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PaddleDetection
提交 96eb9587 编写于 作者: T typhoonzero
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into recv_op_python_with_guard

上级 82d92498 0311fd15
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Showing with 3819 addition and 686 deletion
.copyright.hook
浏览文件 @ 96eb9587
......@@ -9,7 +9,7 @@ import subprocess
import platform
COPYRIGHT = '''
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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.
......
cmake/external/boost.cmake
浏览文件 @ 96eb9587
......@@ -15,9 +15,9 @@
include(ExternalProject)
set(BOOST_PROJECT "extern_boost")
set(BOOST_VER "1.66.0")
set(BOOST_TAR "boost_1_66_0")
set(BOOST_URL "https://dl.bintray.com/boostorg/release/${BOOST_VER}/source/${BOOST_TAR}.tar.gz")
set(BOOST_VER "1.41.0")
set(BOOST_TAR "boost_1_41_0")
set(BOOST_URL "http://sourceforge.net/projects/boost/files/boost/${BOOST_VER}/${BOOST_TAR}.tar.gz")
set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
set(BOOST_DOWNLOAD_DIR "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
......
doc/api/v2/fluid/layers.rst
浏览文件 @ 96eb9587
......@@ -18,6 +18,11 @@ dynamic_lstm
.. autofunction:: paddle.v2.fluid.layers.dynamic_lstm
:noindex:
dynamic_lstmp
-------------
.. autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
:noindex:
dynamic_gru
-----------
.. autofunction:: paddle.v2.fluid.layers.dynamic_gru
......@@ -534,3 +539,8 @@ row_conv
--------
.. autofunction:: paddle.v2.fluid.layers.row_conv
:noindex:
multiplex
---------
.. autofunction:: paddle.v2.fluid.layers.multiplex
:noindex:
doc/api/v2/fluid/nets.rst
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......@@ -26,8 +26,8 @@ glu
:noindex:
dot_product_attention
---------------------
.. autofunction:: paddle.v2.fluid.nets.dot_product_attention
scaled_dot_product_attention
----------------------------
.. autofunction:: paddle.v2.fluid.nets.scaled_dot_product_attention
:noindex:
doc/design/dist_refactor/distributed_architecture.md
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......@@ -152,12 +152,12 @@ for data in train_reader():
`JobDesc` object describe the distributed job resource specification to run on
Cluster environment.
<img src="src/remote_executor.png"/>
<img src="src/remote_executor.png" width="500" align="center" />
`RemoteExecutor.run` sends the `ProgramDesc` and
[TrainingJob](https://github.com/PaddlePaddle/cloud/blob/develop/doc/autoscale/README.md#training-job-resource)
to a server in the cluster which executes `RemoteExecutor.listen`. This server is responsible
to start the final Kubernetes Jobs to run the different role of `ProgramDesc`.
to start the final Kubernetes Jobs to run the different role of `ProgramDesc` from `ConfigMap`.
### Placement Algorithm
......
doc/design/support_new_device.md
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......@@ -2,9 +2,9 @@
## Background
Deep learning has a high demand for computing resources. New high-performance devices and computing libraries are appearing very frequently. Deep learning frameworks have to integrate these high-performance devices and computing libraries flexibly and efficiently.
Deep learning has a high demand for computing resources. New high-performance devices and computing libraries are appearing very frequently. Deep learning frameworks have to integrate these high-performance devices and computing libraries in a flexible and efficient manner.
On one hand, hardware and computing libraries usually do not have a one-to-one correspondence. For example,Intel CPUs support Eigen and MKL computing libraries while Nvidia GPUs support Eigen and cuDNN computing libraries. We have to implement operator specific kernels for each computing library.
On one hand, hardware and computing libraries usually do not have a one-to-one correspondence. For example, Intel CPUs support Eigen and MKL computing libraries while Nvidia GPUs support Eigen and cuDNN computing libraries. We have to implement operator specific kernels for each computing library.
On the other hand, users usually do not want to care about the low-level hardware and computing libraries when writing a neural network configuration. In Fluid, `Layer` is exposed in `Python`, and `Operator` is exposed in `C++`. Both `Layer` and `Operator` are hardware independent.
......@@ -17,7 +17,7 @@ For a general overview of fluid, please refer to the [overview doc](https://gith
There are mainly three parts that we have to consider while integrating a new device/library:
- Place and DeviceContext: indicates the device id and manages hardware resources
- Place and DeviceContext: indicate the device id and manage hardware resources
- Memory and Tensor: malloc/free data on certain device
......@@ -25,10 +25,10 @@ There are mainly three parts that we have to consider while integrating a new de
### Place and DeviceContext
Please remind that device and computing library are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.
Please note that device and computing library are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.
#### Place
Fluid uses class [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L55) to represent the device memory where data is located. If we add another device, we have to add corresponding `DevicePlace`.
Fluid uses class [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L55) to represent the device memory where data is located. If we add another device, we have to add the corresponding `DevicePlace`.
```
| CPUPlace
......@@ -144,7 +144,7 @@ class Tensor {
};
```
`Placeholder` is used to delay memory allocation; that is, we can first define a tensor, using `Resize` to configure its shape, and then call `mutuable_data` to allocate the actual memory.
`Placeholder` is used to delay memory allocation; that is, we can first define a tensor, using `Resize` to configurate its shape, and then call `mutuable_data` to allocate the actual memory.
```cpp
paddle::framework::Tensor t;
......@@ -163,7 +163,7 @@ Fluid implements computing units based on different DeviceContexts. Some computi
Let's take [MaxOutFunctor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/math/maxouting.h#L27) as an example:
The interface is defined in header file.
The interface is defined in the header file.
```
template <typename DeviceContext, typename T>
......@@ -174,7 +174,7 @@ class MaxOutFunctor {
};
```
CPU implemention is in .cc file
CPU implementation is in .cc file
```
template <typename T>
......@@ -188,7 +188,7 @@ class MaxOutFunctor<platform::CPUDeviceContext, T> {
};
```
CUDA implemention is in .cu file
CUDA implementation is in .cu file
```
template <typename T>
......@@ -203,9 +203,9 @@ class MaxOutFunctor<platform::CUDADeviceContext, T> {
```
We get computing handle from a concrete DeviceContext, and make compution on tensors.
We first obtain the computing handle from a concrete DeviceContext and then compute on tensors.
The implemention of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
The implementation of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
Fluid provides different register interfaces in op_registry.h
......@@ -231,7 +231,7 @@ REGISTER_OP_CUDA_KERNEL(
## Advanced topics: How to switch between different Device/Library
Generally, we will impelement OpKernel for all Device/Library of an Operator. We can easily train a Convolutional Neural Network in GPU. However, some OpKernel is not sutibale on a specific Device. For example, crf operator can only run on CPU, whereas most other operators can run at GPU. To achieve high performance in such circumstance, we have to switch between different Device/Library.
Generally, we will implement OpKernel for all Device/Library of an Operator. We can easily train a Convolutional Neural Network in GPU. However, some OpKernel is not suitable on a specific Device. For example, crf operator can only run on CPU, whereas most other operators can run on GPU. To achieve high performance in such circumstance, we have to switch between different Device/Library.
For more details, please refer to following docs:
......
doc/getstarted/build_and_install/pip_install_cn.rst
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......@@ -39,6 +39,7 @@ PaddlePaddle可以使用常用的Python包管理工具
"cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "暂无"
"cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "暂无"
"cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
......
doc/getstarted/build_and_install/pip_install_en.rst
浏览文件 @ 96eb9587
......@@ -42,6 +42,7 @@ If the links below shows up the login form, just click "Log in as guest" to star
"cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "Not Available"
"cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "Not Available"
"cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
......
doc/howto/usage/cluster/fluid_cluster_train_en.md
浏览文件 @ 96eb9587
......@@ -16,6 +16,12 @@ PaddlePaddle must be installed on all nodes. If you have GPU cards on your nodes
PaddlePaddle build and installation guide can be found [here](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html).
In addition to above, the `cmake` command should be run with the option `WITH_DISTRIBUTE` set to on. An example bare minimum `cmake` command would look as follows:
``` bash
cmake .. -DWITH_DOC=OFF -DWITH_GPU=OFF -DWITH_DISTRIBUTE=ON -DWITH_SWIG_PY=ON -DWITH_PYTHON=ON
```
### Update the training script
#### Non-cluster training script
......@@ -119,7 +125,14 @@ for pass_id in range(100):
### E2E demo
Please find the complete demo from [here](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py). In parameter server node run the following in the command line:
Please find the complete demo from [here](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py).
First `cd` into the folder that contains the `python` files. In this case:
```bash
cd /paddle/python/paddle/v2/fluid/tests/book_distribute
```
In parameter server node run the following in the command line:
``` bash
PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=PSERVER python notest_dist_fit_a_line.py
......
paddle/framework/CMakeLists.txt
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......@@ -74,8 +74,10 @@ cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context fill_constant_op)
cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
framework_proto backward glog lod_rank_table profiler)
framework_proto backward glog lod_rank_table profiler feed_fetch_method)
cc_library(prune SRCS prune.cc DEPS framework_proto)
cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
......
paddle/framework/channel.h 0 → 100644
浏览文件 @ 96eb9587
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 <condition_variable>
#include <mutex>
#include <queue>
namespace paddle {
namespace framework {
template <typename T>
class Channel {
public:
explicit Channel(std::size_t capacity) : capacity_(capacity) {}
void Send(T* channel_element) {
std::unique_lock<std::mutex> lock(mu_);
if (IsBounded()) {
full_cond_var_.wait(lock, [this]() {
bool capacity_valid = capacity_ > 0 ? !IsCapacityFull() : true;
return capacity_valid;
});
}
channel_.push_back(std::move(*channel_element));
lock.unlock();
empty_cond_var_.notify_one();
}
T* Receive() {
std::unique_lock<std::mutex> lock(mu_);
empty_cond_var_.wait(lock, [this]() { return !channel_.empty(); });
T* channel_element = std::move(channel_.front());
channel_.pop_front();
NotifyAllSenders(&lock);
return channel_element;
}
size_t Size() {
std::unique_lock<std::mutex> lock(mu_);
return channel_.size();
}
void Clear() {
std::unique_lock<std::mutex> lock(mu_);
channel_.clear();
NotifyAllSenders(&lock);
}
private:
std::size_t capacity_;
std::mutex mu_;
std::condition_variable empty_cond_var_;
std::condition_variable full_cond_var_;
std::deque<T> channel_;
private:
void NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
if (IsBounded()) {
lock->unlock();
full_cond_var_.notify_one();
}
}
bool IsBounded() const { return capacity_ > 0; }
bool IsCapacityFull() const { return channel_.size() >= capacity_; }
};
} // namespace operator
} // namespace paddle
paddle/framework/data_type.h
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......@@ -79,5 +79,33 @@ inline void VisitDataType(proto::DataType type, Visitor visitor) {
}
}
inline std::string DataTypeToString(const proto::DataType type) {
using namespace paddle::framework::proto;
switch (type) {
case DataType::FP16:
return "float16";
case DataType::FP32:
return "float32";
case DataType::FP64:
return "float64";
case DataType::INT16:
return "int16";
case DataType::INT32:
return "int32";
case DataType::INT64:
return "int64";
case DataType::BOOL:
return "bool";
default:
PADDLE_THROW("Not support type %d", type);
}
}
inline std::ostream& operator<<(std::ostream& out,
const proto::DataType& type) {
out << DataTypeToString(type);
return out;
}
} // namespace framework
} // namespace paddle
paddle/framework/executor.cc
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......@@ -17,6 +17,7 @@ limitations under the License. */
#include <set>
#include "gflags/gflags.h"
#include "paddle/framework/feed_fetch_method.h"
#include "paddle/framework/feed_fetch_type.h"
#include "paddle/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor_array.h"
......@@ -149,5 +150,164 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
}
}
// Check whether the block already has feed operators and feed_holder.
// Return false if the block does not have any feed operators.
// If some feed operators have been prepended to the block, check that
// the info contained in these feed operators matches the feed_targets
// and feed_holder_name. Raise exception when any mismatch is found.
// Return true if the block has feed operators and holder of matching info.
static bool has_feed_operators(
BlockDesc* block, std::map<std::string, const LoDTensor*>& feed_targets,
const std::string& feed_holder_name) {
size_t feed_count = 0;
for (auto* op : block->AllOps()) {
if (op->Type() == kFeedOpType) {
feed_count++;
PADDLE_ENFORCE_EQ(op->Input("X")[0], feed_holder_name,
"Input to feed op should be '%s'", feed_holder_name);
std::string feed_target_name = op->Output("Out")[0];
PADDLE_ENFORCE(
feed_targets.find(feed_target_name) != feed_targets.end(),
"Feed operator output name '%s' cannot be found in 'feed_targets'",
feed_target_name);
}
}
if (feed_count > 0) {
PADDLE_ENFORCE_EQ(
feed_count, feed_targets.size(),
"The number of feed operators should match 'feed_targets'");
// When feed operator are present, so should be feed_holder
auto var = block->FindVar(feed_holder_name);
PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
feed_holder_name);
PADDLE_ENFORCE_EQ(var->GetType(), proto::VarDesc::FEED_MINIBATCH,
"'%s' variable should be 'FEED_MINIBATCH' type",
feed_holder_name);
}
return feed_count > 0;
}
// Check whether the block already has fetch operators and fetch_holder.
// Return false if the block does not have any fetch operators.
// If some fetch operators have been appended to the block, check that
// the info contained in these fetch operators matches the fetch_targets
// and fetch_holder_name. Raise exception when any mismatch is found.
// Return true if the block has fetch operators and holder of matching info.
static bool has_fetch_operators(
BlockDesc* block, std::map<std::string, LoDTensor*>& fetch_targets,
const std::string& fetch_holder_name) {
size_t fetch_count = 0;
for (auto* op : block->AllOps()) {
if (op->Type() == kFetchOpType) {
fetch_count++;
PADDLE_ENFORCE_EQ(op->Output("Out")[0], fetch_holder_name,
"Output of fetch op should be '%s'", fetch_holder_name);
std::string fetch_target_name = op->Input("X")[0];
PADDLE_ENFORCE(
fetch_targets.find(fetch_target_name) != fetch_targets.end(),
"Fetch operator input name '%s' cannot be found in 'fetch_targets'",
fetch_target_name);
}
}
if (fetch_count > 0) {
PADDLE_ENFORCE_EQ(
fetch_count, fetch_targets.size(),
"The number of fetch operators should match 'fetch_targets'");
// When fetch operator are present, so should be fetch_holder
auto var = block->FindVar(fetch_holder_name);
PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
fetch_holder_name);
PADDLE_ENFORCE_EQ(var->GetType(), proto::VarDesc::FETCH_LIST,
"'%s' variable should be 'FETCH_LIST' type",
fetch_holder_name);
}
return fetch_count > 0;
}
void Executor::Run(const ProgramDesc& program, Scope* scope,
std::map<std::string, const LoDTensor*>& feed_targets,
std::map<std::string, LoDTensor*>& fetch_targets,
const std::string& feed_holder_name,
const std::string& fetch_holder_name) {
auto* copy_program = new ProgramDesc(program);
auto* global_block = copy_program->MutableBlock(0);
if (!has_feed_operators(global_block, feed_targets, feed_holder_name)) {
// create feed_holder variable
auto* feed_holder = global_block->Var(feed_holder_name);
feed_holder->SetType(proto::VarDesc::FEED_MINIBATCH);
feed_holder->SetPersistable(true);
int i = 0;
for (auto& feed_target : feed_targets) {
std::string var_name = feed_target.first;
VLOG(3) << "feed target's name: " << var_name;
// prepend feed op
auto* op = global_block->PrependOp();
op->SetType(kFeedOpType);
op->SetInput("X", {feed_holder_name});
op->SetOutput("Out", {var_name});
op->SetAttr("col", {static_cast<int>(i)});
op->CheckAttrs();
i++;
}
}
// map the data of feed_targets to feed_holder
for (auto* op : global_block->AllOps()) {
if (op->Type() == kFeedOpType) {
std::string feed_target_name = op->Output("Out")[0];
int idx = boost::get<int>(op->GetAttr("col"));
SetFeedVariable(scope, *feed_targets[feed_target_name], feed_holder_name,
idx);
}
}
if (!has_fetch_operators(global_block, fetch_targets, fetch_holder_name)) {
// create fetch_holder variable
auto* fetch_holder = global_block->Var(fetch_holder_name);
fetch_holder->SetType(proto::VarDesc::FETCH_LIST);
fetch_holder->SetPersistable(true);
int i = 0;
for (auto& fetch_target : fetch_targets) {
std::string var_name = fetch_target.first;
VLOG(3) << "fetch target's name: " << var_name;
// append fetch op
auto* op = global_block->AppendOp();
op->SetType(kFetchOpType);
op->SetInput("X", {var_name});
op->SetOutput("Out", {fetch_holder_name});
op->SetAttr("col", {static_cast<int>(i)});
op->CheckAttrs();
i++;
}
}
Run(*copy_program, scope, 0, true, true);
// obtain the data of fetch_targets from fetch_holder
for (auto* op : global_block->AllOps()) {
if (op->Type() == kFetchOpType) {
std::string fetch_target_name = op->Input("X")[0];
int idx = boost::get<int>(op->GetAttr("col"));
*fetch_targets[fetch_target_name] =
GetFetchVariable(*scope, fetch_holder_name, idx);
}
}
delete copy_program;
}
} // namespace framework
} // namespace paddle
paddle/framework/executor.h
浏览文件 @ 96eb9587
......@@ -41,6 +41,12 @@ class Executor {
void Run(const ProgramDesc&, Scope*, int, bool create_local_scope = true,
bool create_vars = true);
void Run(const ProgramDesc& program, Scope* scope,
std::map<std::string, const LoDTensor*>& feed_targets,
std::map<std::string, LoDTensor*>& fetch_targets,
const std::string& feed_holder_name = "feed",
const std::string& fetch_holder_name = "fetch");
private:
const platform::Place place_;
};
......
paddle/framework/feed_fetch_method.cc 0 → 100644
浏览文件 @ 96eb9587
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/framework/feed_fetch_method.h"
#include "glog/logging.h"
#include "paddle/framework/variable.h"
namespace paddle {
namespace framework {
void SetFeedVariable(Scope* scope, const LoDTensor& input,
const std::string& var_name, size_t index) {
// If var_name Variable is not found in GlobalScope, a new variable will
// be created.
VLOG(3) << "SetFeedVariable name=" << var_name << " index=" << index;
Variable* g_feed_value = scope->Var(var_name);
auto& feed_inputs =
*(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
if (index >= feed_inputs.size()) {
feed_inputs.resize(index + 1);
}
// shared data with input tensor
feed_inputs[index].ShareDataWith(input);
// set lod
feed_inputs[index].set_lod(input.lod());
}
LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
size_t index) {
// Since we want to fetch LodTensor from a variable, the variable must
// be created alreadly.
Variable* g_fetch_value = scope.FindVar(var_name);
PADDLE_ENFORCE(g_fetch_value->IsType<FeedFetchList>(),
"Only %s can be invoked by GetFetchVariable",
typeid(FeedFetchList).name());
auto& fetch_outputs = *g_fetch_value->GetMutable<FeedFetchList>();
auto& tensor = fetch_outputs[index];
VLOG(3) << "Fetch " << var_name << " with index " << index
<< " shape= " << tensor.dims();
PADDLE_ENFORCE_LT(index, fetch_outputs.size());
return tensor;
}
} // namespace framework
} // namespace paddle
paddle/framework/feed_fetch_method.h
浏览文件 @ 96eb9587
......@@ -13,46 +13,18 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "glog/logging.h"
#include "paddle/framework/feed_fetch_type.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/variable.h"
namespace paddle {
namespace framework {
void SetFeedVariable(Scope* scope, const LoDTensor& input,
const std::string& var_name, size_t index) {
// If var_name Variable is not found in GlobalScope, a new variable will
// be created.
VLOG(3) << "SetFeedVariable name=" << var_name << " index=" << index;
Variable* g_feed_value = scope->Var(var_name);
auto& feed_inputs =
*(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
if (index >= feed_inputs.size()) {
feed_inputs.resize(index + 1);
}
// shared data with input tensor
feed_inputs[index].ShareDataWith(input);
// set lod
feed_inputs[index].set_lod(input.lod());
}
const std::string& var_name, size_t index);
LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
size_t index) {
// Since we want to fetch LodTensor from a variable, the variable must
// be created alreadly.
Variable* g_fetch_value = scope.FindVar(var_name);
PADDLE_ENFORCE(g_fetch_value->IsType<FeedFetchList>(),
"Only %s can be invoked by GetFetchVariable",
typeid(FeedFetchList).name());
auto& fetch_outputs = *g_fetch_value->GetMutable<FeedFetchList>();
auto& tensor = fetch_outputs[index];
VLOG(3) << "Fetch " << var_name << " with index " << index
<< " shape= " << tensor.dims();
PADDLE_ENFORCE_LT(index, fetch_outputs.size());
return tensor;
}
size_t index);
} // namespace framework
} // namespace paddle
paddle/framework/op_kernel_type_test.cc
浏览文件 @ 96eb9587
......@@ -26,9 +26,9 @@ TEST(OpKernelType, ToString) {
OpKernelType op_kernel_type(DataType::FP32, CPUPlace(), DataLayout::kNCHW,
LibraryType::kCUDNN);
ASSERT_EQ(
paddle::framework::KernelTypeToString(op_kernel_type),
"data_type[5]:data_layout[NCHW]:place[CPUPlace]:library_type[CUDNN]");
ASSERT_EQ(paddle::framework::KernelTypeToString(op_kernel_type),
"data_type[float32]:data_layout[NCHW]:place[CPUPlace]:library_type["
"CUDNN]");
}
TEST(OpKernelType, Hash) {
......
paddle/framework/threadpool.cc
浏览文件 @ 96eb9587
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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
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,
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. */
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/framework/threadpool.h"
namespace paddle {
namespace framework {
std::unique_ptr<ThreadPool> ThreadPool::threadpool(nullptr);
std::once_flag ThreadPool::init_flag;
std::unique_ptr<ThreadPool> ThreadPool::threadpool_(nullptr);
std::once_flag ThreadPool::init_flag_;
ThreadPool* ThreadPool::GetInstance() {
std::call_once(init_flag_, &ThreadPool::Init);
return threadpool_.get();
}
void ThreadPool::Init() {
if (threadpool_.get() == nullptr) {
// TODO(Yancey1989): specify the max threads number
int num_threads = std::thread::hardware_concurrency();
PADDLE_ENFORCE_GT(num_threads, 0);
threadpool_.reset(new ThreadPool(num_threads));
}
}
ThreadPool::ThreadPool(int num_threads)
: total_threads_(num_threads), idle_threads_(num_threads), running_(true) {
threads_.resize(num_threads);
for (auto& thread : threads_) {
// TODO(Yancey1989): binding the thread on the specify CPU number
thread.reset(new std::thread(std::bind(&ThreadPool::TaskLoop, this)));
}
}
ThreadPool::~ThreadPool() {
{
// notify all threads to stop running
running_ = false;
scheduled_.notify_all();
}
for (auto& t : threads_) {
t->join();
t.reset(nullptr);
}
}
void ThreadPool::Wait() {
std::unique_lock<std::mutex> lock(mutex_);
completed_.wait(lock, [=] { return Done() == true; });
}
void ThreadPool::TaskLoop() {
while (running_) {
std::unique_lock<std::mutex> lock(mutex_);
scheduled_.wait(lock, [=] { return !tasks_.empty() || !running_; });
if (!running_) {
break;
}
// pop a task from the task queue
auto task = std::move(tasks_.front());
tasks_.pop();
--idle_threads_;
lock.unlock();
// run the task
task();
{
std::unique_lock<std::mutex> lock(mutex_);
++idle_threads_;
if (Done()) {
completed_.notify_all();
}
}
}
}
} // namespace framework
} // namespace paddle
paddle/framework/threadpool.h
浏览文件 @ 96eb9587
......@@ -20,52 +20,36 @@ limitations under the License. */
#include <mutex>
#include <queue>
#include <thread>
#include <vector>
#include "paddle/platform/enforce.h"
namespace paddle {
namespace framework {
// ThreadPool maintains a queue of tasks, and runs them using a fixed
// number of threads.
class ThreadPool {
public:
typedef std::packaged_task<void()> Task;
/**
* @brief Get a instance of threadpool, the thread number will
* be specified as the number of hardware thread contexts
*/
static ThreadPool* GetInstance() {
std::call_once(init_flag, &ThreadPool::Init);
return threadpool.get();
}
// Returns the singleton of ThreadPool.
static ThreadPool* GetInstance();
~ThreadPool() {
{
// notify all threads to stop running
running_ = false;
scheduled_.notify_all();
}
for (auto& t : threads_) {
t->join();
t.reset(nullptr);
}
}
~ThreadPool();
int GetNumThreads() const { return num_threads_; }
// Returns the number of threads created by the constructor.
size_t Threads() const { return total_threads_; }
int GetAvailable() {
// Returns the number of currently idle threads.
size_t IdleThreads() {
std::unique_lock<std::mutex> lock(mutex_);
return available_;
return idle_threads_;
}
/**
* @brief Push a function to the queue, and will be scheduled and
* executed if a thread is available.
* @param[in] Task, will be pushed to the task queue.
* @return std::future<void>, we could wait for the task finished by
* f.wait().
*/
// Run pushes a function to the task queue and returns a std::future
// object. To wait for the completion of the task, call
// std::future::wait().
template <typename Callback>
std::future<void> Run(Callback fn) {
std::unique_lock<std::mutex> lock(mutex_);
......@@ -77,84 +61,40 @@ class ThreadPool {
return f;
}
/**
* @brief Wait until all the tasks are completed.
*/
void Wait() {
std::unique_lock<std::mutex> lock(mutex_);
completed_.wait(lock, [=] { return Done() == true; });
}
// Wait until all the tasks are completed.
void Wait();
private:
DISABLE_COPY_AND_ASSIGN(ThreadPool);
explicit ThreadPool(int num_threads)
: num_threads_(num_threads), available_(num_threads), running_(true) {
threads_.resize(num_threads);
for (auto& thread : threads_) {
// TODO(Yancey1989): binding the thread on the specify CPU number
thread.reset(new std::thread(std::bind(&ThreadPool::TaskLoop, this)));
}
}
explicit ThreadPool(int num_threads);
/**
* @brief If the task queue is empty and avaialbe
* is equal to the number of threads, means that
* all tasks are completed.
*
* Note: this function is not thread-safe.
*
* @return true if all tasks are completed.
*/
bool Done() { return tasks_.empty() && available_ == num_threads_; }
void TaskLoop() {
while (running_) {
std::unique_lock<std::mutex> lock(mutex_);
scheduled_.wait(lock, [=] { return !tasks_.empty() || !running_; });
if (!running_) {
break;
}
// pop a task from the task queue
auto task = std::move(tasks_.front());
tasks_.pop();
--available_;
lock.unlock();
// run the task
task();
{
std::unique_lock<std::mutex> lock(mutex_);
++available_;
if (Done()) {
completed_.notify_all();
}
}
}
}
// If the task queue is empty and avaialbe is equal to the number of
// threads, means that all tasks are completed. Note: this function
// is not thread-safe. Returns true if all tasks are completed.
// Note: don't delete the data member total_threads_ and use
// threads_.size() instead; because you'd need to lock the mutex
// before accessing threads_.
bool Done() { return tasks_.empty() && idle_threads_ == total_threads_; }
static void Init() {
if (threadpool.get() == nullptr) {
// TODO(Yancey1989): specify the max threads number
int num_threads = std::thread::hardware_concurrency();
PADDLE_ENFORCE_GT(num_threads, 0);
threadpool.reset(new ThreadPool(num_threads));
}
}
// The constructor starts threads to run TaskLoop, which retrieves
// and runs tasks from the queue.
void TaskLoop();
// Init is called by GetInstance.
static void Init();
private:
static std::unique_ptr<ThreadPool> threadpool;
static std::once_flag init_flag;
static std::unique_ptr<ThreadPool> threadpool_;
static std::once_flag init_flag_;
int num_threads_;
int available_;
bool running_;
std::queue<Task> tasks_;
std::vector<std::unique_ptr<std::thread>> threads_;
const size_t total_threads_;
size_t idle_threads_;
std::queue<Task> tasks_;
std::mutex mutex_;
bool running_;
std::condition_variable scheduled_;
std::condition_variable completed_;
};
......
paddle/framework/threadpool_test.cc
浏览文件 @ 96eb9587
......@@ -22,11 +22,7 @@ namespace framework = paddle::framework;
void do_sum(framework::ThreadPool* pool, std::atomic<int>& sum, int cnt) {
std::vector<std::future<void>> fs;
for (int i = 0; i < cnt; ++i) {
auto f = pool->Run([&sum]() { sum.fetch_add(1); });
fs.push_back(std::move(f));
}
for (auto& f : fs) {
f.wait();
fs.push_back(framework::Async([&sum]() { sum.fetch_add(1); }));
}
}
......
paddle/inference/inference.cc
浏览文件 @ 96eb9587
......@@ -15,18 +15,13 @@ limitations under the License. */
#include "inference.h"
#include <fstream>
#include "paddle/framework/executor.h"
#include "paddle/framework/feed_fetch_method.h"
#include "paddle/framework/init.h"
#include "paddle/framework/scope.h"
#ifdef PADDLE_USE_PTOOLS
#include "chooseser.h"
#endif
namespace paddle {
void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
std::string model_filename = dirname + "/__model__.dat";
std::string model_filename = dirname + "/__model__";
LOG(INFO) << "loading model from " << model_filename;
std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
std::string program_desc_str;
......@@ -52,39 +47,15 @@ void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
}
}
void InferenceEngine::LoadInferenceModel(
const std::string& dirname,
const std::vector<std::string>& feed_var_names,
const std::vector<std::string>& fetch_var_names) {
std::string model_filename = dirname + "/__model__.dat";
LOG(INFO) << "loading model from " << model_filename;
std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
std::string program_desc_str;
inputfs.seekg(0, std::ios::end);
program_desc_str.resize(inputfs.tellg());
inputfs.seekg(0, std::ios::beg);
LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
inputfs.read(&program_desc_str[0], program_desc_str.size());
inputfs.close();
program_ = new framework::ProgramDesc(program_desc_str);
GenerateLoadProgram(dirname);
if (feed_var_names.empty() || fetch_var_names.empty()) {
LOG(FATAL) << "Please specify the feed_var_names and fetch_var_names.";
}
feed_var_names_ = feed_var_names;
fetch_var_names_ = fetch_var_names;
PrependFeedOp();
AppendFetchOp();
}
bool InferenceEngine::IsParameter(const framework::VarDesc* var) {
if (var->Persistable() && var->Name() != "feed" && var->Name() != "fetch") {
if (var->Persistable()) {
// There are many unreachable variables in the program
for (size_t i = 0; i < program_->Size(); ++i) {
const framework::BlockDesc& block = program_->Block(i);
for (auto* op : block.AllOps()) {
if (op->Type() == "feed") {
continue;
}
for (auto input_argument_name : op->InputArgumentNames()) {
if (input_argument_name == var->Name()) {
return true;
......@@ -182,7 +153,7 @@ void InferenceEngine::Execute(const std::vector<framework::LoDTensor>& feeds,
LOG(FATAL) << "Please initialize the program_ and load_program_ first.";
}
if (feeds.size() < feed_var_names_.size()) {
if (feeds.size() != feed_var_names_.size()) {
LOG(FATAL) << "Please feed " << feed_var_names_.size() << " input Tensors.";
}
......@@ -193,19 +164,22 @@ void InferenceEngine::Execute(const std::vector<framework::LoDTensor>& feeds,
executor->Run(*load_program_, scope, 0, true, true);
std::map<std::string, const framework::LoDTensor*> feed_targets;
std::map<std::string, framework::LoDTensor*> fetch_targets;
// set_feed_variable
for (size_t i = 0; i < feed_var_names_.size(); ++i) {
framework::SetFeedVariable(scope, feeds[i], "feed", i);
feed_targets[feed_var_names_[i]] = &feeds[i];
}
executor->Run(*program_, scope, 0, true, true);
// get_fetch_variable
fetchs.resize(fetch_var_names_.size());
for (size_t i = 0; i < fetch_var_names_.size(); ++i) {
fetchs[i] = framework::GetFetchVariable(*scope, "fetch", i);
fetch_targets[fetch_var_names_[i]] = &fetchs[i];
}
executor->Run(*program_, scope, feed_targets, fetch_targets);
delete place;
delete scope;
delete executor;
......
paddle/inference/inference.h
浏览文件 @ 96eb9587
......@@ -29,9 +29,6 @@ public:
}
void LoadInferenceModel(const std::string& dirname);
void LoadInferenceModel(const std::string& dirname,
const std::vector<std::string>& feed_var_names,
const std::vector<std::string>& fetch_var_names);
void Execute(const std::vector<framework::LoDTensor>& feeds,
std::vector<framework::LoDTensor>& fetchs);
......
paddle/operators/CMakeLists.txt
浏览文件 @ 96eb9587
......@@ -147,6 +147,7 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
op_library(sequence_conv_op DEPS context_project)
op_library(sequence_pool_op DEPS sequence_pooling)
op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(lstmp_op DEPS sequence2batch lstm_compute)
op_library(gru_op DEPS sequence2batch gru_compute)
op_library(recurrent_op DEPS executor)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale math_function)
......
paddle/operators/activation_op.h
浏览文件 @ 96eb9587
......@@ -323,7 +323,7 @@ template <typename T>
struct FloorFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.ceil();
out.device(d) = x.floor();
}
};
......
paddle/operators/detail/grpc_client.cc
浏览文件 @ 96eb9587
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "grpc_client.h"
#include "paddle/framework/threadpool.h"
namespace paddle {
namespace operators {
namespace detail {
......@@ -22,25 +23,32 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
const framework::Scope& scope,
const std::string& var_name,
int64_t time_out) {
sendrecv::VariableMessage req;
auto* var = scope.FindVar(var_name);
SerializeToMessage(var_name, var, ctx, &req);
// varhandle
VarHandle var_h;
var_h.ep = ep;
var_h.scope = &scope;
var_h.name = var_name;
var_h.ctx = &ctx;
// stub context
auto ch = GetChannel(ep);
SendProcessor* s = new SendProcessor(ch);
s->Prepare(var_h, time_out);
s->response_call_back_ = NULL;
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
const platform::DeviceContext* p_ctx = &ctx;
const std::string ep_val = ep;
const std::string var_name_val = var_name;
const framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
framework::Async([var_name_val, p_ctx, ep_val, p_scope, time_out, ch, this] {
auto* var = p_scope->FindVar(var_name_val);
sendrecv::VariableMessage req;
SerializeToMessage(var_name_val, var, *p_ctx, &req);
// varhandle
VarHandle var_h;
var_h.ep = ep_val;
var_h.scope = p_scope;
var_h.name = var_name_val;
var_h.ctx = p_ctx;
// stub context
SendProcessor* s = new SendProcessor(ch);
s->Prepare(var_h, time_out);
s->response_call_back_ = NULL;
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
});
req_count_++;
......@@ -50,8 +58,6 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
void ProcGetResponse(const VarHandle& var_h,
const sendrecv::VariableMessage& ret_msg) {
auto* outvar = var_h.scope->FindVar(var_h.name);
std::istringstream iss(ret_msg.serialized());
DeserializeFromMessage(ret_msg, *var_h.ctx, outvar);
}
......@@ -60,24 +66,31 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
const framework::Scope& scope,
const std::string& var_name,
int64_t time_out) {
sendrecv::VariableMessage req;
req.set_varname(var_name);
// varhandle
VarHandle var_h;
var_h.ep = ep;
var_h.scope = &scope;
var_h.name = var_name;
var_h.ctx = &ctx;
// stub context
auto ch = GetChannel(ep);
GetProcessor* s = new GetProcessor(ch);
s->Prepare(var_h, time_out);
s->response_call_back_ = ProcGetResponse;
auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
const platform::DeviceContext* p_ctx = &ctx;
const std::string ep_val = ep;
const std::string var_name_val = var_name;
const framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
framework::Async([var_name_val, ep_val, p_scope, p_ctx, time_out, ch, this] {
sendrecv::VariableMessage req;
req.set_varname(var_name_val);
// varhandle
VarHandle var_h;
var_h.ep = ep_val;
var_h.scope = p_scope;
var_h.name = var_name_val;
var_h.ctx = p_ctx;
// stub context
GetProcessor* s = new GetProcessor(ch);
s->Prepare(var_h, time_out);
s->response_call_back_ = ProcGetResponse;
auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
});
req_count_++;
......@@ -85,19 +98,31 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
}
bool RPCClient::Wait() {
bool ok = true;
if (req_count_ <= 0) {
return true;
}
const size_t kReqCnt = req_count_;
bool a[kReqCnt];
std::vector<std::future<void>> waits(req_count_);
while (true) {
if (req_count_ <= 0) {
break;
}
for (int i = 0; i < req_count_; i++) {
waits[i] = framework::Async([i, &a, this] { a[i] = Proceed(); });
}
for (int i = 0; i < req_count_; i++) {
waits[i].wait();
}
if (!Proceed()) {
int last_req_count = req_count_;
req_count_ = 0;
for (int i = 0; i < last_req_count; i++) {
if (!a[i]) {
return false;
}
}
return ok;
return true;
}
bool RPCClient::Proceed() {
......@@ -124,7 +149,6 @@ bool RPCClient::Proceed() {
c->Process();
delete c;
req_count_--;
return true;
}
......
paddle/operators/gru_op.cc
浏览文件 @ 96eb9587
......@@ -135,14 +135,14 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
GRU Operator implements part calculations of the complete GRU as following:
\f[
update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\
reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r) \\
output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\
$$
update\_gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\
reset\_gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r) \\
output\_candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\
output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)
\f]
$$
@note To implement the complete GRU, fully-connected operator must be used
@note To implement the complete GRU, fully-connected operator must be used
before to feed xu, xr and xc as the Input of GRU operator.
)DOC");
}
......
paddle/operators/lstmp_op.cc 0 → 100644
浏览文件 @ 96eb9587
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/lstmp_op.h"
namespace paddle {
namespace operators {
class LSTMPOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Weight"),
"Input(Weight) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("ProjWeight"),
"Input(ProjWeight) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Bias"),
"Input(Bias) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Projection"),
"Output(Projection) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Cell"),
"Output(Cell) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchGate"),
"Output(BatchGate) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
"Output(BatchCellPreAct) of LSTMP operator should not be "
"null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchHidden"),
"Output(BatchHidden) of LSTMP operator should not be null.");
auto in_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE_EQ(in_dims.size(), 2,
"Input(X)'s rank of LSTMP operator must be 2.");
int frame_size = in_dims[1] / 4;
auto w_dims = ctx->GetInputDim("Weight");
auto proj_dims = ctx->GetInputDim("ProjWeight");
PADDLE_ENFORCE_EQ(w_dims.size(), 2,
"The rank of Input(Weight) should be 2.");
PADDLE_ENFORCE_EQ(w_dims[0], proj_dims[1],
"The first dimension of Input(Weight) "
"should be %d.",
proj_dims[1]);
PADDLE_ENFORCE_EQ(w_dims[1], 4 * frame_size,
"The second dimension of Input(Weight) "
"should be 4 * %d.",
frame_size);
PADDLE_ENFORCE_EQ(proj_dims.size(), 2,
"The rank of Input(ProjWeight) should be 2.");
PADDLE_ENFORCE_EQ(proj_dims[0], frame_size,
"The first dimension of Input(ProjWeight) "
"should be %d.",
frame_size);
if (ctx->HasInput("H0")) {
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");
PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
PADDLE_ENFORCE_EQ(b_dims[0], 1,
"The first dimension of Input(Bias) should be 1.");
if (ctx->Attrs().Get<bool>("use_peepholes")) {
PADDLE_ENFORCE_EQ(b_dims[1], 7 * frame_size,
"The second dimension of Input(Bias) should be "
"7 * %d if enable peepholes connection",
frame_size);
} else {
PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
"The second dimension of Input(Bias) should be "
"4 * %d if disable peepholes connection",
frame_size);
}
framework::DDim out_dims({in_dims[0], frame_size});
framework::DDim proj_out_dims({in_dims[0], proj_dims[1]});
ctx->SetOutputDim("Projection", proj_out_dims);
ctx->SetOutputDim("Cell", out_dims);
ctx->SetOutputDim("BatchGate", in_dims);
ctx->SetOutputDim("BatchCellPreAct", out_dims);
ctx->SetOutputDim("BatchHidden", out_dims);
ctx->ShareLoD("Input", "Projection");
ctx->ShareLoD("Input", "Cell");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("Input")->type()),
ctx.device_context());
}
};
class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LSTMPOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Input",
"(LoDTensor) the input for sequence data, which supports "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X 4D), where T is the "
"total time steps in this mini-batch, D is the hidden size.");
AddInput("H0",
"(Tensor, optional) the initial hidden state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size and D is the hidden size.")
.AsDispensable();
AddInput("C0",
"(Tensor, optional) the initial cell state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size. `C0` should not be null if `H0` provided.")
.AsDispensable();
AddInput("Weight",
"(Tensor) the learnable hidden-hidden weights."
" - The shape is (P x 4D), where P is the projection layer size "
"and D is the hidden size."
" - Weight = {W_cr, W_ir, W_fr, W_or}");
AddInput("ProjWeight",
"(Tensor) the learnable weight of the projection layer."
" - The shape is (D x P), where P is the recurrent projection "
"layer size and D is the hidden size."
" - ProjWeight = {W_rh}");
AddInput("Bias",
"(Tensor) the learnable biases, which contains two parts: "
"input-hidden biases and peephole connections weights if "
"setting `use_peepholes` to `True`. "
"1. `use_peepholes = False` "
" - The shape is (1 x 4D). "
" - Bias = {b_c, b_i, b_f, b_o}."
"2. `use_peepholes = True` "
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
AddOutput("Projection",
"(LoDTensor) the projection of the hidden state of LSTMP "
"operator. The shape is (T x P), and LoD is the same with the "
"`Input`.");
AddOutput("Cell",
"(LoDTensor) the cell state of LSTMP operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("BatchGate",
"(LoDTensor) This LoDTensor contains input gate, forget gate "
"and output gate after the activations. This LoDTensor has the "
"same shape as the reorganized input, which is also be called "
"batch input. The LoD size is 2. The first-level LoD is the "
"batch offsets and the second contains the indices, which "
"denotes the position of reorganized sequence in the raw input.")
.AsIntermediate();
AddOutput("BatchCellPreAct",
"(LoDTensor) the pre-activation cell state reorganized in batch. "
"This LoDTensor is obtained in the forward and used in the "
"backward.")
.AsIntermediate();
AddOutput("BatchHidden",
"(LoDTensor) the hidden state reorganized in batch. "
"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.")
.SetDefault(true);
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"whether to compute reversed LSTMP.")
.SetDefault(false);
AddAttr<std::string>(
"gate_activation",
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default.")
.SetDefault("sigmoid")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("proj_activation",
"(string, default: tanh)"
"The activation for projection output, "
"`tanh` by defalut.")
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddComment(R"DOC(
Long-Short Term Memory with recurrent Projection layer (LSTMP) Operator.
LSTMP has a separate projection layer after the LSTM layer, projecting the
original hidden state to a lower-dimensional one, which is proposed to reduce
the number of total parameters and furthermore computational complexity for
the LSTM, espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
The formula is as follows:
$$
i_t = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
f_t = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
\tilde{c_t} = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c) \\
o_t = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o) \\
c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\
h_t = o_t \odot act_h(c_t) \\
r_t = \overline{act_h}(W_{rh}h_t)
$$
where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
are diagonal weight matrices for peephole connections. In our implementation,
we use vectors to reprenset these diagonal weight matrices. The b terms
denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
is the activation, such as logistic sigmoid function, and
$i, f, o$ and $c$ are the input gate, forget gate, output gate,
and cell activation vectors, respectively, all of which have the same size as
the cell output activation vector $h$. Here $h$ is usually called the hidden
state and $r$ denotes its recurrent projection. And $\tilde{c_t}$ is also
called the candidate hidden state, whose computation is based on the current
input and previous hidden state.
The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
are the cell input and cell output activation functions and `tanh` is usually
used for them. $\overline{act_h}$ is the activation function for the
projection output, usually using `identity` or same as $act_h$.
Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
operations on the input $x_{t}$ are NOT included in this operator.
Users can choose to use fully-connected operator before LSTMP operator.
)DOC");
}
};
class LSTMPGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Projection"),
"Input(Projection) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Cell"),
"Input(Cell) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Weight"),
"Input(Weight) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("ProjWeight"),
"Input(ProjWeight) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Bias"),
"Input(Bias) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
"Input(BatchGate) of LSTMP operator should not be null.");
PADDLE_ENFORCE(ctx->HasInput("BatchCellPreAct"),
"Input(BatchGate) of LSTMP operator should not be null.");
auto SetOutGradDim = [&ctx](const std::string& name) {
auto g_name = framework::GradVarName(name);
if (ctx->HasOutput(g_name))
ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
};
SetOutGradDim("Input");
SetOutGradDim("Weight");
SetOutGradDim("ProjWeight");
SetOutGradDim("Bias");
SetOutGradDim("H0");
SetOutGradDim("C0");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("Input")->type()),
ctx.device_context());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker, lstmp_grad,
ops::LSTMPGradOp);
REGISTER_OP_CPU_KERNEL(
lstmp, ops::LSTMPKernel<paddle::platform::CPUDeviceContext, float>,
ops::LSTMPKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
lstmp_grad, ops::LSTMPGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::LSTMPGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/operators/lstmp_op.cu 0 → 100644
浏览文件 @ 96eb9587
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/lstmp_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
lstmp, ops::LSTMPKernel<paddle::platform::CUDADeviceContext, float>,
ops::LSTMPKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
lstmp_grad,
ops::LSTMPGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::LSTMPGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/lstmp_op.h 0 → 100644
浏览文件 @ 96eb9587
此差异已折叠。
paddle/operators/math/CMakeLists.txt
浏览文件 @ 96eb9587
......@@ -11,7 +11,7 @@ if(WITH_GPU)
nv_library(sequence_pooling SRCS sequence_pooling.cc sequence_pooling.cu DEPS device_context math_function)
nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context tensor)
nv_library(context_project SRCS context_project.cc context_project.cu DEPS device_context math_function)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context tensor)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context tensor math_function)
nv_library(sequence_padding SRCS sequence_padding.cc sequence_padding.cu DEPS lod_tensor device_context)
nv_library(sequence_scale SRCS sequence_scale.cc sequence_scale.cu DEPS lod_tensor device_context)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
......@@ -28,7 +28,7 @@ else()
cc_library(sequence_pooling SRCS sequence_pooling.cc DEPS device_context math_function)
cc_library(vol2col SRCS vol2col.cc DEPS device_context tensor)
cc_library(context_project SRCS context_project.cc DEPS device_context math_function)
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context tensor)
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context tensor math_function)
cc_library(sequence_padding SRCS sequence_padding.cc DEPS lod_tensor device_context)
cc_library(sequence_scale SRCS sequence_scale.cc DEPS lod_tensor device_context)
cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
......
paddle/operators/multiplex_op.cc
浏览文件 @ 96eb9587
......@@ -119,7 +119,13 @@ REGISTER_OPERATOR(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker,
REGISTER_OPERATOR(multiplex_grad, ops::MultiplexGradOp);
REGISTER_OP_CPU_KERNEL(
multiplex,
ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, float>);
ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, float>,
ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, double>,
ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, int>,
ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, int64_t>);
REGISTER_OP_CPU_KERNEL(
multiplex_grad,
ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, float>);
ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, float>,
ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, double>,
ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, int>,
ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/operators/multiplex_op.cu
浏览文件 @ 96eb9587
......@@ -90,7 +90,13 @@ namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
multiplex,
ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, float>);
ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, float>,
ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, double>,
ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, int>,
ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
multiplex_grad,
ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, float>);
ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, float>,
ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, double>,
ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, int>,
ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/operators/nccl_op_test.cu.cc
浏览文件 @ 96eb9587
......@@ -241,7 +241,7 @@ TEST_F(NCCLTester, ncclReduceOp) {
// ncclBcastOp with desc
TEST_F(NCCLTester, ncclBcastOp) {
std::unique_ptr<f::OpDesc> op2(new f::OpDesc);
const int kRoot = 5;
const int kRoot = 0;
op2->SetType("ncclBcast");
op2->SetInput("X", {"st"});
op2->SetInput("Communicator", {"comm"});
......
paddle/operators/one_hot_op.cc 0 → 100644
浏览文件 @ 96eb9587
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
//
// 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/operators/one_hot_op.h"
#include "paddle/framework/framework.pb.h"
namespace paddle {
namespace operators {
class OneHotOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of OneHotOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of OneHotOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"Rank of Input(X) should be at least 2.");
PADDLE_ENFORCE_GE(x_dims[x_dims.size() - 1], 1U,
"Last dimension of Input(X) should be 1.");
int depth = ctx->Attrs().Get<int>("depth");
PADDLE_ENFORCE_GT(depth, 0, "Should provide a positive depth (%d).", depth);
framework::DDim out_dims(x_dims);
out_dims[out_dims.size() - 1] = depth;
ctx->SetOutputDim("Out", out_dims);
ctx->ShareLoD("X", /* --> */ "Out");
}
};
class OneHotOpMaker : public framework::OpProtoAndCheckerMaker {
public:
OneHotOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor, LoDTensor<int>) Input variable with rank at least 2. "
"The last dimension of X should be 1. Each value of X is an index "
"to indicate the position.");
AddOutput("Out",
"(Tensor, Tensor<float>) Output tensor with same rank as X. "
"The tensor consists of one-hot representations of values in X.");
AddAttr<int>("depth",
"A positive integer to specify the length of one-hot vector.");
AddAttr<int>("dtype",
"An integer to specify the data type of one-hot "
"vector. The default value is FP32.")
.SetDefault(paddle::framework::proto::DataType::FP32);
AddComment(R"DOC(
One Hot Operator. This operator creates the one-hot representations for input
index values. The following example will help to explain the function of this
operator:
X is a LoDTensor:
X.lod = [[0, 1, 4]]
X.shape = [4, 1]
X.data = [[1], [1], [3], [0]]
set depth = 4
Out is a LoDTensor:
Out.lod = [[0, 1, 4]]
Out.shape = [4, 4]
Out.data = [[0., 1., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
[1., 0., 0., 0.]]
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(one_hot, ops::OneHotOp, ops::OneHotOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
one_hot, ops::OneHotKernel<paddle::platform::CPUDeviceContext, int>,
ops::OneHotKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/operators/one_hot_op.cu 0 → 100644
浏览文件 @ 96eb9587
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
//
// 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/operators/one_hot_op.h"
#include "paddle/platform/cuda_helper.h"
#include "paddle/platform/gpu_info.h"
namespace paddle {
namespace operators {
using platform::PADDLE_CUDA_NUM_THREADS;
template <typename InT, typename OutT>
__global__ void FillOutputKernel(const InT* p_in_data, OutT* p_out_data,
const int64_t numel, const int depth) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numel) {
*(p_out_data + (idx * depth) + p_in_data[idx]) = 1.0;
}
}
template <typename DeviceContext, typename InT>
struct OneHotOpCUDAFunctor {
const framework::LoDTensor* in_;
framework::LoDTensor* out_;
const DeviceContext& ctx_;
int depth_;
OneHotOpCUDAFunctor(const framework::LoDTensor* in, framework::LoDTensor* out,
int depth, const DeviceContext& ctx)
: in_(in), out_(out), depth_(depth), ctx_(ctx) {}
template <typename OutT>
void operator()() const {
auto* p_in_data = in_->data<InT>();
auto numel = in_->numel();
auto* p_out_data = out_->mutable_data<OutT>(ctx_.GetPlace());
auto stream = ctx_.stream();
math::set_constant(ctx_, out_, 0.0);
FillOutputKernel<<<(numel + PADDLE_CUDA_NUM_THREADS - 1) /
PADDLE_CUDA_NUM_THREADS,
PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
p_in_data, p_out_data, numel, depth_);
}
};
using LoDTensor = framework::LoDTensor;
template <typename DeviceContext, typename T>
class OneHotCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int depth = context.Attr<int>("depth");
framework::VisitDataType(
static_cast<framework::proto::DataType>(context.Attr<int>("dtype")),
OneHotOpCUDAFunctor<DeviceContext, T>(
in, out, depth, context.template device_context<DeviceContext>()));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
one_hot, ops::OneHotCUDAKernel<paddle::platform::CUDADeviceContext, int>,
ops::OneHotCUDAKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/operators/one_hot_op.h 0 → 100644
浏览文件 @ 96eb9587
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename InT>
struct OneHotOpFunctor {
const framework::LoDTensor* in_;
framework::LoDTensor* out_;
int depth_;
const DeviceContext& ctx_;
OneHotOpFunctor(const framework::LoDTensor* in, framework::LoDTensor* out,
int depth, const DeviceContext& ctx)
: in_(in), out_(out), depth_(depth), ctx_(ctx) {}
template <typename OutT>
void operator()() const {
auto* p_in_data = in_->data<InT>();
auto numel = in_->numel();
auto* p_out_data = out_->mutable_data<OutT>(ctx_.GetPlace());
math::set_constant(ctx_, out_, 0.0);
for (int i = 0; i < numel; ++i) {
PADDLE_ENFORCE_GE(p_in_data[i], 0,
"Illegal index value, should be at least 0.");
PADDLE_ENFORCE_LT(p_in_data[i], depth_,
"Illegal index value, should be less than depth (%d).",
depth_);
*(p_out_data + i * depth_ + p_in_data[i]) = 1.0;
}
}
};
using LoDTensor = framework::LoDTensor;
template <typename DeviceContext, typename T>
class OneHotKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int depth = context.Attr<int>("depth");
framework::VisitDataType(
static_cast<framework::proto::DataType>(context.Attr<int>("dtype")),
OneHotOpFunctor<DeviceContext, T>(
in, out, depth, context.template device_context<DeviceContext>()));
}
};
} // namespace operators
} // namespace paddle
paddle/operators/parallel_do_op.cc
浏览文件 @ 96eb9587
......@@ -17,6 +17,7 @@ limitations under the License. */
#include "paddle/framework/executor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/threadpool.h"
#include "paddle/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
......@@ -39,8 +40,10 @@ static void SplitTensorAndMoveTensorToScopes(
const std::vector<std::string> &names) {
size_t num_sub_scopes = 0;
for (auto &argu : names) {
auto *var = scope.FindVar(argu);
const auto &tensor = var->Get<LoDTensor>();
const auto &tensor =
detail::Ref(scope.FindVar(argu),
"Cannot find variable %s in the parent scope", argu)
.Get<LoDTensor>();
auto lod_tensors = tensor.SplitLoDTensor(places);
for (auto &lod : lod_tensors) {
......@@ -60,7 +63,9 @@ static void SplitTensorAndMoveTensorToScopes(
}
for (size_t i = 0; i < lod_tensors.size(); ++i) {
*(*sub_scopes)[i]->Var(argu)->GetMutable<LoDTensor>() = lod_tensors[i];
*detail::Ref(sub_scopes->at(i)->Var(argu),
"Cannot find variable in the sub-scope", argu)
.GetMutable<LoDTensor>() = lod_tensors[i];
}
}
}
......@@ -287,6 +292,17 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
this->InputGrad(input_param, false));
}
}
auto *g_block = this->grad_block_[0];
// All variable name that needed by gradient operators
std::unordered_set<std::string> all_inputs_in_grad_blocks;
for (size_t i = 0; i < g_block->OpSize(); ++i) {
auto *op = g_block->Op(i);
for (auto &var_name : op->InputArgumentNames()) {
all_inputs_in_grad_blocks.insert(var_name);
}
}
for (auto &output_param : this->OutputNames()) {
if (output_param == kParallelScopes) {
......@@ -295,8 +311,17 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
this->Output(output_param));
} else {
grad->SetInput(output_param, this->Output(output_param));
grad->SetInput(framework::GradVarName(output_param),
this->OutputGrad(output_param));
std::vector<std::string> og_names;
for (auto &og_name : this->OutputGrad(output_param)) {
if (all_inputs_in_grad_blocks.count(og_name) != 0) {
// there are some gradient operators who need the OG. So make this
// OG as an input of parallel.do
og_names.push_back(og_name);
}
// else, there is no operator who need the OG. Do not use this OG as
// an input
}
grad->SetInput(framework::GradVarName(output_param), og_names);
}
}
grad->SetAttrMap(this->Attrs());
......
paddle/operators/pool_op.h
浏览文件 @ 96eb9587
......@@ -139,10 +139,8 @@ class PoolGradKernel : public framework::OpKernel<T> {
auto& dev_ctx = context.template device_context<DeviceContext>();
if (in_x_grad) {
in_x_grad->mutable_data<T>(context.GetPlace());
auto temp = framework::EigenVector<T>::Flatten(*in_x_grad);
temp.device(
*context.template device_context<DeviceContext>().eigen_device()) =
temp.constant(static_cast<T>(0));
paddle::operators::math::SetConstant<DeviceContext, T> set_constant;
set_constant(dev_ctx, in_x_grad, 0.0);
switch (ksize.size()) {
case 2: {
......
paddle/operators/reshape_op.cc
浏览文件 @ 96eb9587
......@@ -90,14 +90,10 @@ Reshape Operator.
Reshape Input(X) into the shape specified by Attr(shape).
An example:
Given a 2-D tensor X with 2 rows and 2 columns
[[1, 2], [3, 4]]
Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]]
and target shape = [1, 4], the reshape operator will transform
the tensor X into a 2-D tensor:
[[1, 2, 3, 4]]
the tensor X into a 2-D tensor: [[1, 2, 3, 4]]
One dimension in the target shape can be set -1, representing that its
size is unknown. In this case, the real dimension will be infered from
......
paddle/platform/call_once.h
浏览文件 @ 96eb9587
......@@ -29,20 +29,25 @@ namespace platform {
*/
template <typename Callable, typename... Args>
inline void call_once(std::once_flag& flag, Callable&& f, Args&&... args) {
bool good = false;
bool good = true;
std::exception ex;
std::call_once(flag,
[&](Args&&... args) {
try {
f(args...);
good = true;
} catch (const std::exception& e) {
ex = e;
} catch (...) {
ex = std::runtime_error("excption caught in call_once");
}
},
args...);
try {
std::call_once(flag,
[&](Args&&... args) {
try {
f(args...);
} catch (const std::exception& e) {
ex = e;
good = false;
} catch (...) {
ex = std::runtime_error("excption caught in call_once");
good = false;
}
},
args...);
} catch (std::system_error& x) {
throw std::runtime_error("call once failed");
}
if (!good) {
throw std::exception(ex);
}
......
paddle/pybind/CMakeLists.txt
浏览文件 @ 96eb9587
if(WITH_PYTHON)
cc_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc
DEPS pybind python backward proto_desc paddle_memory executor prune init profiler
DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method
${GLOB_OP_LIB})
if(NOT APPLE AND NOT ANDROID)
target_link_libraries(paddle_pybind rt)
......
paddle/pybind/pybind.cc
浏览文件 @ 96eb9587
......@@ -53,7 +53,7 @@ static size_t UniqueIntegerGenerator(const std::string &prefix) {
return generators[prefix].fetch_add(1);
}
bool IsCompileGPU() {
bool IsCompiledWithCUDA() {
#ifndef PADDLE_WITH_CUDA
return false;
#else
......@@ -424,14 +424,16 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<framework::Executor>(m, "Executor")
.def(py::init<const platform::Place &>())
.def("run", &Executor::Run);
.def("run",
(void (Executor::*)(const ProgramDesc &, Scope *, int, bool, bool)) &
Executor::Run);
m.def("unique_integer", UniqueIntegerGenerator);
m.def("init_gflags", framework::InitGflags);
m.def("init_glog", framework::InitGLOG);
m.def("init_devices", &framework::InitDevices);
m.def("is_compile_gpu", IsCompileGPU);
m.def("is_compiled_with_cuda", IsCompiledWithCUDA);
m.def("set_feed_variable", framework::SetFeedVariable);
m.def("get_fetch_variable", framework::GetFetchVariable);
......
python/paddle/v2/fluid/__init__.py
浏览文件 @ 96eb9587
......@@ -89,7 +89,7 @@ def __bootstrap__():
read_env_flags = [
'use_pinned_memory', 'check_nan_inf', 'do_memory_benchmark'
]
if core.is_compile_gpu():
if core.is_compiled_with_cuda():
read_env_flags += ['fraction_of_gpu_memory_to_use', 'op_sync']
core.init_gflags([sys.argv[0]] +
["--tryfromenv=" + ",".join(read_env_flags)])
......
python/paddle/v2/fluid/backward.py
浏览文件 @ 96eb9587
......@@ -178,7 +178,7 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
if _all_in_set_(
filter(lambda name: name.find(core.grad_var_suffix()) != -1,
op_desc.input_arg_names()), no_grad_set):
no_grad_set.union(out_arg_names)
no_grad_set.update(out_arg_names)
return True
return False
......
python/paddle/v2/fluid/distribute_transpiler.py
浏览文件 @ 96eb9587
......@@ -33,6 +33,10 @@ class VarBlock:
return "%s:%d:%d" % (self.varname, self.offset, self.size)
def same_or_split_var(p_name, var_name):
return p_name == var_name or p_name.startswith(var_name + ".block")
def split_dense_variable(var_list,
pserver_count,
min_block_size=1024,
......@@ -303,8 +307,8 @@ class DistributeTranspiler:
return True
else:
for n in param_names:
if n.startswith(op.inputs["Param"].name+".block") and \
n != op.inputs["Param"].name:
if same_or_split_var(n, op.inputs[
"Param"].name) and n != op.inputs["Param"].name:
return True
return False
else:
......@@ -335,7 +339,7 @@ class DistributeTranspiler:
if key == "Grad":
grad_block = None
for g in self.param_grad_ep_mapping[endpoint]["grads"]:
if g.name.startswith(var.name):
if same_or_split_var(g.name, var.name):
grad_block = g
break
if not grad_block:
......@@ -365,7 +369,7 @@ class DistributeTranspiler:
# param is already created on global program
param_block = None
for p in self.param_grad_ep_mapping[endpoint]["params"]:
if p.name.startswith(var.name):
if same_or_split_var(p.name, var.name):
param_block = p
break
if not param_block:
......@@ -501,7 +505,7 @@ class DistributeTranspiler:
def _get_splited_name_and_shape(varname):
for idx, splited_param in enumerate(params):
pname = splited_param.name
if pname.startswith(varname) and varname != pname:
if same_or_split_var(pname, varname) and varname != pname:
return pname, splited_param.shape
return "", []
......
python/paddle/v2/fluid/executor.py
浏览文件 @ 96eb9587
......@@ -68,6 +68,84 @@ def as_numpy(tensor):
return ans
def has_feed_operators(block, feed_targets, feed_holder_name):
""" Check whether the block already has feed operators.
Return false if the block does not have any feed operators.
If some feed operators have been prepended to the block, check that
the info contained in these feed operators matches the feed_targets
and feed_holder_name. Raise exception when any mismatch is found.
Return true when the block has feed operators with matching info.
Args:
block: a block instance (typically global block of a program)
feed_targets: a dictionary of {feed_target_name: feed_target_data}
feed_holder_name: the name of the variable that holds the data of
all feed targets. The type of this feed_holder variable is
FEED_MINIBATCH, which is essentially vector<LoDTensor>.
Returns:
A boolean value that indicates whether a block has feed operators
that match the info contained in feed_targets and feed_holder_name.
"""
feed_count = 0
for op in block.ops:
if op.desc.type() == 'feed':
feed_count += 1
assert op.desc.input('X')[0] == feed_holder_name
feed_target_name = op.desc.output('Out')[0]
if feed_target_name not in feed_targets:
raise Exception("'feed_targets' does not have {} variable".
format(feed_target_name))
else:
break
if feed_count > 0 and feed_count != len(feed_targets):
raise Exception(
"Feed operators in program desc do not match 'feed_targets'")
return feed_count > 0
def has_fetch_operators(block, fetch_targets, fetch_holder_name):
""" Check whether the block already has fetch operators.
Return false if the block does not have any fetch operators.
If some fetch operators have been appended to the block, check that
the info contained in these fetch operators matches the fetch_targets
and fetch_holder_name. Raise exception when any mismatch is found.
Return true when the block has fetch operators with matching info.
Args:
block: a block instance (typically global block of a program)
fetch_targets: a dictionary of {fetch_target_name: fetch_target_data}
fetch_holder_name: the name of the variable that holds the data of
all fetch targets. The type of this fetch_holder variable is
FETCH_LIST, which is essentially vector<LoDTensor>.
Return:
A boolean value that indicates whether a block has fetch operators
that match the info contained in fetch_targets and fetch_holder_name.
"""
fetch_count = 0
for op in block.ops:
if op.desc.type() == 'fetch':
fetch_count += 1
assert op.desc.output('Out')[0] == fetch_holder_name
fetch_target_name = op.desc.input('X')[0]
if fetch_target_name not in [
var.desc.name() for var in fetch_targets
]:
raise Exception("'fetch_targets' does not have {} variable".
format(fetch_target_name))
idx = op.desc.attr('col')
assert fetch_target_name == fetch_targets[idx].desc.name()
if fetch_count > 0 and fetch_count != len(fetch_targets):
raise Exception(
"Fetch operators in program desc do not match 'fetch_targets'")
return fetch_count > 0
class Executor(object):
def __init__(self, places):
if not isinstance(places, list) and not isinstance(places, tuple):
......@@ -147,33 +225,50 @@ class Executor(object):
program = program.clone()
global_block = program.global_block()
feed_var = global_block.create_var(
name=feed_var_name,
type=core.VarDesc.VarType.FEED_MINIBATCH,
persistable=True)
for i, name in enumerate(feed):
out = global_block.var(name)
global_block.prepend_op(
'feed',
inputs={'X': [feed_var]},
outputs={'Out': [out]},
attrs={'col': i})
cur_feed = feed[name]
if not isinstance(cur_feed, core.LoDTensor):
cur_feed = self.aslodtensor(cur_feed)
core.set_feed_variable(scope, cur_feed, feed_var.name, i)
fetch_var = global_block.create_var(
name=fetch_var_name,
type=core.VarDesc.VarType.FETCH_LIST,
persistable=True)
for i, var in enumerate(fetch_list):
global_block.append_op(
type='fetch',
inputs={'X': [var]},
outputs={'Out': [fetch_var]},
attrs={'col': i})
if feed_var_name in global_block.vars:
feed_var = global_block.var(feed_var_name)
else:
feed_var = global_block.create_var(
name=feed_var_name,
type=core.VarDesc.VarType.FEED_MINIBATCH,
persistable=True)
if fetch_var_name in global_block.vars:
fetch_var = global_block.var(fetch_var_name)
else:
fetch_var = global_block.create_var(
name=fetch_var_name,
type=core.VarDesc.VarType.FETCH_LIST,
persistable=True)
if not has_feed_operators(global_block, feed, feed_var_name):
for i, name in enumerate(feed):
out = global_block.var(name)
global_block.prepend_op(
type='feed',
inputs={'X': [feed_var]},
outputs={'Out': [out]},
attrs={'col': i})
for op in global_block.ops:
if op.desc.type() == 'feed':
feed_target_name = op.desc.output('Out')[0]
cur_feed = feed[feed_target_name]
if not isinstance(cur_feed, core.LoDTensor):
cur_feed = self.aslodtensor(cur_feed)
idx = op.desc.attr('col')
core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
else:
break
if not has_fetch_operators(global_block, fetch_list, fetch_var_name):
for i, var in enumerate(fetch_list):
global_block.append_op(
type='fetch',
inputs={'X': [var]},
outputs={'Out': [fetch_var]},
attrs={'col': i})
self.executor.run(program.desc, scope, 0, True, True)
outs = [
......
python/paddle/v2/fluid/io.py
浏览文件 @ 96eb9587
......@@ -13,8 +13,8 @@
# limitations under the License.
import os
import cPickle as pickle
from paddle.v2.fluid.evaluator import Evaluator
from paddle.v2.fluid.framework import Program, Parameter, default_main_program, Variable
from . import core
......@@ -187,18 +187,28 @@ def get_inference_program(target_vars, main_program=None):
main_program = default_main_program()
if not isinstance(target_vars, list):
target_vars = [target_vars]
pruned_program = main_program.prune(targets=target_vars)
vars = []
for var in target_vars:
if isinstance(var, Evaluator):
vars.extend(var.states)
vars.extend(var.metrics)
else:
vars.append(var)
pruned_program = main_program.prune(targets=vars)
inference_program = pruned_program.inference_optimize()
return inference_program
def prepend_feed_ops(inference_program, feeded_var_names):
def prepend_feed_ops(inference_program,
feed_target_names,
feed_holder_name='feed'):
global_block = inference_program.global_block()
feed_var = global_block.create_var(
name='feed', type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True)
name=feed_holder_name,
type=core.VarDesc.VarType.FEED_MINIBATCH,
persistable=True)
for i, name in enumerate(feeded_var_names):
for i, name in enumerate(feed_target_names):
out = global_block.var(name)
global_block.prepend_op(
type='feed',
......@@ -207,12 +217,16 @@ def prepend_feed_ops(inference_program, feeded_var_names):
attrs={'col': i})
def append_fetch_ops(inference_program, fetch_var_names):
def append_fetch_ops(inference_program,
fetch_target_names,
fetch_holder_name='fetch'):
global_block = inference_program.global_block()
fetch_var = global_block.create_var(
name='fetch', type=core.VarDesc.VarType.FETCH_LIST, persistable=True)
name=fetch_holder_name,
type=core.VarDesc.VarType.FETCH_LIST,
persistable=True)
for i, name in enumerate(fetch_var_names):
for i, name in enumerate(fetch_target_names):
global_block.append_op(
type='fetch',
inputs={'X': [name]},
......@@ -262,21 +276,12 @@ def save_inference_model(dirname,
inference_program = pruned_program.inference_optimize()
fetch_var_names = [v.name for v in target_vars]
model_file_name = dirname + "/__model__"
with open(model_file_name, "w") as f:
pickle.dump({
"program_desc_str": inference_program.desc.serialize_to_string(),
"feed_var_names": feeded_var_names,
"fetch_var_names": fetch_var_names
}, f, -1)
prepend_feed_ops(inference_program, feeded_var_names)
append_fetch_ops(inference_program, fetch_var_names)
# Save only programDesc of inference_program in binary format
# in another file: __model__.dat
with open(model_file_name + ".dat", "wb") as fp:
fp.write(inference_program.desc.serialize_to_string())
model_file_name = dirname + "/__model__"
with open(model_file_name, "wb") as f:
f.write(inference_program.desc.serialize_to_string())
save_params(executor, dirname, main_program)
......@@ -299,6 +304,24 @@ def load_persistables_if_exist(executor, dirname, main_program=None):
predicate=_is_presistable_and_exist_)
def get_feed_targets_names(program):
feed_targets_names = []
global_block = program.global_block()
for op in global_block.ops:
if op.desc.type() == 'feed':
feed_targets_names.insert(0, op.desc.output('Out')[0])
return feed_targets_names
def get_fetch_targets_names(program):
fetch_targets_names = []
global_block = program.global_block()
for op in global_block.ops:
if op.desc.type() == 'fetch':
fetch_targets_names.append(op.desc.input('X')[0])
return fetch_targets_names
def load_inference_model(dirname, executor):
"""
Load inference model from a directory
......@@ -306,24 +329,28 @@ def load_inference_model(dirname, executor):
:param dirname: directory path
:param executor: executor that load inference model
:return: [program, feed_var_names, fetch_var_names]
:return: [program, feed_target_names, fetch_targets]
program: program especially for inference.
feeded_var_names: Names of variables that need to feed data
fetch_vars: Variables from which we can get inference results.
feed_target_names: Names of variables that need to feed data
fetch_targets: Variables from which we can get inference results.
"""
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
model_file_name = dirname + "/__model__"
model = pickle.load(open(model_file_name, "r"))
program_desc_str = model["program_desc_str"]
feed_var_names = model["feed_var_names"]
fetch_var_names = model["fetch_var_names"]
with open(model_file_name, "rb") as f:
program_desc_str = f.read()
program = Program.parse_from_string(program_desc_str)
load_persistables_if_exist(executor, dirname, program)
fetch_vars = [program.global_block().var(name) for name in fetch_var_names]
return [program, feed_var_names, fetch_vars]
feed_target_names = get_feed_targets_names(program)
fetch_target_names = get_fetch_targets_names(program)
fetch_targets = [
program.global_block().var(name) for name in fetch_target_names
]
return [program, feed_target_names, fetch_targets]
def get_parameter_value(para, executor):
......
python/paddle/v2/fluid/layers/nn.py
浏览文件 @ 96eb9587
此差异已折叠。
python/paddle/v2/fluid/nets.py
浏览文件 @ 96eb9587
......@@ -11,14 +11,13 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import layers
__all__ = [
"simple_img_conv_pool",
"sequence_conv_pool",
"glu",
"dot_product_attention",
"scaled_dot_product_attention",
]
......@@ -160,7 +159,11 @@ def glu(input, dim=-1):
return out
def dot_product_attention(querys, keys, values):
def scaled_dot_product_attention(queries,
keys,
values,
num_heads=1,
dropout_rate=0.):
"""
The dot-product attention.
......@@ -174,39 +177,162 @@ def dot_product_attention(querys, keys, values):
.. math::
Attention(Q, K, V)= softmax(QK^\mathrm{T})V
Attention(Q, K, V)= softmax(QK^\mathrm{T})V
Refer to `Attention Is All You Need
<https://arxiv.org/pdf/1706.03762.pdf>`_.
Note that batch data containing sequences with different lengths is not
supported by this because of the (batch) matrix multipication.
Args:
query (Variable): The input variable which is a Tensor or LoDTensor.
key (Variable): The input variable which is a Tensor or LoDTensor.
value (Variable): The input variable which is a Tensor or LoDTensor.
queries (Variable): The input variable which should be a 3-D Tensor.
keys (Variable): The input variable which should be a 3-D Tensor.
values (Variable): The input variable which should be a 3-D Tensor.
num_heads (int): Head number to compute the scaled dot product
attention. Default value is 1.
dropout_rate (float): The dropout rate to drop the attention weight.
Default value is 0.
Returns:
tuple: The Tensor variables representing the output and attention scores.
Variable: A 3-D Tensor computed by multi-head scaled dot product
attention.
Raises:
ValueError: If input queries, keys, values are not 3-D Tensors.
NOTE:
1. When num_heads > 1, three linear projections are learned respectively
to map input queries, keys and values into queries', keys' and values'.
queries', keys' and values' have the same shapes with queries, keys
and values.
1. When num_heads == 1, scaled_dot_product_attention has no learnable
parameters.
Examples:
.. code-block:: python
# Suppose q, k, v are tensor variables with the following shape:
# Suppose q, k, v are Tensors with the following shape:
# q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]
out, attn_scores = fluid.nets.dot_product_attention(q, k, v)
out.shape # [3, 5, 10]
attn_scores.shape # [3, 5, 6]
contexts = fluid.nets.scaled_dot_product_attention(q, k, v)
contexts.shape # [3, 5, 10]
"""
assert keys.shape[-2] == values.shape[
-2], 'The shapes of keys and values mismatch.'
assert querys.shape[-1] == keys.shape[
-1], 'The shapes of querys and keys mismatch.'
product = layers.matmul(x=querys, y=keys, transpose_y=True)
attn_scores = layers.reshape(
if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
raise ValueError(
"Inputs quries, keys and values should all be 3-D tensors.")
if queries.shape[-1] != keys.shape[-1]:
raise ValueError(
"The hidden size of queries and keys should be the same.")
if keys.shape[-2] != values.shape[-2]:
raise ValueError(
"The max sequence length in query batch and in key batch "
"should be the same.")
if keys.shape[-1] % num_heads != 0:
raise ValueError("The hidden size of keys (%d) must be divisible "
"by the number of attention heads (%d)." %
(keys.shape[-1], num_heads))
if values.shape[-1] % num_heads != 0:
raise ValueError("The hidden size of values (%d) must be divisible "
"by the number of attention heads (%d)." %
(values.shape[-1], num_heads))
def __compute_qkv(queries, keys, values, num_heads):
"""
Add linear projection to queries, keys, and values.
Args:
queries(Tensor): a 3-D input Tensor.
keys(Tensor): a 3-D input Tensor.
values(Tensor): a 3-D input Tensor.
num_heads(int): The number of heads. Linearly project the inputs
ONLY when num_heads > 1.
Returns:
Tensor: linearly projected output Tensors: queries', keys' and
values'. They have the same shapes with queries, keys and
values.
"""
if num_heads == 1:
return queries, keys, values
q = layers.fc(input=queries, size=queries.shape[-1], num_flatten_dims=2)
k = layers.fc(input=keys, size=keys.shape[-1], num_flatten_dims=2)
v = layers.fc(input=values, size=values.shape[-1], num_flatten_dims=2)
return q, k, v
def __split_heads(x, num_heads):
"""
Reshape the last dimension of inpunt tensor x so that it becomes two
dimensions.
Args:
x(Tensor): a 3-D input Tensor.
num_heads(int): The number of heads.
Returns:
Tensor: a Tensor with shape [..., n, m/num_heads], where m is size
of the last dimension of x.
"""
if num_heads == 1:
return x
hidden_size = x.shape[-1]
# reshape the 3-D input: [batch_size, max_sequence_length, hidden_dim]
# into a 4-D output:
# [batch_size, max_sequence_length, num_heads, hidden_size_per_head].
reshaped = layers.reshape(
x=x,
shape=list(x.shape[:-1]) + [num_heads, hidden_size // num_heads])
# permuate the dimensions into:
# [batch_size, num_heads, max_sequence_len, hidden_size_per_head]
return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])
def __combine_heads(x):
"""
Reshape the last two dimensions of inpunt tensor x so that it becomes
one dimension.
Args:
x(Tensor): a 4-D input Tensor with shape
[bs, num_heads, max_sequence_length, hidden_dim].
Returns:
Tensor: a Tensor with shape
[bs, max_sequence_length, num_heads * hidden_dim].
"""
if len(x.shape) == 3: return x
if len(x.shape) != 4:
raise ValueError("Input(x) should be a 4-D Tensor.")
trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
return layers.reshape(
x=trans_x,
shape=map(int, [
trans_x.shape[0], trans_x.shape[1],
trans_x.shape[2] * trans_x.shape[3]
]))
q, k, v = __compute_qkv(queries, keys, values, num_heads)
q = __split_heads(q, num_heads)
k = __split_heads(k, num_heads)
v = __split_heads(v, num_heads)
key_dim_per_head = keys.shape[-1] // num_heads
scaled_q = layers.scale(x=q, scale=key_dim_per_head**-0.5)
product = layers.matmul(x=k, y=scaled_q, transpose_y=True)
weights = layers.reshape(
x=layers.reshape(
x=product, shape=[-1, product.shape[-1]], act='softmax'),
x=product, shape=[-1, product.shape[-1]], act="softmax"),
shape=product.shape)
out = layers.matmul(attn_scores, values)
return out, attn_scores
if dropout_rate:
weights = layers.dropout(x, dropout_prob=dropout_rate, is_test=False)
ctx_multiheads = layers.matmul(weights, v)
return __combine_heads(ctx_multiheads)
python/paddle/v2/fluid/tests/book/CMakeLists.txt
浏览文件 @ 96eb9587
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
list(REMOVE_ITEM TEST_OPS test_image_classification_train)
list(REMOVE_ITEM TEST_OPS test_image_classification_train test_recognize_digits)
py_test(test_image_classification_train_resnet SRCS test_image_classification_train.py ARGS resnet)
py_test(test_image_classification_train_vgg SRCS test_image_classification_train.py ARGS vgg)
py_test(test_recognize_digits_mlp_cpu
SRCS test_recognize_digits.py
ARGS mlp)
py_test(test_recognize_digits_mlp_cuda
SRCS test_recognize_digits.py
ARGS mlp --use_cuda)
py_test(test_recognize_digits_conv_cpu
SRCS test_recognize_digits.py
ARGS conv)
py_test(test_recognize_digits_conv_cuda
SRCS test_recognize_digits.py
ARGS conv --use_cuda)
py_test(test_recognize_digits_mlp_cpu_parallel
SRCS test_recognize_digits.py
ARGS mlp --parallel)
py_test(test_recognize_digits_mlp_cuda_parallel
SRCS test_recognize_digits.py
ARGS mlp --use_cuda --parallel)
py_test(test_recognize_digits_conv_cpu_parallel
SRCS test_recognize_digits.py
ARGS conv --parallel)
py_test(test_recognize_digits_conv_cuda_parallel
SRCS test_recognize_digits.py
ARGS conv --use_cuda --parallel)
# default test
foreach(src ${TEST_OPS})
......
python/paddle/v2/fluid/tests/book/__init__.py 0 → 100644
浏览文件 @ 96eb9587
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
python/paddle/v2/fluid/tests/book/test_recognize_digits.py 0 → 100644
浏览文件 @ 96eb9587
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from __future__ import print_function
import argparse
import paddle.v2.fluid as fluid
import paddle.v2 as paddle
import sys
import numpy
def parse_arg():
parser = argparse.ArgumentParser()
parser.add_argument(
"nn_type",
help="The neural network type, in ['mlp', 'conv']",
type=str,
choices=['mlp', 'conv'])
parser.add_argument(
"--parallel",
help='Run in parallel or not',
default=False,
action="store_true")
parser.add_argument(
"--use_cuda",
help="Run the program by using CUDA",
default=False,
action="store_true")
return parser.parse_args()
BATCH_SIZE = 64
def loss_net(hidden, label):
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
return fluid.layers.mean(x=loss), fluid.layers.accuracy(
input=prediction, label=label)
def mlp(img, label):
hidden = fluid.layers.fc(input=img, size=200, act='tanh')
hidden = fluid.layers.fc(input=hidden, size=200, act='tanh')
return loss_net(hidden, label)
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
return loss_net(conv_pool_2, label)
def main():
args = parse_arg()
print("recognize digits with args: {0}".format(" ".join(sys.argv[1:])))
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if args.nn_type == 'mlp':
net_conf = mlp
else:
net_conf = conv_net
if args.parallel:
places = fluid.layers.get_places()
pd = fluid.layers.ParallelDo(places)
with pd.do():
img_ = pd.read_input(img)
label_ = pd.read_input(label)
for o in net_conf(img_, label_):
pd.write_output(o)
avg_loss, acc = pd()
# get mean loss and acc through every devices.
avg_loss = fluid.layers.mean(x=avg_loss)
acc = fluid.layers.mean(x=acc)
else:
avg_loss, acc = net_conf(img, label)
test_program = fluid.default_main_program().clone()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_loss)
place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch, fetch nothing
exe.run(feed=feeder.feed(data))
if (batch_id + 1) % 10 == 0:
acc_set = []
avg_loss_set = []
for test_data in test_reader():
acc_np, avg_loss_np = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[acc, avg_loss])
acc_set.append(float(acc_np))
avg_loss_set.append(float(avg_loss_np))
# get test acc and loss
acc_val = numpy.array(acc_set).mean()
avg_loss_val = numpy.array(avg_loss_set).mean()
if float(acc_val) > 0.85: # test acc > 85%
exit(0)
else:
print(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_val), float(acc_val)))
if __name__ == '__main__':
main()
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py 已删除 100644 → 0
浏览文件 @ 82d92498
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=images,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Adam(learning_rate=0.01)
optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
BATCH_SIZE = 50
PASS_NUM = 3
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
for data in train_reader():
loss, acc = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc=" +
str(pass_acc))
# print loss, acc
if loss < 10.0 and pass_acc > 0.9:
# if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
exit(0)
pass_acc = accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
exit(1)
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py 已删除 100644 → 0
浏览文件 @ 82d92498
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
BATCH_SIZE = 128
image = fluid.layers.data(name='x', shape=[784], dtype='float32')
regularizer = fluid.regularizer.L2Decay(0.0005 * BATCH_SIZE)
hidden1 = fluid.layers.fc(input=image,
size=128,
act='relu',
param_attr=fluid.ParamAttr(
regularizer=regularizer,
gradient_clip=fluid.clip.ClipByValue(10)))
hidden2 = fluid.layers.fc(input=hidden1,
size=64,
act='relu',
param_attr=regularizer)
predict = fluid.layers.fc(input=hidden2,
size=10,
act='softmax',
param_attr=regularizer)
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
opts = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
test_accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
inference_program = fluid.io.get_inference_program(test_target)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
for data in train_reader():
out, acc = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
test_accuracy.reset(exe)
for data in test_reader():
out, acc = exe.run(inference_program,
feed=feeder.feed(data),
fetch_list=[avg_cost] + test_accuracy.metrics)
test_pass_acc = test_accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " train_cost=" + str(
out) + " train_acc=" + str(acc) + " train_pass_acc=" + str(pass_acc)
+ " test_acc=" + str(test_pass_acc))
if test_pass_acc > 0.7:
fluid.io.save_inference_model(
"./recognize_digits_mlp.inference.model/", ["x"], [predict],
exe)
exit(0)
exit(1)
python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py 0 → 100644
浏览文件 @ 96eb9587
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
dict_size = 30000
source_dict_dim = target_dict_dim = dict_size
src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
hidden_dim = 32
embedding_dim = 16
batch_size = 10
max_length = 50
topk_size = 50
encoder_size = decoder_size = hidden_dim
IS_SPARSE = True
USE_PEEPHOLES = False
def bi_lstm_encoder(input_seq, hidden_size):
input_forward_proj = fluid.layers.fc(input=input_seq,
size=hidden_size * 4,
bias_attr=True)
forward, _ = fluid.layers.dynamic_lstm(
input=input_forward_proj,
size=hidden_size * 4,
use_peepholes=USE_PEEPHOLES)
input_backward_proj = fluid.layers.fc(input=input_seq,
size=hidden_size * 4,
bias_attr=True)
backward, _ = fluid.layers.dynamic_lstm(
input=input_backward_proj,
size=hidden_size * 4,
is_reverse=True,
use_peepholes=USE_PEEPHOLES)
forward_last = fluid.layers.sequence_last_step(input=forward)
backward_first = fluid.layers.sequence_first_step(input=backward)
return forward_last, backward_first
# FIXME(peterzhang2029): Replace this function with the lstm_unit_op.
def lstm_step(x_t, hidden_t_prev, cell_t_prev, size):
def linear(inputs):
return fluid.layers.fc(input=inputs, size=size, bias_attr=True)
forget_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
input_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
output_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
cell_tilde = fluid.layers.tanh(x=linear([hidden_t_prev, x_t]))
cell_t = fluid.layers.sums(input=[
fluid.layers.elementwise_mul(
x=forget_gate, y=cell_t_prev), fluid.layers.elementwise_mul(
x=input_gate, y=cell_tilde)
])
hidden_t = fluid.layers.elementwise_mul(
x=output_gate, y=fluid.layers.tanh(x=cell_t))
return hidden_t, cell_t
def lstm_decoder_without_attention(target_embedding, decoder_boot, context,
decoder_size):
rnn = fluid.layers.DynamicRNN()
cell_init = fluid.layers.fill_constant_batch_size_like(
input=decoder_boot,
value=0.0,
shape=[-1, decoder_size],
dtype='float32')
cell_init.stop_gradient = False
with rnn.block():
current_word = rnn.step_input(target_embedding)
context = rnn.static_input(context)
hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True)
cell_mem = rnn.memory(init=cell_init)
decoder_inputs = fluid.layers.concat(
input=[context, current_word], axis=1)
h, c = lstm_step(decoder_inputs, hidden_mem, cell_mem, decoder_size)
rnn.update_memory(hidden_mem, h)
rnn.update_memory(cell_mem, c)
out = fluid.layers.fc(input=h,
size=target_dict_dim,
bias_attr=True,
act='softmax')
rnn.output(out)
return rnn()
def seq_to_seq_net():
"""Construct a seq2seq network."""
src_word_idx = fluid.layers.data(
name='source_sequence', shape=[1], dtype='int64', lod_level=1)
src_embedding = fluid.layers.embedding(
input=src_word_idx,
size=[source_dict_dim, embedding_dim],
dtype='float32')
src_forward_last, src_backward_first = bi_lstm_encoder(
input_seq=src_embedding, hidden_size=encoder_size)
encoded_vector = fluid.layers.concat(
input=[src_forward_last, src_backward_first], axis=1)
decoder_boot = fluid.layers.fc(input=src_backward_first,
size=decoder_size,
bias_attr=False,
act='tanh')
trg_word_idx = fluid.layers.data(
name='target_sequence', shape=[1], dtype='int64', lod_level=1)
trg_embedding = fluid.layers.embedding(
input=trg_word_idx,
size=[target_dict_dim, embedding_dim],
dtype='float32')
prediction = lstm_decoder_without_attention(trg_embedding, decoder_boot,
encoded_vector, decoder_size)
label = fluid.layers.data(
name='label_sequence', shape=[1], dtype='int64', lod_level=1)
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
return avg_cost
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
avg_cost = seq_to_seq_net()
optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
optimizer.minimize(avg_cost)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=batch_size)
place = core.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
batch_id = 0
for pass_id in xrange(2):
for data in train_data():
word_data = to_lodtensor(map(lambda x: x[0], data), place)
trg_word = to_lodtensor(map(lambda x: x[1], data), place)
trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
outs = exe.run(framework.default_main_program(),
feed={
'source_sequence': word_data,
'target_sequence': trg_word,
'label_sequence': trg_word_next
},
fetch_list=[avg_cost])
avg_cost_val = np.array(outs[0])
print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
" avg_cost=" + str(avg_cost_val))
if batch_id > 3:
exit(0)
batch_id += 1
if __name__ == '__main__':
main()
python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py
浏览文件 @ 96eb9587
#Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
# 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
# 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,
#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.
# 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.
from __future__ import print_function
import sys
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import os
......@@ -106,10 +104,10 @@ if len(sys.argv) >= 2:
net_type = sys.argv[1]
if net_type == "vgg":
print("train vgg net")
print("training vgg net")
net = vgg16_bn_drop(images)
elif net_type == "resnet":
print("train resnet")
print("training resnet")
net = resnet_cifar10(images, 32)
else:
raise ValueError("%s network is not supported" % net_type)
......@@ -129,6 +127,7 @@ train_reader = paddle.batch(
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
......@@ -146,17 +145,14 @@ if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
print("start pserver at:", current_endpoint)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
print("pserver run end")
elif training_role == "TRAINER":
print("start trainer")
trainer_prog = t.get_trainer_program()
feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
for data in train_reader():
......@@ -164,9 +160,10 @@ elif training_role == "TRAINER":
feed=feeder.feed(data),
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
# this model is slow, so if we can train two mini batch, we think it works properly.
print("pass_id:" + str(pass_id) + "loss:" + str(loss) + " pass_acc:"
+ str(pass_acc))
# this model is slow, so if we can train two mini batches,
# we think it works properly.
print("trainer run end")
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
......
python/paddle/v2/fluid/tests/book_distribute/notest_recommender_system_dist.py 0 → 100644
浏览文件 @ 96eb9587
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import os
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import paddle.v2.fluid.core as core
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.optimizer import SGDOptimizer
IS_SPARSE = True
BATCH_SIZE = 256
PASS_NUM = 100
def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
param_attr='user_table',
is_sparse=IS_SPARSE)
usr_fc = layers.fc(input=usr_emb, size=32)
USR_GENDER_DICT_SIZE = 2
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
is_sparse=IS_SPARSE)
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
param_attr='age_table')
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
is_sparse=IS_SPARSE)
usr_job_fc = layers.fc(input=usr_job_emb, size=16)
concat_embed = layers.concat(
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return usr_combined_features
def get_mov_combined_features():
MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
param_attr='movie_table',
is_sparse=IS_SPARSE)
mov_fc = layers.fc(input=mov_emb, size=32)
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = layers.data(name='category_id', shape=[1], dtype='int64')
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
input=mov_categories_emb, pool_type="sum")
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = layers.data(name='movie_title', shape=[1], dtype='int64')
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
input=mov_title_emb,
num_filters=32,
filter_size=3,
act="tanh",
pool_type="sum")
concat_embed = layers.concat(
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return mov_combined_features
def model():
usr_combined_features = get_usr_combined_features()
mov_combined_features = get_mov_combined_features()
# need cos sim
inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
scale_infer = layers.scale(x=inference, scale=5.0)
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(x=square_cost)
return avg_cost
def func_feed(feeding, data, place):
feed_tensors = {}
for (key, idx) in feeding.iteritems():
tensor = core.LoDTensor()
if key != "category_id" and key != "movie_title":
if key == "score":
numpy_data = np.array(map(lambda x: x[idx], data)).astype(
"float32")
else:
numpy_data = np.array(map(lambda x: x[idx], data)).astype(
"int64")
else:
numpy_data = map(lambda x: np.array(x[idx]).astype("int64"), data)
lod_info = [len(item) for item in numpy_data]
offset = 0
lod = [offset]
for item in lod_info:
offset += item
lod.append(offset)
numpy_data = np.concatenate(numpy_data, axis=0)
tensor.set_lod([lod])
numpy_data = numpy_data.reshape([numpy_data.shape[0], 1])
tensor.set(numpy_data, place)
feed_tensors[key] = tensor
return feed_tensors
def main():
cost = model()
optimizer = SGDOptimizer(learning_rate=0.2)
optimize_ops, params_grads = optimizer.minimize(cost)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
exe.run(fluid.default_startup_program())
trainer_prog = t.get_trainer_program()
feeding = {
'user_id': 0,
'gender_id': 1,
'age_id': 2,
'job_id': 3,
'movie_id': 4,
'category_id': 5,
'movie_title': 6,
'score': 7
}
for pass_id in range(PASS_NUM):
for data in train_reader():
outs = exe.run(trainer_prog,
feed=func_feed(feeding, data, place),
fetch_list=[cost])
out = np.array(outs[0])
print("cost=" + str(out[0]))
if out[0] < 6.0:
print("Training complete. Average cost is less than 6.0.")
# if avg cost less than 6.0, we think our code is good.
exit(0)
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
if __name__ == '__main__':
main()
python/paddle/v2/fluid/tests/book_distribute/notest_understand_sentiment_dynamic_lstm.py 0 → 100644
浏览文件 @ 96eb9587
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import os
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
def stacked_lstm_net(data,
label,
input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3):
assert stacked_num % 2 == 1
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
# add bias attr
# TODO(qijun) linear act
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = fluid.layers.fc(input=inputs, size=hid_dim)
lstm, cell = fluid.layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
inputs = [fc, lstm]
fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
prediction = fluid.layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
optimize_ops, params_grads = adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0], optimize_ops, params_grads
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
BATCH_SIZE = 100
PASS_NUM = 5
word_dict = paddle.dataset.imdb.word_dict()
print "loaded word dict successfully"
dict_dim = len(word_dict)
class_dim = 2
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost, accuracy, acc_out, optimize_ops, params_grads = stacked_lstm_net(
data, label, input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv(
"TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
elif training_role == "TRAINER":
exe.run(fluid.default_startup_program())
trainer_prog = t.get_trainer_program()
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
for data in train_data():
cost_val, acc_val = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
if cost_val < 1.0 and acc_val > 0.8:
exit(0)
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
if __name__ == '__main__':
main()
python/paddle/v2/fluid/tests/op_test.py
浏览文件 @ 96eb9587
......@@ -334,7 +334,7 @@ class OpTest(unittest.TestCase):
def check_output(self, atol=1e-5):
places = [core.CPUPlace()]
if core.is_compile_gpu() and core.op_support_gpu(self.op_type):
if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
places.append(core.CUDAPlace(0))
for place in places:
self.check_output_with_place(place, atol)
......@@ -367,7 +367,7 @@ class OpTest(unittest.TestCase):
max_relative_error=0.005,
user_defined_grads=None):
places = [core.CPUPlace()]
if core.is_compile_gpu() and core.op_support_gpu(self.op_type):
if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
places.append(core.CUDAPlace(0))
for place in places:
self.check_grad_with_place(place, inputs_to_check, output_names,
......
python/paddle/v2/fluid/tests/test_activation_op.py
浏览文件 @ 96eb9587
......@@ -186,8 +186,7 @@ class TestFloor(OpTest):
self.op_type = "floor"
x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
self.inputs = {'X': x}
# numpy floor need +1
self.outputs = {'Out': np.floor(self.inputs['X']) + 1.0}
self.outputs = {'Out': np.floor(self.inputs['X'])}
def test_check_output(self):
self.check_output()
......
python/paddle/v2/fluid/tests/test_adagrad_op.py
浏览文件 @ 96eb9587
......@@ -180,7 +180,7 @@ class TestSparseAdagradOp(unittest.TestCase):
def test_sparse_adagrad(self):
places = [core.CPUPlace()]
if core.is_compile_gpu():
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
......
python/paddle/v2/fluid/tests/test_adam_op.py
浏览文件 @ 96eb9587
......@@ -305,7 +305,7 @@ class TestSparseAdamOp(unittest.TestCase):
def test_sparse_sgd(self):
places = [core.CPUPlace()]
if core.is_compile_gpu():
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
......
python/paddle/v2/fluid/tests/test_batch_norm_op.py
浏览文件 @ 96eb9587
......@@ -352,7 +352,7 @@ class TestBatchNormOp(OpTest):
print "op test backward passed: ", str(place), data_layout
places = [core.CPUPlace()]
if core.is_compile_gpu() and core.op_support_gpu("batch_norm"):
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
places.append(core.CUDAPlace(0))
for place in places:
......
python/paddle/v2/fluid/tests/test_gaussian_random_op.py
浏览文件 @ 96eb9587
......@@ -33,7 +33,7 @@ class TestGaussianRandomOp(unittest.TestCase):
self.gaussian_random_test(place=fluid.CPUPlace())
def test_gpu(self):
if core.is_compile_gpu():
if core.is_compiled_with_cuda():
self.gaussian_random_test(place=fluid.CUDAPlace(0))
def gaussian_random_test(self, place):
......
python/paddle/v2/fluid/tests/test_iou_similarity_op.py 100755 → 100644
浏览文件 @ 96eb9587
文件模式从 100755 更改为 100644
python/paddle/v2/fluid/tests/test_layers.py
浏览文件 @ 96eb9587
......@@ -202,6 +202,18 @@ class TestBook(unittest.TestCase):
x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
print(str(program))
def test_dynamic_lstmp(self):
program = Program()
with program_guard(program):
hidden_dim, proj_dim = 16, 8
seq_data = layers.data(
name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
fc_out = layers.fc(input=seq_data, size=4 * hidden_dim)
self.assertIsNotNone(
layers.dynamic_lstmp(
input=fc_out, size=4 * hidden_dim, proj_size=proj_dim))
print(str(program))
def test_sequence_softmax(self):
program = Program()
with program_guard(program):
......@@ -279,6 +291,16 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(out)
print(str(program))
def test_multiplex(self):
program = Program()
with program_guard(program):
x1 = layers.data(name='x1', shape=[4], dtype='float32')
x2 = layers.data(name='x2', shape=[4], dtype='float32')
index = layers.data(name='index', shape=[1], dtype='int32')
out = layers.multiplex(inputs=[x1, x2], index=index)
self.assertIsNotNone(out)
print(str(program))
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_lstm_op.py
浏览文件 @ 96eb9587
......@@ -42,7 +42,7 @@ def relu(x):
return np.maximum(x, 0)
ACTVATION = {
ACTIVATION = {
'identity': identity,
'sigmoid': sigmoid,
'tanh': tanh,
......@@ -158,8 +158,8 @@ class TestLstmOp(OpTest):
w_b = b[:, 0:4 * self.D]
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
h, c = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
ACTVATION[self.act_gate], ACTVATION[self.act_cell],
ACTVATION[self.act_cand])
ACTIVATION[self.act_gate], ACTIVATION[self.act_cell],
ACTIVATION[self.act_cand])
self.inputs = {'Input': (x, self.lod), 'Weight': w}
......
python/paddle/v2/fluid/tests/test_lstmp_op.py 0 → 100644
浏览文件 @ 96eb9587
此差异已折叠。
python/paddle/v2/fluid/tests/test_normalization_wrapper.py
浏览文件 @ 96eb9587
......@@ -46,7 +46,7 @@ class TestNormalization(unittest.TestCase):
"""Run the test program.
"""
places = [core.CPUPlace()]
if core.is_compile_gpu():
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
......
python/paddle/v2/fluid/tests/test_one_hot_op.py 0 → 100644
浏览文件 @ 96eb9587
此差异已折叠。
python/paddle/v2/fluid/tests/test_op_support_gpu.py
浏览文件 @ 96eb9587
......@@ -18,7 +18,8 @@ import paddle.v2.fluid.core as core
class TestOpSupportGPU(unittest.TestCase):
def test_case(self):
self.assertEqual(core.is_compile_gpu(), core.op_support_gpu("sum"))
self.assertEqual(core.is_compiled_with_cuda(),
core.op_support_gpu("sum"))
if __name__ == '__main__':
......
python/paddle/v2/fluid/tests/test_parallel_op.py
浏览文件 @ 96eb9587
......@@ -53,7 +53,7 @@ class BaseParallelForTest(unittest.TestCase):
fetch=fetch,
place=cpu,
use_parallel=True)
if fluid.core.is_compile_gpu():
if fluid.core.is_compiled_with_cuda():
gpu = fluid.CUDAPlace(0)
result_gpu = self._run_test_impl_(
callback=callback,
......
python/paddle/v2/fluid/tests/test_profiler.py
浏览文件 @ 96eb9587
......@@ -23,7 +23,7 @@ import paddle.v2.fluid.core as core
class TestProfiler(unittest.TestCase):
def test_nvprof(self):
if not fluid.core.is_compile_gpu():
if not fluid.core.is_compiled_with_cuda():
return
epoc = 8
dshape = [4, 3, 28, 28]
......@@ -42,7 +42,7 @@ class TestProfiler(unittest.TestCase):
os.remove(output_file)
def net_profiler(self, state):
if state == 'GPU' and not core.is_compile_gpu():
if state == 'GPU' and not core.is_compiled_with_cuda():
return
startup_program = fluid.Program()
main_program = fluid.Program()
......
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