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体验新版 GitCode,发现更多精彩内容 >>
提交
ba773fc9
编写于
6月 06, 2018
作者:
F
fengjiayi
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into add_broadcast_test
上级
e819f1b3
ea408d55
变更
57
显示空白变更内容
内联
并排
Showing
57 changed file
with
798 addition
and
302 deletion
+798
-302
Dockerfile
Dockerfile
+2
-3
benchmark/fluid/Dockerfile
benchmark/fluid/Dockerfile
+2
-2
benchmark/fluid/fluid_benchmark.py
benchmark/fluid/fluid_benchmark.py
+10
-5
benchmark/fluid/models/mnist.py
benchmark/fluid/models/mnist.py
+24
-9
benchmark/fluid/models/resnet.py
benchmark/fluid/models/resnet.py
+22
-7
benchmark/fluid/models/stacked_dynamic_lstm.py
benchmark/fluid/models/stacked_dynamic_lstm.py
+1
-2
cmake/external/grpc.cmake
cmake/external/grpc.cmake
+1
-0
doc/fluid/howto/optimization/host_memory_profiling_cn.md
doc/fluid/howto/optimization/host_memory_profiling_cn.md
+89
-0
paddle/contrib/inference/demo/simple_on_word2vec.cc
paddle/contrib/inference/demo/simple_on_word2vec.cc
+3
-0
paddle/contrib/inference/paddle_inference_api.h
paddle/contrib/inference/paddle_inference_api.h
+2
-1
paddle/contrib/inference/paddle_inference_api_impl.cc
paddle/contrib/inference/paddle_inference_api_impl.cc
+27
-17
paddle/contrib/inference/paddle_inference_api_impl.h
paddle/contrib/inference/paddle_inference_api_impl.h
+6
-3
paddle/fluid/framework/CMakeLists.txt
paddle/fluid/framework/CMakeLists.txt
+1
-1
paddle/fluid/framework/details/CMakeLists.txt
paddle/fluid/framework/details/CMakeLists.txt
+1
-0
paddle/fluid/framework/details/execution_strategy.h
paddle/fluid/framework/details/execution_strategy.h
+1
-0
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc
...id/framework/details/scope_buffered_ssa_graph_executor.cc
+76
-0
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h
...uid/framework/details/scope_buffered_ssa_graph_executor.h
+53
-0
paddle/fluid/framework/details/ssa_graph_executor.cc
paddle/fluid/framework/details/ssa_graph_executor.cc
+0
-4
paddle/fluid/framework/details/ssa_graph_executor.h
paddle/fluid/framework/details/ssa_graph_executor.h
+1
-5
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
...le/fluid/framework/details/threaded_ssa_graph_executor.cc
+4
-2
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
+1
-0
paddle/fluid/framework/parallel_executor.cc
paddle/fluid/framework/parallel_executor.cc
+16
-42
paddle/fluid/inference/analysis/CMakeLists.txt
paddle/fluid/inference/analysis/CMakeLists.txt
+6
-0
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h
+68
-0
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc
...fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc
+46
-0
paddle/fluid/operators/conv_cudnn_op.cu.cc
paddle/fluid/operators/conv_cudnn_op.cu.cc
+3
-3
paddle/fluid/operators/detail/grpc_client.cc
paddle/fluid/operators/detail/grpc_client.cc
+41
-55
paddle/fluid/operators/detail/grpc_client.h
paddle/fluid/operators/detail/grpc_client.h
+18
-5
paddle/fluid/operators/detail/grpc_server.cc
paddle/fluid/operators/detail/grpc_server.cc
+1
-6
paddle/fluid/operators/detail/grpc_server_test.cc
paddle/fluid/operators/detail/grpc_server_test.cc
+4
-7
paddle/fluid/operators/detection/box_coder_op.cc
paddle/fluid/operators/detection/box_coder_op.cc
+11
-8
paddle/fluid/operators/detection/box_coder_op.cu
paddle/fluid/operators/detection/box_coder_op.cu
+38
-21
paddle/fluid/operators/detection/box_coder_op.h
paddle/fluid/operators/detection/box_coder_op.h
+52
-33
paddle/fluid/operators/fetch_barrier_op.cc
paddle/fluid/operators/fetch_barrier_op.cc
+2
-2
paddle/fluid/operators/pool_cudnn_op.cu.cc
paddle/fluid/operators/pool_cudnn_op.cu.cc
+5
-1
paddle/fluid/operators/prefetch_op.cc
paddle/fluid/operators/prefetch_op.cc
+1
-1
paddle/fluid/operators/recv_op.cc
paddle/fluid/operators/recv_op.cc
+1
-1
paddle/fluid/operators/reduce_op.h
paddle/fluid/operators/reduce_op.h
+10
-9
paddle/fluid/operators/send_barrier_op.cc
paddle/fluid/operators/send_barrier_op.cc
+2
-2
paddle/fluid/operators/send_op.cc
paddle/fluid/operators/send_op.cc
+4
-4
paddle/fluid/operators/test_send_nccl_id.cc
paddle/fluid/operators/test_send_nccl_id.cc
+6
-7
paddle/fluid/platform/cudnn_helper.h
paddle/fluid/platform/cudnn_helper.h
+17
-3
paddle/fluid/platform/device_context.cc
paddle/fluid/platform/device_context.cc
+0
-1
paddle/fluid/platform/device_context.h
paddle/fluid/platform/device_context.h
+0
-3
paddle/fluid/platform/dynload/cublas.h
paddle/fluid/platform/dynload/cublas.h
+1
-1
paddle/fluid/platform/dynload/cudnn.h
paddle/fluid/platform/dynload/cudnn.h
+1
-1
paddle/fluid/platform/dynload/cupti.h
paddle/fluid/platform/dynload/cupti.h
+1
-1
paddle/fluid/platform/dynload/curand.h
paddle/fluid/platform/dynload/curand.h
+1
-1
paddle/fluid/platform/dynload/nccl.h
paddle/fluid/platform/dynload/nccl.h
+1
-1
paddle/fluid/platform/dynload/tensorrt.h
paddle/fluid/platform/dynload/tensorrt.h
+1
-1
paddle/fluid/platform/dynload/warpctc.h
paddle/fluid/platform/dynload/warpctc.h
+1
-1
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+8
-0
python/paddle/fluid/__init__.py
python/paddle/fluid/__init__.py
+1
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+75
-15
python/paddle/fluid/tests/no_test_concurrency.py
python/paddle/fluid/tests/no_test_concurrency.py
+0
-0
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+2
-4
python/paddle/fluid/tests/unittests/test_box_coder_op.py
python/paddle/fluid/tests/unittests/test_box_coder_op.py
+26
-0
未找到文件。
Dockerfile
浏览文件 @
ba773fc9
...
...
@@ -24,7 +24,7 @@ COPY ./paddle/scripts/docker/root/ /root/
RUN
apt-get update
&&
\
apt-get
install
-y
--allow-downgrades
\
git python-pip python-dev openssh-server bison
\
git python-pip python-dev
python-opencv
openssh-server bison
\
libnccl2
=
2.1.2-1+cuda8.0 libnccl-dev
=
2.1.2-1+cuda8.0
\
wget unzip unrar
tar
xz-utils bzip2
gzip
coreutils ntp
\
curl
sed grep
graphviz libjpeg-dev zlib1g-dev
\
...
...
@@ -76,8 +76,7 @@ RUN easy_install -U pip && \
pip
install
sphinx-rtd-theme
==
0.1.9 recommonmark
RUN
pip
install
pre-commit
'ipython==5.3.0'
&&
\
pip
install
'ipykernel==4.6.0'
'jupyter==1.0.0'
&&
\
pip
install
opencv-python
pip
install
'ipykernel==4.6.0'
'jupyter==1.0.0'
#For docstring checker
RUN
pip
install
pylint pytest astroid isort
...
...
benchmark/fluid/Dockerfile
浏览文件 @
ba773fc9
FROM
nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
RUN
apt-get update
&&
apt-get
install
-y
python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop
RUN
apt-get update
&&
apt-get
install
-y
python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop
python-opencv
RUN
ln
-s
/usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so
&&
ln
-s
/usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/lib/libnccl.so
RUN
pip
install
-U
pip
RUN
pip
install
-U
kubernetes
opencv-python
paddlepaddle
RUN
pip
install
-U
kubernetes paddlepaddle
# IMPORTANT:
# Add "ENV http_proxy=http://ip:port" if your download is slow, and don't forget to unset it at runtime.
...
...
benchmark/fluid/fluid_benchmark.py
浏览文件 @
ba773fc9
...
...
@@ -69,6 +69,11 @@ def parse_args():
type
=
int
,
default
=
1
,
help
=
'If gpus > 1, will use ParallelExecutor to run, else use Executor.'
)
parser
.
add_argument
(
'--cpus'
,
type
=
int
,
default
=
1
,
help
=
'If cpus > 1, will use ParallelDo to run, else use Executor.'
)
parser
.
add_argument
(
'--data_set'
,
type
=
str
,
...
...
@@ -85,8 +90,8 @@ def parse_args():
help
=
'If set, use nvprof for CUDA.'
)
parser
.
add_argument
(
'--no_test'
,
action
=
'store_
fals
e'
,
help
=
'If set, test the testset during training.'
)
action
=
'store_
tru
e'
,
help
=
'If set,
do not
test the testset during training.'
)
parser
.
add_argument
(
'--memory_optimize'
,
action
=
'store_true'
,
...
...
@@ -229,9 +234,9 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
print
(
"Pass: %d, Iter: %d, Loss: %f
\n
"
%
(
pass_id
,
iters
,
np
.
mean
(
train_losses
)))
print_train_time
(
start_time
,
time
.
time
(),
num_samples
)
print
(
"Pass: %d, Loss: %f"
%
(
pass_id
,
np
.
mean
(
train_losses
)))
print
(
"Pass: %d, Loss: %f"
%
(
pass_id
,
np
.
mean
(
train_losses
)))
,
# evaluation
if
not
args
.
no_test
and
batch_acc
!=
None
:
if
not
args
.
no_test
and
batch_acc
:
pass_test_acc
=
test
(
exe
,
infer_prog
,
test_reader
,
feeder
,
batch_acc
)
print
(
", Test Accuracy: %f"
%
pass_test_acc
)
...
...
@@ -310,7 +315,7 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
print
(
"Pass %d, batch %d, loss %s"
%
(
pass_id
,
batch_id
,
np
.
array
(
loss
)))
print_train_time
(
start_time
,
time
.
time
(),
num_samples
)
if
not
args
.
no_test
and
batch_acc
!=
None
:
if
not
args
.
no_test
and
batch_acc
:
test_acc
=
test
(
startup_exe
,
infer_prog
,
test_reader
,
feeder
,
batch_acc
)
print
(
"Pass: %d, Test Accuracy: %f
\n
"
%
(
pass_id
,
test_acc
))
...
...
benchmark/fluid/models/mnist.py
浏览文件 @
ba773fc9
...
...
@@ -69,15 +69,30 @@ def get_model(args):
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
DTYPE
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
if
args
.
device
==
'CPU'
and
args
.
cpus
>
1
:
places
=
fluid
.
layers
.
get_places
(
args
.
cpus
)
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
predict
=
cnn_model
(
pd
.
read_input
(
images
))
label
=
pd
.
read_input
(
label
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
pd
.
write_output
(
avg_cost
)
pd
.
write_output
(
batch_acc
)
avg_cost
,
batch_acc
=
pd
()
avg_cost
=
fluid
.
layers
.
mean
(
avg_cost
)
batch_acc
=
fluid
.
layers
.
mean
(
batch_acc
)
else
:
# Train program
predict
=
cnn_model
(
images
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
# Evaluator
batch_size_tensor
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size_tensor
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
# inference program
inference_program
=
fluid
.
default_main_program
().
clone
()
...
...
benchmark/fluid/models/resnet.py
浏览文件 @
ba773fc9
...
...
@@ -132,18 +132,33 @@ def get_model(args):
input
=
fluid
.
layers
.
data
(
name
=
'data'
,
shape
=
dshape
,
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
predict
=
model
(
input
,
class_dim
)
if
args
.
device
==
'CPU'
and
args
.
cpus
>
1
:
places
=
fluid
.
layers
.
get_places
(
args
.
cpus
)
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
predict
=
model
(
pd
.
read_input
(
input
),
class_dim
)
label
=
pd
.
read_input
(
label
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
batch_size_tensor
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size_tensor
)
pd
.
write_output
(
avg_cost
)
pd
.
write_output
(
batch_acc
)
avg_cost
,
batch_acc
=
pd
()
avg_cost
=
fluid
.
layers
.
mean
(
avg_cost
)
batch_acc
=
fluid
.
layers
.
mean
(
batch_acc
)
else
:
predict
=
model
(
input
,
class_dim
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
inference_program
=
fluid
.
default_main_program
().
clone
()
with
fluid
.
program_guard
(
inference_program
):
inference_program
=
fluid
.
io
.
get_inference_program
(
target_vars
=
[
batch_acc
,
batch_size_tensor
])
target_vars
=
[
batch_acc
])
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
0.01
,
momentum
=
0.9
)
...
...
benchmark/fluid/models/stacked_dynamic_lstm.py
浏览文件 @
ba773fc9
...
...
@@ -101,9 +101,8 @@ def get_model(args):
loss
=
fluid
.
layers
.
mean
(
x
=
loss
)
# add acc
batch_size_tensor
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
logit
,
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
\
shape
=
[
1
],
dtype
=
'int64'
)
,
total
=
batch_size_tensor
)
shape
=
[
1
],
dtype
=
'int64'
))
inference_program
=
fluid
.
default_main_program
().
clone
()
with
fluid
.
program_guard
(
inference_program
):
...
...
cmake/external/grpc.cmake
浏览文件 @
ba773fc9
...
...
@@ -45,6 +45,7 @@ ExternalProject_Add(
# checkout and clean other dirs under third_party
# 4. remove .git, and package the directory.
URL
"http://paddlepaddledeps.bj.bcebos.com/grpc-v1.8.x.tar.gz"
URL_MD5
"c9c58ee7d0e8929a63155af6a2ecdbd0"
PREFIX
${
GRPC_SOURCES_DIR
}
UPDATE_COMMAND
""
CONFIGURE_COMMAND
""
...
...
doc/fluid/howto/optimization/host_memory_profiling_cn.md
0 → 100644
浏览文件 @
ba773fc9
## 堆内存分析和优化
计算机程序都可能有内存泄漏的风险。
**内存泄漏**
一般是由于程序在堆(heap)上分配了内存而没有释放,随着程序的运行占用的内存越来越大,一方面会影响程序的稳定性,可能让运行速度越来越慢,或者造成oom,甚至会影响运行程序的机器的稳定性,造成宕机。
目前有很多内存泄漏分析工具,比较经典的有
[
valgrind
](
http://valgrind.org/docs/manual/quick-start.html#quick-start.intro
)
,
[
gperftools
](
https://gperftools.github.io/gperftools/
)
。
因为Fluid是用Python驱动C++ core来运行,valgrind直接分析非常困难,需要自己编译debug版本的、带valgrind支持的专用Python版本,而且输出的信息中大部分是Python自己的符号和调用信息,分析起来很困难,另外使用valgrind会让程序运行速度变得非常慢,所以不建议使用。
本教程主要介绍
[
gperftools
](
https://gperftools.github.io/gperftools/
)
的使用。
gperftool主要支持以下四个功能:
-
thread-caching malloc
-
heap-checking using tcmalloc
-
heap-profiling using tcmalloc
-
CPU profiler
Paddle也提供了基于gperftool的
[
CPU性能分析教程
](
https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/howto/optimization/cpu_profiling_cn.md
)
。
对于堆内存的分析,主要用到thread-caching malloc和heap-profiling using tcmalloc。
## 使用流程
#### 环境
本教程基于paddle提供的Docker开发环境paddlepaddle/paddle:latest-dev,基于Ubuntu 16.04.4 LTS环境。
#### 使用流程
-
安装google-perftools
```
apt-get install libunwind-dev
apt-get install google-perftools
```
-
安装pprof
```
go get -u github.com/google/pprof
```
-
设置运行环境
```
export PPROF_PATH=/root/gopath/bin/pprof
export PPROF_BINARY_PATH=/root/gopath/bin/pprof
export LD_PRELOAD=/usr/lib/libtcmalloc.so.4
```
-
使用heap profile来运行python程序。本质上是周期性的对堆的分配情况做一次快照。
```
# HEAPPROFILE 设置生成的堆分析文件的目录和文件前缀
# HEAP_PROFILE_ALLOCATION_INTERVAL 设置每分配多少存储dump一次dump,默认1GB
env HEAPPROFILE="./perf_log/test.log" HEAP_PROFILE_ALLOCATION_INTERVAL=209715200 python trainer.py
```
随着程序的运行,会在perf_log这个文件夹下生成很多文件,如下:
```
-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0001.heap
-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0002.heap
-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0003.heap
-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0004.heap
-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0005.heap
-rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0006.heap
```
-
使用pprof对heap文件进行分析。分析有两种模式:
-
完整模式。会对当前heap做一个分析,显示目前分配内存一些调用路径。
```
pprof --pdf python test.log.0012.heap
```
上述命令会生成一个profile00x.pdf的文件,可以直接打开,例如:[memory_cpu_allocator](https://github.com/jacquesqiao/Paddle/blob/bd2ea0e1f84bb6522a66d44a072598153634cade/doc/fluid/howto/optimization/memory_cpu_allocator.pdf)。从下图可以看出,在CPU版本fluid的运行过程中,分配存储最多的模块式CPUAllocator. 而别的模块相对而言分配内存较少,所以被忽略了,这对于分配内存泄漏是很不方便的,因为泄漏是一个缓慢的过程,在这种图中是无法看到的。
![result](https://user-images.githubusercontent.com/3048612/40964027-a54033e4-68dc-11e8-836a-144910c4bb8c.png)
- Diff模式。可以对两个时刻的heap做diff,把一些内存分配没有发生变化的模块去掉,而把增量部分显示出来。
```
pprof --pdf --base test.log.0010.heap python test.log.1045.heap
```
生成的结果为:[`memory_leak_protobuf`](https://github.com/jacquesqiao/Paddle/blob/bd2ea0e1f84bb6522a66d44a072598153634cade/doc/fluid/howto/optimization/memory_leak_protobuf.pdf)
从图中可以看出:ProgramDesc这个结构,在两个版本之间增长了200MB+,所以这里有很大的内存泄漏的可能性,最终结果也确实证明是这里造成了泄漏。
![result](https://user-images.githubusercontent.com/3048612/40964057-b434d5e4-68dc-11e8-894b-8ab62bcf26c2.png)
![result](https://user-images.githubusercontent.com/3048612/40964063-b7dbee44-68dc-11e8-9719-da279f86477f.png)
paddle/contrib/inference/demo/simple_on_word2vec.cc
浏览文件 @
ba773fc9
...
...
@@ -65,7 +65,10 @@ void Main(bool use_gpu) {
}
TEST
(
demo
,
word2vec_cpu
)
{
Main
(
false
/*use_gpu*/
);
}
#ifdef PADDLE_WITH_CUDA
TEST
(
demo
,
word2vec_gpu
)
{
Main
(
true
/*use_gpu*/
);
}
#endif
}
// namespace demo
}
// namespace paddle
paddle/contrib/inference/paddle_inference_api.h
浏览文件 @
ba773fc9
...
...
@@ -63,6 +63,7 @@ class PaddlePredictor {
struct
Config
;
PaddlePredictor
()
=
default
;
PaddlePredictor
(
const
PaddlePredictor
&
)
=
delete
;
PaddlePredictor
&
operator
=
(
const
PaddlePredictor
&
)
=
delete
;
// Predict an record.
// The caller should be responsible for allocating and releasing the memory of
...
...
@@ -76,7 +77,7 @@ class PaddlePredictor {
virtual
std
::
unique_ptr
<
PaddlePredictor
>
Clone
()
=
0
;
// Destroy the Predictor.
virtual
~
PaddlePredictor
()
{}
virtual
~
PaddlePredictor
()
=
default
;
// The common configs for all the predictors.
struct
Config
{
...
...
paddle/contrib/inference/paddle_inference_api_impl.cc
浏览文件 @
ba773fc9
...
...
@@ -54,7 +54,8 @@ std::string num2str(T a) {
}
}
// namespace
bool
NativePaddlePredictor
::
Init
()
{
bool
NativePaddlePredictor
::
Init
(
std
::
shared_ptr
<
framework
::
Scope
>
parent_scope
)
{
VLOG
(
3
)
<<
"Predictor::init()"
;
if
(
config_
.
use_gpu
)
{
...
...
@@ -62,9 +63,15 @@ bool NativePaddlePredictor::Init() {
}
else
{
place_
=
paddle
::
platform
::
CPUPlace
();
}
if
(
parent_scope
)
{
scope_
=
parent_scope
;
sub_scope_
=
&
(
parent_scope
->
NewScope
());
}
else
{
paddle
::
framework
::
InitDevices
(
false
);
executor_
.
reset
(
new
paddle
::
framework
::
Executor
(
place_
));
scope_
.
reset
(
new
paddle
::
framework
::
Scope
());
}
executor_
.
reset
(
new
paddle
::
framework
::
Executor
(
place_
));
// Initialize the inference program
if
(
!
config_
.
model_dir
.
empty
())
{
...
...
@@ -83,13 +90,8 @@ bool NativePaddlePredictor::Init() {
return
false
;
}
ctx_
=
executor_
->
Prepare
(
*
inference_program_
,
0
);
// Create temporary variables first, so that the first batch do not need to
// create variables in the runtime. This is the logics of the old inference
// API.
// TODO(Superjomn) this should be modified when `Clone` is valid for
// multi-thread application.
executor_
->
CreateVariables
(
*
inference_program_
,
scope_
.
get
(),
0
);
executor_
->
CreateVariables
(
*
inference_program_
,
sub_scope_
?
sub_scope_
:
scope_
.
get
(),
0
);
// Get the feed_target_names and fetch_target_names
feed_target_names_
=
inference_program_
->
GetFeedTargetNames
();
...
...
@@ -97,6 +99,13 @@ bool NativePaddlePredictor::Init() {
return
true
;
}
NativePaddlePredictor
::~
NativePaddlePredictor
()
{
if
(
sub_scope_
)
{
PADDLE_ENFORCE_NOT_NULL
(
scope_
,
"Should have parent scope!"
);
scope_
->
DeleteScope
(
sub_scope_
);
}
};
bool
NativePaddlePredictor
::
Run
(
const
std
::
vector
<
PaddleTensor
>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
output_data
)
{
VLOG
(
3
)
<<
"Predictor::predict"
;
...
...
@@ -121,8 +130,9 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
}
// Run the inference program
// if share variables, we need not create variables
executor_
->
RunPreparedContext
(
ctx_
.
get
(),
scope_
.
get
(),
executor_
->
RunPreparedContext
(
ctx_
.
get
(),
sub_scope_
!=
nullptr
?
sub_scope_
:
scope_
.
get
(),
&
feed_targets
,
&
fetch_targets
,
false
/* don't create variable eatch time */
);
...
...
@@ -138,7 +148,7 @@ std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
VLOG
(
3
)
<<
"Predictor::clone"
;
std
::
unique_ptr
<
PaddlePredictor
>
cls
(
new
NativePaddlePredictor
(
config_
));
if
(
!
dynamic_cast
<
NativePaddlePredictor
*>
(
cls
.
get
())
->
Init
())
{
if
(
!
dynamic_cast
<
NativePaddlePredictor
*>
(
cls
.
get
())
->
Init
(
scope_
))
{
LOG
(
ERROR
)
<<
"fail to call Init"
;
return
nullptr
;
}
...
...
@@ -266,7 +276,7 @@ CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
}
std
::
unique_ptr
<
PaddlePredictor
>
predictor
(
new
NativePaddlePredictor
(
config
));
if
(
!
dynamic_cast
<
NativePaddlePredictor
*>
(
predictor
.
get
())
->
Init
())
{
if
(
!
dynamic_cast
<
NativePaddlePredictor
*>
(
predictor
.
get
())
->
Init
(
nullptr
))
{
return
nullptr
;
}
return
std
::
move
(
predictor
);
...
...
paddle/contrib/inference/paddle_inference_api_impl.h
浏览文件 @
ba773fc9
...
...
@@ -34,14 +34,15 @@ class NativePaddlePredictor : public PaddlePredictor {
explicit
NativePaddlePredictor
(
const
NativeConfig
&
config
)
:
config_
(
config
)
{}
bool
Init
();
// will only create sub scope if have global scope
bool
Init
(
std
::
shared_ptr
<
framework
::
Scope
>
parent_scope
);
bool
Run
(
const
std
::
vector
<
PaddleTensor
>
&
inputs
,
std
::
vector
<
PaddleTensor
>
*
output_data
)
override
;
std
::
unique_ptr
<
PaddlePredictor
>
Clone
()
override
;
~
NativePaddlePredictor
()
override
{}
;
~
NativePaddlePredictor
()
override
;
private:
bool
SetFeed
(
const
std
::
vector
<
PaddleTensor
>
&
input_datas
,
...
...
@@ -52,11 +53,13 @@ class NativePaddlePredictor : public PaddlePredictor {
NativeConfig
config_
;
platform
::
Place
place_
;
std
::
unique_ptr
<
framework
::
Executor
>
executor_
;
std
::
unique
_ptr
<
framework
::
Scope
>
scope_
;
std
::
shared
_ptr
<
framework
::
Scope
>
scope_
;
std
::
unique_ptr
<
framework
::
ExecutorPrepareContext
>
ctx_
;
std
::
unique_ptr
<
framework
::
ProgramDesc
>
inference_program_
;
std
::
vector
<
std
::
string
>
feed_target_names_
;
std
::
vector
<
std
::
string
>
fetch_target_names_
;
// Do not use unique_ptr, use parent scope to delete
framework
::
Scope
*
sub_scope_
{
nullptr
};
};
}
// namespace paddle
paddle/fluid/framework/CMakeLists.txt
浏览文件 @
ba773fc9
...
...
@@ -87,7 +87,7 @@ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
framework_proto glog lod_rank_table feed_fetch_method
)
cc_library
(
parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor
)
cc_library
(
parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor
scope_buffered_ssa_graph_executor
)
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/fluid/framework/details/CMakeLists.txt
浏览文件 @
ba773fc9
...
...
@@ -36,5 +36,6 @@ cc_test(broadcast_op_test SRCS broadcast_op_handle_test.cc DEPS var_handle op_ha
device_context broadcast_op_handle
)
cc_test
(
gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
device_context gather_op_handle
)
cc_library
(
scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_executor.cc DEPS ssa_graph_executor
)
#cc_test(reduce_op_handle_test SRCS reduce_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
# device_context reduce_op_handle )
paddle/fluid/framework/details/execution_strategy.h
浏览文件 @
ba773fc9
...
...
@@ -22,6 +22,7 @@ struct ExecutionStrategy {
size_t
num_threads_
{
0
};
bool
use_event_
{
true
};
bool
allow_op_delay_
{
false
};
size_t
num_iteration_per_drop_scope_
{
100
};
};
}
// namespace details
...
...
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc
0 → 100644
浏览文件 @
ba773fc9
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/executor.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
ScopeBufferedSSAGraphExecutor
::
ScopeBufferedSSAGraphExecutor
(
ExecutionStrategy
strategy
,
std
::
vector
<
Scope
*>
local_scopes
,
std
::
vector
<
VariableInfo
>
var_infos
,
std
::
vector
<
platform
::
Place
>
places
,
std
::
unique_ptr
<
SSAGraphExecutor
>
&&
underlying_executor
)
:
strategy_
(
std
::
move
(
strategy
)),
underlying_executor_
(
std
::
move
(
underlying_executor
)),
local_scopes_
(
std
::
move
(
local_scopes
)),
var_infos_
(
std
::
move
(
var_infos
)),
places_
(
std
::
move
(
places
))
{}
FeedFetchList
ScopeBufferedSSAGraphExecutor
::
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
)
{
if
(
drop_scope_counter_
==
0
)
{
// Create local scopes.
for
(
auto
it
=
local_scopes_
.
rbegin
();
it
!=
local_scopes_
.
rend
();
++
it
)
{
auto
&
scope
=
*
it
;
Scope
&
local_scope
=
scope
->
NewScope
();
*
scope
->
Var
(
details
::
kLocalExecScopeName
)
->
GetMutable
<
Scope
*>
()
=
&
local_scope
;
for
(
auto
&
info
:
var_infos_
)
{
if
(
scope
->
FindVar
(
info
.
name_
)
!=
nullptr
)
{
continue
;
}
if
(
info
.
persistable_
)
{
// Persistable
InitializeVariable
(
scope
->
Var
(
info
.
name_
),
info
.
type_
);
}
else
{
InitializeVariable
(
local_scope
.
Var
(
info
.
name_
),
info
.
type_
);
}
}
}
}
auto
fetch_data
=
underlying_executor_
->
Run
(
fetch_tensors
);
drop_scope_counter_
+=
1
;
if
(
!
fetch_tensors
.
empty
()
||
drop_scope_counter_
==
strategy_
.
num_iteration_per_drop_scope_
)
{
drop_scope_counter_
=
0
;
// Wait All computational streams
for
(
auto
p
:
places_
)
{
platform
::
DeviceContextPool
::
Instance
().
Get
(
p
)
->
Wait
();
}
for
(
auto
&
scope
:
local_scopes_
)
{
auto
&
local_scope
=
*
scope
->
Var
(
details
::
kLocalExecScopeName
)
->
GetMutable
<
Scope
*>
();
scope
->
DeleteScope
(
local_scope
);
}
}
return
fetch_data
;
}
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h
0 → 100644
浏览文件 @
ba773fc9
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/place.h"
namespace
paddle
{
namespace
framework
{
namespace
details
{
struct
VariableInfo
{
std
::
string
name_
;
proto
::
VarType
::
Type
type_
;
bool
persistable_
;
};
class
ScopeBufferedSSAGraphExecutor
:
public
SSAGraphExecutor
{
public:
ScopeBufferedSSAGraphExecutor
(
ExecutionStrategy
strategy
,
std
::
vector
<
Scope
*>
local_scopes
,
std
::
vector
<
VariableInfo
>
var_infos
,
std
::
vector
<
platform
::
Place
>
places
,
std
::
unique_ptr
<
SSAGraphExecutor
>&&
underlying_executor
);
FeedFetchList
Run
(
const
std
::
vector
<
std
::
string
>&
fetch_tensors
)
override
;
private:
size_t
drop_scope_counter_
{
0
};
ExecutionStrategy
strategy_
;
std
::
unique_ptr
<
SSAGraphExecutor
>
underlying_executor_
;
std
::
vector
<
Scope
*>
local_scopes_
;
std
::
vector
<
VariableInfo
>
var_infos_
;
std
::
vector
<
platform
::
Place
>
places_
;
};
}
// namespace details
}
// namespace framework
}
// namespace paddle
paddle/fluid/framework/details/ssa_graph_executor.cc
浏览文件 @
ba773fc9
...
...
@@ -17,10 +17,6 @@
namespace
paddle
{
namespace
framework
{
namespace
details
{
SSAGraphExecutor
::
SSAGraphExecutor
(
std
::
unique_ptr
<
SSAGraph
>
&&
graph
)
:
graph_
(
std
::
move
(
graph
))
{}
SSAGraphExecutor
::~
SSAGraphExecutor
()
{}
}
// namespace details
...
...
paddle/fluid/framework/details/ssa_graph_executor.h
浏览文件 @
ba773fc9
...
...
@@ -28,15 +28,11 @@ class SSAGraphExecutor {
DISABLE_COPY_AND_ASSIGN
(
SSAGraphExecutor
);
public:
// Steal graph inside
explicit
SSAGraphExecutor
(
std
::
unique_ptr
<
SSAGraph
>
&&
graph
);
SSAGraphExecutor
()
{}
virtual
~
SSAGraphExecutor
();
virtual
FeedFetchList
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
)
=
0
;
protected:
std
::
unique_ptr
<
SSAGraph
>
graph_
;
};
}
// namespace details
}
// namespace framework
...
...
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @
ba773fc9
...
...
@@ -21,7 +21,7 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
const
ExecutionStrategy
&
strategy
,
const
std
::
vector
<
Scope
*>
&
local_scopes
,
const
std
::
vector
<
platform
::
Place
>
&
places
,
std
::
unique_ptr
<
SSAGraph
>
&&
graph
)
:
SSAGraphExecutor
(
std
::
move
(
graph
)),
:
graph_
(
std
::
move
(
graph
)),
pool_
(
strategy
.
num_threads_
>=
2
?
new
::
ThreadPool
(
strategy
.
num_threads_
)
:
nullptr
),
local_scopes_
(
local_scopes
),
...
...
@@ -189,7 +189,9 @@ void ThreadedSSAGraphExecutor::RunOp(
BlockingQueue
<
VarHandleBase
*>
*
ready_var_q
,
details
::
OpHandleBase
*
op
)
{
auto
op_run
=
[
ready_var_q
,
op
,
this
]
{
try
{
if
(
VLOG_IS_ON
(
10
))
{
VLOG
(
10
)
<<
op
<<
" "
<<
op
->
Name
()
<<
" : "
<<
op
->
DebugString
();
}
op
->
Run
(
strategy_
.
use_event_
);
VLOG
(
10
)
<<
op
<<
" "
<<
op
->
Name
()
<<
" Done "
;
running_ops_
--
;
...
...
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @
ba773fc9
...
...
@@ -51,6 +51,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
details
::
OpHandleBase
*
op
);
private:
std
::
unique_ptr
<
SSAGraph
>
graph_
;
std
::
unique_ptr
<::
ThreadPool
>
pool_
;
std
::
vector
<
Scope
*>
local_scopes_
;
std
::
vector
<
platform
::
Place
>
places_
;
...
...
paddle/fluid/framework/parallel_executor.cc
浏览文件 @
ba773fc9
...
...
@@ -23,6 +23,7 @@ limitations under the License. */
#endif
#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/platform/profiler.h"
...
...
@@ -42,8 +43,6 @@ class ParallelExecutorPrivate {
#ifdef PADDLE_WITH_CUDA
std
::
unique_ptr
<
platform
::
NCCLContextMap
>
nccl_ctxs_
;
#endif
std
::
vector
<
std
::
tuple
<
std
::
string
,
proto
::
VarType
::
Type
,
bool
>>
var_types_
;
bool
own_local_scope
;
};
...
...
@@ -92,9 +91,18 @@ ParallelExecutor::ParallelExecutor(
local_scopes
.
empty
())
{
// Is CUDA
BCastParamsToGPUs
(
bcast_vars
);
}
// Startup Program has been run. All local scopes has correct parameters.
// Startup Program has been run. All local scopes has correct parameters.
// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
// Step 2. Create vars in each scope;
std
::
vector
<
details
::
VariableInfo
>
var_infos
;
for
(
auto
*
var
:
main_program
.
Block
(
0
).
AllVars
())
{
var_infos
.
emplace_back
();
var_infos
.
back
().
name_
=
var
->
Name
();
var_infos
.
back
().
type_
=
var
->
GetType
();
var_infos
.
back
().
persistable_
=
var
->
Persistable
();
}
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
#ifdef PADDLE_WITH_CUDA
details
::
MultiDevSSAGraphBuilder
builder
(
...
...
@@ -105,16 +113,15 @@ ParallelExecutor::ParallelExecutor(
params
,
member_
->
local_scopes_
,
build_strategy
);
#endif
auto
graph
=
builder
.
Build
(
main_program
);
member_
->
executor_
.
reset
(
new
details
::
ThreadedSSAGraphExecutor
(
exec_strategy
,
member_
->
local_scopes_
,
places
,
std
::
move
(
graph
)));
// Step 3. Create vars in each scope;
for
(
auto
*
var
:
main_program
.
Block
(
0
).
AllVars
())
{
member_
->
var_types_
.
emplace_back
(
var
->
Name
(),
var
->
GetType
(),
var
->
Persistable
());
}
member_
->
executor_
.
reset
(
new
details
::
ScopeBufferedSSAGraphExecutor
(
exec_strategy
,
member_
->
local_scopes_
,
std
::
move
(
var_infos
),
member_
->
places_
,
std
::
move
(
member_
->
executor_
)));
}
void
ParallelExecutor
::
BCastParamsToGPUs
(
...
...
@@ -169,42 +176,9 @@ void ParallelExecutor::BCastParamsToGPUs(
void
ParallelExecutor
::
Run
(
const
std
::
vector
<
std
::
string
>
&
fetch_tensors
,
const
std
::
string
&
fetched_var_name
)
{
platform
::
RecordBlock
b
(
0
);
// Create local scopes.
for
(
auto
it
=
member_
->
local_scopes_
.
rbegin
();
it
!=
member_
->
local_scopes_
.
rend
();
++
it
)
{
auto
&
scope
=
*
it
;
Scope
&
local_scope
=
scope
->
NewScope
();
*
scope
->
Var
(
details
::
kLocalExecScopeName
)
->
GetMutable
<
Scope
*>
()
=
&
local_scope
;
for
(
auto
&
name_type_pair
:
member_
->
var_types_
)
{
if
(
scope
->
FindVar
(
std
::
get
<
0
>
(
name_type_pair
))
!=
nullptr
)
{
continue
;
}
if
(
std
::
get
<
2
>
(
name_type_pair
))
{
// Persistable
InitializeVariable
(
scope
->
Var
(
std
::
get
<
0
>
(
name_type_pair
)),
std
::
get
<
1
>
(
name_type_pair
));
}
else
{
InitializeVariable
(
local_scope
.
Var
(
std
::
get
<
0
>
(
name_type_pair
)),
std
::
get
<
1
>
(
name_type_pair
));
}
}
}
auto
fetch_data
=
member_
->
executor_
->
Run
(
fetch_tensors
);
*
member_
->
global_scope_
->
Var
(
fetched_var_name
)
->
GetMutable
<
FeedFetchList
>
()
=
fetch_data
;
// Wait All computational streams
for
(
auto
p
:
member_
->
places_
)
{
platform
::
DeviceContextPool
::
Instance
().
Get
(
p
)
->
Wait
();
}
for
(
auto
&
scope
:
member_
->
local_scopes_
)
{
auto
&
local_scope
=
*
scope
->
Var
(
details
::
kLocalExecScopeName
)
->
GetMutable
<
Scope
*>
();
scope
->
DeleteScope
(
local_scope
);
}
}
void
ParallelExecutor
::
FeedTensorsIntoLocalScopes
(
...
...
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @
ba773fc9
...
...
@@ -15,3 +15,9 @@ cc_test(test_subgraph_splitter
DEPS analysis paddle_fluid tensor
ARGS --inference_model_dir=
${
PYTHON_TESTS_DIR
}
/book/word2vec.inference.model
)
set_tests_properties
(
test_subgraph_splitter PROPERTIES DEPENDS test_word2vec
)
cc_test
(
test_dfg_graphviz_draw_pass
SRCS dfg_graphviz_draw_pass_tester.cc
DEPS analysis
ARGS --inference_model_dir=
${
PYTHON_TESTS_DIR
}
/book/word2vec.inference.model
)
set_tests_properties
(
test_dfg_graphviz_draw_pass PROPERTIES DEPENDS test_word2vec
)
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h
0 → 100644
浏览文件 @
ba773fc9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
/*
* This file create an DFG_GraphvizDrawPass which helps to draw a data flow
* graph's structure using graphviz.
*/
#pragma once
#include <fstream>
#include <string>
#include "paddle/fluid/inference/analysis/pass.h"
namespace
paddle
{
namespace
inference
{
namespace
analysis
{
/*
* Output a dot file and write to some place.
*/
class
DFG_GraphvizDrawPass
:
public
DataFlowGraphPass
{
public:
DFG_GraphvizDrawPass
(
const
std
::
string
&
dir
,
const
std
::
string
&
id
)
:
dir_
(
dir
),
id_
(
id
)
{}
bool
Initialize
()
override
{
return
Pass
::
Initialize
();
}
void
Run
(
DataFlowGraph
*
graph
)
override
{
auto
content
=
Draw
(
graph
);
std
::
ofstream
file
(
GenDotPath
());
file
.
write
(
content
.
c_str
(),
content
.
size
());
file
.
close
();
LOG
(
INFO
)
<<
"draw dot to "
<<
GenDotPath
();
}
bool
Finalize
()
override
{
return
Pass
::
Finalize
();
}
Pass
*
CreatePrinterPass
(
std
::
ostream
&
os
,
const
std
::
string
&
banner
)
const
override
{
return
nullptr
;
}
private:
// Path of the dot file to output.
std
::
string
GenDotPath
()
const
{
return
dir_
+
"/"
+
"graph_"
+
id_
+
".dot"
;
}
std
::
string
Draw
(
DataFlowGraph
*
graph
)
{
return
graph
->
DotString
();
}
std
::
string
dir_
;
std
::
string
id_
;
};
}
// namespace analysis
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc
0 → 100644
浏览文件 @
ba773fc9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
#include <gtest/gtest.h>
#include <fstream>
#include <string>
#include "paddle/fluid/inference/analysis/ut_helper.h"
namespace
paddle
{
namespace
inference
{
namespace
analysis
{
TEST_F
(
DFG_Tester
,
dfg_graphviz_draw_pass_tester
)
{
auto
dfg
=
ProgramDescToDFG
(
desc
);
DFG_GraphvizDrawPass
pass
(
"./"
,
"test"
);
pass
.
Initialize
();
pass
.
Run
(
&
dfg
);
// test content
std
::
ifstream
file
(
"./graph_test.dot"
);
ASSERT_TRUE
(
file
.
is_open
());
std
::
string
line
;
int
no
{
0
};
while
(
std
::
getline
(
file
,
line
))
{
no
++
;
}
ASSERT_EQ
(
no
,
82
);
}
}
// namespace analysis
}
// namespace inference
}
// namespace paddle
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @
ba773fc9
...
...
@@ -20,7 +20,7 @@ limitations under the License. */
#include "paddle/fluid/platform/cudnn_helper.h"
#include "paddle/fluid/platform/float16.h"
DEFINE_bool
(
cudnn_
algo_use_autotune
,
true
,
DEFINE_bool
(
cudnn_
deterministic
,
true
,
"Whether allow using an autotuning algorithm for convolution "
"operator. The autotuning algorithm may be non-deterministic. If "
"false, the algorithm is deterministic."
);
...
...
@@ -272,7 +272,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>();
auto
handle
=
dev_ctx
.
cudnn_handle
();
if
(
input_grad
)
{
if
(
FLAGS_cudnn_
algo_use_autotune
)
{
if
(
FLAGS_cudnn_
deterministic
)
{
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnGetConvolutionBackwardDataAlgorithm
(
handle
,
cudnn_filter_desc
,
...
...
@@ -297,7 +297,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
}
if
(
filter_grad
)
{
if
(
FLAGS_cudnn_
algo_use_autotune
)
{
if
(
FLAGS_cudnn_
deterministic
)
{
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnGetConvolutionBackwardFilterAlgorithm
(
handle
,
cudnn_input_desc
,
cudnn_output_grad_desc
,
...
...
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @
ba773fc9
...
...
@@ -38,6 +38,25 @@ void RPCClient::Init() {
if
(
rpc_client_
.
get
()
==
nullptr
)
{
rpc_client_
.
reset
(
new
RPCClient
());
}
rpc_client_
->
InitEventLoop
();
}
void
RPCClient
::
InitEventLoop
()
{
// start the client process thread
// TODO(wuyi): can make this in a threadpool
client_thread_
.
reset
(
new
std
::
thread
(
std
::
bind
(
&
RPCClient
::
Proceed
,
this
)));
}
RPCClient
::~
RPCClient
()
{
Wait
();
cq_
.
Shutdown
();
{
std
::
lock_guard
<
std
::
mutex
>
guard
(
chan_mutex_
);
for
(
auto
&
it
:
channels_
)
{
it
.
second
.
reset
();
}
}
client_thread_
->
join
();
}
bool
RPCClient
::
AsyncSendVariable
(
const
std
::
string
&
ep
,
...
...
@@ -204,70 +223,37 @@ void RPCClient::AsyncSendFetchBarrier(const std::string& ep, int64_t time_out) {
req_count_
++
;
}
bool
RPCClient
::
Wait
()
{
VLOG
(
3
)
<<
"RPCClient begin Wait()"
<<
" req_count_:"
<<
req_count_
;
if
(
req_count_
<=
0
)
{
return
true
;
}
const
size_t
kReqCnt
=
req_count_
;
bool
a
[
kReqCnt
];
std
::
vector
<
std
::
future
<
void
>>
waits
(
req_count_
);
std
::
mutex
mu
;
for
(
int
i
=
0
;
i
<
req_count_
;
i
++
)
{
waits
[
i
]
=
framework
::
AsyncIO
([
i
,
&
a
,
&
mu
,
this
]
{
bool
ret
=
Proceed
();
std
::
lock_guard
<
std
::
mutex
>
l
(
mu
);
a
[
i
]
=
ret
;
});
}
for
(
int
i
=
0
;
i
<
req_count_
;
i
++
)
{
waits
[
i
].
wait
();
}
int
last_req_count
=
req_count_
;
req_count_
=
0
;
for
(
int
i
=
0
;
i
<
last_req_count
;
i
++
)
{
if
(
!
a
[
i
])
{
return
false
;
}
}
return
true
;
void
RPCClient
::
Wait
()
{
std
::
unique_lock
<
std
::
mutex
>
lk
(
sync_mutex_
);
sync_cond_
.
wait
(
lk
,
[
this
]
{
return
req_count_
==
0
;
});
}
bool
RPCClient
::
Proceed
()
{
void
*
tag
=
NULL
;
void
RPCClient
::
Proceed
()
{
void
*
tag
=
nullptr
;
bool
ok
=
false
;
// request counts.
if
(
!
cq_
.
Next
(
&
tag
,
&
ok
))
{
LOG
(
ERROR
)
<<
"Get meets CompletionQueue error"
;
return
false
;
}
GPR_ASSERT
(
ok
);
PADDLE_ENFORCE
(
tag
);
// TODO(gongwb): add more retries.
while
(
cq_
.
Next
(
&
tag
,
&
ok
))
{
BaseProcessor
*
c
=
static_cast
<
BaseProcessor
*>
(
tag
);
if
(
!
c
->
status_
.
ok
())
{
LOG
(
ERROR
)
<<
"proc param error:"
<<
c
->
var_h_
.
String
()
GPR_ASSERT
(
ok
);
PADDLE_ENFORCE
(
c
);
if
(
c
->
status_
.
ok
())
{
c
->
Process
();
}
else
{
LOG
(
ERROR
)
<<
"var: "
<<
c
->
var_h_
.
String
()
<<
" grpc error:"
<<
c
->
status_
.
error_message
();
delete
c
;
return
false
;
}
c
->
Process
();
delete
c
;
return
true
;
{
std
::
lock_guard
<
std
::
mutex
>
lk
(
sync_mutex_
);
req_count_
--
;
}
sync_cond_
.
notify_all
();
}
}
std
::
shared_ptr
<
grpc
::
Channel
>
RPCClient
::
GetChannel
(
const
std
::
string
&
ep
)
{
// TODO(Yancey1989): make grpc client completely thread-safe
std
::
unique_lock
<
std
::
mutex
>
lock
(
mutex_
);
std
::
lock_guard
<
std
::
mutex
>
guard
(
chan_
mutex_
);
auto
it
=
channels_
.
find
(
ep
);
if
(
it
!=
channels_
.
end
())
{
return
it
->
second
;
...
...
paddle/fluid/operators/detail/grpc_client.h
浏览文件 @
ba773fc9
...
...
@@ -17,14 +17,17 @@ limitations under the License. */
#include <time.h>
#include <chrono> // NOLINT
#include <condition_variable> // NOLINT
#include <ctime>
#include <functional>
#include <iostream>
#include <map>
#include <mutex> // NOLINT
#include <string>
#include <thread> // NOLINT
#include <vector>
#include "grpc++/channel.h"
#include "grpc++/generic/generic_stub.h"
#include "grpc++/grpc++.h"
#include "grpc++/support/byte_buffer.h"
...
...
@@ -164,6 +167,7 @@ class FetchBarrierProcessor : public BaseProcessor {
class
RPCClient
{
public:
RPCClient
()
{}
~
RPCClient
();
static
RPCClient
*
GetInstance
();
...
...
@@ -192,19 +196,28 @@ class RPCClient {
void
AsyncSendFetchBarrier
(
const
std
::
string
&
ep
,
int64_t
time_out
=
600
*
1000
);
bool
Wait
();
void
Wait
();
// InitEventLoop should only be called by Init()
void
InitEventLoop
();
private:
bool
Proceed
();
void
Proceed
();
std
::
shared_ptr
<
grpc
::
Channel
>
GetChannel
(
const
std
::
string
&
ep
);
// Init is called by GetInstance.
static
void
Init
();
private:
grpc
::
CompletionQueue
cq_
;
std
::
map
<
std
::
string
,
std
::
shared_ptr
<
grpc
::
Channel
>>
channels_
;
std
::
unordered_map
<
std
::
string
,
std
::
shared_ptr
<
grpc
::
Channel
>>
channels_
;
std
::
unique_ptr
<
std
::
thread
>
client_thread_
;
// mutex for Wait client sync
std
::
mutex
sync_mutex_
;
std
::
condition_variable
sync_cond_
;
std
::
atomic
<
int64_t
>
req_count_
{
0
};
std
::
mutex
mutex_
;
// mutex for GetChannel thread safety
std
::
mutex
chan_mutex_
;
static
std
::
unique_ptr
<
RPCClient
>
rpc_client_
;
static
std
::
once_flag
init_flag_
;
DISABLE_COPY_AND_ASSIGN
(
RPCClient
);
...
...
paddle/fluid/operators/detail/grpc_server.cc
浏览文件 @
ba773fc9
...
...
@@ -68,9 +68,7 @@ class RequestSend final : public RequestBase {
method_id
,
&
ctx_
,
request_
.
get
(),
&
responder_
,
cq_
,
cq_
,
reinterpret_cast
<
void
*>
(
static_cast
<
intptr_t
>
(
req_id
)));
}
virtual
~
RequestSend
()
{}
std
::
string
GetReqName
()
override
{
return
request_
->
Varname
();
}
void
Process
()
override
{
...
...
@@ -82,7 +80,6 @@ class RequestSend final : public RequestBase {
framework
::
Variable
*
outvar
=
nullptr
;
request_handler_
->
Handle
(
varname
,
scope
,
invar
,
&
outvar
);
status_
=
FINISH
;
responder_
.
Finish
(
reply_
,
::
grpc
::
Status
::
OK
,
reinterpret_cast
<
void
*>
(
static_cast
<
intptr_t
>
(
req_id_
)));
...
...
@@ -125,7 +122,6 @@ class RequestGet final : public RequestBase {
SerializeToByteBuffer
(
varname
,
outvar
,
*
request_handler_
->
dev_ctx
(),
&
reply_
);
}
status_
=
FINISH
;
responder_
.
Finish
(
reply_
,
::
grpc
::
Status
::
OK
,
reinterpret_cast
<
void
*>
(
static_cast
<
intptr_t
>
(
req_id_
)));
...
...
@@ -170,10 +166,9 @@ class RequestPrefetch final : public RequestBase {
SerializeToByteBuffer
(
varname
,
outvar
,
*
request_handler_
->
dev_ctx
(),
&
reply_
);
status_
=
FINISH
;
responder_
.
Finish
(
reply_
,
::
grpc
::
Status
::
OK
,
reinterpret_cast
<
void
*>
(
static_cast
<
intptr_t
>
(
req_id_
)));
status_
=
FINISH
;
}
protected:
...
...
paddle/fluid/operators/detail/grpc_server_test.cc
浏览文件 @
ba773fc9
...
...
@@ -113,10 +113,6 @@ void StartServer() {
std
::
thread
server_thread
(
std
::
bind
(
&
detail
::
AsyncGRPCServer
::
StartServer
,
g_rpc_service
.
get
()));
// FIXME(gongwb): don't use hard time.
sleep
(
10
);
LOG
(
INFO
)
<<
"got nccl id and stop server..."
;
g_rpc_service
->
ShutDown
();
server_thread
.
join
();
}
...
...
@@ -127,7 +123,7 @@ TEST(PREFETCH, CPU) {
std
::
thread
server_thread
(
StartServer
);
g_rpc_service
->
WaitServerReady
();
detail
::
RPCClient
client
;
detail
::
RPCClient
*
client
=
detail
::
RPCClient
::
GetInstance
()
;
int
port
=
g_rpc_service
->
GetSelectedPort
();
std
::
string
ep
=
paddle
::
string
::
Sprintf
(
"127.0.0.1:%d"
,
port
);
...
...
@@ -141,8 +137,8 @@ TEST(PREFETCH, CPU) {
std
::
string
in_var_name
(
"ids"
);
std
::
string
out_var_name
(
"out"
);
client
.
AsyncPrefetchVariable
(
ep
,
ctx
,
scope
,
in_var_name
,
out_var_name
);
client
.
Wait
();
client
->
AsyncPrefetchVariable
(
ep
,
ctx
,
scope
,
in_var_name
,
out_var_name
);
client
->
Wait
();
auto
var
=
scope
.
Var
(
out_var_name
);
auto
value
=
var
->
GetMutable
<
framework
::
SelectedRows
>
()
->
value
();
auto
ptr
=
value
.
mutable_data
<
float
>
(
place
);
...
...
@@ -152,6 +148,7 @@ TEST(PREFETCH, CPU) {
}
}
g_rpc_service
->
ShutDown
();
server_thread
.
join
();
LOG
(
INFO
)
<<
"begin reset"
;
g_rpc_service
.
reset
(
nullptr
);
...
...
paddle/fluid/operators/detection/box_coder_op.cc
浏览文件 @
ba773fc9
...
...
@@ -22,21 +22,21 @@ class BoxCoderOp : public framework::OperatorWithKernel {
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"PriorBox"
),
"Input(PriorBox) of BoxCoderOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"PriorBoxVar"
),
"Input(PriorBoxVar) of BoxCoderOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"TargetBox"
),
"Input(TargetBox) of BoxCoderOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"OutputBox"
),
"Output(OutputBox) of BoxCoderOp should not be null."
);
auto
prior_box_dims
=
ctx
->
GetInputDim
(
"PriorBox"
);
auto
prior_box_var_dims
=
ctx
->
GetInputDim
(
"PriorBoxVar"
);
auto
target_box_dims
=
ctx
->
GetInputDim
(
"TargetBox"
);
PADDLE_ENFORCE_EQ
(
prior_box_dims
.
size
(),
2
,
"The rank of Input of PriorBoxVar must be 2"
);
PADDLE_ENFORCE_EQ
(
prior_box_dims
[
1
],
4
,
"The shape of PriorBox is [N, 4]"
);
if
(
ctx
->
HasInput
(
"PriorBoxVar"
))
{
auto
prior_box_var_dims
=
ctx
->
GetInputDim
(
"PriorBoxVar"
);
PADDLE_ENFORCE_EQ
(
prior_box_dims
,
prior_box_var_dims
);
}
auto
code_type
=
GetBoxCodeType
(
ctx
->
Attrs
().
Get
<
std
::
string
>
(
"code_type"
));
if
(
code_type
==
BoxCodeType
::
kEncodeCenterSize
)
{
...
...
@@ -71,9 +71,11 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
"of the coordinate system. [xmax, ymax] is the right bottom "
"coordinate of the anchor box."
);
AddInput
(
"PriorBoxVar"
,
"(Tensor, default Tensor<float>) "
"(Tensor, default Tensor<float>
, optional
) "
"PriorBoxVar is a 2-D Tensor with shape [M, 4] holds M group "
"of variance."
);
"of variance. PriorBoxVar will set all elements to 1 by "
"default."
)
.
AsDispensable
();
AddInput
(
"TargetBox"
,
"(LoDTensor or Tensor) This input can be a 2-D LoDTensor with shape "
...
...
@@ -131,5 +133,6 @@ width and height.
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
box_coder
,
ops
::
BoxCoderOp
,
ops
::
BoxCoderOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
box_coder
,
ops
::
BoxCoderKernel
<
float
>
,
ops
::
BoxCoderKernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
box_coder
,
ops
::
BoxCoderKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
BoxCoderKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/detection/box_coder_op.cu
浏览文件 @
ba773fc9
...
...
@@ -48,15 +48,18 @@ __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
target_box_data
[
row_idx
*
len
+
1
]
+
(
normalized
==
false
);
output
[
idx
*
len
]
=
(
target_box_center_x
-
prior_box_center_x
)
/
prior_box_width
/
prior_box_var_data
[
col_idx
*
len
];
output
[
idx
*
len
+
1
]
=
(
target_box_center_y
-
prior_box_center_y
)
/
prior_box_height
/
prior_box_var_data
[
col_idx
*
len
+
1
];
output
[
idx
*
len
+
2
]
=
log
(
fabs
(
target_box_width
/
prior_box_width
))
/
prior_box_var_data
[
col_idx
*
len
+
2
];
output
[
idx
*
len
+
3
]
=
log
(
fabs
(
target_box_height
/
prior_box_height
))
/
prior_box_var_data
[
col_idx
*
len
+
3
];
output
[
idx
*
len
]
=
(
target_box_center_x
-
prior_box_center_x
)
/
prior_box_width
;
output
[
idx
*
len
+
1
]
=
(
target_box_center_y
-
prior_box_center_y
)
/
prior_box_height
;
output
[
idx
*
len
+
2
]
=
log
(
fabs
(
target_box_width
/
prior_box_width
));
output
[
idx
*
len
+
3
]
=
log
(
fabs
(
target_box_height
/
prior_box_height
));
if
(
prior_box_var_data
)
{
output
[
idx
*
len
]
/=
prior_box_var_data
[
col_idx
*
len
];
output
[
idx
*
len
+
1
]
/=
prior_box_var_data
[
col_idx
*
len
+
1
];
output
[
idx
*
len
+
2
]
/=
prior_box_var_data
[
col_idx
*
len
+
2
];
output
[
idx
*
len
+
3
]
/=
prior_box_var_data
[
col_idx
*
len
+
3
];
}
}
}
...
...
@@ -79,20 +82,31 @@ __global__ void DecodeCenterSizeKernel(const T* prior_box_data,
T
prior_box_center_y
=
(
prior_box_data
[
col_idx
*
len
+
3
]
+
prior_box_data
[
col_idx
*
len
+
1
])
/
2
;
T
target_box_width
=
exp
(
prior_box_var_data
[
col_idx
*
len
+
2
]
*
T
target_box_width
,
target_box_height
;
T
target_box_center_x
,
target_box_center_y
;
if
(
prior_box_var_data
)
{
target_box_width
=
exp
(
prior_box_var_data
[
col_idx
*
len
+
2
]
*
target_box_data
[
idx
*
len
+
2
])
*
prior_box_width
;
T
target_box_height
=
exp
(
prior_box_var_data
[
col_idx
*
len
+
3
]
*
target_box_height
=
exp
(
prior_box_var_data
[
col_idx
*
len
+
3
]
*
target_box_data
[
idx
*
len
+
3
])
*
prior_box_height
;
T
target_box_center_x
=
prior_box_var_data
[
col_idx
*
len
]
*
target_box_center_x
=
prior_box_var_data
[
col_idx
*
len
]
*
target_box_data
[
idx
*
len
]
*
prior_box_width
+
prior_box_center_x
;
T
target_box_center_y
=
prior_box_var_data
[
col_idx
*
len
+
1
]
*
target_box_center_y
=
prior_box_var_data
[
col_idx
*
len
+
1
]
*
target_box_data
[
idx
*
len
+
1
]
*
prior_box_height
+
prior_box_center_y
;
}
else
{
target_box_width
=
exp
(
target_box_data
[
idx
*
len
+
2
])
*
prior_box_width
;
target_box_height
=
exp
(
target_box_data
[
idx
*
len
+
3
])
*
prior_box_height
;
target_box_center_x
=
target_box_data
[
idx
*
len
]
*
prior_box_width
+
prior_box_center_x
;
target_box_center_y
=
target_box_data
[
idx
*
len
+
1
]
*
prior_box_height
+
prior_box_center_y
;
}
output
[
idx
*
len
]
=
target_box_center_x
-
target_box_width
/
2
;
output
[
idx
*
len
+
1
]
=
target_box_center_y
-
target_box_height
/
2
;
...
...
@@ -103,7 +117,7 @@ __global__ void DecodeCenterSizeKernel(const T* prior_box_data,
}
}
template
<
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
BoxCoderCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
...
...
@@ -114,6 +128,11 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
auto
*
target_box
=
context
.
Input
<
framework
::
LoDTensor
>
(
"TargetBox"
);
auto
*
output_box
=
context
.
Output
<
framework
::
Tensor
>
(
"OutputBox"
);
const
T
*
prior_box_data
=
prior_box
->
data
<
T
>
();
const
T
*
target_box_data
=
target_box
->
data
<
T
>
();
const
T
*
prior_box_var_data
=
nullptr
;
if
(
prior_box_var
)
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
if
(
target_box
->
lod
().
size
())
{
PADDLE_ENFORCE_EQ
(
target_box
->
lod
().
size
(),
1
,
"Only support 1 level of LoD."
);
...
...
@@ -125,10 +144,6 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
int
grid
=
(
row
*
col
+
block
-
1
)
/
block
;
auto
&
device_ctx
=
context
.
cuda_device_context
();
const
T
*
prior_box_data
=
prior_box
->
data
<
T
>
();
const
T
*
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
const
T
*
target_box_data
=
target_box
->
data
<
T
>
();
output_box
->
mutable_data
<
T
>
({
row
,
col
,
len
},
context
.
GetPlace
());
T
*
output
=
output_box
->
data
<
T
>
();
...
...
@@ -150,5 +165,7 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
box_coder
,
ops
::
BoxCoderCUDAKernel
<
float
>
,
ops
::
BoxCoderCUDAKernel
<
double
>
);
REGISTER_OP_CUDA_KERNEL
(
box_coder
,
ops
::
BoxCoderCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
BoxCoderCUDAKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
paddle/fluid/operators/detection/box_coder_op.h
浏览文件 @
ba773fc9
...
...
@@ -28,19 +28,20 @@ inline BoxCodeType GetBoxCodeType(const std::string& type) {
PADDLE_THROW
(
"Not support type %s."
,
type
);
}
template
<
typename
T
>
template
<
typename
DeviceContext
,
typename
T
>
class
BoxCoderKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
EncodeCenterSize
(
const
framework
::
Tensor
&
target_box
,
const
framework
::
Tensor
&
prior_box
,
const
framework
::
Tensor
&
prior_box_var
,
void
EncodeCenterSize
(
const
framework
::
Tensor
*
target_box
,
const
framework
::
Tensor
*
prior_box
,
const
framework
::
Tensor
*
prior_box_var
,
const
bool
normalized
,
T
*
output
)
const
{
int64_t
row
=
target_box
.
dims
()[
0
];
int64_t
col
=
prior_box
.
dims
()[
0
];
int64_t
len
=
prior_box
.
dims
()[
1
];
auto
*
target_box_data
=
target_box
.
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
.
data
<
T
>
();
auto
*
prior_box_var_data
=
prior_box_var
.
data
<
T
>
();
int64_t
row
=
target_box
->
dims
()[
0
];
int64_t
col
=
prior_box
->
dims
()[
0
];
int64_t
len
=
prior_box
->
dims
()[
1
];
auto
*
target_box_data
=
target_box
->
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
->
data
<
T
>
();
const
T
*
prior_box_var_data
=
nullptr
;
if
(
prior_box_var
)
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
...
...
@@ -65,30 +66,35 @@ class BoxCoderKernel : public framework::OpKernel<T> {
(
normalized
==
false
);
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
output
[
offset
]
=
(
target_box_center_x
-
prior_box_center_x
)
/
prior_box_width
/
prior_box_var_data
[
j
*
len
]
;
output
[
offset
+
1
]
=
(
target_box_center_y
-
prior_box_center_y
)
/
prior_box_height
/
prior_box_var_data
[
j
*
len
+
1
]
;
output
[
offset
]
=
(
target_box_center_x
-
prior_box_center_x
)
/
prior_box_width
;
output
[
offset
+
1
]
=
(
target_box_center_y
-
prior_box_center_y
)
/
prior_box_height
;
output
[
offset
+
2
]
=
std
::
log
(
std
::
fabs
(
target_box_width
/
prior_box_width
))
/
prior_box_var_data
[
j
*
len
+
2
];
std
::
log
(
std
::
fabs
(
target_box_width
/
prior_box_width
));
output
[
offset
+
3
]
=
std
::
log
(
std
::
fabs
(
target_box_height
/
prior_box_height
))
/
prior_box_var_data
[
j
*
len
+
3
];
std
::
log
(
std
::
fabs
(
target_box_height
/
prior_box_height
));
if
(
prior_box_var
)
{
output
[
offset
]
/=
prior_box_var_data
[
j
*
len
];
output
[
offset
+
1
]
/=
prior_box_var_data
[
j
*
len
+
1
];
output
[
offset
+
2
]
/=
prior_box_var_data
[
j
*
len
+
2
];
output
[
offset
+
3
]
/=
prior_box_var_data
[
j
*
len
+
3
];
}
}
}
void
DecodeCenterSize
(
const
framework
::
Tensor
&
target_box
,
const
framework
::
Tensor
&
prior_box
,
const
framework
::
Tensor
&
prior_box_var
,
}
void
DecodeCenterSize
(
const
framework
::
Tensor
*
target_box
,
const
framework
::
Tensor
*
prior_box
,
const
framework
::
Tensor
*
prior_box_var
,
const
bool
normalized
,
T
*
output
)
const
{
int64_t
row
=
target_box
.
dims
()[
0
];
int64_t
col
=
prior_box
.
dims
()[
0
];
int64_t
len
=
prior_box
.
dims
()[
1
];
int64_t
row
=
target_box
->
dims
()[
0
];
int64_t
col
=
prior_box
->
dims
()[
0
];
int64_t
len
=
prior_box
->
dims
()[
1
];
auto
*
target_box_data
=
target_box
.
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
.
data
<
T
>
();
auto
*
prior_box_var_data
=
prior_box_var
.
data
<
T
>
();
auto
*
target_box_data
=
target_box
->
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
->
data
<
T
>
();
const
T
*
prior_box_var_data
=
nullptr
;
if
(
prior_box_var
)
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
...
...
@@ -103,19 +109,32 @@ class BoxCoderKernel : public framework::OpKernel<T> {
T
prior_box_center_y
=
(
prior_box_data
[
j
*
len
+
3
]
+
prior_box_data
[
j
*
len
+
1
])
/
2
;
T
target_box_center_x
=
prior_box_var_data
[
j
*
len
]
*
T
target_box_center_x
=
0
,
target_box_center_y
=
0
;
T
target_box_width
=
0
,
target_box_height
=
0
;
if
(
prior_box_var
)
{
target_box_center_x
=
prior_box_var_data
[
j
*
len
]
*
target_box_data
[
offset
]
*
prior_box_width
+
prior_box_center_x
;
T
target_box_center_y
=
prior_box_var_data
[
j
*
len
+
1
]
*
target_box_center_y
=
prior_box_var_data
[
j
*
len
+
1
]
*
target_box_data
[
offset
+
1
]
*
prior_box_height
+
prior_box_center_y
;
T
target_box_width
=
std
::
exp
(
prior_box_var_data
[
j
*
len
+
2
]
*
target_box_width
=
std
::
exp
(
prior_box_var_data
[
j
*
len
+
2
]
*
target_box_data
[
offset
+
2
])
*
prior_box_width
;
T
target_box_height
=
std
::
exp
(
prior_box_var_data
[
j
*
len
+
3
]
*
target_box_height
=
std
::
exp
(
prior_box_var_data
[
j
*
len
+
3
]
*
target_box_data
[
offset
+
3
])
*
prior_box_height
;
}
else
{
target_box_center_x
=
target_box_data
[
offset
]
*
prior_box_width
+
prior_box_center_x
;
target_box_center_y
=
target_box_data
[
offset
+
1
]
*
prior_box_height
+
prior_box_center_y
;
target_box_width
=
std
::
exp
(
target_box_data
[
offset
+
2
])
*
prior_box_width
;
target_box_height
=
std
::
exp
(
target_box_data
[
offset
+
3
])
*
prior_box_height
;
}
output
[
offset
]
=
target_box_center_x
-
target_box_width
/
2
;
output
[
offset
+
1
]
=
target_box_center_y
-
target_box_height
/
2
;
...
...
@@ -147,10 +166,10 @@ class BoxCoderKernel : public framework::OpKernel<T> {
bool
normalized
=
context
.
Attr
<
bool
>
(
"box_normalized"
);
T
*
output
=
output_box
->
data
<
T
>
();
if
(
code_type
==
BoxCodeType
::
kEncodeCenterSize
)
{
EncodeCenterSize
(
*
target_box
,
*
prior_box
,
*
prior_box_var
,
normalized
,
EncodeCenterSize
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
output
);
}
else
if
(
code_type
==
BoxCodeType
::
kDecodeCenterSize
)
{
DecodeCenterSize
(
*
target_box
,
*
prior_box
,
*
prior_box_var
,
normalized
,
DecodeCenterSize
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
output
);
}
}
...
...
paddle/fluid/operators/fetch_barrier_op.cc
浏览文件 @
ba773fc9
...
...
@@ -45,13 +45,13 @@ class FetchBarrierOp : public framework::OperatorBase {
auto
rpc_client
=
detail
::
RPCClient
::
GetInstance
();
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
for
(
auto
&
ep
:
eps
)
{
VLOG
(
3
)
<<
"fetch barrier, ep: "
<<
ep
;
rpc_client
->
AsyncSendFetchBarrier
(
ep
);
}
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
}
};
...
...
paddle/fluid/operators/pool_cudnn_op.cu.cc
浏览文件 @
ba773fc9
...
...
@@ -135,7 +135,11 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
PoolingMode
pooling_mode
;
if
(
pooling_type
==
"max"
)
{
if
(
FLAGS_cudnn_deterministic
)
{
pooling_mode
=
PoolingMode
::
kMaximumDeterministic
;
}
else
{
pooling_mode
=
PoolingMode
::
kMaximum
;
}
}
else
{
pooling_mode
=
PoolingMode
::
kAverage
;
}
...
...
paddle/fluid/operators/prefetch_op.cc
浏览文件 @
ba773fc9
...
...
@@ -53,7 +53,7 @@ class PrefetchOp : public framework::OperatorBase {
VLOG
(
3
)
<<
"don't send no-initialied variable: "
<<
ins
[
i
];
}
}
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
}
};
...
...
paddle/fluid/operators/recv_op.cc
浏览文件 @
ba773fc9
...
...
@@ -51,7 +51,7 @@ class RecvOp : public framework::OperatorBase {
rpc_client
->
AsyncGetVariable
(
epmap
[
i
],
ctx
,
scope
,
outs
[
i
]);
}
if
(
sync_mode
)
{
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
}
}
};
...
...
paddle/fluid/operators/reduce_op.h
浏览文件 @
ba773fc9
...
...
@@ -135,15 +135,16 @@ class ReduceKernel : public framework::OpKernel<T> {
}
else
{
int
ndim
=
context
.
Input
<
Tensor
>
(
"X"
)
->
dims
().
size
();
int
rdim
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dim"
).
size
();
HANDLE_DIM
(
6
,
5
);
HANDLE_DIM
(
6
,
4
);
HANDLE_DIM
(
6
,
3
);
HANDLE_DIM
(
6
,
2
);
HANDLE_DIM
(
6
,
1
);
HANDLE_DIM
(
5
,
4
);
HANDLE_DIM
(
5
,
3
);
HANDLE_DIM
(
5
,
2
);
HANDLE_DIM
(
5
,
1
);
// comments for accelerating compiling temporarily.
// HANDLE_DIM(6, 5);
// HANDLE_DIM(6, 4);
// HANDLE_DIM(6, 3);
// HANDLE_DIM(6, 2);
// HANDLE_DIM(6, 1);
// HANDLE_DIM(5, 4);
// HANDLE_DIM(5, 3);
// HANDLE_DIM(5, 2);
// HANDLE_DIM(5, 1);
HANDLE_DIM
(
4
,
3
);
HANDLE_DIM
(
4
,
2
);
HANDLE_DIM
(
4
,
1
);
...
...
paddle/fluid/operators/send_barrier_op.cc
浏览文件 @
ba773fc9
...
...
@@ -49,13 +49,13 @@ class SendBarrierOp : public framework::OperatorBase {
VLOG
(
3
)
<<
"SendBarrierOp sync_mode:"
<<
sync_mode
;
// need to wait before sending send_barrier message
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
if
(
sync_mode
)
{
for
(
auto
&
ep
:
eps
)
{
VLOG
(
3
)
<<
"send barrier, ep: "
<<
ep
;
rpc_client
->
AsyncSendBatchBarrier
(
ep
);
}
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
}
}
};
...
...
paddle/fluid/operators/send_op.cc
浏览文件 @
ba773fc9
...
...
@@ -59,14 +59,14 @@ class SendOp : public framework::OperatorBase {
VLOG
(
3
)
<<
"don't send no-initialied variable: "
<<
ins
[
i
];
}
}
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
if
(
sync_mode
)
{
for
(
auto
&
ep
:
endpoints
)
{
VLOG
(
3
)
<<
"batch barrier, ep: "
<<
ep
;
rpc_client
->
AsyncSendBatchBarrier
(
ep
);
}
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
}
if
(
outs
.
size
()
>
0
)
{
...
...
@@ -74,13 +74,13 @@ class SendOp : public framework::OperatorBase {
VLOG
(
2
)
<<
"getting "
<<
outs
[
i
]
<<
" from "
<<
epmap
[
i
];
rpc_client
->
AsyncGetVariable
(
epmap
[
i
],
ctx
,
scope
,
outs
[
i
]);
}
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
// tell pservers that current trainer have called fetch
for
(
auto
&
ep
:
endpoints
)
{
VLOG
(
2
)
<<
"send fetch barrier, ep: "
<<
ep
;
rpc_client
->
AsyncSendFetchBarrier
(
ep
);
}
PADDLE_ENFORCE
(
rpc_client
->
Wait
()
);
rpc_client
->
Wait
(
);
}
}
};
...
...
paddle/fluid/operators/test_send_nccl_id.cc
浏览文件 @
ba773fc9
...
...
@@ -61,7 +61,6 @@ void StartServer() {
std
::
bind
(
&
detail
::
AsyncGRPCServer
::
StartServer
,
g_rpc_service
.
get
()));
g_rpc_service
->
SetCond
(
detail
::
kRequestSend
);
std
::
cout
<<
"before WaitFanInOfSend"
<<
std
::
endl
;
g_rpc_service
->
WaitBarrier
(
detail
::
kRequestSend
);
LOG
(
INFO
)
<<
"got nccl id and stop server..."
;
...
...
@@ -88,12 +87,12 @@ TEST(SendNcclId, GrpcServer) {
int
port
=
g_rpc_service
->
GetSelectedPort
();
std
::
string
ep
=
string
::
Sprintf
(
"127.0.0.1:%d"
,
port
);
detail
::
RPCClient
client
;
LOG
(
INFO
)
<<
"connect to server"
<<
ep
;
client
.
AsyncSendVariable
(
ep
,
dev_ctx
,
scope
,
NCCL_ID_VARNAME
);
client
.
Wait
();
client
.
AsyncSendBatchBarrier
(
ep
);
client
.
Wait
();
detail
::
RPCClient
*
client
=
detail
::
RPCClient
::
GetInstance
()
;
LOG
(
INFO
)
<<
"connect to server
"
<<
ep
;
client
->
AsyncSendVariable
(
ep
,
dev_ctx
,
scope
,
NCCL_ID_VARNAME
);
client
->
Wait
();
client
->
AsyncSendBatchBarrier
(
ep
);
client
->
Wait
();
server_thread
.
join
();
g_rpc_service
.
reset
(
nullptr
);
...
...
paddle/fluid/platform/cudnn_helper.h
浏览文件 @
ba773fc9
...
...
@@ -22,6 +22,8 @@ limitations under the License. */
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/macros.h"
DECLARE_bool
(
cudnn_deterministic
);
namespace
paddle
{
namespace
platform
{
...
...
@@ -76,8 +78,22 @@ enum class DataLayout { // Not use
enum
class
PoolingMode
{
kMaximum
,
kAverage
,
kMaximumDeterministic
,
};
inline
cudnnPoolingMode_t
GetPoolingMode
(
const
PoolingMode
&
mode
)
{
switch
(
mode
)
{
case
PoolingMode
::
kMaximumDeterministic
:
return
CUDNN_POOLING_MAX_DETERMINISTIC
;
case
PoolingMode
::
kAverage
:
return
CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING
;
case
PoolingMode
::
kMaximum
:
return
CUDNN_POOLING_MAX
;
default:
PADDLE_THROW
(
"Unexpected pooling mode."
);
}
}
template
<
typename
T
>
class
CudnnDataType
;
...
...
@@ -293,9 +309,7 @@ class ScopedPoolingDescriptor {
PADDLE_ENFORCE_EQ
(
kernel
.
size
(),
pads
.
size
());
PADDLE_ENFORCE_EQ
(
kernel
.
size
(),
strides
.
size
());
PADDLE_ENFORCE
(
dynload
::
cudnnSetPoolingNdDescriptor
(
desc_
,
(
mode
==
PoolingMode
::
kMaximum
?
CUDNN_POOLING_MAX
:
CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING
),
desc_
,
(
GetPoolingMode
(
mode
)),
CUDNN_PROPAGATE_NAN
,
// Always propagate nans.
kernel
.
size
(),
kernel
.
data
(),
pads
.
data
(),
strides
.
data
()));
return
desc_
;
...
...
paddle/fluid/platform/device_context.cc
浏览文件 @
ba773fc9
...
...
@@ -175,7 +175,6 @@ CUDADeviceContext::~CUDADeviceContext() {
Place
CUDADeviceContext
::
GetPlace
()
const
{
return
place_
;
}
void
CUDADeviceContext
::
Wait
()
const
{
std
::
lock_guard
<
std
::
recursive_mutex
>
guard
(
mutex_
);
PADDLE_ENFORCE
(
cudaStreamSynchronize
(
stream_
));
PADDLE_ENFORCE
(
cudaGetLastError
());
}
...
...
paddle/fluid/platform/device_context.h
浏览文件 @
ba773fc9
...
...
@@ -100,7 +100,6 @@ class CUDADeviceContext : public DeviceContext {
template
<
typename
Callback
>
void
RecordEvent
(
cudaEvent_t
ev
,
Callback
callback
)
{
std
::
lock_guard
<
std
::
recursive_mutex
>
guard
(
mutex_
);
callback
();
PADDLE_ENFORCE
(
cudaEventRecord
(
ev
,
stream_
));
}
...
...
@@ -110,8 +109,6 @@ class CUDADeviceContext : public DeviceContext {
std
::
unique_ptr
<
Eigen
::
GpuDevice
>
eigen_device_
;
std
::
unique_ptr
<
EigenCudaStreamDevice
>
eigen_stream_
;
mutable
std
::
recursive_mutex
mutex_
;
cudaStream_t
stream_
;
cudnnHandle_t
cudnn_handle_
;
cublasHandle_t
cublas_handle_
;
...
...
paddle/fluid/platform/dynload/cublas.h
浏览文件 @
ba773fc9
...
...
@@ -45,7 +45,7 @@ extern void *cublas_dso_handle;
std::call_once(cublas_dso_flag, []() { \
cublas_dso_handle = paddle::platform::dynload::GetCublasDsoHandle(); \
}); \
void *p_##__name = dlsym(cublas_dso_handle, #__name);
\
static void *p_##__name = dlsym(cublas_dso_handle, #__name);
\
return reinterpret_cast<FUNC_TYPE>(p_##__name)(args...); \
} \
}; \
...
...
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @
ba773fc9
...
...
@@ -39,7 +39,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
cudnn_dso_handle = paddle::platform::dynload::GetCUDNNDsoHandle(); \
}); \
EnforceCUDNNLoaded(#__name); \
void* p_##__name = dlsym(cudnn_dso_handle, #__name);
\
static void* p_##__name = dlsym(cudnn_dso_handle, #__name);
\
return reinterpret_cast<cudnn_func>(p_##__name)(args...); \
} \
}; \
...
...
paddle/fluid/platform/dynload/cupti.h
浏览文件 @
ba773fc9
...
...
@@ -45,7 +45,7 @@ extern void *cupti_dso_handle;
std::call_once(cupti_dso_flag, []() { \
cupti_dso_handle = paddle::platform::dynload::GetCUPTIDsoHandle(); \
}); \
void *p_##__name = dlsym(cupti_dso_handle, #__name);
\
static void *p_##__name = dlsym(cupti_dso_handle, #__name);
\
return reinterpret_cast<cuptiFunc>(p_##__name)(args...); \
} \
}; \
...
...
paddle/fluid/platform/dynload/curand.h
浏览文件 @
ba773fc9
...
...
@@ -34,7 +34,7 @@ extern void *curand_dso_handle;
std::call_once(curand_dso_flag, []() { \
curand_dso_handle = paddle::platform::dynload::GetCurandDsoHandle(); \
}); \
void *p_##__name = dlsym(curand_dso_handle, #__name);
\
static void *p_##__name = dlsym(curand_dso_handle, #__name);
\
return reinterpret_cast<curandFunc>(p_##__name)(args...); \
} \
}; \
...
...
paddle/fluid/platform/dynload/nccl.h
浏览文件 @
ba773fc9
...
...
@@ -37,7 +37,7 @@ extern void* nccl_dso_handle;
std::call_once(nccl_dso_flag, []() { \
nccl_dso_handle = paddle::platform::dynload::GetNCCLDsoHandle(); \
}); \
void* p_##__name = dlsym(nccl_dso_handle, #__name);
\
static void* p_##__name = dlsym(nccl_dso_handle, #__name);
\
return reinterpret_cast<nccl_func>(p_##__name)(args...); \
} \
}; \
...
...
paddle/fluid/platform/dynload/tensorrt.h
浏览文件 @
ba773fc9
...
...
@@ -40,7 +40,7 @@ extern void* tensorrt_dso_handle;
paddle::platform::dynload::GetTensorRtDsoHandle(); \
PADDLE_ENFORCE(tensorrt_dso_handle, "load tensorrt so failed"); \
}); \
void* p_##__name = dlsym(tensorrt_dso_handle, #__name);
\
static void* p_##__name = dlsym(tensorrt_dso_handle, #__name);
\
PADDLE_ENFORCE(p_##__name, "load %s failed", #__name); \
return reinterpret_cast<tensorrt_func>(p_##__name)(args...); \
} \
...
...
paddle/fluid/platform/dynload/warpctc.h
浏览文件 @
ba773fc9
...
...
@@ -40,7 +40,7 @@ extern void* warpctc_dso_handle;
std::call_once(warpctc_dso_flag, []() { \
warpctc_dso_handle = paddle::platform::dynload::GetWarpCTCDsoHandle(); \
}); \
void* p_##_name = dlsym(warpctc_dso_handle, #__name);
\
static void* p_##_name = dlsym(warpctc_dso_handle, #__name);
\
return reinterpret_cast<warpctcFunc>(p_##_name)(args...); \
} \
}; \
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
ba773fc9
...
...
@@ -519,6 +519,14 @@ All parameter, weight, gradient are variables in Paddle.
[](
const
ExecutionStrategy
&
self
)
{
return
self
.
allow_op_delay_
;
},
[](
ExecutionStrategy
&
self
,
bool
allow_op_delay
)
{
self
.
allow_op_delay_
=
allow_op_delay
;
})
.
def_property
(
"num_iteration_per_drop_scope"
,
[](
const
ExecutionStrategy
&
self
)
{
return
self
.
num_iteration_per_drop_scope_
;
},
[](
ExecutionStrategy
&
self
,
size_t
num_iteration_per_drop_scope
)
{
self
.
num_iteration_per_drop_scope_
=
num_iteration_per_drop_scope
;
});
py
::
class_
<
BuildStrategy
>
build_strategy
(
pe
,
"BuildStrategy"
);
...
...
python/paddle/fluid/__init__.py
浏览文件 @
ba773fc9
...
...
@@ -120,7 +120,7 @@ def __bootstrap__():
]
if
core
.
is_compiled_with_cuda
():
read_env_flags
+=
[
'fraction_of_gpu_memory_to_use'
,
'cudnn_
algo_use_autotune
'
'fraction_of_gpu_memory_to_use'
,
'cudnn_
deterministic
'
]
core
.
init_gflags
([
sys
.
argv
[
0
]]
+
[
"--tryfromenv="
+
","
.
join
(
read_env_flags
)])
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
ba773fc9
...
...
@@ -81,6 +81,8 @@ __all__ = [
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'random_crop'
,
...
...
@@ -3929,22 +3931,25 @@ def dice_loss(input, label, epsilon=0.00001):
return
reduce_mean
(
dice_score
)
def
resize_bilinear
(
input
,
out_shape
=
None
,
scale
=
None
,
name
=
None
):
def
image_resize
(
input
,
out_shape
=
None
,
scale
=
None
,
name
=
None
,
resample
=
'BILINEAR'
):
"""
The mathematical meaning of resize bilinear layer is
Bilinear interpolation.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this layer) on a rectilinear 2D grid.
Resize a batch of images.
For details, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
The input must be a tensor of the shape (num_batches, channels, in_h, in_w),
and the resizing only applies on the last two dimensions(hight and width).
Supporting resample methods:
'BILINEAR' : Bilinear interpolation
Args:
input (Variable): The input tensor of
resize bilinear
layer,
input (Variable): The input tensor of
image resize
layer,
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_shape(list|tuple|Variable|None): Output shape of
resize bilinear
out_shape(list|tuple|Variable|None): Output shape of
image resize
layer, the shape is (out_h, out_w).
Default: None
scale(float|None): The multiplier for the input height or width.
...
...
@@ -3953,6 +3958,8 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
resample(str): The resample method. It can only be 'BILINEAR' currently.
Default: 'BILINEAR'
Returns:
out (Variable): The output is a 4-D tensor of the shape
...
...
@@ -3961,8 +3968,12 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
Examples:
.. code-block:: python
out = fluid.layers.
resize_bilinear
(input, out_shape=[12, 12])
out = fluid.layers.
image_resize
(input, out_shape=[12, 12])
"""
resample_methods
=
{
'BILINEAR'
:
'bilinear_interp'
}
if
resample
not
in
resample_methods
:
raise
ValueError
(
"The 'resample' of image_resize can only be 'BILINEAR' currently."
)
if
out_shape
is
None
and
scale
is
None
:
raise
ValueError
(
"One of out_shape and scale must not be None"
)
helper
=
LayerHelper
(
'bilinear_interp'
,
**
locals
())
...
...
@@ -3990,7 +4001,7 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
out
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"bilinear_interp"
,
type
=
resample_methods
[
resample
]
,
inputs
=
inputs
,
outputs
=
{
"Out"
:
out
},
attrs
=
{
"out_h"
:
out_h
,
...
...
@@ -3998,6 +4009,55 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
return
out
def
resize_bilinear
(
input
,
out_shape
=
None
,
scale
=
None
,
name
=
None
):
"""
This is an alias of layer 'image_resize' with bilinear interpolation.
The mathematical meaning of resize bilinear layer is
Bilinear interpolation.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this layer) on a rectilinear 2D grid.
For details, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
"""
return
image_resize
(
input
,
out_shape
,
scale
,
name
,
'BILINEAR'
)
def
image_resize_short
(
input
,
out_short_len
,
resample
=
'BILINEAR'
):
"""
Resize a batch of images. The short edge of input images will be
resized to the given 'out_short_len'. The long edge of input images
will be resized proportionately to make images' length-width ratio
constant.
Args:
input (Variable): The input tensor of image resize layer,
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_short_len(int): The length of output images' short edge.
Returns:
out (Variable): The output is a 4-D tensor of the shape
(num_batches, channls, out_h, out_w).
"""
in_shape
=
input
.
shape
if
len
(
in_shape
)
!=
4
:
raise
ValueError
(
"The rank of input must be 4 (num_batches, channels, in_h, in_w)."
)
hw
=
in_shape
[
2
:
4
]
short_idx
=
hw
.
index
(
min
(
hw
))
long_idx
=
1
-
short_idx
out_shape
=
list
(
hw
)
out_shape
[
short_idx
]
=
out_short_len
out_shape
[
long_idx
]
=
int
(
float
(
out_shape
[
long_idx
])
*
(
float
(
out_short_len
)
/
float
(
hw
[
short_idx
]))
+
0.5
)
return
image_resize
(
input
=
input
,
out_shape
=
out_shape
,
resample
=
resample
)
def
gather
(
input
,
index
):
"""
Output is obtained by gathering entries of the outer-most dimension
...
...
python/paddle/fluid/tests/test_concurrency.py
→
python/paddle/fluid/tests/
no_
test_concurrency.py
浏览文件 @
ba773fc9
文件已移动
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
ba773fc9
...
...
@@ -43,12 +43,10 @@ list(REMOVE_ITEM TEST_OPS test_warpctc_op)
list
(
REMOVE_ITEM TEST_OPS test_dist_train
)
list
(
REMOVE_ITEM TEST_OPS test_parallel_executor_crf
)
list
(
REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed
)
# TODO(wuyi): this test hungs on CI, will add it back later
list
(
REMOVE_ITEM TEST_OPS test_listen_and_serv_op
)
foreach
(
TEST_OP
${
TEST_OPS
}
)
py_test_modules
(
${
TEST_OP
}
MODULES
${
TEST_OP
}
)
endforeach
(
TEST_OP
)
py_test_modules
(
test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=
${
WARPCTC_LIB_DIR
}
SERIAL
)
py_test_modules
(
test_dist_train MODULES test_dist_train SERIAL
)
# FIXME(Yancey1989): this test would cost much more time on CUDAPlace
# since load cudnn libraries, so we use a longer timeout to make this
# unit test stability.
set_tests_properties
(
test_listen_and_serv_op PROPERTIES TIMEOUT 30
)
python/paddle/fluid/tests/unittests/test_box_coder_op.py
浏览文件 @
ba773fc9
...
...
@@ -120,6 +120,32 @@ class TestBoxCoderOp(OpTest):
self
.
outputs
=
{
'OutputBox'
:
output_box
}
class
TestBoxCoderOpWithoutBoxVar
(
OpTest
):
def
test_check_output
(
self
):
self
.
check_output
()
def
setUp
(
self
):
self
.
op_type
=
"box_coder"
lod
=
[[
0
,
1
,
2
,
3
,
4
,
5
]]
prior_box
=
np
.
random
.
random
((
10
,
4
)).
astype
(
'float32'
)
prior_box_var
=
np
.
ones
((
10
,
4
)).
astype
(
'float32'
)
target_box
=
np
.
random
.
random
((
5
,
10
,
4
)).
astype
(
'float32'
)
code_type
=
"DecodeCenterSize"
box_normalized
=
False
output_box
=
batch_box_coder
(
prior_box
,
prior_box_var
,
target_box
,
lod
[
0
],
code_type
,
box_normalized
)
self
.
inputs
=
{
'PriorBox'
:
prior_box
,
'TargetBox'
:
target_box
,
}
self
.
attrs
=
{
'code_type'
:
'decode_center_size'
,
'box_normalized'
:
False
}
self
.
outputs
=
{
'OutputBox'
:
output_box
}
class
TestBoxCoderOpWithLoD
(
OpTest
):
def
test_check_output
(
self
):
self
.
check_output
()
...
...
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