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PaddleDetection
提交 8bd148dc 编写于 作者: W weixing02
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into hsigmoid_op

上级 2a1fc03e 4c3eb448
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Showing with 15233 addition and 5129 deletion
AUTHORS.md
浏览文件 @ 8bd148dc
......@@ -4,6 +4,7 @@
| backyes | Yan-Fei Wang |
| baiyfbupt | Yi-Fan Bai |
| beckett1124 | Bin Qi |
| ChengduoZH | Cheng-Duo Zhao|
| chengxiaohua1105 | Xiao-Hua Cheng |
| cxwangyi, yiwangbaidu, wangkuiyi | Yi Wang |
| cxysteven | Xing-Yi Cheng |
......@@ -21,6 +22,7 @@
| jczaja | Jacek Czaja |
| JiayiFeng | Jia-Yi Feng |
| kbinias | Krzysztof Binias |
| kexinzhao | Ke-Xin Zhao |
| kuke | Yi-Bing Liu |
| lcy-seso | Ying Cao |
| lipeng-unisound | Peng Li |
......
CMakeLists.txt
浏览文件 @ 8bd148dc
......@@ -55,12 +55,14 @@ option(WITH_FLUID_ONLY "Compile PaddlePaddle fluid only" OFF)
option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF)
option(GLIDE_INSTALL "Download and install go dependencies " ON)
option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
option(WITH_DISTRIBUTE "Compile with grpc distributed support" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(USE_EIGEN_FOR_BLAS "Use matrix multiplication in Eigen" OFF)
option(EIGEN_USE_THREADS "Compile with multi-threaded Eigen" OFF)
option(WITH_ARM_FP16 "Use half precision support on armv8.2-a cpu" OFF)
option(WITH_FAST_BUNDLE_TEST "Bundle tests that can be run in a single process together to reduce launch overhead" OFF)
option(WITH_CONTRIB "Compile the third-party contributation" OFF)
option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
# CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE)
......@@ -147,7 +149,16 @@ include(external/any) # download libn::any
include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
include(external/cares)
include(external/grpc)
if(WITH_DISTRIBUTE)
if(WITH_GRPC)
include(external/grpc)
else()
include(external/leveldb)
include(external/brpc)
endif()
endif()
include(external/snappy) # download snappy
include(external/snappystream)
include(external/threadpool)
......@@ -183,7 +194,10 @@ set(EXTERNAL_LIBS
if(WITH_GPU)
include(cuda)
include(tensorrt)
endif(WITH_GPU)
include(external/anakin)
else()
set(WITH_ANAKIN OFF CACHE STRING "Anakin is valid only when GPU is set." FORCE)
endif()
if(WITH_AMD_GPU)
find_package(HIP)
......
Dockerfile
浏览文件 @ 8bd148dc
......@@ -24,12 +24,12 @@ 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 \
python-matplotlib gcc-4.8 g++-4.8 \
automake locales clang-format swig doxygen cmake \
automake locales clang-format swig cmake \
liblapack-dev liblapacke-dev \
clang-3.8 llvm-3.8 libclang-3.8-dev \
net-tools libtool ccache && \
......@@ -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/.gitignore
浏览文件 @ 8bd148dc
......@@ -7,3 +7,6 @@ paddle/rnn/imdb.pkl
caffe/image/logs
tensorflow/image/logs
tensorflow/rnn/logs
fluid/models/*.pyc
fluid/logs
fluid/nohup.out
benchmark/fluid/Dockerfile 0 → 100644
浏览文件 @ 8bd148dc
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 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 paddlepaddle
# IMPORTANT:
# Add "ENV http_proxy=http://ip:port" if your download is slow, and don't forget to unset it at runtime.
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()\npaddle.dataset.flowers.fetch()" | python'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.mnist.train()\npaddle.dataset.mnist.test()\npaddle.dataset.imdb.fetch()" | python'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.imikolov.fetch()" | python'
RUN pip uninstall -y paddlepaddle && mkdir /workspace
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
ADD *.whl /
RUN pip install /*.whl && rm -f /*.whl && chmod +x /usr/bin/paddle_k8s
ENV LD_LIBRARY_PATH=/usr/local/lib
ADD fluid_benchmark.py recordio_converter.py models/ /workspace/
benchmark/fluid/README.md
浏览文件 @ 8bd148dc
......@@ -24,14 +24,18 @@ Currently supported `--model` argument include:
* Run the following command to start a benchmark job locally:
```bash
python fluid_benchmark.py --model mnist --device GPU
python fluid_benchmark.py --model mnist --device GPU
```
You can choose to use GPU/CPU training. With GPU training, you can specify
`--gpus <gpu_num>` to run multi GPU training.
You can set async mode parameter server. With async mode, you can specify
`--async_mode` to train model asynchronous.
* Run distributed training with parameter servers:
* see [run_fluid_benchmark.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/fluid/run_fluid_benchmark.sh) as an example.
* start parameter servers:
```bash
PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --device GPU --update_method pserver
sleep 15
```
* start trainers:
```bash
......@@ -42,13 +46,37 @@ Currently supported `--model` argument include:
PADDLE_PSERVER_PORT=7164 PADDLE_TRAINER_IPS=192.168.0.2,192.168.0.3 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --device GPU --update_method nccl2
```
## Prepare the RecordIO file to Achieve Better Performance
Run the following command will generate RecordIO files like "mnist.recordio" under the path
and batch_size you choose, you can use batch_size=1 so that later reader can change the batch_size
at any time using `fluid.batch`.
```bash
python -c 'from recordio_converter import *; prepare_mnist("data", 1)'
```
## Run Distributed Benchmark on Kubernetes Cluster
You may need to build a Docker image before submitting a cluster job onto Kubernetes, or you will
have to start all those processes mannually on each node, which is not recommended.
To build the Docker image, you need to choose a paddle "whl" package to run with, you may either
download it from
http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_en.html or
build it by your own. Once you've got the "whl" package, put it under the current directory and run:
```bash
docker build -t [your docker image name]:[your docker image tag] .
```
Then push the image to a Docker registry that your Kubernetes cluster can reach.
We provide a script `kube_gen_job.py` to generate Kubernetes yaml files to submit
distributed benchmark jobs to your cluster. To generate a job yaml, just run:
```bash
python kube_gen_job.py --jobname myjob --pscpu 4 --cpu 8 --gpu 8 --psmemory 20 --memory 40 --pservers 4 --trainers 4 --entry "python fluid_benchmark.py --model mnist --parallel 1 --device GPU --update_method pserver " --disttype pserver
python kube_gen_job.py --jobname myjob --pscpu 4 --cpu 8 --gpu 8 --psmemory 20 --memory 40 --pservers 4 --trainers 4 --entry "python fluid_benchmark.py --model mnist --gpus 8 --device GPU --update_method pserver " --disttype pserver
```
Then the yaml files are generated under directory `myjob`, you can run:
......
benchmark/fluid/args.py 0 → 100644
浏览文件 @ 8bd148dc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
__all__ = ['parse_args', ]
BENCHMARK_MODELS = [
"machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
]
def parse_args():
parser = argparse.ArgumentParser('Fluid model benchmarks.')
parser.add_argument(
'--model',
type=str,
choices=BENCHMARK_MODELS,
default='resnet',
help='The model to run benchmark with.')
parser.add_argument(
'--batch_size', type=int, default=32, help='The minibatch size.')
# args related to learning rate
parser.add_argument(
'--learning_rate', type=float, default=0.001, help='The learning rate.')
# TODO(wuyi): add "--use_fake_data" option back.
parser.add_argument(
'--skip_batch_num',
type=int,
default=5,
help='The first num of minibatch num to skip, for better performance test'
)
parser.add_argument(
'--iterations', type=int, default=80, help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=100, help='The number of passes.')
parser.add_argument(
'--data_format',
type=str,
default='NCHW',
choices=['NCHW', 'NHWC'],
help='The data data_format, now only support NCHW.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
# this option is available only for vgg and resnet.
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,
default='flowers',
choices=['cifar10', 'flowers'],
help='Optional dataset for benchmark.')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
parser.add_argument(
'--use_cprof', action='store_true', help='If set, use cProfile.')
parser.add_argument(
'--use_nvprof',
action='store_true',
help='If set, use nvprof for CUDA.')
parser.add_argument(
'--no_test',
action='store_true',
help='If set, do not test the testset during training.')
parser.add_argument(
'--memory_optimize',
action='store_true',
help='If set, optimize runtime memory before start.')
parser.add_argument(
'--use_fake_data',
action='store_true',
help='If set ommit the actual read data operators.')
parser.add_argument(
'--profile', action='store_true', help='If set, profile a few steps.')
parser.add_argument(
'--update_method',
type=str,
default='local',
choices=['local', 'pserver', 'nccl2'],
help='Choose parameter update method, can be local, pserver, nccl2.')
parser.add_argument(
'--no_split_var',
action='store_true',
default=False,
help='Whether split variables into blocks when update_method is pserver')
parser.add_argument(
'--async_mode',
action='store_true',
default=False,
help='Whether start pserver in async mode to support ASGD')
parser.add_argument(
'--use_reader_op',
action='store_true',
help='Whether to use reader op, and must specify the data path if set this to true.'
)
parser.add_argument(
'--data_path',
type=str,
default="",
help='Directory that contains all the training recordio files.')
args = parser.parse_args()
return args
benchmark/fluid/fluid_benchmark.py
浏览文件 @ 8bd148dc
......@@ -24,90 +24,7 @@ import paddle.fluid.core as core
import paddle.fluid.profiler as profiler
import paddle.fluid.transpiler.distribute_transpiler as distribute_transpiler
BENCHMARK_MODELS = [
"machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
]
def parse_args():
parser = argparse.ArgumentParser('Fluid model benchmarks.')
parser.add_argument(
'--model',
type=str,
choices=BENCHMARK_MODELS,
default='resnet',
help='The model to run benchmark with.')
parser.add_argument(
'--batch_size', type=int, default=32, help='The minibatch size.')
parser.add_argument(
'--learning_rate',
type=float,
default=0.001,
help='The minibatch size.')
# TODO(wuyi): add "--use_fake_data" option back.
parser.add_argument(
'--skip_batch_num',
type=int,
default=5,
help='The first num of minibatch num to skip, for better performance test'
)
parser.add_argument(
'--iterations', type=int, default=80, help='The number of minibatches.')
parser.add_argument(
'--pass_num', type=int, default=100, help='The number of passes.')
parser.add_argument(
'--data_format',
type=str,
default='NCHW',
choices=['NCHW', 'NHWC'],
help='The data data_format, now only support NCHW.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
parser.add_argument(
'--data_set',
type=str,
default='flowers',
choices=['cifar10', 'flowers'],
help='Optional dataset for benchmark.')
parser.add_argument(
'--infer_only', action='store_true', help='If set, run forward only.')
parser.add_argument(
'--use_cprof', action='store_true', help='If set, use cProfile.')
parser.add_argument(
'--use_nvprof',
action='store_true',
help='If set, use nvprof for CUDA.')
parser.add_argument(
'--no_test',
action='store_false',
help='If set, test the testset during training.')
parser.add_argument(
'--memory_optimize',
action='store_true',
help='If set, optimize runtime memory before start.')
parser.add_argument(
'--use_fake_data',
action='store_true',
help='If set ommit the actual read data operators.')
parser.add_argument(
'--profile', action='store_true', help='If set, profile a few steps.')
parser.add_argument(
'--update_method',
type=str,
default='local',
choices=['local', 'pserver', 'nccl2'],
help='Choose parameter update method, can be local, pserver, nccl2.')
args = parser.parse_args()
return args
from args import *
def append_nccl2_prepare(trainer_id):
......@@ -142,7 +59,7 @@ def append_nccl2_prepare(trainer_id):
"nccl-based dist train.")
def dist_transpile(trainer_id):
def dist_transpile(trainer_id, args):
if trainer_id < 0:
return None, None
......@@ -164,7 +81,12 @@ def dist_transpile(trainer_id):
training_role = os.getenv("PADDLE_TRAINING_ROLE")
t = distribute_transpiler.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
t.transpile(
trainer_id,
pservers=pserver_endpoints,
trainers=trainers,
sync_mode=not args.async_mode,
slice_var_up=not args.no_split_var)
if training_role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
......@@ -208,36 +130,57 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(startup_prog)
feed_var_list = [
var for var in train_prog.global_block().vars.itervalues()
if var.is_data
]
feeder = fluid.DataFeeder(feed_var_list, place)
if not args.use_reader_op:
feed_var_list = [
var for var in train_prog.global_block().vars.itervalues()
if var.is_data
]
feeder = fluid.DataFeeder(feed_var_list, place)
iters, num_samples, start_time = 0, 0, time.time()
for pass_id in range(args.pass_num):
train_losses = []
for batch_id, data in enumerate(train_reader()):
if not args.use_reader_op:
reader_generator = train_reader()
batch_id = 0
data = None
while True:
if not args.use_reader_op:
data = next(reader_generator, None)
if data == None:
break
if iters == args.iterations:
break
if iters == args.skip_batch_num:
start_time = time.time()
num_samples = 0
if iters == args.iterations:
break
loss = exe.run(train_prog,
feed=feeder.feed(data),
fetch_list=[avg_loss])
if args.use_reader_op:
try:
loss = exe.run(train_prog, fetch_list=[avg_loss])
except fluid.core.EnforceNotMet as ex:
break
else:
loss = exe.run(train_prog,
feed=feeder.feed(data),
fetch_list=[avg_loss])
iters += 1
num_samples += len(data)
batch_id += 1
# FIXME(wuyi): For use_reader_op, if the current
# pass is not the last, the last batch of this pass
# is also equal to args.batch_size.
if args.use_reader_op:
num_samples += args.batch_size * args.gpus
else:
num_samples += len(data)
train_losses.append(loss)
print("Pass: %d, Iter: %d, Loss: %f\n" %
(pass_id, iters, np.mean(train_losses)))
train_elapsed = time.time() - start_time
examples_per_sec = num_samples / train_elapsed
print('\nTotal examples: %d, total time: %.5f, %.5f examples/sec\n' %
(num_samples, train_elapsed, examples_per_sec))
print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses)))
print_train_time(start_time, time.time(), num_samples)
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 and not args.use_reader_op:
pass_test_acc = test(exe, infer_prog, test_reader, feeder,
batch_acc)
print(", Test Accuracy: %f" % pass_test_acc)
......@@ -251,10 +194,14 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
batch_acc, args, train_prog, startup_prog, nccl_id_var,
num_trainers, trainer_id):
feed_var_list = [
var for var in train_prog.global_block().vars.itervalues()
if var.is_data
]
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
if not args.use_reader_op:
feed_var_list = [
var for var in train_prog.global_block().vars.itervalues()
if var.is_data
]
feeder = fluid.DataFeeder(feed_var_list, place)
# generate fake:
if args.use_fake_data:
for var in feed_var_list:
......@@ -271,7 +218,6 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
"value": 1.0,
"dtype": var.dtype})
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
if nccl_id_var and trainer_id == 0:
#FIXME(wuyi): wait other trainer to start listening
time.sleep(30)
......@@ -288,12 +234,21 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
num_trainers=num_trainers,
trainer_id=trainer_id)
feeder = fluid.DataFeeder(feed_var_list, place)
for pass_id in range(args.pass_num):
num_samples = 0
iters = 0
start_time = time.time()
for batch_id, data in enumerate(train_reader()):
if not args.use_reader_op:
reader_generator = train_reader()
batch_id = 0
data = None
while True:
if not args.use_reader_op:
data = next(reader_generator, None)
if data == None:
break
if iters == args.iterations:
break
if args.profile and pass_id == 0 and batch_id == 5:
profiler.start_profiler("All")
elif args.profile and pass_id == 0 and batch_id == 10:
......@@ -302,39 +257,50 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
if iters == args.skip_batch_num:
start_time = time.time()
num_samples = 0
if iters == args.iterations:
break
if args.use_fake_data:
loss, = exe.run([avg_loss.name])
if args.use_fake_data or args.use_reader_op:
try:
loss, = exe.run([avg_loss.name])
except fluid.core.EnforceNotMet as ex:
break
else:
loss, = exe.run([avg_loss.name], feed=feeder.feed(data))
if args.update_method == "pserver":
exe.bcast_params()
num_samples += len(data)
if args.use_reader_op:
num_samples += args.batch_size * args.gpus
else:
num_samples += len(data)
iters += 1
if batch_id % 1 == 0:
print("Pass %d, batch %d, loss %s" %
(pass_id, batch_id, np.array(loss)))
train_elapsed = time.time() - start_time
examples_per_sec = num_samples / train_elapsed
print('\nTotal examples: %d, total time: %.5f, %.5f examples/sed\n' %
(num_samples, train_elapsed, examples_per_sec))
if not args.no_test and batch_acc != None:
batch_id += 1
print_train_time(start_time, time.time(), num_samples)
if not args.no_test and batch_acc and not args.use_reader_op:
# we have not implement record io for test
# skip test when use args.use_reader_op
test_acc = test(startup_exe, infer_prog, test_reader, feeder,
batch_acc)
print("Pass: %d, Test Accuracy: %f\n" % (pass_id, test_acc))
exit(0)
def print_arguments(args):
vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
vars(args)['device'] == 'GPU')
print('----------- resnet Configuration Arguments -----------')
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
def print_train_time(start_time, end_time, num_samples):
train_elapsed = end_time - start_time
examples_per_sec = num_samples / train_elapsed
print('\nTotal examples: %d, total time: %.5f, %.5f examples/sed\n' %
(num_samples, train_elapsed, examples_per_sec))
def main():
args = parse_args()
print_arguments(args)
......@@ -342,7 +308,7 @@ def main():
# the unique trainer id, starting from 0, needed by trainer
# only
nccl_id_var, num_trainers, trainer_id = (
None, 1, int(os.getenv("PADDLE_TRAINER_ID", "-1")))
None, 1, int(os.getenv("PADDLE_TRAINER_ID", "0")))
if args.use_cprof:
pr = cProfile.Profile()
......@@ -356,7 +322,7 @@ def main():
fluid.memory_optimize(fluid.default_main_program())
if args.update_method == "pserver":
train_prog, startup_prog = dist_transpile(trainer_id)
train_prog, startup_prog = dist_transpile(trainer_id, args)
if not train_prog:
raise Exception(
"Must configure correct environments to run dist train.")
......
benchmark/fluid/kube_gen_job.py
浏览文件 @ 8bd148dc
......@@ -49,7 +49,7 @@ def parse_args():
parser.add_argument(
'--fluid', default=1, type=int, help='whether is fluid job')
parser.add_argument(
'--rdma', action='store_ture', help='whether mount rdma libs')
'--rdma', action='store_true', help='whether mount rdma libs')
parser.add_argument(
'--disttype',
default="pserver",
......
benchmark/fluid/models/machine_translation.py
浏览文件 @ 8bd148dc
......@@ -197,6 +197,8 @@ def lodtensor_to_ndarray(lod_tensor):
def get_model(args):
if args.use_reader_op:
raise Exception("machine_translation do not support reader op for now.")
embedding_dim = 512
encoder_size = 512
decoder_size = 512
......@@ -221,7 +223,7 @@ def get_model(args):
train_batch_generator = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.wmt14.train(dict_size), buf_size=1000),
batch_size=args.batch_size)
batch_size=args.batch_size * args.gpus)
test_batch_generator = paddle.batch(
paddle.reader.shuffle(
......
benchmark/fluid/models/mnist.py
浏览文件 @ 8bd148dc
......@@ -20,6 +20,7 @@ import numpy as np
import argparse
import time
import cProfile
import os
import paddle
import paddle.fluid as fluid
......@@ -65,19 +66,49 @@ def cnn_model(data):
def get_model(args):
# Input data
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# 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)
if args.use_reader_op:
filelist = [
os.path.join(args.data_path, f) for f in os.listdir(args.data_path)
]
data_file = fluid.layers.open_files(
filenames=filelist,
shapes=[[-1, 1, 28, 28], (-1, 1)],
lod_levels=[0, 0],
dtypes=["float32", "int64"],
thread_num=args.gpus,
pass_num=args.pass_num)
data_file = fluid.layers.double_buffer(
fluid.layers.batch(
data_file, batch_size=args.batch_size))
images, label = fluid.layers.read_file(data_file)
else:
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_acc = fluid.layers.accuracy(input=predict, label=label)
# inference program
inference_program = fluid.default_main_program().clone()
......@@ -88,7 +119,7 @@ def get_model(args):
# Reader
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=args.batch_size)
paddle.dataset.mnist.train(), batch_size=args.batch_size * args.gpus)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=args.batch_size)
return avg_cost, inference_program, opt, train_reader, test_reader, batch_acc
benchmark/fluid/models/resnet.py
浏览文件 @ 8bd148dc
......@@ -19,6 +19,7 @@ from __future__ import print_function
import functools
import numpy as np
import time
import os
import cProfile, pstats, StringIO
......@@ -26,6 +27,7 @@ import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.profiler as profiler
from recordio_converter import imagenet_train, imagenet_test
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
......@@ -122,40 +124,85 @@ def get_model(args):
else:
dshape = [32, 32, 3]
model = resnet_cifar10
else:
train_reader = paddle.dataset.cifar.train10()
test_reader = paddle.dataset.cifar.test10()
elif args.data_set == "flowers":
class_dim = 102
if args.data_format == 'NCHW':
dshape = [3, 224, 224]
else:
dshape = [224, 224, 3]
model = resnet_imagenet
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)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(
input=predict, label=label, total=batch_size_tensor)
train_reader = paddle.dataset.flowers.train()
test_reader = paddle.dataset.flowers.test()
elif args.data_set == "imagenet":
class_dim = 1000
if args.data_format == 'NCHW':
dshape = [3, 224, 224]
else:
dshape = [224, 224, 3]
model = resnet_imagenet
if not args.data_path:
raise Exception(
"Must specify --data_path when training with imagenet")
train_reader = imagenet_train(args.data_path)
test_reader = imagenet_test(args.data_path)
if args.use_reader_op:
filelist = [
os.path.join(args.data_path, f) for f in os.listdir(args.data_path)
]
data_file = fluid.layers.open_files(
filenames=filelist,
shapes=[[-1] + dshape, (-1, 1)],
lod_levels=[0, 0],
dtypes=["float32", "int64"],
thread_num=args.gpus,
pass_num=args.pass_num)
data_file = fluid.layers.double_buffer(
fluid.layers.batch(
data_file, batch_size=args.batch_size))
input, label = fluid.layers.read_file(data_file)
else:
input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
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 = 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)
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)
train_reader = paddle.batch(
batched_train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
buf_size=5120),
batch_size=args.batch_size)
test_reader = paddle.batch(
paddle.dataset.cifar.test10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
batch_size=args.batch_size)
return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc
train_reader, buf_size=5120),
batch_size=args.batch_size * args.gpus,
drop_last=True)
batched_test_reader = paddle.batch(
train_reader, batch_size=args.batch_size, drop_last=True)
return avg_cost, inference_program, optimizer, batched_train_reader,\
batched_test_reader, batch_acc
benchmark/fluid/models/stacked_dynamic_lstm.py
浏览文件 @ 8bd148dc
......@@ -44,6 +44,9 @@ def crop_sentence(reader, crop_size):
def get_model(args):
if args.use_reader_op:
raise Exception(
"stacked_dynamic_lstm do not support reader op for now.")
lstm_size = 512
emb_dim = 512
crop_size = 1500
......@@ -115,7 +118,7 @@ def get_model(args):
train_reader = batch(
paddle.reader.shuffle(
crop_sentence(imdb.train(word_dict), crop_size), buf_size=25000),
batch_size=args.batch_size)
batch_size=args.batch_size * args.gpus)
test_reader = batch(
paddle.reader.shuffle(
crop_sentence(imdb.test(word_dict), crop_size), buf_size=25000),
......
benchmark/fluid/models/vgg.py
浏览文件 @ 8bd148dc
......@@ -22,6 +22,7 @@ import paddle.fluid as fluid
import paddle.fluid.core as core
import argparse
import functools
import os
def vgg16_bn_drop(input):
......@@ -65,9 +66,25 @@ def get_model(args):
else:
data_shape = [224, 224, 3]
# Input data
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if args.use_reader_op:
filelist = [
os.path.join(args.data_path, f) for f in os.listdir(args.data_path)
]
data_file = fluid.layers.open_files(
filenames=filelist,
shapes=[[-1] + data_shape, (-1, 1)],
lod_levels=[0, 0],
dtypes=["float32", "int64"],
thread_num=args.gpus,
pass_num=args.pass_num)
data_file = fluid.layers.double_buffer(
fluid.layers.batch(
data_file, batch_size=args.batch_size))
images, label = fluid.layers.read_file(data_file)
else:
images = fluid.layers.data(
name='data', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
net = vgg16_bn_drop(images)
......@@ -95,7 +112,7 @@ def get_model(args):
paddle.dataset.cifar.train10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
buf_size=5120),
batch_size=args.batch_size)
batch_size=args.batch_size * args.gpus)
test_reader = paddle.batch(
paddle.dataset.cifar.test10()
if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
......
benchmark/fluid/recordio_converter.py 0 → 100644
浏览文件 @ 8bd148dc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.dataset import mnist, cifar, flowers, image
def convert_2_recordio(py_reader, outfilepath, batch_size, shape_data,
shape_label):
num_batches = 0
with fluid.program_guard(fluid.Program(), fluid.Program()):
reader = paddle.batch(py_reader(), batch_size=batch_size)
feeder = fluid.DataFeeder(
feed_list=[ # order is image and label
fluid.layers.data(
name='image', shape=shape_data),
fluid.layers.data(
name='label', shape=shape_label, dtype='int64'),
],
place=fluid.CPUPlace())
num_batches = fluid.recordio_writer.convert_reader_to_recordio_file(
outfilepath, reader, feeder)
return num_batches
def prepare_mnist(outpath, batch_size):
outfilepath = os.path.join(outpath, "mnist.recordio")
convert_2_recordio(mnist.train, outfilepath, batch_size, [784], [1])
def prepare_cifar10(outpath, batch_size):
outfilepath = os.path.join(outpath, "cifar.recordio")
convert_2_recordio(cifar.train10, outfilepath, batch_size, [3, 32, 32], [1])
def prepare_flowers(outpath, batch_size):
outfilepath = os.path.join(outpath, "flowers.recordio")
convert_2_recordio(flowers.train, outfilepath, batch_size, [3, 224, 224],
[1])
def default_mapper(sample):
img, label = sample
img = image.simple_transform(
img, 256, 224, True, mean=[103.94, 116.78, 123.68])
return img.flatten().astype('float32'), label
def imagenet_train(data_dir):
contents = os.listdir(data_dir)
if set(contents) != set(
["train", "train.txt", "val", "val_set", "val.txt", "unzip.sh"]):
raise Exception("Imagenet data contents error!")
img2label = dict()
imgfilelist = []
with open(os.path.join(data_dir, "train.txt")) as fn:
while 1:
l = fn.readline()
if not l:
break
img, lbl = l[:-1].split(" ")
img2label[img] = int(lbl)
imgfilelist.append(img)
# shuffle all, this is slow
random.shuffle(imgfilelist)
def train_reader():
for idx, imgfile in enumerate(imgfilelist):
data = image.load_image(
os.path.join(data_dir, "train", imgfile.lower()))
label = [img2label[imgfile], ]
yield [data, label]
return paddle.reader.map_readers(default_mapper, train_reader)
def imagenet_test(data_dir):
contents = os.listdir(data_dir)
if set(contents) != set(
["train", "train.txt", "val", "val_set", "val.txt", "unzip.sh"]):
raise Exception("Imagenet data contents error!")
img2label = dict()
imgfilelist = []
with open(os.path.join(data_dir, "val.txt")) as fn:
while 1:
l = fn.readline()
if not l:
break
img, lbl = l[:-1].split(" ")
img2label[img] = int(lbl)
imgfilelist.append(img)
def test_reader():
for idx, imgfile in enumerate(imgfilelist):
base_path = os.path.join(data_dir, "val", imgfile.split(".")[0])
image_path = ".".join([base_path, "jpeg"])
data = image.load_image(image_path)
label = [img2label[imgfile], ]
yield [data, label]
return paddle.reader.map_readers(default_mapper, test_reader)
# FIXME(wuyi): delete this when https://github.com/PaddlePaddle/Paddle/pull/11066 is merged
def convert_reader_to_recordio_files(
filename,
batch_per_file,
reader_creator,
feeder,
compressor=core.RecordIOWriter.Compressor.Snappy,
max_num_records=1000,
feed_order=None):
if feed_order is None:
feed_order = feeder.feed_names
f_name, f_ext = os.path.splitext(filename)
assert (f_ext == ".recordio")
lines = []
f_idx = 0
counter = 0
for idx, batch in enumerate(reader_creator()):
lines.append(batch)
if idx >= batch_per_file and idx % batch_per_file == 0:
filename = "%s-%05d%s" % (f_name, f_idx, f_ext)
with fluid.recordio_writer.create_recordio_writer(
filename, compressor, max_num_records) as writer:
for l in lines:
res = feeder.feed(l)
for each in feed_order:
writer.append_tensor(res[each])
writer.complete_append_tensor()
counter += 1
lines = []
f_idx += 1
print("written file: ", filename)
return counter
def prepare_imagenet(inpath, outpath, batch_size):
r = paddle.batch(imagenet_train(inpath), batch_size=batch_size)
feeder = fluid.DataFeeder(
feed_list=[
fluid.layers.data(
name="image", shape=[3, 224, 224]), fluid.layers.data(
name="label", shape=[1], dtype='int64')
],
place=fluid.CPUPlace())
outpath = os.path.join(outpath, "imagenet.recordio")
convert_reader_to_recordio_files(outpath, 10000, r, feeder)
benchmark/fluid/run.sh
浏览文件 @ 8bd148dc
......@@ -2,6 +2,7 @@
# This script benchmarking the PaddlePaddle Fluid on
# single thread single GPU.
mkdir -p logs
#export FLAGS_fraction_of_gpu_memory_to_use=0.0
export CUDNN_PATH=/paddle/cudnn_v5
......@@ -35,71 +36,74 @@ nohup stdbuf -oL nvidia-smi \
--format=csv \
--filename=mem.log \
-l 1 &
# mnist
# mnist gpu mnist 128
FLAGS_benchmark=true stdbuf -oL python fluid/mnist.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=mnist \
--device=GPU \
--batch_size=128 \
--skip_batch_num=5 \
--iterations=500 \
2>&1 | tee -a mnist_gpu_128.log
2>&1 | tee -a logs/mnist_gpu_128.log
# vgg16
# gpu cifar10 128
FLAGS_benchmark=true stdbuf -oL python fluid/vgg16.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=vgg16 \
--device=GPU \
--batch_size=128 \
--skip_batch_num=5 \
--iterations=30 \
2>&1 | tee -a vgg16_gpu_128.log
2>&1 | tee -a logs/vgg16_gpu_128.log
# flowers gpu 128
FLAGS_benchmark=true stdbuf -oL python fluid/vgg16.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=vgg16 \
--device=GPU \
--batch_size=32 \
--data_set=flowers \
--skip_batch_num=5 \
--iterations=30 \
2>&1 | tee -a vgg16_gpu_flowers_32.log
2>&1 | tee -a logs/vgg16_gpu_flowers_32.log
# resnet50
# resnet50 gpu cifar10 128
FLAGS_benchmark=true stdbuf -oL python fluid/resnet50.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=resnet \
--device=GPU \
--batch_size=128 \
--data_set=cifar10 \
--model=resnet_cifar10 \
--skip_batch_num=5 \
--iterations=30 \
2>&1 | tee -a resnet50_gpu_128.log
2>&1 | tee -a logs/resnet50_gpu_128.log
# resnet50 gpu flowers 64
FLAGS_benchmark=true stdbuf -oL python fluid/resnet50.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=resnet \
--device=GPU \
--batch_size=64 \
--data_set=flowers \
--model=resnet_imagenet \
--skip_batch_num=5 \
--iterations=30 \
2>&1 | tee -a resnet50_gpu_flowers_64.log
2>&1 | tee -a logs/resnet50_gpu_flowers_64.log
# lstm
# lstm gpu imdb 32 # tensorflow only support batch=32
FLAGS_benchmark=true stdbuf -oL python fluid/stacked_dynamic_lstm.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=stacked_dynamic_lstm \
--device=GPU \
--batch_size=32 \
--skip_batch_num=5 \
--iterations=30 \
--hidden_dim=512 \
--emb_dim=512 \
--crop_size=1500 \
2>&1 | tee -a lstm_gpu_32.log
2>&1 | tee -a logs/lstm_gpu_32.log
# seq2seq
# seq2seq gpu wmb 128
FLAGS_benchmark=true stdbuf -oL python fluid/machine_translation.py \
FLAGS_benchmark=true stdbuf -oL python fluid_benchmark.py \
--model=machine_translation \
--device=GPU \
--batch_size=128 \
--skip_batch_num=5 \
--iterations=30 \
2>&1 | tee -a lstm_gpu_128.log
2>&1 | tee -a logs/lstm_gpu_128.log
benchmark/fluid/run_fluid_benchmark.sh 0 → 100644
浏览文件 @ 8bd148dc
#!/bin/bash
PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model resnet --device CPU --update_method pserver --iterations=10000 &
sleep 15
CUDA_VISIBLE_DEVICES=0,1 PADDLE_TRAINING_ROLE=TRAINER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model resnet --device GPU --update_method pserver --iterations=10000 --gpus 2 &
CUDA_VISIBLE_DEVICES=2,3 PADDLE_TRAINING_ROLE=TRAINER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=1 python fluid_benchmark.py --model resnet --device GPU --update_method pserver --iterations=10000 --gpus 2 &
cmake/configure.cmake
浏览文件 @ 8bd148dc
......@@ -92,6 +92,9 @@ if(WITH_GPU)
if(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
message(FATAL_ERROR "TensorRT needs CUDNN >= 7.0 to compile")
endif()
if(${TENSORRT_MAJOR_VERSION} VERSION_LESS 4)
message(FATAL_ERROR "Paddle needs TensorRT >= 4.0 to compile")
endif()
include_directories(${TENSORRT_INCLUDE_DIR})
endif()
elseif(WITH_AMD_GPU)
......@@ -115,6 +118,10 @@ endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")
if(WITH_DISTRIBUTE)
add_definitions(-DPADDLE_WITH_DISTRIBUTE)
endif()
if(WITH_GOLANG)
# we need to symlink Paddle directory into GOPATH. If we
# don't do it and we have code that depends on Paddle, go
......@@ -163,3 +170,7 @@ if(WITH_GOLANG)
endif()
endif(WITH_GOLANG)
if(WITH_GRPC)
add_definitions(-DPADDLE_WITH_GRPC)
endif(WITH_GRPC)
cmake/external/anakin.cmake 0 → 100644
浏览文件 @ 8bd148dc
if (NOT WITH_ANAKIN)
return()
endif()
set(ANAKIN_INSTALL_DIR "${THIRD_PARTY_PATH}/install/anakin" CACHE PATH
"Anakin install path." FORCE)
set(ANAKIN_INCLUDE "${ANAKIN_INSTALL_DIR}" CACHE STRING "root of Anakin header files")
set(ANAKIN_LIBRARY "${ANAKIN_INSTALL_DIR}" CACHE STRING "path of Anakin library")
set(ANAKIN_COMPILE_EXTRA_FLAGS -Wno-error=unused-variable -Wno-error=format-extra-args -Wno-error=comment -Wno-error=format -Wno-error=switch -Wno-error=return-type -Wno-error=non-virtual-dtor -Wno-reorder -Wno-error=cpp)
set(ANAKIN_LIBRARY_URL "https://github.com/pangge/Anakin/releases/download/3.0/anakin_release_simple.tar.gz")
# A helper function used in Anakin, currently, to use it, one need to recursively include
# nearly all the header files.
function(fetch_include_recursively root_dir)
if (IS_DIRECTORY ${root_dir})
include_directories(${root_dir})
endif()
file(GLOB ALL_SUB RELATIVE ${root_dir} ${root_dir}/*)
foreach(sub ${ALL_SUB})
if (IS_DIRECTORY ${root_dir}/${sub})
fetch_include_recursively(${root_dir}/${sub})
endif()
endforeach()
endfunction()
# download library
message(STATUS "Download Anakin library from ${ANAKIN_LIBRARY_URL}")
execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
execute_process(COMMAND bash -c "rm -rf ${ANAKIN_INSTALL_DIR}/*")
execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; wget -q ${ANAKIN_LIBRARY_URL}")
execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; tar xzf anakin_release_simple.tar.gz")
if (WITH_ANAKIN)
message(STATUS "Anakin for inference is enabled")
message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
fetch_include_recursively(${ANAKIN_INCLUDE})
link_directories(${ANAKIN_LIBRARY})
endif()
cmake/external/brpc.cmake 0 → 100644
浏览文件 @ 8bd148dc
# 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(ExternalProject)
SET(BRPC_SOURCES_DIR ${THIRD_PARTY_PATH}/brpc)
SET(BRPC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/brpc)
SET(BRPC_INCLUDE_DIR "${BRPC_INSTALL_DIR}/include" CACHE PATH "brpc include directory." FORCE)
SET(BRPC_LIBRARIES "${BRPC_INSTALL_DIR}/lib/libbrpc.a" CACHE FILEPATH "brpc library." FORCE)
INCLUDE_DIRECTORIES(${BRPC_INCLUDE_DIR})
# Reference https://stackoverflow.com/questions/45414507/pass-a-list-of-prefix-paths-to-externalproject-add-in-cmake-args
set(prefix_path "${THIRD_PARTY_PATH}/install/gflags|${THIRD_PARTY_PATH}/install/leveldb|${THIRD_PARTY_PATH}/install/snappy|${THIRD_PARTY_PATH}/install/gtest|${THIRD_PARTY_PATH}/install/protobuf")
# If minimal .a is need, you can set WITH_DEBUG_SYMBOLS=OFF
ExternalProject_Add(
extern_brpc
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/brpc/brpc"
GIT_TAG "6d153dd7ff00f960ae6895c9c5fff0ce9f07aff2"
PREFIX ${BRPC_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_INSTALL_PREFIX=${BRPC_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=${BRPC_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
-DCMAKE_PREFIX_PATH=${prefix_path}
-DBRPC_WITH_GLOG=ON
${EXTERNAL_OPTIONAL_ARGS}
LIST_SEPARATOR |
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${BRPC_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR:PATH=${BRPC_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
)
ADD_DEPENDENCIES(extern_brpc protobuf leveldb gflags glog gtest snappy)
ADD_LIBRARY(brpc STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET brpc PROPERTY IMPORTED_LOCATION ${BRPC_LIBRARIES})
ADD_DEPENDENCIES(brpc extern_brpc)
LIST(APPEND external_project_dependencies brpc)
cmake/external/grpc.cmake
浏览文件 @ 8bd148dc
......@@ -33,10 +33,19 @@ ELSE()
SET(BUILD_CMD make HAS_SYSTEM_PROTOBUF=false -s -j ${NUM_OF_PROCESSOR} static grpc_cpp_plugin)
ENDIF()
# FIXME(wuyi): do not build zlib cares protobuf twice, find a way to build grpc with them
ExternalProject_Add(
extern_grpc
DEPENDS protobuf zlib
URL "http://paddlepaddledeps.bj.bcebos.com/grpc.tar.xz"
# NOTE(wuyi):
# this package is generated by following steps:
# 1. git clone -b v1.8.x https://github.com/grpc/grpc.git
# 2. submodule update --init
# 3. keep only zlib, cares, protobuf, boringssl under "third_party",
# 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 ${GRPC_SOURCES_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
......@@ -49,7 +58,6 @@ ExternalProject_Add(
INSTALL_COMMAND make prefix=${GRPC_INSTALL_DIR} install
)
# FIXME(typhoonzero): hack to get static lib path, try a better way like merge them.
ADD_LIBRARY(grpc++_unsecure STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET grpc++_unsecure PROPERTY IMPORTED_LOCATION
"${GRPC_INSTALL_DIR}/lib/libgrpc++_unsecure.a")
......
cmake/external/leveldb.cmake 0 → 100644
浏览文件 @ 8bd148dc
# 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(ExternalProject)
SET(LEVELDB_SOURCES_DIR ${THIRD_PARTY_PATH}/leveldb)
SET(LEVELDB_INSTALL_DIR ${THIRD_PARTY_PATH}/install/leveldb)
SET(LEVELDB_INCLUDE_DIR "${LEVELDB_INSTALL_DIR}/include" CACHE PATH "leveldb include directory." FORCE)
SET(LEVELDB_LIBRARIES "${LEVELDB_INSTALL_DIR}/lib/libleveldb.a" CACHE FILEPATH "leveldb library." FORCE)
INCLUDE_DIRECTORIES(${LEVELDB_INCLUDE_DIR})
ExternalProject_Add(
extern_leveldb
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${LEVELDB_SOURCES_DIR}
URL "https://github.com/google/leveldb/archive/v1.18.tar.gz"
URL_MD5 "73770de34a2a5ab34498d2e05b2b7fa0"
CONFIGURE_COMMAND ""
BUILD_COMMAND CXXFLAGS=-fPIC make -j ${NUM_OF_PROCESSOR} libleveldb.a
INSTALL_COMMAND mkdir -p ${LEVELDB_INSTALL_DIR}/lib/
&& cp ${LEVELDB_SOURCES_DIR}/src/extern_leveldb/libleveldb.a ${LEVELDB_LIBRARIES}
&& cp -r ${LEVELDB_SOURCES_DIR}/src/extern_leveldb/include ${LEVELDB_INSTALL_DIR}/
BUILD_IN_SOURCE 1
)
ADD_DEPENDENCIES(extern_leveldb snappy)
ADD_LIBRARY(leveldb STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET leveldb PROPERTY IMPORTED_LOCATION ${LEVELDB_LIBRARIES})
ADD_DEPENDENCIES(leveldb extern_leveldb)
LIST(APPEND external_project_dependencies leveldb)
cmake/external/openblas.cmake
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......@@ -29,6 +29,8 @@ IF(NOT ${CBLAS_FOUND})
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
CACHE FILEPATH "openblas library." FORCE)
ADD_DEFINITIONS(-DPADDLE_USE_OPENBLAS)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
SET(OPENBLAS_COMMIT "v0.2.20")
......
cmake/generic.cmake
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......@@ -610,3 +610,21 @@ function(grpc_library TARGET_NAME)
COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
cc_library("${TARGET_NAME}" SRCS "${grpc_library_SRCS}" DEPS "${TARGET_NAME}_grpc" "${TARGET_NAME}_proto" "${grpc_library_DEPS}")
endfunction()
function(brpc_library TARGET_NAME)
set(oneValueArgs PROTO)
set(multiValueArgs SRCS DEPS)
set(options "")
cmake_parse_arguments(brpc_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
message(STATUS "generating brpc ${brpc_library_PROTO}")
get_filename_component(ABS_PROTO ${brpc_library_PROTO} ABSOLUTE)
get_filename_component(PROTO_WE ${brpc_library_PROTO} NAME_WE)
get_filename_component(PROTO_PATH ${ABS_PROTO} PATH)
protobuf_generate_cpp(brpc_proto_srcs brpc_proto_hdrs "${ABS_PROTO}")
cc_library("${TARGET_NAME}_proto" SRCS "${brpc_proto_srcs}")
cc_library("${TARGET_NAME}" SRCS "${brpc_library_SRCS}" DEPS "${TARGET_NAME}_proto" "${brpc_library_DEPS}")
endfunction()
cmake/inference_lib.cmake
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......@@ -39,7 +39,7 @@ function(copy TARGET)
message(FATAL_ERROR "${TARGET} source numbers are not equal to destination numbers")
endif()
math(EXPR len "${copy_lib_SRCS_len} - 1")
add_custom_target(${TARGET} DEPENDS ${copy_lib_DEPS})
foreach(index RANGE ${len})
list(GET copy_lib_SRCS ${index} src)
......@@ -155,6 +155,15 @@ copy(inference_lib DEPS paddle_fluid_shared paddle_fluid
DSTS ${dst_dir}/${module} ${dst_dir}/${module}
)
if(WITH_CONTRIB)
set(contrib_dst_dir "${FLUID_INSTALL_DIR}/contrib/inference")
copy(contrib_inference_lib DEPS paddle_inference_api
SRCS ${PADDLE_SOURCE_DIR}/paddle/contrib/inference/paddle_inference_api.h
${PADDLE_BINARY_DIR}/paddle/contrib/inference/libpaddle_inference_api.*
DSTS ${contrib_dst_dir} ${contrib_dst_dir}
)
endif()
set(module "platform")
copy(platform_lib DEPS profiler_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/dynload/*.h ${src_dir}/${module}/details/*.h
......
doc/fluid/api/gen_doc.sh
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#!/bin/bash
python gen_doc.py layers --submodules control_flow device io nn ops tensor > layers.rst
python gen_doc.py layers --submodules control_flow device io nn ops tensor detection learning_rate_scheduler > layers.rst
for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer
do
......
doc/fluid/api/layers.rst
浏览文件 @ 8bd148dc
......@@ -342,6 +342,12 @@ conv2d
.. autofunction:: paddle.fluid.layers.conv2d
:noindex:
conv3d
------
.. autofunction:: paddle.fluid.layers.conv3d
:noindex:
sequence_pool
-------------
......@@ -366,6 +372,12 @@ pool2d
.. autofunction:: paddle.fluid.layers.pool2d
:noindex:
pool3d
------
.. autofunction:: paddle.fluid.layers.pool3d
:noindex:
batch_norm
----------
......@@ -384,6 +396,13 @@ conv2d_transpose
.. autofunction:: paddle.fluid.layers.conv2d_transpose
:noindex:
conv3d_transpose
----------------
.. autofunction:: paddle.fluid.layers.conv2d_transpose
:noindex:
sequence_expand
---------------
......@@ -594,7 +613,6 @@ roi_pool
.. autofunction:: paddle.fluid.layers.roi_pool
:noindex:
ops
===
......@@ -991,21 +1009,93 @@ zeros
.. autofunction:: paddle.fluid.layers.zeros
:noindex:
topk
----
detection
=========
.. autofunction:: paddle.fluid.layers.topk
multi_box_head
--------------
.. autofunction:: paddle.fluid.layers.multi_box_head
:noindex:
dice_loss
----
bipartite_match
---------------
.. autofunction:: paddle.fluid.layers.bipartite_match
:noindex:
target_assign
-------------
.. autofunction:: paddle.fluid.layers.target_assign
:noindex:
detection_output
----------------
.. autofunction:: paddle.fluid.layers.detection_output
:noindex:
ssd_loss
--------
.. autofunction:: paddle.fluid.layers.dice_loss
.. autofunction:: paddle.fluid.layers.ssd_loss
:noindex:
upsampling_bilinear2d
____
detection_map
-------------
.. autofunction:: paddle.fluid.layers.detection_map
:noindex:
iou_similarity
--------------
.. autofunction:: paddle.fluid.layers.iou_similarity
:noindex:
box_coder
---------
.. autofunction:: paddle.fluid.layers.box_coder
:noindex:
learning_rate_scheduler
=======================
exponential_decay
-----------------
.. autofunction:: paddle.fluid.layers.exponential_decay
:noindex:
natural_exp_decay
-----------------
.. autofunction:: paddle.fluid.layers.natural_exp_decay
:noindex:
inverse_time_decay
------------------
.. autofunction:: paddle.fluid.layers.inverse_time_decay
:noindex:
polynomial_decay
----------------
.. autofunction:: paddle.fluid.layers.polynomial_decay
:noindex:
piecewise_decay
---------------
.. autofunction:: paddle.fluid.layers.piecewise_decay
:noindex:
noam_decay
----------
.. autofunction:: paddle.fluid.layers.upsampling_bilinear2d
.. autofunction:: paddle.fluid.layers.noam_decay
:noindex:
doc/fluid/api/optimizer.rst
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......@@ -47,28 +47,6 @@ DecayedAdagrad
:members:
:noindex:
Adadelta
-----------------
.. autoclass:: paddle.fluid.optimizer.Adadelta
:members:
:noindex:
RMSProp
-----------------
.. autoclass:: paddle.fluid.optimizer.RMSProp
:members:
:noindex:
ModelAverage
-----------------
.. autoclass:: paddle.fluid.optimizer.ModelAverage
:members:
:noindex:
SGDOptimizer
------------
......@@ -111,25 +89,24 @@ DecayedAdagradOptimizer
:members:
:noindex:
Adadelta
--------
AdadeltaOptimizer
-----------------
.. autoclass:: paddle.fluid.optimizer.AdadeltaOptimizer
.. autoclass:: paddle.fluid.optimizer.Adadelta
:members:
:noindex:
ModelAverage
------------
RMSPropOptimizer
-----------------
.. autoclass:: paddle.fluid.optimizer.RMSPropOptimizer
.. autoclass:: paddle.fluid.optimizer.ModelAverage
:members:
:noindex:
Optimizer
---------
.. autoclass:: paddle.fluid.optimizer.Optimizer
:members:
:noindex:
doc/fluid/design/concepts/var_desc.md
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......@@ -35,7 +35,7 @@ The computation `Program` consists of nested `Blocks`. Each `Block` will consist
## Definition of VarType
A VarDesc should have a name, type and whether or not it is persistable. The are different kinds of variable types supported in PaddlePaddle, apart from the POD_Types like: `LOD_TENSOR`, `SELECTED_ROWS`, `FEED_MINIBATCH`, `FETCH_LIST`, `STEP_SCOPES`, `LOD_RANK_TABLE`, `LOD_TENSOR_ARRAY`, `PLACE_LIST`, `READER` and `CHANNEL`. These are declared inside `VarType`. A `VarDesc` then looks as the following:
A VarDesc should have a name, type and whether or not it is persistable. There are different kinds of variable types supported in PaddlePaddle, apart from the POD_Types like: `LOD_TENSOR`, `SELECTED_ROWS`, `FEED_MINIBATCH`, `FETCH_LIST`, `STEP_SCOPES`, `LOD_RANK_TABLE`, `LOD_TENSOR_ARRAY`, `PLACE_LIST`, `READER` and `CHANNEL`. These are declared inside `VarType`. A `VarDesc` then looks as the following:
```proto
message VarDesc {
......
doc/fluid/dev/api_doc_std_cn.md
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# API注释撰写标准
- [API注释模块](#API注释模块)
- [格式及示例](#格式及示例)
- [完整示例](#完整示例)
- [API注释撰写标准](#api)
- [API注释模块](#api)
- [格式及示例](#)
- [完整示例](#)
## API注释模块
......@@ -217,4 +218,4 @@ API文档须使用reStructuredText格式撰写，该格式详情请参考[链接
## 完整示例
fc 的完整注释见[示例](src/fc.py)。
fc 的完整注释见[示例](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/dev/src/fc.py)。
doc/fluid/dev/api_doc_std_en.md
浏览文件 @ 8bd148dc
# API Doc Standard
- [API Doc Structure](#API Doc Structure)
- [Format and Examples](#Format and Examples)
- [Complete Example](#Complete Example)
- [API Doc Standard](#api-doc-standard)
- [API Doc Structure](#api-doc-structure)
- [Format and Examples](#format-and-examples)
- [Complete Example](#complete-example)
## API Doc Structure
......@@ -223,4 +224,4 @@ Format and examples of each part of API documantation are as follows: (take fc f
## Complete Example
Complete Example of fc please see [here](src/fc.py)。
Complete Example of fc please see [here](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/dev/src/fc.py)。
doc/fluid/howto/cluster/fluid_recordio.md 0 → 100644
浏览文件 @ 8bd148dc
# How to use RecordIO in Fluid
If you want to use RecordIO as your training data format, you need to convert to your training data
to RecordIO files and reading them in the process of training, PaddlePaddle Fluid provides some
interface to deal with the RecordIO files.
## Generate RecordIO File
Before start training with RecordIO files, you need to convert your training data
to RecordIO format by `fluid.recordio_writer.convert_reader_to_recordio_file`, the sample codes
as follows:
```python
reader = paddle.batch(mnist.train(), batch_size=1)
feeder = fluid.DataFeeder(
feed_list=[ # order is image and label
fluid.layers.data(
name='image', shape=[784]),
fluid.layers.data(
name='label', shape=[1], dtype='int64'),
],
place=fluid.CPUPlace())
fluid.recordio_writer.convert_reader_to_recordio_file('./mnist.recordio', reader, feeder)
```
The above code snippet would generate a RecordIO `./mnist.recordio` on your host.
**NOTE**: we recommend users to set `batch_size=1` when generating the recordio files so that users can
adjust it flexibly while reading it.
## Use the RecordIO file in a Local Training Job
PaddlePaddle Fluid provides an interface `fluid.layers.io.open_recordio_file` to load your RecordIO file
and then you can use them as a Layer in your network configuration, the sample codes as follows:
```python
data_file = fluid.layers.io.open_recordio_file(
filename="./mnist.recordio",
shapes=[(-1, 784),(-1, 1)],
lod_levels=[0, 0],
dtypes=["float32", "int32"])
data_file = fluid.layers.io.batch(data_file, batch_size=4)
img, label = fluid.layers.io.read_file(data_file)
hidden = fluid.layers.fc(input=img, size=100, act='tanh')
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
fluid.optimizer.Adam(learning_rate=1e-3).minimize(avg_loss)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
avg_loss_np = []
# train a pass
batch_id = 0
while True:
tmp, = exe.run(fetch_list=[avg_loss])
avg_loss_np.append(tmp)
print(batch_id)
batch_id += 1
```
## Use the RecordIO files in Distributed Training
1. generate multiple RecordIO files
For a distributed training job, you may have multiple trainer nodes,
and one or more RecordIO files for one trainer node, you can use the interface
`fluid.recordio_writer.convert_reader_to_recordio_files` to convert your training data
into multiple RecordIO files, the sample codes as follows:
```python
reader = paddle.batch(mnist.train(), batch_size=1)
feeder = fluid.DataFeeder(
feed_list=[ # order is image and label
fluid.layers.data(
name='image', shape=[784]),
fluid.layers.data(
name='label', shape=[1], dtype='int64'),
],
place=fluid.CPUPlace())
fluid.recordio_writer.convert_reader_to_recordio_files(
filename_suffix='./mnist.recordio', batch_per_file=100, reader, feeder)
```
The above codes would generate multiple RecordIO files on your host like:
```bash
.
\_mnist-00000.recordio
|-mnist-00001.recordio
|-mnist-00002.recordio
|-mnist-00003.recordio
|-mnist-00004.recordio
```
2. open multiple RecordIO files by `fluid.layers.io.open_files`
For a distributed training job, the distributed operator system will schedule trainer process on multiple nodes,
each trainer process reads parts of the whole training data, we usually take the following approach to make the training
data allocated by each trainer process as uniform as possiable:
```python
def gen_train_list(file_pattern, trainers, trainer_id):
file_list = glob.glob(file_pattern)
ret_list = []
for idx, f in enumerate(file_list):
if (idx + trainers) % trainers == trainer_id:
ret_list.append(f)
return ret_list
trainers = int(os.getenv("TRAINERS"))
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
data_file = fluid.layers.io.open_files(
filenames=gen_train_list("./mnist-[0-9]*.recordio", 2, 0),
thread_num=1,
shapes=[(-1, 784),(-1, 1)],
lod_levels=[0, 0],
dtypes=["float32", "int32"])
img, label = fluid.layers.io.read_file(data_files)
...
```
doc/fluid/howto/index_cn.rst
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......@@ -4,5 +4,5 @@
.. toctree::
:maxdepth: 1
inference/index_cn.rst
optimization/index_cn.rst
inference/inference_support_in_fluid.md
doc/fluid/howto/index_en.rst
浏览文件 @ 8bd148dc
......@@ -5,4 +5,3 @@ HOW TO
:maxdepth: 1
optimization/index_en.rst
inference/inference_support_in_fluid.md
doc/fluid/howto/inference/build_and_install_lib_cn.rst 0 → 100644
浏览文件 @ 8bd148dc
安装与编译C++预测库
===========================
直接下载安装
-------------
====================== ========================================
版本说明 C++预测库
====================== ========================================
cpu_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/fluid.tgz>`_
cpu_avx_openblas `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/fluid.tgz>`_
cpu_noavx_openblas `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/fluid.tgz>`_
cuda7.5_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda8.0_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda8.0_cudnn7_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/fluid.tgz>`_
====================== ========================================
从源码编译
----------
用户也可以从 PaddlePaddle 核心代码编译C++预测库，只需在编译时配制下面这些编译选项：
================= =========
选项 值
================= =========
CMAKE_BUILD_TYPE Release
FLUID_INSTALL_DIR 安装路径
WITH_FLUID_ONLY ON（推荐）
WITH_SWIG_PY OFF（推荐
WITH_PYTHON OFF（推荐）
WITH_GPU ON/OFF
WITH_MKL ON/OFF
================= =========
建议按照推荐值设置，以避免链接不必要的库。其它可选编译选项按需进行设定。
下面的代码片段从github拉取最新代码，配制编译选项（需要将PADDLE_ROOT替换为PaddlePaddle预测库的安装路径）：
.. code-block:: bash
pip install paddlepaddle-gpu
PADDLE_ROOT=/path/of/capi
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
mkdir build
cd build
cmake -DFLUID_INSTALL_DIR=$PADDLE_ROOT \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_FLUID_ONLY=ON \
-DWITH_SWIG_PY=OFF \
-DWITH_PYTHON=OFF \
-DWITH_MKL=OFF \
-DWITH_GPU=OFF \
..
make
make inference_lib_dist
成功编译后，使用C++预测库所需的依赖（包括：（1）编译出的PaddlePaddle预测库和头文件；（2）第三方链接库和头文件；（3）版本信息与编译选项信息）
均会存放于PADDLE_ROOT目录中。目录结构如下：
.. code-block:: text
PaddleRoot/
├── CMakeCache.txt
├── paddle
│   └── fluid
│   ├── framework
│   ├── inference
│   ├── memory
│   ├── platform
│   ├── pybind
│   └── string
├── third_party
│   ├── boost
│   │   └── boost
│   ├── eigen3
│   │   ├── Eigen
│   │   └── unsupported
│   └── install
│   ├── gflags
│   ├── glog
│   ├── mklml
│   ├── protobuf
│   ├── snappy
│   ├── snappystream
│   └── zlib
└── version.txt
version.txt 中记录了该预测库的版本信息，包括Git Commit ID、使用OpenBlas或MKL数学库、CUDA/CUDNN版本号，如：
.. code-block:: text
GIT COMMIT ID: c95cd4742f02bb009e651a00b07b21c979637dc8
WITH_MKL: ON
WITH_GPU: ON
CUDA version: 8.0
CUDNN version: v5
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预测库
------------
.. toctree::
:maxdepth: 1
build_and_install_lib_cn.rst
inference_support_in_fluid_cn.md
doc/fluid/howto/inference/inference_support_in_fluid.md → doc/fluid/howto/inference/inference_support_in_fluid_cn.md
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# Fluid Inference使用指南
# 使用指南
## 目录：
- Python Inference API
- 编译Fluid Inference库
- Inference C++ API
- Inference实例
- Inference计算优化
......@@ -55,62 +54,6 @@
return [program, feed_target_names, fetch_targets]
```
## 编译Fluid Inference库
- **不需要额外的CMake选项**
- 1、 配置CMake命令，更多配置请参考[源码编译PaddlePaddle](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/build_from_source_cn.html)
```bash
$ git clone https://github.com/PaddlePaddle/Paddle.git
$ cd Paddle
$ mkdir build
$ cd build
$ cmake -DCMAKE_INSTALL_PREFIX=your/path/to/paddle_inference_lib \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_PYTHON=ON \
-DWITH_MKL=OFF \
-DWITH_GPU=OFF \
..
```
- 2、 编译PaddlePaddle
```bash
$ make
```
- 3、 部署。执行如下命令将PaddlePaddle Fluid Inference库部署到`your/path/to/paddle_inference_lib`目录。
```bash
$ make inference_lib_dist
```
- 目录结构
```bash
$ cd your/path/to/paddle_inference_lib
$ tree
.
|-- paddle
| `-- fluid
| |-- framework
| |-- inference
| | |-- io.h
| | `-- libpaddle_fluid.so
| |-- memory
| |-- platform
| `-- string
|-- third_party
| |-- eigen3
| `-- install
| |-- gflags
| |-- glog
| `-- protobuf
`-- ...
```
假设`PADDLE_ROOT=your/path/to/paddle_inference_lib`。
## 链接Fluid Inference库
- 示例项目([链接](https://github.com/luotao1/fluid_inference_example.git))
......
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## 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)
doc/mobile/cross_compiling_for_android_cn.md
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......@@ -63,16 +63,16 @@ Android的Docker开发镜像向用户提供两个可配置的参数：
- 编译`armeabi-v7a`，`Android API 21`的PaddlePaddle库
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" username/paddle-android:dev
$ docker run -it --rm -v $PWD:/paddle -w /paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" username/paddle-android:dev ./paddle/scripts/paddle_build.sh build_android
```
- 编译`arm64-v8a`，`Android API 21`的PaddlePaddle库
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=arm64-v8a" -e "ANDROID_API=21" username/paddle-android:dev
$ docker run -it --rm -v $PWD:/paddle -w /paddle -e "ANDROID_ABI=arm64-v8a" -e "ANDROID_API=21" username/paddle-android:dev ./paddle/scripts/paddle_build.sh build_android
```
执行上述`docker run`命令时，容器默认执行[paddle/scripts/docker/build_android.sh](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build_android.sh)脚本。该脚本中记录了交叉编译Android版PaddlePaddle库常用的CMake配置，并且会根据`ANDROID_ABI`和`ANDROID_API`自动构建独立工具链、进行编译和安装。由于arm64架构要求Android API不小于21。因此当`ANDROID_ABI=arm64-v8a`，`ANDROID_API<21`时，Docker容器中将默认使用`Android API 21`的编译工具链。用户可以参考下文[配置交叉编译参数](#配置交叉编译参数)章节，根据个人的需求修改定制Docker容器所执行的脚本。编译安装结束之后，PaddlePaddle的C-API库将被安装到`$PWD/install_android`目录，所依赖的第三方库同时也被安装到`$PWD/install_android/third_party`目录。
执行上述`docker run`命令时，容器执行[paddle/scripts/paddle_build.sh build_android](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/paddle_build.sh)脚本。该脚本中记录了交叉编译Android版PaddlePaddle库常用的CMake配置，并且会根据`ANDROID_ABI`和`ANDROID_API`自动构建独立工具链、进行编译和安装。由于arm64架构要求Android API不小于21。因此当`ANDROID_ABI=arm64-v8a`，`ANDROID_API<21`时，Docker容器中将默认使用`Android API 21`的编译工具链。用户可以参考下文[配置交叉编译参数](#配置交叉编译参数)章节，根据个人的需求修改定制Docker容器所执行的脚本。编译安装结束之后，PaddlePaddle的C-API库将被安装到`$PWD/install_android`目录，所依赖的第三方库同时也被安装到`$PWD/install_android/third_party`目录。
## 基于Linux交叉编译环境的编译方式
本文档将以Linux x86-64平台为例，介绍交叉编译Android平台上适用的PaddlePaddle库的方法和步骤。
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......@@ -36,7 +36,7 @@ $ docker pull docker.paddlepaddlehub.com/paddle:latest-dev-android
We can run the Docker image we just created to build the inference library of PaddlePaddle for Android using the command below:
```bash
$ docker run -it --rm -v $PWD:/paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" paddle:dev-android
$ docker run -it --rm -v $PWD:/paddle -w /paddle -e "ANDROID_ABI=armeabi-v7a" -e "ANDROID_API=21" paddle:dev-android ./paddle/scripts/paddle_build.sh build_android
```
The Docker image accepts two arguments `ANDROID_ABI` and `ANDROID_API`:
......@@ -70,7 +70,7 @@ The Docker image accepts two arguments `ANDROID_ABI` and `ANDROID_API`:
The ARM-64 architecture (`arm64-v8a`) requires at least level 21 of Android API.
The default entry-point of the Docker image, [`paddle/scripts/docker/build_android.sh`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build_android.sh) generates the [Android cross-compiling standalone toolchain](https://developer.android.com/ndk/guides/standalone_toolchain.html) based on the argument: `ANDROID_ABI` or `ANDROID_API`. For information about other configuration arguments, please continue reading.
The build command, [`paddle/scripts/paddle_build.sh build_android`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/paddle_build.sh) generates the [Android cross-compiling standalone toolchain](https://developer.android.com/ndk/guides/standalone_toolchain.html) based on the argument: `ANDROID_ABI` or `ANDROID_API`. For information about other configuration arguments, please continue reading.
The above command generates and outputs the inference library in `$PWD/install_android` and puts third-party libraries in `$PWD/install_android/third_party`.
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# Automatic Differentiation with the Tape
## Automatic Differentiation
A key challenge in the field of deep learning is to automatically derive the backward pass from the forward pass described algorithmically by researchers. Such a derivation, or a transformation of the forward pass program, has been long studied before the recent prosperity of deep learning in the field known as [automatic differentiation](https://arxiv.org/pdf/1502.05767.pdf).
## The Tape
Given the forward pass program (usually in Python in practices), there are two strategies to derive the backward pass:
1. from the forward pass program itself, or
1. from the execution trace of the forward pass program, which is often known as the *tape*.
This article surveys systems that follow the latter strategy.
## Dynamic Network
When we train a deep learning model, the tape changes every iteration as the input data change, so we have to re-derive the backward pass every iteration. This is known as *dynamic network*.
Deep learning systems that utilize the idea of dynamic network gained their popularities in recent years. This article surveys two representative systems: [PyTorch](https://pytorch.org/) and [DyNet](https://dynet.readthedocs.io/en/latest/).
## An Overview
Both frameworks record a ‘tape’ of the computation and interpreting (or run-time compiling) a transformation of the tape played back in reverse. This tape is a different kind of entity than the original program.[[link]](http://www.bcl.hamilton.ie/~barak/papers/toplas-reverse.pdf)
Consider the following code feedforward model.
```python
x = Variable(randn(20, 1)))
label = Variable(randint(1))
W_1, W_2 = Variable(randn(20, 20)), Variable(randn(10, 20))
h = matmul(W_1, x)
pred = matmul(W_2, x)
loss = softmax(pred, label)
loss.backward()
```
### 1) Dynet uses List to encode the Tape
During the forward execution, a list of operators, in this case `matmul`, `matmul` and `softmax`, are recorded in the tape, along with the necessary information needed to do the backward such as pointers to the inputs and outputs. Then the tape is played in reverse order at `loss.backward()`.
<details>
<summary></summary>
digraph g {
graph [
rankdir = "LR"
];
node [
fontsize = "16"
shape = "ellipse"
];
edge [];
"node0" [
label = "<f0> type: matmul | <f1> input: W_1, x | <f2> output: h"
shape = "record"
];
"node1" [
label = "<f0> type: matmul | <f1> input: W_2, h | <f2> output: pred"
shape = "record"
];
"node2" [
label = "<f0> type: softmax | <f1> input: pred, label | <f2> output: loss"
shape = "record"
];
"node0":f0 -> "node1":f0 [];
"node1":f0 -> "node2":f0 [];
}
</details>
![Alt text](https://g.gravizo.com/svg?digraph%20g%20{%20graph%20[%20rankdir%20=%20%22LR%22%20];%20node%20[%20fontsize%20=%20%2216%22%20shape%20=%20%22ellipse%22%20];%20edge%20[];%20%22node0%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20matmul%20|%20%3Cf1%3E%20input:%20W_1,%20x%20|%20%3Cf2%3E%20output:%20h%22%20shape%20=%20%22record%22%20];%20%22node1%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20matmul%20|%20%3Cf1%3E%20input:%20W_2,%20h%20|%20%3Cf2%3E%20output:%20pred%22%20shape%20=%20%22record%22%20];%20%22node2%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20softmax%20|%20%3Cf1%3E%20input:%20pred,%20label%20|%20%3Cf2%3E%20output:%20loss%22%20shape%20=%20%22record%22%20];%20%22node0%22:f0%20-%3E%20%22node1%22:f0%20[%20id%20=%200%20];%20%22node1%22:f0%20-%3E%20%22node2%22:f0%20[%20id%20=%201%20];%20})
### 2) Pytorch uses Node Graph to encode the Tape
The graph is composed of `Variable`s and `Function`s. During the forward execution, a `Variable` records its creator function, e.g. `h.creator = matmul`. And a Function records its inputs' previous/dependent functions `prev_func` through `creator`, e.g. `matmul.prev_func = matmul1`. At `loss.backward()`, a topological sort is performed on all `prev_func`s. Then the grad op is performed by the sorted order.
<details>
<summary></summary>
digraph g {
graph [
rankdir = "LR"
];
subgraph function {
node [
fontsize = "16"
style = filled
shape = "record"
];
"matmul0" [ label = "<f0> type: matmul | prev_func: None" ];
"matmul1" [ label = "<f0> type: matmul | prev_func: matmul" ];
"softmax" [ label = "<f0> type: softmax | prev_func: matmul" ];
}
subgraph variable {
node [
fontsize = "16"
shape = "Mrecord"
style = filled
fillcolor = white
];
"x" [ label = "<f0> x | <f1> creator: None" ];
"label" [ label = "<f0> label | <f1> creator: None" ];
"W_1" [ label = "<f0> W_1 | <f1> creator: None" ];
"W_2" [ label = "<f0> W_2 | <f1> creator: None" ];
"h" [ label = "<f0> h | <f1> creator: None" ];
"pred" [ label = "<f0> pred | <f1> creator: matmul" ];
"loss" [ label = "<f0> loss | <f1> creator: softmax" ];
}
subgraph data_flow {
"x":f0 -> "matmul0":f0;
"W_1":f0 -> "matmul0":f0;
"matmul0":f0 -> "h":f0;
"h":f0 -> "matmul1":f0;
"W_2":f0 -> "matmul1":f0;
"matmul1":f0 -> "pred":f0;
"pred":f0 -> "softmax":f0;
"label":f0 -> "softmax":f0;
"softmax":f0 -> "loss":f0;
}
subgraph prev_func {
edge [color="red", arrowsize="0.6", penwidth="1", constraint=false];
"matmul1":f1 -> "matmul0":f0;
"softmax":f1 -> "matmul1":f0;
label = "prev_func";
}
}
</details>
![Alt text](https://g.gravizo.com/svg?digraph%20g%20{%20graph%20[%20rankdir%20=%20%22LR%22%20];%20subgraph%20function%20{%20node%20[%20fontsize%20=%20%2216%22%20style%20=%20filled%20shape%20=%20%22record%22%20];%20%22matmul0%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20matmul%20|%20prev_func:%20None%22%20];%20%22matmul1%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20matmul%20|%20prev_func:%20matmul%22%20];%20%22softmax%22%20[%20label%20=%20%22%3Cf0%3E%20type:%20softmax%20|%20prev_func:%20matmul%22%20];%20}%20subgraph%20variable%20{%20node%20[%20fontsize%20=%20%2216%22%20shape%20=%20%22Mrecord%22%20style%20=%20filled%20fillcolor%20=%20white%20];%20%22x%22%20[%20label%20=%20%22%3Cf0%3E%20x%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22label%22%20[%20label%20=%20%22%3Cf0%3E%20label%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22W_1%22%20[%20label%20=%20%22%3Cf0%3E%20W_1%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22W_2%22%20[%20label%20=%20%22%3Cf0%3E%20W_2%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22h%22%20[%20label%20=%20%22%3Cf0%3E%20h%20|%20%3Cf1%3E%20creator:%20None%22%20];%20%22pred%22%20[%20label%20=%20%22%3Cf0%3E%20pred%20|%20%3Cf1%3E%20creator:%20matmul%22%20];%20%22loss%22%20[%20label%20=%20%22%3Cf0%3E%20loss%20|%20%3Cf1%3E%20creator:%20softmax%22%20];%20}%20subgraph%20data_flow%20{%20%22x%22:f0%20-%3E%20%22matmul0%22:f0;%20%22W_1%22:f0%20-%3E%20%22matmul0%22:f0;%20%22matmul0%22:f0%20-%3E%20%22h%22:f0;%20%22h%22:f0%20-%3E%20%22matmul1%22:f0;%20%22W_2%22:f0%20-%3E%20%22matmul1%22:f0;%20%22matmul1%22:f0%20-%3E%20%22pred%22:f0;%20%22pred%22:f0%20-%3E%20%22softmax%22:f0;%20%22label%22:f0%20-%3E%20%22softmax%22:f0;%20%22softmax%22:f0%20-%3E%20%22loss%22:f0;%20}%20subgraph%20prev_func%20{%20edge%20[color=%22red%22,%20arrowsize=%220.6%22,%20penwidth=%221%22,%20constraint=false];%20%22matmul1%22:f1%20-%3E%20%22matmul0%22:f0;%20%22softmax%22:f1%20-%3E%20%22matmul1%22:f0;%20label%20=%20%22prev_func%22;%20}%20})
Chainer and Autograd uses the similar techniques to record the forward pass. For details please refer to the appendix.
## Design choices
### 1) Dynet's List vs Pytorch's Node Graph
What's good about List:
1. It avoids a topological sort. One only needs to traverse the list of operators in reverse and calling the corresponding backward operator.
1. It promises effient data parallelism implementations. One could count the time of usage of a certain variable during the construction list. Then in the play back, one knows the calculation of a variable has completed. This enables communication and computation overlapping.
What's good about Node Graph:
1. More flexibility. PyTorch users can mix and match independent graphs however they like, in whatever threads they like (without explicit synchronization). An added benefit of structuring graphs this way is that when a portion of the graph becomes dead, it is automatically freed. [[2]](https://openreview.net/pdf?id=BJJsrmfCZ) Consider the following example, Pytorch only does backward on SmallNet while Dynet does both BigNet and SmallNet.
```python
result = BigNet(data)
loss = SmallNet(data)
loss.backward()
```
### 2) Dynet's Lazy evaluation vs Pytorch's Immediate evaluation
Dynet builds the list in a symbolic matter. Consider the following example
```python
for epoch in range(num_epochs):
for in_words, out_label in training_data:
dy.renew_cg()
W = dy.parameter(W_p)
b = dy.parameter(b_p)
score_sym = dy.softmax(W*dy.concatenate([E[in_words[0]],E[in_words[1]]])+b)
loss_sym = dy.pickneglogsoftmax(score_sym, out_label)
loss_val = loss_sym.value()
loss_sym.backward()
```
The computation of `lookup`, `concat`, `matmul` and `softmax` didn't happen until the call of `loss_sym.value()`. This defered execution is useful because it allows some graph-like optimization possible, e.g. kernel fusion.
Pytorch chooses immediate evaluation. It avoids ever materializing a "forward graph"/"tape" (no need to explicitly call `dy.renew_cg()` to reset the list), recording only what is necessary to differentiate the computation, i.e. `creator` and `prev_func`.
## What can fluid learn from them?
Please refer to `paddle/contrib/dynamic/`.
# Appendix
### Overview
| Framework | Has Tape | Core in C++ | First Release Date |
|-----------|----------|-------------|--------------------|
| Autograd | No | No | Mar 5, 2015 |
| Chainer | No | No | Jun 5, 2015 |
| Pytorch | No | Yes | Aug 31, 2016 |
| Dynet | Yes | Yes | Oct 12, 2016 |
### Source Code
#### Autograd
[Backward code](https://github.com/HIPS/autograd/blob/442205dfefe407beffb33550846434baa90c4de7/autograd/core.py#L8-L40). In the forward pass, a graph of VJPNode is constructed.
```python
# User API
def make_grad(fun, x):
start_node = VJPNode.new_root()
end_value, end_node = trace(start_node, fun, x)
return backward_pass(g, end_node), end_value
# trace the forward pass by creating VJPNodes
def trace(start_node, fun, x):
with trace_stack.new_trace() as t:
start_box = new_box(x, t, start_node)
end_box = fun(start_box)
return end_box._value, end_box._node
def backward_pass(g, end_node):
outgrads = {end_node : (g, False)}
for node in toposort(end_node):
outgrad = outgrads.pop(node)
ingrads = node.vjp(outgrad[0])
for parent, ingrad in zip(node.parents, ingrads):
outgrads[parent] = add_outgrads(outgrads.get(parent), ingrad)
return outgrad[0]
# Every VJPNode corresponds to a op_grad
class VJPNode(Node):
__slots__ = ['parents', 'vjp']
def __init__(self, value, fun, args, kwargs, parent_argnums, parents):
self.parents = parents
vjpmaker = primitive_vjps[fun]
self.vjp = vjpmaker(parent_argnums, value, args, kwargs)
```
#### Chainer
Example Code
```python
# (1) Function Set definition, creates FunctionNode
model = FunctionSet(
l1=F.Linear(784, 100),
l2=F.Linear(100, 100),
l3=F.Linear(100, 10)).to_gpu()
# (2) Optimizer Setup
opt = optimizers.SGD()
opt.setup(model)
# (3) Forward computation
def forward(x, t):
h1 = F.relu(model.l1(x))
h2 = F.relu(model.l2(h1))
y = model.l3(h2)
return F.softmax_cross_entropy(y, t)
# (4) Training loop
for epoch in xrange(n_epoch):
for i in xrange(0, N, b_size):
x = Variable(to_gpu(...))
t = Variable(to_gpu(...))
opt.zero_grads()
loss = forward(x, t)
loss.backward()
opt.update()
```
In `forward(x, t)`, a graph of [`VariableNode`](https://github.com/chainer/chainer/blob/master/chainer/variable.py#L110) and [`FunctionNode`](https://github.com/chainer/chainer/blob/a69103a4aa59d5b318f39b01dbcb858d465b89cf/chainer/function_node.py#L19) is constructed. Every output's `VariableNode.creator` is pointed to the `FunctionNode`.
```python
class FunctionNode(object):
...
def apply(self, inputs):
outputs = self.forward(inputs)
ret = tuple([variable.Variable(y, requires_grad=requires_grad)
for y in outputs])
# Topological ordering
self.rank = max([x.rank for x in inputs]) if input_vars else 0
# Add backward edges
for y in ret:
y.creator_node = self
self.inputs = tuple([x.node for x in input_vars])
self.outputs = tuple([y.node for y in ret])
return ret
```
`loss.backward()` will calculate the accumulated gradient of all variables. All the backward of `FunctionNode`s will be called based on the topological order.
```python
class VariableNode(object):
...
def backward(self, retain_grad, loss_scale):
if self.creator_node is None:
return
cand_funcs = []
seen_set = set()
grads = {}
# Initialize error by 1, if this is a loss variable
if self.data.size == 1 and self._grad_var is None:
self.grad = numpy.ones_like(self.data)
grads[self._node] = self._grad_var
def add_cand(cand):
if cand not in seen_set:
# Negate since heapq is min-heap. This is a global variable
heapq.heappush(cand_funcs, (-cand.rank, len(seen_set), cand))
seen_set.add(cand)
add_cand(self.creator_node)
while cand_funcs:
_, _, func = heapq.heappop(cand_funcs)
gxs = func.backward_accumulate(func.inputs, func.outputs, func.outputs.grad)
for x, gx in enumerate(gxs):
if x in grads:
grads[x] += gx
else:
grads[x] = gx
if x.creator_node is not None:
add_cand(x.creator_node)
```
#### PyTorch
Example Code
```python
x = Variable(torch.ones(5, 5))
y = Variable(torch.ones(5, 5) * 4)
z = x ** 2 + x * 2 + x * y + y
z.backward(torch.ones(5, 5))
```
The trace is done by `Variable.creator` and `Function.previous_functions`.
```python
class Variable(object):
def __init__(self, tensor, creator=None, requires_grad=True):
if creator is None:
creator = Leaf(self, requires_grad)
self.data = tensor
self.creator = creator
self._grad = None
def backward(self, gradient=None):
if gradient is None:
if self.data.numel() != 1:
raise RuntimeError('backward should be called only on a scalar (i.e. 1-element tensor) or with gradient w.r.t. the variable')
gradient = self.data.new(1).fill_(1)
self._execution_engine.run_backward(self, gradient)
class Function(obejct):
# ...
def _do_forward(self, *input):
unpacked_input = tuple(arg.data for arg in input)
raw_output = self.forward(*unpacked_input)
# mark output.creator = self for backward trace
output = tuple(Variable(tensor, self) for tensor in raw_output)
self.previous_functions = [(arg.creator, id(arg)) for arg in input]
self.output_ids = {id(var): i for i, var in enumerate(output)}
return output
def _do_backward(self, grad_output):
return self.backwaerd(grad_output)
```
The [backward](https://github.com/pytorch/pytorch/blob/v0.1.1/torch/autograd/engine.py) is similar to Autograd.
#### DyNet
Example code
```python
model = dy.model()
W_p = model.add_parameters((20, 100))
b_p = model.add_parameters(20)
E = model.add_lookup_parameters((20000, 50))
for epoch in range(num_epochs):
for in_words, out_label in training_data:
dy.renew_cg() # init tape
W = dy.parameter(W_p)
b = dy.parameter(b_p)
score_sym = dy.softmax(W*dy.concatenate([E[in_words[0]],E[in_words[1]]])+b)
loss_sym = dy.pickneglogsoftmax(score_sym, out_label)
loss_val = loss_sym.value()
loss_sym.backward()
```
[forward](https://github.com/clab/dynet/blob/740a9626a13a2732544de142e256ad0d0a166658/dynet/exec.cc#L84-L158), [backward](https://github.com/clab/dynet/blob/740a9626a13a2732544de142e256ad0d0a166658/dynet/exec.cc#L166-L284). The trace is done by creating a tape of expressions in every iteration. Backward is done by traverse the tape in the reverse order.
```c++
void SimpleExecutionEngine::backward(VariableIndex from_where, bool full) {
...
for (int i = num_nodes - 1; i >= 0; --i) {
// each node corresponds to an op
node->backward(xs, node_fx, node_dEdfx, ai, node_dEdxai);
}
...
}
```
doc/v2/api/config/evaluators.rst
浏览文件 @ 8bd148dc
......@@ -101,7 +101,7 @@ value_printer
:noindex:
Detection
=====
==========
detection_map
-------------
......
doc/v2/api/config/layer.rst
浏览文件 @ 8bd148dc
......@@ -11,7 +11,7 @@ Data layer
data
----
.. autoclass:: paddle.v2.layer.data
.. autofunction:: paddle.v2.layer.data
:noindex:
Fully Connected Layers
......@@ -21,12 +21,12 @@ Fully Connected Layers
fc
--
.. autoclass:: paddle.v2.layer.fc
.. autofunction:: paddle.v2.layer.fc
:noindex:
selective_fc
------------
.. autoclass:: paddle.v2.layer.selective_fc
.. autofunction:: paddle.v2.layer.selective_fc
:noindex:
Conv Layers
......@@ -34,34 +34,34 @@ Conv Layers
conv_operator
-------------
.. autoclass:: paddle.v2.layer.conv_operator
.. autofunction:: paddle.v2.layer.conv_operator
:noindex:
conv_projection
---------------
.. autoclass:: paddle.v2.layer.conv_projection
.. autofunction:: paddle.v2.layer.conv_projection
:noindex:
conv_shift
----------
.. autoclass:: paddle.v2.layer.conv_shift
.. autofunction:: paddle.v2.layer.conv_shift
:noindex:
img_conv
--------
.. autoclass:: paddle.v2.layer.img_conv
.. autofunction:: paddle.v2.layer.img_conv
:noindex:
.. _api_v2.layer_context_projection:
context_projection
------------------
.. autoclass:: paddle.v2.layer.context_projection
.. autofunction:: paddle.v2.layer.context_projection
:noindex:
row_conv
--------
.. autoclass:: paddle.v2.layer.row_conv
.. autofunction:: paddle.v2.layer.row_conv
:noindex:
Image Pooling Layer
......@@ -69,27 +69,27 @@ Image Pooling Layer
img_pool
--------
.. autoclass:: paddle.v2.layer.img_pool
.. autofunction:: paddle.v2.layer.img_pool
:noindex:
spp
---
.. autoclass:: paddle.v2.layer.spp
.. autofunction:: paddle.v2.layer.spp
:noindex:
maxout
------
.. autoclass:: paddle.v2.layer.maxout
.. autofunction:: paddle.v2.layer.maxout
:noindex:
roi_pool
--------
.. autoclass:: paddle.v2.layer.roi_pool
.. autofunction:: paddle.v2.layer.roi_pool
:noindex:
pad
----
.. autoclass:: paddle.v2.layer.pad
.. autofunction:: paddle.v2.layer.pad
:noindex:
Norm Layer
......@@ -97,27 +97,27 @@ Norm Layer
img_cmrnorm
-----------
.. autoclass:: paddle.v2.layer.img_cmrnorm
.. autofunction:: paddle.v2.layer.img_cmrnorm
:noindex:
batch_norm
----------
.. autoclass:: paddle.v2.layer.batch_norm
.. autofunction:: paddle.v2.layer.batch_norm
:noindex:
sum_to_one_norm
---------------
.. autoclass:: paddle.v2.layer.sum_to_one_norm
.. autofunction:: paddle.v2.layer.sum_to_one_norm
:noindex:
cross_channel_norm
------------------
.. autoclass:: paddle.v2.layer.cross_channel_norm
.. autofunction:: paddle.v2.layer.cross_channel_norm
:noindex:
row_l2_norm
-----------
.. autoclass:: paddle.v2.layer.row_l2_norm
.. autofunction:: paddle.v2.layer.row_l2_norm
:noindex:
Recurrent Layers
......@@ -125,22 +125,22 @@ Recurrent Layers
recurrent
---------
.. autoclass:: paddle.v2.layer.recurrent
.. autofunction:: paddle.v2.layer.recurrent
:noindex:
lstmemory
---------
.. autoclass:: paddle.v2.layer.lstmemory
.. autofunction:: paddle.v2.layer.lstmemory
:noindex:
grumemory
---------
.. autoclass:: paddle.v2.layer.grumemory
.. autofunction:: paddle.v2.layer.grumemory
:noindex:
gated_unit
-----------
.. autoclass:: paddle.v2.layer.gated_unit
.. autofunction:: paddle.v2.layer.gated_unit
:noindex:
Recurrent Layer Group
......@@ -148,32 +148,32 @@ Recurrent Layer Group
memory
------
.. autoclass:: paddle.v2.layer.memory
.. autofunction:: paddle.v2.layer.memory
:noindex:
recurrent_group
---------------
.. autoclass:: paddle.v2.layer.recurrent_group
.. autofunction:: paddle.v2.layer.recurrent_group
:noindex:
lstm_step
---------
.. autoclass:: paddle.v2.layer.lstm_step
.. autofunction:: paddle.v2.layer.lstm_step
:noindex:
gru_step
--------
.. autoclass:: paddle.v2.layer.gru_step
.. autofunction:: paddle.v2.layer.gru_step
:noindex:
beam_search
------------
.. autoclass:: paddle.v2.layer.beam_search
.. autofunction:: paddle.v2.layer.beam_search
:noindex:
get_output
----------
.. autoclass:: paddle.v2.layer.get_output
.. autofunction:: paddle.v2.layer.get_output
:noindex:
Mixed Layer
......@@ -183,54 +183,54 @@ Mixed Layer
mixed
-----
.. autoclass:: paddle.v2.layer.mixed
.. autofunction:: paddle.v2.layer.mixed
:noindex:
.. _api_v2.layer_embedding:
embedding
---------
.. autoclass:: paddle.v2.layer.embedding
.. autofunction:: paddle.v2.layer.embedding
:noindex:
scaling_projection
------------------
.. autoclass:: paddle.v2.layer.scaling_projection
.. autofunction:: paddle.v2.layer.scaling_projection
:noindex:
dotmul_projection
-----------------
.. autoclass:: paddle.v2.layer.dotmul_projection
.. autofunction:: paddle.v2.layer.dotmul_projection
:noindex:
dotmul_operator
---------------
.. autoclass:: paddle.v2.layer.dotmul_operator
.. autofunction:: paddle.v2.layer.dotmul_operator
:noindex:
full_matrix_projection
----------------------
.. autoclass:: paddle.v2.layer.full_matrix_projection
.. autofunction:: paddle.v2.layer.full_matrix_projection
:noindex:
identity_projection
-------------------
.. autoclass:: paddle.v2.layer.identity_projection
.. autofunction:: paddle.v2.layer.identity_projection
:noindex:
slice_projection
-------------------
.. autoclass:: paddle.v2.layer.slice_projection
.. autofunction:: paddle.v2.layer.slice_projection
:noindex:
table_projection
----------------
.. autoclass:: paddle.v2.layer.table_projection
.. autofunction:: paddle.v2.layer.table_projection
:noindex:
trans_full_matrix_projection
----------------------------
.. autoclass:: paddle.v2.layer.trans_full_matrix_projection
.. autofunction:: paddle.v2.layer.trans_full_matrix_projection
:noindex:
Aggregate Layers
......@@ -245,51 +245,46 @@ AggregateLevel
pooling
-------
.. autoclass:: paddle.v2.layer.pooling
.. autofunction:: paddle.v2.layer.pooling
:noindex:
.. _api_v2.layer_last_seq:
last_seq
--------
.. autoclass:: paddle.v2.layer.last_seq
.. autofunction:: paddle.v2.layer.last_seq
:noindex:
.. _api_v2.layer_first_seq:
first_seq
---------
.. autoclass:: paddle.v2.layer.first_seq
.. autofunction:: paddle.v2.layer.first_seq
:noindex:
sub_seq
---------
.. autoclass:: paddle.v2.layer.sub_seq
.. autofunction:: paddle.v2.layer.sub_seq
:noindex:
concat
------
.. autoclass:: paddle.v2.layer.concat
.. autofunction:: paddle.v2.layer.concat
:noindex:
seq_concat
----------
.. autoclass:: paddle.v2.layer.seq_concat
.. autofunction:: paddle.v2.layer.seq_concat
:noindex:
seq_slice
---------
.. autoclass:: paddle.v2.layer.seq_slice
:noindex:
kmax_sequence_score
-------------------
.. autoclass:: paddle.v2.layer.kmax_sequence_score
.. autofunction:: paddle.v2.layer.seq_slice
:noindex:
sub_nested_seq
--------------
.. autoclass:: paddle.v2.layer.sub_nested_seq
.. autofunction:: paddle.v2.layer.sub_nested_seq
:noindex:
Reshaping Layers
......@@ -297,7 +292,7 @@ Reshaping Layers
block_expand
------------
.. autoclass:: paddle.v2.layer.block_expand
.. autofunction:: paddle.v2.layer.block_expand
:noindex:
.. _api_v2.layer_expand:
......@@ -309,22 +304,22 @@ ExpandLevel
expand
------
.. autoclass:: paddle.v2.layer.expand
.. autofunction:: paddle.v2.layer.expand
:noindex:
repeat
------
.. autoclass:: paddle.v2.layer.repeat
.. autofunction:: paddle.v2.layer.repeat
:noindex:
rotate
------
.. autoclass:: paddle.v2.layer.rotate
.. autofunction:: paddle.v2.layer.rotate
:noindex:
seq_reshape
-----------
.. autoclass:: paddle.v2.layer.seq_reshape
.. autofunction:: paddle.v2.layer.seq_reshape
:noindex:
Math Layers
......@@ -332,94 +327,94 @@ Math Layers
addto
-----
.. autoclass:: paddle.v2.layer.addto
.. autofunction:: paddle.v2.layer.addto
:noindex:
linear_comb
-----------
.. autoclass:: paddle.v2.layer.linear_comb
.. autofunction:: paddle.v2.layer.linear_comb
:noindex:
interpolation
-------------
.. autoclass:: paddle.v2.layer.interpolation
.. autofunction:: paddle.v2.layer.interpolation
:noindex:
bilinear_interp
---------------
.. autoclass:: paddle.v2.layer.bilinear_interp
.. autofunction:: paddle.v2.layer.bilinear_interp
:noindex:
dropout
--------
.. autoclass:: paddle.v2.layer.dropout
.. autofunction:: paddle.v2.layer.dropout
:noindex:
dot_prod
---------
.. autoclass:: paddle.v2.layer.dot_prod
.. autofunction:: paddle.v2.layer.dot_prod
:noindex:
out_prod
--------
.. autoclass:: paddle.v2.layer.out_prod
.. autofunction:: paddle.v2.layer.out_prod
:noindex:
power
-----
.. autoclass:: paddle.v2.layer.power
.. autofunction:: paddle.v2.layer.power
:noindex:
scaling
-------
.. autoclass:: paddle.v2.layer.scaling
.. autofunction:: paddle.v2.layer.scaling
:noindex:
clip
----
.. autoclass:: paddle.v2.layer.clip
.. autofunction:: paddle.v2.layer.clip
:noindex:
resize
------
.. autoclass:: paddle.v2.layer.resize
.. autofunction:: paddle.v2.layer.resize
:noindex:
slope_intercept
---------------
.. autoclass:: paddle.v2.layer.slope_intercept
.. autofunction:: paddle.v2.layer.slope_intercept
:noindex:
tensor
------
.. autoclass:: paddle.v2.layer.tensor
.. autofunction:: paddle.v2.layer.tensor
:noindex:
.. _api_v2.layer_cos_sim:
cos_sim
-------
.. autoclass:: paddle.v2.layer.cos_sim
.. autofunction:: paddle.v2.layer.cos_sim
:noindex:
l2_distance
-----------
.. autoclass:: paddle.v2.layer.l2_distance
.. autofunction:: paddle.v2.layer.l2_distance
:noindex:
trans
-----
.. autoclass:: paddle.v2.layer.trans
.. autofunction:: paddle.v2.layer.trans
:noindex:
scale_shift
-----------
.. autoclass:: paddle.v2.layer.scale_shift
.. autofunction:: paddle.v2.layer.scale_shift
:noindex:
factorization_machine
---------------------
.. autoclass:: paddle.v2.layer.factorization_machine
.. autofunction:: paddle.v2.layer.factorization_machine
:noindex:
Sampling Layers
......@@ -427,17 +422,17 @@ Sampling Layers
maxid
-----
.. autoclass:: paddle.v2.layer.max_id
.. autofunction:: paddle.v2.layer.max_id
:noindex:
sampling_id
-----------
.. autoclass:: paddle.v2.layer.sampling_id
.. autofunction:: paddle.v2.layer.sampling_id
:noindex:
multiplex
---------
.. autoclass:: paddle.v2.layer.multiplex
.. autofunction:: paddle.v2.layer.multiplex
:noindex:
.. _api_v2.layer_costs:
......@@ -447,97 +442,97 @@ Cost Layers
cross_entropy_cost
------------------
.. autoclass:: paddle.v2.layer.cross_entropy_cost
.. autofunction:: paddle.v2.layer.cross_entropy_cost
:noindex:
cross_entropy_with_selfnorm_cost
--------------------------------
.. autoclass:: paddle.v2.layer.cross_entropy_with_selfnorm_cost
.. autofunction:: paddle.v2.layer.cross_entropy_with_selfnorm_cost
:noindex:
multi_binary_label_cross_entropy_cost
-------------------------------------
.. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
.. autofunction:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
:noindex:
classification_cost
-------------------
.. autoclass:: paddle.v2.layer.classification_cost
.. autofunction:: paddle.v2.layer.classification_cost
:noindex:
huber_regression_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_regression_cost
.. autofunction:: paddle.v2.layer.huber_regression_cost
:noindex:
huber_classification_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_classification_cost
.. autofunction:: paddle.v2.layer.huber_classification_cost
:noindex:
lambda_cost
-----------
.. autoclass:: paddle.v2.layer.lambda_cost
.. autofunction:: paddle.v2.layer.lambda_cost
:noindex:
square_error_cost
-----------------
.. autoclass:: paddle.v2.layer.square_error_cost
.. autofunction:: paddle.v2.layer.square_error_cost
:noindex:
rank_cost
---------
.. autoclass:: paddle.v2.layer.rank_cost
.. autofunction:: paddle.v2.layer.rank_cost
:noindex:
sum_cost
---------
.. autoclass:: paddle.v2.layer.sum_cost
.. autofunction:: paddle.v2.layer.sum_cost
:noindex:
crf
---
.. autoclass:: paddle.v2.layer.crf
.. autofunction:: paddle.v2.layer.crf
:noindex:
crf_decoding
------------
.. autoclass:: paddle.v2.layer.crf_decoding
.. autofunction:: paddle.v2.layer.crf_decoding
:noindex:
ctc
---
.. autoclass:: paddle.v2.layer.ctc
.. autofunction:: paddle.v2.layer.ctc
:noindex:
warp_ctc
--------
.. autoclass:: paddle.v2.layer.warp_ctc
.. autofunction:: paddle.v2.layer.warp_ctc
:noindex:
nce
---
.. autoclass:: paddle.v2.layer.nce
.. autofunction:: paddle.v2.layer.nce
:noindex:
hsigmoid
---------
.. autoclass:: paddle.v2.layer.hsigmoid
.. autofunction:: paddle.v2.layer.hsigmoid
:noindex:
smooth_l1_cost
--------------
.. autoclass:: paddle.v2.layer.smooth_l1_cost
.. autofunction:: paddle.v2.layer.smooth_l1_cost
:noindex:
multibox_loss
--------------
.. autoclass:: paddle.v2.layer.multibox_loss
.. autofunction:: paddle.v2.layer.multibox_loss
:noindex:
detection_output
----------------
.. autoclass:: paddle.v2.layer.detection_output
.. autofunction:: paddle.v2.layer.detection_output
:noindex:
Check Layer
......@@ -545,7 +540,7 @@ Check Layer
eos
---
.. autoclass:: paddle.v2.layer.eos
.. autofunction:: paddle.v2.layer.eos
:noindex:
Activation
......@@ -553,5 +548,5 @@ Activation
prelu
--------
.. autoclass:: paddle.v2.layer.prelu
.. autofunction:: paddle.v2.layer.prelu
:noindex:
doc/v2/api/index_en.rst
浏览文件 @ 8bd148dc
......@@ -8,4 +8,3 @@ API
model_configs.rst
data.rst
run_logic.rst
fluid/index.rst
doc/v2/build_and_install/build_from_source_cn.rst
浏览文件 @ 8bd148dc
......@@ -23,7 +23,7 @@ PaddlePaddle需要使用Docker环境完成编译，这样可以免去单独安
在 `这里 <https://github.com/PaddlePaddle/Paddle/tree/develop/tools/manylinux1/>`__ 找到 paddle_manylinux_devel
镜像的编译以及使用方法。或者参考下述可选步骤，从源码中构建用于编译PaddlePaddle的Docker镜像。
如果您选择不使用Docker镜像，则需要在本机安装下面章节列出的 `编译依赖`_ 之后才能开始编译的步骤。
如果您选择不使用Docker镜像，则需要在本机安装下面章节列出的 :ref:`编译依赖 <_compile_deps>` 之后才能开始编译的步骤。
编译PaddlePaddle，需要执行：
......@@ -106,7 +106,7 @@ PaddlePaddle需要使用Docker环境完成编译，这样可以免去单独安
- 学习 Docker 有多难？
理解 Docker 并不难，大概花十分钟看一下[这篇文章](https://zhuanlan.zhihu.com/p/19902938)。这可以帮您省掉花一小时安装和配置各种开发工具，以及切换机器时需要新安装的辛苦。别忘了 PaddlePaddle 更新可能导致需要新的开发工具。更别提简化问题复现带来的好处了。
理解 Docker 并不难，大概花十分钟看一下 `如何使用Docker <https://zhuanlan.zhihu.com/p/19902938>`_ 。这可以帮您省掉花一小时安装和配置各种开发工具，以及切换机器时需要新安装的辛苦。别忘了 PaddlePaddle 更新可能导致需要新的开发工具。更别提简化问题复现带来的好处了。
- 我可以用 IDE 吗？
......@@ -123,7 +123,7 @@ PaddlePaddle需要使用Docker环境完成编译，这样可以免去单独安
- 可以并行编译吗？
是的。我们的 Docker image 运行一个 [Bash 脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh)。这个脚本调用 `make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。
是的。我们的 Docker image 运行一个 `Paddle编译Bash脚本 <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh>`_ 。这个脚本调用 `make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。
- Docker 需要 sudo
......@@ -131,11 +131,11 @@ PaddlePaddle需要使用Docker环境完成编译，这样可以免去单独安
- 在 Windows/MacOS 上编译很慢
Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考[这个issue](https://github.com/PaddlePaddle/Paddle/issues/627)。
Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考 `如何为Windows/Mac计算机上的Docker增加内存和虚拟机 <https://github.com/PaddlePaddle/Paddle/issues/627>`_ 。
- 磁盘不够
本文中的例子里，`docker run` 命令里都用了 `--rm` 参数，这样保证运行结束之后的 containers 不会保留在磁盘上。可以用 `docker ps -a` 命令看到停止后但是没有删除的 containers。`docker build` 命令有时候会产生一些中间结果，是没有名字的 images，也会占用磁盘。可以参考[这篇文章](https://zaiste.net/posts/removing_docker_containers/)来清理这些内容。
本文中的例子里，`docker run` 命令里都用了 `--rm` 参数，这样保证运行结束之后的 containers 不会保留在磁盘上。可以用 `docker ps -a` 命令看到停止后但是没有删除的 containers。`docker build` 命令有时候会产生一些中间结果，是没有名字的 images，也会占用磁盘。可以参考 `如何删除Docker Container <https://zaiste.net/posts/removing_docker_containers/>`_ 来清理这些内容。
.. _compile_deps:
......@@ -195,7 +195,7 @@ BLAS
PaddlePaddle支持 `MKL <https://software.intel.com/en-us/intel-mkl>`_ 和
`OpenBlAS <http://www.openblas.net/>`_ 两种BLAS库。默认使用MKL。如果使用MKL并且机器含有AVX2指令集，
还会下载MKL-DNN数学库，详细参考 `这里 <https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn#cmake>`_ 。
还会下载MKL-DNN数学库，详细参考 `mkldnn设计文档 <https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn#cmake>`_ 。
如果关闭MKL，则会使用OpenBLAS作为BLAS库。
......@@ -211,7 +211,7 @@ PaddlePaddle可以使用cuDNN v5.1之后的任何一个版本来编译运行，
编译选项的设置
++++++++++++++
PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。cmake编译时，首先在系统路径（ :code:`/usr/lib:/usr/local/lib` ）中搜索这几个库，同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置，例如
PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。cmake编译时，首先在系统路径（ :code:`/usr/lib:/usr/local/lib` ）中搜索这几个库，同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置，例如
.. code-block:: bash
......
doc/v2/build_and_install/build_from_source_en.rst
浏览文件 @ 8bd148dc
......@@ -11,7 +11,7 @@ To build PaddlePaddle, you need
1. A computer -- Linux, Windows, MacOS.
2. Docker.
Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image.
Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image.
We run all the tools by running this image.
.. _build_step:
......@@ -26,6 +26,8 @@ you can also find how to build and use paddle_manylinux_devel Docker image from
`here <https://github.com/PaddlePaddle/Paddle/tree/develop/tools/manylinux1/>`__
Or you can build your own image from source as the optional step below:
If you don't wish to use docker，you need to install several compile dependencies manually as :ref:`Compile Dependencies <_compile_deps>` shows to start compilation.
.. code-block:: bash
# 1. clone the source code
......@@ -108,7 +110,7 @@ Frequently Asked Questions
- How difficult is it to learn Docker?
It takes you ten minutes to read [an introductory article](https://docs.docker.com/get-started) and saves you more than one hour to install all required build tools, configure them, especially when new versions of PaddlePaddle require some new tools. Not even to mention the time saved when other people trying to reproduce the issue you have.
It takes you ten minutes to read `an introductory article <https://docs.docker.com/get-started>`_ and saves you more than one hour to install all required build tools, configure them, especially when new versions of PaddlePaddle require some new tools. Not even to mention the time saved when other people trying to reproduce the issue you have.
- Can I use my favorite IDE?
......@@ -125,7 +127,7 @@ Frequently Asked Questions
- Does Docker do parallel building?
Our building Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh), which calls `make -j$(nproc)` to starts as many processes as the number of your CPU cores.
Our building Docker image runs a `Bash script <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh>`_ , which calls `make -j$(nproc)` to starts as many processes as the number of your CPU cores.
- Docker requires sudo
......@@ -133,11 +135,11 @@ Frequently Asked Questions
- Docker on Windows/MacOS builds slowly
On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to [this issue](https://github.com/PaddlePaddle/Paddle/issues/627) for details.
On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to `this issue <https://github.com/PaddlePaddle/Paddle/issues/627>`_ for details.
- Not enough disk space
Examples in this article use option `--rm` with the `docker run` command. This option ensures that stopped containers do not exist on hard disks. We can use `docker ps -a` to list all containers, including stopped. Sometimes `docker build` generates some intermediate dangling images, which also take disk space. To clean them, please refer to [this article](https://zaiste.net/posts/removing_docker_containers/).
Examples in this article use option `--rm` with the `docker run` command. This option ensures that stopped containers do not exist on hard disks. We can use `docker ps -a` to list all containers, including stopped. Sometimes `docker build` generates some intermediate dangling images, which also take disk space. To clean them, please refer to `this article <https://zaiste.net/posts/removing_docker_containers/>`_ .
.. _compile_deps:
......
doc/v2/build_and_install/pip_install_cn.rst
浏览文件 @ 8bd148dc
......@@ -60,6 +60,7 @@ paddlepaddle-gpu==0.11.0 使用CUDA 7.5和cuDNN 5编译的0.11.0版
"cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda9.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
.. _pip_dependency:
......
doc/v2/build_and_install/pip_install_en.rst
浏览文件 @ 8bd148dc
......@@ -63,6 +63,7 @@ If the links below shows up the login form, just click "Log in as guest" to star
"cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
"cuda9.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`__", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`__"
.. _pip_dependency:
......
doc/v2/dev/contribute_to_paddle_cn.md
浏览文件 @ 8bd148dc
......@@ -104,7 +104,7 @@ no changes added to commit (use "git add" and/or "git commit -a")
➜ docker run -it -v $(pwd):/paddle paddle:latest-dev bash -c "cd /paddle/build && ctest"
```
关于构建和测试的更多信息，请参见[这篇文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
关于构建和测试的更多信息，请参见[使用Docker安装运行](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/v2/build_and_install/docker_install_cn.rst)。
## 提交（commit）
......
paddle/contrib/CMakeLists.txt
浏览文件 @ 8bd148dc
......@@ -14,3 +14,4 @@
#
add_subdirectory(inference)
add_subdirectory(tape)
paddle/contrib/inference/CMakeLists.txt
浏览文件 @ 8bd148dc
......@@ -17,46 +17,52 @@ if(APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
endif(APPLE)
function(inference_api_test TARGET_NAME TEST_SRC)
set(options "")
set(oneValueArgs "")
set(multiValueArgs ARGS)
cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
set(arg_list "")
if(inference_test_ARGS)
foreach(arg ${inference_test_ARGS})
list(APPEND arg_list "_${arg}")
endforeach()
else()
list(APPEND arg_list "_")
endif()
foreach(arg ${arg_list})
string(REGEX REPLACE "^_$" "" arg "${arg}")
set(inference_deps paddle_inference_api paddle_fluid_api)
function(inference_api_test TARGET_NAME)
if (WITH_TESTING)
set(options "")
set(oneValueArgs "")
set(multiValueArgs ARGS)
cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
cc_test(${TARGET_NAME}
SRCS ${TEST_SRC}
DEPS paddle_fluid_api paddle_inference_api paddle_inference_api_impl
SRCS ${TARGET_NAME}.cc
DEPS "${inference_deps}"
ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
# TODO(panyx0178): Figure out how to add word2vec and image_classification
# as deps.
# set_tests_properties(${TARGET_NAME}
# PROPERTIES DEPENDS ${DEP_TEST})
endforeach()
if(inference_test_ARGS)
set_tests_properties(${TARGET_NAME}
PROPERTIES DEPENDS "${inference_test_ARGS}")
endif()
endif(WITH_TESTING)
endfunction(inference_api_test)
cc_library(paddle_inference_api
SRCS paddle_inference_api.cc
SRCS paddle_inference_api.cc paddle_inference_api_impl.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
cc_library(paddle_inference_api_impl
SRCS paddle_inference_api_impl.cc
DEPS paddle_inference_api paddle_fluid_api)
cc_test(test_paddle_inference_api
SRCS test_paddle_inference_api.cc
DEPS paddle_inference_api)
inference_api_test(test_paddle_inference_api_impl
test_paddle_inference_api_impl.cc)
ARGS test_word2vec test_image_classification)
if (WITH_ANAKIN AND WITH_TESTING) # only needed in CI
# Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
# so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
# compile the libinference_anakin_api.a and compile with anakin.so.
nv_library(inference_anakin_api SHARED SRCS paddle_inference_api.cc paddle_inference_api_anakin_engine.cc)
target_compile_options(inference_anakin_api BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
target_link_libraries(inference_anakin_api anakin anakin_saber_common)
cc_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc
ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api)
target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endif()
if(WITH_TESTING)
add_subdirectory(demo)
endif()
paddle/contrib/inference/demo/CMakeLists.txt 0 → 100644
浏览文件 @ 8bd148dc
# 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.
#
inference_api_test(simple_on_word2vec ARGS test_word2vec)
paddle/contrib/inference/demo/simple_on_word2vec.cc 0 → 100644
浏览文件 @ 8bd148dc
/* 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 contains a simple demo for how to take a model for inference.
*/
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <memory>
#include <thread>
#include "paddle/contrib/inference/paddle_inference_api.h"
namespace paddle {
namespace demo {
DEFINE_string(dirname, "", "Directory of the inference model.");
void Main(bool use_gpu) {
//# 1. Create PaddlePredictor with a config.
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
config.use_gpu = use_gpu;
config.fraction_of_gpu_memory = 0.15;
config.device = 0;
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
for (int batch_id = 0; batch_id < 3; batch_id++) {
//# 2. Prepare input.
int64_t data[4] = {1, 2, 3, 4};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = buf,
.dtype = PaddleDType::INT64};
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> slots(4, tensor);
//# 3. Run
std::vector<PaddleTensor> outputs;
CHECK(predictor->Run(slots, &outputs));
//# 4. Get output.
ASSERT_EQ(outputs.size(), 1UL);
LOG(INFO) << "output buffer size: " << outputs.front().data.length;
const size_t num_elements = outputs.front().data.length / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
}
// TODO(Superjomn): this is should be free automatically
free(outputs[0].data.data);
}
}
void MainThreads(int num_threads, bool use_gpu) {
// Multi-threads only support on CPU
// 0. Create PaddlePredictor with a config.
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
config.use_gpu = use_gpu;
config.fraction_of_gpu_memory = 0.15;
config.device = 0;
auto main_predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
std::vector<std::thread> threads;
for (int tid = 0; tid < num_threads; ++tid) {
threads.emplace_back([&, tid]() {
// 1. clone a predictor which shares the same parameters
auto predictor = main_predictor->Clone();
constexpr int num_batches = 3;
for (int batch_id = 0; batch_id < num_batches; ++batch_id) {
// 2. Dummy Input Data
int64_t data[4] = {1, 2, 3, 4};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = buf,
.dtype = PaddleDType::INT64};
std::vector<PaddleTensor> inputs(4, tensor);
std::vector<PaddleTensor> outputs;
// 3. Run
CHECK(predictor->Run(inputs, &outputs));
// 4. Get output.
ASSERT_EQ(outputs.size(), 1UL);
LOG(INFO) << "TID: " << tid << ", "
<< "output buffer size: " << outputs.front().data.length;
const size_t num_elements = outputs.front().data.length / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
}
free(outputs[0].data.data);
}
});
}
for (int i = 0; i < num_threads; ++i) {
threads[i].join();
}
}
TEST(demo, word2vec_cpu) { Main(false /*use_gpu*/); }
TEST(demo_multi_threads, word2vec_cpu_1) { MainThreads(1, false /*use_gpu*/); }
TEST(demo_multi_threads, word2vec_cpu_4) { MainThreads(4, false /*use_gpu*/); }
#ifdef PADDLE_WITH_CUDA
TEST(demo, word2vec_gpu) { Main(true /*use_gpu*/); }
TEST(demo_multi_threads, word2vec_gpu_1) { MainThreads(1, true /*use_gpu*/); }
TEST(demo_multi_threads, word2vec_gpu_4) { MainThreads(4, true /*use_gpu*/); }
#endif
} // namespace demo
} // namespace paddle
paddle/contrib/inference/paddle_inference_api.h
浏览文件 @ 8bd148dc
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
/*
* This file contains the definition of a simple Inference API for Paddle.
......@@ -40,20 +40,30 @@ struct PaddleBuf {
struct PaddleTensor {
std::string name; // variable name.
std::vector<int> shape;
// TODO(Superjomn) for LoD support, add a vector<vector<int>> field if needed.
PaddleBuf data; // blob of data.
PaddleDType dtype;
};
enum class PaddleEngineKind {
kNative = 0, // Use the native Fluid facility.
kAnakin, // Use Anakin for inference.
// TODO(Superjomn) support following engines latter.
// kTensorRT, // Use TensorRT for inference.
// kAutoMixedAnakin, // Automatically mix Fluid with Anakin.
// kAutoMixedTensorRT, // Automatically mix Fluid with TensorRT.
};
/*
* A simple Inference API for Paddle. Currently this API might just be used by
* non-sequence scenerios.
* TODO(Superjomn) Prepare another API for NLP-related usages.
*/
* A simple Inference API for Paddle. Currently this API can be used by
* non-sequence scenerios.
*/
class PaddlePredictor {
public:
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
......@@ -66,34 +76,41 @@ class PaddlePredictor {
// be thread-safe.
virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
virtual bool InitShared() { return false; }
// Destroy the Predictor.
virtual ~PaddlePredictor() {}
friend std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(
const PaddlePredictor::Config& config);
virtual ~PaddlePredictor() = default;
// The common configs for all the predictors.
struct Config {
enum class EngineKind;
std::string model_dir; // path to the model directory.
bool enable_engine{false}; // Enable to execute (part of) the model on
// third-party engines.
EngineKind engine_kind{Config::EngineKind::kNone};
enum class EngineKind {
kNone = -1, // Use the native Fluid facility.
kAnakin, // Use Anakin for inference.
kTensorRT, // Use TensorRT for inference.
kAutoMixedAnakin, // Automatically mix Fluid with Anakin.
kAutoMixedTensorRT, // Automatically mix Fluid with TensorRT.
};
};
};
// A factory to help create difference predictor.
template <typename ConfigT>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
struct NativeConfig : public PaddlePredictor::Config {
// GPU related fields.
bool use_gpu{false};
int device{0};
float fraction_of_gpu_memory{-1.f}; // Negative to notify initialization.
std::string prog_file;
std::string param_file;
};
// Configurations for Anakin engine.
struct AnakinConfig : public PaddlePredictor::Config {
int device;
std::string model_file;
int max_batch_size{-1};
};
// A factory to help create different predictors.
//
// FOR EXTENSION DEVELOPER:
// Different predictors are designated by config type and engine kind. Similar
// configs can be merged, but there shouldn't be a huge config containing
// different fields for more than one kind of predictors.
//
// Similarly, each engine kind should map to a unique predictor implementation.
template <typename ConfigT, PaddleEngineKind engine = PaddleEngineKind::kNative>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
} // namespace paddle
paddle/contrib/inference/paddle_inference_api_anakin_engine.cc 0 → 100644
浏览文件 @ 8bd148dc
// 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/contrib/inference/paddle_inference_api_anakin_engine.h"
#include <cuda.h>
namespace paddle {
PaddleInferenceAnakinPredictor::PaddleInferenceAnakinPredictor(
const AnakinConfig &config) {
CHECK(Init(config));
}
bool PaddleInferenceAnakinPredictor::Init(const AnakinConfig &config) {
if (!(graph_.load(config.model_file))) {
return false;
}
graph_.ResetBatchSize("input_0", config.max_batch_size);
// optimization for graph
if (!(graph_.Optimize())) {
return false;
}
// construct executer
executor_.init(graph_);
return true;
}
bool PaddleInferenceAnakinPredictor::Run(
const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data) {
for (const auto &input : inputs) {
if (input.dtype != PaddleDType::FLOAT32) {
LOG(ERROR) << "Only support float type inputs. " << input.name
<< "'s type is not float";
return false;
}
auto d_tensor_in_p = executor_.get_in(input.name);
float *d_data_p = d_tensor_in_p->mutable_data();
if (cudaMemcpy(d_data_p,
static_cast<float *>(input.data.data),
d_tensor_in_p->valid_size() * sizeof(float),
cudaMemcpyHostToDevice) != 0) {
LOG(ERROR) << "copy data from CPU to GPU error";
return false;
}
}
executor_.prediction();
if (output_data->empty()) {
LOG(ERROR) << "At least one output should be set with tensors' names.";
return false;
}
for (auto &output : *output_data) {
auto *tensor = executor_.get_out(output.name);
output.shape = tensor->shape();
// Copy data from GPU -> CPU
if (cudaMemcpy(output.data.data,
tensor->mutable_data(),
tensor->valid_size() * sizeof(float),
cudaMemcpyDeviceToHost) != 0) {
LOG(ERROR) << "copy data from GPU to CPU error";
return false;
}
}
return true;
}
anakin::Net<anakin::NV, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
&PaddleInferenceAnakinPredictor::get_executer() {
return executor_;
}
// the cloned new Predictor of anakin share the same net weights from original
// Predictor
std::unique_ptr<PaddlePredictor> PaddleInferenceAnakinPredictor::Clone() {
VLOG(3) << "Anakin Predictor::clone";
std::unique_ptr<PaddlePredictor> cls(new PaddleInferenceAnakinPredictor());
// construct executer from other graph
auto anakin_predictor_p =
dynamic_cast<PaddleInferenceAnakinPredictor *>(cls.get());
if (!anakin_predictor_p) {
LOG(ERROR) << "fail to call Init";
return nullptr;
}
anakin_predictor_p->get_executer().init(graph_);
return std::move(cls);
}
// A factory to help create difference predictor.
template <>
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(
const AnakinConfig &config) {
VLOG(3) << "Anakin Predictor create.";
std::unique_ptr<PaddlePredictor> x(
new PaddleInferenceAnakinPredictor(config));
return x;
};
} // namespace paddle
paddle/contrib/inference/paddle_inference_api_anakin_engine.h 0 → 100644
浏览文件 @ 8bd148dc
/* 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 contains the implementation of inference API with Anakin engine
* embeded, this API can only support Anakin models.
*/
#pragma once
#include "paddle/contrib/inference/paddle_inference_api.h"
// from anakin
#include "framework/core/net/net.h"
#include "saber/saber_types.h"
namespace paddle {
class PaddleInferenceAnakinPredictor : public PaddlePredictor {
public:
PaddleInferenceAnakinPredictor() {}
PaddleInferenceAnakinPredictor(const AnakinConfig& config);
// NOTE Unlike the native engine, the buffers of anakin engine's output_data
// should be allocated first.
bool Run(const std::vector<PaddleTensor>& inputs,
std::vector<PaddleTensor>* output_data) override;
std::unique_ptr<PaddlePredictor> Clone() override;
anakin::Net<anakin::NV, anakin::saber::AK_FLOAT, anakin::Precision::FP32>&
get_executer();
~PaddleInferenceAnakinPredictor() override{};
private:
bool Init(const AnakinConfig& config);
anakin::graph::Graph<anakin::NV,
anakin::saber::AK_FLOAT,
anakin::Precision::FP32>
graph_;
anakin::Net<anakin::NV, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
executor_;
AnakinConfig config_;
};
} // namespace paddle
paddle/contrib/inference/paddle_inference_api_anakin_engine_tester.cc 0 → 100644
浏览文件 @ 8bd148dc
/* 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 <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/contrib/inference/paddle_inference_api.h"
DEFINE_string(model, "", "Directory of the inference model.");
namespace paddle {
AnakinConfig GetConfig() {
AnakinConfig config;
config.model_file = FLAGS_model;
config.device = 0;
config.max_batch_size = 1;
return config;
}
TEST(inference, anakin) {
AnakinConfig config = GetConfig();
auto predictor =
CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(config);
float data[1 * 3 * 224 * 224] = {1.0f};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "input_0",
.shape = std::vector<int>({1, 3, 224, 224}),
.data = buf,
.dtype = PaddleDType::FLOAT32};
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
float data_out[1000];
PaddleBuf buf_out{.data = data_out, .length = sizeof(data)};
PaddleTensor tensor_out{.name = "prob_out",
.shape = std::vector<int>({1000, 1}),
.data = buf_out,
.dtype = PaddleDType::FLOAT32};
std::vector<PaddleTensor> outputs(1, tensor_out);
ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
float* data_o = static_cast<float*>(outputs[0].data.data);
for (size_t j = 0; j < 1000; ++j) {
LOG(INFO) << "output[" << j << "]: " << data_o[j];
}
}
} // namespace paddle
paddle/contrib/inference/paddle_inference_api_impl.cc
浏览文件 @ 8bd148dc
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <sys/time.h>
#include <algorithm>
......@@ -54,18 +54,24 @@ std::string num2str(T a) {
}
} // namespace
bool PaddlePredictorImpl::Init() {
bool NativePaddlePredictor::Init(
std::shared_ptr<framework::Scope> parent_scope) {
VLOG(3) << "Predictor::init()";
// TODO(panyx0718): Should CPU vs GPU device be decided by id?
if (config_.device >= 0) {
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
} else {
place_ = paddle::platform::CPUPlace();
}
paddle::framework::InitDevices(false);
if (parent_scope) {
scope_ = parent_scope;
sub_scope_ = &(parent_scope->NewScope());
} else {
paddle::framework::InitDevices(false);
scope_.reset(new paddle::framework::Scope());
}
executor_.reset(new paddle::framework::Executor(place_));
scope_.reset(new paddle::framework::Scope());
// Initialize the inference program
if (!config_.model_dir.empty()) {
......@@ -84,20 +90,24 @@ bool PaddlePredictorImpl::Init() {
return false;
}
ctx_ = executor_->Prepare(*inference_program_, 0);
executor_->CreateVariables(
*inference_program_, sub_scope_ ? sub_scope_ : scope_.get(), 0);
// Create variables
// TODO(panyx0718): Why need to test share_variables here?
if (config_.share_variables) {
executor_->CreateVariables(*inference_program_, scope_.get(), 0);
}
// Get the feed_target_names and fetch_target_names
feed_target_names_ = inference_program_->GetFeedTargetNames();
fetch_target_names_ = inference_program_->GetFetchTargetNames();
return true;
}
bool PaddlePredictorImpl::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data) {
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";
Timer timer;
timer.tic();
......@@ -120,11 +130,12 @@ bool PaddlePredictorImpl::Run(const std::vector<PaddleTensor> &inputs,
}
// Run the inference program
// if share variables, we need not create variables
executor_->RunPreparedContext(ctx_.get(),
scope_.get(),
&feed_targets,
&fetch_targets,
!config_.share_variables);
executor_->RunPreparedContext(
ctx_.get(),
sub_scope_ != nullptr ? sub_scope_ : scope_.get(),
&feed_targets,
&fetch_targets,
false /* don't create variable eatch time */);
if (!GetFetch(fetchs, output_data)) {
LOG(ERROR) << "fail to get fetchs";
return false;
......@@ -133,59 +144,20 @@ bool PaddlePredictorImpl::Run(const std::vector<PaddleTensor> &inputs,
return true;
}
std::unique_ptr<PaddlePredictor> PaddlePredictorImpl::Clone() {
std::unique_ptr<PaddlePredictor> NativePaddlePredictor::Clone() {
VLOG(3) << "Predictor::clone";
std::unique_ptr<PaddlePredictor> cls(new PaddlePredictorImpl(config_));
if (!cls->InitShared()) {
LOG(ERROR) << "fail to call InitShared";
std::unique_ptr<PaddlePredictor> cls(new NativePaddlePredictor(config_));
if (!dynamic_cast<NativePaddlePredictor *>(cls.get())->Init(scope_)) {
LOG(ERROR) << "fail to call Init";
return nullptr;
}
// fix manylinux compile error.
return std::move(cls);
}
// TODO(panyx0718): Consider merge with Init()?
bool PaddlePredictorImpl::InitShared() {
VLOG(3) << "Predictor::init_shared";
// 1. Define place, executor, scope
if (this->config_.device >= 0) {
place_ = platform::CUDAPlace();
} else {
place_ = platform::CPUPlace();
}
this->executor_.reset(new framework::Executor(this->place_));
this->scope_.reset(new framework::Scope());
// Initialize the inference program
if (!this->config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
this->inference_program_ = inference::Load(
this->executor_.get(), this->scope_.get(), this->config_.model_dir);
} else if (!this->config_.prog_file.empty() &&
!this->config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
// in Python API `fluid.io.save_inference_model`.
this->inference_program_ = inference::Load(this->executor_.get(),
this->scope_.get(),
this->config_.prog_file,
this->config_.param_file);
}
this->ctx_ = this->executor_->Prepare(*this->inference_program_, 0);
// 3. create variables
// TODO(panyx0718): why test share_variables.
if (config_.share_variables) {
this->executor_->CreateVariables(
*this->inference_program_, this->scope_.get(), 0);
}
// 4. Get the feed_target_names and fetch_target_names
this->feed_target_names_ = this->inference_program_->GetFeedTargetNames();
this->fetch_target_names_ = this->inference_program_->GetFetchTargetNames();
return true;
}
bool PaddlePredictorImpl::SetFeed(const std::vector<PaddleTensor> &inputs,
std::vector<framework::LoDTensor> *feeds) {
bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
std::vector<framework::LoDTensor> *feeds) {
VLOG(3) << "Predictor::set_feed";
if (inputs.size() != feed_target_names_.size()) {
LOG(ERROR) << "wrong feed input size.";
......@@ -213,7 +185,7 @@ bool PaddlePredictorImpl::SetFeed(const std::vector<PaddleTensor> &inputs,
return true;
}
bool PaddlePredictorImpl::GetFetch(
bool NativePaddlePredictor::GetFetch(
const std::vector<framework::LoDTensor> &fetchs,
std::vector<PaddleTensor> *outputs) {
VLOG(3) << "Predictor::get_fetch";
......@@ -280,23 +252,31 @@ bool PaddlePredictorImpl::GetFetch(
}
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(
const ConfigImpl &config) {
VLOG(3) << "create PaddlePredictorImpl";
// 1. GPU memeroy
std::vector<std::string> flags;
if (config.fraction_of_gpu_memory >= 0.0f ||
config.fraction_of_gpu_memory <= 0.95f) {
flags.push_back("dummpy");
std::string flag = "--fraction_of_gpu_memory_to_use=" +
num2str<float>(config.fraction_of_gpu_memory);
flags.push_back(flag);
VLOG(3) << "set flag: " << flag;
framework::InitGflags(flags);
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
const NativeConfig &config) {
VLOG(3) << "create NativePaddlePredictor";
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(
config.fraction_of_gpu_memory,
0.f,
"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
std::vector<std::string> flags;
if (config.fraction_of_gpu_memory >= 0.0f ||
config.fraction_of_gpu_memory <= 0.95f) {
flags.push_back("dummpy");
std::string flag = "--fraction_of_gpu_memory_to_use=" +
num2str<float>(config.fraction_of_gpu_memory);
flags.push_back(flag);
VLOG(3) << "set flag: " << flag;
framework::InitGflags(flags);
}
}
std::unique_ptr<PaddlePredictor> predictor(new PaddlePredictorImpl(config));
if (!dynamic_cast<PaddlePredictorImpl *>(predictor.get())->Init()) {
std::unique_ptr<PaddlePredictor> predictor(new NativePaddlePredictor(config));
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 @ 8bd148dc
......@@ -29,42 +29,37 @@
namespace paddle {
struct ConfigImpl : public PaddlePredictor::Config {
int device;
float fraction_of_gpu_memory;
std::string prog_file;
std::string param_file;
bool share_variables;
};
class PaddlePredictorImpl : public PaddlePredictor {
class NativePaddlePredictor : public PaddlePredictor {
public:
explicit PaddlePredictorImpl(const ConfigImpl &config) : config_(config) {}
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;
~PaddlePredictorImpl() override{};
~NativePaddlePredictor() override;
private:
bool InitShared() override;
bool SetFeed(const std::vector<PaddleTensor> &input_datas,
std::vector<framework::LoDTensor> *feeds);
bool GetFetch(const std::vector<framework::LoDTensor> &fetchs,
std::vector<PaddleTensor> *output_data);
ConfigImpl config_;
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/contrib/inference/test_paddle_inference_api_impl.cc
浏览文件 @ 8bd148dc
......@@ -15,6 +15,8 @@ limitations under the License. */
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <thread>
#include "gflags/gflags.h"
#include "paddle/contrib/inference/paddle_inference_api_impl.h"
#include "paddle/fluid/inference/tests/test_helper.h"
......@@ -40,19 +42,24 @@ PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
return pt;
}
ConfigImpl GetConfig() {
ConfigImpl config;
NativeConfig GetConfig() {
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.15;
#ifdef PADDLE_WITH_CUDA
config.use_gpu = true;
#else
config.use_gpu = false;
#endif
config.device = 0;
config.share_variables = true;
return config;
}
TEST(paddle_inference_api_impl, word2vec) {
ConfigImpl config = GetConfig();
std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
void MainWord2Vec(bool use_gpu) {
NativeConfig config = GetConfig();
auto predictor = CreatePaddlePredictor<NativeConfig>(config);
config.use_gpu = use_gpu;
framework::LoDTensor first_word, second_word, third_word, fourth_word;
framework::LoD lod{{0, 1}};
......@@ -74,7 +81,7 @@ TEST(paddle_inference_api_impl, word2vec) {
ASSERT_EQ(outputs.size(), 1UL);
size_t len = outputs[0].data.length;
float* data = static_cast<float*>(outputs[0].data.data);
for (int j = 0; j < len / sizeof(float); ++j) {
for (size_t j = 0; j < len / sizeof(float); ++j) {
ASSERT_LT(data[j], 1.0);
ASSERT_GT(data[j], -1.0);
}
......@@ -92,7 +99,7 @@ TEST(paddle_inference_api_impl, word2vec) {
TestInference<platform::CPUPlace>(config.model_dir, cpu_feeds, cpu_fetchs1);
float* lod_data = output1.data<float>();
for (size_t i = 0; i < output1.numel(); ++i) {
for (int i = 0; i < output1.numel(); ++i) {
EXPECT_LT(lod_data[i] - data[i], 1e-3);
EXPECT_GT(lod_data[i] - data[i], -1e-3);
}
......@@ -100,11 +107,11 @@ TEST(paddle_inference_api_impl, word2vec) {
free(outputs[0].data.data);
}
TEST(paddle_inference_api_impl, image_classification) {
void MainImageClassification(bool use_gpu) {
int batch_size = 2;
bool use_mkldnn = false;
bool repeat = false;
ConfigImpl config = GetConfig();
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
......@@ -126,14 +133,10 @@ TEST(paddle_inference_api_impl, image_classification) {
std::vector<framework::LoDTensor*> cpu_fetchs1;
cpu_fetchs1.push_back(&output1);
TestInference<platform::CPUPlace, false, true>(config.model_dir,
cpu_feeds,
cpu_fetchs1,
repeat,
is_combined,
use_mkldnn);
TestInference<platform::CPUPlace, false, true>(
config.model_dir, cpu_feeds, cpu_fetchs1, repeat, is_combined);
std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
auto predictor = CreatePaddlePredictor(config);
std::vector<PaddleTensor> paddle_tensor_feeds;
paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&input));
......@@ -144,10 +147,148 @@ TEST(paddle_inference_api_impl, image_classification) {
float* data = static_cast<float*>(outputs[0].data.data);
float* lod_data = output1.data<float>();
for (size_t j = 0; j < len / sizeof(float); ++j) {
EXPECT_LT(lod_data[j] - data[j], 1e-10);
EXPECT_GT(lod_data[j] - data[j], -1e-10);
EXPECT_NEAR(lod_data[j], data[j], 1e-3);
}
free(data);
}
void MainThreadsWord2Vec(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
// prepare inputs data and reference results
constexpr int num_jobs = 3;
std::vector<std::vector<framework::LoDTensor>> jobs(num_jobs);
std::vector<std::vector<PaddleTensor>> paddle_tensor_feeds(num_jobs);
std::vector<framework::LoDTensor> refs(num_jobs);
for (size_t i = 0; i < jobs.size(); ++i) {
// each job has 4 words
jobs[i].resize(4);
for (size_t j = 0; j < 4; ++j) {
framework::LoD lod{{0, 1}};
int64_t dict_size = 2073; // The size of dictionary
SetupLoDTensor(&jobs[i][j], lod, static_cast<int64_t>(0), dict_size - 1);
paddle_tensor_feeds[i].push_back(LodTensorToPaddleTensor(&jobs[i][j]));
}
// get reference result of each job
std::vector<paddle::framework::LoDTensor*> ref_feeds;
std::vector<paddle::framework::LoDTensor*> ref_fetches(1, &refs[i]);
for (auto& word : jobs[i]) {
ref_feeds.push_back(&word);
}
TestInference<platform::CPUPlace>(config.model_dir, ref_feeds, ref_fetches);
}
// create threads and each thread run 1 job
std::vector<std::thread> threads;
for (int tid = 0; tid < num_jobs; ++tid) {
threads.emplace_back([&, tid]() {
auto predictor = main_predictor->Clone();
auto& local_inputs = paddle_tensor_feeds[tid];
std::vector<PaddleTensor> local_outputs;
ASSERT_TRUE(predictor->Run(local_inputs, &local_outputs));
// check outputs range
ASSERT_EQ(local_outputs.size(), 1UL);
const size_t len = local_outputs[0].data.length;
float* data = static_cast<float*>(local_outputs[0].data.data);
for (size_t j = 0; j < len / sizeof(float); ++j) {
ASSERT_LT(data[j], 1.0);
ASSERT_GT(data[j], -1.0);
}
// check outputs correctness
float* ref_data = refs[tid].data<float>();
EXPECT_EQ(refs[tid].numel(), static_cast<int64_t>(len / sizeof(float)));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
}
free(data);
});
}
for (int i = 0; i < num_jobs; ++i) {
threads[i].join();
}
}
void MainThreadsImageClassification(bool use_gpu) {
constexpr int num_jobs = 4; // each job run 1 batch
constexpr int batch_size = 1;
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "image_classification_resnet.inference.model";
auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
std::vector<framework::LoDTensor> jobs(num_jobs);
std::vector<std::vector<PaddleTensor>> paddle_tensor_feeds(num_jobs);
std::vector<framework::LoDTensor> refs(num_jobs);
for (size_t i = 0; i < jobs.size(); ++i) {
// prepare inputs
std::vector<std::vector<int64_t>> feed_target_shapes =
GetFeedTargetShapes(config.model_dir, /*is_combined*/ false);
feed_target_shapes[0][0] = batch_size;
framework::DDim input_dims = framework::make_ddim(feed_target_shapes[0]);
SetupTensor<float>(&jobs[i], input_dims, 0.f, 1.f);
paddle_tensor_feeds[i].push_back(LodTensorToPaddleTensor(&jobs[i]));
// get reference result of each job
std::vector<framework::LoDTensor*> ref_feeds(1, &jobs[i]);
std::vector<framework::LoDTensor*> ref_fetches(1, &refs[i]);
TestInference<platform::CPUPlace>(config.model_dir, ref_feeds, ref_fetches);
}
// create threads and each thread run 1 job
std::vector<std::thread> threads;
for (int tid = 0; tid < num_jobs; ++tid) {
threads.emplace_back([&, tid]() {
auto predictor = main_predictor->Clone();
auto& local_inputs = paddle_tensor_feeds[tid];
std::vector<PaddleTensor> local_outputs;
ASSERT_TRUE(predictor->Run(local_inputs, &local_outputs));
// check outputs correctness
ASSERT_EQ(local_outputs.size(), 1UL);
const size_t len = local_outputs[0].data.length;
float* data = static_cast<float*>(local_outputs[0].data.data);
float* ref_data = refs[tid].data<float>();
EXPECT_EQ(refs[tid].numel(), len / sizeof(float));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
}
free(data);
});
}
for (int i = 0; i < num_jobs; ++i) {
threads[i].join();
}
}
TEST(inference_api_native, word2vec_cpu) { MainWord2Vec(false /*use_gpu*/); }
TEST(inference_api_native, word2vec_cpu_threads) {
MainThreadsWord2Vec(false /*use_gpu*/);
}
TEST(inference_api_native, image_classification_cpu) {
MainThreadsImageClassification(false /*use_gpu*/);
}
TEST(inference_api_native, image_classification_cpu_threads) {
MainThreadsImageClassification(false /*use_gpu*/);
}
#ifdef PADDLE_WITH_CUDA
TEST(inference_api_native, word2vec_gpu) { MainWord2Vec(true /*use_gpu*/); }
TEST(inference_api_native, word2vec_gpu_threads) {
MainThreadsWord2Vec(true /*use_gpu*/);
}
TEST(inference_api_native, image_classification_gpu) {
MainThreadsImageClassification(true /*use_gpu*/);
}
TEST(inference_api_native, image_classification_gpu_threads) {
MainThreadsImageClassification(true /*use_gpu*/);
}
#endif
} // namespace paddle
paddle/contrib/tape/CMakeLists.txt 0 → 100644
浏览文件 @ 8bd148dc
# 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.
#
if(APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
endif(APPLE)
cc_library(tape_variable SRCS variable.cc DEPS ${FLUID_CORE_MODULES} device_context framework_proto proto_desc operator)
cc_library(tape SRCS tape.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB} tape_variable)
cc_test(test_tape
SRCS test_tape.cc
DEPS tape tape_variable)
paddle/contrib/tape/README.md 0 → 100644
浏览文件 @ 8bd148dc
# Dynamic Graph on Fluid
PaddlePaddle Fluid is targeting the autodiff without tape, which, however, is very
challenging and we are still way from there. DyNet and PyTorch provide a good design
idea, the *tape*, that significantly eases the challenge. Also, DyNet provides
a C++ API that is as convenient as Python but with higher efficiency and could
conveniently integrate with industrial/production systems. This package, `tape`,
combines the good of
1. tape from PyTorch and DyNet
2. C++ API and core from DyNet
3. rich set of operators from PaddlePaddle
## Overview
We can implement Dynet-like Tape(See this [survey](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/survey/dynamic_graph.md))
by wrapping Paddle Fluid's `Operator` and `Variable`.
The user API is straight forward since
1. it is imperative. And it uses host language's control flow logic.
1. it avoids extra concepts such as `Scope` and `Executor`.
All of these benefits come at the cost of just adding one line `reset_global_tape`
at every iteration.
## Code Structure
In short, the `Tape` contains a vector of `OpHandle`s. And an `OpHandle` contains its
`type`, the pointers to the `Variable`s, and necessary attributes.
```c++
class Variable {
public:
VriableHandle Grad(); // returns its gradient variable
private:
framework::VarDesc desc_; // compile time infershape, necessary for lazy execution
framework::Variable var_; // run time variable, holds data memory
};
using VariableHandle = shared_ptr<Variable>;
struct OpHandle {
string type_;
map<string, vector<VariableHandle>> inputs_;
map<string, vector<VariableHandle>> outputs_;
AttributeMap attrs_;
};
class Tape {
public:
void AddOp(OpHandle); // add op
void Forward(); // execute the tape_
void Backward(); // execute the backward of the tape_
private:
vector<OpHandle> tape_;
};
```
We uses `Function` to indicate layers. It takes care of parameter
initialization and `AddOp` to the Tape when it is called.
```c++
class Linear {
public:
Linear(int in_dim, int out_dim, const std::string &act)
: w_(new Variable("LinearWeight")),
b_(new Variable("LinearBias")),
act_(act) {
Tape init_tape;
std::string initializer = "fill_constant";
framework::AttributeMap attrs;
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{in_dim, out_dim};
attrs["value"] = 1.0f;
init_tape.AddOp(initializer, {}, {{"Out", {w_}}}, attrs);
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{out_dim};
attrs["value"] = 1.0f;
init_tape.AddOp(initializer, {}, {{"Out", {b_}}}, attrs);
init_tape.Forward();
}
VariableHandle operator()(VariableHandle input) {
VariableHandle pre_bias(new Variable("linear"));
get_global_tape().AddOp("mul",
{{"X", {input}}, {"Y", {w_}}},
{{"Out", {pre_bias}}},
{{"x_num_col_dims", 1}, {"y_num_col_dims", 1}});
VariableHandle pre_act(new Variable("linear"));
get_global_tape().AddOp("elementwise_add",
{{"X", {pre_bias}}, {"Y", {b_}}},
{{"Out", {pre_act}}},
{{"axis", 1}});
VariableHandle post_act(new Variable("linear"));
get_global_tape().AddOp(act_,
{{"X", {pre_act}}},
{{"Out", {post_act}}},
{});
return post_act;
}
std::vector<VariableHandle> Params() { return {w_, b_}; }
private:
VariableHandle w_;
VariableHandle b_;
std::string act_;
};
```
## User API
```c++
// Model function
paddle::tape::Linear linear1(3, 3, "relu"); // init weight and bias
paddle::tape::Linear linear2(3, 3, "relu"); // init weight and bias
paddle::tape::Mean mean;
// Optimizer
paddle::tape::SGD sgd(0.001);
// Data Feeder
paddle::tape::Fill data_feeder(...);
VariableHandle input(new paddle::tape::Variable("input"));
VariableHandle label(new paddle::tape::Variable("label"));
for (int i = 0; i < 2; ++i) {
reset_global_tape();
data_feeder(input, label);
auto loss = softmax(linear2(linear1(input)), label); // compile time InferShape & InferVarType
LOG(INFO) << loss.value(); // Run forward up to loss
// Run backward, store gradient of w at w->Grad()
get_global_tape.Backward(loss);
// Update w
sgd(linear1.Params());
sgd(linear2.Params());
}
```
<details>
<summary></summary>
digraph G {
subgraph cluster_0 {
node [shape=record,style=filled];
style=filled;
color=lightgrey;
linear1 [label="{type: mul | {input | {<before_mul1>X: before_mul1 |<weight1> Y: weight1}} | {output |<before_bias1> Out: before_bias1}}"];
elementwise_add1 [label="{type: elementwise_add | {input | {<before_bias1>X: before_bias1 |<bias1> Y: bias1}} | {output |<before_act1> Out: before_act1}}"];
relu1 [label="{type: relu | {input | {<before_act1>X: before_act1 }} | {output |<after_act1> Out: after_act1}}"];
linear1 -> elementwise_add1->relu1;
label = "forward tape";
}
linear1:before_mul1->before_mul1
linear1:weight1->weight1
linear1:before_bias1->before_bias1
elementwise_add1:bias1->bias1
elementwise_add1:before_bias1->before_bias1
elementwise_add1:before_act1->before_act1
relu1:before_act1->before_act1
relu1:after_act1->after_act1
subgraph cluster_1 {
node [shape=record,style=filled];
style=filled;
color=lightgrey;
linear1_grad [label="{type: mul_grad | {input | {<before_mul1>X: before_mul1 |<weight1> Y: weight1|<before_bias1_grad> Out_grad: before_bias1_grad}} | {output |{<before_mul1_grad>X_grad: before_mul1_grad |<weight1_grad> Y_grad: weight1_grad}}}"];
elementwise_add1_grad [label="{type: elementwise_add_grad | {input | <before_act1_grad> Out_grad: before_act1_grad} | {output |{<before_bias1_grad>X_grad: before_bias1_grad |<bias1_grad> Y_grad: bias1_grad}}}"];
relu1_grad [label="{type: relu_grad | {input |<after_act1_grad> Out_grad: after_act1_grad} | {ouput | {<before_act1_grad>X_grad: before_act1_grad }}}"];
linear1_grad -> elementwise_add1_grad ->relu1_grad [dir=back];
label = "backward tape";
}
relu1_grad:after_act1_grad->after_act1_grad
relu1_grad:before_act1_grad->before_act1_grad
elementwise_add1_grad:before_act1_grad->before_act1_grad
elementwise_add1_grad:before_bias1_grad->before_bias1_grad
elementwise_add1_grad:bias1_grad->bias1_grad
linear1_grad:before_mul1->before_mul1
linear1_grad:weight1->weight1
linear1_grad:before_bias1_grad->before_bias1_grad
linear1_grad:before_mul1_grad->before_mul1_grad
linear1_grad:weight1_grad->weight1_grad
subgraph cluster_2 {
node [shape=record];
label = "Linear1";
weight1
bias1
}
weight1 -> weight1_grad [ label="Grad()", style="dashed" ];
bias1 -> bias1_grad [ label="Grad()", style="dashed"];
}
</details>
![Image](https://github.com/tonyyang-svail/Paddle/blob/cpp_tap/paddle/contrib/tape/computation_graph.png)
## Code Reuse
We want to stay close to Paddle Fluid as much as possible.
### Reuse All Operators
As all Ops are registered at `OpInfoMap`, the effort of adding a new `Function`
is about 10 lines of code, similar to expose an operator to Python.
### Reuse Compile Time InferShape and InferVarType
Note that all the symbolic information is stored at `tape::Varaible::desc_`, instead
of `ProgramDesc.block.vars`, we create a temporary `BlockDesc` to do `InferShape` and
`InferVarType` every time we `AddOp` to the tape.
### Reuse Operator::Run
We use smart pointer, instead of `Scope`, to manage memory. So we create a temporary
`Scope` for every `Operator::Run()`.
## Possible Feature
### Release Memory on Backward
We can release memory aggressively. During backward, we can delete the OpHandle once
we have finished its backward. Since all the variable is managed by smart pointer, the
memory is automatically released when its `ref_count` goes to 0.
### Kernel Fusion
As a symbolic representation of the Tape is constructed first before the actual
execution, it would be possible to perform graph optimization. One use case is kernel
fusion.
paddle/contrib/tape/function.h 0 → 100644
浏览文件 @ 8bd148dc
// 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 <string>
#include "paddle/contrib/tape/tape.h"
#include "paddle/contrib/tape/variable.h"
#include "paddle/fluid/framework/type_defs.h"
namespace paddle {
namespace tape {
class Function {};
class Fill {
public:
Fill(const std::string &initializer, const framework::AttributeMap &attrs)
: initializer_(initializer), attrs_(attrs) {}
void operator()(VariableHandle var) {
get_global_tape().AddOp(initializer_, {}, {{"Out", {var}}}, attrs_);
}
private:
const std::string initializer_;
const framework::AttributeMap attrs_;
};
class Mean {
public:
VariableHandle operator()(VariableHandle var) {
VariableHandle out(new Variable("mean"));
get_global_tape().AddOp("mean", {{"X", {var}}}, {{"Out", {out}}}, {});
return out;
}
};
class Linear {
public:
Linear(int in_dim, int out_dim, const std::string &act)
: w_(new Variable("LinearWeight")),
b_(new Variable("LinearBias")),
act_(act) {
Tape init_tape;
std::string initializer = "fill_constant";
framework::AttributeMap attrs;
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{in_dim, out_dim};
attrs["value"] = 1.0f;
init_tape.AddOp(initializer, {}, {{"Out", {w_}}}, attrs);
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{out_dim};
attrs["value"] = 1.0f;
init_tape.AddOp(initializer, {}, {{"Out", {b_}}}, attrs);
init_tape.Forward();
}
VariableHandle operator()(VariableHandle input) {
VariableHandle pre_bias(new Variable("linear"));
get_global_tape().AddOp("mul",
{{"X", {input}}, {"Y", {w_}}},
{{"Out", {pre_bias}}},
{{"x_num_col_dims", 1}, {"y_num_col_dims", 1}});
VariableHandle pre_act(new Variable("linear"));
get_global_tape().AddOp("elementwise_add",
{{"X", {pre_bias}}, {"Y", {b_}}},
{{"Out", {pre_act}}},
{{"axis", 1}});
VariableHandle post_act(new Variable("linear"));
get_global_tape().AddOp(
act_, {{"X", {pre_act}}}, {{"Out", {post_act}}}, {});
return post_act;
}
std::vector<VariableHandle> Params() { return {w_, b_}; }
private:
VariableHandle w_;
VariableHandle b_;
std::string act_;
};
class SGD {
public:
SGD(float learning_rate) : learning_rate_(new Variable("sgd")) {
Tape init_tape;
std::string initializer = "fill_constant";
framework::AttributeMap attrs;
attrs["dtype"] = paddle::framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{1};
attrs["value"] = learning_rate;
init_tape.AddOp(initializer, {}, {{"Out", {learning_rate_}}}, attrs);
init_tape.Forward();
}
void operator()(VariableHandle input) {
PADDLE_ENFORCE(get_global_tape().HasBeenBackwarded(),
"optimization must happen after the backward");
Tape temp_tape;
temp_tape.AddOp("sgd",
{{"Param", {input}},
{"LearningRate", {learning_rate_}},
{"Grad", {input->Grad()}}},
{{"ParamOut", {input}}},
{});
temp_tape.Forward();
}
private:
VariableHandle learning_rate_;
};
}
}
paddle/contrib/tape/tape.cc 0 → 100644
浏览文件 @ 8bd148dc
// 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/contrib/tape/tape.h"
#include <list>
#include <map>
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/dim.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/pybind/pybind.h"
namespace paddle {
namespace tape {
// borrowed from
// https://stackoverflow.com/questions/874134/find-if-string-ends-with-another-string-in-c
inline bool ends_with(std::string const &value, std::string const &ending) {
if (ending.size() > value.size()) return false;
return std::equal(ending.rbegin(), ending.rend(), value.rbegin());
}
std::ostream &operator<<(std::ostream &os, const framework::VarDesc &var_desc) {
os << var_desc.Name();
os << "[" << var_desc.GetType() << "]";
os << "[" << var_desc.GetDataType() << "]";
os << "{";
for (auto &i : var_desc.GetShape()) {
os << i << ",";
}
os << "}";
return os;
}
std::string to_string(const std::string &type,
const VariableHandleMap &in_vars,
const VariableHandleMap &out_vars,
const framework::AttributeMap &attrs) {
std::stringstream ss;
ss << type << " ";
for (auto &param_name : in_vars) {
for (auto &var : param_name.second) {
ss << param_name.first << ":(" << var->Desc() << ") ";
}
}
for (auto &param_name : out_vars) {
for (auto &var : param_name.second) {
ss << param_name.first << ":(" << var->Desc() << ") ";
}
}
return ss.str();
}
framework::OpDesc CreateOpDesc(const std::string &type,
const VariableHandleMap &in_vars,
const VariableHandleMap &out_vars,
const framework::AttributeMap &attrs) {
framework::VariableNameMap inputs;
for (auto &param_name : in_vars) {
for (auto &var : param_name.second) {
inputs[param_name.first].emplace_back(var->Name());
}
}
framework::VariableNameMap outputs;
for (auto &param_name : out_vars) {
for (auto &var : param_name.second) {
outputs[param_name.first].emplace_back(var->Name());
}
}
return framework::OpDesc(type, inputs, outputs, attrs);
}
void InferShapeAndVarType(const std::string &type,
const VariableHandleMap &in_vars,
VariableHandleMap *out_vars,
const framework::AttributeMap &attrs) {
framework::OpDesc op_desc = CreateOpDesc(type, in_vars, *out_vars, attrs);
// Create a temporary block for compile-time
framework::ProgramDesc program_desc;
framework::BlockDesc *block_desc = program_desc.MutableBlock(0);
PADDLE_ENFORCE(block_desc);
for (auto &param_name : in_vars) {
for (auto &var : param_name.second) {
*block_desc->Var(var->Name())->Proto() = *var->MutableDesc()->Proto();
}
}
for (auto &param_name : *out_vars) {
for (auto &var : param_name.second) {
*block_desc->Var(var->Name())->Proto() = *var->MutableDesc()->Proto();
}
}
LOG(INFO) << "- " << to_string(type, in_vars, *out_vars, attrs);
op_desc.InferShape(*block_desc);
op_desc.InferVarType(block_desc);
for (auto &param_name : *out_vars) {
for (auto &var : param_name.second) {
*var->MutableDesc()->Proto() = *block_desc->Var(var->Name())->Proto();
}
}
LOG(INFO) << "+ " << to_string(type, in_vars, *out_vars, attrs);
}
void Tape::AddOp(const std::string &type,
const VariableHandleMap &in_vars,
VariableHandleMap out_vars,
const framework::AttributeMap &attrs) {
InferShapeAndVarType(type, in_vars, &out_vars, attrs);
tape_.emplace_back(type, in_vars, out_vars, attrs);
}
// Temporary Scope for Operator::Run()
class ScopeWrapper : public framework::Scope {
public:
ScopeWrapper(const VariableHandleMap &in_vars,
const VariableHandleMap &out_vars) {
for (auto &v : in_vars) {
for (auto &vv : v.second) {
if (!vars_.count(vv->Name())) {
vars_[vv->Name()].reset(vv->Var());
}
}
}
for (auto &v : out_vars) {
for (auto &vv : v.second) {
if (!vars_.count(vv->Name())) {
vars_[vv->Name()].reset(vv->Var());
}
}
}
}
~ScopeWrapper() {
for (auto &pair : vars_) {
pair.second.release();
}
}
};
void Tape::Forward() {
LOG(INFO) << "Starting forward -------------------------";
PADDLE_ENFORCE(!has_been_backwarded_);
while (current_position_ < tape_.size()) {
OpHandle &op = tape_[current_position_];
// Create Output Tensor, this is only necessary for OpWithKernel
for (auto &param2var : op.outputs_) {
for (auto &var : param2var.second) {
var->InitializeVariable();
}
}
framework::OpDesc op_desc =
CreateOpDesc(op.type_, op.inputs_, op.outputs_, op.attrs_);
ScopeWrapper scope(op.inputs_, op.outputs_);
framework::OpRegistry::CreateOp(op_desc)->Run(scope, platform::CPUPlace());
current_position_++;
}
LOG(INFO) << "Finishing forward -------------------------";
}
void Tape::Backward(VariableHandle target) {
PADDLE_ENFORCE(!has_been_backwarded_);
Forward();
// TODO(tonyyang-svail): check output of last op is target
backward_tape_.reset(new Tape());
framework::AttributeMap attrs;
// FIXME(tonyyang-svail): Need to infer_data_type
attrs["dtype"] = framework::proto::VarType::Type::VarType_Type_FP32;
attrs["shape"] = std::vector<int>{1};
attrs["value"] = 1.0f;
backward_tape_->AddOp(
"fill_constant", {}, {{"Out", {target->Grad()}}}, attrs);
for (auto it = tape_.rbegin(); it != tape_.rend(); ++it) {
framework::OpDesc op_desc =
CreateOpDesc(it->type_, it->inputs_, it->outputs_, it->attrs_);
std::unordered_map<std::string, std::string> grad_to_var;
std::vector<std::unique_ptr<framework::OpDesc>> grad_op_descs =
framework::OpInfoMap::Instance()
.Get(op_desc.Type())
.GradOpMaker()(op_desc, {}, &grad_to_var, {});
for (auto &op_desc : grad_op_descs) {
std::unordered_map<std::string, VariableHandle> name2var;
for (auto &param2vars : it->inputs_) {
for (auto &a : param2vars.second) {
name2var[a->Name()] = a;
}
}
for (auto &param2vars : it->outputs_) {
for (auto &a : param2vars.second) {
name2var[a->Name()] = a;
}
}
VariableHandleMap in_vars;
VariableHandleMap out_vars;
std::map<const framework::VariableNameMap *, VariableHandleMap *>
loop_over{{&op_desc->Inputs(), &in_vars},
{&op_desc->Outputs(), &out_vars}};
for (auto &each : loop_over) {
auto &vmp = *each.first;
auto &vhm = *each.second;
for (auto &p2a : vmp) {
for (auto &argu : p2a.second) {
if (name2var.count(argu)) {
vhm[p2a.first].push_back(name2var[argu]);
} else {
PADDLE_ENFORCE(ends_with(argu, framework::kGradVarSuffix),
argu.c_str());
std::string name = argu.substr(
0, argu.size() - std::strlen(framework::kGradVarSuffix));
PADDLE_ENFORCE(name2var.count(name), name.c_str());
vhm[p2a.first].push_back(name2var[name]->Grad());
}
}
}
}
backward_tape_->AddOp(
op_desc->Type(), in_vars, out_vars, op_desc->GetAttrMap());
}
// TODO(tonyyang-svail): how to fill empty grad?
// TODO(tonyyang-svail): Sum var grad is necessary
}
backward_tape_->Forward();
has_been_backwarded_ = true;
}
Tape &get_global_tape() {
static Tape T;
return T;
}
void reset_global_tape() { get_global_tape() = Tape(); }
}
}
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