory::format::blocked;
}
};
auto out_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(out.dims()),
mkldnn_fmt(out.dims().size()));
out.set_mkldnn_prim_desc(out_mem_pd);
#endif
} else {
// Case2 - transfrom from MKLDNN OPKernel to Non-MKLDNN OPKernel
......
paddle/fluid/framework/details/all_reduce_deps_pass.cc
浏览文件 @ cf0511f2
......@@ -50,7 +50,7 @@ std::unique_ptr<ir::Graph> AllReduceDepsPass::ApplyImpl(
std::unordered_map<std::string, int> vars;
// TODO(gongwb): use graph topology sort to find the order of operators.
// Note that must assert topology sort is stable
auto& ops = Get<const std::vector<OpDesc*>>(kAllOpDescs);
auto& ops = graph->Get<const std::vector<OpDesc*>>(kStaleProgramOpDescs);
for (auto* op_desc : ops) {
auto outputs = op_desc->Outputs();
for (auto& o_it : outputs) {
......@@ -120,4 +120,4 @@ std::unique_ptr<ir::Graph> AllReduceDepsPass::ApplyImpl(
REGISTER_PASS(all_reduce_deps_pass,
paddle::framework::details::AllReduceDepsPass)
.RequirePassAttr(paddle::framework::details::kAllOpDescs);
.RequireGraphAttr(paddle::framework::details::kStaleProgramOpDescs);
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ cf0511f2
......@@ -136,14 +136,17 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
ir::Pass *multi_devices_pass;
if (strategy_.is_distribution_) {
VLOG(3) << "multi device parameter server mode";
multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
} else if (strategy_.async_mode_) {
multi_devices_pass = AppendPass("async_multi_devices_pass").get();
} else {
if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
VLOG(3) << "multi devices collective mode with allreduce";
multi_devices_pass =
AppendPass("allreduce_mode_multi_devices_pass").get();
} else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
VLOG(3) << "multi deivces collective mode with reduce";
multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get();
} else {
PADDLE_THROW("Unknown reduce strategy.");
......@@ -174,7 +177,8 @@ bool BuildStrategy::IsMultiDevPass(const std::string &pass_name) const {
}
std::unique_ptr<ir::Graph> BuildStrategy::Apply(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> graph,
const std::vector<platform::Place> &places,
const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
const size_t &nranks,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
......@@ -185,7 +189,6 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
// Create a default one if not finalized by user.
CreatePassesFromStrategy(false);
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
if (IsMultiDevPass(pass->Type())) {
pass->Erase(kPlaces);
......@@ -203,41 +206,12 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
} else if (pass->Type() == "memory_optimize_pass") {
if (graph->Has(kAllOpDescs)) {
graph->Erase(kAllOpDescs);
}
const std::vector<OpDesc *> *all_op_descs =
new std::vector<OpDesc *>(main_program.Block(0).AllOps());
graph->Set<const std::vector<OpDesc *>>(kAllOpDescs,
all_op_descs); // take ownership
pass->Erase(kAllOpDescs);
pass->SetNotOwned<const std::vector<OpDesc *>>(kAllOpDescs, all_op_descs);
} else if (pass->Type() == "sequential_execution_pass") {
LOG(INFO) << "set enable_sequential_execution:"
<< enable_sequential_execution_;
pass->Erase(kAllOpDescs);
pass->Set<const std::vector<OpDesc *>>(
kAllOpDescs,
new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
} else if (pass->Type() == "all_reduce_deps_pass") {
LOG(INFO) << "SeqOnlyAllReduceOps:" << SeqOnlyAllReduceOps(*this)
<< ", num_trainers:" << num_trainers_;
pass->Erase(kAllOpDescs);
pass->Set<const std::vector<OpDesc *>>(
kAllOpDescs,
new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
} else if (pass->Type() == "inplace_pass") {
if (graph->Has(kAllOpDescs)) {
graph->Erase(kAllOpDescs);
}
graph->Set<const std::vector<OpDesc *>>(
kAllOpDescs,
new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
} else if (pass->Type() == "fuse_relu_depthwise_conv_pass") {
if (!use_cuda) {
LOG(WARNING) << "fuse_relu_depthwise_conv_pass is only supported on "
......
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ cf0511f2
......@@ -115,7 +115,7 @@ struct BuildStrategy {
// Apply the passes built by the pass_builder_. The passes will be
// applied to the Program and output an ir::Graph.
std::unique_ptr<ir::Graph> Apply(const ProgramDesc &main_program,
std::unique_ptr<ir::Graph> Apply(std::unique_ptr<ir::Graph> graph,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::vector<Scope *> &local_scopes,
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc
浏览文件 @ cf0511f2
......@@ -24,12 +24,11 @@ namespace details {
FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph)
const std::vector<platform::Place> &places, ir::Graph *graph)
: strategy_(strategy),
local_scopes_(local_scopes),
places_(places),
graph_(std::move(graph)),
graph_(graph),
pool_(strategy.num_threads_),
prepare_pool_(1), // add one more thread for generate op_deps
fetch_ctxs_(places) {
......@@ -110,14 +109,14 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
}
}
if (exception_.IsCaught()) {
ClearFetchOp(graph_.get(), &fetch_ops);
ClearFetchOp(graph_, &fetch_ops);
exception_.ReThrow();
}
}
num_complete += num_comp;
}
// Wait FetchOps.
ClearFetchOp(graph_.get(), &fetch_ops);
ClearFetchOp(graph_, &fetch_ops);
return fetches;
}
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h
浏览文件 @ cf0511f2
......@@ -32,7 +32,7 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
FastThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph);
ir::Graph *graph);
FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
const ir::Graph &Graph() const override;
......@@ -40,7 +40,7 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
ExecutionStrategy strategy_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
std::unique_ptr<ir::Graph> graph_;
ir::Graph *graph_;
std::unordered_map<OpHandleBase *, int> op_deps_;
std::vector<OpHandleBase *> bootstrap_ops_;
......
paddle/fluid/framework/details/memory_optimize_helper.cc
浏览文件 @ cf0511f2
......@@ -33,10 +33,10 @@ namespace details {
using paddle::framework::VarDesc;
std::vector<ir::Node*> SortOpLikeDescOrder(const ir::Graph& graph) {
PADDLE_ENFORCE(graph.Has(kAllOpDescs),
"Graph has no attribute of kAllOpDescs.");
PADDLE_ENFORCE(graph.Has(kStaleProgramOpDescs),
"Graph has no attribute of kStaleProgramOpDescs.");
// 1. get op desc order
auto& op_descs = graph.Get<const std::vector<OpDesc*>>(kAllOpDescs);
auto& op_descs = graph.Get<const std::vector<OpDesc*>>(kStaleProgramOpDescs);
// 2. topology sort order
auto nodes = graph.Nodes();
......@@ -461,11 +461,21 @@ void ControlFlowGraph::LiveVariableAnalysis() {
}
}
}
for (auto* op : ops_) {
unlived_vars_[op] = std::set<std::string>();
for (auto& var : this->LiveIn(op)) {
if (!this->LiveOut(op).count(var)) {
unlived_vars_[op].insert(var);
}
}
}
}
void ControlFlowGraph::RenameVarInCFGGraph(const std::string& old_node,
const std::string& new_node,
int begin_idx) {
std::vector<bool> need_update(ops_.size(), false);
// update graph from begin idx to the end
for (size_t i = begin_idx; i != ops_.size(); ++i) {
auto* op = ops_[i];
......@@ -480,15 +490,27 @@ void ControlFlowGraph::RenameVarInCFGGraph(const std::string& old_node,
if (live_in_[op].find(old_node) != live_in_[op].end()) {
live_in_[op].erase(old_node);
live_in_[op].insert(new_node);
need_update[i] = true;
}
if (live_out_[op].find(old_node) != live_out_[op].end()) {
live_out_[op].erase(old_node);
live_out_[op].insert(new_node);
need_update[i] = true;
}
}
for (size_t i = begin_idx; i < ops_.size(); ++i) {
if (!need_update[i]) continue;
auto* op = ops_[i];
for (auto& var : this->LiveIn(op)) {
if (!this->LiveOut(op).count(var)) {
unlived_vars_[op].insert(var);
}
}
}
}
const std::set<std::string> ControlFlowGraph::LiveIn(ir::Node* op) const {
const std::set<std::string>& ControlFlowGraph::LiveIn(ir::Node* op) const {
auto it = live_in_.find(op);
PADDLE_ENFORCE(
it != live_in_.end(),
......@@ -496,7 +518,7 @@ const std::set<std::string> ControlFlowGraph::LiveIn(ir::Node* op) const {
return it->second;
}
const std::set<std::string> ControlFlowGraph::LiveOut(ir::Node* op) const {
const std::set<std::string>& ControlFlowGraph::LiveOut(ir::Node* op) const {
auto it = live_out_.find(op);
PADDLE_ENFORCE(
it != live_out_.end(),
......@@ -504,15 +526,24 @@ const std::set<std::string> ControlFlowGraph::LiveOut(ir::Node* op) const {
return it->second;
}
const std::set<std::string> ControlFlowGraph::Use(ir::Node* op) const {
const std::set<std::string>& ControlFlowGraph::Use(ir::Node* op) const {
auto it = uses_.find(op);
PADDLE_ENFORCE(
it != uses_.end(),
string::Sprintf("Expect %s in live_out, but Not Found.", op->Name()));
string::Sprintf("Expect %s in use, but Not Found.", op->Name()));
return it->second;
}
const std::set<std::string>& ControlFlowGraph::Unlived(ir::Node* op) const {
auto it = unlived_vars_.find(op);
PADDLE_ENFORCE(
it != unlived_vars_.end(),
string::Sprintf("Expect %s in unlived_set, but Not Found.", op->Name()));
return it->second;
return it->second;
}
const std::vector<ir::Node*> ControlFlowGraph::Ops() const { return ops_; }
const std::vector<ir::Node*>& ControlFlowGraph::Ops() const { return ops_; }
std::vector<ir::Node*>& ControlFlowGraph::Ops() { return ops_; }
......
paddle/fluid/framework/details/memory_optimize_helper.h
浏览文件 @ cf0511f2
......@@ -92,10 +92,11 @@ class ControlFlowGraph {
void RenameVarInCFGGraph(const std::string& old_node,
const std::string& new_node, int begin_idx);
const std::set<std::string> LiveIn(ir::Node* op) const;
const std::set<std::string> LiveOut(ir::Node* op) const;
const std::set<std::string> Use(ir::Node* op) const;
const std::vector<ir::Node*> Ops() const;
const std::set<std::string>& LiveIn(ir::Node* op) const;
const std::set<std::string>& LiveOut(ir::Node* op) const;
const std::set<std::string>& Use(ir::Node* op) const;
const std::set<std::string>& Unlived(ir::Node* op) const;
const std::vector<ir::Node*>& Ops() const;
std::vector<ir::Node*>& Ops();
// for ssa-graph nodes
......@@ -117,6 +118,7 @@ class ControlFlowGraph {
VarSetMap live_out_;
VarSetMap uses_; // op inputs
VarSetMap defs_; // op outputs
std::unordered_map<ir::Node*, std::set<std::string>> unlived_vars_;
std::vector<ir::Node*> ops_; // op sequence by topology sort
};
......
paddle/fluid/framework/details/memory_optimize_helper_test.cc
浏览文件 @ cf0511f2
......@@ -228,9 +228,6 @@ TEST(CFGGraph, IRGraph) {
// prepare ir graph
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
ControlFlowGraph cfg(graph);
cfg.LiveVariableAnalysis();
......@@ -256,9 +253,6 @@ TEST(CFGGraph, IRGraph) {
TEST(SortOpLikeDescOrder, NormalTest) {
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto nodes = SortOpLikeDescOrder(graph);
auto op_descs = prog.Block(0).AllOps();
......@@ -273,9 +267,6 @@ TEST(SortOpLikeDescOrder, NormalTest) {
TEST(SortOpLikeDescOrder, RemoveOpDesc) {
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto nodes = graph.Nodes();
auto op_descs = prog.Block(0).AllOps();
ir::Node* found_node = nullptr;
......@@ -324,8 +315,6 @@ TEST(SortOpLikeDescOrder, RemoveOpDesc) {
// 3. add some op_desc
TEST(SortOpLikeDescOrder, AddOpDesc) {
auto prog = FillProgramDesc();
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
ir::Graph graph(prog);
auto find_node_in_graph = [&](std::string s) {
......@@ -342,9 +331,7 @@ TEST(SortOpLikeDescOrder, AddOpDesc) {
// cached desc different with real one
// mimic the intermidiete pass modify the programdesc.
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto op_descs = prog.Block(0).AllOps();
std::vector<OpDesc*> op_descs = graph.OriginProgram().Block(0).AllOps();
auto op = prog.MutableBlock(0)->AppendOp();
prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
......@@ -376,9 +363,6 @@ TEST(SortOpLikeDescOrder, AddOpDesc) {
TEST(SortOpLikeDescOrder, AddAndDeleteOpDesc) {
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
auto find_node_in_graph = [&](std::string s) {
ir::Node* ret = nullptr;
......@@ -392,8 +376,9 @@ TEST(SortOpLikeDescOrder, AddAndDeleteOpDesc) {
return ret;
};
std::vector<OpDesc*> op_descs = graph.OriginProgram().Block(0).AllOps();
// remove sum node
auto op_descs = prog.Block(0).AllOps();
ir::Node* found_node = nullptr;
auto nodes = graph.Nodes();
for (auto node : nodes) {
......@@ -454,9 +439,7 @@ TEST(SortOpLikeDescOrder, AddAndDeleteOpDesc) {
TEST(SortOpLikeDescOrder, AddAndReplaceOpDescInplace) {
auto prog = FillProgramDesc();
ir::Graph graph(prog);
const std::vector<OpDesc*>* all_op_descs =
new std::vector<OpDesc*>(prog.Block(0).AllOps());
graph.Set(details::kAllOpDescs, all_op_descs); // take ownership
std::vector<OpDesc*> op_descs = graph.OriginProgram().Block(0).AllOps();
auto find_node_in_graph = [&](std::string s) {
ir::Node* ret = nullptr;
......@@ -470,7 +453,6 @@ TEST(SortOpLikeDescOrder, AddAndReplaceOpDescInplace) {
return ret;
};
auto op_descs = prog.Block(0).AllOps();
// add node
auto op = prog.MutableBlock(0)->AppendOp();
prog.MutableBlock(0)->Var("d1")->SetType(proto::VarType::LOD_TENSOR);
......
paddle/fluid/framework/details/memory_optimize_pass.cc
浏览文件 @ cf0511f2
......@@ -118,13 +118,11 @@ std::unique_ptr<ir::Graph> MemoryOptimizePass::ApplyImpl(
}
}
// fill the pool
for (auto var : cfg_->LiveIn(op)) {
if (cfg_->LiveOut(op).count(var) == 0) {
ir::Node* var_node = cfg_->GetNodeByName(var, op);
if (var_node == nullptr || var_node->IsCtrlVar()) continue;
if (NodeCanReused(var_node) && !pool_.Has(var_node)) {
pool_.Insert(var_node);
}
for (auto& var : cfg_->Unlived(op)) {
ir::Node* var_node = cfg_->GetNodeByName(var, op);
if (var_node == nullptr || var_node->IsCtrlVar()) continue;
if (NodeCanReused(var_node) && !pool_.Has(var_node)) {
pool_.Insert(var_node);
}
}
}
......@@ -337,4 +335,4 @@ void MemoryOptimizePass::RenameVarInGraphNode(const std::string& var,
REGISTER_PASS(memory_optimize_pass,
paddle::framework::details::MemoryOptimizePass)
.RequireGraphAttr(paddle::framework::details::kAllOpDescs);
.RequireGraphAttr(paddle::framework::details::kStaleProgramOpDescs);
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ cf0511f2
......@@ -969,9 +969,21 @@ void DistSSAGraphBuilder::InsertCollectiveOp(ir::Graph *result,
}
void DistSSAGraphBuilder::InsertPostprocessOps(ir::Graph *result) const {
if (need_broadcast_var_ ||
(UseGPU() &&
strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce)) {
// broad cast received parameters when training in parameter server mode.
if (need_broadcast_var_) {
// There are 4 conditions:
// 1. GPU && Reduce: Reduce gradient then broadcast gradient to other GPUS.
// Need to broadcast received parameters to other GPU.
// 2. GPU && AllReduce: AllReduce all graident to each GPU. Need to
// broadcast received parameters to other GPU.
// 3. CPU && AllReduce: AllReduce all gradient to each thread. Need to
// broadcast received parameters to other scope.
// 4. CPU && Reduce: because all parameters share the same memory, did not
// broadcast received parameters.
if (!UseGPU() &&
strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
return;
}
if (strategy_.fuse_broadcast_op_) {
CreateFusedBroadcastOp(result, bcast_var_name_set_);
} else {
......
paddle/fluid/framework/details/parallel_ssa_graph_executor.cc
浏览文件 @ cf0511f2
......@@ -20,8 +20,7 @@ namespace framework {
namespace details {
std::vector<std::unique_ptr<ir::Graph>>
ParallelSSAGraphExecutor::SeparateMultiDevicesGraph(
std::unique_ptr<ir::Graph> &&graph) {
ParallelSSAGraphExecutor::SeparateMultiDevicesGraph(ir::Graph *graph) {
std::vector<std::unique_ptr<ir::Graph>> graphs;
graphs.reserve(places_.size());
for (size_t i = 0; i < places_.size(); ++i) {
......@@ -77,24 +76,18 @@ ParallelSSAGraphExecutor::SeparateMultiDevicesGraph(
ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const framework::ProgramDesc &main_prog, std::unique_ptr<ir::Graph> &&graph)
const std::vector<platform::Place> &places, ir::Graph *graph)
: strategy_(std::move(strategy)),
local_scopes_(std::move(local_scopes)),
pool_(places.size() >= 2 ? new ::ThreadPool(places.size()) : nullptr),
places_(std::move(places)),
main_prog_(main_prog),
// TODO(Yancey1989): Copying graphs is not safely since it deleted the
// attrs.
graphs_(SeparateMultiDevicesGraph(std::move(graph))) {
graphs_(SeparateMultiDevicesGraph(graph)) {
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
auto seq_allreduce_pass =
ir::PassRegistry::Instance().Get("all_reduce_deps_pass");
seq_allreduce_pass->Erase(details::kAllOpDescs);
seq_allreduce_pass->Set<const std::vector<OpDesc *>>(
details::kAllOpDescs,
new std::vector<OpDesc *>(main_prog_.Block(0).AllOps()));
for (size_t i = 0; i < graphs_.size(); ++i) {
graphs_[i] = seq_allreduce_pass->Apply(std::move(graphs_[i]));
}
......@@ -107,7 +100,7 @@ ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
<< " to run the operators of the graph on each device.";
for (size_t i = 0; i < places.size(); ++i) {
executors_.emplace_back(new details::ThreadedSSAGraphExecutor(
strategy_, local_scopes_, {places_[i]}, std::move(graphs_.at(i))));
strategy_, local_scopes_, {places_[i]}, graphs_.at(i).get()));
}
}
......
paddle/fluid/framework/details/parallel_ssa_graph_executor.h
浏览文件 @ cf0511f2
......@@ -31,8 +31,7 @@ class ParallelSSAGraphExecutor : public SSAGraphExecutor {
ParallelSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const framework::ProgramDesc &main_prog,
std::unique_ptr<ir::Graph> &&graph);
ir::Graph *graph);
~ParallelSSAGraphExecutor() final = default;
const ir::Graph &Graph() const override { return *graphs_[0]; }
......@@ -41,13 +40,12 @@ class ParallelSSAGraphExecutor : public SSAGraphExecutor {
private:
std::vector<std::unique_ptr<ir::Graph>> SeparateMultiDevicesGraph(
std::unique_ptr<ir::Graph> &&graph);
ir::Graph *graph);
ExecutionStrategy strategy_;
std::vector<Scope *> local_scopes_;
std::unique_ptr<::ThreadPool> pool_{nullptr};
std::vector<platform::Place> places_;
framework::ProgramDesc main_prog_;
std::vector<std::unique_ptr<ir::Graph>> graphs_;
std::vector<std::unique_ptr<details::ThreadedSSAGraphExecutor>> executors_;
......
paddle/fluid/framework/details/sequential_execution_pass.cc
浏览文件 @ cf0511f2
......@@ -40,7 +40,7 @@ std::unique_ptr<ir::Graph> SequentialExecutionPass::ApplyImpl(
static std::unordered_set<std::string> skip_dist_ops{
"send", "recv", "send_barrier", "fetch_barrier"};
auto &ops = Get<const std::vector<OpDesc *>>(kAllOpDescs);
auto &ops = graph->Get<const std::vector<OpDesc *>>(kStaleProgramOpDescs);
std::vector<ir::Node *> op_node_list;
op_node_list.reserve(ops.size());
......@@ -107,4 +107,4 @@ std::unique_ptr<ir::Graph> SequentialExecutionPass::ApplyImpl(
REGISTER_PASS(sequential_execution_pass,
paddle::framework::details::SequentialExecutionPass)
.RequirePassAttr(paddle::framework::details::kAllOpDescs);
.RequireGraphAttr(paddle::framework::details::kStaleProgramOpDescs);
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ cf0511f2
......@@ -23,9 +23,8 @@ namespace framework {
namespace details {
ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph)
: graph_(std::move(graph)),
const std::vector<platform::Place> &places, ir::Graph *graph)
: graph_(graph),
pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
: nullptr),
local_scopes_(local_scopes),
......@@ -123,7 +122,7 @@ inline FeedFetchList ThreadedSSAGraphExecutor::RunImpl(
for (auto &run_op_future : run_op_futures_) {
run_op_future.wait();
}
ClearFetchOp(graph_.get(), &fetch_ops);
ClearFetchOp(graph_, &fetch_ops);
exception_holder_.ReThrow();
} else {
continue;
......@@ -148,7 +147,7 @@ inline FeedFetchList ThreadedSSAGraphExecutor::RunImpl(
}
PADDLE_ENFORCE(ready_ops.empty());
// Wait FetchOps.
ClearFetchOp(graph_.get(), &fetch_ops);
ClearFetchOp(graph_, &fetch_ops);
return fetch_data;
}
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ cf0511f2
......@@ -41,7 +41,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
ThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph);
ir::Graph *graph);
const ir::Graph &Graph() const override { return *graph_; }
// Run a SSAGraph by a thread pool
......@@ -56,7 +56,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
details::OpHandleBase *op);
private:
std::unique_ptr<ir::Graph> graph_;
ir::Graph *graph_;
std::unique_ptr<::ThreadPool> pool_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ cf0511f2
......@@ -102,6 +102,7 @@ cc_test(test_seqpool_concat_fuse_pass SRCS seqpool_concat_fuse_pass_tester.cc DE
cc_test(test_is_test_pass SRCS is_test_pass_tester.cc DEPS is_test_pass)
if (WITH_MKLDNN)
cc_test(test_depthwise_conv_mkldnn_pass SRCS mkldnn/depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass)
cc_test(test_conv_bias_mkldnn_fuse_pass SRCS mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc DEPS conv_bias_mkldnn_fuse_pass naive_executor)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS mkldnn/conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass)
endif ()
paddle/fluid/framework/ir/attention_lstm_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -22,7 +22,8 @@ namespace ir {
class AttentionLSTMFusePass : public FusePassBase {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
......
paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -31,7 +31,8 @@ class ConvAffineChannelFusePass : public FusePassBase {
virtual ~ConvAffineChannelFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"conv_affine_channel_fuse"};
};
......@@ -40,7 +41,8 @@ class ConvEltwiseAddAffineChannelFusePass : public FusePassBase {
virtual ~ConvEltwiseAddAffineChannelFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"conv_eltwiseadd_affine_channel_fuse"};
};
......
paddle/fluid/framework/ir/conv_bn_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -31,7 +31,8 @@ class ConvBNFusePass : public FusePassBase {
virtual ~ConvBNFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"conv_bn_fuse"};
};
......@@ -40,7 +41,8 @@ class ConvEltwiseAddBNFusePass : public FusePassBase {
virtual ~ConvEltwiseAddBNFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"conv_eltwiseadd_bn_fuse"};
};
......
paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -25,7 +25,8 @@ class ConvElementwiseAdd2ActFusePass : public FusePassBase {
virtual ~ConvElementwiseAdd2ActFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
......
paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -25,7 +25,8 @@ class ConvElementwiseAddActFusePass : public FusePassBase {
virtual ~ConvElementwiseAddActFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
......
paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -25,7 +25,8 @@ class ConvElementwiseAddFusePass : public FusePassBase {
virtual ~ConvElementwiseAddFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
......
paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -14,6 +14,8 @@
#pragma once
#include <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
......@@ -30,7 +32,8 @@ class EmbeddingFCLSTMFusePass : public FusePassBase {
virtual ~EmbeddingFCLSTMFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"embedding_fc_lstm_fuse"};
};
......
paddle/fluid/framework/ir/fc_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
......@@ -29,7 +31,8 @@ class FCFusePass : public FusePassBase {
virtual ~FCFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
......
paddle/fluid/framework/ir/fc_gru_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -30,7 +30,8 @@ class FCGRUFusePass : public FusePassBase {
virtual ~FCGRUFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"fc_gru_fuse"};
};
......@@ -41,7 +42,8 @@ class MulGRUFusePass : public FusePassBase {
virtual ~MulGRUFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"fc_nobias_gru_fuse"};
};
......
paddle/fluid/framework/ir/fc_lstm_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -14,6 +14,8 @@
#pragma once
#include <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
......@@ -30,7 +32,8 @@ class FCLstmFusePass : public FusePassBase {
virtual ~FCLstmFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"fc_lstm_fuse"};
};
......@@ -40,7 +43,8 @@ class MulLstmFusePass : public FusePassBase {
virtual ~MulLstmFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"fc_nobias_lstm_fuse"};
};
......
paddle/fluid/framework/ir/fuse_elewise_add_act_pass.h
浏览文件 @ cf0511f2
......@@ -32,7 +32,8 @@ class FuseElewiseAddActPass : public FusePassBase {
virtual ~FuseElewiseAddActPass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
std::unique_ptr<ir::Graph> FuseElewiseAddAct(
std::unique_ptr<ir::Graph> graph,
......
paddle/fluid/framework/ir/fuse_relu_depthwise_conv_pass.h
浏览文件 @ cf0511f2
......@@ -32,7 +32,8 @@ class FuseReluDepthwiseConvPass : public FusePassBase {
virtual ~FuseReluDepthwiseConvPass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
std::unique_ptr<ir::Graph> FuseReluDepthwiseConv(
std::unique_ptr<ir::Graph> graph, bool only_forward) const;
};
......
paddle/fluid/framework/ir/graph.cc
浏览文件 @ cf0511f2
......@@ -76,6 +76,9 @@ std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
var->inputs.push_back(node);
}
}
Set<const std::vector<OpDesc *>>(
details::kStaleProgramOpDescs,
new std::vector<OpDesc *>(program.Block(0).AllOps()));
return var_nodes;
}
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ cf0511f2
......@@ -31,7 +31,7 @@ namespace details {
// This attr is not recommended, because the graph should not dependence
// the program once it is built.
constexpr char kAllOpDescs[] = "all_op_descs";
constexpr char kStaleProgramOpDescs[] = "stale_program_op_descs";
} // namespace details
namespace ir {
......@@ -195,6 +195,12 @@ class Graph {
return nullptr;
}
// Returns reference to the original program.
// WARN: After a series of passes, the current graph can be quite
// different from OriginProgram. Caller shouldn't assume much from
// the returned OriginProgram.
const ProgramDesc &OriginProgram() const { return program_; }
// This method takes ownership of `node`.
ir::Node *AddNode(ir::Node *node) {
PADDLE_ENFORCE(node_set_.find(node) == node_set_.end());
......
paddle/fluid/framework/ir/identity_scale_op_clean_pass.h
浏览文件 @ cf0511f2
......@@ -22,7 +22,8 @@ namespace ir {
class IdentityScaleOpCleanPass : public FusePassBase {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
private:
virtual ~IdentityScaleOpCleanPass() = default;
......
paddle/fluid/framework/ir/lock_free_optimize_pass.h
浏览文件 @ cf0511f2
......@@ -60,7 +60,8 @@ class LockFreeOptimizePass : public Pass {
virtual ~LockFreeOptimizePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
private:
// Create a new sgd node via current optimizer node
......
paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -29,7 +29,8 @@ class ConvBiasFusePass : public FusePassBase {
virtual bool is_conv3d() const { return false; }
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"conv_bias_mkldnn_fuse"};
};
/*
......
paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc 0 → 100644
浏览文件 @ cf0511f2
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/platform/place.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/op_proto_maker.h"
namespace paddle {
namespace framework {
namespace ir {
void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
if (type == "conv2d") {
op->SetAttr("use_mkldnn", true);
op->SetAttr("name", name);
op->SetInput("Input", {inputs[0]});
op->SetInput("Filter", {inputs[1]});
if (inputs.size() > 2)
op->SetInput("Bias", {inputs[2]});
else
op->SetInput("Bias", {});
} else if (type == "elementwise_add") {
op->SetAttr("use_mkldnn", true);
op->SetInput("X", {inputs[0]});
op->SetInput("Y", {inputs[1]});
}
op->SetOutput("Out", outputs);
op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kForward));
}
// (c, weights)->conv->f
// (f)->elementwise_add->g
ProgramDesc BuildProgramDesc(bool convWithExistingBias) {
ProgramDesc prog;
std::vector<std::string> nodes{"c", "weights", "f", "eltwise_bias", "g"};
if (convWithExistingBias) nodes.push_back("conv_bias");
for (auto& v : nodes) {
auto* var = prog.MutableBlock(0)->Var(v);
var->SetType(proto::VarType::LOD_TENSOR);
if (v == "weights" || v == "conv_bias" || v == "eltwise_bias") {
var->SetPersistable(true);
}
}
// conv+bias, both with MKL-DNN
if (convWithExistingBias) {
SetOp(&prog, "conv2d", "conv",
std::vector<std::string>({"c", "weights", "conv_bias"}),
std::vector<std::string>({"f"}));
} else {
SetOp(&prog, "conv2d", "conv", std::vector<std::string>({"c", "weights"}),
std::vector<std::string>({"f"}));
}
SetOp(&prog, "elementwise_add", "eltwise",
std::vector<std::string>({"f", "eltwise_bias"}),
std::vector<std::string>({"g"}));
return prog;
}
void InitTensorHolder(Scope* scope, const paddle::platform::Place& place,
const char* var_name) {
auto x = scope->Var(var_name);
auto tensor = x->GetMutable<LoDTensor>();
tensor->mutable_data(place, proto::VarType::FP32,
::paddle::memory::Allocator::kDefault, 1);
}
void MainTest(bool convWithExistingBias) {
auto prog = BuildProgramDesc(convWithExistingBias);
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
auto place = paddle::platform::CPUPlace();
NaiveExecutor exe{place};
Scope scope;
// Init scope, as it is used in pass
exe.CreateVariables(prog, 0, true, &scope);
if (convWithExistingBias) {
InitTensorHolder(&scope, place, "conv_bias");
InitTensorHolder(&scope, place, "eltwise_bias");
}
graph->Set(kParamScopeAttr, new framework::Scope*(&scope));
auto pass = PassRegistry::Instance().Get("conv_bias_mkldnn_fuse_pass");
int original_nodes_num = graph->Nodes().size();
graph = pass->Apply(std::move(graph));
int current_nodes_num = graph->Nodes().size();
// Remove 3 Nodes: Conv, Bias, conv_out
// Add 1 Node: ConvBias
EXPECT_EQ(original_nodes_num - 2, current_nodes_num);
// Assert conv_bias op in newly generated graph
int conv_bias_count = 0;
for (auto* node : graph->Nodes()) {
if (node->IsOp() && node->Op()->Type() == "conv2d") {
auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(boost::get<bool>(op->GetAttr("use_mkldnn")));
// check if "conv" convolution is fused
auto op_name = boost::get<std::string>(op->GetAttr("name"));
if (op_name == "conv") {
auto input_names = op->InputNames();
ASSERT_TRUE(std::find(input_names.begin(), input_names.end(), "Bias") !=
input_names.end());
auto bias = boost::get<std::vector<std::string>>(op->Input("Bias"));
if (bias.size()) {
++conv_bias_count;
}
}
}
}
EXPECT_EQ(conv_bias_count, 1);
}
TEST(ConvBiasFusePass, bias_free_conv) { MainTest(false); }
TEST(ConvBiasFusePass, conv_with_existing_bias) { MainTest(true); }
TEST(ConvBiasFusePass, conv3d) {
Conv3DBiasFusePass pass;
ASSERT_TRUE(pass.is_conv3d());
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(conv_bias_mkldnn_fuse_pass);
paddle/fluid/framework/ir/repeated_fc_relu_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -31,7 +31,8 @@ class RepeatedFCReluFusePass : public FusePassBase {
virtual ~RepeatedFCReluFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"repeated_fc_relu_fuse"};
};
......
paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
......@@ -25,7 +27,8 @@ class SeqConcatFcFusePass : public FusePassBase {
virtual ~SeqConcatFcFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
......
paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -28,7 +28,8 @@ class SeqConvEltAddReluFusePass : public FusePassBase {
virtual ~SeqConvEltAddReluFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"seqconv_eltadd_relu_fuse"};
};
......
paddle/fluid/framework/ir/seqpool_concat_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -42,7 +42,8 @@ class SeqPoolConcatFusePass : public FusePassBase {
virtual ~SeqPoolConcatFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"seqpool_concat_fuse"};
};
......
paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -31,7 +31,8 @@ class SquaredMatSubFusePass : public FusePassBase {
virtual ~SquaredMatSubFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
const std::string name_scope_{"squared_mat_sub_fuse"};
};
......
paddle/fluid/framework/ir/transpose_flatten_concat_fuse_pass.h
浏览文件 @ cf0511f2
......@@ -30,7 +30,8 @@ class TransposeFlattenConcatFusePass : public FusePassBase {
virtual ~TransposeFlattenConcatFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ cf0511f2
......@@ -27,7 +27,7 @@ enum class OpRole {
kForward = 0x0000,
kBackward = 0x0001,
kOptimize = 0x0002,
// RPC role is for send/recv releated op
// RPC role is for send/recv related op
kRPC = 0x0004,
// Dist role is for split_byref/split_selected_rows/concat
// used for distributed training.
......
paddle/fluid/framework/operator.cc
浏览文件 @ cf0511f2
......@@ -904,6 +904,16 @@ void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
this->InferShape(&infer_shape_ctx);
}
std::vector<KernelConfig>* OperatorWithKernel::GetKernelConfig(
const OpKernelType& key) const {
auto config_iter = kernel_configs_map_.find(key);
std::vector<KernelConfig>* kernel_configs = nullptr;
if (config_iter != kernel_configs_map_.end()) {
kernel_configs = &(config_iter->second);
}
return kernel_configs;
}
void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place) const {
RuntimeContext ctx(Inputs(), Outputs(), scope);
......@@ -921,7 +931,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
OpKernelMap& kernels = kernels_iter->second;
auto expected_kernel_key = this->GetExpectedKernelType(
ExecutionContext(*this, scope, *dev_ctx, ctx));
ExecutionContext(*this, scope, *dev_ctx, ctx, nullptr));
VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
auto kernel_iter = kernels.find(expected_kernel_key);
......@@ -940,6 +950,9 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
KernelTypeToString(expected_kernel_key));
}
std::vector<KernelConfig>* kernel_configs =
GetKernelConfig(expected_kernel_key);
// do data transformScope &transfer_scope;
std::vector<std::string> transfered_inplace_vars;
auto* transfer_scope =
......@@ -957,7 +970,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
this->InferShape(&infer_shape_ctx);
// TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
// not Scope. Imperative mode only pass inputs and get outputs.
kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx, ctx));
kernel_iter->second(
ExecutionContext(*this, exec_scope, *dev_ctx, ctx, kernel_configs));
if (!transfered_inplace_vars.empty()) {
// there is inplace variable has been transfered.
......
paddle/fluid/framework/operator.h
浏览文件 @ cf0511f2
......@@ -28,6 +28,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/operator_kernel_configs.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
......@@ -184,12 +185,30 @@ class OperatorBase {
const platform::Place& place) const = 0;
};
#ifdef PADDLE_WITH_CUDA
using KernelConfig = boost::variant<
std::shared_ptr<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>,
std::shared_ptr<AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>,
std::shared_ptr<AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>>;
#else
using KernelConfig = boost::variant<boost::blank>;
#endif
using OpKernelConfigsMap =
std::unordered_map<OpKernelType, std::vector<KernelConfig>,
OpKernelType::Hash>;
class ExecutionContext {
public:
ExecutionContext(const OperatorBase& op, const Scope& scope,
const platform::DeviceContext& device_context,
const RuntimeContext& ctx)
: op_(op), scope_(scope), device_context_(device_context), ctx_(ctx) {}
const RuntimeContext& ctx,
std::vector<KernelConfig>* configs)
: op_(op),
scope_(scope),
device_context_(device_context),
ctx_(ctx),
kernel_configs_(configs) {}
const OperatorBase& op() const { return op_; }
......@@ -398,11 +417,20 @@ class ExecutionContext {
return temp_tensor;
}
template <typename T>
T& GetKernelConfig(int idx) const {
PADDLE_ENFORCE(kernel_configs_ && kernel_configs_->size() > idx,
"%s selected kernel doesn't have kernel config %lu <= %d",
op_.Type().c_str(), kernel_configs_->size(), idx);
return *boost::get<std::shared_ptr<T>>(kernel_configs_->at(idx));
}
private:
const OperatorBase& op_;
const Scope& scope_;
const platform::DeviceContext& device_context_;
const RuntimeContext& ctx_;
mutable std::vector<KernelConfig>* kernel_configs_;
};
template <>
......@@ -483,6 +511,8 @@ class OperatorWithKernel : public OperatorBase {
virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
std::vector<KernelConfig>* GetKernelConfig(const OpKernelType& key) const;
protected:
virtual OpKernelType GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor,
......@@ -508,6 +538,9 @@ class OperatorWithKernel : public OperatorBase {
void TransferInplaceVarsBack(const Scope& scope,
const std::vector<std::string>& inplace_vars,
const Scope& exec_scope) const;
protected:
mutable OpKernelConfigsMap kernel_configs_map_;
};
extern bool OpSupportGPU(const std::string& op_type);
......
paddle/fluid/framework/operator_kernel_configs.h 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include <unordered_map>
#include <vector>
namespace paddle {
namespace framework {
// Not thread-safe. Should be owned per-kernel.
template <typename TAlgorithm>
class AlgorithmsCache {
public:
AlgorithmsCache() : search_times_(0) { hash_.clear(); }
// Caches the best algorithm for a given
// combination of tensor dimensions & compute data type.
TAlgorithm GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations,
int algorithmFlags, // can set for different data type
std::function<TAlgorithm()> gen_func);
TAlgorithm GetAlgorithm(int64_t area, int search_times, int algorithmFlags,
std::function<TAlgorithm()> gen_func);
private:
std::unordered_map<int64_t, TAlgorithm> hash_;
int search_times_;
};
template <typename TAlgorithm>
TAlgorithm framework::AlgorithmsCache<TAlgorithm>::GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
int64_t seed = 0;
// Hash all of the inputs, use to try and look up a previously
// discovered algorithm, or fall back to generating a new one.
std::hash<int64_t> hashFn;
// do hash like boost
// https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
for (const auto num : dims1) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
for (const auto num : dims2) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2) + 1;
}
for (const auto num : strides) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 2;
}
for (const auto num : paddings) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 3;
}
for (const auto num : dilations) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 4;
}
seed ^= hashFn(static_cast<int64_t>(algorithmFlags)) + 0x9e3779b9 +
(seed << 6) + (seed >> 2) + 5;
if (seed == 0) return gen_func();
if (hash_.find(seed) == hash_.end()) {
TAlgorithm value = gen_func();
hash_[seed] = value;
}
return hash_[seed];
}
template <typename TAlgorithm>
TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm(
int64_t area, int search_times, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
if (hash_.find(area) != hash_.end()) {
return hash_[area];
}
if (search_times_ < search_times) {
auto algo = gen_func();
hash_[area] = algo;
++search_times_;
return algo;
}
TAlgorithm algo;
int64_t min = static_cast<uint64_t>(INT_MAX);
for (const auto& m : hash_) {
if (m.first < min) {
min = m.first;
algo = m.second;
}
}
return algo;
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ cf0511f2
......@@ -185,9 +185,10 @@ std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
ParallelExecutor::ParallelExecutor(
const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program, const std::string &loss_var_name,
Scope *scope, const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy)
const std::string &loss_var_name, Scope *scope,
const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy,
ir::Graph *graph)
: member_(new ParallelExecutorPrivate(places)) {
member_->global_scope_ = scope;
member_->use_cuda_ = exec_strategy.use_cuda_;
......@@ -221,12 +222,13 @@ ParallelExecutor::ParallelExecutor(
PADDLE_ENFORCE(!member_->use_cuda_,
"gpu mode does not support async_mode_ now!");
}
std::unique_ptr<ir::Graph> temp_owned_graph(graph);
// FIXME(Yancey1989): parallel graph mode get better performance
// in GPU allreduce distributed training. Need an elegant way to
// choice the execution strategy.
build_strategy.enable_parallel_graph_ =
EnableParallelGraphExecution(main_program, exec_strategy, build_strategy);
build_strategy.enable_parallel_graph_ = EnableParallelGraphExecution(
*temp_owned_graph, exec_strategy, build_strategy);
if (build_strategy.enable_parallel_graph_)
VLOG(0) << "The Executor would execute the graph by ParallelGraph "
"Execution which can get better performance,"
......@@ -260,41 +262,45 @@ ParallelExecutor::ParallelExecutor(
if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
BCastParamsToDevices(bcast_vars);
}
// Startup Program has been run. All local scopes has correct parameters.
// Startup Program has been run. All local scopes has correct parameters.
// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
std::unique_ptr<ir::Graph> graph;
// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
if (build_strategy.async_mode_ && !build_strategy.is_distribution_) {
VLOG(3) << "use local async mode";
graph =
build_strategy.Apply(main_program, {member_->places_[0]}, loss_var_name,
temp_owned_graph =
build_strategy.Apply(std::move(temp_owned_graph), {member_->places_[0]}, loss_var_name,
{member_->local_scopes_[0]}, member_->nranks_,
member_->use_cuda_, member_->nccl_ctxs_.get());
} else {
graph = build_strategy.Apply(main_program, member_->places_, loss_var_name,
temp_owned_graph = build_strategy.Apply(std::move(temp_owned_graph), member_->places_, loss_var_name,
member_->local_scopes_, member_->nranks_,
member_->use_cuda_, member_->nccl_ctxs_.get());
}
#else
if (build_strategy.async_mode_ && !build_strategy.is_distribution_) {
VLOG(3) << "use local async mode";
graph = build_strategy.Apply(main_program, {member_->places_[0]},
temp_owned_graph = build_strategy.Apply(std::move(temp_owned_graph), {member_->places_[0]},
loss_var_name, {member_->local_scopes_[0]},
member_->nranks_, member_->use_cuda_);
} else {
graph = build_strategy.Apply(main_program, member_->places_, loss_var_name,
temp_owned_graph = build_strategy.Apply(std::move(temp_owned_graph), member_->places_, loss_var_name,
member_->local_scopes_, member_->nranks_,
member_->use_cuda_);
}
#endif
auto max_memory_size = GetEagerDeletionThreshold();
VLOG(10) << "Eager Deletion Threshold "
<< static_cast<float>(max_memory_size) / (1 << 30);
if (max_memory_size >= 0) {
graph = member_->PrepareGCAndRefCnts(std::move(graph),
static_cast<size_t>(max_memory_size));
graph = member_
->PrepareGCAndRefCnts(std::move(temp_owned_graph),
static_cast<size_t>(max_memory_size))
.release();
} else {
graph = temp_owned_graph.release();
}
// Step 3. Create vars in each scope. Passes may also create new vars.
......@@ -328,15 +334,14 @@ ParallelExecutor::ParallelExecutor(
VLOG(3) << "use AsyncSSAGraphExecutor";
member_->executor_.reset(new details::AsyncSSAGraphExecutor(
exec_strategy, member_->local_scopes_, member_->places_,
std::move(graph)));
graph));
} else if (build_strategy.enable_parallel_graph_) {
VLOG(3) << "use ParallelSSAGraphExecutor";
#ifdef PADDLE_WITH_CUDA
// TODO(Yancey1989): Remove passing in the main_program when
// allreduce_seq_pass doesn't need it as the attr.
member_->executor_.reset(new details::ParallelSSAGraphExecutor(
exec_strategy, member_->local_scopes_, member_->places_, main_program,
std::move(graphs[0])));
exec_strategy, member_->local_scopes_, member_->places_, graph));
#else
PADDLE_THROW(
"Paddle should be compiled with CUDA for ParallelGraph Execution.");
......@@ -345,13 +350,11 @@ ParallelExecutor::ParallelExecutor(
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
VLOG(3) << "use ThreadedSSAGraphExecutor";
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, member_->places_,
std::move(graph)));
exec_strategy, member_->local_scopes_, member_->places_, graph));
} else {
VLOG(3) << "use FastThreadedSSAGraphExecutor";
member_->executor_.reset(new details::FastThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, member_->places_,
std::move(graph)));
exec_strategy, member_->local_scopes_, member_->places_, graph));
}
}
......@@ -491,24 +494,33 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
}
}
ParallelExecutor::~ParallelExecutor() {
for (auto &p : member_->places_) {
platform::DeviceContextPool::Instance().Get(p)->Wait();
}
delete member_;
}
bool ParallelExecutor::EnableParallelGraphExecution(
const ProgramDesc &main_program, const ExecutionStrategy &exec_strategy,
const ir::Graph &graph, const ExecutionStrategy &exec_strategy,
const BuildStrategy &build_strategy) const {
if (!FLAGS_enable_parallel_graph) return false;
bool enable_parallel_graph = true;
// TODO(Yancey1989): support sparse update in ParallelGraph mode.
for (auto &var_desc : main_program.Block(0).AllVars()) {
if (var_desc->GetType() == proto::VarType::SELECTED_ROWS) {
enable_parallel_graph = false;
}
}
// TODO(Yancey1989): support pserver mode
for (auto &op_desc : main_program.Block(0).AllOps()) {
if (op_desc->Type() == "send" || op_desc->Type() == "recv") {
enable_parallel_graph = false;
break;
for (ir::Node *node : graph.Nodes()) {
if (node->IsVar() && node->Var()) {
// TODO(Yancey1989): support sparse update in ParallelGraph mode.
if (node->Var()->GetType() == proto::VarType::SELECTED_ROWS) {
enable_parallel_graph = false;
break;
}
} else if (node->IsOp() && node->Op()) {
// TODO(Yancey1989): support pserver mode
if (node->Op()->Type() == "send" || node->Op()->Type() == "recv") {
enable_parallel_graph = false;
break;
}
}
}
......@@ -520,13 +532,6 @@ bool ParallelExecutor::EnableParallelGraphExecution(
return enable_parallel_graph;
}
ParallelExecutor::~ParallelExecutor() {
for (auto &p : member_->places_) {
platform::DeviceContextPool::Instance().Get(p)->Wait();
}
delete member_;
}
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ cf0511f2
......@@ -46,11 +46,11 @@ class ParallelExecutor {
public:
explicit ParallelExecutor(const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &bcast_vars,
const ProgramDesc &main_program,
const std::string &loss_var_name, Scope *scope,
const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy,
const BuildStrategy &build_strategy);
const BuildStrategy &build_strategy,
ir::Graph *graph);
~ParallelExecutor();
......@@ -71,7 +71,7 @@ class ParallelExecutor {
private:
void BCastParamsToDevices(const std::unordered_set<std::string> &vars) const;
bool EnableParallelGraphExecution(const ProgramDesc &main_program,
bool EnableParallelGraphExecution(const ir::Graph &graph,
const ExecutionStrategy &exec_strategy,
const BuildStrategy &build_strategy) const;
......
paddle/fluid/framework/tensor.h
浏览文件 @ cf0511f2
......@@ -27,6 +27,10 @@ limitations under the License. */
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_utils.h"
#endif
namespace paddle {
namespace framework {
......@@ -37,10 +41,34 @@ class Tensor {
#ifdef PADDLE_WITH_MKLDNN
public:
inline mkldnn::memory::format format() const { return format_; }
// TODO(jczaja): This is depracted and will be removed
inline mkldnn::memory::format format() const {
if (layout_ == DataLayout::kMKLDNN) {
return static_cast<mkldnn::memory::format>(mem_pd_.desc().data.format);
} else {
return mkldnn::memory::format::format_undef;
}
}
inline void set_format(const mkldnn::memory::format format) {
format_ = format;
// TODO(jczaja): This is depracted and will be removed
inline void set_format(
const mkldnn::memory::format fmt,
mkldnn::memory::data_type data_type = mkldnn::memory::f32) {
mem_pd_ = paddle::platform::create_prim_desc_from_format(
paddle::framework::vectorize2int(dims()), fmt, data_type);
layout_ = DataLayout::kMKLDNN;
}
inline mkldnn::memory::primitive_desc get_mkldnn_prim_desc() const {
return mem_pd_;
}
inline void set_mkldnn_prim_desc(
const mkldnn::memory::primitive_desc& mem_pd) {
// Internally MKL-DNN is just copying (increasing reference counter)
// to shared_ptr. So asignment should be quite cheap
mem_pd_ = mem_pd;
layout_ = DataLayout::kMKLDNN;
}
protected:
......@@ -48,12 +76,9 @@ class Tensor {
* @brief the detail format of memory block which have layout as kMKLDNN
*
* @note MKLDNN lib support various memory format like nchw, nhwc, nChw8C,
* nChw16c, etc. For a MKLDNN memory block, layout will be set as
* DataLayout::kMKLDNN meanwhile detail memory format will be kept in
* this field.
* nChw16c, etc. For a MKLDNN memory block, we store memory descriptor
*/
mkldnn::memory::format format_ = mkldnn::memory::format::format_undef;
mutable mkldnn::memory::primitive_desc mem_pd_;
#endif
public:
......
paddle/fluid/framework/var_type_traits.h
浏览文件 @ cf0511f2
......@@ -50,8 +50,6 @@ class Scope;
} // namespace framework
namespace operators {
template <typename T>
class AlgorithmsCache;
class CudnnRNNCache;
......@@ -144,9 +142,6 @@ using VarTypeRegistry = detail::VarTypeRegistryImpl<
#ifndef _WIN32
ncclUniqueId, platform::Communicator,
#endif
operators::AlgorithmsCache<cudnnConvolutionFwdAlgo_t>,
operators::AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>,
operators::AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>,
operators::CudnnRNNCache,
#endif
int, float>;
......
paddle/fluid/imperative/layer.cc
浏览文件 @ cf0511f2
......@@ -249,7 +249,8 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
framework::Scope scope;
PreparedOp p = PreparedOp::Prepare(ctx, *op_kernel, place_);
p.op.RuntimeInferShape(scope, place_, ctx);
p.func(framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx));
p.func(
framework::ExecutionContext(p.op, scope, *p.dev_ctx, p.ctx, nullptr));
}
}
......
paddle/fluid/imperative/layer.h
浏览文件 @ cf0511f2
......@@ -44,8 +44,13 @@ class PreparedOp {
PreparedOp(const framework::OperatorBase& op,
const framework::RuntimeContext& ctx,
framework::OperatorWithKernel::OpKernelFunc func,
platform::DeviceContext* dev_ctx)
: op(op), ctx(ctx), func(func), dev_ctx(dev_ctx) {}
platform::DeviceContext* dev_ctx,
std::vector<framework::KernelConfig>* kernel_configs)
: op(op),
ctx(ctx),
func(func),
dev_ctx(dev_ctx),
kernel_configs(kernel_configs) {}
static PreparedOp Prepare(const framework::RuntimeContext& ctx,
const framework::OperatorWithKernel& op,
......@@ -64,8 +69,9 @@ class PreparedOp {
framework::OperatorWithKernel::OpKernelMap& kernels = kernels_iter->second;
auto expected_kernel_key = op.GetExpectedKernelType(
framework::ExecutionContext(op, framework::Scope(), *dev_ctx, ctx));
auto expected_kernel_key =
op.GetExpectedKernelType(framework::ExecutionContext(
op, framework::Scope(), *dev_ctx, ctx, nullptr));
VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
auto kernel_iter = kernels.find(expected_kernel_key);
......@@ -83,7 +89,9 @@ class PreparedOp {
PADDLE_THROW("op %s does not have kernel for %s", op.Type(),
KernelTypeToString(expected_kernel_key));
}
return PreparedOp(op, ctx, kernel_iter->second, dev_ctx);
std::vector<framework::KernelConfig>* kernel_configs =
op.GetKernelConfig(expected_kernel_key);
return PreparedOp(op, ctx, kernel_iter->second, dev_ctx, kernel_configs);
}
inline platform::DeviceContext* GetDeviceContext() const { return dev_ctx; }
......@@ -92,6 +100,7 @@ class PreparedOp {
const framework::RuntimeContext& ctx;
framework::OperatorWithKernel::OpKernelFunc func;
platform::DeviceContext* dev_ctx;
std::vector<framework::KernelConfig>* kernel_configs;
};
class OpBase;
......@@ -105,23 +114,23 @@ class VarBase {
public:
VarBase() : VarBase(new framework::Variable(), new VarBase(true)) {}
// Owns `var` and `grad`
explicit VarBase(bool stop_gradient)
: VarBase(new framework::Variable(),
stop_gradient ? nullptr : new VarBase(true), stop_gradient) {}
VarBase(framework::Variable* var, VarBase* grad)
: VarBase(var, grad, false) {}
private:
VarBase(framework::Variable* var, VarBase* grad, bool stop_gradient)
: var_desc_(nullptr),
var_(var),
grads_(grad),
stop_gradient_(false),
pre_op_(nullptr),
pre_op_out_idx_(-1) {}
explicit VarBase(bool stop_gradient)
: var_desc_(nullptr),
var_(new framework::Variable()),
grads_(stop_gradient ? nullptr : new VarBase(true)),
stop_gradient_(stop_gradient),
pre_op_(nullptr),
pre_op_out_idx_(-1) {}
public:
virtual ~VarBase() {
if (var_) {
delete var_;
......@@ -132,11 +141,13 @@ class VarBase {
}
}
OpBase* PreOp() const { return pre_op_; }
int PreOpOutIdx() const { return pre_op_out_idx_; }
inline OpBase* PreOp() const { return pre_op_; }
inline int PreOpOutIdx() const { return pre_op_out_idx_; }
void SetStopGradient(bool stop_gradient) { stop_gradient_ = stop_gradient; }
bool IsStopGradient() const { return stop_gradient_; }
inline void SetStopGradient(bool stop_gradient) {
stop_gradient_ = stop_gradient;
}
inline bool IsStopGradient() const { return stop_gradient_; }
void RunBackward();
......
paddle/fluid/imperative/tracer.cc
浏览文件 @ cf0511f2
......@@ -14,6 +14,8 @@
#include "paddle/fluid/imperative/tracer.h"
#include <set>
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
......@@ -66,16 +68,18 @@ platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs) {
return result;
}
void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
const VarBasePtrMap& outputs, framework::BlockDesc* block,
const platform::Place expected_place,
const bool stop_gradient) {
std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
const VarBasePtrMap& outputs,
framework::BlockDesc* block,
const platform::Place expected_place,
const bool stop_gradient) {
std::map<std::string, VarBase*> vars;
framework::OpDesc* op_desc = op->op_desc_;
VLOG(3) << "tracer tracing " << op_desc->Type();
op_desc->InferShape(*block);
op_desc->InferVarType(block);
std::unique_ptr<framework::OperatorBase> op_base =
framework::OpRegistry::CreateOp(*op_desc);
......@@ -92,7 +96,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
invars.emplace_back(inp->var_);
vars[inp->var_desc_->Name()] = inp;
if (inp->PreOp()) {
if (inp->PreOp() && !inp->IsStopGradient()) {
op->pre_ops_[it.first].push_back(inp->PreOp());
op->pre_ops_out_idx_[it.first].push_back(inp->PreOpOutIdx());
} else {
......@@ -138,8 +142,11 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
op->place_ = GetExpectedPlace(expected_place, inputs);
PreparedOp prepared_op = PreparedOp::Prepare(ctx, *op_kernel, op->place_);
prepared_op.op.RuntimeInferShape(scope, op->place_, ctx);
prepared_op.func(framework::ExecutionContext(
prepared_op.op, scope, *prepared_op.dev_ctx, prepared_op.ctx));
prepared_op.func(
framework::ExecutionContext(prepared_op.op, scope, *prepared_op.dev_ctx,
prepared_op.ctx, prepared_op.kernel_configs));
std::set<std::string> vars_saved_for_backward;
if (!stop_gradient) {
std::unique_ptr<std::unordered_map<std::string, std::string>> grad_to_var(
......@@ -160,6 +167,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
PADDLE_ENFORCE(fwd_var_it != vars.end());
// Forward inputs or outputs.
grad_in_vars.push_back(fwd_var_it->second->var_);
vars_saved_for_backward.insert(it.first);
} else {
VarBase* var = vars[var_it->second];
if (!var->grads_->var_->IsInitialized()) {
......@@ -193,6 +201,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
}
op->block_ = block;
return vars_saved_for_backward;
}
std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
......@@ -202,7 +211,7 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
op->input_vars_[PyLayer::kFwdInp] = inputs;
op->output_vars_[PyLayer::kFwdOut] = PyLayer::Apply(op->forward_id_, inputs);
for (VarBase* inp : inputs) {
if (inp->PreOp()) {
if (inp->PreOp() && !inp->IsStopGradient()) {
op->pre_ops_[PyLayer::kFwdInp].push_back(inp->PreOp());
op->pre_ops_out_idx_[PyLayer::kFwdInp].push_back(inp->PreOpOutIdx());
} else {
......
paddle/fluid/imperative/tracer.h
浏览文件 @ cf0511f2
......@@ -15,6 +15,7 @@
#pragma once
#include <map>
#include <set>
#include <string>
#include <vector>
......@@ -43,10 +44,11 @@ class Tracer {
virtual ~Tracer() {}
void Trace(OpBase* op, const VarBasePtrMap& inputs,
const VarBasePtrMap& outputs, framework::BlockDesc* block,
const platform::Place expected_place,
const bool stop_gradient = false);
std::set<std::string> Trace(OpBase* op, const VarBasePtrMap& inputs,
const VarBasePtrMap& outputs,
framework::BlockDesc* block,
const platform::Place expected_place,
const bool stop_gradient = false);
std::vector<VarBase*> PyTrace(OpBase* op, const std::vector<VarBase*>& inputs,
bool stop_gradient = false);
......
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @ cf0511f2
......@@ -89,7 +89,7 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
CP_MEMBER(params_file_);
CP_MEMBER(model_from_memory_); // the memory model reuses prog_file_ and
// params_file_ fields.
// Gpu releated.
// Gpu related.
CP_MEMBER(use_gpu_);
CP_MEMBER(device_id_);
CP_MEMBER(memory_pool_init_size_mb_);
......@@ -97,13 +97,13 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
CP_MEMBER(enable_memory_optim_);
CP_MEMBER(static_memory_optim_);
CP_MEMBER(static_memory_optim_force_update_);
// TensorRT releated.
// TensorRT related.
CP_MEMBER(use_tensorrt_);
CP_MEMBER(tensorrt_workspace_size_);
CP_MEMBER(tensorrt_max_batchsize_);
CP_MEMBER(tensorrt_min_subgraph_size_);
CP_MEMBER(tensorrt_precision_mode_);
// MKLDNN releated.
// MKLDNN related.
CP_MEMBER(use_mkldnn_);
CP_MEMBER(mkldnn_enabled_op_types_);
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ cf0511f2
......@@ -392,7 +392,7 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig &config) {
VLOG(3) << "create AnalysisConfig";
if (config.use_gpu()) {
// 1. GPU memeroy
// 1. GPU memory
PADDLE_ENFORCE_GT(config.memory_pool_init_size_mb(), 0.f);
PADDLE_ENFORCE_GE(config.gpu_device_id(), 0, "Invalid device id %d",
config.gpu_device_id());
......@@ -726,7 +726,7 @@ bool AnalysisPredictor::need_collect_var_shapes_for_memory_optim() {
return need;
}
std::string AnalysisPredictor::GetSeriazlizedProgram() const {
std::string AnalysisPredictor::GetSerializedProgram() const {
return inference_program_->Proto()->SerializeAsString();
}
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ cf0511f2
......@@ -74,7 +74,7 @@ class AnalysisPredictor : public PaddlePredictor {
void SetMkldnnThreadID(int tid);
std::string GetSeriazlizedProgram() const override;
std::string GetSerializedProgram() const override;
protected:
// For memory optimization.
......
paddle/fluid/inference/api/analysis_predictor_tester.cc
浏览文件 @ cf0511f2
......@@ -214,8 +214,8 @@ TEST(AnalysisPredictor, memory_optim) {
{
// The first predictor help to cache the memory optimize strategy.
auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
LOG(INFO) << "serialized program: " << predictor->GetSeriazlizedProgram();
ASSERT_FALSE(predictor->GetSeriazlizedProgram().empty());
LOG(INFO) << "serialized program: " << predictor->GetSerializedProgram();
ASSERT_FALSE(predictor->GetSerializedProgram().empty());
// Run several times to check the parameters are not reused by mistake.
for (int i = 0; i < 5; i++) {
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ cf0511f2
......@@ -290,7 +290,7 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
NativeConfig, PaddleEngineKind::kNative>(const NativeConfig &config) {
VLOG(3) << "create NativePaddlePredictor";
if (config.use_gpu) {
// 1. GPU memeroy
// 1. GPU memory
PADDLE_ENFORCE_GE(
config.fraction_of_gpu_memory, 0.f,
"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
......
paddle/fluid/inference/api/paddle_analysis_config.h
浏览文件 @ cf0511f2
......@@ -212,12 +212,12 @@ struct AnalysisConfig {
std::string prog_file_;
std::string params_file_;
// GPU releated.
// GPU related.
bool use_gpu_{false};
int device_id_{0};
uint64_t memory_pool_init_size_mb_{100}; // initial size is 100MB.
// TensorRT releated.
// TensorRT related.
bool use_tensorrt_{false};
// For workspace_size, refer it from here:
// https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
......
paddle/fluid/inference/api/paddle_api.h
浏览文件 @ cf0511f2
......@@ -248,7 +248,7 @@ class PaddlePredictor {
/** \brief Get the serialized model program that executes in inference phase.
* Its data type is ProgramDesc, which is a protobuf message.
*/
virtual std::string GetSeriazlizedProgram() const {
virtual std::string GetSerializedProgram() const {
assert(false); // Force raise error.
return "NotImplemented";
}
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ cf0511f2
......@@ -60,10 +60,13 @@ set(RNN2_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/rnn2")
download_model_and_data(${RNN2_INSTALL_DIR} "rnn2_model.tar.gz" "rnn2_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_rnn2 ${RNN2_INSTALL_DIR} analyzer_rnn2_tester.cc)
# TODO(luotao, Superjom) Disable DAM test, temporarily fix
# https://github.com/PaddlePaddle/Paddle/issues/15032#issuecomment-455990914.
# After inference framework refactor, will reopen it.
# normal DAM
set(DAM_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/dam")
download_model_and_data(${DAM_INSTALL_DIR} "DAM_model.tar.gz" "DAM_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_dam ${DAM_INSTALL_DIR} analyzer_dam_tester.cc EXTRA_DEPS legacy_allocator SERIAL)
#inference_analysis_api_test(test_analyzer_dam ${DAM_INSTALL_DIR} analyzer_dam_tester.cc EXTRA_DEPS legacy_allocator SERIAL)
# small DAM
set(DAM_SMALL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/small_dam")
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ cf0511f2
......@@ -66,7 +66,7 @@ set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler tree2col)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel_helper concat_and_split cross_entropy softmax vol2col im2col sampler sample_prob tree2col)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions beam_search)
if (WITH_GPU)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv prelu)
......@@ -97,3 +97,4 @@ if (WITH_PYTHON)
endif()
set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
add_subdirectory(benchmark)
paddle/fluid/operators/beam_search_decode_op.cc
浏览文件 @ cf0511f2
......@@ -123,7 +123,7 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
auto& dev_ctx = *pool.Get(dev_place);
framework::RuntimeContext run_ctx(Inputs(), Outputs(), scope);
framework::ExecutionContext ctx(*this, scope, dev_ctx, run_ctx);
framework::ExecutionContext ctx(*this, scope, dev_ctx, run_ctx, nullptr);
const LoDTensorArray* ids = ctx.Input<LoDTensorArray>("Ids");
const LoDTensorArray* scores = ctx.Input<LoDTensorArray>("Scores");
......
paddle/fluid/operators/beam_search_decode_op.h
浏览文件 @ cf0511f2
......@@ -122,7 +122,7 @@ void BeamSearchDecoder<T>::ConvertSentenceVectorToLodTensor(
auto cpu_place = std::unique_ptr<paddle::platform::CPUPlace>(
new paddle::platform::CPUPlace());
paddle::platform::CPUDeviceContext cpu_ctx(*cpu_place.get());
paddle::platform::CPUDeviceContext cpu_ctx(*cpu_place);
framework::LoD lod;
lod.push_back(source_level_lod);
......
paddle/fluid/operators/benchmark/CMakeLists.txt 0 → 100644
浏览文件 @ cf0511f2
cc_test(op_tester SRCS op_tester.cc op_tester_config.cc
DEPS memory timer framework_proto proto_desc lod_tensor op_registry
device_context scope ${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS})
paddle/fluid/operators/benchmark/op_tester.cc 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/benchmark/op_tester.h"
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/platform/timer.h"
#include "paddle/fluid/pybind/pybind.h"
namespace paddle {
namespace operators {
namespace benchmark {
DEFINE_string(op_config_list, "", "Path of op config file.");
void OpTester::Init(const std::string &filename) {
Init(OpTesterConfig(filename));
}
void OpTester::Init(const OpTesterConfig &config) {
config_ = config;
auto &op_desc_info = framework::OpInfoMap::Instance();
// Initialize the OpDesc
if (op_desc_info.Has(config_.op_type)) {
type_ = config_.op_type;
op_desc_.SetType(config_.op_type);
CreateInputVarDesc();
CreateOutputVarDesc();
} else {
LOG(FATAL) << "Op \"" << config_.op_type << "\" is not registered.";
}
if (config_.device_id >= 0) {
place_ = paddle::platform::CUDAPlace(config_.device_id);
} else {
place_ = paddle::platform::CPUPlace();
}
framework::InitDevices(false);
scope_.reset(new paddle::framework::Scope());
op_ = framework::OpRegistry::CreateOp(op_desc_);
CreateVariables(scope_.get());
}
void OpTester::Run() {
if (config_.print_debug_string) {
LOG(INFO) << DebugString();
}
// Warm up
RunImpl();
platform::Timer timer;
if (config_.profile) {
if (platform::is_cpu_place(place_)) {
platform::EnableProfiler(platform::ProfilerState::kCPU);
} else {
#ifdef PADDLE_WITH_CUDA
platform::EnableProfiler(platform::ProfilerState::kAll);
platform::SetDeviceId(config_.device_id);
#else
PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
#endif
}
timer.Start();
for (int i = config_.repeat; i > 0; --i) {
RunImpl();
}
timer.Pause();
platform::DisableProfiler(platform::EventSortingKey::kDefault,
"op_tester_profiler");
} else {
timer.Start();
for (int i = config_.repeat; i > 0; --i) {
RunImpl();
}
timer.Pause();
}
config_.runtime = timer.ElapsedMS() / config_.repeat;
LOG(INFO) << "=== Run " << config_.repeat
<< " times, latency: " << config_.runtime << " ms ===";
}
void OpTester::RunImpl() {
op_->Run(*scope_, place_);
platform::DeviceContextPool::Instance().Get(place_)->Wait();
scope_->DropKids();
}
std::vector<std::string> OpTester::GetOpProtoInputNames() {
std::vector<std::string> input_names;
const framework::proto::OpProto &proto =
framework::OpInfoMap::Instance().Get(type_).Proto();
for (int i = 0; i != proto.inputs_size(); ++i) {
const auto &input = proto.inputs(i);
input_names.push_back(input.name());
}
return input_names;
}
std::vector<std::string> OpTester::GetOpProtoOutputNames() {
std::vector<std::string> output_names;
const framework::proto::OpProto &proto =
framework::OpInfoMap::Instance().Get(type_).Proto();
for (int i = 0; i != proto.outputs_size(); ++i) {
const auto &output = proto.outputs(i);
output_names.push_back(output.name());
}
return output_names;
}
void OpTester::CreateInputVarDesc() {
std::vector<std::string> input_names = GetOpProtoInputNames();
for (auto &name : input_names) {
const OpInputConfig *input = config_.GetInput(name);
if (input == nullptr) {
LOG(FATAL) << "The input " << name << " of op " << config_.op_type
<< " is not correctlly provided.";
}
std::string var_name = config_.op_type + "." + name;
framework::VarDesc *var = Var(var_name);
// Need to support more type
var->SetType(framework::proto::VarType::LOD_TENSOR);
var->SetPersistable(false);
var->SetDataType(framework::proto::VarType::FP32);
var->SetShape(input->dims);
op_desc_.SetInput(name, {var_name});
inputs_.push_back(var_name);
}
}
void OpTester::CreateOutputVarDesc() {
std::vector<std::string> output_names = GetOpProtoOutputNames();
for (auto &name : output_names) {
std::string var_name = config_.op_type + "." + name;
framework::VarDesc *var = Var(var_name);
// Need to support more type
var->SetType(framework::proto::VarType::LOD_TENSOR);
var->SetPersistable(false);
var->SetDataType(framework::proto::VarType::FP32);
op_desc_.SetOutput(name, {var_name});
outputs_.push_back(var_name);
}
}
framework::VarDesc *OpTester::Var(const std::string &name) {
auto it = vars_.find(name);
if (it != vars_.end()) {
return it->second.get();
}
auto *var = new framework::VarDesc(name);
vars_[name].reset(var);
return var;
}
template <typename T>
void OpTester::SetupTensor(framework::LoDTensor *tensor,
const std::vector<int64_t> &shape, T lower,
T upper) {
static unsigned int seed = 100;
std::mt19937 rng(seed++);
std::uniform_real_distribution<double> uniform_dist(0, 1);
T *ptr = tensor->mutable_data<T>(framework::make_ddim(shape), place_);
if (platform::is_cpu_place(place_)) {
for (int i = 0; i < tensor->numel(); ++i) {
ptr[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
} else {
framework::LoDTensor cpu_tensor;
T *cpu_ptr = cpu_tensor.mutable_data<T>(framework::make_ddim(shape),
platform::CPUPlace());
for (int i = 0; i < cpu_tensor.numel(); ++i) {
cpu_ptr[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
TensorCopySync(cpu_tensor, place_, tensor);
}
}
void OpTester::CreateVariables(framework::Scope *scope) {
for (auto &item : vars_) {
auto &var = item.second;
if (var->Name() == framework::kEmptyVarName) {
continue;
}
auto *ptr = scope->Var(var->Name());
framework::InitializeVariable(ptr, var->GetType());
if (var->Persistable()) {
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else {
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
// Allocate memory for input tensor
for (auto &name : inputs_) {
VLOG(3) << "Allocate memory for tensor " << name;
auto &var_desc = vars_[name];
std::vector<int64_t> shape = var_desc->GetShape();
auto *var = scope->Var(name);
auto *tensor = var->GetMutable<framework::LoDTensor>();
SetupTensor<float>(tensor, shape, static_cast<float>(0.0),
static_cast<float>(1.0));
}
}
static std::string GenSpaces(int count) {
std::stringstream ss;
for (int i = 0; i < count; ++i) {
ss << " ";
}
return ss.str();
}
std::string OpTester::DebugString() {
std::stringstream ss;
int count = 0;
for (auto &item : vars_) {
auto &var = item.second;
ss << GenSpaces(count++) << "vars {\n";
ss << GenSpaces(count) << "name: \"" << var->Name() << "\"\n";
ss << GenSpaces(count++) << "type: {\n";
ss << GenSpaces(count) << "type: LOD_TENSOR\n";
ss << GenSpaces(count++) << "lod_tensor {\n";
ss << GenSpaces(count++) << "tensor {\n";
ss << GenSpaces(count) << "data_type: FP32\n";
std::vector<int64_t> shape = var->GetShape();
for (auto d : shape) {
ss << GenSpaces(count) << "dims: " << d << "\n";
}
ss << GenSpaces(--count) << "}\n";
ss << GenSpaces(--count) << "}\n";
ss << GenSpaces(--count) << "}\n";
ss << GenSpaces(count) << "persistable: " << var->Persistable() << "\n";
ss << GenSpaces(--count) << "}\n";
}
ss << GenSpaces(count++) << "ops {\n";
for (auto &name : op_desc_.InputNames()) {
ss << GenSpaces(count++) << "inputs {\n";
ss << GenSpaces(count) << "parameters: \"" << name << "\"\n";
ss << GenSpaces(count) << "arguments: \"" << op_desc_.Input(name)[0]
<< "\"\n";
ss << GenSpaces(--count) << "}\n";
}
for (auto &name : op_desc_.OutputNames()) {
ss << GenSpaces(count++) << "outputs {\n";
ss << GenSpaces(count) << "parameters: \"" << name << "\"\n";
ss << GenSpaces(count) << "arguments: \"" << op_desc_.Output(name)[0]
<< "\"\n";
ss << GenSpaces(--count) << "}\n";
}
ss << GenSpaces(count) << "type: " << op_desc_.Type() << "\n";
ss << GenSpaces(--count) << "}\n";
return ss.str();
}
TEST(op_tester, base) {
OpTester tester;
if (!FLAGS_op_config_list.empty()) {
tester.Init(FLAGS_op_config_list);
} else {
OpTesterConfig config;
config.op_type = "elementwise_add";
config.inputs.resize(2);
config.inputs[0].name = "X";
config.inputs[0].dims = {64, 64};
config.inputs[1].name = "Y";
config.inputs[1].dims = {64, 1};
tester.Init(config);
}
tester.Run();
}
} // namespace benchmark
} // namespace operators
} // namespace paddle
paddle/fluid/operators/benchmark/op_tester.h 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/benchmark/op_tester_config.h"
namespace paddle {
namespace operators {
namespace benchmark {
class OpTester {
public:
OpTester() {}
void Init(const std::string &filename);
void Init(const OpTesterConfig &config);
void Run();
std::string DebugString();
private:
std::vector<std::string> GetOpProtoInputNames();
std::vector<std::string> GetOpProtoOutputNames();
void CreateInputVarDesc();
void CreateOutputVarDesc();
framework::VarDesc *Var(const std::string &name);
void CreateVariables(framework::Scope *scope);
template <typename T>
void SetupTensor(framework::LoDTensor *input,
const std::vector<int64_t> &shape, T lower, T upper);
void RunImpl();
private:
OpTesterConfig config_;
std::string type_;
framework::OpDesc op_desc_;
std::unordered_map<std::string, std::unique_ptr<framework::VarDesc>> vars_;
std::vector<std::string> inputs_;
std::vector<std::string> outputs_;
std::unique_ptr<framework::OperatorBase> op_;
platform::Place place_;
std::unique_ptr<framework::Scope> scope_;
};
} // namespace benchmark
} // namespace operators
} // namespace paddle
paddle/fluid/operators/benchmark/op_tester_config.cc 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/benchmark/op_tester_config.h"
#include <fstream>
#include "glog/logging.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace operators {
namespace benchmark {
static const char kStartSeparator[] = "{";
static const char kEndSeparator[] = "}";
static const char kSepBetweenItems[] = ";";
static bool StartWith(const std::string& str, const std::string& substr) {
return str.find(substr) == 0;
}
static bool EndWith(const std::string& str, const std::string& substr) {
return str.rfind(substr) == (str.length() - substr.length());
}
static void EraseEndSep(std::string* str) {
std::string substr = kSepBetweenItems;
if (EndWith(*str, substr)) {
str->erase(str->length() - substr.length(), str->length());
}
}
static std::vector<int64_t> ParseDims(std::string dims_str) {
std::vector<int64_t> dims;
std::string token;
std::istringstream token_stream(dims_str);
while (std::getline(token_stream, token, 'x')) {
dims.push_back(std::stoi(token));
}
return dims;
}
OpInputConfig::OpInputConfig(std::istream& is) {
std::string sep;
is >> sep;
if (sep == kStartSeparator) {
while (sep != kEndSeparator) {
is >> sep;
if (sep == "name" || sep == "name:") {
is >> name;
EraseEndSep(&name);
} else if (sep == "dims" || sep == "dims:") {
std::string dims_str;
is >> dims_str;
dims = ParseDims(dims_str);
}
}
}
}
OpTesterConfig::OpTesterConfig(const std::string& filename) {
std::ifstream fin(filename, std::ios::in | std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s",
filename.c_str());
Init(fin);
}
void OpTesterConfig::Init(std::istream& is) {
std::string sep;
is >> sep;
if (sep == kStartSeparator) {
while (sep != kEndSeparator) {
is >> sep;
if (sep == "op_type" || sep == "op_type:") {
is >> op_type;
} else if (sep == "device_id" || sep == "device_id:") {
is >> device_id;
} else if (sep == "repeat" || sep == "repeat:") {
is >> repeat;
} else if (sep == "profile" || sep == "profile:") {
is >> profile;
} else if (sep == "print_debug_string" || sep == "print_debug_string:") {
is >> print_debug_string;
} else if (sep == "input" || sep == "input:") {
OpInputConfig input_config(is);
inputs.push_back(input_config);
}
}
}
}
const OpInputConfig* OpTesterConfig::GetInput(const std::string& name) {
for (size_t i = 0; i < inputs.size(); ++i) {
if (inputs[i].name == name) {
return &inputs[i];
}
}
return nullptr;
}
} // namespace benchmark
} // namespace operators
} // namespace paddle
paddle/fluid/operators/benchmark/op_tester_config.h 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <istream>
#include <string>
#include <vector>
namespace paddle {
namespace operators {
namespace benchmark {
struct OpInputConfig {
OpInputConfig() {}
explicit OpInputConfig(std::istream& is);
std::string name;
std::vector<int64_t> dims;
};
struct OpTesterConfig {
OpTesterConfig() {}
explicit OpTesterConfig(const std::string& filename);
void Init(std::istream& is);
const OpInputConfig* GetInput(const std::string& name);
std::string op_type;
std::vector<OpInputConfig> inputs;
int device_id{-1}; // CPU: -1
int repeat{1};
int profile{0};
int print_debug_string{0};
double runtime{0.0};
};
} // namespace benchmark
} // namespace operators
} // namespace paddle
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ cf0511f2
......@@ -42,6 +42,7 @@ using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
using DataLayout = platform::DataLayout;
template <typename T>
using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
using framework::AlgorithmsCache;
template <typename T>
class CUDNNConvOpKernel : public framework::OpKernel<T> {
......@@ -169,18 +170,8 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
workspace_size_limit, &algo));
VLOG(3) << "cuDNN forward algo " << algo;
} else if (exhaustive_search && (!half_float)) {
AlgorithmsCache<cudnnConvolutionFwdAlgo_t>* algo_cache = nullptr;
if (ctx.scope().FindVar(kCUDNNFwdAlgoCache)) {
algo_cache =
ctx.scope()
.FindVar(kCUDNNFwdAlgoCache)
->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
} else {
algo_cache =
const_cast<framework::Scope&>(ctx.scope())
.Var(kCUDNNFwdAlgoCache)
->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
}
AlgorithmsCache<cudnnConvolutionFwdAlgo_t>& algo_cache =
ctx.GetKernelConfig<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>(0);
cudnn_workspace =
ctx.AllocateTmpTensor<int8_t, platform::CUDADeviceContext>(
framework::make_ddim(
......@@ -188,7 +179,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
dev_ctx);
cudnn_workspace_ptr = static_cast<void*>(cudnn_workspace.data<int8_t>());
algo = algo_cache->GetAlgorithm(
algo = algo_cache.GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionFwdAlgoPerf_t, kNUM_CUDNN_FWD_ALGS>
......@@ -382,22 +373,11 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
if (exhaustive_search) {
AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>* data_algo_cache;
if (ctx.scope().FindVar(kCUDNNBwdDataAlgoCache)) {
data_algo_cache =
ctx.scope()
.FindVar(kCUDNNBwdDataAlgoCache)
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>();
} else {
data_algo_cache =
const_cast<framework::Scope&>(ctx.scope())
.Var(kCUDNNBwdDataAlgoCache)
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>();
}
data_algo = data_algo_cache->GetAlgorithm(
AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>& data_algo_cache =
ctx.GetKernelConfig<AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>(
0);
data_algo = data_algo_cache.GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionBwdDataAlgoPerf_t,
......@@ -448,22 +428,11 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
if (filter_grad) {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
if (exhaustive_search) {
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>* f_algo_cache;
if (ctx.scope().FindVar(kCUDNNBwdFilterAlgoCache)) {
f_algo_cache =
ctx.scope()
.FindVar(kCUDNNBwdFilterAlgoCache)
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>();
} else {
f_algo_cache =
const_cast<framework::Scope&>(ctx.scope())
.Var(kCUDNNBwdFilterAlgoCache)
->GetMutable<
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>();
}
filter_algo = f_algo_cache->GetAlgorithm(
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>& f_algo_cache =
ctx.GetKernelConfig<
AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>(1);
filter_algo = f_algo_cache.GetAlgorithm(
x_dims, f_dims, strides, paddings, dilations, 0, [&]() {
int returned_algo_count;
std::array<cudnnConvolutionBwdFilterAlgoPerf_t,
......
paddle/fluid/operators/conv_cudnn_op_cache.h
浏览文件 @ cf0511f2
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <functional>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/cudnn_helper.h"
DECLARE_uint64(conv_workspace_size_limit);
......@@ -46,100 +47,5 @@ static constexpr size_t kNUM_CUDNN_BWD_FILTER_ALGS = 4;
static constexpr size_t kNUM_CUDNN_BWD_DATA_ALGS = 5;
#endif
template <typename TAlgorithm>
class AlgorithmsCache {
public:
AlgorithmsCache() : search_times_(0) { hash_.clear(); }
// Caches the best algorithm for a given
// combination of tensor dimensions & compute data type.
TAlgorithm GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations,
int algorithmFlags, // can set for different data type
std::function<TAlgorithm()> gen_func);
TAlgorithm GetAlgorithm(int64_t area, int search_times, int algorithmFlags,
std::function<TAlgorithm()> gen_func);
private:
std::unordered_map<int64_t, TAlgorithm> hash_;
std::mutex mutex_;
int search_times_;
};
template <typename TAlgorithm>
TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm(
const std::vector<int64_t>& dims1, const std::vector<int64_t>& dims2,
const std::vector<int>& strides, const std::vector<int>& paddings,
const std::vector<int>& dilations, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
std::lock_guard<std::mutex> lock(mutex_);
int64_t seed = 0;
// Hash all of the inputs, use to try and look up a previously
// discovered algorithm, or fall back to generating a new one.
std::hash<int64_t> hashFn;
// do hash like boost
// https://stackoverflow.com/questions/2590677/how-do-i-combine-hash-values-in-c0x
for (const auto num : dims1) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
for (const auto num : dims2) {
seed ^= hashFn(num) + 0x9e3779b9 + (seed << 6) + (seed >> 2) + 1;
}
for (const auto num : strides) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 2;
}
for (const auto num : paddings) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 3;
}
for (const auto num : dilations) {
seed ^= hashFn(static_cast<int64_t>(num)) + 0x9e3779b9 + (seed << 6) +
(seed >> 2) + 4;
}
seed ^= hashFn(static_cast<int64_t>(algorithmFlags)) + 0x9e3779b9 +
(seed << 6) + (seed >> 2) + 5;
if (seed == 0) return gen_func();
if (hash_.find(seed) == hash_.end()) {
TAlgorithm value = gen_func();
hash_[seed] = value;
}
return hash_[seed];
}
template <typename TAlgorithm>
TAlgorithm AlgorithmsCache<TAlgorithm>::GetAlgorithm(
int64_t area, int search_times, int algorithmFlags,
std::function<TAlgorithm()> gen_func) {
if (hash_.find(area) != hash_.end()) {
return hash_[area];
}
if (search_times_ < search_times) {
auto algo = gen_func();
hash_[area] = algo;
++search_times_;
return algo;
}
TAlgorithm algo;
int64_t min = static_cast<uint64_t>(INT_MAX);
for (const auto& m : hash_) {
if (m.first < min) {
min = m.first;
algo = m.second;
}
}
return algo;
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/conv_fusion_op.cu.cc
浏览文件 @ cf0511f2
......@@ -30,6 +30,8 @@ using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
using ScopedActivationDescriptor = platform::ScopedActivationDescriptor;
using DataLayout = platform::DataLayout;
using framework::AlgorithmsCache;
template <typename T>
using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
......@@ -139,38 +141,21 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
}
return fwd_perf_stat[0].algo;
};
AlgorithmsCache<cudnnConvolutionFwdAlgo_t>* algo_cache = nullptr;
AlgorithmsCache<cudnnConvolutionFwdAlgo_t>& algo_cache =
ctx.GetKernelConfig<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>(0);
int search_times = ctx.Attr<int>("search_times");
search_times = std::max(
static_cast<int>(FLAGS_cudnn_exhaustive_search_times), search_times);
// TODO(dangqingqing): Unify this if-else.
if (search_times > 0) {
// The searched algo will be cached by `search_times` times for
// different input dimension. For other dimensions, select the algo
// of closest area.
auto var_name = ctx.Inputs("AlgoCache")[0];
algo_cache =
ctx.scope()
.FindVar(var_name)
->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
algo = algo_cache->GetAlgorithm(x_dims[2] * x_dims[3], search_times, 0,
search_func);
algo = algo_cache.GetAlgorithm(x_dims[2] * x_dims[3], search_times, 0,
search_func);
} else {
// Cache searched algo in Var(kCUDNNFwdAlgoCache).
// all conv ops use the same kCUDNNFwdAlgoCache variable.
if (ctx.scope().FindVar(kCUDNNFwdAlgoCache)) {
algo_cache =
ctx.scope()
.FindVar(kCUDNNFwdAlgoCache)
->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
} else {
// TODO(qingqing) remove const_cast
algo_cache =
const_cast<framework::Scope*>(ctx.scope().parent())
->Var(kCUDNNFwdAlgoCache)
->GetMutable<AlgorithmsCache<cudnnConvolutionFwdAlgo_t>>();
}
algo = algo_cache->GetAlgorithm(x_dims, f_dims, strides, paddings,
dilations, 0, search_func);
algo = algo_cache.GetAlgorithm(x_dims, f_dims, strides, paddings,
dilations, 0, search_func);
}
VLOG(3) << "choose algo " << algo;
}
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ cf0511f2
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <vector>
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/operators/conv_cudnn_op_cache.h"
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
#ifdef PADDLE_WITH_MKLDNN
......@@ -109,8 +110,20 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
"float16 can only be used when CUDNN is used");
}
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
library, customized_type_value);
auto type = framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
library, customized_type_value);
#ifdef PADDLE_WITH_CUDA
std::vector<framework::KernelConfig>& configs = kernel_configs_map_[type];
// TODO(dangqingqing): Currently conv_fusion_op use cudnn but sets use_cudnn
// to false. It should be fixed and then here should only create if library
// is kCUDNN.
if (configs.empty()) {
std::shared_ptr<framework::AlgorithmsCache<cudnnConvolutionFwdAlgo_t>> p(
new framework::AlgorithmsCache<cudnnConvolutionFwdAlgo_t>());
configs.push_back(p);
}
#endif
return type;
}
void Conv2DOpMaker::Make() {
......@@ -410,9 +423,25 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
}
#endif
return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
ctx.GetPlace(), layout_, library_,
customized_type_value);
auto type = framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
ctx.GetPlace(), layout_, library_,
customized_type_value);
#ifdef PADDLE_WITH_CUDA
if (library_ == framework::LibraryType::kCUDNN) {
std::vector<framework::KernelConfig>& configs = kernel_configs_map_[type];
if (configs.empty()) {
std::shared_ptr<framework::AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>>
p(new framework::AlgorithmsCache<cudnnConvolutionBwdDataAlgo_t>());
configs.push_back(p);
std::shared_ptr<
framework::AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>>
p2(new framework::AlgorithmsCache<cudnnConvolutionBwdFilterAlgo_t>());
configs.push_back(p2);
}
}
#endif
return type;
}
class Conv2dGradMaker : public framework::SingleGradOpDescMaker {
......
paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @ cf0511f2
......@@ -144,34 +144,40 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"The ignore threshold to ignore confidence loss.")
.SetDefault(0.7);
AddComment(R"DOC(
This operator generate yolov3 loss by given predict result and ground
This operator generates yolov3 loss based on given predict result and ground
truth boxes.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, specify the grid size, each grid point predict given
number boxes, this given number is specified by anchors, it should be
half anchors length, which following will be represented as S. In the
second dimention(the channel dimention), C should be S * (class_num + 5),
class_num is the box categoriy number of source dataset(such as coco),
so in the second dimention, stores 4 box location coordinates x, y, w, h
and confidence score of the box and class one-hot key of each anchor box.
should be the same, H and W specify the grid size, each grid point predict
given number boxes, this given number, which following will be represented as S,
is specified by the number of anchors, In the second dimension(the channel
dimension), C should be equal to S * (class_num + 5), class_num is the object
category number of source dataset(such as 80 in coco dataset), so in the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor box.
While the 4 location coordinates if $$tx, ty, tw, th$$, the box predictions
correspnd to:
Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box predictions
should be as follows:
$$
b_x = \sigma(t_x) + c_x
b_y = \sigma(t_y) + c_y
b_x = \\sigma(t_x) + c_x
$$
$$
b_y = \\sigma(t_y) + c_y
$$
$$
b_w = p_w e^{t_w}
$$
$$
b_h = p_h e^{t_h}
$$
While $$c_x, c_y$$ is the left top corner of current grid and $$p_w, p_h$$
is specified by anchors.
In the equation above, :math:`c_x, c_y` is the left top corner of current grid
and :math:`p_w, p_h` is specified by anchors.
As for confidence score, it is the logistic regression value of IoU between
anchor boxes and ground truth boxes, the score of the anchor box which has
the max IoU should be 1, and if the anchor box has IoU bigger then ignore
the max IoU should be 1, and if the anchor box has IoU bigger than ignore
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
......@@ -186,13 +192,13 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
In order to trade off box coordinate losses between big boxes and small
boxes, box coordinate losses will be mutiplied by scale weight, which is
calculated as follow.
calculated as follows.
$$
weight_{box} = 2.0 - t_w * t_h
$$
Final loss will be represented as follow.
Final loss will be represented as follows.
$$
loss = (loss_{xy} + loss_{wh}) * weight_{box}
......
paddle/fluid/operators/jit/test.cc
浏览文件 @ cf0511f2
/* 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. */
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 <random>
#include <string>
......@@ -259,7 +259,7 @@ struct TestFuncWithRefer<jit::SeqPoolTuples<T>, std::vector<T>, std::vector<T>,
const std::vector<T>& x, const std::vector<T>& yref,
const typename jit::SeqPoolTuples<T>::attr_type& attr) {
EXPECT_TRUE(tgt != nullptr);
EXPECT_EQ(x.size() % yref.size(), 0);
EXPECT_EQ(x.size() % yref.size(), static_cast<size_t>(0));
int w = yref.size();
std::vector<T> y(w);
const T* x_data = x.data();
......
paddle/fluid/operators/lstm_op.h
浏览文件 @ cf0511f2
......@@ -151,9 +151,10 @@ class LSTMKernel : public framework::OpKernel<T> {
lstm_value.output_value = out_t.data<T>();
lstm_value.state_value = cell_t.data<T>();
lstm_value.state_active_value = cell_pre_act_t.data<T>();
T cell_clip = 0.0;
math::LstmUnitFunctor<DeviceContext, T>::compute(
device_ctx, lstm_value, frame_size, cur_batch_size, gate_act,
cell_act, cand_act);
device_ctx, lstm_value, frame_size, cur_batch_size, cell_clip,
gate_act, cell_act, cand_act);
lstm_value.prev_state_value = lstm_value.state_value;
}
......@@ -316,9 +317,10 @@ class LSTMGradKernel : public framework::OpKernel<T> {
lstm_value.output_value = nullptr;
lstm_grad.state_active_grad = nullptr;
int cur_batch_size = bend - bstart;
T cell_clip = 0.0;
math::LstmUnitGradFunctor<DeviceContext, T>::compute(
device_ctx, lstm_value, lstm_grad, frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
cell_clip, gate_act, cell_act, cand_act);
if (n > 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
......
paddle/fluid/operators/lstmp_op.cc
浏览文件 @ cf0511f2
......@@ -73,12 +73,6 @@ class LSTMPOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasInput("C0"),
"Input(C0) of LSTMP operator should not be null after "
"Input(H0) provided.");
auto h_dims = ctx->GetInputDim("H0");
auto c_dims = ctx->GetInputDim("C0");
PADDLE_ENFORCE(h_dims == c_dims,
"The dimension of Input(H0) and Input(C0) "
"should be the same.");
ctx->SetOutputDim("OrderedP0", {h_dims[0], proj_dims[1]});
}
auto b_dims = ctx->GetInputDim("Bias");
......@@ -180,11 +174,6 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
"This LoDTensor is obtained in the forward and used in the "
"backward.")
.AsIntermediate();
AddOutput("OrderedP0",
"(Tensor) the projection of the initial hidden state "
"H0. This is a tensor with shape (N x P), where N is the "
"batch size and P is the hidden size.")
.AsIntermediate();
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
......@@ -193,6 +182,16 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
"(bool, defalut: False) "
"whether to compute reversed LSTMP.")
.SetDefault(false);
AddAttr<float>("cell_clip",
"(float, defalut: 0.0) "
"Clip for Tensor for cell state tensor when clip value is "
"greater than 0.0")
.SetDefault(0.0);
AddAttr<float>("proj_clip",
"(float, defalut: 0.0) "
"Clip for Tensor for projection tensor when clip value is "
"greater than 0.0")
.SetDefault(0.0);
AddAttr<std::string>(
"gate_activation",
"(string, default: sigmoid)"
......
paddle/fluid/operators/lstmp_op.h
浏览文件 @ cf0511f2
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/activation_op.h"
......@@ -21,17 +22,50 @@ limitations under the License. */
#include "paddle/fluid/operators/math/detail/activation_functions.h"
#include "paddle/fluid/operators/math/lstm_compute.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/transform.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
using platform::Transform;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
class _ClipFunctor {
public:
explicit _ClipFunctor(const T min, const T max) : min_(min), max_(max) {}
HOSTDEVICE T operator()(const T& x) const {
if (x < min_)
return min_;
else if (x > max_)
return max_;
else
return x;
}
private:
T min_;
T max_;
};
template <typename T>
class _ClipGradFunctor {
public:
explicit _ClipGradFunctor(const T min, const T max) : min_(min), max_(max) {}
HOSTDEVICE T operator()(const T& x, const T& y) const {
return (y > min_ && y < max_) ? x : 0;
}
private:
T min_;
T max_;
};
template <typename DeviceContext, typename T>
inline void ReorderInitState(const DeviceContext& ctx,
const framework::Tensor& src,
......@@ -67,9 +101,11 @@ class LSTMPKernel : public framework::OpKernel<T> {
auto* bias = ctx.Input<Tensor>("Bias");
auto* hidden_t0 = ctx.Input<Tensor>("H0");
auto* ordered_proj0 = ctx.Output<Tensor>("OrderedP0");
auto* cell_t0 = ctx.Input<Tensor>("C0");
auto proj_clip = static_cast<T>(ctx.Attr<float>("proj_clip"));
auto cell_clip = static_cast<T>(ctx.Attr<float>("cell_clip"));
auto* batch_gate = ctx.Output<LoDTensor>("BatchGate");
batch_gate->mutable_data<T>(ctx.GetPlace());
auto* proj_out = ctx.Output<LoDTensor>("Projection");
......@@ -110,6 +146,7 @@ class LSTMPKernel : public framework::OpKernel<T> {
}
lstmp_value.prev_state_value = nullptr;
Tensor ordered_c0;
Tensor ordered_h0;
framework::Vector<size_t> order(batch_gate->lod()[2]);
......@@ -169,18 +206,9 @@ class LSTMPKernel : public framework::OpKernel<T> {
// Since the batch computing for LSTMP reorders the input sequence
// according to their length. The initialized hidden state also needs
// to reorder.
Tensor ordered_h0;
ordered_proj0->mutable_data<T>(ctx.GetPlace());
ReorderInitState<DeviceContext, T>(device_ctx, *hidden_t0, order,
&ordered_h0, true);
blas.MatMul(ordered_h0, false, *proj_weight, false, static_cast<T>(1.0),
ordered_proj0, static_cast<T>(0.0));
if (proj_act != math::detail::ActivationType::kIdentity) {
auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
ActCompute(cell_act, place, proj0_dev, proj0_dev);
}
blas.MatMul(*ordered_proj0, false, *weight, false, static_cast<T>(1.0),
blas.MatMul(ordered_h0, false, *weight, false, static_cast<T>(1.0),
&gate_t, static_cast<T>(1.0));
}
......@@ -189,8 +217,8 @@ class LSTMPKernel : public framework::OpKernel<T> {
lstmp_value.state_value = cell_t.data<T>();
lstmp_value.state_active_value = cell_pre_act_t.data<T>();
math::LstmUnitFunctor<DeviceContext, T>::compute(
device_ctx, lstmp_value, frame_size, cur_batch_size, gate_act,
cell_act, cand_act);
device_ctx, lstmp_value, frame_size, cur_batch_size, cell_clip,
gate_act, cell_act, cand_act);
lstmp_value.prev_state_value = lstmp_value.state_value;
blas.MatMul(hidden_t, false, *proj_weight, false, static_cast<T>(1.0),
&proj_t, static_cast<T>(0.0));
......@@ -198,6 +226,14 @@ class LSTMPKernel : public framework::OpKernel<T> {
auto proj_t_dev = EigenMatrix<T>::From(proj_t);
ActCompute(cell_act, place, proj_t_dev, proj_t_dev);
}
if (proj_clip && proj_clip > 0.0) {
T* x_data = proj_t.data<T>();
int64_t numel = proj_t.numel();
Transform<DeviceContext> trans;
trans(ctx.template device_context<DeviceContext>(), x_data,
x_data + numel, x_data,
_ClipFunctor<T>(-1.0 * proj_clip, proj_clip));
}
}
math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
......@@ -239,6 +275,9 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
auto* proj_out = ctx.Input<LoDTensor>("Projection");
auto* cell_out = ctx.Input<LoDTensor>("Cell");
auto proj_clip = static_cast<T>(ctx.Attr<float>("proj_clip"));
auto cell_clip = static_cast<T>(ctx.Attr<float>("cell_clip"));
auto* batch_gate = ctx.Input<LoDTensor>("BatchGate");
auto* batch_cell_pre_act = ctx.Input<LoDTensor>("BatchCellPreAct");
auto* batch_hidden = ctx.Input<LoDTensor>("BatchHidden");
......@@ -253,7 +292,6 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
auto* bias_g = ctx.Output<Tensor>(framework::GradVarName("Bias"));
auto* h0 = ctx.Input<Tensor>("H0");
auto* ordered_proj0 = ctx.Input<Tensor>("OrderedP0");
auto* c0 = ctx.Input<Tensor>("C0");
auto* h0_g = ctx.Output<Tensor>(framework::GradVarName("H0"));
......@@ -363,6 +401,17 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
Tensor cur_proj = batch_proj.Slice(bstart, bend);
Tensor proj_g = batch_proj_g.Slice(bstart, bend);
if (proj_clip && proj_clip > 0.0) {
T* dx_data = proj_g.data<T>();
T* x_data = cur_proj.data<T>();
int64_t numel = proj_g.numel();
Transform<DeviceContext> trans;
trans(ctx.template device_context<DeviceContext>(), dx_data,
dx_data + numel, x_data, dx_data,
_ClipGradFunctor<T>(-1.0 * proj_clip, proj_clip));
}
if (proj_act != math::detail::ActivationType::kIdentity) {
auto cur_proj_dev = EigenMatrix<T>::From(cur_proj);
auto proj_g_dev = EigenMatrix<T>::From(proj_g);
......@@ -412,7 +461,7 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
math::LstmUnitGradFunctor<DeviceContext, T>::compute(
device_ctx, lstmp_value, lstmp_grad, frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
cell_clip, gate_act, cell_act, cand_act);
if (n > 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
......@@ -431,31 +480,14 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
ReorderInitState<DeviceContext, T>(device_ctx, *h0, order,
&ordered_h0, true);
if (weight_g) {
blas.MatMul(*ordered_proj0, true, gate_g, false,
static_cast<T>(1.0), weight_g, static_cast<T>(1.0));
blas.MatMul(ordered_h0, true, gate_g, false, static_cast<T>(1.0),
weight_g, static_cast<T>(1.0));
}
}
if (h0 && (h0_g || proj_weight_g)) {
ordered_h0_g.mutable_data<T>(h0_g->dims(), ctx.GetPlace());
Tensor proj0_g;
proj0_g.Resize({in_dims[0], proj_weight->dims()[1]});
proj0_g.mutable_data<T>(ctx.GetPlace());
blas.MatMul(gate_g, false, *weight, true, static_cast<T>(1.0),
&proj0_g, static_cast<T>(0.0));
if (proj_act != math::detail::ActivationType::kIdentity) {
auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
auto proj0_g_dev = EigenMatrix<T>::From(proj0_g);
ActGradCompute(cell_act, place, proj0_dev, proj0_dev, proj0_g_dev,
proj0_g_dev);
}
if (h0_g) {
blas.MatMul(proj0_g, false, *proj_weight, true, static_cast<T>(1.0),
&ordered_h0_g, static_cast<T>(0.0));
}
if (proj_weight_g) {
blas.MatMul(ordered_h0, true, proj0_g, false, static_cast<T>(1.0),
proj_weight_g, static_cast<T>(1.0));
}
&ordered_h0_g, static_cast<T>(0.0));
}
}
}
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ cf0511f2
......@@ -39,6 +39,7 @@ math_library(cross_entropy)
math_library(cos_sim_functor)
math_library(depthwise_conv DEPS cub)
math_library(im2col)
math_library(sample_prob)
math_library(sampler)
math_library(gru_compute DEPS activation_functions math_function)
......
paddle/fluid/operators/math/detail/lstm_cpu_kernel.h
浏览文件 @ cf0511f2
......@@ -32,7 +32,8 @@ namespace detail {
template <class T, class Op>
void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
int frame_size, ActivationType active_node,
int frame_size, T cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
T r_value_in;
......@@ -67,7 +68,7 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
value_in[i] = r_value_in;
value_ig[i] = r_value_ig;
......@@ -82,7 +83,7 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frame_size,
ActivationType active_node,
T cell_clip, ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
T r_value_in;
......@@ -135,7 +136,7 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
&r_grad_ig, &r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad,
&r_state, &r_state_grad, &r_state_atv, &r_output_grad, &r_checkI,
&r_checkF, &r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
grad_in[i] = r_grad_in;
grad_ig[i] = r_grad_ig;
......@@ -154,7 +155,8 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
int frame_size, ActivationType active_node,
int frame_size, T cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
#ifdef __AVX__
......@@ -194,7 +196,7 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
value_in[i] = r_value_in;
value_ig[i] = r_value_ig;
......@@ -210,7 +212,7 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frame_size,
ActivationType active_node,
T cell_clip, ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
#ifdef __AVX__
......@@ -268,7 +270,7 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
&r_grad_ig, &r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad,
&r_state, &r_state_grad, &r_state_atv, &r_output_grad, &r_checkI,
&r_checkF, &r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
grad_in[i] = r_grad_in;
grad_ig[i] = r_grad_ig;
......@@ -292,27 +294,27 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void cpu_lstm_forward(Op op, LstmMetaValue<T> value, int frame_size,
ActivationType active_node, ActivationType active_gate,
ActivationType active_state) {
T cell_clip, ActivationType active_node,
ActivationType active_gate, ActivationType active_state) {
if (Op::avx && !(frame_size & (8 - 1)) && (std::is_same<T, float>::value)) {
avx_lstm_forward_one_sequence<T>(op, value, frame_size, active_node,
active_gate, active_state);
avx_lstm_forward_one_sequence<T>(op, value, frame_size, cell_clip,
active_node, active_gate, active_state);
} else {
naive_lstm_forward_one_sequence<T>(op, value, frame_size, active_node,
active_gate, active_state);
naive_lstm_forward_one_sequence<T>(op, value, frame_size, cell_clip,
active_node, active_gate, active_state);
}
}
template <class T, class Op>
void cpu_lstm_backward(Op op, LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, ActivationType active_node,
int frame_size, T cell_clip, ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
if (Op::avx && !(frame_size & (8 - 1)) && (std::is_same<T, float>::value)) {
avx_lstm_backward_one_sequence<T>(op, value, grad, frame_size, active_node,
active_gate, active_state);
avx_lstm_backward_one_sequence<T>(op, value, grad, frame_size, cell_clip,
active_node, active_gate, active_state);
} else {
naive_lstm_backward_one_sequence<T>(op, value, grad, frame_size,
naive_lstm_backward_one_sequence<T>(op, value, grad, frame_size, cell_clip,
active_node, active_gate, active_state);
}
}
......
paddle/fluid/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @ cf0511f2
......@@ -31,7 +31,8 @@ namespace detail {
*/
template <class T, class Op, bool is_batch>
__global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frame_size,
int batch_size, ActivationType active_node,
int batch_size, T cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -72,7 +73,7 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frame_size,
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_prev_state,
&r_state, &r_state_atv, &r_out, &r_checkI, &r_checkF, &r_checkO,
active_node, active_gate, active_state);
&cell_clip, active_node, active_gate, active_state);
value.gate_value[frame_idx] = r_value_in;
value.gate_value[frame_idx + frame_size] = r_value_ig;
......@@ -91,7 +92,8 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frame_size,
template <class T, class Op, bool is_batch>
__global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frame_size,
int batch_size, ActivationType active_node,
int batch_size, T cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -148,8 +150,8 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
op(&r_value_in, &r_value_ig, &r_value_fg, &r_value_og, &r_grad_in, &r_grad_ig,
&r_grad_fg, &r_grad_og, &r_prev_state, &r_prev_state_grad, &r_state,
&r_state_grad, &r_state_atv, &r_output_grad, &r_checkI, &r_checkF,
&r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad, active_node,
active_gate, active_state);
&r_checkO, &r_checkIGrad, &r_checkFGrad, &r_checkOGrad, &cell_clip,
active_node, active_gate, active_state);
grad.gate_grad[frame_idx] = r_grad_in;
grad.gate_grad[frame_idx + frame_size] = r_grad_ig;
......@@ -185,8 +187,8 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
LstmMetaValue<T> value, int frame_size, int batch_size,
ActivationType active_node, ActivationType active_gate,
ActivationType active_state) {
T cell_clip, ActivationType active_node,
ActivationType active_gate, ActivationType active_state) {
dim3 threads;
dim3 grid;
if (batch_size == 1) {
......@@ -205,12 +207,12 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
if (batch_size == 1) {
KeLstmForward<T, Op,
/* is_batch= */ false><<<grid, threads, 0, stream>>>(
op, value, frame_size, batch_size, active_node, active_gate,
op, value, frame_size, batch_size, cell_clip, active_node, active_gate,
active_state);
} else {
KeLstmForward<T, Op,
/* is_batch= */ true><<<grid, threads, 0, stream>>>(
op, value, frame_size, batch_size, active_node, active_gate,
op, value, frame_size, batch_size, cell_clip, active_node, active_gate,
active_state);
}
}
......@@ -218,7 +220,7 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
template <class T, class Op>
void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, int batch_size,
int frame_size, int batch_size, T cell_clip,
ActivationType active_node, ActivationType active_gate,
ActivationType active_state) {
dim3 threads;
......@@ -239,13 +241,13 @@ void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
if (batch_size == 1) {
KeLstmBackward<T, Op,
/* is_batch= */ false><<<grid, threads, 0, stream>>>(
op, value, grad, frame_size, batch_size, active_node, active_gate,
active_state);
op, value, grad, frame_size, batch_size, cell_clip, active_node,
active_gate, active_state);
} else {
KeLstmBackward<T, Op,
/* is_batch= */ true><<<grid, threads, 0, stream>>>(
op, value, grad, frame_size, batch_size, active_node, active_gate,
active_state);
op, value, grad, frame_size, batch_size, cell_clip, active_node,
active_gate, active_state);
}
}
......
paddle/fluid/operators/math/detail/lstm_kernel.h
浏览文件 @ cf0511f2
......@@ -29,7 +29,7 @@ class lstm {
public:
HOSTDEVICE void operator()(T *value_in, T *value_ig, T *value_fg, T *value_og,
T *prev_state, T *state, T *state_atv, T *output,
T *checkI, T *checkF, T *checkO,
T *checkI, T *checkF, T *checkO, T *cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
......@@ -37,6 +37,15 @@ class lstm {
*value_ig = activation(*value_ig + (*prev_state) * (*checkI), active_gate);
*value_fg = activation(*value_fg + (*prev_state) * (*checkF), active_gate);
*state = (*value_in) * (*value_ig) + (*prev_state) * (*value_fg);
if (*cell_clip > 0.0) {
if (*state < -1.0 * (*cell_clip)) {
*state = -1.0 * (*cell_clip);
}
if (*state > *cell_clip) {
*state = *cell_clip;
}
}
*value_og = activation(*value_og + (*state) * (*checkO), active_gate);
*state_atv = activation(*state, active_state);
*output = (*value_og) * (*state_atv);
......@@ -52,7 +61,7 @@ class lstm {
__m256 *value_fg, __m256 *value_og,
__m256 *prev_state, __m256 *state,
__m256 *state_atv, __m256 *output, __m256 *checkI,
__m256 *checkF, __m256 *checkO,
__m256 *checkF, __m256 *checkO, T *cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
......@@ -65,6 +74,13 @@ class lstm {
active_gate);
*state = _mm256_add_ps(_mm256_mul_ps(*value_in, *value_ig),
_mm256_mul_ps(*prev_state, *value_fg));
if (*cell_clip > 0.0f) {
__m256 min = _mm256_set1_ps(0.0f - *cell_clip);
__m256 max = _mm256_set1_ps(*cell_clip);
*state = _mm256_min_ps(max, *state);
*state = _mm256_max_ps(min, *state);
}
*value_og = activation(
_mm256_add_ps(*value_og, _mm256_mul_ps(*state, *checkO)), active_gate);
*state_atv = activation(*state, active_state);
......@@ -86,15 +102,26 @@ class lstm {
T *prev_state, T *prev_state_grad, T *state,
T *state_grad, T *state_atv, T *output_grad,
T *checkI, T *checkF, T *checkO, T *checkIGrad,
T *checkFGrad, T *checkOGrad,
T *checkFGrad, T *checkOGrad, T *cell_clip,
ActivationType active_node,
ActivationType active_gate,
ActivationType active_state) {
*grad_og =
activation((*output_grad) * (*state_atv), *value_og, active_gate);
*state_grad +=
activation((*output_grad) * (*value_og), *state_atv, active_state) +
(*grad_og) * (*checkO);
if (*cell_clip > 0.0f) {
if (*state >= (*cell_clip) || *state <= (0.0f - (*cell_clip))) {
*state_grad = 0.0f;
} else {
*state_grad +=
activation((*output_grad) * (*value_og), *state_atv, active_state) +
(*grad_og) * (*checkO);
}
} else {
*state_grad +=
activation((*output_grad) * (*value_og), *state_atv, active_state) +
(*grad_og) * (*checkO);
}
*grad_in = activation((*state_grad) * (*value_ig), *value_in, active_node);
*grad_ig = activation((*state_grad) * (*value_in), *value_ig, active_gate);
*grad_fg =
......@@ -117,15 +144,24 @@ class lstm {
__m256 *prev_state, __m256 *prev_state_grad, __m256 *state,
__m256 *state_grad, __m256 *state_atv, __m256 *output_grad,
__m256 *checkI, __m256 *checkF, __m256 *checkO, __m256 *checkIGrad,
__m256 *checkFGrad, __m256 *checkOGrad, ActivationType active_node,
ActivationType active_gate, ActivationType active_state) {
__m256 *checkFGrad, __m256 *checkOGrad, T *cell_clip,
ActivationType active_node, ActivationType active_gate,
ActivationType active_state) {
*grad_og = activation(_mm256_mul_ps(*output_grad, *state_atv), *value_og,
active_gate);
*state_grad =
_mm256_add_ps(activation(_mm256_mul_ps(*output_grad, *value_og),
*state_atv, active_state),
*state_grad);
*state_grad = _mm256_add_ps(_mm256_mul_ps(*grad_og, *checkO), *state_grad);
if (*cell_clip > 0.0f) {
T *state_ = reinterpret_cast<T *>(state);
if (*state_ >= (*cell_clip) || *state_ <= (0.0f - (*cell_clip))) {
*state_grad = _mm256_set1_ps(0.0f);
} else {
*state_grad =
_mm256_add_ps(activation(_mm256_mul_ps(*output_grad, *value_og),
*state_atv, active_state),
*state_grad);
*state_grad =
_mm256_add_ps(_mm256_mul_ps(*grad_og, *checkO), *state_grad);
}
}
*grad_in = activation(_mm256_mul_ps(*state_grad, *value_ig), *value_in,
active_node);
*grad_ig = activation(_mm256_mul_ps(*state_grad, *value_in), *value_ig,
......
paddle/fluid/operators/math/lstm_compute.cc
浏览文件 @ cf0511f2
......@@ -24,12 +24,12 @@ template <class T>
struct LstmUnitFunctor<platform::CPUDeviceContext, T> {
static void compute(const platform::CPUDeviceContext& context,
LstmMetaValue<T> value, int frame_size, int batch_size,
const detail::ActivationType& gate_act,
T cell_clip, const detail::ActivationType& gate_act,
const detail::ActivationType& cell_act,
const detail::ActivationType& cand_act) {
for (int b = 0; b < batch_size; b++) {
detail::cpu_lstm_forward(detail::forward::lstm<T>(), value, frame_size,
cand_act, gate_act, cell_act);
cell_clip, cand_act, gate_act, cell_act);
value.gate_value += frame_size * 4;
value.state_value += frame_size;
value.state_active_value += frame_size;
......@@ -45,13 +45,14 @@ template <class T>
struct LstmUnitGradFunctor<platform::CPUDeviceContext, T> {
static void compute(const platform::CPUDeviceContext& context,
LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, int batch_size,
int frame_size, int batch_size, T cell_clip,
const detail::ActivationType& gate_act,
const detail::ActivationType& cell_act,
const detail::ActivationType& cand_act) {
for (int b = 0; b < batch_size; b++) {
detail::cpu_lstm_backward(detail::backward::lstm<T>(), value, grad,
frame_size, cand_act, gate_act, cell_act);
frame_size, cell_clip, cand_act, gate_act,
cell_act);
value.gate_value += frame_size * 4;
value.state_value += frame_size;
......
paddle/fluid/operators/math/lstm_compute.cu
浏览文件 @ cf0511f2
......@@ -24,12 +24,12 @@ template <class T>
struct LstmUnitFunctor<platform::CUDADeviceContext, T> {
static void compute(const platform::CUDADeviceContext& context,
LstmMetaValue<T> value, int frame_size, int batch_size,
const detail::ActivationType& gate_act,
T cell_clip, const detail::ActivationType& gate_act,
const detail::ActivationType& cell_act,
const detail::ActivationType& cand_act) {
detail::gpu_lstm_forward<T>(context, detail::forward::lstm<T>(), value,
frame_size, batch_size, cand_act, gate_act,
cell_act);
frame_size, batch_size, cell_clip, cand_act,
gate_act, cell_act);
}
};
......@@ -37,13 +37,13 @@ template <class T>
struct LstmUnitGradFunctor<platform::CUDADeviceContext, T> {
static void compute(const platform::CUDADeviceContext& context,
LstmMetaValue<T> value, LstmMetaGrad<T> grad,
int frame_size, int batch_size,
int frame_size, int batch_size, T cell_clip,
const detail::ActivationType& gate_act,
const detail::ActivationType& cell_act,
const detail::ActivationType& cand_act) {
detail::gpu_lstm_backward(context, detail::backward::lstm<T>(), value, grad,
frame_size, batch_size, cand_act, gate_act,
cell_act);
frame_size, batch_size, cell_clip, cand_act,
gate_act, cell_act);
}
};
......
paddle/fluid/operators/math/lstm_compute.h
浏览文件 @ cf0511f2
......@@ -50,7 +50,7 @@ template <typename DeviceContext, typename T>
class LstmUnitFunctor {
public:
static void compute(const DeviceContext &context, LstmMetaValue<T> value,
int frame_size, int batch_size,
int frame_size, int batch_size, T cell_clip,
const detail::ActivationType &gate_act,
const detail::ActivationType &cell_act,
const detail::ActivationType &cand_act);
......@@ -61,7 +61,7 @@ class LstmUnitGradFunctor {
public:
static void compute(const DeviceContext &context, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frame_size, int batch_size,
const detail::ActivationType &gate_act,
T cell_clip, const detail::ActivationType &gate_act,
const detail::ActivationType &cell_act,
const detail::ActivationType &cand_act);
};
......
paddle/fluid/operators/ngraph/ngraph_ops.h → paddle/fluid/operators/math/sample_prob.cc
浏览文件 @ cf0511f2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 2019 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
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,
......@@ -12,28 +12,15 @@ 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. */
/*
* This file contains the list of the ngraph operators for Paddle.
*
* ATTENTION: It requires some C++11 features, for lower version C++ or C, we
* might release another API.
*/
#include "paddle/fluid/operators/math/sample_prob.h"
#pragma once
namespace paddle {
namespace operators {
namespace math {
#include "ops/accuracy_op.h"
#include "ops/activation_op.h"
#include "ops/batch_norm_op.h"
#include "ops/binary_unary_op.h"
#include "ops/conv2d_op.h"
#include "ops/cross_entropy_op.h"
#include "ops/elementwise_add_op.h"
#include "ops/fill_constant_op.h"
#include "ops/mean_op.h"
#include "ops/momentum_op.h"
#include "ops/mul_op.h"
#include "ops/pool2d_op.h"
#include "ops/scale_op.h"
#include "ops/softmax_op.h"
#include "ops/sum_op.h"
#include "ops/top_k_op.h"
template class SampleWithProb<platform::CPUDeviceContext, float>;
template class SampleWithProb<platform::CPUDeviceContext, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/sample_prob.cu 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2019 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 <thrust/random.h>
#include <thrust/sort.h>
#include <iostream>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sample_prob.h"
#include "paddle/fluid/operators/math/sampler.h"
namespace paddle {
namespace operators {
namespace math {
using Tensor = framework::Tensor;
template <typename T>
__device__ T gpu_adjust_prob(const T prob, const int num_samples,
const int num_tries) {
if (num_samples == num_tries) {
return prob * num_samples;
} else {
return -expm1(num_tries * log1p(-prob));
}
}
class GPULogUniformSampler {
public:
__device__ int64_t Sample(float random, const int range,
const float log_range) const;
__device__ float Probability(int64_t value, const float log_range) const;
};
__device__ int64_t GPULogUniformSampler::Sample(float random, const int range,
const float log_range) const {
// Got Log Uniform distribution from uniform distribution by
// inverse_transform_sampling method
const int64_t value = static_cast<int64_t>(exp(random * log_range)) - 1;
// Mathematically, value should be <= range_, but might not be due to some
// floating point roundoff, so we mod by range_.
return value % range;
}
__device__ float GPULogUniformSampler::Probability(
int64_t value, const float log_range) const {
// Given f(x) = 1/[(x+1) * log_range_]
// The value's probability is integral of f(x) from value to (value + 1)
return (log((value + 2.0) / (value + 1.0))) / log_range;
}
template <typename T>
__global__ void SamplingCondidate(
const size_t n, const int num_tries, const int range, const float log_range,
const int num_true, const std::size_t num_samples,
const int64_t* label_data, int64_t* samples_data, T* probabilities_data) {
const int num_sampled_classes = num_true + num_samples;
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = 0;
GPULogUniformSampler sampler;
for (; idx < n; idx += blockDim.x * gridDim.x) {
int col_idx = idx % num_sampled_classes;
int row_idx = idx / num_sampled_classes;
if (col_idx < num_true) {
samples_data[idx] = label_data[row_idx * num_true + col_idx];
} else {
samples_data[idx] = samples_data[col_idx];
}
probabilities_data[idx] = sampler.Probability(samples_data[idx], log_range);
probabilities_data[idx] =
gpu_adjust_prob(probabilities_data[idx], num_samples, num_tries);
}
}
template <typename T>
int UniqSampler(const Sampler& sampler, const std::size_t num_samples,
int64_t* samples_data) {
// sample num_samles unique samples for an example, note that they are not
// all negative samples
std::unordered_set<int64_t> tmp_samples;
tmp_samples.clear();
int num_tries = 0;
int j = 0;
while (j < num_samples) {
++num_tries;
auto v = sampler.Sample();
auto insert_ok = tmp_samples.insert(v).second;
if (!insert_ok) {
continue;
}
samples_data[j] = v;
++j;
}
return num_tries;
}
template <typename T>
void GPUSampleWithProb<T>::operator()(
const platform::CUDADeviceContext& context, const int seed,
const int dict_size, const bool uniq, const std::size_t num_samples,
const Tensor* L, Tensor* S, Tensor* P) {
// UNDERSTAND: dimension issues
const auto lbl_dim = L->dims();
const int batch_size = lbl_dim[0];
const int num_true = lbl_dim[1];
const int num_sampled_classes = num_true + num_samples;
framework::DDim ret_dim{batch_size, num_sampled_classes};
// UNDERSTAND: raw data view
const int64_t* label_data = L->data<int64_t>();
int64_t* samples_data = S->data<int64_t>();
T* probabilities_data = P->data<T>();
int s_size = num_samples;
framework::DDim s_dim{s_size};
Tensor s;
int64_t* s_data = s.mutable_data<int64_t>(s_dim, platform::CPUPlace());
math::LogUniformSampler sampler(dict_size, seed);
int range = dict_size;
float log_range = log(range + 1);
int num_tries = UniqSampler<T>(sampler, num_samples, s_data);
VLOG(1) << "num_tries: " << num_tries;
PADDLE_ENFORCE(cudaMemcpy(samples_data + num_true, s_data,
sizeof(int64_t) * num_samples,
cudaMemcpyHostToDevice));
int threads = 512;
const size_t size = batch_size * num_sampled_classes;
int grid = (batch_size * num_sampled_classes + threads - 1) / threads;
SamplingCondidate<T><<<grid, threads, 0, context.stream()>>>(
size, num_tries, range, log_range, num_true, num_samples, label_data,
samples_data, probabilities_data);
}
template class GPUSampleWithProb<float>;
template class GPUSampleWithProb<double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/sample_prob.h 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2019 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 <iostream>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/sampler.h"
namespace paddle {
namespace operators {
namespace math {
using Tensor = framework::Tensor;
/* UNDERSTAND: utility function to adjust probability for unique sampling,
return whatever as it is if not using unique samping */
template <typename T>
static T adjust_prob(const T prob, const int num_samples, const int num_tries) {
if (num_samples == num_tries) {
return prob * num_samples;
} else {
return -expm1(num_tries * log1p(-prob));
}
}
template <typename DeviceContext, typename T>
class SampleWithProb {
public:
void operator()(const DeviceContext& context, const Sampler& sampler,
const std::size_t num_samples, const Tensor* L, Tensor* S,
Tensor* P) {
// UNDERSTAND: dimension issues
const auto lbl_dim = L->dims();
const int batch_size = lbl_dim[0];
const int num_true = lbl_dim[1];
const int num_sampled_classes = num_true + num_samples;
framework::DDim ret_dim{batch_size, num_sampled_classes};
// UNDERSTAND: raw data view
const int64_t* label_data = L->data<int64_t>();
int64_t* samples_data =
S->mutable_data<int64_t>(ret_dim, context.GetPlace());
T* probabilities_data = P->mutable_data<T>(ret_dim, context.GetPlace());
// temp sets for unique sampling
std::unordered_set<int64_t> tmp_samples;
int j = 0; // column index
// add true labels, not that efficient
while (j < num_true) {
for (int i = 0; i < batch_size; ++i) {
auto samples_index = i * num_sampled_classes + j;
auto v = label_data[i * num_true + j];
samples_data[samples_index] = v;
probabilities_data[samples_index] = sampler.Probability(v);
}
++j;
}
// sample num_samles unique samples for an example, note that they are not
// all negative samples
tmp_samples.clear();
int num_tries = 0;
while (j < num_sampled_classes) {
++num_tries;
auto v = sampler.Sample();
auto insert_ok = tmp_samples.insert(v).second;
if (!insert_ok) {
continue;
}
auto p = sampler.Probability(v);
for (int i = 0; i < batch_size; ++i) {
auto samples_index = i * num_sampled_classes + j;
samples_data[samples_index] = v;
probabilities_data[samples_index] = p;
}
++j;
}
// compute Q(y|x), because of unique sampling, probabilities need to be
// adjusted
for (int k = 0; k < num_sampled_classes; ++k) {
for (int i = 0; i < batch_size; ++i) {
auto samples_index = i * num_sampled_classes + k;
probabilities_data[samples_index] = adjust_prob(
probabilities_data[samples_index], num_samples, num_tries);
}
}
}
};
#ifdef PADDLE_WITH_CUDA
template <typename T>
class GPUSampleWithProb {
public:
void operator()(const platform::CUDADeviceContext& context, const int seed,
const int dict_size, const bool uniq,
const std::size_t num_samples, const Tensor* L, Tensor* S,
Tensor* P);
};
#endif
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
浏览文件 @ cf0511f2
......@@ -52,11 +52,6 @@ class MKLDNNActivationKernel
"Wrong layout/format set for Input x tensor");
Functor functor;
auto attrs = functor.GetAttrs();
for (auto &attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(ctx);
}
};
......@@ -76,11 +71,6 @@ class MKLDNNActivationGradKernel
"is_test attribute should be set to False in training phase.");
Functor functor;
auto attrs = functor.GetAttrs();
for (auto &attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(ctx);
}
};
......@@ -235,7 +225,7 @@ void eltwise_grad(const framework::ExecutionContext &ctx,
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
PADDLE_ENFORCE(src_memory != nullptr,
"Fail to find src_memory in device context");
src_memory->set_data_handle(*p_src_data.get());
src_memory->set_data_handle(*p_src_data);
std::shared_ptr<memory> diff_src_memory;
......
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
浏览文件 @ cf0511f2
......@@ -96,12 +96,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto* bias = ctx.HasInput("Bias") ? ctx.Input<Tensor>("Bias") : nullptr;
auto* output = ctx.Output<Tensor>("Output");
PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN &&
input->format() != memory::format::format_undef,
"Wrong layout/format set for Input tensor");
PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN &&
filter->format() != memory::format::format_undef,
"Wrong layout/format set for Filter tensor");
PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN);
PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN);
PADDLE_ENFORCE(input->dims().size() == 4 || input->dims().size() == 5,
"Input must be with 4 or 5 dimensions, i.e. NCHW or NCDHW");
PADDLE_ENFORCE(filter->dims().size() == 4 || filter->dims().size() == 5,
......@@ -148,14 +144,19 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std::vector<primitive> pipeline;
auto src_format = input->format();
mkldnn::memory::format weights_format =
GetWeightsFormat(filter->format(), g, is_conv3d);
auto user_src_md = platform::MKLDNNMemDesc(
{src_tz}, platform::MKLDNNGetDataType<T>(), src_format);
auto user_weights_md = platform::MKLDNNMemDesc(
{weights_tz}, platform::MKLDNNGetDataType<T>(), weights_format);
// For convolution with groups we need to recreate primitive descriptor
// as Paddle tensor is not having group dims while mkldnn treats
// group as another dimensions
mkldnn::memory::primitive_desc user_weights_mpd =
filter->get_mkldnn_prim_desc();
if (g > 1) {
mkldnn::memory::format weights_format =
GetWeightsFormat(filter->format(), g, is_conv3d);
auto user_weights_md = platform::MKLDNNMemDesc(
{weights_tz}, platform::MKLDNNGetDataType<T>(), weights_format);
user_weights_mpd =
mkldnn::memory::primitive_desc(user_weights_md, mkldnn_engine);
}
/* create memory descriptor for convolution without specified format
* ('any') which lets a primitive (convolution in this case) choose
......@@ -165,7 +166,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto chosen_memory_format =
platform::data_format_to_memory_format(data_format);
weights_format = mkldnn::memory::format::any;
mkldnn::memory::format weights_format = mkldnn::memory::format::any;
// Check the format for user's special output
if (chosen_memory_format != mkldnn::memory::format::any) {
if (is_conv3d) {
......@@ -205,10 +206,10 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
platform::ConvMKLDNNHandler handler(conv_pd, dev_ctx, mkldnn_engine, key);
// create mkldnn memory from input tensors (data/weights)
auto user_src_memory_p =
handler.AcquireSrcMemory(user_src_md, to_void_cast<T>(input_data));
auto user_src_memory_p = handler.AcquireSrcMemory(
input->get_mkldnn_prim_desc(), to_void_cast<T>(input_data));
auto user_weights_memory_p = handler.AcquireWeightsMemory(
user_weights_md, to_void_cast<T>(filter_data));
user_weights_mpd, to_void_cast<T>(filter_data));
// create reorder primitive if the input format is not the preferred one
auto src_memory_p =
......@@ -281,8 +282,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
pipeline.push_back(*conv_p);
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
output->set_format(GetMKLDNNFormat(*dst_memory_p));
auto dst_mpd = dst_memory_p->get_primitive_desc();
output->set_mkldnn_prim_desc(dst_mpd);
}
void ComputeINT8(const paddle::framework::ExecutionContext& ctx) const {
const bool is_test = ctx.Attr<bool>("is_test");
......@@ -947,8 +948,8 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
// push primitive to stream and wait until it's executed
pipeline.push_back(*conv_bwd_weights_p);
filter_grad->set_layout(DataLayout::kMKLDNN);
filter_grad->set_format(GetMKLDNNFormat(*diff_weights_memory_p));
auto filter_grad_mpd = diff_weights_memory_p->get_primitive_desc();
filter_grad->set_mkldnn_prim_desc(filter_grad_mpd);
}
if (input_grad) {
......
paddle/fluid/operators/mkldnn/gaussian_random_mkldnn_op.cc
浏览文件 @ cf0511f2
......@@ -42,8 +42,12 @@ class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
// The format of output is set as the mkldnn's format
// TODO(@mozga-intel) The format of matrix sets inside the another layers.
tensor->set_layout(DataLayout::kMKLDNN);
tensor->set_format(mkldnn::memory::format::oihw);
// TODO(jczaja): Remove this hack after checking performance on block layout
auto tensor_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(tensor->dims()),
mkldnn::memory::format::oihw);
tensor->set_mkldnn_prim_desc(tensor_mem_pd);
}
};
} // namespace operators
......
paddle/fluid/operators/mkldnn/pool_mkldnn_op.cc
浏览文件 @ cf0511f2
......@@ -198,7 +198,7 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline{*(pool_p.get())};
std::vector<mkldnn::primitive> pipeline{*pool_p};
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
......@@ -367,8 +367,7 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
dev_ctx.SetBlob(key_pool_diff_dst_mem_p, diff_dst_memory);
pool_bwd_p = std::make_shared<pooling_backward>(
pool_bwd_pd, *(diff_dst_memory.get()), *workspace_memory,
*(diff_src_memory));
pool_bwd_pd, *diff_dst_memory, *workspace_memory, *diff_src_memory);
dev_ctx.SetBlob(key_pool_bwd_p, pool_bwd_p);
} else {
......@@ -404,7 +403,7 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
if (is_diff_dst_reordered) {
pipeline.push_back(reorder_diff_dst);
}
pipeline.push_back(*(pool_bwd_p.get()));
pipeline.push_back(*pool_bwd_p);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
in_x_grad->set_layout(DataLayout::kMKLDNN);
......
paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc
浏览文件 @ cf0511f2
......@@ -66,8 +66,7 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
"Fail to find softmax primitive in device context");
if (softmax_p == nullptr) {
softmax_p = std::make_shared<mkldnn::softmax_forward>(
*(softmax_pd_.get()),
*(static_cast<mkldnn::memory*>(src_memory_p.get())),
*softmax_pd_, *(static_cast<mkldnn::memory*>(src_memory_p.get())),
*(static_cast<mkldnn::memory*>(dst_memory_p.get())));
dev_ctx_.SetBlob(prim_key, softmax_p);
} else {
......@@ -88,8 +87,8 @@ class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
"Fail to find softmax backward primitive in device context");
if (softmax_bwd_p == nullptr) {
softmax_bwd_p = std::make_shared<mkldnn::softmax_backward>(
*softmax_bwd_pd_, *(dst_memory_p.get()), *(diff_dst_memory_p.get()),
*(diff_src_memory_p.get()));
*softmax_bwd_pd_, *dst_memory_p, *diff_dst_memory_p,
*diff_src_memory_p);
dev_ctx_.SetBlob(prim_key, softmax_bwd_p);
} else {
is_reusing_ = true;
......
paddle/fluid/operators/mkldnn/sum_mkldnn_op.cc
浏览文件 @ cf0511f2
......@@ -160,7 +160,7 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto get_selected_row = [&](size_t i) -> const SelectedRows& {
if (i == 0 && in0) {
return *in0.get();
return *in0;
} else {
return in_vars[i]->Get<SelectedRows>();
}
......
paddle/fluid/operators/mkldnn/transpose_mkldnn_op.cc
浏览文件 @ cf0511f2
......@@ -52,7 +52,7 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
mkldnn_engine, key);
auto transpose_src_memory_p = handler.AcquireSrcMemory(
input->format(), platform::to_void_cast<T>(input_data));
input->get_mkldnn_prim_desc(), platform::to_void_cast<T>(input_data));
auto transpose_dst_memory_p =
handler.AcquireDstMemory(output, ctx.GetPlace());
auto transpose_p = handler.AcquireTranspose(transpose_dst_memory_p,
......@@ -61,6 +61,15 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std::vector<mkldnn::primitive> pipeline;
pipeline.push_back(*transpose_p);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
// Transpose did change logical dimensions of Tensor, but reorder does not.
// Reorder does change only physical layout eg. format , strides
// so we need to create new primitive descriptor with changed logical layout
// so it match output shape
auto output_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(output->dims()),
mkldnn::memory::format::blocked);
output->set_mkldnn_prim_desc(output_mem_pd);
}
};
......@@ -102,8 +111,9 @@ class TransposeMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
platform::TransposeMKLDNNHandler handler(nchw_tz, reversed_axis, dev_ctx,
mkldnn_engine, key);
auto transpose_src_memory_p = handler.AcquireSrcMemory(
out_grad->format(), platform::to_void_cast<T>(out_grad_data));
auto transpose_src_memory_p =
handler.AcquireSrcMemory(out_grad->get_mkldnn_prim_desc(),
platform::to_void_cast<T>(out_grad_data));
auto transpose_dst_memory_p =
handler.AcquireDstMemory(x_grad, ctx.GetPlace());
auto transpose_p = handler.AcquireTranspose(transpose_dst_memory_p,
......@@ -112,6 +122,15 @@ class TransposeMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
std::vector<mkldnn::primitive> pipeline;
pipeline.push_back(*transpose_p);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
// Transpose did change logical dimensions of Tensor, but reorder does not.
// Reorder does change only physical layout eg. format , strides
// so we need to create new primitive descriptor with changed logical layout
// so it match output shape
auto x_grad_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(x_grad->dims()),
mkldnn::memory::format::blocked);
x_grad->set_mkldnn_prim_desc(x_grad_mem_pd);
}
};
......
paddle/fluid/operators/ngraph/CMakeLists.txt
浏览文件 @ cf0511f2
......@@ -2,4 +2,5 @@ if(WITH_NGRAPH)
cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto ngraph)
cc_library(ngraph_engine SRCS ngraph_engine.cc DEPS ngraph_bridge framework_proto)
op_library(ngraph_engine_op DEPS ngraph_engine op_registry op_info device_context)
add_subdirectory(ops)
endif()
paddle/fluid/operators/ngraph/ngraph_bridge.cc
浏览文件 @ cf0511f2
......@@ -19,50 +19,21 @@ limitations under the License. */
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ngraph_bridge.h"
#include "paddle/fluid/operators/ngraph/ngraph_ops.h"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace NG_OPS = paddle::operators::ngraphs;
std::map<std::string,
std::function<void(const std::shared_ptr<framework::OperatorBase>&,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {
{"accuracy", NG_OPS::BuildAccuracyNode},
{"conv2d", NG_OPS::BuildConv2dNode},
{"conv2d_grad", NG_OPS::BuildConv2dGradNode},
{"batch_norm", NG_OPS::BuildBatchNormNode},
{"batch_norm_grad", NG_OPS::BuildBatchNormGradNode},
{"cross_entropy", NG_OPS::BuildCrossEntropyNode},
{"cross_entropy_grad", NG_OPS::BuildCrossEntropyGradNode},
{"elementwise_add", NG_OPS::BuildElementwiseAddNode},
{"elementwise_add_grad", NG_OPS::BuildElementwiseAddGradNode},
{"fill_constant", NG_OPS::BuildFillConstantNode},
{"mean", NG_OPS::BuildMeanNode},
{"mean_grad", NG_OPS::BuildMeanGradNode},
{"momentum", NG_OPS::BuildMomentumNode},
{"mul", NG_OPS::BuildMulNode},
{"mul_grad", NG_OPS::BuildMulGradNode},
{"pool2d", NG_OPS::BuildPool2dNode},
{"pool2d_grad", NG_OPS::BuildPool2dGradNode},
{"softmax", NG_OPS::BuildSoftmaxNode},
{"softmax_grad", NG_OPS::BuildSoftmaxGradNode},
{"scale", NG_OPS::BuildScaleNode},
{"sigmoid", NG_OPS::BuildUnaryNode<ngraph::op::Sigmoid>},
{"sum", NG_OPS::BuildSumNode},
{"relu", NG_OPS::BuildUnaryNode<ngraph::op::Relu>},
{"relu_grad", NG_OPS::BuildReluGradNode},
{"tanh", NG_OPS::BuildUnaryNode<ngraph::op::Tanh>},
{"tanh_grad", NG_OPS::BuildTanhGradNode},
{"top_k", NG_OPS::BuildTopKNode}};
bool NgraphBridge::isRegister(const std::string& str) {
return ops::NgraphSingleton::Lookup(str);
}
void NgraphBridge::BuildNgNode(
const std::shared_ptr<framework::OperatorBase>& op) {
auto& op_type = op->Type();
NG_NODE_MAP[op_type](op, ngb_node_map_);
ops::NgraphSingleton::BuildNode(ngb_node_map_, op, op_type);
}
} // namespace operators
......
paddle/fluid/operators/ngraph/ngraph_bridge.h
浏览文件 @ cf0511f2
......@@ -28,13 +28,6 @@ namespace operators {
class NgraphBridge {
public:
static std::map<
std::string,
std::function<void(const std::shared_ptr<framework::OperatorBase>&,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NG_NODE_MAP;
explicit NgraphBridge(
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
......@@ -43,6 +36,8 @@ class NgraphBridge {
void BuildNgNode(const std::shared_ptr<framework::OperatorBase>& op);
static bool isRegister(const std::string& str);
private:
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
......
paddle/fluid/operators/ngraph/ngraph_engine.cc
浏览文件 @ cf0511f2
......@@ -88,14 +88,12 @@ static std::vector<std::vector<int>> NgraphOpIntervals(
int pivot = left;
while (pivot < right) {
auto op_type = ops.at(pivot)->Type();
if (NgraphBridge::NG_NODE_MAP.find(op_type) ==
NgraphBridge::NG_NODE_MAP.end()) {
if (NgraphBridge::isRegister(op_type)) {
++pivot;
} else {
int start = pivot, end = start;
while (pivot < right &&
(NgraphBridge::NG_NODE_MAP.find(ops.at(pivot)->Type()) !=
NgraphBridge::NG_NODE_MAP.end())) {
(!NgraphBridge::isRegister(ops.at(pivot)->Type()))) {
++pivot;
++end;
}
......
paddle/fluid/operators/ngraph/ops/CMakeLists.txt 0 → 100644
浏览文件 @ cf0511f2
file(GLOB LIST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.h")
set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/operators/ngraph/ngraph_ops.h)
file(APPEND ${pass_file} "\#pragma once\n")
file(WRITE ${pass_file} "// Generated by the /paddle/fluid/operators/ngraph/ops/CMakeLists.txt. DO NOT EDIT!\n\n")
foreach(OPS_NAME ${LIST_OPS})
file(APPEND ${pass_file} "\#include \"paddle/fluid/operators/ngraph/ops/${OPS_NAME}\"\n")
endforeach(OPS_NAME)
paddle/fluid/operators/ngraph/ops/accuracy_op.h
浏览文件 @ cf0511f2
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -63,3 +64,5 @@ void BuildAccuracyNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(accuracy, BuildAccuracyNode);
paddle/fluid/operators/ngraph/ops/activation_op.h
浏览文件 @ cf0511f2
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -50,3 +51,6 @@ void BuildTanhGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(relu_grad, BuildReluGradNode);
REGISTER_NG_OP(than_grad, BuildTanhGradNode);
paddle/fluid/operators/ngraph/ops/batch_norm_op.h
浏览文件 @ cf0511f2
......@@ -20,6 +20,7 @@ limitations under the License. */
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_node.h"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -155,3 +156,6 @@ void BuildBatchNormGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(batch_norm, BuildBatchNormNode);
REGISTER_NG_OP(batch_norm_grad, BuildBatchNormGradNode);
paddle/fluid/operators/ngraph/ops/binary_unary_op.h
浏览文件 @ cf0511f2
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -47,3 +48,7 @@ static void BuildUnaryNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(relu, BuildUnaryNode<ngraph::op::Relu>);
REGISTER_NG_OP(tanh, BuildUnaryNode<ngraph::op::Tanh>);
REGISTER_NG_OP(sigmoid, BuildUnaryNode<ngraph::op::Sigmoid>);
paddle/fluid/operators/ngraph/ops/conv2d_op.h
浏览文件 @ cf0511f2
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -233,3 +234,6 @@ void BuildConv2dGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(conv2d, BuildConv2dNode);
REGISTER_NG_OP(conv2d_grad, BuildConv2dGradNode);
paddle/fluid/operators/ngraph/ops/cross_entropy_op.h
浏览文件 @ cf0511f2
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -143,3 +144,6 @@ void BuildCrossEntropyGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(cross_entropy, BuildCrossEntropyNode);
REGISTER_NG_OP(cross_entropy_grad, BuildCrossEntropyGradNode);
paddle/fluid/operators/ngraph/ops/elementwise_add_op.h
浏览文件 @ cf0511f2
......@@ -19,6 +19,7 @@ limitations under the License. */
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_node.h"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -85,3 +86,6 @@ void BuildElementwiseAddGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(elementwise_add, BuildElementwiseAddNode);
REGISTER_NG_OP(elementwise_add_grad, BuildElementwiseAddGradNode);
paddle/fluid/operators/ngraph/ops/fill_constant_op.h
浏览文件 @ cf0511f2
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -55,3 +56,5 @@ void BuildFillConstantNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(fill_constant, BuildFillConstantNode);
paddle/fluid/operators/ngraph/ops/mean_op.h
浏览文件 @ cf0511f2
......@@ -19,6 +19,7 @@ limitations under the License. */
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -64,3 +65,6 @@ void BuildMeanGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(mean, BuildMeanNode);
REGISTER_NG_OP(mean_grad, BuildMeanGradNode);
paddle/fluid/operators/ngraph/ops/momentum_op.h
浏览文件 @ cf0511f2
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -99,3 +100,5 @@ void BuildMomentumNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(momentum, BuildMomentumNode);
paddle/fluid/operators/ngraph/ops/mul_op.h
浏览文件 @ cf0511f2
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -130,3 +131,6 @@ static void BuildMulGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(mul, BuildMulNode);
REGISTER_NG_OP(mul_grad, BuildMulGradNode);
paddle/fluid/operators/ngraph/ops/op_bridge.h 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include <map>
#include <string>
#include <unordered_map>
#include "ngraph/node.hpp"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/ngraph/ngraph_bridge.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace operators {
namespace ops {
class NgraphSingleton {
NgraphSingleton() = default;
NgraphSingleton(NgraphSingleton const&) = delete;
void operator=(NgraphSingleton const) = delete;
~NgraphSingleton() = default;
static std::map<
std::string,
std::function<void(const std::shared_ptr<framework::OperatorBase>&,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
ng_node_maps_;
public:
template <typename TF>
static void Register(TF&& tf, const std::string& name) {
ng_node_maps_[name] = tf;
}
static bool Lookup(const std::string& name) {
auto it = ng_node_maps_.find(name);
if (it == ng_node_maps_.end()) {
return true;
}
return false;
}
static void BuildNode(
const std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>& ng_maps,
const std::shared_ptr<framework::OperatorBase>& op,
const std::string& name) {
ng_node_maps_[name](op, ng_maps);
}
};
std::map<std::string,
std::function<void(const std::shared_ptr<framework::OperatorBase>&,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphSingleton::ng_node_maps_;
} // namespace ops
} // namespace operators
} // namespace paddle
#define REGISTER_NG_OP(op_type__, Converter__) \
struct ng_##op_type__##_converter { \
ng_##op_type__##_converter() { \
paddle::operators::ops::NgraphSingleton::Register( \
paddle::operators::ngraphs::Converter__, #op_type__); \
} \
}; \
ng_##op_type__##_converter ng_##op_type__##_converter__;
paddle/fluid/operators/ngraph/ops/pool2d_op.h
浏览文件 @ cf0511f2
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -172,3 +173,6 @@ void BuildPool2dGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(pool2d, BuildPool2dNode);
REGISTER_NG_OP(pool2d_grad, BuildPool2dGradNode);
paddle/fluid/operators/ngraph/ops/scale_op.h
浏览文件 @ cf0511f2
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -37,3 +38,5 @@ void BuildScaleNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(scale, BuildScaleNode);
paddle/fluid/operators/ngraph/ops/softmax_op.h
浏览文件 @ cf0511f2
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -72,3 +73,6 @@ void BuildSoftmaxGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(softmax, BuildSoftmaxNode);
REGISTER_NG_OP(softmax_grad, BuildSoftmaxGradNode);
paddle/fluid/operators/ngraph/ops/top_k_op.h
浏览文件 @ cf0511f2
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/op_bridge.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
......@@ -42,3 +43,5 @@ void BuildTopKNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(top_k, BuildTopKNode);
paddle/fluid/operators/pool_op.cc
浏览文件 @ cf0511f2
......@@ -168,9 +168,10 @@ void Pool2dOpMaker::Make() {
"be ignored."); // TODO(Chengduo): Add checker.
// (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>("global_pooling",
"(bool, default false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings will be ignored.")
AddAttr<bool>(
"global_pooling",
"(bool, default false) Whether to use the global pooling. "
"If global_pooling = true, kernel size and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"(vector<int>, default {1, 1}), strides(height, "
......@@ -182,7 +183,7 @@ void Pool2dOpMaker::Make() {
"paddings",
"(vector<int>, default {0,0}), paddings(height, width) of pooling "
"operator."
"If global_pooling = true, paddings and ksize will be ignored.")
"If global_pooling = true, paddings and kernel size will be ignored.")
.SetDefault({0, 0});
AddAttr<bool>(
"exclusive",
......@@ -204,7 +205,7 @@ void Pool2dOpMaker::Make() {
.SetDefault(false);
AddAttr<bool>(
"ceil_mode",
"(bool, default false) Wether to use the ceil function to calculate "
"(bool, default false) Whether to use the ceil function to calculate "
"output height and width. False is the default. If it is set to False, "
"the floor function will be used.")
.SetDefault(false);
......@@ -262,28 +263,37 @@ Example:
For exclusive = false:
$$
hstart = i * strides[0] - paddings[0]
$$
$$
hend = hstart + ksize[0]
$$
$$
wstart = j * strides[1] - paddings[1]
$$
$$
wend = wstart + ksize[1]
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$
For exclusive = true:
$$
hstart = max(0, i * strides[0] - paddings[0])
$$
$$
hend = min(H, hstart + ksize[0])
$$
$$
wstart = max(0, j * strides[1] - paddings[1])
$$
$$
wend = min(W, wstart + ksize[1])
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
For adaptive = true:
$$
hstart = floor(i * H_{in} / H_{out})
hend = ceil((i + 1) * H_{in} / H_{out})
wstart = floor(j * W_{in} / W_{out})
wend = ceil((j + 1) * W_{in} / W_{out})
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
)DOC");
}
......@@ -324,7 +334,7 @@ void Pool3dOpMaker::Make() {
AddAttr<bool>(
"global_pooling",
"(bool, default false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings wille be ignored.")
"If global_pooling = true, kernel size and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>(
"strides",
......@@ -359,7 +369,7 @@ void Pool3dOpMaker::Make() {
.SetDefault(false);
AddAttr<bool>(
"ceil_mode",
"(bool, default false) Wether to use the ceil function to calculate "
"(bool, default false) Whether to use the ceil function to calculate "
"output height and width. False is the default. If it is set to False, "
"the floor function will be used.")
.SetDefault(false);
......@@ -392,48 +402,68 @@ Example:
Output:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
For ceil_mode = false:
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
$$
D_{out} = \\frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1
$$
$$
H_{out} = \\frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[2]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
For ceil_mode = true:
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0] + strides[0] -1)}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
$$
D_{out} = \\frac{(D_{in} - ksize[0] + 2 * paddings[0] + strides[0] -1)}{strides[0]} + 1
$$
$$
H_{out} = \\frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
For exclusive = false:
$$
dstart = i * strides[0] - paddings[0]
dend = dstart + ksize[0]
hstart = j * strides[1] - paddings[1]
hend = hstart + ksize[1]
wstart = k * strides[2] - paddings[2]
wend = wstart + ksize[2]
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
$$
dstart = i * strides[0] - paddings[0]
$$
$$
dend = dstart + ksize[0]
$$
$$
hstart = j * strides[1] - paddings[1]
$$
$$
hend = hstart + ksize[1]
$$
$$
wstart = k * strides[2] - paddings[2]
$$
$$
wend = wstart + ksize[2]
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
For exclusive = true:
$$
dstart = max(0, i * strides[0] - paddings[0])
dend = min(D, dstart + ksize[0])
hstart = max(0, j * strides[1] - paddings[1])
hend = min(H, hstart + ksize[1])
wstart = max(0, k * strides[2] - paddings[2])
wend = min(W, wstart + ksize[2])
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
For adaptive = true:
$$
dstart = floor(i * D_{in} / D_{out})
dend = ceil((i + 1) * D_{in} / D_{out})
hstart = floor(j * H_{in} / H_{out})
hend = ceil((j + 1) * H_{in} / H_{out})
wstart = floor(k * W_{in} / W_{out})
wend = ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
$$
dstart = max(0, i * strides[0] - paddings[0])
$$
$$
dend = min(D, dstart + ksize[0])
$$
$$
hend = min(H, hstart + ksize[1])
$$
$$
wstart = max(0, k * strides[2] - paddings[2])
$$
$$
wend = min(W, wstart + ksize[2])
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
)DOC");
}
......
paddle/fluid/operators/sample_logits_op.cc 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/sample_logits_op.h"
#include "paddle/fluid/operators/math/sample_prob.h"
namespace paddle {
namespace operators {
class SampleLogitsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Logits",
"(Tensor, default: Tensor<float>), The unscaled log probabilities "
"which is a 2-D tensor with shape [N x K]. N is the batch_size, "
"and K is the class number.");
AddInput("Labels",
"(Tensor) The ground truth which is a 2-D tensor. Labels is a "
"Tensor<int64> with shape [N x NT], where NT is the number of"
"true labels for each example.");
AddInput("CustomizedSamples",
"(Tensor, default: Tensor<int64_t>), A 2-D tensor with shape [N, "
"NT + S],"
" where N is the batch size, NT is the number of true labels "
"and S is the number of negtive sample for each example."
"The first NT elements of each row should be the same with true "
"labels, "
"followed by S custom negtive samples. This tensor"
"is only used when use_customized_samples is true.")
.AsDispensable();
AddInput(
"CustomizedProbabilities",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape [N, NT + S]."
"The tensor has the same shape with CustomSamples,"
"and each element represents probability of element in CustomSamples. "
"This "
"tensor is only used when use_customized_samples is true.")
.AsDispensable();
AddOutput("Samples",
"(Tensor, default: Tensor<int64_t>), A 2-D tensor with shape [N, "
"NT + S]."
"The outputs value of sampler, including NT true lables and S "
"negetive samples "
"for each example. This will be used in"
"backward calculation.")
.AsIntermediate();
AddOutput(
"Probabilities",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape [N, NT + S]."
"The probabilites of sampled positive and negtive labels.")
.AsIntermediate();
AddOutput("SampledLogits",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape"
"[N, NT + S]. The outputs value of sampled logits, which will be"
"used in backward propagation.")
.AsIntermediate();
AddOutput(
"SampledLabels",
"(Tensor, default: Tensor<int64>), A 2-D tensor. The sampled labels"
"with shape [N, NT]. The tonsor contains hard labels as input to "
" softmax op, that is 0, 1, ..., NT-1 because of the first NT elements"
" of Sampels are positive lables.");
AddAttr<bool>(
"use_customized_samples",
"An indicator whether to use customized samples with probabilities, if "
"True"
"the operator will use customized samples and customized probabilities"
"otherwise, the operator will generate them by itself.")
.SetDefault(false);
AddAttr<bool>(
"uniq",
"An indicator whether to sample non-repetitive negtive labels, if True"
"the operator will sample negtive labels without replacement."
"Otherwise, the operator will sample negtive labels with replacement.")
.SetDefault(true);
AddAttr<bool>(
"remove_accidental_hits",
"An indicator whether to remove accidental hits when samples hits true"
"labels, the removal is implemented by subtracting the corresponding"
"logits by float_max to subpress their softmax to be zero.")
.SetDefault(true);
AddAttr<int>("num_samples", "The number of negative samples.");
AddAttr<int>("seed", "Random seed for generating samples").SetDefault(0);
AddComment(R"DOC(
"""
Computes sampled output training logits and labels suitable for implementing
sampled softmax.
"""
)DOC");
}
};
class SampleLogitsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Logits"),
"Input(Logits) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Samples"),
"Output(Samples) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Probabilities"),
"Output(Probabilities) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("SampledLogits"),
"Output(SampledLogits) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("SampledLabels"),
"Output(SampledLabels) should be not null.");
auto logits_dims = ctx->GetInputDim("Logits");
auto labels_dims = ctx->GetInputDim("Labels");
PADDLE_ENFORCE_EQ(
logits_dims.size(), 2UL,
"The logits of softmax_with_cross_entropy should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
"The labels should be a 2-D tensor.");
const int num_samples = ctx->Attrs().Get<int>("num_samples");
const int num_sampled_classes = labels_dims[1] + num_samples;
ctx->SetOutputDim("Samples", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("Probabilities", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("SampledLogits", {logits_dims[0], num_sampled_classes});
ctx->SetOutputDim("SampledLabels", {logits_dims[0], labels_dims[1]});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Logits"));
framework::OpKernelType kt =
framework::OpKernelType(data_type, ctx.device_context());
return kt;
}
};
// UNDERSTAND: InferShape for Grad
class SampleLogitsOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Logits"),
"Input(Logits) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Labels"),
"Input(Labels) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Samples"),
"Input(Samples) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("SampledLogits"),
"Input(SampledLogits) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("SampledLogits")),
"Input(SampledLogits@Grad) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Logits")),
"Output(Logits@Grad) should be not null.");
auto logit_dims = ctx->GetInputDim("Logits");
auto label_dims = ctx->GetInputDim("Labels");
PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
"The label should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(logit_dims.size(), 2UL,
"The logits should be a 2-D tensor.");
ctx->SetOutputDim(framework::GradVarName("Logits"),
ctx->GetInputDim("Logits"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto data_type = framework::GetDataTypeOfVar(
ctx.InputVar(framework::GradVarName("SampledLogits")));
framework::OpKernelType kt =
framework::OpKernelType(data_type, ctx.device_context());
return kt;
}
};
// UNDERSTAND: what's the rule for making a GradMaker TODO
class SampleLogitsGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* grad_op = new framework::OpDesc();
grad_op->SetType("sample_logits_grad");
grad_op->SetInput("Logits", Input("Logits"));
grad_op->SetInput("Labels", Input("Labels"));
grad_op->SetInput("Samples", Output("Samples"));
grad_op->SetInput("SampledLogits", Output("SampledLogits"));
grad_op->SetInput(framework::GradVarName("SampledLogits"),
OutputGrad("SampledLogits"));
grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(grad_op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(sample_logits, ops::SampleLogitsOp, ops::SampleLogitsOpMaker,
ops::SampleLogitsGradMaker);
REGISTER_OPERATOR(sample_logits_grad, ops::SampleLogitsOpGrad);
REGISTER_OP_CPU_KERNEL(sample_logits, ops::SampleLogitsKernel<float>,
ops::SampleLogitsKernel<double>);
REGISTER_OP_CPU_KERNEL(sample_logits_grad, ops::SampleLogitsGradKernel<float>,
ops::SampleLogitsGradKernel<double>);
paddle/fluid/operators/sample_logits_op.cu 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sample_prob.h"
#include "paddle/fluid/operators/math/softmax.h"
#include "paddle/fluid/operators/sample_logits_op.h"
namespace paddle {
namespace operators {
// UNDERSTAND: something like take_along_axis in numpy.
template <typename T>
__global__ void GPUTakeAlongD1(size_t size, const int batch_size,
const int array_slice_size,
const int idx_slice_size, const T* p_array,
const int64_t* p_index, T* p_value) {
const auto value_slice_size = idx_slice_size;
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = blockDim.x * gridDim.x;
for (; idx < size; idx += step_size) {
int i = idx / idx_slice_size;
auto array_index = p_index[idx];
p_value[idx] = p_array[i * array_slice_size + array_index];
}
}
// UNDERSTAND: something like put_along_axis in numpy but if there is duplicate
// indices, scatter is done in += way.
template <typename T>
__global__ void GPUPutAlongD1(size_t size, const int batch_size,
const int array_slice_size,
const int idx_slice_size, T* p_array,
const int64_t* p_index, const T* p_value) {
const auto value_slice_size = idx_slice_size;
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = blockDim.x * gridDim.x;
// size == batch_size
for (; idx < size; idx += step_size) {
int i = idx;
for (int j = 0; j < idx_slice_size; ++j) {
auto array_index = p_index[i * idx_slice_size + j];
p_array[i * array_slice_size + array_index] +=
p_value[i * idx_slice_size + j];
}
}
}
// UNDERSTAND: set label as 0,1,...,num_true-1
template <typename T>
__global__ void GPUSetLabel(size_t size, const int num_true, int64_t* p_array) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = blockDim.x * gridDim.x;
for (; idx < size; idx += step_size) {
p_array[idx] = idx % num_true;
}
}
// UNDERSTAND: compute accidentdal hits from samples and minus corresponding
// logits by a float max, here 1e20
template <typename T>
__global__ void gpu_compute_remove_accidental_hits(const int size,
const int num_true,
const int idx_slice_size,
const int64_t* p_index,
T* p_value) {
const auto value_slice_size = idx_slice_size;
int idx = blockDim.x * blockIdx.x + threadIdx.x;
int step_size = blockDim.x * gridDim.x;
for (; idx < size; idx += step_size) {
int i = idx / idx_slice_size;
if (idx % idx_slice_size < num_true) continue;
for (int j = 0; j < num_true; ++j) {
const auto true_idx = i * idx_slice_size + j;
if (p_index[true_idx] == p_index[idx]) {
p_value[idx] -= 1e20;
break;
}
}
}
}
template <typename T>
class SampleLogitsCUDAKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& context) const override {
// get necessary inputs
const Tensor* logits = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Labels");
VLOG(3) << "Enter SampleLogitsCUDAKernel";
// get necessary outputs
Tensor* samples = context.Output<Tensor>("Samples");
Tensor* probabilities = context.Output<Tensor>("Probabilities");
Tensor* sampled_logits = context.Output<Tensor>("SampledLogits");
Tensor* sampled_labels = context.Output<Tensor>("SampledLabels");
// shapes
const auto batch_size = logits->dims()[0];
const auto num_classes = logits->dims()[1];
const auto labels_dim = labels->dims();
const auto num_true = labels_dim[1];
const auto samples_dim = samples->dims();
// attrs
const auto num_samples = context.Attr<int>("num_samples");
const bool use_customized_samples =
context.Attr<bool>("use_customized_samples");
const bool uniq = context.Attr<bool>("uniq");
const bool remove_accidental_hits =
context.Attr<bool>("remove_accidental_hits");
// device contexts
auto& dev_ctx = context.cuda_device_context();
// UNDERSTAND: allocate memories for temporaries
sampled_logits->mutable_data<T>(samples_dim, context.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
set_zero(dev_ctx, sampled_logits, static_cast<T>(0));
auto sampled_labels_data =
sampled_labels->mutable_data<int64_t>(labels_dim, context.GetPlace());
int threads = 512;
size_t size = batch_size * num_true;
int grid = (size + threads - 1) / threads;
GPUSetLabel<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, num_true, sampled_labels_data);
if (use_customized_samples) {
const Tensor* customized_samples =
context.Input<Tensor>("CustomizedSamples");
const Tensor* customized_probabilities =
context.Input<Tensor>("CustomizedProbabilities");
samples->ShareDataWith(*customized_samples);
probabilities->ShareDataWith(*customized_probabilities);
} else {
samples->mutable_data<int64_t>(context.GetPlace());
probabilities->mutable_data<T>(samples_dim, context.GetPlace());
// UNDERSTAND: sampling
const auto seed = context.Attr<int>("seed");
auto sampler_with_prob = math::GPUSampleWithProb<T>();
sampler_with_prob(context.cuda_device_context(), seed, num_classes, uniq,
num_samples, labels, samples, probabilities);
}
// UNDERSTAND: gather sampled logits and remove accidental hits if needed
const auto num_take = samples->dims()[1];
const auto array_dims = logits->dims();
const auto idx_dims = samples->dims();
const T* p_array = logits->data<T>();
const int64_t* p_index = samples->data<int64_t>();
T* p_value = sampled_logits->data<T>();
// src slice size
const auto array_slice_size = array_dims[1];
// index slice size
const auto idx_slice_size = idx_dims[1];
size = batch_size * num_take;
grid = (size + threads - 1) / threads;
GPUTakeAlongD1<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, batch_size, array_slice_size, idx_slice_size, p_array, p_index,
p_value);
if (remove_accidental_hits) {
const size_t size = batch_size * (num_true + num_samples);
int grid = (size + threads - 1) / threads;
gpu_compute_remove_accidental_hits<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, num_true, idx_slice_size, p_index, p_value);
}
// subtracted sampled logits with logQ(y|x)
auto probs = EigenMatrix<T>::From(*probabilities);
auto smp_logits = EigenMatrix<T>::From(*sampled_logits);
smp_logits.device(*dev_ctx.eigen_device()) =
(smp_logits - probs.log().unaryExpr(TolerableValue<T>()))
.unaryExpr(TolerableValue<T>());
}
};
template <typename T>
class SampleLogitsGradCUDAKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& context) const override {
auto logits_grad = context.Output<Tensor>(framework::GradVarName("Logits"));
const Tensor* samples = context.Input<Tensor>("Samples");
const Tensor* sampled_logits_grad =
context.Input<Tensor>(framework::GradVarName("SampledLogits"));
logits_grad->mutable_data<T>(context.GetPlace());
auto& dev_ctx = context.cuda_device_context();
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
set_zero(dev_ctx, logits_grad, static_cast<T>(0));
// UNDERSTAND: scatter it back to logit_grad
const auto batch_size = samples->dims()[0];
const auto num_put = samples->dims()[1];
const auto array_dims = logits_grad->dims();
const auto idx_dims = samples->dims();
T* p_array = logits_grad->data<T>();
const int64_t* p_index = samples->data<int64_t>();
const T* p_value = sampled_logits_grad->data<T>();
// src slice size
const auto array_slice_size = array_dims[1];
// index slice size
const auto idx_slice_size = idx_dims[1];
int threads = 128;
const size_t size = batch_size;
int grid = (size + threads - 1) / threads;
GPUPutAlongD1<
T><<<grid, threads, 0, context.cuda_device_context().stream()>>>(
size, batch_size, array_slice_size, idx_slice_size, p_array, p_index,
p_value);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(sample_logits, ops::SampleLogitsCUDAKernel<float>,
ops::SampleLogitsCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(sample_logits_grad,
ops::SampleLogitsGradCUDAKernel<float>,
ops::SampleLogitsGradCUDAKernel<double>);
paddle/fluid/operators/sample_logits_op.h 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sample_prob.h"
#include "paddle/fluid/operators/math/softmax.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
struct TolerableValue {
HOSTDEVICE T operator()(const T& x) const {
PADDLE_ASSERT(std::is_floating_point<T>::value);
const T kApproInf = 1e20;
if (x == INFINITY) return kApproInf;
if (x == -INFINITY) return -kApproInf;
return x;
}
};
// UNDERSTAND: something like take_along_axis in numpy.
template <typename T>
static void CPUTakeAlongD1(const platform::DeviceContext& ctx,
const framework::Tensor& array,
const framework::Tensor& index,
framework::Tensor* value) {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()));
// UNDERSTAND: check shape src(B, C), index(B, K), out should also be (B, K)
PADDLE_ENFORCE(index.dims().size() == 2 && array.dims().size() == 2 &&
index.dims()[0] == array.dims()[0] &&
index.dims() == value->dims());
const auto batch_size = index.dims()[0];
const auto num_take = index.dims()[1];
const auto array_dims = array.dims();
const auto idx_dims = index.dims();
// UNDERSTAND: no allocations here
const T* p_array = array.data<T>();
const int64_t* p_index = index.data<int64_t>();
T* p_value = value->data<T>();
// src slice size
const auto array_slice_size = array_dims[1];
// index slice size
const auto idx_slice_size = idx_dims[1];
const auto value_slice_size = idx_slice_size;
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < num_take; ++j) {
auto array_index = p_index[i * idx_slice_size + j];
p_value[i * value_slice_size + j] =
p_array[i * array_slice_size + array_index];
}
}
}
// UNDERSTAND: something like put_along_axis in numpy but if there is duplicate
// indices, scatter is done in += way.
template <typename T>
static void CPUPutAlongD1(const platform::DeviceContext& ctx,
framework::Tensor* array,
const framework::Tensor& index,
const framework::Tensor& value) {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()));
// UNDERSTAND: check shape src(B, C), index(B, K), out should also be (B, K)
PADDLE_ENFORCE(index.dims().size() == 2 && array->dims().size() == 2 &&
index.dims()[0] == array->dims()[0] &&
index.dims() == value.dims());
const auto batch_size = index.dims()[0];
const auto num_put = index.dims()[1];
auto array_dims = array->dims();
auto idx_dims = index.dims();
// UNDERSTAND: no allocations here
T* p_array = array->data<T>();
const int64_t* p_index = index.data<int64_t>();
const T* p_value = value.data<T>();
// slice sizes
const auto array_slice_size = array_dims[1];
const auto idx_slice_size = idx_dims[1];
const auto value_slice_size = idx_slice_size;
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < num_put; ++j) {
auto array_index = p_index[i * idx_slice_size + j];
p_array[i * array_slice_size + array_index] +=
p_value[i * value_slice_size + j];
}
}
}
// UNDERSTAND: compute accidentdal hits from samples and minus corresponding
// logits by a float max, here 1e20
template <typename T>
static void compute_remove_accidental_hits(const platform::DeviceContext& ctx,
framework::Tensor* sampled_logits,
const framework::Tensor& samples,
const int num_true) {
const auto batch_size = sampled_logits->dims()[0];
const auto num_sampled_classes = sampled_logits->dims()[1];
T* sampled_logits_data = sampled_logits->data<T>();
const auto samples_data = samples.data<int64_t>();
std::unordered_set<int64_t> tmp_true_labels;
for (int i = 0; i < batch_size; ++i) {
tmp_true_labels.clear();
tmp_true_labels.insert(samples_data + i * num_sampled_classes,
samples_data + i * num_sampled_classes + num_true);
for (int j = num_true; j < num_sampled_classes; ++j) {
const auto idx = i * num_sampled_classes + j;
if (tmp_true_labels.find(samples_data[idx]) != tmp_true_labels.end())
sampled_logits_data[idx] -= 1e20;
}
}
}
template <typename T>
class SampleLogitsKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()),
"This kernel only runs on CPU.");
VLOG(3) << "Enter SampleLogitsKernel";
// get necessary inputs
const Tensor* logits = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Labels");
// get necessary outputs
Tensor* samples = context.Output<Tensor>("Samples");
Tensor* probabilities = context.Output<Tensor>("Probabilities");
Tensor* sampled_logits = context.Output<Tensor>("SampledLogits");
Tensor* sampled_labels = context.Output<Tensor>("SampledLabels");
// shapes
const auto batch_size = logits->dims()[0];
const auto num_classes = logits->dims()[1];
const auto labels_dim = labels->dims();
const auto num_true = labels_dim[1];
const auto samples_dim = samples->dims();
// attrs
const auto num_samples = context.Attr<int>("num_samples");
const bool use_customized_samples =
context.Attr<bool>("use_customized_samples");
const bool remove_accidental_hits =
context.Attr<bool>("remove_accidental_hits");
// device contexts
auto& dev_ctx =
context.template device_context<platform::CPUDeviceContext>();
// UNDERSTAND: allocate memories for temporaries
sampled_logits->mutable_data<T>(samples_dim, context.GetPlace());
auto sampled_labels_data =
sampled_labels->mutable_data<int64_t>(labels_dim, context.GetPlace());
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < num_true; ++j) {
sampled_labels_data[i * num_true + j] = j;
}
}
if (use_customized_samples) {
const Tensor* customized_samples =
context.Input<Tensor>("CustomizedSamples");
const Tensor* customized_probabilities =
context.Input<Tensor>("CustomizedProbabilities");
samples->ShareDataWith(*customized_samples);
probabilities->ShareDataWith(*customized_probabilities);
} else {
samples->mutable_data<int64_t>(context.GetPlace());
probabilities->mutable_data<T>(samples_dim, context.GetPlace());
// UNDERSTAND: sampling
const auto seed = context.Attr<int>("seed");
auto sampler_with_prob =
math::SampleWithProb<platform::CPUDeviceContext, T>();
sampler_with_prob(dev_ctx, math::LogUniformSampler(num_classes, seed),
num_samples, labels, samples, probabilities);
}
// UNDERSTAND: gather sampled logits and remove accidental hits if needed
CPUTakeAlongD1<T>(dev_ctx, *logits, *samples, sampled_logits);
if (remove_accidental_hits) {
compute_remove_accidental_hits<T>(dev_ctx, sampled_logits, *samples,
num_true);
}
// subtracted sampled logits with logQ(y|x)
auto probs = EigenMatrix<T>::From(*probabilities);
auto smp_logits = EigenMatrix<T>::From(*sampled_logits);
smp_logits.device(*dev_ctx.eigen_device()) =
(smp_logits - probs.log().unaryExpr(TolerableValue<T>()))
.unaryExpr(TolerableValue<T>());
}
};
template <typename T>
class SampleLogitsGradKernel : public framework::OpKernel<T> {
public:
using Tensor = framework::Tensor;
void Compute(const framework::ExecutionContext& context) const override {
auto logits_grad = context.Output<Tensor>(framework::GradVarName("Logits"));
const Tensor* samples = context.Input<Tensor>("Samples");
const Tensor* sampled_logits_grad =
context.Input<Tensor>(framework::GradVarName("SampledLogits"));
logits_grad->mutable_data<T>(context.GetPlace());
auto& dev_ctx =
context.template device_context<platform::CPUDeviceContext>();
math::SetConstant<platform::CPUDeviceContext, T> set_zero;
set_zero(dev_ctx, logits_grad, static_cast<T>(0));
// UNDERSTAND: scatter it back to logit_grad
CPUPutAlongD1<T>(dev_ctx, logits_grad, *samples, *sampled_logits_grad);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ cf0511f2
......@@ -89,9 +89,9 @@ cc_test(timer_test SRCS timer_test.cc DEPS timer)
cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto framework_proto ${GPU_CTX_DEPS})
if(WITH_GPU)
nv_library(profiler SRCS profiler.cc profiler.cu DEPS device_context device_tracer)
nv_library(profiler SRCS profiler.cc profiler.cu DEPS device_tracer gpu_info enforce)
else()
cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
cc_library(profiler SRCS profiler.cc DEPS device_tracer enforce)
endif()
cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
......
paddle/fluid/platform/device_context.cc
浏览文件 @ cf0511f2
......@@ -394,7 +394,7 @@ void MKLDNNDeviceContext::SetBlob(const std::string& name,
int tid = platform::get_cur_thread_id();
std::lock_guard<std::mutex> lock(*p_mutex_.get());
std::lock_guard<std::mutex> lock(*p_mutex_);
// Find KeyBlob for current thread
auto map_it = pMap->find(tid);
......@@ -427,7 +427,7 @@ std::shared_ptr<void> MKLDNNDeviceContext::GetBlob(
int tid = platform::get_cur_thread_id();
std::lock_guard<std::mutex> lock(*p_mutex_.get());
std::lock_guard<std::mutex> lock(*p_mutex_);
// Find KeyBlob for current thread firstly
auto map_it = pMap->find(tid);
......
paddle/fluid/platform/device_tracer.cc
浏览文件 @ cf0511f2
......@@ -136,7 +136,7 @@ void EnableActivity() {
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_DRIVER));
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_RUNTIME));
// We don't track these activities for now.
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MEMSET));
CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_MEMSET));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_OVERHEAD));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_DEVICE));
// CUPTI_CALL(dynload::cuptiActivityEnable(CUPTI_ACTIVITY_KIND_CONTEXT));
......@@ -155,7 +155,7 @@ void DisableActivity() {
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_CONTEXT));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_DRIVER));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_RUNTIME));
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MEMSET));
CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MEMSET));
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_NAME));
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_MARKER));
// CUPTI_CALL(dynload::cuptiActivityDisable(CUPTI_ACTIVITY_KIND_OVERHEAD));
......@@ -212,6 +212,14 @@ void CUPTIAPI bufferCompleted(CUcontext ctx, uint32_t streamId, uint8_t *buffer,
memcpy->correlationId, memcpy->bytes);
break;
}
case CUPTI_ACTIVITY_KIND_MEMSET: {
auto *memset =
reinterpret_cast<const CUpti_ActivityMemset *>(record);
tracer->AddKernelRecords("MEMSET", memset->start, memset->end,
memset->deviceId, memset->streamId,
memset->correlationId);
break;
}
case CUPTI_ACTIVITY_KIND_DRIVER: {
auto *api = reinterpret_cast<const CUpti_ActivityAPI *>(record);
if (api->start != 0 && api->end != 0)
......@@ -348,6 +356,8 @@ class DeviceTracerImpl : public DeviceTracer {
const std::vector<int> cbids {
CUPTI_RUNTIME_TRACE_CBID_cudaMemcpy_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaMemcpyAsync_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaMemset_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaMemsetAsync_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaLaunch_v3020,
CUPTI_RUNTIME_TRACE_CBID_cudaLaunchKernel_v7000
#if CUDA_VERSION >= 9000
......@@ -601,6 +611,8 @@ void initCuptiCbidStr() {
REGISTER_RUNTIME_CBID_STR(cudaStreamSynchronize_v3020);
REGISTER_RUNTIME_CBID_STR(cudaStreamWaitEvent_v3020);
REGISTER_RUNTIME_CBID_STR(cudaUnbindTexture_v3020);
REGISTER_RUNTIME_CBID_STR(cudaSetupArgument_v3020);
REGISTER_RUNTIME_CBID_STR(cudaLaunch_v3020);
#if CUDA_VERSION >= 9000
REGISTER_RUNTIME_CBID_STR(cudaLaunchCooperativeKernel_v9000);
REGISTER_RUNTIME_CBID_STR(cudaLaunchCooperativeKernelMultiDevice_v9000);
......
paddle/fluid/platform/device_tracer.h
浏览文件 @ cf0511f2
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <string>
#include "paddle/fluid/platform/dynload/cupti.h"
#include "paddle/fluid/platform/event.h"
#include "paddle/fluid/platform/port.h"
#include "paddle/fluid/platform/profiler.pb.h"
......@@ -32,8 +33,6 @@ inline uint64_t PosixInNsec() {
return 1000 * (static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec);
}
class Event;
// DeviceTracer performs the following tasks:
// 1. Register cuda callbacks for various events: kernel, memcpy, etc.
// 2. Collect cuda statistics: start/end ts, memory, etc.
......
paddle/fluid/platform/enforce.h
浏览文件 @ cf0511f2
......@@ -34,6 +34,7 @@ limitations under the License. */
#include <type_traits>
#include <utility>
#define GLOG_NO_ABBREVIATED_SEVERITIES // msvc conflict logging with windows.h
#include "glog/logging.h"
#include "paddle/fluid/platform/macros.h"
#include "paddle/fluid/platform/port.h"
......
paddle/fluid/platform/event.h 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
namespace paddle {
namespace platform {
enum EventType { kMark, kPushRange, kPopRange };
class Event {
public:
// The DeviceContext is used to get the cuda stream.
// If CPU profiling mode, can pass nullptr.
Event(EventType type, std::string name, uint32_t thread_id);
const EventType& type() const;
std::string name() const { return name_; }
uint32_t thread_id() const { return thread_id_; }
#ifdef PADDLE_WITH_CUDA
#ifndef PADDLE_WITH_CUPTI
cudaEvent_t event() const { return event_; }
int device() const { return device_; }
#endif
#endif
double CpuElapsedMs(const Event& e) const;
double CudaElapsedMs(const Event& e) const;
private:
EventType type_;
std::string name_;
uint32_t thread_id_;
int64_t cpu_ns_;
#ifdef PADDLE_WITH_CUDA
#ifdef PADDLE_WITH_CUPTI
int64_t gpu_ns_ = 0;
public:
void AddCudaElapsedTime(int64_t start_ns, int64_t end_ns) {
gpu_ns_ += end_ns - start_ns;
}
private:
#else
cudaEvent_t event_ = nullptr;
int device_ = -1;
#endif
#endif
};
} // namespace platform
} // namespace paddle
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @ cf0511f2
......@@ -39,6 +39,45 @@ class MKLDNNHandler {
return this->AcquireMemory(md, ptr, "@user_src_mem_p");
}
// TODO(jczaja): extract common part and make AcquireMemory
std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
const mkldnn::memory::primitive_desc& mpd, void* ptr) {
auto local_key = key_ + "@user_src_mem_p";
auto mem_p =
std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false),
" find mem primitive in device context");
if (mem_p == nullptr) {
mem_p = std::make_shared<mkldnn::memory>(mpd, ptr);
dev_ctx_.SetBlob(local_key, mem_p);
} else {
mem_p->set_data_handle(ptr);
// Mark that reusing happenned. All primitives from operator instance
// should be reused or none of them. So we check consistency
is_reusing_ = true;
}
return mem_p;
}
std::shared_ptr<mkldnn::memory> AcquireWeightsMemory(
const mkldnn::memory::primitive_desc& mpd, void* ptr) {
auto local_key = key_ + "@user_weights_mem_p";
auto mem_p =
std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false),
" find mem primitive in device context");
if (mem_p == nullptr) {
mem_p = std::make_shared<mkldnn::memory>(mpd, ptr);
dev_ctx_.SetBlob(local_key, mem_p);
} else {
mem_p->set_data_handle(ptr);
// Mark that reusing happenned. All primitives from operator instance
// should be reused or none of them. So we check consistency
is_reusing_ = true;
}
return mem_p;
}
std::shared_ptr<mkldnn::memory> AcquireWeightsMemory(
const mkldnn::memory::desc& md, void* ptr,
user_function custom_func = {}) {
......@@ -273,37 +312,7 @@ class TransposeMKLDNNHandler : public MKLDNNHandler {
mkldnn::engine engine, const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key),
dims_(dims),
axis_(axis),
logical_axis_(dims.size(), 0) {}
std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
const mkldnn::memory::format& fmt, void* ptr) {
auto local_key = key_ + "@user_src_mem_p";
auto mem_p =
std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false),
" find mem primitive in device context");
if (mem_p == nullptr) {
// Make memory descriptor using input format, unless it
// cannot be trusted (nchw) then make up memory fmt manually
for (size_t i = 0; i < logical_axis_.size(); ++i) {
logical_axis_[i] = i;
}
auto src_md = fmt != mkldnn::memory::format::nchw
? platform::MKLDNNMemDesc(
dims_, platform::MKLDNNGetDataType<float>(), fmt)
: Axis2MemoryDesc(dims_, logical_axis_);
mem_p = std::make_shared<mkldnn::memory>(
mkldnn::memory::primitive_desc{src_md, engine_}, ptr);
dev_ctx_.SetBlob(local_key, mem_p);
} else {
mem_p->set_data_handle(ptr);
// Mark that reusing happenned. All primitives from operator instance
// should be reused or none of them. So we check consistency
is_reusing_ = true;
}
return mem_p;
}
axis_(axis) {}
std::shared_ptr<mkldnn::memory> AcquireDstMemory(framework::Tensor* output,
platform::Place place) {
......@@ -388,7 +397,6 @@ class TransposeMKLDNNHandler : public MKLDNNHandler {
private:
std::vector<int> dims_;
std::vector<int> axis_;
std::vector<int> logical_axis_;
};
template <class forward_t, class backward_data_t, class backward_weights_t>
......@@ -548,9 +556,8 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false),
"Fail to find convolution primitive in device context");
if (conv_p == nullptr) {
conv_p = std::make_shared<forward_t>(*conv_pd_, *(src_memory_p),
*(weights_memory_p.get()),
*(dst_memory_p.get()));
conv_p = std::make_shared<forward_t>(*conv_pd_, *src_memory_p,
*weights_memory_p, *dst_memory_p);
dev_ctx_.SetBlob(prim_key, conv_p);
} else {
......@@ -570,9 +577,9 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
PADDLE_ENFORCE((conv_p != nullptr) || (is_reusing_ == false),
"Fail to find convolution primitive in device context");
if (conv_p == nullptr) {
conv_p = std::make_shared<forward_t>(
*conv_pd_, *(src_memory_p), *(weights_memory_p.get()),
*(bias_memory_p.get()), *(dst_memory_p.get()));
conv_p = std::make_shared<forward_t>(*conv_pd_, *src_memory_p,
*weights_memory_p, *bias_memory_p,
*dst_memory_p);
dev_ctx_.SetBlob(prim_key, conv_p);
} else {
......
paddle/fluid/platform/mkldnn_utils.h 0 → 100644
浏览文件 @ cf0511f2
/* Copyright (c) 2019 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 <mkldnn.h>
#include <string>
namespace paddle {
namespace platform {
inline mkldnn::memory::primitive_desc create_prim_desc_from_dims(
const std::vector<int>& ltz, mkldnn::memory::format fmt,
mkldnn::memory::data_type data_type = mkldnn::memory::data_type::f32) {
mkldnn_memory_desc_t mem_fmt;
mem_fmt.primitive_kind = mkldnn_memory;
mem_fmt.ndims = ltz.size();
for (unsigned int i = 0; i < ltz.size(); ++i) {
mem_fmt.dims[i] = ltz[i]; // logical dimensions (nchw format,
// regardless physical layout)
}
mem_fmt.data_type = static_cast<mkldnn_data_type_t>(data_type);
mem_fmt.format = static_cast<mkldnn_memory_format_t>(fmt);
unsigned int total_stride = 1;
for (int i = ltz.size() - 1; i >= 0; --i) {
mem_fmt.layout_desc.blocking.padding_dims[i] =
ltz[i]; // logical dimensions (nchw format, regardless physical
// layout)
mem_fmt.layout_desc.blocking.block_dims[i] = 1;
mem_fmt.layout_desc.blocking.offset_padding_to_data[i] = 0; // no offset
mem_fmt.layout_desc.blocking.strides[0][i] = total_stride;
mem_fmt.layout_desc.blocking.strides[1][i] = 1;
total_stride *= ltz[i];
}
mem_fmt.layout_desc.blocking.offset_padding = 0; // no initial offset
auto& pool = platform::DeviceContextPool::Instance();
auto place = paddle::platform::CPUPlace();
auto* dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(pool.Get(place));
auto& cpu_engine = dev_ctx->GetEngine();
return mkldnn::memory::primitive_desc(mem_fmt, cpu_engine);
}
inline mkldnn::memory::primitive_desc create_prim_desc_from_format(
const std::vector<int>& ltz, const mkldnn::memory::format format,
const mkldnn::memory::data_type data_type) {
auto md = mkldnn::memory::desc({ltz}, data_type, format);
auto& pool = platform::DeviceContextPool::Instance();
auto place = paddle::platform::CPUPlace();
auto dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(pool.Get(place));
PADDLE_ENFORCE_NOT_NULL(dev_ctx, "Could not get valid device");
auto& cpu_engine = dev_ctx->GetEngine();
return mkldnn::memory::primitive_desc(md, cpu_engine);
}
} // namespace platform
} // namespace paddle
paddle/fluid/platform/profiler.cc
浏览文件 @ cf0511f2
......@@ -112,12 +112,11 @@ double Event::CpuElapsedMs(const Event& e) const {
}
double Event::CudaElapsedMs(const Event& e) const {
#ifdef PADDLE_WITH_CUDA
#ifdef PADDLE_WITH_CUPTI
return gpu_ns_ / 1000000.0;
#endif
#else
PADDLE_THROW("CUDA is not enabled");
LOG_FIRST_N(WARNING, 1) << "CUDA CUPTI is not enabled";
return 0;
#endif
}
......@@ -255,9 +254,11 @@ struct EventItem {
std::string name;
int calls;
double total_time;
double min_time;
double max_time;
double ave_time;
double min_time;
double cpu_time;
double gpu_time;
float ratio;
};
......@@ -291,8 +292,12 @@ void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
// Output events table
std::cout.setf(std::ios::left);
std::cout << std::setw(name_width) << "Event" << std::setw(data_width)
<< "Calls" << std::setw(data_width) << "Total"
<< std::setw(data_width) << "Min." << std::setw(data_width)
<< "Calls" << std::setw(data_width) << "Total";
if (g_state == ProfilerState::kAll) {
std::cout << std::setw(data_width * 2) << "CPU Time (Ratio)"
<< std::setw(data_width * 2) << "GPU Time (Ratio)";
}
std::cout << std::setw(data_width) << "Min." << std::setw(data_width)
<< "Max." << std::setw(data_width) << "Ave."
<< std::setw(data_width) << "Ratio." << std::endl;
for (size_t i = 0; i < events_table.size(); ++i) {
......@@ -300,8 +305,18 @@ void PrintProfiler(const std::vector<std::vector<EventItem>>& events_table,
const EventItem& event_item = events_table[i][j];
std::cout << std::setw(name_width) << event_item.name
<< std::setw(data_width) << event_item.calls
<< std::setw(data_width) << event_item.total_time
<< std::setw(data_width) << event_item.min_time
<< std::setw(data_width) << event_item.total_time;
if (g_state == ProfilerState::kAll) {
std::cout << std::setw(data_width * 2)
<< string::Sprintf(
"%f (%f)", event_item.cpu_time,
(event_item.cpu_time / event_item.total_time))
<< std::setw(data_width * 2)
<< string::Sprintf(
"%f (%f)", event_item.gpu_time,
(event_item.gpu_time / event_item.total_time));
}
std::cout << std::setw(data_width) << event_item.min_time
<< std::setw(data_width) << event_item.max_time
<< std::setw(data_width) << event_item.ave_time
<< std::setw(data_width) << event_item.ratio << std::endl;
......@@ -350,6 +365,18 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
return a.ave_time > b.ave_time;
};
break;
case EventSortingKey::kGPUTime:
sorted_domain = "average time";
sorted_func = [](const EventItem& a, const EventItem& b) {
return a.gpu_time > b.gpu_time;
};
break;
case EventSortingKey::kCPUTime:
sorted_domain = "average time";
sorted_func = [](const EventItem& a, const EventItem& b) {
return a.cpu_time > b.cpu_time;
};
break;
default:
sorted_domain = "event first end time";
}
......@@ -388,10 +415,17 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
}
if (rit != pushed_events.rend()) {
double event_time = (g_state == ProfilerState::kCUDA ||
g_state == ProfilerState::kAll)
? rit->CudaElapsedMs((*analyze_events)[i][j])
: rit->CpuElapsedMs((*analyze_events)[i][j]);
double event_time = 0;
double gpu_time = rit->CudaElapsedMs((*analyze_events)[i][j]);
double cpu_time = rit->CpuElapsedMs((*analyze_events)[i][j]);
if (g_state == ProfilerState::kCUDA) {
event_time = gpu_time;
} else if (g_state == ProfilerState::kCPU) {
event_time = cpu_time;
} else {
event_time = gpu_time + cpu_time;
}
total += event_time;
std::string event_name;
......@@ -408,7 +442,7 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
event_idx[event_name] = event_items.size();
EventItem event_item = {event_name, 1, event_time,
event_time, event_time, event_time,
0.};
gpu_time, cpu_time, 0.};
event_items.push_back(event_item);
} else {
int index = event_idx[event_name];
......@@ -421,6 +455,8 @@ void ParseEvents(const std::vector<std::vector<Event>>& events,
// max time
event_items[index].max_time =
std::max(event_time, event_items[index].max_time);
event_items[index].gpu_time += gpu_time;
event_items[index].cpu_time += cpu_time;
}
// remove the push marker from the list
......
paddle/fluid/platform/profiler.cu
浏览文件 @ cf0511f2
......@@ -12,9 +12,8 @@ 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/platform/profiler.h"
#include <cuda.h>
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace platform {
......@@ -22,26 +21,27 @@ namespace platform {
__global__ void DummyKernel(int *a) { a[0] = 0; }
static void ForEachDevice(std::function<void(int)> func) {
auto original_device = GetCurrentDeviceId();
int count = GetCUDADeviceCount();
auto original_device = platform::GetCurrentDeviceId();
int count = platform::GetCUDADeviceCount();
for (int i = 0; i < count; i++) {
SetDeviceId(i);
platform::SetDeviceId(i);
func(i);
}
SetDeviceId(original_device);
platform::SetDeviceId(original_device);
}
void DummyKernelAndEvent() {
for (int i = 0; i < 5; i++) {
ForEachDevice([](int d) {
CUDADeviceContext *dev_ctx = new CUDADeviceContext(CUDAPlace(d));
platform::SetDeviceId(d);
cudaStream_t stream;
PADDLE_ENFORCE(cudaStreamCreate(&stream));
Mark("_cuda_startup_");
int *ptr;
PADDLE_ENFORCE(cudaMalloc(&ptr, sizeof(int)));
DummyKernel<<<1, 1, 0, dev_ctx->stream()>>>(ptr);
dev_ctx->Wait();
DummyKernel<<<1, 1, 0, stream>>>(ptr);
PADDLE_ENFORCE(cudaStreamSynchronize(stream));
PADDLE_ENFORCE(cudaFree(ptr));
delete dev_ctx;
});
}
}
......
paddle/fluid/platform/profiler.h
浏览文件 @ cf0511f2
......@@ -17,54 +17,13 @@ limitations under the License. */
#include <list>
#include <string>
#include <vector>
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace platform {
enum EventType { kMark, kPushRange, kPopRange };
class Event {
public:
// The DeviceContext is used to get the cuda stream.
// If CPU profiling mode, can pass nullptr.
Event(EventType type, std::string name, uint32_t thread_id);
const EventType& type() const;
std::string name() const { return name_; }
uint32_t thread_id() const { return thread_id_; }
#ifdef PADDLE_WITH_CUDA
#ifndef PADDLE_WITH_CUPTI
cudaEvent_t event() const { return event_; }
int device() const { return device_; }
#endif
#endif
double CpuElapsedMs(const Event& e) const;
double CudaElapsedMs(const Event& e) const;
private:
EventType type_;
std::string name_;
uint32_t thread_id_;
int64_t cpu_ns_;
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/event.h"
#ifdef PADDLE_WITH_CUDA
#ifdef PADDLE_WITH_CUPTI
int64_t gpu_ns_ = 0;
public:
void AddCudaElapsedTime(int64_t start_ns, int64_t end_ns) {
gpu_ns_ += end_ns - start_ns;
}
private:
#else
cudaEvent_t event_ = nullptr;
int device_ = -1;
#endif
#include "paddle/fluid/platform/gpu_info.h"
#endif
};
namespace paddle {
namespace platform {
enum ProfilerState {
kDisabled, // disabled state
......@@ -117,7 +76,16 @@ struct RecordBlock {
std::vector<std::vector<Event>> GetAllEvents();
// Candidate keys to sort the profiling report
enum EventSortingKey { kDefault, kCalls, kTotal, kMin, kMax, kAve };
enum EventSortingKey {
kDefault,
kCalls,
kTotal,
kMin,
kMax,
kAve,
kCPUTime,
kGPUTime
};
// Enable the profiling function.
void EnableProfiler(ProfilerState state);
......
paddle/fluid/platform/profiler_test.cc
浏览文件 @ cf0511f2
......@@ -33,7 +33,6 @@ TEST(Event, CpuElapsedTime) {
}
TEST(RecordEvent, RecordEvent) {
using paddle::platform::DeviceContext;
using paddle::platform::Event;
using paddle::platform::EventType;
using paddle::platform::RecordEvent;
......
paddle/fluid/platform/temporary_allocator_test.cc
浏览文件 @ cf0511f2
......@@ -141,7 +141,7 @@ TEST(temporary_allocator, create_tensor_with_allocationptr) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx =
static_cast<platform::CPUDeviceContext*>(pool.Get(cpu_place));
framework::ExecutionContext ctx(op, scope, *dev_ctx, run_ctx);
framework::ExecutionContext ctx(op, scope, *dev_ctx, run_ctx, nullptr);
int numel = memory_size / sizeof(float);
framework::Tensor tensor =
......@@ -156,7 +156,7 @@ TEST(temporary_allocator, create_tensor_with_allocationptr) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx =
static_cast<platform::CUDADeviceContext*>(pool.Get(gpu_place));
framework::ExecutionContext ctx(op, scope, *dev_ctx, run_ctx);
framework::ExecutionContext ctx(op, scope, *dev_ctx, run_ctx, nullptr);
int numel = memory_size / sizeof(float);
framework::Tensor tensor =
ctx.AllocateTmpTensor<float, platform::CUDADeviceContext>(
......@@ -179,7 +179,7 @@ TEST(temporary_allocator, create_tensor_with_allocationptr2) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx =
static_cast<platform::CPUDeviceContext*>(pool.Get(cpu_place));
framework::ExecutionContext ctx(op, scope, *dev_ctx, run_ctx);
framework::ExecutionContext ctx(op, scope, *dev_ctx, run_ctx, nullptr);
int numel = memory_size / sizeof(float);
framework::Tensor out_side_tensor;
......@@ -200,7 +200,7 @@ TEST(temporary_allocator, create_tensor_with_allocationptr2) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx =
static_cast<platform::CUDADeviceContext*>(pool.Get(gpu_place));
framework::ExecutionContext ctx(op, scope, *dev_ctx, run_ctx);
framework::ExecutionContext ctx(op, scope, *dev_ctx, run_ctx, nullptr);
size_t memory_size = 500;
int numel = memory_size / sizeof(float);
......
paddle/fluid/pybind/imperative.cc
浏览文件 @ cf0511f2
......@@ -34,8 +34,8 @@ void BindTracer(pybind11::module* m) {
framework::BlockDesc* block,
const platform::CPUPlace expected_place,
const bool stop_gradient = false) {
self.Trace(op, inputs, outputs, block, expected_place,
stop_gradient);
return self.Trace(op, inputs, outputs, block, expected_place,
stop_gradient);
})
.def("trace",
[](imperative::Tracer& self, imperative::OpBase* op,
......@@ -44,8 +44,8 @@ void BindTracer(pybind11::module* m) {
framework::BlockDesc* block,
const platform::CUDAPlace expected_place,
const bool stop_gradient = false) {
self.Trace(op, inputs, outputs, block, expected_place,
stop_gradient);
return self.Trace(op, inputs, outputs, block, expected_place,
stop_gradient);
})
.def("py_trace", &imperative::Tracer::PyTrace,
pybind11::return_value_policy::take_ownership);
......
paddle/fluid/pybind/ir.cc
浏览文件 @ cf0511f2
......@@ -101,7 +101,8 @@ void BindGraph(py::module *m) {
[](Graph &self, Node &node) { return self.RemoveNode(&node); })
.def("retrieve_node", &Graph::RetrieveNode,
return_value_policy::reference)
.def("resolve_hazard", &Graph::ResolveHazard);
.def("resolve_hazard", &Graph::ResolveHazard)
.def("origin_program_desc", &Graph::OriginProgram);
}
void BindNode(py::module *m) {
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ cf0511f2
......@@ -189,6 +189,8 @@ void BindBlockDesc(pybind11::module *m) {
return self.HasVar(name);
},
pybind11::return_value_policy::reference)
.def("_clear_block", [](pd::BlockDesc &self) { return self.Clear(); },
pybind11::return_value_policy::reference)
.def("_rename_var",
[](pd::BlockDesc &self, const pybind11::bytes &byte_name,
const pybind11::bytes &byte_name_new) {
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ cf0511f2
......@@ -976,6 +976,7 @@ All parameter, weight, gradient are variables in Paddle.
[](ir::PassBuilder &self, size_t idx) { self.RemovePass(idx); });
// -- python binds for parallel executor.
py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy", R"DOC(
ExecutionStrategy allows the user to more preciously control how to run
......@@ -1227,9 +1228,9 @@ All parameter, weight, gradient are variables in Paddle.
cannot be updated after being finalized.)DOC");
pe.def(py::init<const std::vector<platform::Place> &,
const std::unordered_set<std::string> &, const ProgramDesc &,
const std::string &, Scope *, std::vector<Scope *> &,
const ExecutionStrategy &, const BuildStrategy &>())
const std::unordered_set<std::string> &, const std::string &,
Scope *, std::vector<Scope *> &, const ExecutionStrategy &,
const BuildStrategy &, ir::Graph *>())
// NOTE: even we return a vec<Scope*>* to Python use reference policy.
// We still cannot get local_scope from this vector, since the element
// of vec<Scope*> will be freed by Python GC. We can only return Scope*
......
paddle/fluid/train/demo/demo_trainer.cc
浏览文件 @ cf0511f2
......@@ -73,7 +73,7 @@ int main() {
PADDLE_ENFORCE_NE(loss_name, "", "loss not found");
// init all parameters
executor.Run(*startup_program.get(), &scope, 0);
executor.Run(*startup_program, &scope, 0);
// prepare data
auto x_var = scope.Var("x");
......@@ -101,7 +101,7 @@ int main() {
clock_t t1 = clock();
for (int i = 0; i < 10; ++i) {
executor.Run(*train_program.get(), &scope, 0, false, true);
executor.Run(*train_program, &scope, 0, false, true);
std::cout << "step: " << i << " loss: "
<< loss_var->Get<paddle::framework::LoDTensor>().data<float>()[0]
<< std::endl;
......
paddle/fluid/train/test_train_recognize_digits.cc
浏览文件 @ cf0511f2
......@@ -74,7 +74,7 @@ void Train() {
float first_loss = 0.0;
float last_loss = 0.0;
for (int i = 0; i < 100; ++i) {
executor.Run(*train_program.get(), &scope, 0, false, true);
executor.Run(*train_program, &scope, 0, false, true);
if (i == 0) {
first_loss = loss_var->Get<framework::LoDTensor>().data<float>()[0];
} else if (i == 99) {
......
paddle/scripts/paddle_build.sh
浏览文件 @ cf0511f2
......@@ -444,6 +444,7 @@ function assert_api_spec_approvals() {
"paddle/fluid/framework/ir/node.h"
"paddle/fluid/framework/ir/graph.h"
"paddle/fluid/framework/framework.proto"
"python/paddle/fluid/compiler.py"
"paddle/fluid/operators/distributed/send_recv.proto.in")
for API_FILE in ${API_FILES[*]}; do
API_CHANGE=`git diff --name-only upstream/$BRANCH | grep "${API_FILE}" || true`
......
python/paddle/fluid/compiler.py
浏览文件 @ cf0511f2
......@@ -17,8 +17,10 @@ import os
import six
import sys
from .. import compat as cpt
from . import framework
from . import core
from . import framework
__all__ = ['CompiledProgram', 'ExecutionStrategy', 'BuildStrategy']
......@@ -36,7 +38,7 @@ def _place_obj(place):
class CompiledProgram(object):
"""
Compiles a Program for execution.
Compiles to Graph for execution.
1. Users first create the program with layers.
2. Optionally, users use CompiledProgram to optimize the program before run.
......@@ -51,7 +53,7 @@ class CompiledProgram(object):
Example:
.. code-block:: python
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup)
compiled_prog = compiler.CompiledProgram(main).with_data_parallel(
......@@ -62,11 +64,25 @@ class CompiledProgram(object):
fetch_list=[loss.name])
Args:
program: Program instance that contains the model logic.
program_or_graph (Graph|Program): If it's Program, it will be first
lowered to a graph for further optimizations. If it's a graph
(potentially optimized before), it will be directly used for
further optimizations. Note: graph is only supported when compiled
with with_data_parallel option.
"""
def __init__(self, program):
self._program = program
def __init__(self, program_or_graph):
if isinstance(program_or_graph, core.Graph):
self._graph = program_or_graph
self._program = None
elif isinstance(program_or_graph, framework.Program):
self._graph = core.Graph(program_or_graph.desc)
self._program = program_or_graph
else:
raise ValueError("Wrong program_to_graph type: %s" %
type(program_or_graph))
self._program_desc = self._graph.origin_program_desc()
self._scope = None
self._place = None
self._executor = None
......@@ -101,6 +117,7 @@ class CompiledProgram(object):
self
"""
assert not self._is_data_parallel, "Already compiled with parallel."
assert not self._is_inference, "Cannot compile both data parallel and inference"
self._is_data_parallel = True
self._build_strategy = build_strategy
self._exec_strategy = exec_strategy
......@@ -110,6 +127,8 @@ class CompiledProgram(object):
self._exec_strategy = ExecutionStrategy()
if self._build_strategy is None:
self._build_strategy = BuildStrategy()
self._build_strategy.is_distribution = framework.is_pserver_mode(
self._program)
return self
def with_inference_optimize(self, config):
......@@ -120,11 +139,13 @@ class CompiledProgram(object):
Returns:
self
"""
assert not self._is_data_parallel, "Cannot compile both data parallel and inference"
assert not self._is_inference, "Already compiled with inference"
assert any([
isinstance(config, InferNativeConfig),
isinstance(config, InferAnalysisConfig)
])
self._is_data_parallel = False
self._is_inference = True
self._infer_config = config
return self
......@@ -173,37 +194,41 @@ class CompiledProgram(object):
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
self._exec_strategy.num_threads = cpu_num * 2
trainers_endpoints = self._program._trainers_endpoints
# FIXME(dzhwinter): enable_inplace should be after memory_optimize
# if turn on python memory optimize, turn off the inplace_pass.
if self._build_strategy.memory_optimize is None:
self._build_strategy.memory_optimize = False if self._program._is_mem_optimized else True
self._build_strategy.memory_optimize = False if self._program and self._program._is_mem_optimized else True
if self._build_strategy.enable_inplace is None:
self._build_strategy.enable_inplace = False if self._program._is_mem_optimized else True
self._build_strategy.enable_inplace = False if self._program and self._program._is_mem_optimized else True
# TODO(wuyi): trainer endpoings should be passed in through
# build_strategy, not program.xxx.
if self._program and self._build_strategy.num_trainers > 1 and \
self._program._trainers_endpoints:
tps = self._program._trainers_endpoints
if self._build_strategy.num_trainers > 1 and trainers_endpoints:
assert self._build_strategy.num_trainers == len(
trainers_endpoints), "num_trainers == len(end_points)"
self._build_strategy.trainers_endpoints = trainers_endpoints
self._persistable_vars = set([
cpt.to_text(v.name)
for v in [
var for var in self._program.list_vars()
if var.persistable and var.type != core.VarDesc.VarType.RAW
]
])
tps), "num_trainers == len(end_points)"
self._build_strategy.trainers_endpoints = tps
self._persistable_vars = []
for block_id in range(self._program_desc.num_blocks()):
bdesc = self._program_desc.block(block_id)
self._persistable_vars.extend([
cpt.to_text(v.name()) for v in bdesc.all_vars()
if v.persistable() and v.type() != core.VarDesc.VarType.RAW
])
places = list(map(_place_obj, self._places))
return core.ParallelExecutor(
places, self._persistable_vars, self._program.desc,
places,
set(self._persistable_vars),
cpt.to_text(self._loss_name)
if self._loss_name else six.u(''), self._scope, self._local_scopes,
self._exec_strategy, self._build_strategy)
self._exec_strategy, self._build_strategy, self._graph)
def _compile_inference(self):
assert self._is_data_parallel is False
return core.create_paddle_predictor(self._infer_config)
def _compile(self, scope, place):
......
python/paddle/fluid/executor.py
浏览文件 @ cf0511f2
......@@ -538,6 +538,8 @@ class Executor(object):
else:
# TODO(panyx0718): Can compile program to optimize executor
# performance.
# TODO(panyx0718): executor should be able to run graph.
assert program._program, "CompiledProgram is compiled from graph, can only run with_data_parallel."
return self._run(
program._program,
self._default_executor,
......
python/paddle/fluid/framework.py
浏览文件 @ cf0511f2
......@@ -87,6 +87,15 @@ def _current_expected_place():
return _imperative_current_expected_place_
def is_pserver_mode(main_program):
main = main_program if main_program \
else default_main_program()
for op in main.global_block().ops:
if op.type in ["send", "recv"]:
return True
return False
class NameScope(object):
def __init__(self, name="", parent=None):
self._children = dict()
......@@ -378,16 +387,19 @@ class Variable(object):
# get_capacity is implemented
pass
self.block.vars[name] = self
self.op = None
self.stop_gradient = stop_gradient
self.is_data = is_data
if _in_imperative_mode():
# record vars in tracer rather than blocks
self._ivar = kwargs.get("ivar", None)
if not self._ivar:
self._ivar = core.VarBase()
self._ivar = core.VarBase(stop_gradient)
self._ivar.desc = self.desc
self._ivar.stop_gradient = stop_gradient
if persistable:
self.block.vars[name] = self
else:
self.block.vars[name] = self
self.op = None
self.stop_gradient = stop_gradient
self.is_data = is_data
def _numpy(self):
new_ivar = self._ivar._copy_to(core.CPUPlace(), True)
......@@ -723,7 +735,6 @@ class Operator(object):
self._update_desc_attr(attr_name, attr_val)
self.desc.check_attrs()
if self._has_kernel(type):
self.desc.infer_var_type(self.block.desc)
self.desc.infer_shape(self.block.desc)
......@@ -731,6 +742,7 @@ class Operator(object):
if _in_imperative_mode():
self.iop = core.OpBase()
self.iop.desc = self.desc
self.inputs = defaultdict(list)
if inputs is not None:
for k, v in six.iteritems(inputs):
......@@ -738,6 +750,7 @@ class Operator(object):
self.inputs[k].append(v._ivar)
elif isinstance(v, list) or isinstance(v, tuple):
self.inputs[k].extend([var._ivar for var in v])
self.outputs = defaultdict(list)
if outputs is not None:
for k, v in six.iteritems(outputs):
......@@ -1187,6 +1200,15 @@ class Block(object):
else:
raise ValueError("Var {0} is not found recursively".format(name))
def _clear_block(self):
# TODO(minqiyang): move this to backward_hooks
self.desc._clear_block()
for name in self.vars.keys():
assert self.vars[name].persistable
del self.ops[:]
def all_parameters(self):
return list(self.iter_parameters())
......@@ -1317,18 +1339,31 @@ class Block(object):
inputs=kwargs.get("inputs", None),
outputs=kwargs.get("outputs", None),
attrs=kwargs.get("attrs", None))
if _in_imperative_mode():
# record ops in tracer rather than blocks
#
# TODO(minqiyang): add op stop_gradient support in static mode too.
# currently, we only support stop_gradient in imperative mode.
self._trace_op(op, kwargs.get("stop_gradient", False))
self.ops.append(op)
# TODO(minqiyang): add stop_gradient support in static mode too.
# currently, we only support stop_gradient in imperative mode.
self._trace_op(op, kwargs.get("stop_gradient", False))
return op
def _trace_op(self, op, stop_gradient=False):
if _in_imperative_mode():
_imperative_tracer().trace(op.iop, op.inputs, op.outputs, self.desc,
_imperative_current_expected_place_,
stop_gradient)
backward_refs = _imperative_tracer().trace(
op.iop, op.inputs, op.outputs, self.desc,
_imperative_current_expected_place_, stop_gradient)
# TODO(minqiyang): support backward_hooks to eager remove backward_refs
op.backward_refs = defaultdict(list)
for k, v in six.iteritems(op.inputs):
if k in backward_refs:
op.backward_refs[k] = op.inputs[k]
for k, v in six.iteritems(op.outputs):
if k in backward_refs:
op.backward_refs[k] = op.outputs[k]
def _insert_op(self, index, *args, **kwargs):
"""
......@@ -1383,7 +1418,8 @@ class Block(object):
outputs=kwargs.get("outputs", None),
attrs=kwargs.get("attrs", None))
self.ops.insert(0, op)
self._trace_op(op, kwargs.get("stop_gradient", False))
if _in_imperative_mode():
self._trace_op(op, kwargs.get("stop_gradient", False))
return op
def _sync_with_cpp(self):
......
python/paddle/fluid/imperative/layers.py
浏览文件 @ cf0511f2
......@@ -17,7 +17,7 @@ import contextlib
import sys
import numpy as np
import collections
from .. import unique_name
from paddle.fluid import core
from paddle.fluid import framework
from paddle.fluid.imperative import base
......@@ -26,14 +26,33 @@ __all__ = ['Layer', 'PyLayer']
class Layer(core.Layer):
"""Layers composed of operators."""
def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
"""Layers composed of operators.
Args:
name_scope: prefix name used by the layer to name parameters.
If prefix is "my_model/layer_1", parameter name in MyLayer
can be "my_model/layer_1/MyLayer/w_n", where w is the parameter
base name and n is an unique suffix auto-generated.
dtype: data type for the variables in the layer.
"""
def __init__(self, name_scope, dtype=core.VarDesc.VarType.FP32):
self._full_name = unique_name.generate(name_scope + "/" +
self.__class__.__name__)
self._built = False
self._dtype = dtype
self._parameters = collections.OrderedDict()
self._sub_layers = collections.OrderedDict()
def full_name(self):
"""Full name for this layers.
Full name is composed by name_scope + "/" + MyLayer.__class__.__name__
Returns full name of this name.
"""
return self._full_name
def parameters(self, include_sublayers=True):
"""Returns a list of Parameters from current and sub-layers.
......
python/paddle/fluid/imperative/nn.py
浏览文件 @ cf0511f2
......@@ -27,6 +27,7 @@ __all__ = ['Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding']
class Conv2D(layers.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
......@@ -38,19 +39,17 @@ class Conv2D(layers.Layer):
act=None,
param_attr=None,
bias_attr=None,
name=None,
dtype=core.VarDesc.VarType.FP32):
assert param_attr is not False, "param_attr should not be False here."
super(Conv2D, self).__init__(name=name, dtype=dtype)
super(Conv2D, self).__init__(name_scope, dtype=dtype)
# TODO(minqiyang): Move this to the top.
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
type(self).__name__,
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
dtype=dtype,
name=name,
act=act)
self._groups = groups
......@@ -143,6 +142,7 @@ class Conv2D(layers.Layer):
class Pool2D(layers.Layer):
def __init__(self,
name_scope,
pool_size=-1,
pool_type="max",
pool_stride=1,
......@@ -151,7 +151,6 @@ class Pool2D(layers.Layer):
use_cudnn=True,
ceil_mode=False,
exclusive=True,
name=None,
dtype=core.VarDesc.VarType.FP32):
if pool_type not in ["max", "avg"]:
raise ValueError(
......@@ -166,10 +165,10 @@ class Pool2D(layers.Layer):
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
super(Pool2D, self).__init__(name=name, dtype=dtype)
super(Pool2D, self).__init__(name_scope, dtype=dtype)
from ..layer_helper import LayerHelper
self._helper = LayerHelper(type(self).__name__, dtype=dtype, name=name)
self._helper = LayerHelper(self.full_name(), dtype=dtype)
self._pool_type = pool_type
self._pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
......@@ -205,25 +204,24 @@ class Pool2D(layers.Layer):
class FC(layers.Layer):
def __init__(self,
name_scope,
size,
param_attr=None,
bias_attr=None,
num_flatten_dims=1,
dtype=core.VarDesc.VarType.FP32,
act=None,
name=None):
super(FC, self).__init__()
act=None):
super(FC, self).__init__(name_scope)
self._size = size
self._num_flatten_dims = num_flatten_dims
self._dtype = dtype
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
'FC',
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
act=act,
name=name)
act=act)
def _build_once(self, input):
input_shape = input.shape
......@@ -282,6 +280,7 @@ class FC(layers.Layer):
class BatchNorm(layers.Layer):
def __init__(self,
name_scope,
num_channels,
act=None,
is_test=False,
......@@ -292,22 +291,20 @@ class BatchNorm(layers.Layer):
dtype=core.VarDesc.VarType.FP32,
data_layout='NCHW',
in_place=False,
name=None,
moving_mean_name=None,
moving_variance_name=None,
do_model_average_for_mean_and_var=False,
fuse_with_relu=False,
use_global_stats=False):
super(BatchNorm, self).__init__()
super(BatchNorm, self).__init__(name_scope)
assert bias_attr is not False, "bias_attr should not be False in batch_norm."
from ..layer_helper import LayerHelper
self._helper = LayerHelper(
'batch_norm',
self.full_name(),
param_attr=param_attr,
bias_attr=bias_attr,
name=name,
act=act)
if dtype == core.VarDesc.VarType.FP16:
......@@ -419,6 +416,7 @@ class Embedding(layers.Layer):
constructor.
Args:
name_scope: See base class.
size(tuple|list): The shape of the look up table parameter. It should
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
......@@ -446,6 +444,7 @@ class Embedding(layers.Layer):
"""
def __init__(self,
name_scope,
size,
is_sparse=False,
is_distributed=False,
......@@ -453,7 +452,7 @@ class Embedding(layers.Layer):
param_attr=None,
dtype='float32'):
super(Embedding, self).__init__()
super(Embedding, self).__init__(name_scope)
self._size = size
self._is_sparse = is_sparse
self._is_distributed = is_distributed
......@@ -468,7 +467,7 @@ class Embedding(layers.Layer):
assert self._is_sparse is True and self._is_distributed is False
from ..layer_helper import LayerHelper
self._helper = LayerHelper('embedding', param_attr=param_attr)
self._helper = LayerHelper(self.full_name(), param_attr=param_attr)
self._w = self._helper.create_parameter(
attr=self._param_attr,
shape=self._size,
......
python/paddle/fluid/layer_helper.py
浏览文件 @ cf0511f2
......@@ -34,6 +34,9 @@ class LayerHelper(object):
self.kwargs = kwargs
self.layer_type = layer_type
name = self.kwargs.get('name', None)
# TODO(panyx0718, minqiyang): imperative mode
# can not use both `layer_type` and `name`. Deprecate LayerHelper
# and write a Helper for imperative mode.
if name is None:
self.kwargs['name'] = unique_name.generate(self.layer_type)
......
python/paddle/fluid/layers/detection.py
浏览文件 @ cf0511f2
......@@ -545,15 +545,16 @@ def yolov3_loss(x,
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchors = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80, anchors=anchors,
ignore_thresh=0.5, downsample_ratio=32)
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
anchor_mask = [0, 1, 2]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel, anchors=anchors,
anchor_mask=anchor_mask, class_num=80,
ignore_thresh=0.7, downsample_ratio=32)
"""
helper = LayerHelper('yolov3_loss', **locals())
......
python/paddle/fluid/layers/nn.py
浏览文件 @ cf0511f2
......@@ -87,6 +87,7 @@ __all__ = [
'transpose',
'im2sequence',
'nce',
'sampled_softmax_with_cross_entropy',
'hsigmoid',
'beam_search',
'row_conv',
......@@ -668,7 +669,11 @@ def dynamic_lstmp(input,
candidate_activation='tanh',
proj_activation='tanh',
dtype='float32',
name=None):
name=None,
h_0=None,
c_0=None,
cell_clip=None,
proj_clip=None):
"""
**Dynamic LSTMP Layer**
......@@ -785,6 +790,17 @@ def dynamic_lstmp(input,
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
h_0(Variable): The initial hidden state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size and D is the projection size.
c_0(Variable): The initial cell state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size. `h_0` and `c_0` can be NULL but only at the same time.
cell_clip(float): If provided the cell state is clipped
by this value prior to the cell output activation.
proj_clip(float): If `num_proj > 0` and `proj_clip` is
provided, then the projected values are clipped elementwise to within
`[-proj_clip, proj_clip]`.
Returns:
tuple: A tuple of two output variable: the projection of hidden state, \
......@@ -831,25 +847,41 @@ def dynamic_lstmp(input,
batch_hidden = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
inputs = {
'Input': input,
'Weight': weight,
'ProjWeight': proj_weight,
'Bias': bias
}
batch_size = input.shape[0]
if h_0:
assert h_0.shape == (batch_size, proj_size), \
'The shape of h0 should be (batch_size, %d)' % proj_size
inputs['H0'] = h_0
if c_0:
assert c_0.shape == (batch_size, size), \
'The shape of c0 should be (batch_size, %d)' % size
inputs['C0'] = c_0
if cell_clip:
assert cell_clip >= 0, "cell_clip should not be negtive."
if proj_clip:
assert proj_clip >= 0, "proj_clip should not be negtive."
helper.append_op(
type='lstmp',
inputs={
'Input': input,
'Weight': weight,
'ProjWeight': proj_weight,
'Bias': bias
},
inputs=inputs,
outputs={
'Projection': projection,
'Cell': cell,
'OrderedP0': ordered_proj0,
'BatchHidden': batch_hidden,
'BatchGate': batch_gate,
'BatchCellPreAct': batch_cell_pre_act
},
attrs={
'use_peepholes': use_peepholes,
'cell_clip': cell_clip,
'proj_clip': proj_clip,
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'cell_activation': cell_activation,
......@@ -2441,7 +2473,7 @@ def pool2d(input,
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
conv2d = fluid.layers.pool2d(
pool2d = fluid.layers.pool2d(
input=data,
pool_size=2,
pool_type='max',
......@@ -2490,6 +2522,7 @@ def pool2d(input,
return pool_out
@templatedoc()
def pool3d(input,
pool_size=-1,
pool_type="max",
......@@ -2501,13 +2534,19 @@ def pool3d(input,
name=None,
exclusive=True):
"""
This function adds the operator for pooling in 3-dimensions, using the
pooling configurations mentioned in input parameters.
${comment}
Args:
input (Variable): ${input_comment}
pool_size (int): ${ksize_comment}
pool_type (str): ${pooling_type_comment}
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCDHW, where N is batch size, C is
the number of channels, D is the depth of the feature,
H is the height of the feature, and W is the width
of the feature.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size
is a tuple or list, it must contain three integers,
(pool_size_Depth, pool_size_Height, pool_size_Width).
Otherwise, the pool kernel size will be the cube of an int.
pool_type (string): ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling (bool): ${global_pooling_comment}
......@@ -2520,6 +2559,19 @@ def pool3d(input,
Returns:
Variable: output of pool3d layer.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32, 32], dtype='float32')
pool3d = fluid.layers.pool3d(
input=data,
pool_size=2,
pool_type='max',
pool_stride=1,
global_pooling=False)
"""
if pool_type not in ["max", "avg"]:
raise ValueError(
......@@ -2569,7 +2621,27 @@ def adaptive_pool2d(input,
require_index=False,
name=None):
"""
${comment}
**Adaptive Pool2d Operator**
The adaptive_pool2d operation calculates the output based on the input, pool_size,
pool_type parameters. Input(X) and output(Out) are in NCHW format, where N is batch
size, C is the number of channels, H is the height of the feature, and W is
the width of the feature. Parameters(pool_size) should contain two elements which
represent height and width, respectively. Also the H and W dimensions of output(Out)
is same as Parameter(pool_size).
For average adaptive pool2d:
.. math::
hstart &= floor(i * H_{in} / H_{out})
hend &= ceil((i + 1) * H_{in} / H_{out})
wstart &= floor(j * W_{in} / W_{out})
wend &= ceil((j + 1) * W_{in} / W_{out})
Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
Args:
input (Variable): The input tensor of pooling operator. The format of
......@@ -2579,8 +2651,8 @@ def adaptive_pool2d(input,
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (pool_size_Height, pool_size_Width).
pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs.
it cannot be set in average pooling type.
require_index (bool): If true, the index of max pooling point will be returned along
with outputs. It cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
......@@ -2661,18 +2733,42 @@ def adaptive_pool3d(input,
require_index=False,
name=None):
"""
${comment}
**Adaptive Pool3d Operator**
The adaptive_pool3d operation calculates the output based on the input, pool_size,
pool_type parameters. Input(X) and output(Out) are in NCDHW format, where N is batch
size, C is the number of channels, D is the depth of the feature, H is the height of
the feature, and W is the width of the feature. Parameters(pool_size) should contain
three elements which represent height and width, respectively. Also the D, H and W
dimensions of output(Out) is same as Parameter(pool_size).
For average adaptive pool3d:
.. math::
dstart &= floor(i * D_{in} / D_{out})
dend &= ceil((i + 1) * D_{in} / D_{out})
hstart &= floor(j * H_{in} / H_{out})
hend &= ceil((j + 1) * H_{in} / H_{out})
wstart &= floor(k * W_{in} / W_{out})
wend &= ceil((k + 1) * W_{in} / W_{out})
Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
Args:
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is
the number of channels, H is the height of the
feature, and W is the width of the feature.
input tensor is NCDHW, where N is batch size, C is
the number of channels, D is the depth of the feature,
H is the height of the feature, and W is the width of the feature.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (Depth, Height, Width).
it must contain three integers, (Depth, Height, Width).
pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs.
it cannot be set in average pooling type.
require_index (bool): If true, the index of max pooling point will be returned along
with outputs. It cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
......@@ -2709,7 +2805,7 @@ def adaptive_pool3d(input,
name='data', shape=[3, 32, 32], dtype='float32')
pool_out, mask = fluid.layers.adaptive_pool3d(
input=data,
pool_size=[3, 3],
pool_size=[3, 3, 3],
pool_type='avg')
"""
if pool_type not in ["max", "avg"]:
......@@ -5765,6 +5861,132 @@ def softmax_with_cross_entropy(logits,
return loss
def sampled_softmax_with_cross_entropy(logits,
label,
num_samples,
num_true=1,
remove_accidental_hits=True,
use_customized_samples=False,
customized_samples=None,
customized_probabilities=None,
seed=0):
"""
**Sampled Softmax With Cross Entropy Operator.**
Cross entropy loss with sampled softmax is used as the output layer for
larger output classes extensively. This operator samples a number of samples
for all examples, and computes the softmax normalized values for each
row of the sampled tensor, after which cross-entropy loss is computed.
Because this operator performs a softmax on logits internally, it expects
unscaled logits. This operator should not be used with the output of
softmax operator since that would produce incorrect results.
For examples with T true labels (T >= 1), we assume that each true label has
a probability of 1/T. For each sample, S samples are generated using a
log uniform distribution. True labels are concatenated with these samples to
form T + S samples for each example. So, assume the shape of logits is
[N x K], the shape for samples is [N x (T+S)]. For each sampled label, a
probability is calculated, which corresponds to the Q(y|x) in
[Jean et al., 2014](http://arxiv.org/abs/1412.2007).
Logits are sampled according to the sampled labels. Then if
remove_accidental_hits is True, if a sample[i, j] accidentally hits true
labels, then the corresponding sampled_logits[i, j] is minus by 1e20 to
make its softmax result close to zero. Then sampled logits are subtracted by
logQ(y|x), these sampled logits and re-indexed labels are used to compute
a softmax with cross entropy.
Args:
logits (Variable): The unscaled log probabilities, which is a 2-D tensor
with shape [N x K]. N is the batch_size, and K is the class number.
label (Variable): The ground truth which is a 2-D tensor. Label is a
Tensor<int64> with shape [N x T], where T is the number of true
labels per example.
num_samples (int): The number for each example, num_samples should be
less than the number of class.
num_true(int): The number of target classes per training example.
remove_accidental_hits (bool): A flag indicating whether to remove
accidental hits when sampling. If True and if a sample[i, j]
accidentally hits true labels, then the corresponding
sampled_logits[i, j] is minus by 1e20 to make its softmax result
close to zero. Default is True.
use_customized_samples (bool): Whether to use custom samples and probabities to sample
logits.
customized_samples (Variable): User defined samples, which is a 2-D tensor
with shape [N, T + S]. S is the num_samples, and T is the number of true
labels per example.
customized_probabilities (Variable): User defined probabilities of samples,
a 2-D tensor which has the same shape with customized_samples.
seed (int): The random seed for generating random number, which is used
in the process of sampling. Default is 0.
Returns:
Variable: Return the cross entropy loss which is a 2-D tensor with shape
[N x 1].
Examples:
.. code-block:: python
logits = fluid.layers.data(name='data', shape=[256], dtype='float32')
label = fluid.layers.data(name='label', shape=[5], dtype='int64')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.sampled_softmax_with_cross_entropy(
logits=fc, label=label, num_samples=25)
"""
helper = LayerHelper('sample_logits', **locals())
samples = helper.create_variable_for_type_inference(dtype='int64')
probabilities = helper.create_variable_for_type_inference(
dtype=logits.dtype)
sampled_logits \
= helper.create_variable_for_type_inference(dtype=logits.dtype)
sampled_label = helper.create_variable_for_type_inference(dtype='int64')
sampled_softlabel = helper.create_variable_for_type_inference(
dtype=logits.dtype)
helper.append_op(
type='sample_logits',
inputs={
'Logits': logits,
'Labels': label,
'CustomizedSamples': customized_samples,
'CustomizedProbabilities': customized_probabilities
},
outputs={
'Samples': samples,
'Probabilities': probabilities,
'SampledLabels': sampled_label,
'SampledLogits': sampled_logits
},
attrs={
'use_customized_samples': use_customized_samples,
'uniq': True,
'remove_accidental_hits': remove_accidental_hits,
'num_samples': num_samples,
'seed': seed
})
loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
helper.append_op(
type='one_hot',
inputs={'X': sampled_label},
attrs={'depth': num_samples + 1},
outputs={'Out': sampled_softlabel})
helper.append_op(
type='softmax_with_cross_entropy',
inputs={'Logits': sampled_logits,
'Label': sampled_softlabel},
outputs={'Softmax': softmax,
'Loss': loss},
attrs={
'soft_label': True,
'ignore_index': False,
'numeric_stable_mode': False
})
return loss / num_true
def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
"""
This layer computes the smooth L1 loss for Variable :attr:`x` and :attr:`y`.
......
python/paddle/fluid/layers/ops.py
浏览文件 @ cf0511f2
......@@ -60,7 +60,28 @@ __all__ += ["uniform_random"]
_uniform_random_ = generate_layer_fn('uniform_random')
def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
"""
This operator initializes a variable with random values sampled from a
uniform distribution. The random result is in set [min, max].
Args:
shape (list): The shape of output variable.
dtype(np.dtype|core.VarDesc.VarType|str): The type of data, such as
float32, float64 etc. Default: float32.
min (float): Minimum value of uniform random. Default -1.0.
max (float): Maximun value of uniform random. Default 1.0.
seed (int): Random seed used for generating samples. 0 means use a
seed generated by the system. Note that if seed is not 0, this
operator will always generate the same random numbers every time.
Default 0.
Examples:
.. code-block:: python
result = fluid.layers.uniform_random(shape=[32, 784])
"""
locals_var = locals().keys()
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
......@@ -72,12 +93,6 @@ def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
return _uniform_random_(**kwargs)
uniform_random.__doc__ = _uniform_random_.__doc__ + """
Examples:
>>> result = fluid.layers.uniform_random(shape=[32, 784])
"""
__all__ += ['hard_shrink']
_hard_shrink_ = generate_layer_fn('hard_shrink')
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ cf0511f2
......@@ -29,15 +29,6 @@ ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
def _is_pserver_mode(main_program):
main = main_program if main_program \
else framework.default_main_program()
for op in main.global_block().ops:
if op.type in ["send", "recv"]:
return True
return False
class ParallelExecutor(object):
"""
ParallelExecutor is designed for data parallelism, which focuses on distributing
......@@ -140,7 +131,7 @@ class ParallelExecutor(object):
# FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
# num_trainers is 1, so the current fields of build_strategy doesn't tell if
# it's distributed model.
build_strategy.is_distribution = _is_pserver_mode(
build_strategy.is_distribution = framework.is_pserver_mode(
main_program) or num_trainers > 1
# step4: get main_program, scope, local_scopes
......@@ -185,10 +176,13 @@ class ParallelExecutor(object):
places = list(map(place_obj, self._places))
# step7: init ParallelExecutor
# ParallelExecutor API will be deprecated, don't support parallel graph.
self._graph = core.Graph(main.desc)
self.executor = core.ParallelExecutor(
places, persistable_vars, main.desc,
places, persistable_vars,
cpt.to_text(loss_name) if loss_name else six.u(''), scope,
local_scopes, exec_strategy, build_strategy)
local_scopes, exec_strategy, build_strategy, self._graph)
self.scope = scope
......
python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py
浏览文件 @ cf0511f2
......@@ -18,8 +18,8 @@ import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest
from scipy.special import expit
from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
import paddle.fluid as fluid
class TestMKLDNNReluDim2(TestRelu):
......@@ -97,5 +97,64 @@ class TestMKLDNNAbsDim4(TestAbs):
self.attrs = {"use_mkldnn": True}
# Check if primitives already exist in backward
class TestMKLDNNReluPrimitivesAlreadyExist(unittest.TestCase):
def __assert_close(self, tensor, np_array, msg, atol=1e-4):
self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
def test_check_forward_backward(self):
place = core.CPUPlace()
np.random.seed(123)
x = np.random.uniform(-1, 1, [2, 2]).astype(np.float32)
out = np.abs(x)
out_grad = np.random.random_sample(x.shape).astype(np.float32)
x_grad = out_grad * np.sign(x) # Abs grad calculation
var_dict = {'x': x, 'out': out, 'out@GRAD': out_grad, 'x@GRAD': x_grad}
var_names = list(var_dict.keys())
ground_truth = {name: var_dict[name] for name in var_names}
program = fluid.Program()
with fluid.program_guard(program):
block = program.global_block()
for name in ground_truth:
block.create_var(
name=name, dtype='float32', shape=ground_truth[name].shape)
relu_op = block.append_op(
type="abs",
inputs={"X": block.var('x'), },
outputs={"Out": block.var('out')},
attrs={"use_mkldnn": True})
# Generate backward op_desc
grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
relu_op.desc, set(), [])
grad_op_desc = grad_op_desc_list[0]
new_op_desc = block.desc.append_op()
new_op_desc.copy_from(grad_op_desc)
for var_name in grad_op_desc.output_arg_names():
block.desc.var(var_name.encode("ascii"))
grad_op_desc.infer_var_type(block.desc)
grad_op_desc.infer_shape(block.desc)
for arg in grad_op_desc.output_arg_names():
grad_var = block.desc.find_var(arg.encode("ascii"))
grad_var.set_dtype(core.VarDesc.VarType.FP32)
exe = fluid.Executor(place)
# Do at least 2 iterations
for i in range(2):
out = exe.run(
program,
feed={name: var_dict[name]
for name in ['x', 'out@GRAD']},
fetch_list=['x@GRAD'])
self.__assert_close(x_grad, out[0], "x@GRAD")
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_base_layer.py
浏览文件 @ cf0511f2
......@@ -20,10 +20,10 @@ from paddle.fluid.layer_helper import LayerHelper
class L1(fluid.imperative.Layer):
def __init__(self):
super(L1, self).__init__()
def __init__(self, prefix):
super(L1, self).__init__(prefix)
self._helper = LayerHelper(
'MyLayer',
self.full_name(),
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
......@@ -43,20 +43,20 @@ class L1(fluid.imperative.Layer):
class L2(fluid.imperative.Layer):
def __init__(self):
super(L2, self).__init__()
self.layer1 = L1()
self.layer2 = L1()
def __init__(self, prefix):
super(L2, self).__init__(prefix)
self.layer1 = L1(self.full_name())
self.layer2 = L1(self.full_name())
def forward(self):
return self.layer1() + self.layer2()
class L3(fluid.imperative.Layer):
def __init__(self):
super(L3, self).__init__()
self.layer1 = L2()
self.layer2 = L2()
def __init__(self, prefix):
super(L3, self).__init__(prefix)
self.layer1 = L2(self.full_name())
self.layer2 = L2(self.full_name())
def forward(self):
return self.layer1() + self.layer2()
......@@ -65,16 +65,23 @@ class L3(fluid.imperative.Layer):
class TestBaseLayer(unittest.TestCase):
def test_one_level(self):
with fluid.imperative.guard():
l = L1()
l = L1('test_one_level')
ret = l()
self.assertEqual(l.w1.name, "MyLayer_0.w_0")
self.assertEqual(l.w2.name, "MyLayer_0.w_1")
self.assertEqual(l.w1.name, "test_one_level/L1_0_0.w_0")
self.assertEqual(l.w2.name, "test_one_level/L1_0_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
def test_three_level(self):
with fluid.imperative.guard():
l = L3()
l = L3('test_three_level')
names = [p.name for p in l.parameters()]
ret = l()
self.assertEqual(names[0], "test_three_level/L3_0/L2_0/L1_0_0.w_0")
self.assertEqual(names[1], "test_three_level/L3_0/L2_0/L1_0_0.w_1")
self.assertEqual(names[2], "test_three_level/L3_0/L2_0/L1_1_0.w_0")
self.assertEqual(names[3], "test_three_level/L3_0/L2_0/L1_1_0.w_1")
self.assertEqual(names[4], "test_three_level/L3_0/L2_1/L1_0_0.w_0")
self.assertEqual(names[5], "test_three_level/L3_0/L2_1/L1_0_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
......
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @ cf0511f2
......@@ -15,7 +15,6 @@
import contextlib
import unittest
import numpy as np
import sys
import paddle.fluid as fluid
from paddle.fluid import core
......@@ -24,8 +23,8 @@ from test_imperative_base import new_program_scope
class MyLayer(fluid.imperative.Layer):
def __init__(self):
super(MyLayer, self).__init__()
def __init__(self, name_scope):
super(MyLayer, self).__init__(name_scope)
def forward(self, inputs):
x = fluid.layers.relu(inputs)
......@@ -50,12 +49,14 @@ class MyPyLayer(fluid.imperative.PyLayer):
class MLP(fluid.imperative.Layer):
def __init__(self):
super(MLP, self).__init__()
self._fc1 = FC(3,
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._fc1 = FC(self.full_name(),
3,
fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
self._fc2 = FC(4,
self._fc2 = FC(self.full_name(),
4,
fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
......@@ -67,8 +68,9 @@ class MLP(fluid.imperative.Layer):
class SimpleRNNCell(fluid.imperative.Layer):
def __init__(self, step_input_size, hidden_size, output_size, param_attr):
super(SimpleRNNCell, self).__init__()
def __init__(self, name_scope, step_input_size, hidden_size, output_size,
param_attr):
super(SimpleRNNCell, self).__init__(name_scope)
self.step_input_size = step_input_size
self.hidden_size = hidden_size
self.output_size = output_size
......@@ -158,10 +160,11 @@ class SimpleRNNCell(fluid.imperative.Layer):
class SimpleRNN(fluid.imperative.Layer):
def __init__(self):
super(SimpleRNN, self).__init__()
def __init__(self, name_scope):
super(SimpleRNN, self).__init__(name_scope)
self.seq_len = 4
self._cell = SimpleRNNCell(
self.full_name(),
3,
3,
3,
......@@ -205,7 +208,7 @@ class TestImperative(unittest.TestCase):
with fluid.imperative.guard():
cl = core.Layer()
cl.forward([])
l = fluid.imperative.Layer()
l = fluid.imperative.Layer("l")
self.assertRaises(NotImplementedError, l.forward, [])
def test_pylayer_func_id(self):
......@@ -281,7 +284,7 @@ class TestImperative(unittest.TestCase):
np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
l = MyLayer()
l = MyLayer("my_layer")
x = l(var_inp)[0]
self.assertIsNotNone(x)
dy_out = x._numpy()
......@@ -291,7 +294,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[3], append_batch_size=False)
l = MyLayer()
l = MyLayer("my_layer")
x = l(inp)[0]
param_grads = fluid.backward.append_backward(
x, parameter_list=[l._x_for_debug.name])[0]
......@@ -309,7 +312,7 @@ class TestImperative(unittest.TestCase):
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
mlp = MLP()
mlp = MLP("mlp")
out = mlp(var_inp)
dy_out = out._numpy()
out._backward()
......@@ -318,7 +321,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
mlp = MLP()
mlp = MLP("mlp")
out = mlp(inp)
param_grads = fluid.backward.append_backward(
out, parameter_list=[mlp._fc1._w.name])[0]
......@@ -334,10 +337,10 @@ class TestImperative(unittest.TestCase):
self.assertTrue(np.allclose(dy_grad, static_grad))
params = mlp.parameters(True)
self.assertEqual("FC_0.w_0", params[0].name)
self.assertEqual("FC_0.b_0", params[1].name)
self.assertEqual("FC_1.w_0", params[2].name)
self.assertEqual("FC_1.b_0", params[3].name)
self.assertEqual("mlp/MLP_0/FC_0_0.w_0", params[0].name)
self.assertEqual("mlp/MLP_0/FC_0_0.b_0", params[1].name)
self.assertEqual("mlp/MLP_0/FC_1_0.w_0", params[2].name)
self.assertEqual("mlp/MLP_0/FC_1_0.b_0", params[3].name)
self.assertEqual(len(params), 4)
sublayers = mlp.sublayers(True)
......@@ -353,7 +356,7 @@ class TestImperative(unittest.TestCase):
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
simple_rnn = SimpleRNN()
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn.forward(var_inp)
dy_out = outs[3]._numpy()
outs[3]._backward()
......@@ -364,7 +367,7 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[1, 4, 3], append_batch_size=False)
simple_rnn = SimpleRNN()
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn(inp)
param_grads = fluid.backward.append_backward(outs[3])
exe = fluid.Executor(fluid.CPUPlace())
......
python/paddle/fluid/tests/unittests/test_imperative_gan.py
浏览文件 @ cf0511f2
......@@ -28,10 +28,10 @@ from paddle.fluid.imperative.base import to_variable
class Discriminator(fluid.imperative.Layer):
def __init__(self):
super(Discriminator, self).__init__()
self._fc1 = FC(size=32, act='elu', name="d_fc1")
self._fc2 = FC(size=1, name="d_fc2")
def __init__(self, name_scope):
super(Discriminator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=32, act='elu')
self._fc2 = FC(self.full_name(), size=1)
def forward(self, inputs):
x = self._fc1(inputs)
......@@ -39,11 +39,11 @@ class Discriminator(fluid.imperative.Layer):
class Generator(fluid.imperative.Layer):
def __init__(self):
super(Generator, self).__init__()
self._fc1 = FC(size=64, act='elu', name="g_fc1")
self._fc2 = FC(size=64, act='elu', name="g_fc2")
self._fc3 = FC(size=1, name="g_fc3")
def __init__(self, name_scope):
super(Generator, self).__init__(name_scope)
self._fc1 = FC(self.full_name(), size=64, act='elu')
self._fc2 = FC(self.full_name(), size=64, act='elu')
self._fc3 = FC(self.full_name(), size=1)
def forward(self, inputs):
x = self._fc1(inputs)
......@@ -65,8 +65,8 @@ class TestImperativeMnist(unittest.TestCase):
scope = fluid.core.Scope()
with new_program_scope(
main=discriminate_p, startup=startup, scope=scope):
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
img = fluid.layers.data(
name="img", shape=[2, 1], append_batch_size=False)
......@@ -93,8 +93,8 @@ class TestImperativeMnist(unittest.TestCase):
sgd.minimize(d_loss)
with new_program_scope(main=generate_p, startup=startup, scope=scope):
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
noise = fluid.layers.data(
name="noise", shape=[2, 2], append_batch_size=False)
......@@ -134,8 +134,8 @@ class TestImperativeMnist(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
discriminator = Discriminator()
generator = Generator()
discriminator = Discriminator("d")
generator = Generator("g")
sgd = SGDOptimizer(learning_rate=1e-3)
d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @ cf0511f2
......@@ -28,6 +28,7 @@ from test_imperative_base import new_program_scope
class SimpleImgConvPool(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
......@@ -44,9 +45,10 @@ class SimpleImgConvPool(fluid.imperative.Layer):
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__()
super(SimpleImgConvPool, self).__init__(name_scope)
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
......@@ -59,6 +61,7 @@ class SimpleImgConvPool(fluid.imperative.Layer):
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
......@@ -73,19 +76,20 @@ class SimpleImgConvPool(fluid.imperative.Layer):
class MNIST(fluid.imperative.Layer):
def __init__(self, param_attr=None, bias_attr=None):
super(MNIST, self).__init__()
def __init__(self, name_scope, param_attr=None, bias_attr=None):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
1, 20, 5, 2, 2, act="relu")
self.full_name(), 1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
20, 50, 5, 2, 2, act="relu")
self.full_name(), 20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
self._fc = FC(10,
self._fc = FC(self.full_name(),
10,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)),
......@@ -101,47 +105,48 @@ class MNIST(fluid.imperative.Layer):
class TestImperativeMnist(unittest.TestCase):
def test_mnist_float32(self):
seed = 90
batch_num = 2
epoch_num = 1
with fluid.imperative.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mnist = MNIST()
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
dy_param_init_value = {}
for batch_id, data in enumerate(train_reader()):
if batch_id >= batch_num:
break
dy_x_data = np.array(
[x[0].reshape(1, 28, 28) for x in data]).astype('float32')
y_data = np.array([x[1] for x in data]).astype('int64').reshape(
128, 1)
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy()
if batch_id == 0:
for param in fluid.default_main_program().global_block(
).all_parameters():
dy_param_init_value[param.name] = param._numpy()
avg_loss._backward()
sgd.minimize(avg_loss)
mnist.clear_gradients()
dy_param_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
dy_param_value[param.name] = param._numpy()
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array(
[x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array(
[x[1] for x in data]).astype('int64').reshape(128, 1)
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy()
if epoch == 0 and batch_id == 0:
for param in mnist.parameters():
dy_param_init_value[param.name] = param._numpy()
avg_loss._backward()
sgd.minimize(avg_loss)
mnist.clear_gradients()
fluid.default_main_program().global_block()._clear_block()
dy_param_value = {}
for param in mnist.parameters():
dy_param_value[param.name] = param._numpy()
with new_program_scope():
fluid.default_startup_program().random_seed = seed
......@@ -150,10 +155,10 @@ class TestImperativeMnist(unittest.TestCase):
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
mnist = MNIST()
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
img = fluid.layers.data(
name='pixel', shape=[1, 28, 28], dtype='float32')
......@@ -166,8 +171,7 @@ class TestImperativeMnist(unittest.TestCase):
# initialize params and fetch them
static_param_init_value = {}
static_param_name_list = []
for param in fluid.default_startup_program().global_block(
).all_parameters():
for param in mnist.parameters():
static_param_name_list.append(param.name)
out = exe.run(fluid.default_startup_program(),
......@@ -176,26 +180,29 @@ class TestImperativeMnist(unittest.TestCase):
for i in range(len(static_param_name_list)):
static_param_init_value[static_param_name_list[i]] = out[i]
for batch_id, data in enumerate(train_reader()):
if batch_id >= batch_num:
break
static_x_data = np.array(
[x[0].reshape(1, 28, 28) for x in data]).astype('float32')
y_data = np.array([x[1] for x in data]).astype('int64').reshape(
[128, 1])
fetch_list = [avg_loss.name]
fetch_list.extend(static_param_name_list)
out = exe.run(fluid.default_main_program(),
feed={"pixel": static_x_data,
"label": y_data},
fetch_list=fetch_list)
static_param_value = {}
static_out = out[0]
for i in range(1, len(out)):
static_param_value[static_param_name_list[i - 1]] = out[i]
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
static_x_data = np.array(
[x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array(
[x[1] for x in data]).astype('int64').reshape([128, 1])
fetch_list = [avg_loss.name]
fetch_list.extend(static_param_name_list)
out = exe.run(
fluid.default_main_program(),
feed={"pixel": static_x_data,
"label": y_data},
fetch_list=fetch_list)
static_param_value = {}
static_out = out[0]
for i in range(1, len(out)):
static_param_value[static_param_name_list[i - 1]] = out[
i]
self.assertTrue(np.allclose(dy_x_data.all(), static_x_data.all()))
for key, value in six.iteritems(static_param_init_value):
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
......@@ -203,7 +210,7 @@ class TestImperativeMnist(unittest.TestCase):
self.assertTrue(np.allclose(static_out, dy_out))
for key, value in six.iteritems(static_param_value):
self.assertTrue(np.allclose(value, dy_param_value[key]))
self.assertTrue(np.allclose(value, dy_param_value[key], atol=1e-5))
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ cf0511f2
......@@ -28,12 +28,13 @@ from paddle.fluid.backward import append_backward
class SimpleLSTMRNN(fluid.imperative.Layer):
def __init__(self,
name_scope,
hidden_size,
num_steps,
num_layers=2,
init_scale=0.1,
dropout=None):
super(SimpleLSTMRNN, self).__init__()
super(SimpleLSTMRNN, self).__init__(name_scope)
self._hidden_size = hidden_size
self._num_layers = num_layers
self._init_scale = init_scale
......@@ -130,13 +131,14 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
class PtbModel(fluid.imperative.Layer):
def __init__(self,
name_scope,
hidden_size,
vocab_size,
num_layers=2,
num_steps=20,
init_scale=0.1,
dropout=None):
super(PtbModel, self).__init__()
super(PtbModel, self).__init__(name_scope)
self.hidden_size = hidden_size
self.vocab_size = vocab_size
self.init_scale = init_scale
......@@ -146,12 +148,14 @@ class PtbModel(fluid.imperative.Layer):
from paddle.fluid.layer_helper import LayerHelper
self._helper = LayerHelper('PtbModel', act="tanh")
self.simple_lstm_rnn = SimpleLSTMRNN(
self.full_name(),
hidden_size,
num_steps,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
self.embedding = Embedding(
self.full_name(),
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False,
......@@ -226,6 +230,7 @@ class TestImperativePtbRnn(unittest.TestCase):
fluid.default_main_program().random_seed = seed
# TODO: marsyang1993 Change seed to
ptb_model = PtbModel(
"ptb_model",
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
......@@ -265,6 +270,7 @@ class TestImperativePtbRnn(unittest.TestCase):
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
ptb_model = PtbModel(
"ptb_model",
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
......
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @ cf0511f2
......@@ -70,15 +70,17 @@ def optimizer_setting(params):
class ConvBNLayer(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__()
super(ConvBNLayer, self).__init__(name_scope)
self._conv = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
......@@ -88,7 +90,7 @@ class ConvBNLayer(fluid.imperative.Layer):
act=None,
bias_attr=None)
self._batch_norm = BatchNorm(num_filters, act=act)
self._batch_norm = BatchNorm(self.full_name(), num_filters, act=act)
def forward(self, inputs):
y = self._conv(inputs)
......@@ -98,21 +100,29 @@ class ConvBNLayer(fluid.imperative.Layer):
class BottleneckBlock(fluid.imperative.Layer):
def __init__(self, num_channels, num_filters, stride, shortcut=True):
super(BottleneckBlock, self).__init__()
def __init__(self,
name_scope,
num_channels,
num_filters,
stride,
shortcut=True):
super(BottleneckBlock, self).__init__(name_scope)
self.conv0 = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act='relu')
self.conv1 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu')
self.conv2 = ConvBNLayer(
self.full_name(),
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
......@@ -120,6 +130,7 @@ class BottleneckBlock(fluid.imperative.Layer):
if not shortcut:
self.short = ConvBNLayer(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
......@@ -141,13 +152,13 @@ class BottleneckBlock(fluid.imperative.Layer):
y = fluid.layers.elementwise_add(x=short, y=conv2)
layer_helper = LayerHelper('elementwise_add_activation', act='relu')
layer_helper = LayerHelper(self.full_name(), act='relu')
return layer_helper.append_activation(y)
class ResNet(fluid.imperative.Layer):
def __init__(self, layers=50, class_dim=102):
super(ResNet, self).__init__()
def __init__(self, name_scope, layers=50, class_dim=102):
super(ResNet, self).__init__(name_scope)
self.layers = layers
supported_layers = [50, 101, 152]
......@@ -163,9 +174,18 @@ class ResNet(fluid.imperative.Layer):
num_filters = [64, 128, 256, 512]
self.conv = ConvBNLayer(
num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
self.full_name(),
num_channels=3,
num_filters=64,
filter_size=7,
stride=2,
act='relu')
self.pool2d_max = Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
self.full_name(),
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
self.bottleneck_block_list = []
num_channels = 64
......@@ -175,6 +195,7 @@ class ResNet(fluid.imperative.Layer):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
......@@ -184,12 +205,13 @@ class ResNet(fluid.imperative.Layer):
shortcut = True
self.pool2d_avg = Pool2D(
pool_size=7, pool_type='avg', global_pooling=True)
self.full_name(), pool_size=7, pool_type='avg', global_pooling=True)
import math
stdv = 1.0 / math.sqrt(2048 * 1.0)
self.out = FC(size=class_dim,
self.out = FC(self.full_name(),
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
......@@ -209,12 +231,12 @@ class TestImperativeResnet(unittest.TestCase):
seed = 90
batch_size = train_parameters["batch_size"]
batch_num = 1
batch_num = 2
with fluid.imperative.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
resnet = ResNet()
resnet = ResNet("resnet")
optimizer = optimizer_setting(train_parameters)
np.random.seed(seed)
import random
......@@ -264,6 +286,8 @@ class TestImperativeResnet(unittest.TestCase):
optimizer.minimize(avg_loss)
resnet.clear_gradients()
fluid.default_main_program().global_block()._clear_block()
dy_param_value = {}
for param in resnet.parameters():
dy_param_value[param.name] = param._numpy()
......@@ -275,7 +299,7 @@ class TestImperativeResnet(unittest.TestCase):
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
resnet = ResNet()
resnet = ResNet("resnet")
optimizer = optimizer_setting(train_parameters)
np.random.seed(seed)
......@@ -297,11 +321,9 @@ class TestImperativeResnet(unittest.TestCase):
static_param_init_value = {}
static_param_name_list = []
static_grad_name_list = []
for param in fluid.default_startup_program().global_block(
).all_parameters():
for param in resnet.parameters():
static_param_name_list.append(param.name)
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
if not param.stop_gradient:
static_grad_name_list.append(param.name +
core.grad_var_suffix())
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ cf0511f2
......@@ -374,6 +374,17 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(output)
print(str(program))
def test_sampled_softmax_with_cross_entropy(self):
program = Program()
with program_guard(program):
logits = layers.data(name='Logits', shape=[256], dtype='float64')
label = layers.data(name='Label', shape=[1], dtype='int64')
num_samples = 25
output = layers.sampled_softmax_with_cross_entropy(logits, label,
num_samples)
self.assertIsNotNone(output)
print(str(program))
@decorators.prog_scope()
def test_nce(self):
window_size = 5
......
python/paddle/fluid/tests/unittests/test_lstmp_op.py
浏览文件 @ cf0511f2
......@@ -36,12 +36,14 @@ def lstmp(
w_b=None, # 1 x 4D
w_c=None, # 1 x 3D
is_reverse=False,
proj_clip=0.0,
cell_clip=0.0,
act_gate=None,
act_cell=None,
act_cand=None,
act_proj=None):
def _step(x, w_r, w_rh, w_c, r_pre, c_pre, act_gate, act_cell, act_cand,
act_proj):
def _step(x, w_r, w_rh, w_c, r_pre, c_pre, proj_clip, cell_clip, act_gate,
act_cell, act_cand, act_proj):
g = np.dot(r_pre, w_r) # 1 x 4D
g = g + x
g = np.reshape(g, (1, g.size))
......@@ -55,6 +57,17 @@ def lstmp(
g_f = act_gate(g_f + w_fc * c_pre) # 1 x D
c = g_f * c_pre + g_i * act_cand(c) # 1 x D
def array_clip(a, clip):
size = np.prod(a.shape)
new_a = np.reshape(a, (size))
for i in range(size):
new_a[i] = max(new_a[i], -1.0 * clip)
new_a[i] = min(new_a[i], clip)
new_a = np.reshape(new_a, a.shape)
return new_a
if cell_clip > 0.0:
c = array_clip(c, cell_clip)
if w_c is None:
g_o = act_gate(g_o) # 1 x D
else:
......@@ -64,6 +77,8 @@ def lstmp(
# projection
r = np.dot(h, w_rh)
r = act_proj(r)
if proj_clip > 0.0:
r = array_clip(r, proj_clip)
return r, c
def _reverse(x, offset):
......@@ -87,13 +102,13 @@ def lstmp(
# compute one sequence
seq_len = lod[0][i]
x = input[offset[i]:offset[i + 1], :]
r_pre = np.dot(h0[i], w_rh) # 1 x P
r_pre = act_proj(r_pre)
r_pre = h0[i]
c_pre = c0[i] # 1 x D
for j in range(seq_len):
# compute one step
r_pre, c_pre = _step(x[j], w_r, w_rh, w_c, r_pre, c_pre, act_gate,
act_cell, act_cand, act_proj)
r_pre, c_pre = _step(x[j], w_r, w_rh, w_c, r_pre, c_pre, proj_clip,
cell_clip, act_gate, act_cell, act_cand,
act_proj)
projection.append(r_pre.flatten())
cell.append(c_pre.flatten())
......@@ -123,13 +138,12 @@ class TestLstmpOp(LstmTest.TestLstmOp):
T = sum(self.lod[0])
N = len(self.lod[0])
x = np.random.normal(size=(T, 4 * self.D)).astype('float64')
if self.has_initial_state:
h0 = np.random.normal(size=(N, self.D)).astype('float64')
h0 = np.random.normal(size=(N, self.P)).astype('float64')
c0 = np.random.normal(size=(N, self.D)).astype('float64')
else:
h0 = np.zeros((N, self.D)).astype('float64')
h0 = np.zeros((N, self.P)).astype('float64')
c0 = np.zeros((N, self.D)).astype('float64')
w = np.random.normal(size=(self.P, 4 * self.D)).astype('float64')
if self.use_peepholes:
......@@ -140,9 +154,12 @@ class TestLstmpOp(LstmTest.TestLstmOp):
w_b = b[:, 0:4 * self.D]
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
w_rh = np.random.normal(size=(self.D, self.P)).astype('float64')
proj_clip = 0.1
cell_clip = 0.1
r, c = lstmp(x, self.lod, h0, c0, w, w_rh, w_b, w_c, self.is_reverse,
ACTIVATION[self.act_gate], ACTIVATION[self.act_cell],
ACTIVATION[self.act_cand], ACTIVATION[self.act_proj])
proj_clip, cell_clip, ACTIVATION[self.act_gate],
ACTIVATION[self.act_cell], ACTIVATION[self.act_cand],
ACTIVATION[self.act_proj])
self.inputs = {'Input': (x, self.lod), 'Weight': w, 'ProjWeight': w_rh}
......@@ -159,6 +176,8 @@ class TestLstmpOp(LstmTest.TestLstmOp):
self.attrs = {
'use_peepholes': self.use_peepholes,
'is_reverse': self.is_reverse,
'proj_clip': proj_clip,
'cell_clip': cell_clip,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
'candidate_activation': self.act_cand,
......@@ -171,14 +190,14 @@ class TestLstmpOp(LstmTest.TestLstmOp):
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'ProjWeight', 'Bias'], ['Projection'],
max_relative_error=1e-2)
max_relative_error=1e-2,
numeric_grad_delta=0.0000005)
class TestLstmpOpHasInitial(TestLstmpOp):
......@@ -188,7 +207,6 @@ class TestLstmpOpHasInitial(TestLstmpOp):
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -196,11 +214,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'Weight', 'ProjWeight', 'Bias', 'H0', 'C0'],
['Projection'],
numeric_grad_delta=0.0000005,
max_relative_error=1e-2)
def test_check_grad_ingore_bias(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -208,11 +226,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'ProjWeight', 'Weight'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('Bias'))
def test_check_grad_ingore_weight(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -220,11 +238,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'ProjWeight', 'Bias'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('Weight'))
def test_check_grad_ingore_proj_weight(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -232,11 +250,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'Weight', 'Bias'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('ProjWeight'))
def test_check_grad_ingore_input(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -244,11 +262,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Weight', 'ProjWeight', 'Bias'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('Input'))
def test_check_grad_ingore_h0(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -256,11 +274,11 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'Weight', 'ProjWeight', 'Bias', 'C0'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('H0'))
def test_check_grad_ingore_c0(self):
N = len(self.lod[0])
self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
......@@ -268,6 +286,7 @@ class TestLstmpOpHasInitial(TestLstmpOp):
self.check_grad(
['Input', 'Weight', 'ProjWeight', 'Bias', 'H0'], ['Projection'],
max_relative_error=1e-2,
numeric_grad_delta=0.0000005,
no_grad_set=set('C0'))
......
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