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提交 afb5496c 编写于 作者: S sweetsky0901
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into detection_output

上级 d52fd00a 62166317
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Showing with 700 addition and 78 deletion
benchmark/IntelOptimizedPaddle.md
浏览文件 @ afb5496c
......@@ -93,6 +93,15 @@ Test on batch size 1, 2, 4, 8, 16 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
| MKLML | 22.74 | 41.56 | 81.22 | 133.47 | 210.53 |
| MKL-DNN | 175.10 | 272.92 | 450.70 | 512.00 | 600.94 |
- Alexnet
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|--------|--------|--------|--------|--------|
| OpenBLAS | | | | | |
| MKLML | 21.32 | 36.55 | 73.06 | 131.15 | 192.77 |
| MKL-DNN | 442.91 | 656.41 | 719.10 | 847.68 | 850.51 |
chart TBD
### Laptop
TBD
benchmark/paddle/image/alexnet.py
浏览文件 @ afb5496c
......@@ -19,7 +19,11 @@ args = {
'num_samples': num_samples
}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
......
benchmark/paddle/image/run_openblas_infer.sh
浏览文件 @ afb5496c
......@@ -8,15 +8,19 @@ function clock_to_seconds() {
}
function infer() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
thread=`nproc`
if [ $thread -gt $bs ]; then
thread=$bs
trainers=`nproc`
if [ $trainers -gt $bs ]; then
trainers=$bs
fi
log="logs/infer-${topology}-${layer_num}-${thread}openblas-${bs}.log"
log="logs/infer-${topology}-${layer_num}-${trainers}openblas-${bs}.log"
threads=$((`nproc` / trainers))
if [ $threads -eq 0 ]; then
threads=1
fi
export OPENBLAS_NUM_THREADS=$threads
models_in="models/${topology}-${layer_num}/pass-00000/"
if [ ! -d $models_in ]; then
......@@ -28,7 +32,7 @@ function infer() {
--config="${topology}.py" \
--use_mkldnn=False \
--use_gpu=False \
--trainer_count=$thread \
--trainer_count=$trainers \
--log_period=$log_period \
--config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True,num_samples=256" \
--init_model_path=$models_in \
......
benchmark/paddle/image/run_openblas_train.sh
浏览文件 @ afb5496c
set -e
function train() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
export OPENBLAS_NUM_THREADS=1
topology=$1
layer_num=$2
bs=$3
......
doc/getstarted/build_and_install/docker_install_cn.rst
浏览文件 @ afb5496c
......@@ -15,7 +15,7 @@
获取PaddlePaddle的Docker镜像
------------------------------
执行下面的命令获取最新的PaddlePaddle Docker镜像
执行下面的命令获取最新的PaddlePaddle Docker镜像，版本为cpu_avx_mkl：
.. code-block:: bash
......@@ -27,7 +27,7 @@
docker pull docker.paddlepaddle.org/paddle
下载GPU版本的Docker镜像：
下载GPU版本（cuda8.0_cudnn5_avx_mkl）的Docker镜像：
.. code-block:: bash
......@@ -54,7 +54,7 @@
.. _docker_run:
在Docker中执行PaddlePaddle训练程序
------------------------------
----------------------------------
假设您已经在当前目录（比如在/home/work）编写了一个PaddlePaddle的程序 :code:`train.py` （可以参考
`PaddlePaddleBook <http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.cn.html>`_
......@@ -82,7 +82,7 @@
.. _docker_run_book:
使用Docker启动PaddlePaddle Book教程
------------------------------
-----------------------------------
使用Docker可以快速在本地启动一个包含了PaddlePaddle官方Book教程的Jupyter Notebook，可以通过网页浏览。
PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Notebook。
......
doc/getstarted/build_and_install/docker_install_en.rst
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......@@ -16,7 +16,7 @@ After you've read above tutorials you may proceed the following steps.
Pull PaddlePaddle Docker Image
------------------------------
Run the following command to download the latest Docker images:
Run the following command to download the latest Docker images, the version is cpu_avx_mkl:
.. code-block:: bash
......@@ -28,7 +28,7 @@ For users in China, we provide a faster mirror:
docker pull docker.paddlepaddle.org/paddle
Download GPU version images:
Download GPU version (cuda8.0_cudnn5_avx_mkl) images:
.. code-block:: bash
......@@ -58,7 +58,7 @@ and run:
.. _docker_run:
Launch your training program in Docker
------------------------------
--------------------------------------
Assume that you have already written a PaddlePaddle program
named :code:`train.py` under directory :code:`/home/work` (refer to
......
doc/getstarted/build_and_install/pip_install_cn.rst
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......@@ -11,14 +11,14 @@ PaddlePaddle可以使用常用的Python包管理工具
------------------------------
执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件。
执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件，版本为cpu_avx_openblas。
.. code-block:: bash
pip install paddlepaddle
如果需要安装支持GPU的版本，需要执行：
如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
.. code-block:: bash
......
doc/getstarted/build_and_install/pip_install_en.rst
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......@@ -12,14 +12,14 @@ Install Using pip
------------------------------
Run the following command to install PaddlePaddle on the current
machine, it will also download requirements.
machine, it will also download requirements, the version is cpu_avx_openblas.
.. code-block:: bash
pip install paddlepaddle
If you wish to install GPU version, just run:
If you wish to install GPU version (cuda7.5_cudnn5_avx_openblas), just run:
.. code-block:: bash
......
doc/getstarted/index_cn.rst
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......@@ -7,13 +7,13 @@
++++++++
PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12，并安装有Python2.7。
执行下面的命令完成快速安装：
执行下面的命令完成快速安装，版本为cpu_avx_openblas：
.. code-block:: bash
pip install paddlepaddle
如果需要安装支持GPU的版本，需要执行：
如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
.. code-block:: bash
......
doc/getstarted/index_en.rst
浏览文件 @ afb5496c
......@@ -8,13 +8,13 @@ Quick Install
You can use pip to install PaddlePaddle with a single command, supports
CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
Simply run the following command to install:
Simply run the following command to install, the version is cpu_avx_openblas:
.. code-block:: bash
pip install paddlepaddle
If you need to install GPU version, run:
If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
.. code-block:: bash
......
paddle/framework/CMakeLists.txt
浏览文件 @ afb5496c
......@@ -5,10 +5,18 @@ cc_library(ddim SRCS ddim.cc DEPS eigen3)
cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
cc_library(tensor SRCS tensor.cc DEPS ddim place paddle_memory device_context framework_proto)
if (WITH_GPU)
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS ddim place paddle_memory device_context framework_proto)
else()
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS ddim place paddle_memory device_context framework_proto)
endif ()
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
cc_test(tensor_util_test SRCS tensor_util_test.cc DEPS tensor)
if (WITH_GPU)
nv_test(tensor_util_test SRCS tensor_util_test.cc tensor_util_test.cu DEPS tensor)
else()
cc_test(tensor_util_test SRCS tensor_util_test.cc DEPS tensor)
endif()
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
......
paddle/framework/data_transform.h
浏览文件 @ afb5496c
......@@ -27,9 +27,8 @@ limitations under the License. */
namespace paddle {
namespace framework {
using DataTransformFn =
std::function<void(const std::vector<platform::DeviceContext*> ctx,
const Variable& in, Variable* out)>;
using DataTransformFn = std::function<void(const platform::DeviceContext* ctx,
const Variable& in, Variable* out)>;
using KernelTypePair = std::pair<OpKernelType, OpKernelType>;
struct KernelTypePairHash {
......
paddle/framework/data_transform_test.cc
浏览文件 @ afb5496c
......@@ -54,18 +54,18 @@ auto kernel1 = GenFromBit({0, 0, 0, 1});
auto kernel2 = GenFromBit({0, 0, 1, 0});
auto kernel3 = GenFromBit({0, 0, 1, 1});
void TransDataType_t(std::vector<platform::DeviceContext*> ctx,
const Variable& in, Variable* out) {
void TransDataType_t(const platform::DeviceContext* ctx, const Variable& in,
Variable* out) {
test_value++;
}
void TransDataLayout_t(std::vector<platform::DeviceContext*> ctx,
const Variable& in, Variable* out) {
void TransDataLayout_t(const platform::DeviceContext* ctx, const Variable& in,
Variable* out) {
test_value--;
}
void TransLibraryType_t(std::vector<platform::DeviceContext*> ctx,
const Variable& in, Variable* out) {
void TransLibraryType_t(const platform::DeviceContext* ctx, const Variable& in,
Variable* out) {
test_value += 2;
}
......@@ -83,7 +83,8 @@ TEST(DataTransform, Register) {
using namespace paddle::platform;
auto& instance = DataTransformFnMap::Instance();
std::vector<DeviceContext*> ctx;
ASSERT_EQ(instance.Map().size(), 3UL);
DeviceContext* ctx = nullptr;
paddle::framework::Variable in;
paddle::framework::Variable out;
......
paddle/framework/executor.cc
浏览文件 @ afb5496c
......@@ -14,18 +14,17 @@ limitations under the License. */
#include "paddle/framework/executor.h"
#include <algorithm>
#include <iostream>
#include <memory>
#include <set>
#include <vector>
#include "gflags/gflags.h"
#include "paddle/framework/feed_fetch_type.h"
#include "paddle/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/scope.h"
DEFINE_bool(check_nan_inf, false,
"Checking whether operator produce NAN/INF or not. It will be "
"extremely slow so please use this flag wisely.");
namespace paddle {
namespace framework {
......@@ -58,6 +57,19 @@ static void CreateTensor(Variable* var, proto::VarDesc::VarType var_type) {
}
}
static void CheckTensorNANOrInf(const std::string& name,
const framework::Tensor& tensor) {
if (tensor.memory_size() == 0) {
return;
}
if (tensor.type().hash_code() != typeid(float).hash_code() &&
tensor.type().hash_code() != typeid(double).hash_code()) {
return;
}
PADDLE_ENFORCE(!framework::HasInf(tensor), "Tensor %s has Inf", name);
PADDLE_ENFORCE(!framework::HasNAN(tensor), "Tensor %s has NAN", name);
}
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
bool create_local_scope, bool create_vars) {
// TODO(tonyyang-svail):
......@@ -101,6 +113,15 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
VLOG(3) << op->DebugString();
op->Run(*local_scope, place_);
if (FLAGS_check_nan_inf) {
for (auto& vname : op->OutputVars(true)) {
auto* var = local_scope->FindVar(vname);
if (var == nullptr) continue;
if (var->IsType<framework::LoDTensor>()) {
CheckTensorNANOrInf(vname, var->Get<framework::LoDTensor>());
}
}
}
}
if (create_vars && create_local_scope) {
scope->DeleteScope(local_scope);
......
paddle/framework/operator.cc
浏览文件 @ afb5496c
......@@ -384,6 +384,24 @@ class RuntimeInferShapeContext : public InferShapeContext {
const Scope& scope_;
};
const platform::DeviceContext* GetDeviceContext(
framework::KernelTypePair& kernel_pair) {
auto& actual_kernel_key = kernel_pair.first;
auto& expected_kernel_key = kernel_pair.second;
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
if (platform::is_gpu_place(actual_kernel_key.place_) &&
platform::is_cpu_place(expected_kernel_key.place_)) {
return pool.Get(actual_kernel_key.place_);
} else if (platform::is_cpu_place(actual_kernel_key.place_) &&
platform::is_gpu_place(expected_kernel_key.place_)) {
return pool.Get(expected_kernel_key.place_);
} else {
PADDLE_THROW(
"Currently, model parallelism is only supported between CPU and CUDA");
}
}
void OperatorWithKernel::Run(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
......@@ -418,9 +436,9 @@ void OperatorWithKernel::Run(const Scope& scope,
"CPU and other devices. For example, multi-GPU model "
"parallelism will failed.");
} else {
auto kernel_pair = std::make_pair(actual_kernel_key, expected_kernel_key);
const DataTransformFn* trans_fun =
DataTransformFnMap::Instance().GetNullable(
std::make_pair(actual_kernel_key, expected_kernel_key));
DataTransformFnMap::Instance().GetNullable(kernel_pair);
if (trans_fun) {
auto input_vars = this->InputVars();
// TODO(qijun) filter the input vars that do not need to be transformed
......@@ -437,22 +455,18 @@ void OperatorWithKernel::Run(const Scope& scope,
}
if (!need_trans.empty()) {
// TODO(qijun) get appropriate DeviceContext from DeviceContext pool
platform::DeviceContext* trans_dev_ctx = nullptr;
std::vector<platform::DeviceContext*> trans_dev_ctx_vec{trans_dev_ctx};
auto trans_dev_ctx = GetDeviceContext(kernel_pair);
// Wait for transform starting
dev_ctx->Wait();
for (auto var_name : need_trans) {
(*trans_fun)(trans_dev_ctx_vec, *(scope.FindVar(var_name)),
(*trans_fun)(trans_dev_ctx, *(scope.FindVar(var_name)),
scope.FindVar(var_name + framework::KernelTypeToString(
expected_kernel_key)));
}
// Wait for data transform finishing
for (auto ctx : trans_dev_ctx_vec) {
ctx->Wait();
}
trans_dev_ctx->Wait();
}
}
}
......
paddle/framework/tensor_util.cc 0 → 100644
浏览文件 @ afb5496c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/tensor_util.h"
namespace paddle {
namespace framework {
template <typename Predicate, typename DevCtx>
struct AnyDTypeVisitor {
Predicate predicate_;
const Tensor& tensor_;
const DevCtx& ctx_;
Tensor* out_;
AnyDTypeVisitor(Predicate predicate, const Tensor& tensor, const DevCtx& ctx,
Tensor* out)
: predicate_(predicate), tensor_(tensor), ctx_(ctx), out_(out) {}
template <typename T>
void operator()() const {
auto t = EigenVector<T>::Flatten(tensor_);
auto o = EigenScalar<bool>::From(*out_);
// return any of predicate_(t) is true.
o.device(*ctx_.eigen_device()) = predicate_(t).any();
}
};
template <typename Predicate, typename DevCtx>
inline void AnyImpl(Predicate predicate, const framework::Tensor& tensor,
const DevCtx& ctx, framework::Tensor* out) {
VisitDataType(ToDataType(tensor.type()), AnyDTypeVisitor<Predicate, DevCtx>(
predicate, tensor, ctx, out));
}
template <typename Predicate>
struct AnyVisitor : public boost::static_visitor<bool> {
const framework::Tensor& tensor_;
Predicate predicate_;
AnyVisitor(const framework::Tensor& tensor, Predicate predicate)
: tensor_(tensor), predicate_(std::move(predicate)) {}
template <typename Place>
bool operator()(const Place& place) const {
framework::Tensor out;
out.Resize({1});
out.mutable_data<bool>(place);
auto* ctx = platform::DeviceContextPool::Instance().GetByPlace(place);
AnyImpl(predicate_, tensor_, *ctx, &out);
return this->GetResult(out, place);
}
bool GetResult(const framework::Tensor& out,
const platform::CUDAPlace& gpu) const {
platform::CPUPlace cpu;
framework::Tensor tmp;
tmp.Resize({1});
tmp.mutable_data<bool>(cpu);
auto gpuctx = platform::DeviceContextPool::Instance().Get(gpu);
gpuctx->Wait();
CopyFrom(out, cpu, *gpuctx, &tmp);
gpuctx->Wait();
return GetResult(tmp, cpu);
}
bool GetResult(const framework::Tensor& out,
const platform::CPUPlace& cpu) const {
return *out.data<bool>();
}
};
template <typename Predicate>
inline bool Any(const framework::Tensor& tensor, Predicate predicate) {
AnyVisitor<Predicate> visitor(tensor, predicate);
auto place = tensor.place();
return platform::VisitPlace(place, visitor);
}
struct HasNANPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
-> decltype(std::declval<T>().isnan()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isnan();
}
};
bool HasNAN(const framework::Tensor& tensor) {
HasNANPredicate predicate;
return Any(tensor, predicate);
}
struct HasInfPredicate {
template <typename T>
auto operator()(const T& eigen_vec) const
-> decltype(std::declval<T>().isinf()) {
// Cast eigen_vector to vector of bool. true if is inf.
return eigen_vec.isinf();
}
};
bool HasInf(const framework::Tensor& tensor) {
HasInfPredicate predicate;
return Any(tensor, predicate);
}
} // namespace framework
} // namespace paddle
paddle/framework/tensor_util.cu 0 → 120000
浏览文件 @ afb5496c
./tensor_util.cc
\ No newline at end of file
paddle/framework/tensor_util.h
浏览文件 @ afb5496c
......@@ -14,8 +14,10 @@ limitations under the License. */
#pragma once
#include "paddle/framework/data_type.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace framework {
......@@ -207,6 +209,12 @@ inline void CopyToVector(const Tensor& src, std::vector<T>* dst) {
src_ptr, size);
}
// Returns true if a tensor contains NAN, i.e., Not A Number.
bool HasNAN(const framework::Tensor& tensor);
// Returns true if a tensor contains Inf, i.e., Infinity.
bool HasInf(const framework::Tensor& tensor);
inline void SerializeToStream(std::ostream& os, const Tensor& tensor,
const platform::DeviceContext& dev_ctx) {
// TODO(typhoonzero): serialize to ostream
......
paddle/framework/tensor_util_test.cc
浏览文件 @ afb5496c
......@@ -13,6 +13,7 @@
#include "paddle/framework/tensor_util.h"
#include <gtest/gtest.h>
#include <cmath>
#include <string>
namespace paddle {
......@@ -230,6 +231,29 @@ TEST(CopyToVector, Tensor) {
#endif
}
TEST(HasNAN, CPU) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
float* buf = src.mutable_data<float>({3}, CPUPlace());
buf[0] = 0.0;
buf[1] = NAN;
buf[2] = 0.0;
ASSERT_TRUE(HasNAN(src));
}
TEST(HasInf, CPU) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
double* buf = src.mutable_data<double>({3}, CPUPlace());
buf[0] = 1.0;
buf[1] = INFINITY;
buf[2] = 0.0;
ASSERT_TRUE(HasInf(src));
}
TEST(Tensor, SerializeAndDeserialize) {
framework::Tensor src_tensor;
int array[6] = {1, 2, 3, 4, 5, 6};
......
paddle/framework/tensor_util_test.cu 0 → 100644
浏览文件 @ afb5496c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "gtest/gtest.h"
#include "paddle/framework/tensor_util.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/place.h"
namespace paddle {
namespace framework {
static __global__ void FillNAN(float* buf) {
buf[0] = 0.0;
buf[1] = 0.1;
buf[2] = NAN;
}
static __global__ void FillInf(float* buf) {
buf[0] = 0.0;
buf[1] = INFINITY;
buf[2] = 0.5;
}
TEST(HasNAN, GPU) {
Tensor tensor;
platform::CUDAPlace gpu(0);
auto& pool = platform::DeviceContextPool::Instance();
auto* cuda_ctx = pool.GetByPlace(gpu);
float* buf = tensor.mutable_data<float>({3}, gpu);
FillNAN<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
ASSERT_TRUE(HasNAN(tensor));
}
TEST(HasInf, GPU) {
Tensor tensor;
platform::CUDAPlace gpu(0);
auto& pool = platform::DeviceContextPool::Instance();
auto* cuda_ctx = pool.GetByPlace(gpu);
float* buf = tensor.mutable_data<float>({3}, gpu);
FillInf<<<1, 1, 0, cuda_ctx->stream()>>>(buf);
cuda_ctx->Wait();
ASSERT_TRUE(HasInf(tensor));
}
} // namespace framework
} // namespace paddle
paddle/framework/threadpool.h
浏览文件 @ afb5496c
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <condition_variable>
#include <functional>
#include <future>
#include <mutex>
#include <queue>
#include <thread>
......@@ -25,10 +26,11 @@ limitations under the License. */
namespace paddle {
namespace framework {
typedef std::function<void()> Task;
class ThreadPool {
public:
typedef std::packaged_task<void()> Task;
typedef std::function<void()> Fun;
/**
* @brief Get a instance of threadpool, the thread number will
* be specified as the number of hardware thread contexts
......@@ -61,13 +63,18 @@ class ThreadPool {
/**
* @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.
* @param[in] Task, will be pushed to the task queue.
* @return std::future<void>, we could wait for the task finished by
* f.wait().
*/
void Run(const Task& fn) {
std::future<void> Run(const Fun& fn) {
std::unique_lock<std::mutex> lock(mutex_);
tasks_.push(fn);
Task task(std::bind(fn));
std::future<void> f = task.get_future();
tasks_.push(std::move(task));
lock.unlock();
scheduled_.notify_one();
return f;
}
/**
......@@ -110,7 +117,7 @@ class ThreadPool {
break;
}
// pop a task from the task queue
auto task = tasks_.front();
auto task = std::move(tasks_.front());
tasks_.pop();
--available_;
......
paddle/framework/threadpool_test.cc
浏览文件 @ afb5496c
......@@ -20,16 +20,21 @@ limitations under the License. */
namespace framework = paddle::framework;
void do_sum(framework::ThreadPool* pool, std::atomic<int>& sum, int cnt) {
std::vector<std::future<void>> fs;
for (int i = 0; i < cnt; ++i) {
pool->Run([&sum]() { sum.fetch_add(1); });
auto f = pool->Run([&sum]() { sum.fetch_add(1); });
fs.push_back(std::move(f));
}
for (auto& f : fs) {
f.wait();
}
}
TEST(ThreadPool, ConcurrentInit) {
framework::ThreadPool* pool;
int concurrent_cnt = 50;
int n = 50;
std::vector<std::thread> threads;
for (int i = 0; i < concurrent_cnt; ++i) {
for (int i = 0; i < n; ++i) {
std::thread t([&pool]() { pool = framework::ThreadPool::GetInstance(); });
threads.push_back(std::move(t));
}
......@@ -38,13 +43,13 @@ TEST(ThreadPool, ConcurrentInit) {
}
}
TEST(ThreadPool, ConcurrentStart) {
TEST(ThreadPool, ConcurrentRun) {
framework::ThreadPool* pool = framework::ThreadPool::GetInstance();
std::atomic<int> sum(0);
std::vector<std::thread> threads;
int concurrent_cnt = 50;
int n = 50;
// sum = (n * (n + 1)) / 2
for (int i = 1; i <= concurrent_cnt; ++i) {
for (int i = 1; i <= n; ++i) {
std::thread t(do_sum, pool, std::ref(sum), i);
threads.push_back(std::move(t));
}
......@@ -52,5 +57,5 @@ TEST(ThreadPool, ConcurrentStart) {
t.join();
}
pool->Wait();
EXPECT_EQ(sum, ((concurrent_cnt + 1) * concurrent_cnt) / 2);
EXPECT_EQ(sum, ((n + 1) * n) / 2);
}
paddle/function/GemmConvOp.cpp
浏览文件 @ afb5496c
......@@ -126,14 +126,165 @@ public:
inputData += inputChannels * inputHeight * inputWidth;
outputData += outputChannels * outputHeight * outputWidth;
}
}
};
#ifdef PADDLE_MOBILE_INFERENCE
if (Device == DEVICE_TYPE_CPU) {
memory_.reset();
/*
* \brief Forward calculation of convolution, optimized for mobile.
*/
template <DeviceType Device>
class GemmConvMobileFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
// TODO(hedaoyuan): Need to define some index macros,
// to avoid useing 0 and 1.
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
real beta;
if (outputs[0].getArgType() == ADD_TO) {
beta = 1.0;
} else {
beta = 0.0;
}
#endif
size_t batchSize = input[0];
size_t inputChannels = input[1];
size_t inputHeight = input[2];
size_t inputWidth = input[3];
size_t filterHeight = getFilterHeight(filter);
size_t filterWidth = getFilterWidth(filter);
size_t outputChannels = output[1];
size_t outputHeight = output[2];
size_t outputWidth = output[3];
real* inputData = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
real* outputData = outputs[0].data<real>();
bool needIm2col = isNeedIm2col(filter);
TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape;
real* colData = NULL;
size_t colHeight = inputChannels / groups_ * filterHeight * filterWidth;
size_t colWidth = outputHeight * outputWidth;
// Max col matrix height 256, Max col matrix width 1024
size_t stepColHeight = std::min(colHeight, static_cast<size_t>(256));
size_t stepColWidth = std::min(colWidth, static_cast<size_t>(2048));
if (needIm2col) {
colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(stepColHeight * stepColWidth * sizeof(real));
colData = reinterpret_cast<real*>(memory_->getBuf());
}
Im2ColMobileFunctor<real> im2col;
size_t inputOffset = imShape.getElements();
size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth;
size_t filterOffset = filter.getElements() / groups_;
int nStride = colWidth;
int kStride = colHeight;
for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) {
if (needIm2col) {
real beta_ = beta;
for (size_t colHeightStart = 0; colHeightStart < colHeight;
colHeightStart += stepColHeight) {
for (size_t colWidthStart = 0; colWidthStart < colWidth;
colWidthStart += stepColWidth) {
int N = std::min(colWidth - colWidthStart, stepColWidth);
int K = std::min(colHeight - colHeightStart, stepColHeight);
// im2col
im2col(inputData + g * inputOffset,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW(),
dilationH(),
dilationW(),
colHeightStart,
K,
colWidthStart,
N);
// gemm
int M = outputChannels / groups_;
BlasGemm<Device, real>::compute(
false,
false,
M,
N,
K,
1.0f,
filterData + g * filterOffset + colHeightStart,
kStride,
colData,
N,
beta_,
outputData + g * outputOffset + colWidthStart,
nStride);
}
beta_ = 1.0;
}
} else {
int M = outputChannels / groups_;
int N = outputHeight * outputWidth;
int K = inputChannels / groups_ * filterHeight * filterWidth;
BlasGemm<Device, real>::compute(false,
false,
M,
N,
K,
1.0f,
filterData + g * filterOffset,
K,
inputData + g * inputOffset,
N,
beta,
outputData + g * outputOffset,
N);
}
}
inputData += inputChannels * inputHeight * inputWidth;
outputData += outputChannels * outputHeight * outputWidth;
}
memory_.reset();
}
};
#endif
/*
* \brief Backward input calculation of convolution.
*/
......@@ -348,7 +499,11 @@ public:
}
};
#ifdef PADDLE_MOBILE_INFERENCE
REGISTER_TYPED_FUNC(GemmConv, CPU, GemmConvMobileFunction);
#else
REGISTER_TYPED_FUNC(GemmConv, CPU, GemmConvFunction);
#endif
REGISTER_TYPED_FUNC(GemmConvGradInput, CPU, GemmConvGradInputFunction);
REGISTER_TYPED_FUNC(GemmConvGradFilter, CPU, GemmConvGradFilterFunction);
#ifdef PADDLE_WITH_CUDA
......
paddle/function/Im2Col.h
浏览文件 @ afb5496c
......@@ -98,4 +98,54 @@ public:
int dilationWidth = 1);
};
template <class T>
class Im2ColMobileFunctor {
public:
void operator()(const T* imData,
const TensorShape& imShape,
T* colData,
const TensorShape& colShape,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth,
int dilationHeight,
int dilationWidth,
int colHeightStart,
int colHeightSize,
int colWidthStart,
int colWidthSize) {
int inputHeight = imShape[1];
int inputWidth = imShape[2];
int filterHeight = colShape[1];
int filterWidth = colShape[2];
int outputWidth = colShape[4];
for (int colh = 0; colh < colHeightSize; colh++) {
int wOffset = (colHeightStart + colh) % filterWidth;
int hOffset = ((colHeightStart + colh) / filterWidth) % filterHeight;
int c_im = (colHeightStart + colh) / filterWidth / filterHeight;
for (int colw = 0; colw < colWidthSize; colw++) {
int h = (colWidthStart + colw) / outputWidth;
int w = (colWidthStart + colw) % outputWidth;
int imRowIdx = h * strideHeight + hOffset * dilationHeight;
int imColIdx = w * strideWidth + wOffset * dilationWidth;
if ((imRowIdx - paddingHeight) < 0 ||
(imRowIdx - paddingHeight) >= inputHeight ||
(imColIdx - paddingWidth) < 0 ||
(imColIdx - paddingWidth) >= inputWidth) {
colData[colh * colWidthSize + colw] = static_cast<T>(0);
} else {
imRowIdx += c_im * inputHeight - paddingHeight;
imColIdx -= paddingWidth;
colData[colh * colWidthSize + colw] =
imData[imRowIdx * inputWidth + imColIdx];
}
}
}
}
};
} // namespace paddle
paddle/function/Im2ColTest.cpp
浏览文件 @ afb5496c
......@@ -138,4 +138,86 @@ TEST(Im2ColFunctor, GPU) { TestIm2ColFunctor<DEVICE_TYPE_GPU, float>(); }
#endif
template <class T>
void TestIm2ColMobileFunctor() {
for (size_t channels : {32}) {
for (size_t inputHeight : {33, 100}) {
for (size_t inputWidth : {32, 96}) {
for (size_t filterHeight : {5}) {
for (size_t filterWidth : {7}) {
for (size_t stride : {2}) {
for (size_t padding : {1}) {
for (size_t dilation : {1, 3}) {
size_t filterSizeH = (filterHeight - 1) * dilation + 1;
size_t filterSizeW = (filterWidth - 1) * dilation + 1;
if (inputHeight + 2 * padding < filterSizeH ||
inputWidth + 2 * padding < filterSizeW)
break;
if (padding >= filterSizeH || padding >= filterSizeW) break;
size_t outputHeight =
(inputHeight - filterSizeH + 2 * padding) / stride + 1;
size_t outputWidth =
(inputWidth - filterSizeW + 2 * padding) / stride + 1;
TensorShape imShape =
TensorShape({channels, inputHeight, inputWidth});
TensorShape colShape1 = TensorShape({channels,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
size_t height = channels * filterHeight * filterWidth;
size_t width = outputHeight * outputWidth;
VectorPtr input1 =
Vector::create(imShape.getElements(), false);
VectorPtr input2 =
Vector::create(imShape.getElements(), false);
MatrixPtr output1 =
Matrix::create(height, width, false, false);
MatrixPtr output2 =
Matrix::create(height, width, false, false);
input1->uniform(0.001, 1);
input2->copyFrom(*input1);
Im2ColFunctor<kCFO, DEVICE_TYPE_CPU, T> im2Col1;
Im2ColMobileFunctor<T> im2Col2;
im2Col1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding,
dilation,
dilation);
im2Col2(input2->getData(),
imShape,
output2->getData(),
colShape1,
stride,
stride,
padding,
padding,
dilation,
dilation,
0,
height,
0,
width);
autotest::TensorCheckEqual(*output1, *output2);
}
}
}
}
}
}
}
}
}
TEST(Im2ColFunctor, Mobile) { TestIm2ColMobileFunctor<float>(); }
} // namespace paddle
paddle/platform/device_context.h
浏览文件 @ afb5496c
......@@ -52,6 +52,14 @@ class CPUDeviceContext : public DeviceContext {
std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
};
template <typename Place>
struct DefaultDeviceContextType;
template <>
struct DefaultDeviceContextType<platform::CPUPlace> {
using TYPE = CPUDeviceContext;
};
#ifdef PADDLE_WITH_CUDA
class EigenCudaStreamDevice;
......@@ -90,6 +98,11 @@ class CUDADeviceContext : public DeviceContext {
cublasHandle_t cublas_handle_;
};
template <>
struct DefaultDeviceContextType<platform::CUDAPlace> {
using TYPE = CUDADeviceContext;
};
class CUDNNDeviceContext : public CUDADeviceContext {
public:
explicit CUDNNDeviceContext(CUDAPlace place);
......@@ -125,6 +138,13 @@ class DeviceContextPool {
/*! \brief Return handle of single device context. */
const platform::DeviceContext* Get(const platform::Place& place);
template <typename Place>
const typename DefaultDeviceContextType<Place>::TYPE* GetByPlace(
const Place& place) {
return reinterpret_cast<
const typename DefaultDeviceContextType<Place>::TYPE*>(Get(place));
}
private:
static DeviceContextPool* pool;
constexpr static int LEFT_SHIFT = 8;
......
paddle/platform/place.h
浏览文件 @ afb5496c
......@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once
#include <iostream>
#include "paddle/platform/enforce.h"
#include "paddle/platform/variant.h"
namespace paddle {
......@@ -64,5 +64,31 @@ bool places_are_same_class(const Place &, const Place &);
std::ostream &operator<<(std::ostream &, const Place &);
template <typename Visitor>
struct PlaceVisitorWrapper
: public boost::static_visitor<typename Visitor::result_type> {
const Visitor &visitor_;
explicit PlaceVisitorWrapper(const Visitor &visitor) : visitor_(visitor) {}
typename Visitor::result_type operator()(const CPUPlace &cpu) const {
return visitor_(cpu);
}
typename Visitor::result_type operator()(const CUDAPlace &cuda) const {
#ifdef PADDLE_WITH_CUDA
return visitor_(cuda);
#else
PADDLE_THROW("Paddle is not compiled with CUDA. Cannot visit cuda device");
return typename Visitor::result_type();
#endif
}
};
template <typename Visitor>
typename Visitor::result_type VisitPlace(const Place &place,
const Visitor &visitor) {
return boost::apply_visitor(PlaceVisitorWrapper<Visitor>(visitor), place);
}
} // namespace platform
} // namespace paddle
paddle/pybind/CMakeLists.txt
浏览文件 @ afb5496c
......@@ -3,6 +3,9 @@ if(WITH_PYTHON)
SRCS pybind.cc exception.cc protobuf.cc const_value.cc
DEPS pybind python backward proto_desc paddle_memory executor prune init
${GLOB_OP_LIB})
if(NOT APPLE AND NOT ANDROID)
target_link_libraries(paddle_pybind rt)
endif(NOT APPLE AND NOT ANDROID)
endif(WITH_PYTHON)
if(WITH_DOC)
......
paddle/scripts/submit_local.sh.in
浏览文件 @ afb5496c
......@@ -71,9 +71,7 @@ function threads_config() {
# auto set OMP_NUM_THREADS and MKL_NUM_THREADS
# according to trainer_count and total processors
# only when MKL enabled
if [ "@WITH_MKL@" == "OFF" ]; then
return 0
fi
# auto set OPENBLAS_NUM_THREADS when do not use MKL
processors=`grep "processor" /proc/cpuinfo|sort -u|wc -l`
trainers=`grep -Eo 'trainer_count.[0-9]+' <<< "$@" |grep -Eo '[0-9]+'|xargs`
if [ -z $trainers ]; then
......@@ -83,12 +81,19 @@ function threads_config() {
if [ $threads -eq 0 ]; then
threads=1
fi
if [ -z "$OMP_NUM_THREADS" ]; then
export OMP_NUM_THREADS=$threads
fi
if [ -z "$MKL_NUM_THREADS" ]; then
export MKL_NUM_THREADS=$threads
if [ "@WITH_MKL@" == "ON" ]; then
if [ -z "$OMP_NUM_THREADS" ]; then
export OMP_NUM_THREADS=$threads
fi
if [ -z "$MKL_NUM_THREADS" ]; then
export MKL_NUM_THREADS=$threads
fi
else
if [ -z "$OPENBLAS_NUM_THREADS" ]; then
export OPENBLAS_NUM_THREADS=$threads
fi
fi
}
PADDLE_CONF_HOME="$HOME/.config/paddle"
......@@ -150,7 +155,7 @@ fi
case "$1" in
"train")
threads_config $@
# echo $OMP_NUM_THREADS $MKL_NUM_THREADS
# echo $OMP_NUM_THREADS $MKL_NUM_THREADS $OPENBLAS_NUM_THREADS
${DEBUGGER} $PADDLE_BIN_PATH/paddle_trainer ${@:2}
;;
"merge_model")
......
python/paddle/v2/dataset/flowers.py
浏览文件 @ afb5496c
......@@ -44,7 +44,7 @@ __all__ = ['train', 'test', 'valid']
DATA_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz'
LABEL_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat'
SETID_URL = 'http://www.robots.ox.ac.uk/~vgg/data/flowers/102/setid.mat'
DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
DATA_MD5 = '33bfc11892f1e405ca193ae9a9f2a118'
LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
# In official 'readme', tstid is the flag of test data
......
python/paddle/v2/fluid/__init__.py
浏览文件 @ afb5496c
......@@ -36,7 +36,7 @@ def __read_gflags_from_env__():
"""
import sys
import core
read_env_flags = ['use_pinned_memory']
read_env_flags = ['use_pinned_memory', 'check_nan_inf']
if core.is_compile_gpu():
read_env_flags.append('fraction_of_gpu_memory_to_use')
core.init_gflags([sys.argv[0]] +
......
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