Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into feature/python_doc

AUTHORS.md

@@ -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 |

Dockerfile

@@ -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

@@ -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

+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 dataset.py models/ /workspace/

benchmark/fluid/README.md

@@ -44,11 +44,25 @@ Currently supported `--model` argument include:
 
 ## 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/fluid_benchmark.py

@@ -40,10 +40,7 @@ def parse_args():
     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.')
+        '--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',
@@ -72,6 +69,11 @@ def parse_args():
         type=int,
         default=1,
         help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
+    parser.add_argument(
+        '--cpus',
+        type=int,
+        default=1,
+        help='If cpus > 1, will use ParallelDo to run, else use Executor.')
     parser.add_argument(
         '--data_set',
         type=str,
@@ -88,8 +90,8 @@ def parse_args():
         help='If set, use nvprof for CUDA.')
     parser.add_argument(
         '--no_test',
-        action='store_false',
-        help='If set, test the testset during training.')
+        action='store_true',
+        help='If set, do not test the testset during training.')
     parser.add_argument(
         '--memory_optimize',
         action='store_true',
@@ -231,13 +233,10 @@ def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
             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:
             pass_test_acc = test(exe, infer_prog, test_reader, feeder,
                                  batch_acc)
             print(", Test Accuracy: %f" % pass_test_acc)
@@ -315,11 +314,8 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
             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:
+        print_train_time(start_time, time.time(), num_samples)
+        if not args.no_test and batch_acc:
             test_acc = test(startup_exe, infer_prog, test_reader, feeder,
                             batch_acc)
             print("Pass: %d, Test Accuracy: %f\n" % (pass_id, test_acc))
@@ -329,12 +325,19 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
 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 +345,7 @@ 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()

benchmark/fluid/kube_gen_job.py

@@ -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/mnist.py

@@ -69,15 +69,30 @@ def get_model(args):
     images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
+    if args.device == 'CPU' and args.cpus > 1:
+        places = fluid.layers.get_places(args.cpus)
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            predict = cnn_model(pd.read_input(images))
+            label = pd.read_input(label)
+            cost = fluid.layers.cross_entropy(input=predict, label=label)
+            avg_cost = fluid.layers.mean(x=cost)
+            batch_acc = fluid.layers.accuracy(input=predict, label=label)
+
+            pd.write_output(avg_cost)
+            pd.write_output(batch_acc)
+
+        avg_cost, batch_acc = pd()
+        avg_cost = fluid.layers.mean(avg_cost)
+        batch_acc = fluid.layers.mean(batch_acc)
+    else:
         # Train program
         predict = cnn_model(images)
         cost = fluid.layers.cross_entropy(input=predict, label=label)
         avg_cost = fluid.layers.mean(x=cost)
 
         # Evaluator
-    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-    batch_acc = fluid.layers.accuracy(
-        input=predict, label=label, total=batch_size_tensor)
+        batch_acc = fluid.layers.accuracy(input=predict, label=label)
 
     # inference program
     inference_program = fluid.default_main_program().clone()

benchmark/fluid/models/resnet.py

@@ -132,18 +132,33 @@ def get_model(args):
 
     input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-    predict = model(input, class_dim)
+
+    if args.device == 'CPU' and args.cpus > 1:
+        places = fluid.layers.get_places(args.cpus)
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            predict = model(pd.read_input(input), class_dim)
+            label = pd.read_input(label)
             cost = fluid.layers.cross_entropy(input=predict, label=label)
             avg_cost = fluid.layers.mean(x=cost)
+            batch_acc = fluid.layers.accuracy(input=predict, label=label)
 
-    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
-    batch_acc = fluid.layers.accuracy(
-        input=predict, label=label, total=batch_size_tensor)
+            pd.write_output(avg_cost)
+            pd.write_output(batch_acc)
+
+        avg_cost, batch_acc = pd()
+        avg_cost = fluid.layers.mean(avg_cost)
+        batch_acc = fluid.layers.mean(batch_acc)
+    else:
+        predict = model(input, class_dim)
+        cost = fluid.layers.cross_entropy(input=predict, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+        batch_acc = fluid.layers.accuracy(input=predict, label=label)
 
     inference_program = fluid.default_main_program().clone()
     with fluid.program_guard(inference_program):
         inference_program = fluid.io.get_inference_program(
-            target_vars=[batch_acc, batch_size_tensor])
+            target_vars=[batch_acc])
 
     optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
 

benchmark/fluid/models/stacked_dynamic_lstm.py

@@ -101,9 +101,8 @@ def get_model(args):
     loss = fluid.layers.mean(x=loss)
 
     # add acc
-    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
     batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
-                shape=[1], dtype='int64'), total=batch_size_tensor)
+                shape=[1], dtype='int64'))
 
     inference_program = fluid.default_main_program().clone()
     with fluid.program_guard(inference_program):

benchmark/fluid/run.sh

@@ -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

cmake/external/grpc.cmake

@@ -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")

doc/fluid/api/io.rst

@@ -59,3 +59,21 @@ get_inference_program
 ..  autofunction:: paddle.fluid.io.get_inference_program
     :noindex:
 
+save_checkpoint
+---------------
+
+..  autofunction:: paddle.fluid.io.save_checkpoint
+    :noindex:
+
+load_checkpoint
+---------------
+
+..  autofunction:: paddle.fluid.io.load_checkpoint
+    :noindex:
+
+clean_checkpoint
+----------------
+
+..  autofunction:: paddle.fluid.io.clean_checkpoint
+    :noindex:
+

doc/fluid/api/layers.rst

@@ -181,6 +181,12 @@ Print
 ..  autofunction:: paddle.fluid.layers.Print
     :noindex:
 
+is_empty
+--------
+
+..  autofunction:: paddle.fluid.layers.is_empty
+    :noindex:
+
 device
 ======
 
@@ -255,6 +261,19 @@ double_buffer
 ..  autofunction:: paddle.fluid.layers.double_buffer
     :noindex:
 
+random_data_generator
+---------------------
+
+..  autofunction:: paddle.fluid.layers.random_data_generator
+    :noindex:
+
+Preprocessor
+------------
+
+..  autoclass:: paddle.fluid.layers.Preprocessor
+    :members:
+    :noindex:
+
 nn
 ==
 
@@ -594,6 +613,29 @@ roi_pool
 ..  autofunction:: paddle.fluid.layers.roi_pool
     :noindex:
 
+dice_loss
+---------
+
+..  autofunction:: paddle.fluid.layers.dice_loss
+    :noindex:
+
+resize_bilinear
+---------------
+
+..  autofunction:: paddle.fluid.layers.resize_bilinear
+    :noindex:
+
+gather
+------
+
+..  autofunction:: paddle.fluid.layers.gather
+    :noindex:
+
+random_crop
+-----------
+
+..  autofunction:: paddle.fluid.layers.random_crop
+    :noindex:
 
 ops
 ===
@@ -742,6 +784,12 @@ sum
 ..  autofunction:: paddle.fluid.layers.sum
     :noindex:
 
+shape
+-----
+
+..  autofunction:: paddle.fluid.layers.shape
+    :noindex:
+
 sigmoid
 -------
 
@@ -991,21 +1039,3 @@ zeros
 ..  autofunction:: paddle.fluid.layers.zeros
     :noindex:
 
-topk
-----
-
-..  autofunction:: paddle.fluid.layers.topk
-    :noindex:
-
-dice_loss
-----
-
-..  autofunction:: paddle.fluid.layers.dice_loss
-    :noindex:
-
-upsampling_bilinear2d
-____
-
-..  autofunction:: paddle.fluid.layers.upsampling_bilinear2d
-    :noindex:
-

doc/fluid/api/optimizer.rst

@@ -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
 ------------
 
@@ -111,19 +89,24 @@ DecayedAdagradOptimizer
     :members:
     :noindex:
 
+RMSPropOptimizer
+----------------
 
-AdadeltaOptimizer
------------------
-
-..  autoclass:: paddle.fluid.optimizer.AdadeltaOptimizer
+..  autoclass:: paddle.fluid.optimizer.RMSPropOptimizer
     :members:
     :noindex:
 
+Adadelta
+--------
 
-RMSPropOptimizer
------------------
+..  autoclass:: paddle.fluid.optimizer.Adadelta
+    :members:
+    :noindex:
 
-..  autoclass:: paddle.fluid.optimizer.RMSPropOptimizer
+ModelAverage
+------------
+
+..  autoclass:: paddle.fluid.optimizer.ModelAverage
     :members:
     :noindex:
 
@@ -133,3 +116,4 @@ Optimizer
 ..  autoclass:: paddle.fluid.optimizer.Optimizer
     :members:
     :noindex:
+

doc/fluid/api/profiler.rst

@@ -23,3 +23,15 @@ profiler
 ..  autofunction:: paddle.fluid.profiler.profiler
     :noindex:
 
+start_profiler
+--------------
+
+..  autofunction:: paddle.fluid.profiler.start_profiler
+    :noindex:
+
+stop_profiler
+-------------
+
+..  autofunction:: paddle.fluid.profiler.stop_profiler
+    :noindex:
+

doc/fluid/design/concepts/var_desc.md

@@ -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/howto/cluster/fluid_recordio.md

+# 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

@@ -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

@@ -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

+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

doc/fluid/howto/inference/index_cn.rst

+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

-# 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))
 

doc/fluid/howto/optimization/benchmark/README.md

-../../../../../benchmark/cluster/README.md
\ No newline at end of file

doc/fluid/howto/optimization/benchmark/vgg16/README.md

-../../../../../../benchmark/cluster/vgg16/README.md
\ No newline at end of file

doc/fluid/howto/optimization/host_memory_profiling_cn.md

+## 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

@@ -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。

doc/mobile/cross_compiling_for_android_en.md

@@ -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`.
 

doc/v2/build_and_install/build_from_source_cn.rst

@@ -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 `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。
 
 - 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$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。
+  是的。我们的 Docker image 运行一个 `Bash脚本 <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh>`_ 。这个脚本调用 `make -j$(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 `这个issue <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 `这篇文章 <https://zaiste.net/posts/removing_docker_containers/>`_ 来清理这些内容。
 
 
 .. _compile_deps:

doc/v2/build_and_install/build_from_source_en.rst

@@ -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:
 

paddle/contrib/inference/CMakeLists.txt

@@ -17,46 +17,77 @@ if(APPLE)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
 endif(APPLE)
 
-function(inference_api_test TARGET_NAME TEST_SRC)
+set(ANAKIN_INCLUDE "" CACHE STRING "root of Anakin header files")
+set(ANAKIN_LIBRARY "" CACHE STRING "path of Anakin library")
+
+
+set(inference_deps paddle_inference_api paddle_fluid_api)
+
+# if anakin is set enable anakin api implementation
+if(ANAKIN_INCLUDE_DIR AND ANAKIN_LIBRARY)
+    set(ANAKIN_FOUND ON)
+else()
+    set(ANAKIN_FOUND OFF)
+endif()
+
+if (ANAKIN_FOUND)
+    # Anakin's code style doesn't follow google c style.
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=comment
+                                            -Wno-error=reorder
+                                            -Wno-error=format
+                                            -Wno-error=switch
+                                            -Wno-error=return-type
+                                            -Wno-error=non-virtual-dtor
+                                            -Wno-error=cpp")
+
+    message(STATUS "Anakin for inference is enabled")
+    message(STATUS "Anakin is set INCLUDE:${ANAKIN_INCLUDE} LIBRARY:${ANAKIN_LIBRARY}")
+    include_directories("${ANAKIN_INCLUDE}")
+    # Anakin's source path is a mass, need to set sub-directories trivially.
+    include_directories("${ANAKIN_INCLUDE}/saber")
+    link_directories("${ANAKIN_LIBRARY}")
+
+    nv_library(inference_anakin_api SRCS paddle_inference_api_anakin_engine.cc)
+    target_link_libraries(inference_anakin_api anakin)
+    list(APPEND inference_deps inference_anakin_api)
+endif()
+
+
+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)
-    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 "${arg}")
         cc_test(${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 (ANAKIN_FOUND)
+  nv_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc
+    DEPS ${inference_deps} protobuf)
+endif()
+
+if(WITH_TESTING)
+    add_subdirectory(demo)
+endif()

paddle/contrib/inference/demo/CMakeLists.txt

+# 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

+/* 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 "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];
+    }
+  }
+}
+
+TEST(demo, word2vec_cpu) { Main(false /*use_gpu*/); }
+
+#ifdef PADDLE_WITH_CUDA
+TEST(demo, word2vec_gpu) { Main(true /*use_gpu*/); }
+#endif
+
+}  // namespace demo
+}  // namespace paddle

paddle/contrib/inference/paddle_inference_api.h

 /* 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 +76,42 @@ 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>
+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

+// 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 <cuda.h>
+
+#include "paddle/contrib/inference/paddle_inference_api_anakin_engine.h"
+
+namespace paddle {
+
+PaddleInferenceAnakinPredictor::PaddleInferenceAnakinPredictor(
+    const AnakinConfig &config) {
+  CHECK(Init(config));
+}
+
+bool PaddleInferenceAnakinPredictor::Init(const AnakinConfig &config) {
+  // TODO(Superjomn) Tell anakin to support return code.
+  engine_.Build(config.model_file, config.max_batch_size);
+  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;
+    }
+    engine_.SetInputFromCPU(
+        input.name, static_cast<float *>(input.data.data), input.data.length);
+  }
+
+  // TODO(Superjomn) Tell anakin to support return code.
+  engine_.Execute();
+
+  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 = engine_.GetOutputInGPU(output.name);
+    output.shape = tensor->shape();
+    // Copy data from GPU -> CPU
+    if (cudaMemcpy(output.data.data,
+                   tensor->data(),
+                   tensor->size(),
+                   cudaMemcpyDeviceToHost) != 0) {
+      LOG(ERROR) << "copy data from GPU to CPU error";
+      return false;
+    }
+  }
+  return true;
+}
+
+// TODO(Superjomn) To implement latter.
+std::unique_ptr<PaddlePredictor> PaddleInferenceAnakinPredictor::Clone() {
+  return nullptr;
+}
+
+// A factory to help create difference predictor.
+template <>
+std::unique_ptr<PaddlePredictor>
+CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(
+    const AnakinConfig &config) {
+  std::unique_ptr<PaddlePredictor> x(
+      new PaddleInferenceAnakinPredictor(config));
+  return x;
+};
+
+}  // namespace paddle

paddle/contrib/inference/paddle_inference_api_anakin_engine.h

+/* 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
+
+// NOTE This header file do not have namespace.
+// TODO(Superjomn) Tell Anakin to provide better APIs.
+#include <test/framework/net/paddle_api.h>
+#include "paddle/contrib/inference/paddle_inference_api.h"
+
+namespace paddle {
+
+class PaddleInferenceAnakinPredictor : public PaddlePredictor {
+ public:
+  PaddleInferenceAnakinPredictor(const AnakinConfig& config);
+
+  // NOTE Unlike the native engine, the buffers of anakin engine's output_data
+  // should be allocated first.
+  // TODO(Superjomn) should unify all the behaviors of output_data accross all
+  // the engines.
+  bool Run(const std::vector<PaddleTensor>& inputs,
+           std::vector<PaddleTensor>* output_data) override;
+
+  std::unique_ptr<PaddlePredictor> Clone() override;
+
+ private:
+  bool Init(const AnakinConfig& config);
+
+  anakin::AnakinEngine<anakin::NV,
+                       anakin::saber::AK_FLOAT,
+                       anakin::Precision::FP32>
+      engine_;
+};
+
+}  // namespace paddle

paddle/contrib/inference/paddle_inference_api_anakin_engine_tester.cc

+/* 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.h"
+#include <gtest/gtest.h>
+
+namespace paddle {
+
+TEST(inference, anakin) {
+  AnakinConfig config;
+
+  auto engine =
+      CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(config);
+}
+
+}  // namespace paddle

paddle/contrib/inference/paddle_inference_api_impl.cc

 /* 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();
   }
+  if (parent_scope) {
+    scope_ = parent_scope;
+    sub_scope_ = &(parent_scope->NewScope());
+  } else {
     paddle::framework::InitDevices(false);
-  executor_.reset(new paddle::framework::Executor(place_));
     scope_.reset(new paddle::framework::Scope());
+  }
+
+  executor_.reset(new paddle::framework::Executor(place_));
 
   // Initialize the inference program
   if (!config_.model_dir.empty()) {
@@ -84,19 +90,23 @@ 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,
+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;
@@ -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(),
+  executor_->RunPreparedContext(
+      ctx_.get(),
+      sub_scope_ != nullptr ? sub_scope_ : scope_.get(),
       &feed_targets,
       &fetch_targets,
-                                !config_.share_variables);
+      false /* don't create variable eatch time */);
   if (!GetFetch(fetchs, output_data)) {
     LOG(ERROR) << "fail to get fetchs";
     return false;
@@ -133,58 +144,19 @@ 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,
+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()) {
@@ -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";
@@ -280,10 +252,17 @@ bool PaddlePredictorImpl::GetFetch(
 }
 
 template <>
-std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(
-    const ConfigImpl &config) {
-  VLOG(3) << "create PaddlePredictorImpl";
+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) {
@@ -294,9 +273,10 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(
       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

@@ -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

@@ -40,19 +40,19 @@ 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;
+  config.use_gpu = true;
   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);
+  NativeConfig config = GetConfig();
+  auto predictor = CreatePaddlePredictor<NativeConfig>(config);
 
   framework::LoDTensor first_word, second_word, third_word, fourth_word;
   framework::LoD lod{{0, 1}};
@@ -74,7 +74,7 @@ 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 +92,7 @@ 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);
   }
@@ -104,7 +104,7 @@ TEST(paddle_inference_api_impl, image_classification) {
   int batch_size = 2;
   bool use_mkldnn = false;
   bool repeat = false;
-  ConfigImpl config = GetConfig();
+  NativeConfig config = GetConfig();
   config.model_dir =
       FLAGS_dirname + "image_classification_resnet.inference.model";
 
@@ -133,7 +133,7 @@ TEST(paddle_inference_api_impl, image_classification) {
                                                  is_combined,
                                                  use_mkldnn);
 
-  std::unique_ptr<PaddlePredictor> predictor = CreatePaddlePredictor(config);
+  auto predictor = CreatePaddlePredictor(config);
   std::vector<PaddleTensor> paddle_tensor_feeds;
   paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&input));
 
@@ -144,8 +144,7 @@ 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);
 }

paddle/fluid/framework/CMakeLists.txt

@@ -87,7 +87,7 @@ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
 framework_proto glog lod_rank_table feed_fetch_method)
 
 
-cc_library(parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor)
+cc_library(parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor scope_buffered_ssa_graph_executor)
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)

paddle/fluid/framework/block_desc.cc

@@ -169,17 +169,13 @@ void BlockDesc::Flush() {
   }
 
   if (need_update_) {
-    auto &op_field = *this->desc_->mutable_ops();
-    this->ClearPBOps();
-    op_field.Reserve(static_cast<int>(ops_.size()));
+    this->desc_->mutable_ops()->Clear();
     for (auto &op_desc : ops_) {
-      op_field.AddAllocated(op_desc->Proto());
+      this->desc_->mutable_ops()->Add()->CopyFrom(*op_desc->Proto());
     }
-    auto &var_field = *this->desc_->mutable_vars();
-    this->ClearPBVars();
-    var_field.Reserve(static_cast<int>(vars_.size()));
+    this->desc_->mutable_vars()->Clear();
     for (auto &var_desc : vars_) {
-      var_field.AddAllocated(var_desc.second->Proto());
+      this->desc_->mutable_vars()->Add()->CopyFrom(*var_desc.second->Proto());
     }
     need_update_ = false;
   }
@@ -200,7 +196,7 @@ BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
     vars_[var_desc.name()].reset(new VarDesc(var_desc));
   }
   for (const proto::OpDesc &op_desc : desc_->ops()) {
-    ops_.emplace_back(new OpDesc(op_desc, prog, this));
+    ops_.emplace_back(new OpDesc(op_desc, this));
   }
 }
 
@@ -209,7 +205,7 @@ BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
     : prog_(prog), desc_(desc) {
   need_update_ = true;
   for (auto &op : other.ops_) {
-    ops_.emplace_back(new OpDesc(*op->Proto(), prog, this));
+    ops_.emplace_back(new OpDesc(*op, this));
   }
   for (auto &it : other.vars_) {
     auto *var = new VarDesc(*it.second);
@@ -217,22 +213,6 @@ BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
   }
 }
 
-void BlockDesc::ClearPBOps() {
-  auto ops = this->desc_->mutable_ops();
-  while (!ops->empty()) {
-    // we do not own the OpDesc, so release the ownership.
-    ops->ReleaseLast();
-  }
-}
-
-void BlockDesc::ClearPBVars() {
-  auto vars = this->desc_->mutable_vars();
-  while (!vars->empty()) {
-    // we do not own the VarDesc, so release the ownership.
-    vars->ReleaseLast();
-  }
-}
-
 void BlockDesc::SetForwardBlockID(int32_t forward_block_id) {
   PADDLE_ENFORCE(!desc_->has_forward_block_idx(),
                  "Parent block ID has been set to %d. Cannot set to %d",

paddle/fluid/framework/block_desc.h

@@ -41,11 +41,6 @@ class BlockDesc {
 
   BlockDesc(const BlockDesc &other, proto::BlockDesc *desc, ProgramDesc *prog);
 
-  ~BlockDesc() {
-    this->ClearPBVars();
-    this->ClearPBOps();
-  }
-
   int32_t ID() const { return desc_->idx(); }
 
   int32_t Parent() const { return desc_->parent_idx(); }
@@ -105,7 +100,7 @@ class BlockDesc {
 
   size_t OpSize() const { return ops_.size(); }
 
-  OpDesc *Op(int idx) { return ops_.at(idx).get(); }
+  OpDesc *Op(int idx) const { return ops_.at(idx).get(); }
 
   void Flush();
 
@@ -113,10 +108,6 @@ class BlockDesc {
 
   ProgramDesc *Program() const { return this->prog_; }
 
- private:
-  void ClearPBOps();
-  void ClearPBVars();
-
  private:
   ProgramDesc *prog_;       // not_own
   proto::BlockDesc *desc_;  // not_own

paddle/fluid/framework/details/CMakeLists.txt

@@ -36,5 +36,6 @@ cc_test(broadcast_op_test SRCS broadcast_op_handle_test.cc DEPS var_handle op_ha
         device_context broadcast_op_handle)
 cc_test(gather_op_test SRCS gather_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
         device_context gather_op_handle)
+cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_executor.cc DEPS ssa_graph_executor)
 #cc_test(reduce_op_handle_test SRCS reduce_op_handle_test.cc DEPS var_handle op_handle_base scope ddim memory
 #        device_context reduce_op_handle )

paddle/fluid/framework/details/execution_strategy.h

@@ -22,6 +22,7 @@ struct ExecutionStrategy {
   size_t num_threads_{0};
   bool use_event_{true};
   bool allow_op_delay_{false};
+  size_t num_iteration_per_drop_scope_{100};
 };
 
 }  //  namespace details

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -11,11 +11,15 @@
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
-#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
+#include <algorithm>
 #include <fstream>
+#include <string>
 #include <utility>
+#include <vector>
+
 #include "paddle/fluid/framework/details/broadcast_op_handle.h"
 #include "paddle/fluid/framework/details/computation_op_handle.h"
+#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/rpc_op_handle.h"
 #include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
@@ -26,9 +30,6 @@
 #include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
 #endif
 
-#include <string>
-#include <vector>
-
 DEFINE_string(ssa_graph_path, "/tmp/ssa_graph.dot",
               "the ssa graph path only print with GLOG_v=10,"
               "default /tmp/graph.dot");
@@ -148,9 +149,9 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(
 
 std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
     const ProgramDesc &program) const {
-  std::unordered_map<std::string, proto::VarType::Type> var_types;
+  std::unordered_map<std::string, VarDesc *> all_vars;
   for (auto *var : program.Block(0).AllVars()) {
-    var_types[var->Name()] = var->GetType();
+    all_vars[var->Name()] = var;
   }
 
   auto graph = new SSAGraph();
@@ -167,12 +168,28 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
   auto send_vars = FindDistTrainSendVars(program);
   auto recv_vars = FindDistTrainRecvVars(program);
 
-  size_t cur_device_id = 0;
   std::vector<std::unordered_set<std::string>> var_name_on_devices;
   std::vector<std::unordered_set<std::string>> bcast_var_name_set;
   var_name_on_devices.resize(places_.size());
   bcast_var_name_set.resize(places_.size());
 
+  size_t cur_device_id = 0;
+  std::vector<int64_t> balance_grads(places_.size(), 0);
+
+  auto get_appropriate_dev = [&](std::string &g_name) -> size_t {
+    auto var_desc = all_vars.at(g_name);
+    PADDLE_ENFORCE_NOT_NULL(var_desc);
+    auto dim = framework::make_ddim(var_desc->GetShape());
+    int64_t numel = framework::product(dim);
+    PADDLE_ENFORCE_GE(numel, 0);
+    auto smallest =
+        std::min_element(std::begin(balance_grads), std::end(balance_grads));
+    size_t dev_id =
+        static_cast<size_t>(std::distance(std::begin(balance_grads), smallest));
+    balance_grads[dev_id] += numel;
+    return dev_id;
+  };
+
   bool is_forwarding = true;
   for (auto *op : program.Block(0).AllOps()) {
     if (boost::get<int>(
@@ -220,13 +237,13 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
 
               switch (strategy_.reduce_) {
                 case BuildStrategy::ReduceStrategy::kReduce:
+                  cur_device_id = get_appropriate_dev(g_name);
                   CreateReduceOp(&result, g_name, cur_device_id);
                   var_name_on_devices[cur_device_id].emplace(g_name);
                   bcast_var_name_set[cur_device_id].emplace(p_name);
-                  cur_device_id = (cur_device_id + 1) % places_.size();
                   break;
                 case BuildStrategy::ReduceStrategy::kAllReduce:
-                  if (IsSparseGradient(var_types, g_name)) {
+                  if (IsSparseGradient(all_vars, g_name)) {
                     CreateReduceOp(&result, g_name, 0);
                     CreateBroadcastOp(&result, g_name, 0);
                   } else {
@@ -269,10 +286,10 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
 }
 
 bool MultiDevSSAGraphBuilder::IsSparseGradient(
-    const std::unordered_map<std::string, proto::VarType::Type> &var_types,
+    const std::unordered_map<std::string, VarDesc *> &all_vars,
     const std::string &og) const {
-  PADDLE_ENFORCE(var_types.count(og) != 0);
-  if (var_types.at(og) == proto::VarType::SELECTED_ROWS) {
+  PADDLE_ENFORCE(all_vars.count(og) != 0);
+  if (all_vars.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
     return true;
   }
   return false;

paddle/fluid/framework/details/multi_devices_graph_builder.h

@@ -106,7 +106,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
                          size_t src_dev_id) const;
 
   bool IsSparseGradient(
-      const std::unordered_map<std::string, proto::VarType::Type> &var_types,
+      const std::unordered_map<std::string, VarDesc *> &all_vars,
       const std::string &og) const;
 
  private:

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/executor.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+ScopeBufferedSSAGraphExecutor::ScopeBufferedSSAGraphExecutor(
+    ExecutionStrategy strategy, std::vector<Scope *> local_scopes,
+    std::vector<VariableInfo> var_infos, std::vector<platform::Place> places,
+    std::unique_ptr<SSAGraphExecutor> &&underlying_executor)
+    : strategy_(std::move(strategy)),
+      underlying_executor_(std::move(underlying_executor)),
+      local_scopes_(std::move(local_scopes)),
+      var_infos_(std::move(var_infos)),
+      places_(std::move(places)) {}
+
+FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
+    const std::vector<std::string> &fetch_tensors) {
+  if (drop_scope_counter_ == 0) {
+    // Create local scopes.
+    for (auto it = local_scopes_.rbegin(); it != local_scopes_.rend(); ++it) {
+      auto &scope = *it;
+      Scope &local_scope = scope->NewScope();
+      *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
+          &local_scope;
+
+      for (auto &info : var_infos_) {
+        if (scope->FindVar(info.name_) != nullptr) {
+          continue;
+        }
+
+        if (info.persistable_) {  // Persistable
+          InitializeVariable(scope->Var(info.name_), info.type_);
+        } else {
+          InitializeVariable(local_scope.Var(info.name_), info.type_);
+        }
+      }
+    }
+  }
+
+  auto fetch_data = underlying_executor_->Run(fetch_tensors);
+  drop_scope_counter_ += 1;
+  if (!fetch_tensors.empty() ||
+      drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {
+    drop_scope_counter_ = 0;
+    // Wait All computational streams
+    for (auto p : places_) {
+      platform::DeviceContextPool::Instance().Get(p)->Wait();
+    }
+    for (auto &scope : local_scopes_) {
+      auto &local_scope =
+          *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
+      scope->DeleteScope(local_scope);
+    }
+  }
+  return fetch_data;
+}
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/details/execution_strategy.h"
+#include "paddle/fluid/framework/details/ssa_graph_executor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/platform/place.h"
+namespace paddle {
+namespace framework {
+namespace details {
+
+struct VariableInfo {
+  std::string name_;
+  proto::VarType::Type type_;
+  bool persistable_;
+};
+
+class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor {
+ public:
+  ScopeBufferedSSAGraphExecutor(
+      ExecutionStrategy strategy, std::vector<Scope*> local_scopes,
+      std::vector<VariableInfo> var_infos, std::vector<platform::Place> places,
+      std::unique_ptr<SSAGraphExecutor>&& underlying_executor);
+  FeedFetchList Run(const std::vector<std::string>& fetch_tensors) override;
+
+ private:
+  size_t drop_scope_counter_{0};
+
+  ExecutionStrategy strategy_;
+  std::unique_ptr<SSAGraphExecutor> underlying_executor_;
+  std::vector<Scope*> local_scopes_;
+  std::vector<VariableInfo> var_infos_;
+  std::vector<platform::Place> places_;
+};
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/details/ssa_graph_executor.cc

@@ -17,10 +17,6 @@
 namespace paddle {
 namespace framework {
 namespace details {
-
-SSAGraphExecutor::SSAGraphExecutor(std::unique_ptr<SSAGraph> &&graph)
-    : graph_(std::move(graph)) {}
-
 SSAGraphExecutor::~SSAGraphExecutor() {}
 
 }  // namespace details

paddle/fluid/framework/details/ssa_graph_executor.h

@@ -28,15 +28,11 @@ class SSAGraphExecutor {
   DISABLE_COPY_AND_ASSIGN(SSAGraphExecutor);
 
  public:
-  // Steal graph inside
-  explicit SSAGraphExecutor(std::unique_ptr<SSAGraph> &&graph);
+  SSAGraphExecutor() {}
 
   virtual ~SSAGraphExecutor();
 
   virtual FeedFetchList Run(const std::vector<std::string> &fetch_tensors) = 0;
-
- protected:
-  std::unique_ptr<SSAGraph> graph_;
 };
 }  // namespace details
 }  // namespace framework

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -21,7 +21,7 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
     const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
     const std::vector<platform::Place> &places,
     std::unique_ptr<SSAGraph> &&graph)
-    : SSAGraphExecutor(std::move(graph)),
+    : graph_(std::move(graph)),
       pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
                                        : nullptr),
       local_scopes_(local_scopes),
@@ -189,7 +189,9 @@ void ThreadedSSAGraphExecutor::RunOp(
     BlockingQueue<VarHandleBase *> *ready_var_q, details::OpHandleBase *op) {
   auto op_run = [ready_var_q, op, this] {
     try {
+      if (VLOG_IS_ON(10)) {
         VLOG(10) << op << " " << op->Name() << " : " << op->DebugString();
+      }
       op->Run(strategy_.use_event_);
       VLOG(10) << op << " " << op->Name() << " Done ";
       running_ops_--;

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -51,6 +51,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
              details::OpHandleBase *op);
 
  private:
+  std::unique_ptr<SSAGraph> graph_;
   std::unique_ptr<::ThreadPool> pool_;
   std::vector<Scope *> local_scopes_;
   std::vector<platform::Place> places_;

paddle/fluid/framework/executor.cc

@@ -220,8 +220,10 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
       has_fetch_operators(program.Block(0), *fetch_targets, fetch_holder_name);
 
   ProgramDesc* copy_program = const_cast<ProgramDesc*>(&program);
+  std::unique_ptr<ProgramDesc> unique_ptr_of_copy_program;
   if (!has_feed_ops || !has_fetch_ops) {
-    copy_program = std::unique_ptr<ProgramDesc>(new ProgramDesc(program)).get();
+    unique_ptr_of_copy_program.reset(new ProgramDesc(program));
+    copy_program = unique_ptr_of_copy_program.get();
   }
 
   auto* global_block = copy_program->MutableBlock(0);

paddle/fluid/framework/op_desc.cc

@@ -103,7 +103,7 @@ void OpDesc::CopyFrom(const OpDesc &op_desc) {
   need_update_ = true;
 }
 
-OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
+OpDesc::OpDesc(const proto::OpDesc &desc, BlockDesc *block)
     : desc_(desc), need_update_(false) {
   // restore inputs_
   int input_size = desc_.inputs_size();

paddle/fluid/framework/op_desc.h

@@ -33,13 +33,14 @@ class OpDesc {
   OpDesc(const std::string &type, const VariableNameMap &inputs,
          const VariableNameMap &outputs, const AttributeMap &attrs);
 
-  OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block);
+  OpDesc(const proto::OpDesc &desc, BlockDesc *block);
 
   explicit OpDesc(BlockDesc *block) : block_(block) {}
 
   OpDesc(const OpDesc &other, BlockDesc *block) {
     *this = other;
     block_ = block;
+    need_update_ = true;
   }
 
   void CopyFrom(const OpDesc &op_desc);

paddle/fluid/framework/parallel_executor.cc

@@ -23,6 +23,7 @@ limitations under the License. */
 #endif
 
 #include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
+#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -42,8 +43,6 @@ class ParallelExecutorPrivate {
 #ifdef PADDLE_WITH_CUDA
   std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
 #endif
-
-  std::vector<std::tuple<std::string, proto::VarType::Type, bool>> var_types_;
   bool own_local_scope;
 };
 
@@ -92,9 +91,18 @@ ParallelExecutor::ParallelExecutor(
       local_scopes.empty()) {  // Is CUDA
     BCastParamsToGPUs(bcast_vars);
   }
-// Startup Program has been run. All local scopes has correct parameters.
+  // Startup Program has been run. All local scopes has correct parameters.
 
-// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
+  // Step 2. Create vars in each scope;
+  std::vector<details::VariableInfo> var_infos;
+  for (auto *var : main_program.Block(0).AllVars()) {
+    var_infos.emplace_back();
+    var_infos.back().name_ = var->Name();
+    var_infos.back().type_ = var->GetType();
+    var_infos.back().persistable_ = var->Persistable();
+  }
+
+// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
 // ncclOp
 #ifdef PADDLE_WITH_CUDA
   details::MultiDevSSAGraphBuilder builder(
@@ -105,16 +113,15 @@ ParallelExecutor::ParallelExecutor(
                                            params, member_->local_scopes_,
                                            build_strategy);
 #endif
+
   auto graph = builder.Build(main_program);
 
   member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
       exec_strategy, member_->local_scopes_, places, std::move(graph)));
 
-  // Step 3. Create vars in each scope;
-  for (auto *var : main_program.Block(0).AllVars()) {
-    member_->var_types_.emplace_back(var->Name(), var->GetType(),
-                                     var->Persistable());
-  }
+  member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
+      exec_strategy, member_->local_scopes_, std::move(var_infos),
+      member_->places_, std::move(member_->executor_)));
 }
 
 void ParallelExecutor::BCastParamsToGPUs(
@@ -169,42 +176,9 @@ void ParallelExecutor::BCastParamsToGPUs(
 void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
                            const std::string &fetched_var_name) {
   platform::RecordBlock b(0);
-  // Create local scopes.
-  for (auto it = member_->local_scopes_.rbegin();
-       it != member_->local_scopes_.rend(); ++it) {
-    auto &scope = *it;
-    Scope &local_scope = scope->NewScope();
-    *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
-        &local_scope;
-
-    for (auto &name_type_pair : member_->var_types_) {
-      if (scope->FindVar(std::get<0>(name_type_pair)) != nullptr) {
-        continue;
-      }
-
-      if (std::get<2>(name_type_pair)) {  // Persistable
-        InitializeVariable(scope->Var(std::get<0>(name_type_pair)),
-                           std::get<1>(name_type_pair));
-      } else {
-        InitializeVariable(local_scope.Var(std::get<0>(name_type_pair)),
-                           std::get<1>(name_type_pair));
-      }
-    }
-  }
-
   auto fetch_data = member_->executor_->Run(fetch_tensors);
   *member_->global_scope_->Var(fetched_var_name)->GetMutable<FeedFetchList>() =
       fetch_data;
-
-  // Wait All computational streams
-  for (auto p : member_->places_) {
-    platform::DeviceContextPool::Instance().Get(p)->Wait();
-  }
-  for (auto &scope : member_->local_scopes_) {
-    auto &local_scope =
-        *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
-    scope->DeleteScope(local_scope);
-  }
 }
 
 void ParallelExecutor::FeedTensorsIntoLocalScopes(

paddle/fluid/framework/program_desc.cc

@@ -51,12 +51,15 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
     auto *block = desc_.mutable_blocks(i);
     blocks_.emplace_back(new BlockDesc(*o.blocks_[i], block, this));
   }
-  for (auto &block : blocks_) {
-    for (auto *op : block->AllOps()) {
-      for (const auto &attr : op->Proto()->attrs()) {
-        if (attr.type() == proto::AttrType::BLOCK) {
-          size_t blk_idx = attr.block_idx();
-          op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
+  for (size_t block_id = 0; block_id < blocks_.size(); ++block_id) {
+    auto all_ops = blocks_[block_id]->AllOps();
+    for (size_t op_id = 0; op_id < all_ops.size(); ++op_id) {
+      auto &op = all_ops[op_id];
+      for (const std::string &attr_name : op->AttrNames()) {
+        if (op->GetAttrType(attr_name) == proto::AttrType::BLOCK) {
+          int sub_block_id =
+              o.Block(block_id).Op(op_id)->GetBlockAttr(attr_name);
+          op->SetBlockAttr(attr_name, MutableBlock(sub_block_id));
         }
       }
     }
@@ -86,6 +89,16 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
   for (auto &block_desc : *desc_.mutable_blocks()) {
     blocks_.emplace_back(new BlockDesc(this, &block_desc));
   }
+  for (auto &block : blocks_) {
+    for (auto *op : block->AllOps()) {
+      for (const auto &attr : op->Proto()->attrs()) {
+        if (attr.type() == proto::AttrType::BLOCK) {
+          size_t blk_idx = attr.block_idx();
+          op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
+        }
+      }
+    }
+  }
 }
 
 const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {

paddle/fluid/framework/reader.cc

@@ -25,8 +25,10 @@ void FileReader::ReadNext(std::vector<LoDTensor> *out) {
   if (out->empty()) {
     return;
   }
+
+  PADDLE_ENFORCE_EQ(out->size(), dims_.size());
   for (size_t i = 0; i < dims_.size(); ++i) {
-    auto &actual = out->at(i).dims();
+    auto &actual = (*out)[i].dims();
     auto &expect = dims_[i];
 
     PADDLE_ENFORCE_EQ(actual.size(), expect.size());

paddle/fluid/framework/tensor_impl.h

@@ -39,7 +39,7 @@ template <typename T>
 inline const T* Tensor::data() const {
   check_memory_size();
   PADDLE_ENFORCE(std::is_same<T, void>::value ||
-                     holder_->type().hash_code() == typeid(T).hash_code(),
+                     holder_->type() == std::type_index(typeid(T)),
                  "Tensor holds the wrong type, it holds %s",
                  this->holder_->type().name());
 
@@ -53,7 +53,7 @@ template <typename T>
 inline T* Tensor::data() {
   check_memory_size();
   PADDLE_ENFORCE(std::is_same<T, void>::value ||
-                     holder_->type().hash_code() == typeid(T).hash_code(),
+                     holder_->type() == std::type_index(typeid(T)),
                  "Tensor holds the wrong type, it holds %s",
                  this->holder_->type().name());
   return reinterpret_cast<T*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +

paddle/fluid/inference/CMakeLists.txt

@@ -5,14 +5,19 @@ cc_library(paddle_fluid_api
     SRCS io.cc
     DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
 
-# Create static library
 get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
-cc_library(paddle_fluid DEPS ${fluid_modules})
 
+if(WITH_CONTRIB)
+  set(fluid_modules "${fluid_modules}" paddle_inference_api)
+endif()
+
+# Create static library
+cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api)
 # Create shared library
 cc_library(paddle_fluid_shared SHARED
     SRCS io.cc
-    DEPS ${fluid_modules})
+    DEPS ${fluid_modules} paddle_fluid_api)
+
 set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
 if(NOT APPLE)
   # TODO(liuyiqun): Temporarily disable the link flag because it is not support on Mac.

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -15,3 +15,9 @@ cc_test(test_subgraph_splitter
         DEPS analysis paddle_fluid tensor
         ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
 set_tests_properties(test_subgraph_splitter PROPERTIES DEPENDS test_word2vec)
+
+cc_test(test_dfg_graphviz_draw_pass
+        SRCS dfg_graphviz_draw_pass_tester.cc
+        DEPS analysis
+        ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
+set_tests_properties(test_dfg_graphviz_draw_pass PROPERTIES DEPENDS test_word2vec)

paddle/fluid/inference/analysis/data_flow_graph.h

@@ -21,7 +21,10 @@ limitations under the License. */
 
 #include <deque>
 #include <stack>
+#include <string>
 #include <unordered_set>
+#include <utility>
+#include <vector>
 
 #include "paddle/fluid/inference/analysis/graph_traits.h"
 #include "paddle/fluid/inference/analysis/node.h"

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass_tester.cc

@@ -44,6 +44,6 @@ TEST_F(DFG_Tester, Test) {
   LOG(INFO) << graph.nodes.size();
 }
 
-}  // analysis
-}  // inference
-}  // paddle
+};  // namespace analysis
+};  // namespace inference
+};  // namespace paddle

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+/*
+ * This file create an DFG_GraphvizDrawPass which helps to draw a data flow
+ * graph's structure using graphviz.
+ */
+
+#pragma once
+
+#include <fstream>
+#include <string>
+#include "paddle/fluid/inference/analysis/pass.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+/*
+ * Output a dot file and write to some place.
+ */
+class DFG_GraphvizDrawPass : public DataFlowGraphPass {
+ public:
+  DFG_GraphvizDrawPass(const std::string& dir, const std::string& id)
+      : dir_(dir), id_(id) {}
+
+  bool Initialize() override { return Pass::Initialize(); }
+  void Run(DataFlowGraph* graph) override {
+    auto content = Draw(graph);
+    std::ofstream file(GenDotPath());
+    file.write(content.c_str(), content.size());
+    file.close();
+    LOG(INFO) << "draw dot to " << GenDotPath();
+  }
+
+  bool Finalize() override { return Pass::Finalize(); }
+
+  Pass* CreatePrinterPass(std::ostream& os,
+                          const std::string& banner) const override {
+    return nullptr;
+  }
+
+ private:
+  // Path of the dot file to output.
+  std::string GenDotPath() const {
+    return dir_ + "/" + "graph_" + id_ + ".dot";
+  }
+
+  std::string Draw(DataFlowGraph* graph) { return graph->DotString(); }
+
+  std::string dir_;
+  std::string id_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
+
+#include <gtest/gtest.h>
+#include <fstream>
+#include <string>
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+TEST_F(DFG_Tester, dfg_graphviz_draw_pass_tester) {
+  auto dfg = ProgramDescToDFG(desc);
+  DFG_GraphvizDrawPass pass("./", "test");
+  pass.Initialize();
+  pass.Run(&dfg);
+
+  // test content
+  std::ifstream file("./graph_test.dot");
+  ASSERT_TRUE(file.is_open());
+
+  std::string line;
+  int no{0};
+  while (std::getline(file, line)) {
+    no++;
+  }
+  ASSERT_EQ(no, 82);
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.cc

@@ -12,9 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
+#include <string>
 #include <vector>
 
+#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
+
 namespace paddle {
 namespace inference {
 namespace analysis {

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h

@@ -19,6 +19,8 @@
 
 #pragma once
 
+#include <string>
+
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
 #include "paddle/fluid/inference/analysis/pass.h"

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass_tester.cc

@@ -32,6 +32,6 @@ TEST_F(DFG_Tester, Init) {
   LOG(INFO) << '\n' << graph.DotString();
 }
 
-}  // analysis
-}  // inference
-}  // paddle
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/helper.h

@@ -50,7 +50,7 @@ struct DataTypeNamer {
     return dic_.at(x);
   }
 
-  const std::string &repr(size_t &hash) const {
+  const std::string &repr(size_t &hash) const {  // NOLINT
     PADDLE_ENFORCE(dic_.count(hash), "unknown type for representation");
     return dic_.at(hash);
   }
@@ -62,7 +62,9 @@ struct DataTypeNamer {
     SET_TYPE(float);
   }
 
-  std::unordered_map<decltype(typeid(int).hash_code()), std::string> dic_;
+  std::unordered_map<decltype(typeid(int).hash_code()),  // NOLINT
+                     std::string>
+      dic_;
 };
 #undef SET_TYPE
 

paddle/fluid/inference/analysis/pass.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <glog/logging.h>
 #include <iosfwd>
+#include <string>
 
 #include "paddle/fluid/framework/framework.pb.h"
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"

paddle/fluid/inference/analysis/subgraph_splitter.h

@@ -18,6 +18,8 @@ limitations under the License. */
 
 #pragma once
 
+#include <vector>
+
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
 #include "paddle/fluid/inference/analysis/node.h"
 

paddle/fluid/inference/analysis/ut_helper.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 #include <gflags/gflags.h>
 #include <gtest/gtest.h>
+#include <string>
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
 #include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

@@ -8,3 +8,5 @@ nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS
 nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)
 nv_test(test_trt_mul_op SRCS test_mul_op.cc mul_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
+nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc
+        DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)

paddle/fluid/inference/tensorrt/convert/activation_op.cc

@@ -24,7 +24,7 @@ class ReluOpConverter : public OpConverter {
   void operator()(const framework::proto::OpDesc& op) override {
     // Here the two nullptr looks strange, that's because the
     // framework::OpDesc's constructor is strange.
-    framework::OpDesc op_desc(op, nullptr, nullptr);
+    framework::OpDesc op_desc(op, nullptr);
     LOG(INFO) << "convert a fluid relu op to tensorrt activation layer whose "
                  "type is Relu";
     const nvinfer1::ITensor* input_tensor =

paddle/fluid/inference/tensorrt/convert/conv2d_op.cc

@@ -21,7 +21,8 @@ namespace tensorrt {
 class Conv2dOpConverter : public OpConverter {
  public:
   Conv2dOpConverter() {}
-  void operator()(const framework::proto::OpDesc& op) override {
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope) override {
     LOG(INFO)
         << "convert a fluid conv2d op to tensorrt conv layer without bias";
   }

paddle/fluid/inference/tensorrt/convert/fc_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/engine.h"
+#include "paddle/fluid/platform/place.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+// Reorder the elements from istrides to ostrides, borrowed from TRT convert in
+// tensorflow.
+// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/tensorrt/convert/convert_nodes.cc#L318
+template <typename T>
+void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides,
+              T* odata, nvinfer1::DimsHW ostrides) {
+  for (int h = 0; h < shape.h(); ++h) {
+    for (int w = 0; w < shape.w(); ++w) {
+      odata[h * ostrides.h() + w * ostrides.w()] =
+          idata[h * ostrides.h() + w * ostrides.w()];
+    }
+  }
+}
+
+// Reorder the data layout from CK to KC.
+void ReorderCKtoKC(TensorRTEngine::Weight& iweights,
+                   TensorRTEngine::Weight* oweights) {
+  int c = iweights.dims[0];
+  int k = iweights.dims[1];
+  oweights->dims.assign({k, c});
+  nvinfer1::DimsHW istrides = {1, k};
+  nvinfer1::DimsHW ostrides = {c, 1};
+  Reorder2({k, c}, static_cast<float const*>(iweights.get().values), istrides,
+           static_cast<float*>(const_cast<void*>(oweights->get().values)),
+           ostrides);
+}
+
+/*
+ * FC converter convert a MUL op in Fluid to a FC layer in TRT.
+ */
+class FcOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope) override {
+    VLOG(4) << "convert a fluid fc op to tensorrt fc layer without bias";
+
+    framework::OpDesc op_desc(op, nullptr);
+    PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
+    PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);  // Y is a weight
+    PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
+
+    // Declare inputs
+    auto* X = engine_->GetITensor(op_desc.Input("X").front());
+
+    // Declare weights
+    auto* Y_v = scope.FindVar(op_desc.Input("Y").front());
+    PADDLE_ENFORCE_NOT_NULL(Y_v);
+    auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
+    // This may trigger a GPU->CPU copy, because TRT's weight can only be
+    // assigned from CPU memory, that can't be avoided.
+    auto* weight_data = Y_t->mutable_data<float>(platform::CPUPlace());
+    PADDLE_ENFORCE_EQ(Y_t->dims().size(), 2UL);  // a matrix
+    size_t n_output = Y_t->dims()[1];
+
+    framework::LoDTensor tmp;
+    tmp.Resize(Y_t->dims());
+    memcpy(tmp.mutable_data<float>(platform::CPUPlace()), Y_t->data<float>(),
+           Y_t->dims()[0] * Y_t->dims()[1]);
+
+    TensorRTEngine::Weight weight{nvinfer1::DataType::kFLOAT,
+                                  static_cast<void*>(weight_data),
+                                  Y_t->memory_size() / sizeof(float)};
+    TensorRTEngine::Weight tmp_weight(nvinfer1::DataType::kFLOAT,
+                                      static_cast<void*>(tmp.data<float>()),
+                                      Y_t->memory_size() / sizeof(float));
+    weight.dims.assign({Y_t->dims()[0], Y_t->dims()[1]});
+    tmp_weight.dims = weight.dims;
+
+    // The data layout of TRT FC layer's weight is different from fluid's FC,
+    // need to reorder the elements.
+    ReorderCKtoKC(tmp_weight, &weight);
+
+    // Currently, the framework can only handle one fluid op -> one TRT layer,
+    // but fc fuses `mul` and `bias` (2 fluid ops), so here is a trick, just
+    // handle `mul`, leave `add` as another layer.
+    // DEBUG
+    TensorRTEngine::Weight bias{nvinfer1::DataType::kFLOAT, nullptr, 0};
+
+    auto* layer = TRT_ENGINE_ADD_LAYER(engine_, FullyConnected,
+                                       *const_cast<nvinfer1::ITensor*>(X),
+                                       n_output, weight.get(), bias.get());
+
+    auto output_name = op_desc.Output("Out").front();
+    engine_->DeclareOutput(layer, 0, output_name);
+  }
+};
+
+REGISTER_TRT_OP_CONVERTER(fc, FcOpConverter);
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(mul);

paddle/fluid/inference/tensorrt/convert/mul_op.cc

@@ -24,10 +24,11 @@ namespace tensorrt {
 class MulOpConverter : public OpConverter {
  public:
   MulOpConverter() {}
-  void operator()(const framework::proto::OpDesc& op) override {
-    VLOG(4) << "convert a fluid mul op to tensorrt fc layer without bias";
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope) override {
+    VLOG(4) << "convert a fluid mul op to tensorrt mul layer without bias";
 
-    framework::OpDesc op_desc(op, nullptr, nullptr);
+    framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
     auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
     auto* input2 = engine_->GetITensor(op_desc.Input("Y")[0]);

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -31,27 +31,42 @@ namespace tensorrt {
 class OpConverter {
  public:
   OpConverter() {}
-  virtual void operator()(const framework::proto::OpDesc& op) {}
 
-  void Run(const framework::proto::OpDesc& op, TensorRTEngine* engine) {
-    std::string type = op.type();
-    auto* it = Registry<OpConverter>::Lookup(type);
-    PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]", type);
-    it->SetEngine(engine);
-    (*it)(op);
-  }
+  // Converter logic for an op.
+  virtual void operator()(const framework::proto::OpDesc& op,
+                          const framework::Scope& scope) {}
+
+  // Convert a single fluid operaotr and add the corresponding layer to TRT.
+  void ConvertOp(const framework::proto::OpDesc& op,
+                 const std::unordered_set<std::string>& parameters,
+                 const framework::Scope& scope, TensorRTEngine* engine) {
+    framework::OpDesc op_desc(op, nullptr);
+
+    OpConverter* it{nullptr};
 
-  // convert fluid op to tensorrt layer
-  void ConvertOp(const framework::proto::OpDesc& op, TensorRTEngine* engine) {
-    OpConverter::Run(op, engine);
+    if (op_desc.Type() == "mul") {
+      PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL);
+      std::string Y = op_desc.Input("Y")[0];
+      if (parameters.count(Y)) {
+        it = Registry<OpConverter>::Lookup("fc");
+      }
+    }
+    if (!it) {
+      it = Registry<OpConverter>::Lookup(op_desc.Type());
+    }
+    PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
+                            op_desc.Type());
+    it->SetEngine(engine);
+    (*it)(op, scope);
   }
 
   // convert fluid block to tensorrt network
   void ConvertBlock(const framework::proto::BlockDesc& block,
-                    TensorRTEngine* engine) {
+                    const std::unordered_set<std::string>& parameters,
+                    const framework::Scope& scope, TensorRTEngine* engine) {
     for (int i = 0; i < block.ops_size(); i++) {
       const auto& op = block.ops(i);
-      OpConverter::Run(op, engine);
+      ConvertOp(op, parameters, scope, engine);
     }
   }
 

paddle/fluid/inference/tensorrt/convert/test_fc_op.cc

+/* 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 <gtest/gtest.h>
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+TEST(fc_op, test) {
+  std::unordered_set<std::string> parameters({"mul-Y"});
+  framework::Scope scope;
+  TRTConvertValidation validator(20, parameters, scope, 1000);
+
+  validator.DeclInputVar("mul-X", nvinfer1::Dims4(8, 3, 1, 1));
+  validator.DeclParamVar("mul-Y", nvinfer1::Dims2(3, 2));
+  validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(8, 2));
+
+  // Prepare Op description
+  framework::OpDesc desc;
+  desc.SetType("mul");
+  desc.SetInput("X", {"mul-X"});
+  desc.SetInput("Y", {"mul-Y"});
+  desc.SetOutput("Out", {"mul-Out"});
+
+  validator.SetOp(*desc.Proto());
+
+  validator.Execute(10);
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/convert/test_mul_op.cc

@@ -21,7 +21,9 @@ namespace inference {
 namespace tensorrt {
 
 TEST(MulOpConverter, main) {
-  TRTConvertValidation validator(10, 1000);
+  framework::Scope scope;
+  std::unordered_set<std::string> parameters;
+  TRTConvertValidation validator(10, parameters, scope, 1000);
   validator.DeclInputVar("mul-X", nvinfer1::Dims2(10, 6));
   validator.DeclInputVar("mul-Y", nvinfer1::Dims2(6, 10));
   validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(10, 10));

paddle/fluid/inference/tensorrt/convert/test_op_converter.cc

@@ -12,9 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
 #include <gtest/gtest.h>
 #include "paddle/fluid/framework/program_desc.h"
-#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
 
 namespace paddle {
 namespace inference {
@@ -27,7 +28,9 @@ TEST(OpConverter, ConvertBlock) {
   conv2d_op->SetType("conv2d");
 
   OpConverter converter;
-  converter.ConvertBlock(*block->Proto(), nullptr /*TensorRTEngine*/);
+  framework::Scope scope;
+  converter.ConvertBlock(*block->Proto(), {}, scope,
+                         nullptr /*TensorRTEngine*/);
 }
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/convert/ut_helper.h

@@ -19,6 +19,9 @@ limitations under the License. */
 
 #pragma once
 
+#include <string>
+#include <vector>
+
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/inference/analysis/helper.h"
@@ -58,7 +61,10 @@ class TRTConvertValidation {
  public:
   TRTConvertValidation() = delete;
 
-  TRTConvertValidation(int batch_size, int workspace_size = 1 << 10) {
+  TRTConvertValidation(int batch_size,
+                       const std::unordered_set<std::string>& parameters,
+                       framework::Scope& scope, int workspace_size = 1 << 10)
+      : parameters_(parameters), scope_(scope) {
     // create engine.
     engine_.reset(new TensorRTEngine(10, 1 << 10, &stream_));
     engine_->InitNetwork();
@@ -73,19 +79,22 @@ class TRTConvertValidation {
     engine_->DeclareInput(name, nvinfer1::DataType::kFLOAT, dims);
   }
 
+  // Declare a parameter varaible in the scope.
+  void DeclParamVar(const std::string& name, const nvinfer1::Dims& dims) {
+    DeclVar(name, dims);
+  }
+
   void DeclOutputVar(const std::string& name, const nvinfer1::Dims& dims) {
     DeclVar(name, dims);
   }
 
+  // Declare a variable in a fluid Scope.
   void DeclVar(const std::string& name, const nvinfer1::Dims& dims) {
     platform::CPUPlace place;
     platform::CPUDeviceContext ctx(place);
 
     // Init Fluid tensor.
-    std::vector<int> dim_vec(dims.nbDims);
-    for (int i = 0; i < dims.nbDims; i++) {
-      dim_vec[i] = dims.d[i];
-    }
+    std::vector<int> dim_vec(dims.d, dims.d + dims.nbDims);
     auto* x = scope_.Var(name);
     auto* x_tensor = x->GetMutable<framework::LoDTensor>();
     x_tensor->Resize(framework::make_ddim(dim_vec));
@@ -96,20 +105,22 @@ class TRTConvertValidation {
     op_ = framework::OpRegistry::CreateOp(desc);
 
     OpConverter op_converter;
-    op_converter.ConvertOp(desc, engine_.get());
+    op_converter.ConvertOp(desc, parameters_, scope_, engine_.get());
 
     engine_->FreezeNetwork();
 
     // Declare outputs.
-    op_desc_.reset(new framework::OpDesc(desc, nullptr, nullptr));
+    op_desc_.reset(new framework::OpDesc(desc, nullptr));
 
     // Set Inputs.
     for (const auto& input : op_desc_->InputArgumentNames()) {
+      if (parameters_.count(input)) continue;
       auto* var = scope_.FindVar(input);
       PADDLE_ENFORCE(var);
       auto tensor = var->GetMutable<framework::LoDTensor>();
+
       engine_->SetInputFromCPU(
-          input, static_cast<void*>(tensor->data<float>()),
+          input, static_cast<void*>(tensor->data<void>()),
           sizeof(float) *
               analysis::AccuDims(tensor->dims(), tensor->dims().size()));
     }
@@ -117,18 +128,21 @@ class TRTConvertValidation {
 
   void Execute(int batch_size) {
     // Execute Fluid Op
-    // Execute TRT
     platform::CPUPlace place;
     platform::CPUDeviceContext ctx(place);
-    engine_->Execute(batch_size);
-
     op_->Run(scope_, place);
+    // Execute TRT.
+    engine_->Execute(batch_size);
+    cudaStreamSynchronize(*engine_->stream());
 
     ASSERT_FALSE(op_desc_->OutputArgumentNames().empty());
+    const size_t output_space_size = 200;
     for (const auto& output : op_desc_->OutputArgumentNames()) {
       std::vector<float> fluid_out;
-      std::vector<float> trt_out(200);
-      engine_->GetOutputInCPU(output, &trt_out[0], 200 * sizeof(float));
+      std::vector<float> trt_out(output_space_size);
+      engine_->GetOutputInCPU(output, &trt_out[0],
+                              output_space_size * sizeof(float));
+      cudaStreamSynchronize(*engine_->stream());
 
       auto* var = scope_.FindVar(output);
       auto tensor = var->GetMutable<framework::LoDTensor>();
@@ -136,7 +150,7 @@ class TRTConvertValidation {
       // Compare two output
       ASSERT_FALSE(fluid_out.empty());
       for (size_t i = 0; i < fluid_out.size(); i++) {
-        EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 0.001);
+        EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 1e-6);
       }
     }
   }
@@ -146,9 +160,10 @@ class TRTConvertValidation {
  private:
   std::unique_ptr<TensorRTEngine> engine_;
   cudaStream_t stream_;
-  framework::Scope scope_;
   std::unique_ptr<framework::OperatorBase> op_;
   std::unique_ptr<framework::OpDesc> op_desc_;
+  const std::unordered_set<std::string>& parameters_;
+  framework::Scope& scope_;
 };
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/engine.cc

@@ -106,6 +106,7 @@ void TensorRTEngine::DeclareOutput(const nvinfer1::ILayer* layer, int offset,
                     name);
 
   auto* output = layer->getOutput(offset);
+  SetITensor(name, output);
   PADDLE_ENFORCE(output != nullptr);
   output->setName(name.c_str());
   infer_network_->markOutput(*output);

paddle/fluid/inference/tensorrt/engine.h

@@ -37,13 +37,15 @@ class TensorRTEngine : public EngineBase {
   // Weight is model parameter.
   class Weight {
    public:
-    Weight(nvinfer1::DataType dtype, void* value, int num_elem) {
+    Weight(nvinfer1::DataType dtype, void* value, size_t num_elem) {
       w_.type = dtype;
       w_.values = value;
       w_.count = num_elem;
     }
     const nvinfer1::Weights& get() { return w_; }
 
+    std::vector<int64_t> dims;
+
    private:
     nvinfer1::Weights w_;
   };

paddle/fluid/inference/tests/book/CMakeLists.txt

@@ -38,3 +38,11 @@ inference_test(recommender_system)
 #inference_test(rnn_encoder_decoder)
 #inference_test(understand_sentiment ARGS conv)
 inference_test(word2vec)
+
+# This is an unly work around to make this test run
+# TODO(TJ): clean me up
+cc_test(test_inference_nlp
+  SRCS test_inference_nlp.cc
+  DEPS paddle_fluid
+  ARGS
+  --model_path=${PADDLE_BINARY_DIR}/python/paddle/fluid/tests/book/recognize_digits_mlp.inference.model)

paddle/fluid/inference/tests/book/test_inference_nlp.cc

+/* 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 <sys/time.h>
+#include <time.h>
+#include <fstream>
+#include <thread>  // NOLINT
+#include "gflags/gflags.h"
+#include "gtest/gtest.h"
+#include "paddle/fluid/inference/tests/test_helper.h"
+#ifdef PADDLE_WITH_MKLML
+#include <mkl_service.h>
+#include <omp.h>
+#endif
+
+DEFINE_string(model_path, "", "Directory of the inference model.");
+DEFINE_string(data_file, "", "File of input index data.");
+DEFINE_int32(repeat, 100, "Running the inference program repeat times");
+DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run inference");
+DEFINE_bool(prepare_vars, true, "Prepare variables before executor");
+DEFINE_int32(num_threads, 1, "Number of threads should be used");
+
+inline double GetCurrentMs() {
+  struct timeval time;
+  gettimeofday(&time, NULL);
+  return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
+}
+
+// This function just give dummy data for recognize_digits model.
+size_t DummyData(std::vector<paddle::framework::LoDTensor>* out) {
+  paddle::framework::LoDTensor input;
+  SetupTensor<float>(&input, {1, 1, 28, 28}, -1.f, 1.f);
+  out->emplace_back(input);
+  return 1;
+}
+
+// Load the input word index data from file and save into LodTensor.
+// Return the size of words.
+size_t LoadData(std::vector<paddle::framework::LoDTensor>* out,
+                const std::string& filename) {
+  if (filename.empty()) {
+    return DummyData(out);
+  }
+
+  size_t sz = 0;
+  std::fstream fin(filename);
+  std::string line;
+  out->clear();
+  while (getline(fin, line)) {
+    std::istringstream iss(line);
+    std::vector<int64_t> ids;
+    std::string field;
+    while (getline(iss, field, ' ')) {
+      ids.push_back(stoi(field));
+    }
+    if (ids.size() >= 1024) {
+      // Synced with NLP guys, they will ignore input larger then 1024
+      continue;
+    }
+
+    paddle::framework::LoDTensor words;
+    paddle::framework::LoD lod{{0, ids.size()}};
+    words.set_lod(lod);
+    int64_t* pdata = words.mutable_data<int64_t>(
+        {static_cast<int64_t>(ids.size()), 1}, paddle::platform::CPUPlace());
+    memcpy(pdata, ids.data(), words.numel() * sizeof(int64_t));
+    out->emplace_back(words);
+    sz += ids.size();
+  }
+  return sz;
+}
+
+// Split input data samples into small pieces jobs as balanced as possible,
+// according to the number of threads.
+void SplitData(
+    const std::vector<paddle::framework::LoDTensor>& datasets,
+    std::vector<std::vector<const paddle::framework::LoDTensor*>>* jobs,
+    const int num_threads) {
+  size_t s = 0;
+  jobs->resize(num_threads);
+  while (s < datasets.size()) {
+    for (auto it = jobs->begin(); it != jobs->end(); it++) {
+      it->emplace_back(&datasets[s]);
+      s++;
+      if (s >= datasets.size()) {
+        break;
+      }
+    }
+  }
+}
+
+void ThreadRunInfer(
+    const int tid, paddle::framework::Executor* executor,
+    paddle::framework::Scope* scope,
+    const std::unique_ptr<paddle::framework::ProgramDesc>& inference_program,
+    const std::vector<std::vector<const paddle::framework::LoDTensor*>>& jobs) {
+  auto copy_program = std::unique_ptr<paddle::framework::ProgramDesc>(
+      new paddle::framework::ProgramDesc(*inference_program));
+  auto& sub_scope = scope->NewScope();
+
+  std::string feed_holder_name = "feed_" + paddle::string::to_string(tid);
+  std::string fetch_holder_name = "fetch_" + paddle::string::to_string(tid);
+  copy_program->SetFeedHolderName(feed_holder_name);
+  copy_program->SetFetchHolderName(fetch_holder_name);
+
+  const std::vector<std::string>& feed_target_names =
+      copy_program->GetFeedTargetNames();
+  const std::vector<std::string>& fetch_target_names =
+      copy_program->GetFetchTargetNames();
+
+  PADDLE_ENFORCE_EQ(fetch_target_names.size(), 1UL);
+  std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+  paddle::framework::LoDTensor outtensor;
+  fetch_targets[fetch_target_names[0]] = &outtensor;
+
+  std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
+  PADDLE_ENFORCE_EQ(feed_target_names.size(), 1UL);
+
+  auto& inputs = jobs[tid];
+  auto start_ms = GetCurrentMs();
+  for (size_t i = 0; i < inputs.size(); ++i) {
+    feed_targets[feed_target_names[0]] = inputs[i];
+    executor->Run(*copy_program, &sub_scope, &feed_targets, &fetch_targets,
+                  true /*create_local_scope*/, true /*create_vars*/,
+                  feed_holder_name, fetch_holder_name);
+  }
+  auto stop_ms = GetCurrentMs();
+  scope->DeleteScope(&sub_scope);
+  LOG(INFO) << "Tid: " << tid << ", process " << inputs.size()
+            << " samples, avg time per sample: "
+            << (stop_ms - start_ms) / inputs.size() << " ms";
+}
+
+TEST(inference, nlp) {
+  if (FLAGS_model_path.empty()) {
+    LOG(FATAL) << "Usage: ./example --model_path=path/to/your/model";
+  }
+  if (FLAGS_data_file.empty()) {
+    LOG(WARNING) << "No data file provided, will use dummy data!"
+                 << "Note: if you use nlp model, please provide data file.";
+  }
+  LOG(INFO) << "Model Path: " << FLAGS_model_path;
+  LOG(INFO) << "Data File: " << FLAGS_data_file;
+
+  std::vector<paddle::framework::LoDTensor> datasets;
+  size_t num_total_words = LoadData(&datasets, FLAGS_data_file);
+  LOG(INFO) << "Number of samples (seq_len<1024): " << datasets.size();
+  LOG(INFO) << "Total number of words: " << num_total_words;
+
+  const bool model_combined = false;
+  // 0. Call `paddle::framework::InitDevices()` initialize all the devices
+  // 1. Define place, executor, scope
+  auto place = paddle::platform::CPUPlace();
+  auto executor = paddle::framework::Executor(place);
+  std::unique_ptr<paddle::framework::Scope> scope(
+      new paddle::framework::Scope());
+
+  // 2. Initialize the inference_program and load parameters
+  std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
+  inference_program =
+      InitProgram(&executor, scope.get(), FLAGS_model_path, model_combined);
+  if (FLAGS_use_mkldnn) {
+    EnableMKLDNN(inference_program);
+  }
+
+#ifdef PADDLE_WITH_MKLML
+  // only use 1 thread number per std::thread
+  omp_set_dynamic(0);
+  omp_set_num_threads(1);
+  mkl_set_num_threads(1);
+#endif
+
+  double start_ms = 0, stop_ms = 0;
+  if (FLAGS_num_threads > 1) {
+    std::vector<std::vector<const paddle::framework::LoDTensor*>> jobs;
+    SplitData(datasets, &jobs, FLAGS_num_threads);
+    std::vector<std::unique_ptr<std::thread>> threads;
+    start_ms = GetCurrentMs();
+    for (int i = 0; i < FLAGS_num_threads; ++i) {
+      threads.emplace_back(
+          new std::thread(ThreadRunInfer, i, &executor, scope.get(),
+                          std::ref(inference_program), std::ref(jobs)));
+    }
+    for (int i = 0; i < FLAGS_num_threads; ++i) {
+      threads[i]->join();
+    }
+    stop_ms = GetCurrentMs();
+  } else {
+    if (FLAGS_prepare_vars) {
+      executor.CreateVariables(*inference_program, scope.get(), 0);
+    }
+    // always prepare context
+    std::unique_ptr<paddle::framework::ExecutorPrepareContext> ctx;
+    ctx = executor.Prepare(*inference_program, 0);
+
+    // preapre fetch
+    const std::vector<std::string>& fetch_target_names =
+        inference_program->GetFetchTargetNames();
+    PADDLE_ENFORCE_EQ(fetch_target_names.size(), 1UL);
+    std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+    paddle::framework::LoDTensor outtensor;
+    fetch_targets[fetch_target_names[0]] = &outtensor;
+
+    // prepare feed
+    const std::vector<std::string>& feed_target_names =
+        inference_program->GetFeedTargetNames();
+    PADDLE_ENFORCE_EQ(feed_target_names.size(), 1UL);
+    std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
+
+    // feed data and run
+    start_ms = GetCurrentMs();
+    for (size_t i = 0; i < datasets.size(); ++i) {
+      feed_targets[feed_target_names[0]] = &(datasets[i]);
+      executor.RunPreparedContext(ctx.get(), scope.get(), &feed_targets,
+                                  &fetch_targets, !FLAGS_prepare_vars);
+    }
+    stop_ms = GetCurrentMs();
+    LOG(INFO) << "Tid: 0, process " << datasets.size()
+              << " samples, avg time per sample: "
+              << (stop_ms - start_ms) / datasets.size() << " ms";
+  }
+  LOG(INFO) << "Total inference time with " << FLAGS_num_threads
+            << " threads : " << (stop_ms - start_ms) / 1000.0
+            << " sec, QPS: " << datasets.size() / ((stop_ms - start_ms) / 1000);
+}

paddle/fluid/operators/activation_mkldnn_op.cc

@@ -222,35 +222,35 @@ struct MKLDNNActivationGradFunc : public BaseActivationFunctor<T> {
 };
 
 template <typename T>
-using ReluMkldnnFunctor =
+using ReluMKLDNNFunctor =
     MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_relu>;
 
 template <typename T>
-using TanhMkldnnFunctor =
+using TanhMKLDNNFunctor =
     MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_tanh>;
 
 template <typename T>
-using SqrtMkldnnFunctor =
+using SqrtMKLDNNFunctor =
     MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_sqrt>;
 
 template <typename T>
-using AbsMkldnnFunctor =
+using AbsMKLDNNFunctor =
     MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_abs>;
 
 template <typename T>
-using ReluMkldnnGradFunctor =
+using ReluMKLDNNGradFunctor =
     MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_relu>;
 
 template <typename T>
-using TanhMkldnnGradFunctor =
+using TanhMKLDNNGradFunctor =
     MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_tanh>;
 
 template <typename T>
-using SqrtMkldnnGradFunctor =
+using SqrtMKLDNNGradFunctor =
     MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_sqrt>;
 
 template <typename T>
-using AbsMkldnnGradFunctor =
+using AbsMKLDNNGradFunctor =
     MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_abs>;
 }  // namespace operators
 }  // namespace paddle
@@ -265,9 +265,9 @@ namespace ops = paddle::operators;
       ops::MKLDNNActivationGradKernel<ops::grad_functor<float>>);
 
 #define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro)            \
-  __macro(relu, ReluMkldnnFunctor, ReluMkldnnGradFunctor); \
-  __macro(tanh, TanhMkldnnFunctor, TanhMkldnnGradFunctor); \
-  __macro(sqrt, SqrtMkldnnFunctor, SqrtMkldnnGradFunctor); \
-  __macro(abs, AbsMkldnnFunctor, AbsMkldnnGradFunctor);
+  __macro(relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \
+  __macro(tanh, TanhMKLDNNFunctor, TanhMKLDNNGradFunctor); \
+  __macro(sqrt, SqrtMKLDNNFunctor, SqrtMKLDNNGradFunctor); \
+  __macro(abs, AbsMKLDNNFunctor, AbsMKLDNNGradFunctor);
 
 FOR_EACH_MKLDNN_KERNEL_FUNCTOR(REGISTER_ACTIVATION_MKLDNN_KERNEL);

paddle/fluid/operators/bilinear_interp_op.cc

@@ -34,9 +34,22 @@ class BilinearInterpOp : public framework::OperatorWithKernel {
     int out_w = ctx->Attrs().Get<int>("out_w");
     PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
 
+    if (ctx->HasInput("OutSize")) {
+      auto out_size_dim = ctx->GetInputDim("OutSize");
+      PADDLE_ENFORCE_EQ(out_size_dim.size(), 1,
+                        "OutSize's dimension size must be 1");
+      PADDLE_ENFORCE_EQ(out_size_dim[0], 2, "OutSize's dim[0] must be 2");
+    }
     std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
     ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
+  }
 };
 
 class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -45,6 +58,10 @@ class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X",
              "(Tensor) The input tensor of bilinear interpolation, "
              "This is a 4-D tensor with shape of (N x C x h x w)");
+    AddInput("OutSize",
+             "(Tensor) This is a 1-D tensor with two number. "
+             "The first number is height and the second number is width.")
+        .AsDispensable();
     AddOutput("Out",
               "(Tensor) The dimension of output is (N x C x out_h x out_w]");
 
@@ -78,6 +95,12 @@ class BilinearInterpOpGrad : public framework::OperatorWithKernel {
       ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
     }
   }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
+  }
 };
 
 }  // namespace operators

paddle/fluid/operators/bilinear_interp_op.cu

@@ -102,10 +102,21 @@ class BilinearInterpOpCUDAKernel : public framework::OpKernel<T> {
     auto* input_t = ctx.Input<Tensor>("X");      // float tensor
     auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
     auto* input = input_t->data<T>();
-    auto* output = output_t->mutable_data<T>(ctx.GetPlace());
 
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+    auto out_dims = output_t->dims();
+    auto out_size_t = ctx.Input<Tensor>("OutSize");
+    if (out_size_t != nullptr) {
+      Tensor sizes;
+      framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
+      auto size_data = sizes.data<int>();
+      out_h = size_data[0];
+      out_w = size_data[1];
+    }
+    auto* output = output_t->mutable_data<T>(
+        {out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
+
     int batch_size = input_t->dims()[0];
     int channels = input_t->dims()[1];
     int in_h = input_t->dims()[2];
@@ -139,8 +150,8 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
     auto* d_output = d_output_t->data<T>();
+    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
 
     auto& device_ctx =
         ctx.template device_context<platform::CUDADeviceContext>();
@@ -149,6 +160,16 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
 
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+
+    auto out_size_t = ctx.Input<Tensor>("OutSize");
+    if (out_size_t != nullptr) {
+      Tensor sizes;
+      framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
+      auto size_data = sizes.data<int>();
+      out_h = size_data[0];
+      out_w = size_data[1];
+    }
+
     int batch_size = d_input_t->dims()[0];
     int channels = d_input_t->dims()[1];
     int in_h = d_input_t->dims()[2];

paddle/fluid/operators/bilinear_interp_op.h

@@ -24,11 +24,18 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* input_t = ctx.Input<Tensor>("X");      // float tensor
     auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
+    auto out_dims = output_t->dims();
     auto* input = input_t->data<T>();
-    auto* output = output_t->mutable_data<T>(ctx.GetPlace());
-
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+    auto out_size_t = ctx.Input<Tensor>("OutSize");
+    if (out_size_t != nullptr) {
+      auto out_size_data = out_size_t->data<int>();
+      out_h = out_size_data[0];
+      out_w = out_size_data[1];
+    }
+    auto* output = output_t->mutable_data<T>(
+        {out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
     int batch_size = input_t->dims()[0];
     int channels = input_t->dims()[1];
     int in_h = input_t->dims()[2];
@@ -83,9 +90,8 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
     auto* d_output = d_output_t->data<T>();
-
+    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
     auto& device_ctx =
         ctx.template device_context<platform::CPUDeviceContext>();
     math::SetConstant<platform::CPUDeviceContext, T> zero;
@@ -93,6 +99,14 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
 
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+
+    auto out_size_t = ctx.Input<Tensor>("OutSize");
+    if (out_size_t != nullptr) {
+      auto out_size_data = out_size_t->data<int>();
+      out_h = out_size_data[0];
+      out_w = out_size_data[1];
+    }
+
     int batch_size = d_input_t->dims()[0];
     int channels = d_input_t->dims()[1];
     int in_h = d_input_t->dims()[2];

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -20,7 +20,7 @@ limitations under the License. */
 #include "paddle/fluid/platform/cudnn_helper.h"
 #include "paddle/fluid/platform/float16.h"
 
-DEFINE_bool(cudnn_algo_use_autotune, true,
+DEFINE_bool(cudnn_deterministic, true,
             "Whether allow using an autotuning algorithm for convolution "
             "operator. The autotuning algorithm may be non-deterministic. If "
             "false, the algorithm is deterministic.");
@@ -272,7 +272,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
     auto handle = dev_ctx.cudnn_handle();
     if (input_grad) {
-      if (FLAGS_cudnn_algo_use_autotune) {
+      if (FLAGS_cudnn_deterministic) {
         PADDLE_ENFORCE(
             platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
                 handle, cudnn_filter_desc,
@@ -297,7 +297,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     }
 
     if (filter_grad) {
-      if (FLAGS_cudnn_algo_use_autotune) {
+      if (FLAGS_cudnn_deterministic) {
         PADDLE_ENFORCE(
             platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
                 handle, cudnn_input_desc, cudnn_output_grad_desc,

paddle/fluid/operators/detail/CMakeLists.txt

 if(WITH_DISTRIBUTE)
   grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
-      grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor selected_rows)
+      request_handler_impl.cc rpc_server.cc grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor
+      selected_rows memory)
   set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
   set_source_files_properties(serde_test.cc grpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
   cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr

paddle/fluid/operators/detail/grpc_client.cc

@@ -38,6 +38,25 @@ void RPCClient::Init() {
   if (rpc_client_.get() == nullptr) {
     rpc_client_.reset(new RPCClient());
   }
+  rpc_client_->InitEventLoop();
+}
+
+void RPCClient::InitEventLoop() {
+  // start the client process thread
+  // TODO(wuyi): can make this in a threadpool
+  client_thread_.reset(new std::thread(std::bind(&RPCClient::Proceed, this)));
+}
+
+RPCClient::~RPCClient() {
+  Wait();
+  cq_.Shutdown();
+  {
+    std::lock_guard<std::mutex> guard(chan_mutex_);
+    for (auto& it : channels_) {
+      it.second.reset();
+    }
+  }
+  client_thread_->join();
 }
 
 bool RPCClient::AsyncSendVariable(const std::string& ep,
@@ -204,68 +223,37 @@ void RPCClient::AsyncSendFetchBarrier(const std::string& ep, int64_t time_out) {
   req_count_++;
 }
 
-bool RPCClient::Wait() {
-  if (req_count_ <= 0) {
-    return true;
-  }
-  const size_t kReqCnt = req_count_;
-  bool a[kReqCnt];
-  std::vector<std::future<void>> waits(req_count_);
-  std::mutex mu;
-
-  for (int i = 0; i < req_count_; i++) {
-    waits[i] = framework::AsyncIO([i, &a, &mu, this] {
-      bool ret = Proceed();
-      std::lock_guard<std::mutex> l(mu);
-      a[i] = ret;
-    });
-  }
-
-  for (int i = 0; i < req_count_; i++) {
-    waits[i].wait();
-  }
-
-  int last_req_count = req_count_;
-  req_count_ = 0;
-
-  for (int i = 0; i < last_req_count; i++) {
-    if (!a[i]) {
-      return false;
-    }
-  }
-
-  return true;
+void RPCClient::Wait() {
+  std::unique_lock<std::mutex> lk(sync_mutex_);
+  sync_cond_.wait(lk, [this] { return req_count_ == 0; });
 }
 
-bool RPCClient::Proceed() {
-  void* tag = NULL;
+void RPCClient::Proceed() {
+  void* tag = nullptr;
   bool ok = false;
 
-  // request counts.
-  if (!cq_.Next(&tag, &ok)) {
-    LOG(ERROR) << "Get meets CompletionQueue error";
-    return false;
-  }
-
-  GPR_ASSERT(ok);
-  PADDLE_ENFORCE(tag);
-
-  // TODO(gongwb): add more retries.
+  while (cq_.Next(&tag, &ok)) {
     BaseProcessor* c = static_cast<BaseProcessor*>(tag);
-  if (!c->status_.ok()) {
-    LOG(ERROR) << "proc param error:" << c->var_h_.String()
+    GPR_ASSERT(ok);
+    PADDLE_ENFORCE(c);
+    if (c->status_.ok()) {
+      c->Process();
+    } else {
+      LOG(ERROR) << "var: " << c->var_h_.String()
                  << " grpc error:" << c->status_.error_message();
-    delete c;
-    return false;
     }
-
-  c->Process();
     delete c;
-  return true;
+    {
+      std::lock_guard<std::mutex> lk(sync_mutex_);
+      req_count_--;
+    }
+    sync_cond_.notify_all();
+  }
 }
+
 std::shared_ptr<grpc::Channel> RPCClient::GetChannel(const std::string& ep) {
   // TODO(Yancey1989): make grpc client completely thread-safe
-  std::unique_lock<std::mutex> lock(mutex_);
+  std::lock_guard<std::mutex> guard(chan_mutex_);
   auto it = channels_.find(ep);
   if (it != channels_.end()) {
     return it->second;

paddle/fluid/operators/detail/grpc_client.h

@@ -17,14 +17,17 @@ limitations under the License. */
 #include <time.h>
 
 #include <chrono>              // NOLINT
+#include <condition_variable>  // NOLINT
 #include <ctime>
 #include <functional>
 #include <iostream>
 #include <map>
 #include <mutex>  // NOLINT
 #include <string>
+#include <thread>  // NOLINT
 #include <vector>
 
+#include "grpc++/channel.h"
 #include "grpc++/generic/generic_stub.h"
 #include "grpc++/grpc++.h"
 #include "grpc++/support/byte_buffer.h"
@@ -164,6 +167,7 @@ class FetchBarrierProcessor : public BaseProcessor {
 class RPCClient {
  public:
   RPCClient() {}
+  ~RPCClient();
 
   static RPCClient* GetInstance();
 
@@ -192,19 +196,28 @@ class RPCClient {
   void AsyncSendFetchBarrier(const std::string& ep,
                              int64_t time_out = 600 * 1000);
 
-  bool Wait();
+  void Wait();
+  // InitEventLoop should only be called by Init()
+  void InitEventLoop();
 
  private:
-  bool Proceed();
+  void Proceed();
   std::shared_ptr<grpc::Channel> GetChannel(const std::string& ep);
   // Init is called by GetInstance.
   static void Init();
 
  private:
   grpc::CompletionQueue cq_;
-  std::map<std::string, std::shared_ptr<grpc::Channel>> channels_;
+  std::unordered_map<std::string, std::shared_ptr<grpc::Channel>> channels_;
+  std::unique_ptr<std::thread> client_thread_;
+
+  // mutex for Wait client sync
+  std::mutex sync_mutex_;
+  std::condition_variable sync_cond_;
   std::atomic<int64_t> req_count_{0};
-  std::mutex mutex_;
+
+  // mutex for GetChannel thread safety
+  std::mutex chan_mutex_;
   static std::unique_ptr<RPCClient> rpc_client_;
   static std::once_flag init_flag_;
   DISABLE_COPY_AND_ASSIGN(RPCClient);

paddle/fluid/operators/detail/grpc_server.cc

-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/*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.
@@ -12,19 +12,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/fluid/operators/detail/grpc_server.h"
-
 #include <limits>
 #include <string>
 
-using ::grpc::ServerAsyncResponseWriter;
+#include "paddle/fluid/operators/detail/grpc_server.h"
 
-DEFINE_int32(rpc_server_handle_send_threads, 20,
-             "Number of threads used to handle send at rpc server.");
-DEFINE_int32(rpc_server_handle_get_threads, 20,
-             "Number of threads used to handle get at rpc server.");
-DEFINE_int32(rpc_server_handle_prefetch_threads, 1,
-             "Number of threads used to handle prefetch at rpc server.");
+using ::grpc::ServerAsyncResponseWriter;
 
 namespace paddle {
 namespace operators {
@@ -36,64 +29,57 @@ enum CallStatus { PROCESS = 0, FINISH };
 class RequestBase {
  public:
   explicit RequestBase(GrpcService::AsyncService* service,
-                       ::grpc::ServerCompletionQueue* cq, bool sync_mode,
-                       const platform::DeviceContext* dev_ctx)
+                       ::grpc::ServerCompletionQueue* cq,
+                       RequestHandler* request_handler, int req_id)
       : service_(service),
         cq_(cq),
-        sync_mode_(sync_mode),
         status_(PROCESS),
-        dev_ctx_(dev_ctx) {
+        request_handler_(request_handler),
+        req_id_(req_id) {
     PADDLE_ENFORCE(cq_);
   }
   virtual ~RequestBase() {}
-  virtual void Process() { assert(false); }
+  virtual void Process() = 0;
 
   CallStatus Status() { return status_; }
   void SetStatus(CallStatus status) { status_ = status; }
-  virtual std::string GetReqName() {
-    assert(false);
-    return "";
-  }
+  virtual std::string GetReqName() = 0;
 
  protected:
   ::grpc::ServerContext ctx_;
   GrpcService::AsyncService* service_;
   ::grpc::ServerCompletionQueue* cq_;
-  const bool sync_mode_;
   CallStatus status_;
-  const platform::DeviceContext* dev_ctx_;
+  RequestHandler* request_handler_;
+  int req_id_;
 };
 
 class RequestSend final : public RequestBase {
  public:
   explicit RequestSend(GrpcService::AsyncService* service,
-                       ::grpc::ServerCompletionQueue* cq, bool sync_mode,
-                       framework::Scope* scope, ReceivedQueue* queue,
-                       const platform::DeviceContext* dev_ctx, int req_id)
-      : RequestBase(service, cq, sync_mode, dev_ctx),
-        queue_(queue),
-        responder_(&ctx_),
-        req_id_(req_id) {
-    if (sync_mode_) {
-      request_.reset(new VariableResponse(scope, dev_ctx_, false));
-    } else {
-      request_.reset(new VariableResponse(scope, dev_ctx_, true));
-    }
+                       ::grpc::ServerCompletionQueue* cq,
+                       RequestHandler* request_handler, int req_id)
+      : RequestBase(service, cq, request_handler, req_id), responder_(&ctx_) {
+    request_.reset(new VariableResponse(request_handler->scope(),
+                                        request_handler->dev_ctx(),
+                                        !request_handler->sync_mode()));
     int method_id = static_cast<int>(detail::GrpcMethod::kSendVariable);
     service_->RequestAsyncUnary(
         method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
         reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
   }
-
   virtual ~RequestSend() {}
+  std::string GetReqName() override { return request_->Varname(); }
 
-  virtual std::string GetReqName() { return request_->Varname(); }
+  void Process() override {
+    std::string varname = GetReqName();
+    VLOG(3) << "RequestSend var_name:" << varname;
 
-  virtual void Process() {
-    std::string var_name = GetReqName();
-    VLOG(3) << "RequestSend " << var_name;
-    queue_->Push(std::make_pair(var_name, request_));
+    auto scope = request_->GetMutableLocalScope();
+    auto invar = request_->GetVar();
+    framework::Variable* outvar = nullptr;
 
+    request_handler_->Handle(varname, scope, invar, &outvar);
     status_ = FINISH;
     responder_.Finish(reply_, ::grpc::Status::OK,
                       reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
@@ -102,311 +88,256 @@ class RequestSend final : public RequestBase {
  protected:
   sendrecv::VoidMessage reply_;
   std::shared_ptr<VariableResponse> request_;
-  ReceivedQueue* queue_;
   ServerAsyncResponseWriter<sendrecv::VoidMessage> responder_;
-  int req_id_;
 };
 
 class RequestGet final : public RequestBase {
  public:
   explicit RequestGet(GrpcService::AsyncService* service,
-                      ::grpc::ServerCompletionQueue* cq, bool sync_mode,
-                      framework::Scope* scope,
-                      const platform::DeviceContext* dev_ctx,
-                      framework::BlockingQueue<MessageWithName>* queue,
-                      int req_id)
-      : RequestBase(service, cq, sync_mode, dev_ctx),
-        responder_(&ctx_),
-        scope_(scope),
-        queue_(queue),
-        req_id_(req_id) {
+                      ::grpc::ServerCompletionQueue* cq,
+                      RequestHandler* request_handler, int req_id)
+      : RequestBase(service, cq, request_handler, req_id), responder_(&ctx_) {
     auto method_id = static_cast<int>(detail::GrpcMethod::kGetVariable);
     service_->RequestAsyncUnary(
         method_id, &ctx_, &request_, &responder_, cq_, cq_,
-        reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
+        reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
   }
 
   virtual ~RequestGet() {}
 
-  virtual std::string GetReqName() { return request_.varname(); }
+  std::string GetReqName() override { return request_.varname(); }
 
-  virtual void Process() {
+  void Process() override {
     // proc request.
-    std::string var_name = request_.varname();
-    VLOG(3) << "RequestGet " << var_name;
-    auto* var = scope_->FindVar(var_name);
+    std::string varname = request_.varname();
+    VLOG(3) << "RequestGet " << varname;
 
-    if (var_name != FETCH_BARRIER_MESSAGE) {
-      SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply_);
-    }
+    auto scope = request_handler_->scope();
+    auto invar = scope->FindVar(varname);
+    framework::Variable* outvar = nullptr;
 
+    request_handler_->Handle(varname, scope, invar, &outvar);
+
+    if (outvar) {
+      SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
+                            &reply_);
+    }
     status_ = FINISH;
     responder_.Finish(reply_, ::grpc::Status::OK,
                       reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
-
-    if (var_name == FETCH_BARRIER_MESSAGE) {
-      sendrecv::VariableMessage msg;
-      MessageWithName msg_with_name = std::make_pair(var_name, msg);
-      queue_->Push(msg_with_name);
-    }
   }
 
  protected:
   sendrecv::VariableMessage request_;
   ::grpc::ByteBuffer reply_;
   ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
-  framework::Scope* scope_;
-  framework::BlockingQueue<MessageWithName>* queue_;
-  int req_id_;
 };
 
 class RequestPrefetch final : public RequestBase {
  public:
   explicit RequestPrefetch(GrpcService::AsyncService* service,
-                           ::grpc::ServerCompletionQueue* cq, bool sync_mode,
-                           framework::Scope* scope,
-                           const platform::DeviceContext* dev_ctx,
-                           framework::Executor* executor,
-                           framework::ProgramDesc* program,
-                           framework::ExecutorPrepareContext* prefetch_ctx,
-                           int req_id)
-      : RequestBase(service, cq, sync_mode, dev_ctx),
+                           ::grpc::ServerCompletionQueue* cq,
+                           RequestHandler* request_handler, int req_id)
+      : RequestBase(service, cq, request_handler, req_id),
         responder_(&ctx_),
-        scope_(scope),
-        executor_(executor),
-        program_(program),
-        prefetch_ctx_(prefetch_ctx),
-        req_id_(req_id) {
-    // prefetch always create a new sub scope
-    request_.reset(new VariableResponse(scope, dev_ctx_, true));
+        local_scope_(nullptr) {
+    request_.reset(new VariableResponse(request_handler->scope(),
+                                        request_handler->dev_ctx(), true));
     int method_id = static_cast<int>(detail::GrpcMethod::kPrefetchVariable);
     service_->RequestAsyncUnary(
         method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
-        reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
+        reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
   }
 
   virtual ~RequestPrefetch() {}
 
-  virtual std::string GetReqName() { return request_->Varname(); }
+  std::string GetReqName() override { return request_->Varname(); }
 
-  virtual void Process() {
+  void Process() override {
     // prefetch process...
+    std::string varname = request_->OutVarname();
+    VLOG(3) << "RequestPrefetch " << varname;
 
-    std::string var_name = request_->OutVarname();
-    VLOG(3) << "RequestPrefetch " << var_name;
-    auto var_desc = program_->Block(0).FindVar(var_name);
-    framework::Scope* local_scope = request_->GetMutableLocalScope();
-    auto* var = local_scope->FindVar(var_name);
-    InitializeVariable(var, var_desc->GetType());
-    executor_->RunPreparedContext(prefetch_ctx_, local_scope);
+    auto scope = request_->GetMutableLocalScope();
+    auto invar = scope->FindVar(varname);
+    framework::Variable* outvar = nullptr;
 
-    SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply_);
+    request_handler_->Handle(varname, scope, invar, &outvar);
 
-    status_ = FINISH;
+    SerializeToByteBuffer(varname, outvar, *request_handler_->dev_ctx(),
+                          &reply_);
     responder_.Finish(reply_, ::grpc::Status::OK,
                       reinterpret_cast<void*>(static_cast<intptr_t>(req_id_)));
+    status_ = FINISH;
   }
 
  protected:
   std::shared_ptr<VariableResponse> request_;
   ::grpc::ByteBuffer reply_;
   ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
-  framework::Scope* scope_;
-  framework::Executor* executor_;
-  framework::ProgramDesc* program_;
-  framework::ExecutorPrepareContext* prefetch_ctx_;
-  int req_id_;
+  framework::Scope* local_scope_;
 };
 
-void AsyncGRPCServer::WaitClientGet(int count) {
-  int fetch_barriers = 0;
-  while (fetch_barriers < count) {
-    auto msg = var_get_queue_.Pop();
-    if (msg.first == FETCH_BARRIER_MESSAGE) {
-      fetch_barriers++;
-    }
-  }
-}
-
 void AsyncGRPCServer::WaitServerReady() {
+  VLOG(3) << "AsyncGRPCServer is wait server ready";
   std::unique_lock<std::mutex> lock(this->mutex_ready_);
   condition_ready_.wait(lock, [=] { return this->ready_ == 1; });
+  VLOG(3) << "AsyncGRPCServer WaitSeverReady";
 }
 
-void AsyncGRPCServer::RunSyncUpdate() {
+void AsyncGRPCServer::StartServer() {
   ::grpc::ServerBuilder builder;
-  builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials(),
+  builder.AddListeningPort(bind_address_, ::grpc::InsecureServerCredentials(),
                            &selected_port_);
+
   builder.SetMaxSendMessageSize(std::numeric_limits<int>::max());
   builder.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
   builder.RegisterService(&service_);
 
-  cq_send_ = builder.AddCompletionQueue();
-  cq_get_ = builder.AddCompletionQueue();
-  cq_prefetch_ = builder.AddCompletionQueue();
+  for (auto t : rpc_call_map_) {
+    rpc_cq_[t.first].reset(builder.AddCompletionQueue().release());
+  }
 
   server_ = builder.BuildAndStart();
-  LOG(INFO) << "Server listening on " << address_
+  LOG(INFO) << "Server listening on " << bind_address_
             << " selected port: " << selected_port_;
 
-  std::function<void(int)> send_register = std::bind(
-      &AsyncGRPCServer::TryToRegisterNewSendOne, this, std::placeholders::_1);
-  std::function<void(int)> get_register = std::bind(
-      &AsyncGRPCServer::TryToRegisterNewGetOne, this, std::placeholders::_1);
-  std::function<void(int)> prefetch_register =
-      std::bind(&AsyncGRPCServer::TryToRegisterNewPrefetchOne, this,
-                std::placeholders::_1);
+  std::function<void(const std::string&, int)> f =
+      std::bind(&AsyncGRPCServer::TryToRegisterNewOne, this,
+                std::placeholders::_1, std::placeholders::_2);
 
-  for (int i = 0; i < kSendReqsBufSize; ++i) {
-    TryToRegisterNewSendOne(i);
-  }
-  for (int i = 0; i < kGetReqsBufSize; ++i) {
-    TryToRegisterNewGetOne(i);
-  }
-  for (int i = 0; i < kPrefetchReqsBufSize; ++i) {
-    TryToRegisterNewPrefetchOne(i);
-  }
+  for (auto& t : rpc_call_map_) {
+    auto& rpc_name = t.first;
+    auto& cq = rpc_cq_[rpc_name];
+    auto threadnum = rpc_thread_num_[rpc_name];
+    auto& reqs = rpc_reqs_[rpc_name];
+
+    reqs.reserve(kRequestBufSize);
 
-  for (int i = 0; i < FLAGS_rpc_server_handle_send_threads; ++i) {
-    t_sends_.emplace_back(
-        new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
-                                  cq_send_.get(), "cq_send", send_register)));
+    for (int i = 0; i < kRequestBufSize; i++) {
+      TryToRegisterNewOne(rpc_name, i);
     }
-  for (int i = 0; i < FLAGS_rpc_server_handle_get_threads; ++i) {
-    t_gets_.emplace_back(
-        new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
-                                  cq_get_.get(), "cq_get", get_register)));
+
+    for (int i = 0; i < threadnum; i++) {
+      rpc_threads_[rpc_name].emplace_back(new std::thread(std::bind(
+          &AsyncGRPCServer::HandleRequest, this, cq.get(), rpc_name, f)));
+      VLOG(3) << t.first << " creates threads!";
     }
-  for (int i = 0; i < FLAGS_rpc_server_handle_prefetch_threads; ++i) {
-    t_prefetchs_.emplace_back(new std::thread(
-        std::bind(&AsyncGRPCServer::HandleRequest, this, cq_prefetch_.get(),
-                  "cq_prefetch", prefetch_register)));
   }
+
   {
     std::lock_guard<std::mutex> lock(this->mutex_ready_);
     ready_ = 1;
   }
   condition_ready_.notify_all();
+
   // wait server
   server_->Wait();
-  for (int i = 0; i < FLAGS_rpc_server_handle_send_threads; ++i) {
-    t_sends_[i]->join();
-  }
-  for (int i = 0; i < FLAGS_rpc_server_handle_get_threads; ++i) {
-    t_gets_[i]->join();
+
+  for (auto& t : rpc_threads_) {
+    auto& threads = t.second;
+    for (size_t i = 0; i < threads.size(); ++i) {
+      threads[i]->join();
+      VLOG(3) << t.first << " threads ends!";
     }
-  for (int i = 0; i < FLAGS_rpc_server_handle_prefetch_threads; ++i) {
-    t_prefetchs_[i]->join();
   }
 }
 
 void AsyncGRPCServer::ShutdownQueue() {
-  std::unique_lock<std::mutex> lock(cq_mutex_);
-  cq_send_->Shutdown();
-  cq_get_->Shutdown();
-  cq_prefetch_->Shutdown();
+  for (auto& t : rpc_cq_) {
+    t.second->Shutdown();
+    VLOG(3) << t.first << " shutdown!";
+  }
 }
 
-// This URL explains why shutdown is complicate:
-void AsyncGRPCServer::ShutDown() {
+void AsyncGRPCServer::ShutDownImpl() {
+  std::unique_lock<std::mutex> lock(cq_mutex_);
   is_shut_down_ = true;
   ShutdownQueue();
+
+  VLOG(3) << "server_ shutdown!";
   server_->Shutdown();
 }
 
-void AsyncGRPCServer::TryToRegisterNewSendOne(int i) {
+void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
+                                          int req_id) {
   std::unique_lock<std::mutex> lock(cq_mutex_);
   if (is_shut_down_) {
     VLOG(3) << "shutdown, do not TryToRegisterNewSendOne";
     return;
   }
-  RequestSend* send = new RequestSend(&service_, cq_send_.get(), sync_mode_,
-                                      scope_, &var_recv_queue_, dev_ctx_, i);
-  send_reqs_[i] = static_cast<RequestBase*>(send);
-  VLOG(4) << "Create RequestSend status:" << send->Status();
-}
 
-void AsyncGRPCServer::TryToRegisterNewGetOne(int req_id) {
-  std::unique_lock<std::mutex> lock(cq_mutex_);
-  if (is_shut_down_) {
-    VLOG(3) << "shutdown, do not TryToRegisterNewGetOne";
-    return;
-  }
-  RequestGet* get = new RequestGet(&service_, cq_get_.get(), sync_mode_, scope_,
-                                   dev_ctx_, &var_get_queue_, req_id);
-  get_reqs_[req_id] = static_cast<RequestBase*>(get);
-  VLOG(4) << "Create RequestGet status:" << get->Status();
-}
+  VLOG(4) << "register send rpc_name:" << rpc_name
+          << ", handler:" << rpc_call_map_[kRequestSend];
 
-void AsyncGRPCServer::TryToRegisterNewPrefetchOne(int req_id) {
-  std::unique_lock<std::mutex> lock(cq_mutex_);
-  if (is_shut_down_) {
-    VLOG(3) << "shutdown, do not TryToRegisterNewPrefetchOne";
-    return;
+  auto& reqs = rpc_reqs_[rpc_name];
+  auto& handler = rpc_call_map_[rpc_name];
+  auto& cq = rpc_cq_[rpc_name];
+
+  RequestBase* b = nullptr;
+  if (rpc_name == kRequestSend) {
+    b = new RequestSend(&service_, cq.get(), handler, req_id);
+  } else if (rpc_name == kRequestGet) {
+    b = new RequestGet(&service_, cq.get(), handler, req_id);
+  } else if (rpc_name == kRequestPrefetch) {
+    b = new RequestPrefetch(&service_, cq.get(), handler, req_id);
+  } else {
+    PADDLE_ENFORCE(false, "not surpported rpc");
   }
-  RequestPrefetch* prefetch = new RequestPrefetch(
-      &service_, cq_prefetch_.get(), sync_mode_, scope_, dev_ctx_, executor_,
-      program_, prefetch_ctx_.get(), req_id);
-  prefetch_reqs_[req_id] = static_cast<RequestBase*>(prefetch);
 
-  VLOG(4) << "Create RequestPrefetch status:" << prefetch->Status();
+  reqs[req_id] = b;
+
+  VLOG(4) << "Create RequestSend status:" << b->Status();
 }
 
-// FIXME(typhoonzero): change cq_name to enum.
 void AsyncGRPCServer::HandleRequest(
-    ::grpc::ServerCompletionQueue* cq, const std::string& cq_name,
-    std::function<void(int)> TryToRegisterNewOne) {
+    ::grpc::ServerCompletionQueue* cq, const std::string& rpc_name,
+    std::function<void(const std::string&, int)> TryToRegisterNewOne) {
   void* tag = NULL;
   bool ok = false;
 
   while (true) {
-    VLOG(3) << "HandleRequest for " << cq_name << " wait Next";
+    VLOG(3) << "HandleRequest " << rpc_name << " wait next";
     if (!cq->Next(&tag, &ok)) {
-      LOG(INFO) << cq_name << " CompletionQueue shutdown!";
+      LOG(INFO) << "CompletionQueue " << rpc_name << " shutdown!";
       break;
     }
-    VLOG(3) << "HandleRequest for " << cq_name << " get Next";
-    int req_id = static_cast<int>(reinterpret_cast<intptr_t>(tag));
 
-    if (sync_mode_) {
-      // FIXME(typhoonzero): de-couple the barriers with recv_op
-      if (!is_shut_down_ && cq_name == "cq_get") WaitCond(1);
-      if (!is_shut_down_ && cq_name == "cq_send") WaitCond(0);
-      VLOG(3) << "HandleRequest for " << cq_name << " after WaitCond";
-    }
+    int req_id = static_cast<int>(reinterpret_cast<intptr_t>(tag));
+    VLOG(3) << "HandleRequest " << rpc_name << ", req_id:" << req_id
+            << " get next";
 
+    auto& reqs = rpc_reqs_[rpc_name];
     RequestBase* base = nullptr;
     {
-      std::lock_guard<std::mutex> l(cq_mutex_);
-      if (cq_name == "cq_get") {
-        base = get_reqs_[req_id];
-      } else if (cq_name == "cq_send") {
-        base = send_reqs_[req_id];
-      } else if (cq_name == "cq_prefetch") {
-        base = prefetch_reqs_[req_id];
-      }
+      PADDLE_ENFORCE(req_id >= 0 && req_id < kRequestBufSize);
+      std::unique_lock<std::mutex> lock(cq_mutex_);
+      base = reqs[req_id];
     }
+
     // reference:
     // https://github.com/tensorflow/tensorflow/issues/5596
     // https://groups.google.com/forum/#!topic/grpc-io/xftlRy-IQwM
     // https://groups.google.com/forum/#!topic/grpc-io/ywATt88Ef_I
     if (!ok) {
-      LOG(WARNING) << cq_name << " recv no regular event:argument name["
+      LOG(WARNING) << "completion queue:" << rpc_name
+                   << " recv no regular event:argument name["
                    << base->GetReqName() << "]";
-      TryToRegisterNewOne(req_id);
+      TryToRegisterNewOne(rpc_name, req_id);
       delete base;
       continue;
     }
 
+    VLOG(3) << "queue id:" << rpc_name << ", req_id:" << req_id
+            << ", status:" << base->Status();
+
     switch (base->Status()) {
       case PROCESS: {
         base->Process();
-        VLOG(4) << cq_name << " PROCESS status:" << base->Status();
         break;
       }
       case FINISH: {
-        TryToRegisterNewOne(req_id);
-        VLOG(4) << cq_name << " FINISH status:" << base->Status();
+        TryToRegisterNewOne(rpc_name, req_id);
         delete base;
         break;
       }
@@ -415,20 +346,6 @@ void AsyncGRPCServer::HandleRequest(
   }
 }
 
-void AsyncGRPCServer::WaitCond(int cond) {
-  std::unique_lock<std::mutex> lock(this->barrier_mutex_);
-  barrier_condition_.wait(lock,
-                          [=] { return this->barrier_cond_step_ == cond; });
-}
-
-void AsyncGRPCServer::SetCond(int cond) {
-  {
-    std::lock_guard<std::mutex> lock(this->barrier_mutex_);
-    barrier_cond_step_ = cond;
-  }
-  barrier_condition_.notify_all();
-}
-
 }  // namespace detail
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/detail/grpc_server.h

@@ -14,6 +14,8 @@ limitations under the License. */
 
 #pragma once
 
+#include <map>
+#include <set>
 #include <string>
 #include <thread>  // NOLINT
 #include <utility>
@@ -28,6 +30,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/operators/detail/grpc_service.h"
+#include "paddle/fluid/operators/detail/request_handler.h"
+#include "paddle/fluid/operators/detail/rpc_server.h"
 #include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
 #include "paddle/fluid/operators/detail/send_recv.pb.h"
 #include "paddle/fluid/operators/detail/sendrecvop_utils.h"
@@ -37,106 +41,46 @@ namespace paddle {
 namespace operators {
 namespace detail {
 
-typedef std::pair<std::string, std::shared_ptr<VariableResponse>>
-    ReceivedMessage;
-typedef framework::BlockingQueue<ReceivedMessage> ReceivedQueue;
-
-typedef std::pair<std::string, sendrecv::VariableMessage> MessageWithName;
 class RequestBase;
 
-class AsyncGRPCServer final {
+class AsyncGRPCServer final : public RPCServer {
  public:
-  explicit AsyncGRPCServer(const std::string &address, bool sync_mode)
-      : address_(address), sync_mode_(sync_mode), ready_(0) {}
-
-  ~AsyncGRPCServer() {}
-  void WaitServerReady();
-  void RunSyncUpdate();
-
-  // functions to sync server barrier status.
-  void WaitCond(int cond);
-  void SetCond(int cond);
-  void WaitClientGet(int count);
-
-  void SetScope(framework::Scope *scope) { scope_ = scope; }
-
-  void SetDevCtx(const platform::DeviceContext *dev_ctx) { dev_ctx_ = dev_ctx; }
-
-  void SetProgram(framework::ProgramDesc *program) { program_ = program; }
-
-  void SetExecutor(framework::Executor *executor) { executor_ = executor; }
-
-  void SetPrefetchPreparedCtx(
-      std::unique_ptr<framework::ExecutorPrepareContext> prepared) {
-    prefetch_ctx_.reset(prepared.release());
-  }
-
-  int GetSelectedPort() const { return selected_port_; }
-
-  const ReceivedMessage Get() { return this->var_recv_queue_.Pop(); }
+  explicit AsyncGRPCServer(const std::string& address, int client_num)
+      : RPCServer(address, client_num), ready_(0) {}
 
-  void Push(const std::string &msg_name) {
-    this->var_recv_queue_.Push(std::make_pair(msg_name, nullptr));
-  }
+  virtual ~AsyncGRPCServer() {}
+  void WaitServerReady() override;
+  void StartServer() override;
 
-  void ShutDown();
+ private:
+  void HandleRequest(
+      ::grpc::ServerCompletionQueue* cq, const std::string& rpc_name,
+      std::function<void(const std::string&, int)> TryToRegisterNewOne);
 
- protected:
-  void HandleRequest(::grpc::ServerCompletionQueue *cq,
-                     const std::string &cq_name,
-                     std::function<void(int)> TryToRegisterNewOne);
-  void TryToRegisterNewSendOne(int req_id);
-  void TryToRegisterNewGetOne(int req_id);
-  void TryToRegisterNewPrefetchOne(int req_id);
+  void TryToRegisterNewOne(const std::string& rpc_name, int req_id);
   void ShutdownQueue();
+  void ShutDownImpl() override;
 
  private:
-  static const int kSendReqsBufSize = 100;
-  static const int kGetReqsBufSize = 100;
-  static const int kPrefetchReqsBufSize = 10;
+  static const int kRequestBufSize = 100;
 
   std::mutex cq_mutex_;
   volatile bool is_shut_down_ = false;
-  std::unique_ptr<::grpc::ServerCompletionQueue> cq_send_;
-  std::unique_ptr<::grpc::ServerCompletionQueue> cq_get_;
-  std::unique_ptr<::grpc::ServerCompletionQueue> cq_prefetch_;
-
-  RequestBase *send_reqs_[kSendReqsBufSize];
-  RequestBase *get_reqs_[kGetReqsBufSize];
-  RequestBase *prefetch_reqs_[kPrefetchReqsBufSize];
 
   GrpcService::AsyncService service_;
   std::unique_ptr<::grpc::Server> server_;
 
-  std::string address_;
-  const bool sync_mode_;
-  framework::Scope *scope_;
-  const platform::DeviceContext *dev_ctx_;
-
-  // received variable from RPC, operators fetch variable from this queue.
-  framework::BlockingQueue<MessageWithName> var_get_queue_;
-  // client send variable to this queue.
-  ReceivedQueue var_recv_queue_;
-
   // condition of the sub program
-  std::mutex barrier_mutex_;
-  mutable int barrier_cond_step_;
   std::condition_variable barrier_condition_;
 
-  std::vector<std::unique_ptr<std::thread>> t_sends_;
-  std::vector<std::unique_ptr<std::thread>> t_gets_;
-  std::vector<std::unique_ptr<std::thread>> t_prefetchs_;
-
-  std::unique_ptr<std::thread> t_prefetch_;
-
-  std::unique_ptr<framework::ExecutorPrepareContext> prefetch_ctx_;
-  framework::ProgramDesc *program_;
-  framework::Executor *executor_;
-  int selected_port_;
-
   std::mutex mutex_ready_;
   std::condition_variable condition_ready_;
+
   int ready_;
+
+  std::map<std::string, std::unique_ptr<::grpc::ServerCompletionQueue>> rpc_cq_;
+  std::map<std::string, std::vector<std::unique_ptr<std::thread>>> rpc_threads_;
+  std::map<std::string, std::vector<RequestBase*>> rpc_reqs_;
 };
 
 };  // namespace detail

paddle/fluid/operators/detail/grpc_server_test.cc

@@ -24,13 +24,16 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
 
+#include "paddle/fluid/operators/detail/request_handler_impl.h"
+
 namespace framework = paddle::framework;
 namespace platform = paddle::platform;
 namespace detail = paddle::operators::detail;
 
 USE_OP(lookup_table);
 
-std::unique_ptr<detail::AsyncGRPCServer> rpc_service_;
+std::unique_ptr<detail::AsyncGRPCServer> g_rpc_service;
+std::unique_ptr<detail::RequestHandler> g_req_handler;
 
 framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
   auto root_block = program->MutableBlock(0);
@@ -88,8 +91,7 @@ void InitTensorsOnServer(framework::Scope* scope, platform::CPUPlace* place,
   }
 }
 
-void StartServer(const std::string& endpoint) {
-  rpc_service_.reset(new detail::AsyncGRPCServer(endpoint, true));
+void StartServer() {
   framework::ProgramDesc program;
   framework::Scope scope;
   platform::CPUPlace place;
@@ -99,42 +101,56 @@ void StartServer(const std::string& endpoint) {
   auto prepared = exe.Prepare(program, block->ID());
   InitTensorsOnServer(&scope, &place, 10);
 
-  rpc_service_->SetProgram(&program);
-  rpc_service_->SetPrefetchPreparedCtx(std::move(prepared));
-  rpc_service_->SetDevCtx(&ctx);
-  rpc_service_->SetScope(&scope);
-  rpc_service_->SetExecutor(&exe);
+  g_req_handler->SetProgram(&program);
+  g_req_handler->SetPrefetchPreparedCtx(std::move(prepared));
+  g_req_handler->SetDevCtx(&ctx);
+  g_req_handler->SetScope(&scope);
+  g_req_handler->SetExecutor(&exe);
+
+  g_rpc_service->RegisterRPC(detail::kRequestPrefetch, g_req_handler.get());
+  g_req_handler->SetRPCServer(g_rpc_service.get());
 
-  rpc_service_->RunSyncUpdate();
+  std::thread server_thread(
+      std::bind(&detail::AsyncGRPCServer::StartServer, g_rpc_service.get()));
+
+  server_thread.join();
 }
 
-TEST(PREFETCH, DISABLED_CPU) {
-  // start up a server instance backend
-  std::thread server_thread(StartServer, "127.0.0.1:8889");
-  sleep(2);
+TEST(PREFETCH, CPU) {
+  g_req_handler.reset(new detail::RequestPrefetchHandler(true));
+  g_rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", 1));
+
+  std::thread server_thread(StartServer);
+  g_rpc_service->WaitServerReady();
+
+  detail::RPCClient* client = detail::RPCClient::GetInstance();
+  int port = g_rpc_service->GetSelectedPort();
+  std::string ep = paddle::string::Sprintf("127.0.0.1:%d", port);
+
   framework::Scope scope;
   platform::CPUPlace place;
   platform::CPUDeviceContext ctx(place);
+  {
     // create var on local scope
     int64_t rows_numel = 5;
     InitTensorsOnClient(&scope, &place, rows_numel);
     std::string in_var_name("ids");
     std::string out_var_name("out");
 
-  auto client = detail::RPCClient::GetInstance();
-  client->AsyncPrefetchVariable("127.0.0.1:8889", ctx, scope, in_var_name,
-                                out_var_name);
+    client->AsyncPrefetchVariable(ep, ctx, scope, in_var_name, out_var_name);
     client->Wait();
-
     auto var = scope.Var(out_var_name);
     auto value = var->GetMutable<framework::SelectedRows>()->value();
     auto ptr = value.mutable_data<float>(place);
 
-  rpc_service_->ShutDown();
-  server_thread.join();
-  rpc_service_.reset(nullptr);
-
     for (int64_t i = 0; i < rows_numel; ++i) {
       EXPECT_EQ(ptr[0 + i * value.dims()[1]], static_cast<float>(i * 2));
     }
+  }
+
+  g_rpc_service->ShutDown();
+  server_thread.join();
+  LOG(INFO) << "begin reset";
+  g_rpc_service.reset(nullptr);
+  g_req_handler.reset(nullptr);
 }

paddle/fluid/operators/detail/request_handler.h

+// 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 <time.h>
+
+#include <functional>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/framework/var_type.h"
+#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
+
+namespace paddle {
+namespace operators {
+namespace detail {
+
+constexpr char kRequestSend[] = "RequestSend";
+constexpr char kRequestGet[] = "RequestGet";
+constexpr char kRequestPrefetch[] = "RequestPrefetch";
+
+class RPCServer;
+
+class RequestHandler {
+ public:
+  explicit RequestHandler(bool sync_mode)
+      : sync_mode_(sync_mode),
+        dev_ctx_(nullptr),
+        executor_(nullptr),
+        scope_(nullptr),
+        program_(nullptr),
+        rpc_server_(nullptr) {}
+
+  virtual ~RequestHandler() {}
+
+  // Set attributes.
+  void SetScope(framework::Scope* scope) { scope_ = scope; }
+  void SetDevCtx(const platform::DeviceContext* dev_ctx) { dev_ctx_ = dev_ctx; }
+  void SetProgram(framework::ProgramDesc* program) { program_ = program; }
+  void SetExecutor(framework::Executor* executor) { executor_ = executor; }
+  void SetPrefetchPreparedCtx(
+      std::unique_ptr<framework::ExecutorPrepareContext> prepared) {
+    prefetch_ctx_.reset(prepared.release());
+  }
+
+  // Used for async.
+  void SetGradToPreparedCtx(
+      std::unordered_map<
+          std::string, std::shared_ptr<framework::ExecutorPrepareContext>>* g) {
+    grad_to_prepared_ctx_ = g;
+  }
+
+  void SetRPCServer(RPCServer* rpc_server) { rpc_server_ = rpc_server; }
+
+  // Get attributes.
+  bool sync_mode() { return sync_mode_; }
+  framework::Scope* scope() { return scope_; }
+  const platform::DeviceContext* dev_ctx() { return dev_ctx_; }
+  framework::ExecutorPrepareContext* prefetch_ctx() {
+    return prefetch_ctx_.get();
+  }
+  framework::ProgramDesc* program() { return program_; }
+  framework::Executor* executor() { return executor_; }
+  std::vector<framework::Variable*>& sparse_vars() { return sparse_vars_; }
+
+  // This function processes user's rpc request.
+  // The implemention is in request_handler_impl.
+  // example:
+  //    std::string varname = request_.varname();
+  //
+  //    auto scope = request_handler_->scope();
+  //    auto invar = scope->FindVar(varname);
+  //    framework::Variable* outvar = nullptr;
+  //
+  //    request_handler_->Handle(varname, scope, invar, &outvar);
+  //    if (outvar) {
+  //        SerializeToByteBuffer(varname, outvar,
+  //           *request_handler_->dev_ctx(), &reply_);
+  //    }
+  virtual bool Handle(const std::string& varname, framework::Scope* scope,
+                      framework::Variable* var,
+                      framework::Variable** outvar) = 0;
+
+ protected:
+  const bool sync_mode_;
+
+  const platform::DeviceContext* dev_ctx_;
+  framework::Executor* executor_;
+  framework::Scope* scope_;
+  framework::ProgramDesc* program_;
+  std::unique_ptr<framework::ExecutorPrepareContext> prefetch_ctx_;
+
+  // Used for async.
+  std::unordered_map<std::string,
+                     std::shared_ptr<framework::ExecutorPrepareContext>>*
+      grad_to_prepared_ctx_;
+
+  // Record received sparse variables, so that
+  // we could reset those after execute optimize program
+  std::vector<framework::Variable*> sparse_vars_;
+  RPCServer* rpc_server_;
+
+  std::mutex sparse_var_mutex_;
+};
+
+}  // namespace detail
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/detail/request_handler_impl.cc

+// 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 <iostream>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/blocking_queue.h"
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/operators/detail/request_handler_impl.h"
+#include "paddle/fluid/operators/detail/rpc_server.h"
+#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
+#include "paddle/fluid/operators/detail/variable_response.h"
+
+namespace paddle {
+namespace operators {
+namespace detail {
+
+bool RequestSendHandler::Handle(const std::string& varname,
+                                framework::Scope* scope,
+                                framework::Variable* invar,
+                                framework::Variable** outvar) {
+  VLOG(4) << "RequestSendHandler:" << varname;
+
+  // Async
+  if (!sync_mode_) {
+    try {
+      executor_->RunPreparedContext((*grad_to_prepared_ctx_)[varname].get(),
+                                    scope);
+    } catch (std::exception& e) {
+      LOG(ERROR) << "async: run sub program error " << e.what();
+      return false;
+    }
+    return true;
+  }
+
+  // Sync
+  if (varname == BATCH_BARRIER_MESSAGE) {
+    VLOG(3) << "sync: recv batch barrier message";
+    rpc_server_->IncreaseBatchBarrier(kRequestSend);
+  } else {
+    VLOG(3) << "sync: received var_name: " << varname;
+    if (sync_mode_) {
+      rpc_server_->WaitCond(kRequestSend);
+    }
+
+    if (invar == nullptr) {
+      LOG(ERROR) << "sync: Can not find server side var: " << varname;
+      PADDLE_THROW("sync: Can not find server side var");
+      return false;
+    }
+
+    if (invar->IsType<framework::SelectedRows>()) {
+      std::unique_lock<std::mutex> lock(sparse_var_mutex_);
+      sparse_vars_.push_back(invar);
+    }
+  }
+
+  return true;
+}
+
+bool RequestGetHandler::Handle(const std::string& varname,
+                               framework::Scope* scope,
+                               framework::Variable* invar,
+                               framework::Variable** outvar) {
+  VLOG(4) << "RequestGetHandler:" << varname;
+
+  if (varname != FETCH_BARRIER_MESSAGE) {
+    if (sync_mode_) {
+      rpc_server_->WaitCond(kRequestGet);
+    }
+    *outvar = scope_->FindVar(varname);
+    return true;
+  }
+
+  // FETCH_BARRIER_MESSAGE
+  if (sync_mode_) {
+    VLOG(3) << "sync: recv fetch barrier message";
+    rpc_server_->IncreaseBatchBarrier(kRequestGet);
+  }
+
+  return true;
+}
+
+bool RequestPrefetchHandler::Handle(const std::string& varname,
+                                    framework::Scope* scope,
+                                    framework::Variable* invar,
+                                    framework::Variable** outvar) {
+  VLOG(4) << "RequestPrefetchHandler " << varname;
+
+  auto var_desc = program_->Block(0).FindVar(varname);
+  *outvar = scope->FindVar(varname);
+  InitializeVariable(*outvar, var_desc->GetType());
+  executor_->RunPreparedContext(prefetch_ctx_.get(), scope);
+
+  return true;
+}
+
+}  // namespace detail
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/detail/request_handler_impl.h

+// 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 <time.h>
+
+#include <functional>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/selected_rows.h"
+#include "paddle/fluid/framework/var_type.h"
+#include "paddle/fluid/operators/detail/request_handler.h"
+#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
+
+namespace paddle {
+namespace operators {
+namespace detail {
+
+class RequestSendHandler final : public RequestHandler {
+ public:
+  explicit RequestSendHandler(bool sync_mode) : RequestHandler(sync_mode) {}
+  virtual ~RequestSendHandler() {}
+  bool Handle(const std::string& varname, framework::Scope* scope,
+              framework::Variable* var, framework::Variable** outvar) override;
+};
+
+class RequestGetHandler final : public RequestHandler {
+ public:
+  explicit RequestGetHandler(bool sync_mode) : RequestHandler(sync_mode) {}
+  virtual ~RequestGetHandler() {}
+  bool Handle(const std::string& varname, framework::Scope* scope,
+              framework::Variable* var, framework::Variable** outvar) override;
+};
+
+class RequestPrefetchHandler final : public RequestHandler {
+ public:
+  explicit RequestPrefetchHandler(bool sync_mode) : RequestHandler(sync_mode) {}
+  virtual ~RequestPrefetchHandler() {}
+  bool Handle(const std::string& varname, framework::Scope* scope,
+              framework::Variable* var, framework::Variable** outvar) override;
+};
+
+}  // namespace detail
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/detail/rpc_server.cc

+// 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 <fstream>
+#include <iostream>
+#include <limits>
+#include <string>
+
+#include "paddle/fluid/operators/detail/rpc_server.h"
+
+namespace paddle {
+namespace operators {
+namespace detail {
+
+void RPCServer::ShutDown() {
+  LOG(INFO) << "RPCServer ShutDown ";
+  ShutDownImpl();
+
+  exit_flag_ = true;
+  barrier_cond_.notify_all();
+  rpc_cond_.notify_all();
+}
+
+void RPCServer::SavePort() const {
+  auto file_path = string::Sprintf("/tmp/paddle.%d.port", ::getpid());
+  std::ofstream port_file;
+  port_file.open(file_path);
+  port_file << selected_port_;
+  port_file.close();
+  VLOG(4) << "selected port written to " << file_path;
+}
+
+void RPCServer::WaitBarrier(const std::string& rpc_name) {
+  std::unique_lock<std::mutex> lock(this->mutex_);
+  barrier_cond_.wait(lock, [=] {
+    return (barrier_counter_[rpc_name] >= client_num_ || exit_flag_.load());
+  });
+
+  VLOG(3) << "batch_barrier_:" << barrier_counter_[rpc_name];
+}
+
+void RPCServer::IncreaseBatchBarrier(const std::string rpc_name) {
+  VLOG(3) << "RPCServer begin IncreaseBatchBarrier " << rpc_name;
+  int b = 0;
+  {
+    std::unique_lock<std::mutex> lock(mutex_);
+    b = ++barrier_counter_[rpc_name];
+  }
+
+  VLOG(3) << "RPCServer IncreaseBatchBarrier " << rpc_name
+          << ", barrier_count:" << b << ", fan_in" << client_num_;
+
+  if (b >= client_num_) {
+    barrier_cond_.notify_all();
+  }
+}
+
+void RPCServer::ResetBarrierCounter() {
+  VLOG(3) << "RPCServer ResetBarrierCounter ";
+  std::unique_lock<std::mutex> lock(mutex_);
+  for (auto& t : barrier_counter_) {
+    t.second = 0;
+  }
+}
+
+void RPCServer::RegisterRPC(const std::string& rpc_name,
+                            RequestHandler* handler, int thread_num) {
+  rpc_call_map_[rpc_name] = handler;
+  rpc_thread_num_[rpc_name] = thread_num;
+
+  static int cond = -1;
+  rpc_cond_map_[rpc_name] = ++cond;
+  VLOG(4) << "RegisterRPC rpc_name:" << rpc_name << ", handler:" << handler
+          << ", cond:" << rpc_cond_map_[rpc_name];
+}
+
+void RPCServer::SetCond(const std::string& rpc_name) {
+  VLOG(3) << "RPCServer SetCond " << rpc_name;
+  {
+    std::unique_lock<std::mutex> lock(mutex_);
+    cur_cond_ = rpc_cond_map_[rpc_name];
+  }
+
+  rpc_cond_.notify_all();
+}
+
+void RPCServer::WaitCond(const std::string& rpc_name) {
+  VLOG(3) << "RPCServer WaitCond " << rpc_name;
+  int cond = 0;
+  {
+    std::unique_lock<std::mutex> lock(mutex_);
+    cond = rpc_cond_map_[rpc_name];
+  }
+
+  std::unique_lock<std::mutex> lock(mutex_);
+  rpc_cond_.wait(
+      lock, [=] { return (cur_cond_.load() == cond || exit_flag_.load()); });
+}
+
+}  // namespace detail
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/detail/rpc_server.h

+// 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 <set>
+#include <string>
+#include <thread>  // NOLINT
+#include <utility>
+#include <vector>
+#include "paddle/fluid/operators/detail/request_handler.h"
+
+namespace paddle {
+namespace operators {
+namespace detail {
+
+class RPCServer {
+ public:
+  explicit RPCServer(const std::string& address, int client_num)
+      : cur_cond_(0),
+        bind_address_(address),
+        exit_flag_(false),
+        selected_port_(0),
+        client_num_(client_num) {}
+
+  virtual ~RPCServer() {}
+  virtual void StartServer() = 0;
+  virtual void WaitServerReady() = 0;
+
+  void ShutDown();
+
+  bool IsExit() { return exit_flag_.load(); }
+
+  int GetSelectedPort() const { return selected_port_; }
+  void SavePort() const;
+
+  // RegisterRPC, register the rpc method name to a handler
+  // class, and auto generate a condition id for this call
+  // to be used for the barrier.
+  void RegisterRPC(const std::string& rpc_name, RequestHandler* handler,
+                   int thread_num = 5);
+
+  // Wait util all the clients have reached the barrier for one
+  // rpc method. This function should be called in the
+  // RequestHandler if you want to run the server/client in a
+  // synchronous mode.
+  void WaitBarrier(const std::string& rpc_name);
+
+  void SetCond(const std::string& rpc_name);
+  void WaitCond(const std::string& rpc_name);
+  void IncreaseBatchBarrier(const std::string rpc_name);
+  void ResetBarrierCounter();
+
+ protected:
+  virtual void ShutDownImpl() = 0;
+
+ private:
+  std::mutex mutex_;
+  std::unordered_map<std::string, int> barrier_counter_;
+  std::condition_variable barrier_cond_;
+
+  std::unordered_map<std::string, int> rpc_cond_map_;
+  std::atomic<int> cur_cond_;
+  std::condition_variable rpc_cond_;
+
+ protected:
+  std::string bind_address_;
+  std::atomic<int> exit_flag_;
+  int selected_port_;
+
+  const int client_num_;
+
+  std::unordered_map<std::string, RequestHandler*> rpc_call_map_;
+  std::unordered_map<std::string, int> rpc_thread_num_;
+  friend class RequestHandler;
+};
+
+};  // namespace detail
+};  // namespace operators
+};  // namespace paddle

paddle/fluid/operators/detail/variable_response.h

@@ -67,8 +67,8 @@ class VariableResponse {
 
   framework::Scope* GetMutableLocalScope() const { return local_scope_; }
 
-  inline std::string Varname() { return meta_.varname(); }
-  inline std::string OutVarname() { return meta_.out_varname(); }
+  inline std::string Varname() const { return meta_.varname(); }
+  inline std::string OutVarname() const { return meta_.out_varname(); }
 
   // should call parse first.
   framework::Variable* GetVar() {

paddle/fluid/operators/detection/box_coder_op.cc

@@ -22,21 +22,21 @@ class BoxCoderOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("PriorBox"),
                    "Input(PriorBox) of BoxCoderOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("PriorBoxVar"),
-                   "Input(PriorBoxVar) of BoxCoderOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("TargetBox"),
                    "Input(TargetBox) of BoxCoderOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("OutputBox"),
                    "Output(OutputBox) of BoxCoderOp should not be null.");
 
     auto prior_box_dims = ctx->GetInputDim("PriorBox");
-    auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
     auto target_box_dims = ctx->GetInputDim("TargetBox");
 
     PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2,
                       "The rank of Input of PriorBoxVar must be 2");
     PADDLE_ENFORCE_EQ(prior_box_dims[1], 4, "The shape of PriorBox is [N, 4]");
+    if (ctx->HasInput("PriorBoxVar")) {
+      auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
       PADDLE_ENFORCE_EQ(prior_box_dims, prior_box_var_dims);
+    }
 
     auto code_type = GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
     if (code_type == BoxCodeType::kEncodeCenterSize) {
@@ -71,9 +71,11 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
         "of the coordinate system. [xmax, ymax] is the right bottom "
         "coordinate of the anchor box.");
     AddInput("PriorBoxVar",
-             "(Tensor, default Tensor<float>) "
+             "(Tensor, default Tensor<float>, optional) "
              "PriorBoxVar is a 2-D Tensor with shape [M, 4] holds M group "
-             "of variance.");
+             "of variance. PriorBoxVar will set all elements to 1 by "
+             "default.")
+        .AsDispensable();
     AddInput(
         "TargetBox",
         "(LoDTensor or Tensor) This input can be a 2-D LoDTensor with shape "
@@ -91,6 +93,10 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
                          "the code type used with the target box")
         .SetDefault("encode_center_size")
         .InEnum({"encode_center_size", "decode_center_size"});
+    AddAttr<bool>("box_normalized",
+                  "(bool, default true) "
+                  "whether treat the priorbox as a noramlized box")
+        .SetDefault(true);
     AddOutput("OutputBox",
               "(LoDTensor or Tensor) "
               "When code_type is 'encode_center_size', the output tensor of "
@@ -127,5 +133,6 @@ width and height.
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(box_coder, ops::BoxCoderOp, ops::BoxCoderOpMaker,
                   paddle::framework::EmptyGradOpMaker);
-REGISTER_OP_CPU_KERNEL(box_coder, ops::BoxCoderKernel<float>,
-                       ops::BoxCoderKernel<double>);
+REGISTER_OP_CPU_KERNEL(
+    box_coder, ops::BoxCoderKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::BoxCoderKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/detection/box_coder_op.cu

@@ -20,15 +20,16 @@ __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
                                        const T* prior_box_var_data,
                                        const T* target_box_data, const int row,
                                        const int col, const int len,
-                                       T* output) {
+                                       const bool normalized, T* output) {
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < row * col) {
     const int row_idx = idx / col;
     const int col_idx = idx % col;
-    T prior_box_width =
-        prior_box_data[col_idx * len + 2] - prior_box_data[col_idx * len];
-    T prior_box_height =
-        prior_box_data[col_idx * len + 3] - prior_box_data[col_idx * len + 1];
+    T prior_box_width = prior_box_data[col_idx * len + 2] -
+                        prior_box_data[col_idx * len] + (normalized == false);
+    T prior_box_height = prior_box_data[col_idx * len + 3] -
+                         prior_box_data[col_idx * len + 1] +
+                         (normalized == false);
     T prior_box_center_x =
         (prior_box_data[col_idx * len + 2] + prior_box_data[col_idx * len]) / 2;
     T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
@@ -41,20 +42,24 @@ __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
     T target_box_center_y = (target_box_data[row_idx * len + 3] +
                              target_box_data[row_idx * len + 1]) /
                             2;
-    T target_box_width =
-        target_box_data[row_idx * len + 2] - target_box_data[row_idx * len];
-    T target_box_height =
-        target_box_data[row_idx * len + 3] - target_box_data[row_idx * len + 1];
+    T target_box_width = target_box_data[row_idx * len + 2] -
+                         target_box_data[row_idx * len] + (normalized == false);
+    T target_box_height = target_box_data[row_idx * len + 3] -
+                          target_box_data[row_idx * len + 1] +
+                          (normalized == false);
 
-    output[idx * len] = (target_box_center_x - prior_box_center_x) /
-                        prior_box_width / prior_box_var_data[col_idx * len];
-    output[idx * len + 1] = (target_box_center_y - prior_box_center_y) /
-                            prior_box_height /
-                            prior_box_var_data[col_idx * len + 1];
-    output[idx * len + 2] = log(fabs(target_box_width / prior_box_width)) /
-                            prior_box_var_data[col_idx * len + 2];
-    output[idx * len + 3] = log(fabs(target_box_height / prior_box_height)) /
-                            prior_box_var_data[col_idx * len + 3];
+    output[idx * len] =
+        (target_box_center_x - prior_box_center_x) / prior_box_width;
+    output[idx * len + 1] =
+        (target_box_center_y - prior_box_center_y) / prior_box_height;
+    output[idx * len + 2] = log(fabs(target_box_width / prior_box_width));
+    output[idx * len + 3] = log(fabs(target_box_height / prior_box_height));
+    if (prior_box_var_data) {
+      output[idx * len] /= prior_box_var_data[col_idx * len];
+      output[idx * len + 1] /= prior_box_var_data[col_idx * len + 1];
+      output[idx * len + 2] /= prior_box_var_data[col_idx * len + 2];
+      output[idx * len + 3] /= prior_box_var_data[col_idx * len + 3];
+    }
   }
 }
 
@@ -63,42 +68,56 @@ __global__ void DecodeCenterSizeKernel(const T* prior_box_data,
                                        const T* prior_box_var_data,
                                        const T* target_box_data, const int row,
                                        const int col, const int len,
-                                       T* output) {
+                                       const bool normalized, T* output) {
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < row * col) {
     const int col_idx = idx % col;
-    T prior_box_width =
-        prior_box_data[col_idx * len + 2] - prior_box_data[col_idx * len];
-    T prior_box_height =
-        prior_box_data[col_idx * len + 3] - prior_box_data[col_idx * len + 1];
+    T prior_box_width = prior_box_data[col_idx * len + 2] -
+                        prior_box_data[col_idx * len] + (normalized == false);
+    T prior_box_height = prior_box_data[col_idx * len + 3] -
+                         prior_box_data[col_idx * len + 1] +
+                         (normalized == false);
     T prior_box_center_x =
         (prior_box_data[col_idx * len + 2] + prior_box_data[col_idx * len]) / 2;
     T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
                             prior_box_data[col_idx * len + 1]) /
                            2;
-
-    T target_box_width = exp(prior_box_var_data[col_idx * len + 2] *
+    T target_box_width, target_box_height;
+    T target_box_center_x, target_box_center_y;
+    if (prior_box_var_data) {
+      target_box_width = exp(prior_box_var_data[col_idx * len + 2] *
                              target_box_data[idx * len + 2]) *
                          prior_box_width;
-    T target_box_height = exp(prior_box_var_data[col_idx * len + 3] *
+      target_box_height = exp(prior_box_var_data[col_idx * len + 3] *
                               target_box_data[idx * len + 3]) *
                           prior_box_height;
-    T target_box_center_x = prior_box_var_data[col_idx * len] *
+      target_box_center_x = prior_box_var_data[col_idx * len] *
                                 target_box_data[idx * len] * prior_box_width +
                             prior_box_center_x;
-    T target_box_center_y = prior_box_var_data[col_idx * len + 1] *
+      target_box_center_y = prior_box_var_data[col_idx * len + 1] *
                                 target_box_data[idx * len + 1] *
                                 prior_box_height +
                             prior_box_center_y;
+    } else {
+      target_box_width = exp(target_box_data[idx * len + 2]) * prior_box_width;
+      target_box_height =
+          exp(target_box_data[idx * len + 3]) * prior_box_height;
+      target_box_center_x =
+          target_box_data[idx * len] * prior_box_width + prior_box_center_x;
+      target_box_center_y = target_box_data[idx * len + 1] * prior_box_height +
+                            prior_box_center_y;
+    }
 
     output[idx * len] = target_box_center_x - target_box_width / 2;
     output[idx * len + 1] = target_box_center_y - target_box_height / 2;
-    output[idx * len + 2] = target_box_center_x + target_box_width / 2;
-    output[idx * len + 3] = target_box_center_y + target_box_height / 2;
+    output[idx * len + 2] =
+        target_box_center_x + target_box_width / 2 - (normalized == false);
+    output[idx * len + 3] =
+        target_box_center_y + target_box_height / 2 - (normalized == false);
   }
 }
 
-template <typename T>
+template <typename DeviceContext, typename T>
 class BoxCoderCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
@@ -109,6 +128,11 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
     auto* output_box = context.Output<framework::Tensor>("OutputBox");
 
+    const T* prior_box_data = prior_box->data<T>();
+    const T* target_box_data = target_box->data<T>();
+    const T* prior_box_var_data = nullptr;
+    if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
+
     if (target_box->lod().size()) {
       PADDLE_ENFORCE_EQ(target_box->lod().size(), 1,
                         "Only support 1 level of LoD.");
@@ -120,22 +144,19 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     int grid = (row * col + block - 1) / block;
     auto& device_ctx = context.cuda_device_context();
 
-    const T* prior_box_data = prior_box->data<T>();
-    const T* prior_box_var_data = prior_box_var->data<T>();
-    const T* target_box_data = target_box->data<T>();
-
     output_box->mutable_data<T>({row, col, len}, context.GetPlace());
     T* output = output_box->data<T>();
 
     auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
+    bool normalized = context.Attr<bool>("box_normalized");
     if (code_type == BoxCodeType::kEncodeCenterSize) {
       EncodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
           prior_box_data, prior_box_var_data, target_box_data, row, col, len,
-          output);
+          normalized, output);
     } else if (code_type == BoxCodeType::kDecodeCenterSize) {
       DecodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
           prior_box_data, prior_box_var_data, target_box_data, row, col, len,
-          output);
+          normalized, output);
     }
   }
 };
@@ -144,5 +165,7 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(box_coder, ops::BoxCoderCUDAKernel<float>,
-                        ops::BoxCoderCUDAKernel<double>);
+REGISTER_OP_CUDA_KERNEL(
+    box_coder,
+    ops::BoxCoderCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::BoxCoderCUDAKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/detection/box_coder_op.h

@@ -28,26 +28,28 @@ inline BoxCodeType GetBoxCodeType(const std::string& type) {
   PADDLE_THROW("Not support type %s.", type);
 }
 
-template <typename T>
+template <typename DeviceContext, typename T>
 class BoxCoderKernel : public framework::OpKernel<T> {
  public:
-  void EncodeCenterSize(const framework::Tensor& target_box,
-                        const framework::Tensor& prior_box,
-                        const framework::Tensor& prior_box_var,
-                        T* output) const {
-    int64_t row = target_box.dims()[0];
-    int64_t col = prior_box.dims()[0];
-    int64_t len = prior_box.dims()[1];
-    auto* target_box_data = target_box.data<T>();
-    auto* prior_box_data = prior_box.data<T>();
-    auto* prior_box_var_data = prior_box_var.data<T>();
+  void EncodeCenterSize(const framework::Tensor* target_box,
+                        const framework::Tensor* prior_box,
+                        const framework::Tensor* prior_box_var,
+                        const bool normalized, T* output) const {
+    int64_t row = target_box->dims()[0];
+    int64_t col = prior_box->dims()[0];
+    int64_t len = prior_box->dims()[1];
+    auto* target_box_data = target_box->data<T>();
+    auto* prior_box_data = prior_box->data<T>();
+    const T* prior_box_var_data = nullptr;
+    if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
 
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
-        T prior_box_width =
-            prior_box_data[j * len + 2] - prior_box_data[j * len];
-        T prior_box_height =
-            prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
+        T prior_box_width = prior_box_data[j * len + 2] -
+                            prior_box_data[j * len] + (normalized == false);
+        T prior_box_height = prior_box_data[j * len + 3] -
+                             prior_box_data[j * len + 1] +
+                             (normalized == false);
         T prior_box_center_x =
             (prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
         T prior_box_center_y =
@@ -57,67 +59,89 @@ class BoxCoderKernel : public framework::OpKernel<T> {
             (target_box_data[i * len + 2] + target_box_data[i * len]) / 2;
         T target_box_center_y =
             (target_box_data[i * len + 3] + target_box_data[i * len + 1]) / 2;
-        T target_box_width =
-            target_box_data[i * len + 2] - target_box_data[i * len];
-        T target_box_height =
-            target_box_data[i * len + 3] - target_box_data[i * len + 1];
+        T target_box_width = target_box_data[i * len + 2] -
+                             target_box_data[i * len] + (normalized == false);
+        T target_box_height = target_box_data[i * len + 3] -
+                              target_box_data[i * len + 1] +
+                              (normalized == false);
 
         size_t offset = i * col * len + j * len;
-        output[offset] = (target_box_center_x - prior_box_center_x) /
-                         prior_box_width / prior_box_var_data[j * len];
-        output[offset + 1] = (target_box_center_y - prior_box_center_y) /
-                             prior_box_height / prior_box_var_data[j * len + 1];
+        output[offset] =
+            (target_box_center_x - prior_box_center_x) / prior_box_width;
+        output[offset + 1] =
+            (target_box_center_y - prior_box_center_y) / prior_box_height;
         output[offset + 2] =
-            std::log(std::fabs(target_box_width / prior_box_width)) /
-            prior_box_var_data[j * len + 2];
+            std::log(std::fabs(target_box_width / prior_box_width));
         output[offset + 3] =
-            std::log(std::fabs(target_box_height / prior_box_height)) /
-            prior_box_var_data[j * len + 3];
+            std::log(std::fabs(target_box_height / prior_box_height));
+        if (prior_box_var) {
+          output[offset] /= prior_box_var_data[j * len];
+          output[offset + 1] /= prior_box_var_data[j * len + 1];
+          output[offset + 2] /= prior_box_var_data[j * len + 2];
+          output[offset + 3] /= prior_box_var_data[j * len + 3];
         }
       }
     }
-  void DecodeCenterSize(const framework::Tensor& target_box,
-                        const framework::Tensor& prior_box,
-                        const framework::Tensor& prior_box_var,
-                        T* output) const {
-    int64_t row = target_box.dims()[0];
-    int64_t col = prior_box.dims()[0];
-    int64_t len = prior_box.dims()[1];
-
-    auto* target_box_data = target_box.data<T>();
-    auto* prior_box_data = prior_box.data<T>();
-    auto* prior_box_var_data = prior_box_var.data<T>();
+  }
+  void DecodeCenterSize(const framework::Tensor* target_box,
+                        const framework::Tensor* prior_box,
+                        const framework::Tensor* prior_box_var,
+                        const bool normalized, T* output) const {
+    int64_t row = target_box->dims()[0];
+    int64_t col = prior_box->dims()[0];
+    int64_t len = prior_box->dims()[1];
+
+    auto* target_box_data = target_box->data<T>();
+    auto* prior_box_data = prior_box->data<T>();
+    const T* prior_box_var_data = nullptr;
+    if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
 
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
         size_t offset = i * col * len + j * len;
-        T prior_box_width =
-            prior_box_data[j * len + 2] - prior_box_data[j * len];
-        T prior_box_height =
-            prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
+        T prior_box_width = prior_box_data[j * len + 2] -
+                            prior_box_data[j * len] + (normalized == false);
+        T prior_box_height = prior_box_data[j * len + 3] -
+                             prior_box_data[j * len + 1] +
+                             (normalized == false);
         T prior_box_center_x =
             (prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
         T prior_box_center_y =
             (prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;
 
-        T target_box_center_x = prior_box_var_data[j * len] *
+        T target_box_center_x = 0, target_box_center_y = 0;
+        T target_box_width = 0, target_box_height = 0;
+        if (prior_box_var) {
+          target_box_center_x = prior_box_var_data[j * len] *
                                     target_box_data[offset] * prior_box_width +
                                 prior_box_center_x;
-        T target_box_center_y = prior_box_var_data[j * len + 1] *
+          target_box_center_y = prior_box_var_data[j * len + 1] *
                                     target_box_data[offset + 1] *
                                     prior_box_height +
                                 prior_box_center_y;
-        T target_box_width = std::exp(prior_box_var_data[j * len + 2] *
+          target_box_width = std::exp(prior_box_var_data[j * len + 2] *
                                       target_box_data[offset + 2]) *
                              prior_box_width;
-        T target_box_height = std::exp(prior_box_var_data[j * len + 3] *
+          target_box_height = std::exp(prior_box_var_data[j * len + 3] *
                                        target_box_data[offset + 3]) *
                               prior_box_height;
+        } else {
+          target_box_center_x =
+              target_box_data[offset] * prior_box_width + prior_box_center_x;
+          target_box_center_y = target_box_data[offset + 1] * prior_box_height +
+                                prior_box_center_y;
+          target_box_width =
+              std::exp(target_box_data[offset + 2]) * prior_box_width;
+          target_box_height =
+              std::exp(target_box_data[offset + 3]) * prior_box_height;
+        }
 
         output[offset] = target_box_center_x - target_box_width / 2;
         output[offset + 1] = target_box_center_y - target_box_height / 2;
-        output[offset + 2] = target_box_center_x + target_box_width / 2;
-        output[offset + 3] = target_box_center_y + target_box_height / 2;
+        output[offset + 2] =
+            target_box_center_x + target_box_width / 2 - (normalized == false);
+        output[offset + 3] =
+            target_box_center_y + target_box_height / 2 - (normalized == false);
       }
     }
   }
@@ -139,11 +163,14 @@ class BoxCoderKernel : public framework::OpKernel<T> {
     output_box->mutable_data<T>({row, col, len}, context.GetPlace());
 
     auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
+    bool normalized = context.Attr<bool>("box_normalized");
     T* output = output_box->data<T>();
     if (code_type == BoxCodeType::kEncodeCenterSize) {
-      EncodeCenterSize(*target_box, *prior_box, *prior_box_var, output);
+      EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
+                       output);
     } else if (code_type == BoxCodeType::kDecodeCenterSize) {
-      DecodeCenterSize(*target_box, *prior_box, *prior_box_var, output);
+      DecodeCenterSize(target_box, prior_box, prior_box_var, normalized,
+                       output);
     }
   }
 };

paddle/fluid/operators/fetch_barrier_op.cc

@@ -45,13 +45,13 @@ class FetchBarrierOp : public framework::OperatorBase {
 
     auto rpc_client = detail::RPCClient::GetInstance();
 
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
 
     for (auto& ep : eps) {
       VLOG(3) << "fetch barrier, ep: " << ep;
       rpc_client->AsyncSendFetchBarrier(ep);
     }
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
   }
 };
 

paddle/fluid/operators/gather_op.cc

@@ -33,7 +33,6 @@ class GatherOp : public framework::OperatorWithKernel {
     auto index_dims = ctx->GetInputDim("Index");
     PADDLE_ENFORCE(index_dims.size() == 1);
     int batch_size = ctx->GetInputDim("Index")[0];
-    PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
     framework::DDim output_dims(ctx->GetInputDim("X"));
     output_dims[0] = batch_size;
     ctx->SetOutputDim("Out", output_dims);

paddle/fluid/operators/gen_nccl_id_op.cc

@@ -23,6 +23,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/threadpool.h"
 #include "paddle/fluid/operators/detail/grpc_client.h"
 #include "paddle/fluid/operators/detail/grpc_server.h"
+#include "paddle/fluid/operators/detail/request_handler_impl.h"
 #include "paddle/fluid/platform/nccl_helper.h"
 
 namespace paddle {
@@ -75,19 +76,23 @@ class GenNCCLIdOp : public framework::OperatorBase {
     // NOTE: Can not use unique_ptr here because the default
     // deleter will call GRPC Server's base class's dtor and
     // that will cause a wired crash.
-    detail::AsyncGRPCServer rpc_service(endpoint, true);
+    detail::RequestSendHandler rpc_h(true);
+    detail::AsyncGRPCServer rpc_service(endpoint, 1);
+    rpc_service.RegisterRPC(detail::kRequestSend, &rpc_h);
+    rpc_h.SetRPCServer(&rpc_service);
+
     framework::ProgramDesc empty_program;
     framework::Executor executor(dev_ctx.GetPlace());
-    rpc_service.SetScope(scope);
-    rpc_service.SetDevCtx(&dev_ctx);
-    rpc_service.SetProgram(&empty_program);
-    rpc_service.SetExecutor(&executor);
+    rpc_h.SetScope(scope);
+    rpc_h.SetDevCtx(&dev_ctx);
+    rpc_h.SetProgram(&empty_program);
+    rpc_h.SetExecutor(&executor);
 
     std::thread server_thread(
-        std::bind(&detail::AsyncGRPCServer::RunSyncUpdate, &rpc_service));
-    rpc_service.SetCond(0);
+        std::bind(&detail::AsyncGRPCServer::StartServer, &rpc_service));
+    rpc_service.SetCond(detail::kRequestSend);
     VLOG(3) << "start getting nccl id from trainer 0...";
-    auto recv = rpc_service.Get();
+    rpc_service.WaitBarrier(detail::kRequestSend);
     VLOG(3) << "got nccl id and stop server...";
     rpc_service.ShutDown();
     VLOG(3) << "rpc server stopped";

paddle/fluid/operators/listen_and_serv_op.cc

@@ -19,14 +19,16 @@ limitations under the License. */
 #include <thread>  // NOLINT
 #include <vector>
 
+#include "paddle/fluid/operators/detail/grpc_server.h"
+#include "paddle/fluid/operators/detail/request_handler_impl.h"
 #include "paddle/fluid/operators/listen_and_serv_op.h"
 #include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
 
-void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service) {
-  service->RunSyncUpdate();
+void RunServer(std::shared_ptr<detail::RPCServer> service) {
+  service->StartServer();
   VLOG(4) << "RunServer thread end";
 }
 static void split(const std::string &str, char sep,
@@ -67,8 +69,6 @@ static void ParallelExecuteBlocks(
   for (size_t i = 0; i < fs.size(); ++i) fs[i].wait();
 }
 
-std::atomic_int ListenAndServOp::selected_port_{0};
-
 ListenAndServOp::ListenAndServOp(const std::string &type,
                                  const framework::VariableNameMap &inputs,
                                  const framework::VariableNameMap &outputs,
@@ -78,7 +78,6 @@ ListenAndServOp::ListenAndServOp(const std::string &type,
 ListenAndServOp::~ListenAndServOp() { Stop(); }
 
 void ListenAndServOp::Stop() {
-  rpc_service_->Push(LISTEN_TERMINATE_MESSAGE);
   rpc_service_->ShutDown();
   server_thread_->join();
   auto file_path = string::Sprintf("/tmp/paddle.%d.port", ::getpid());
@@ -87,26 +86,13 @@ void ListenAndServOp::Stop() {
 
 void ListenAndServOp::SavePort() const {
   // NOTE: default write file to /tmp/paddle.selected_port
-  selected_port_ = rpc_service_->GetSelectedPort();
-  auto file_path = string::Sprintf("/tmp/paddle.%d.port", ::getpid());
-  std::ofstream port_file;
-  port_file.open(file_path);
-  port_file << selected_port_.load();
-  port_file.close();
-  VLOG(4) << "selected port written to " << file_path;
-}
-
-void ListenAndServOp::WaitServerReady() {
-  while (selected_port_.load() == 0) {
-  }
+  rpc_service_->SavePort();
 }
 
 void ListenAndServOp::RunSyncLoop(framework::Executor *executor,
                                   framework::ProgramDesc *program,
                                   framework::Scope *recv_scope,
                                   framework::BlockDesc *prefetch_block) const {
-  auto fan_in = Attr<int>("Fanin");
-
   size_t num_blocks = program->Size();
   PADDLE_ENFORCE_GE(num_blocks, 2,
                     "server program should have at least 2 blocks");
@@ -121,49 +107,24 @@ void ListenAndServOp::RunSyncLoop(framework::Executor *executor,
       optimize_prepared.begin(),
       std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
 
-  bool exit_flag = false;
+  rpc_service_->ResetBarrierCounter();
   // Record received sparse variables, so that
   // we could reset those after execute optimize program
   std::vector<framework::Variable *> sparse_vars;
-  while (!exit_flag && !SignalHandler::IsProgramExit()) {
+  while (true) {
     // Get from multiple trainers, we don't care about the order in which
     // the gradients arrives, just add suffix 0~n and merge the gradient.
-    rpc_service_->SetCond(0);
-    size_t recv_var_cnt = 0;
-    int batch_barrier = 0;
-    while (batch_barrier != fan_in) {
-      const detail::ReceivedMessage v = rpc_service_->Get();
-      auto recv_var_name = v.first;
-      if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
-        LOG(INFO) << "received terminate message and exit";
-        exit_flag = true;
-        break;
-      } else if (recv_var_name == BATCH_BARRIER_MESSAGE) {
-        VLOG(3) << "recv batch barrier message";
-        batch_barrier++;
-        continue;
-      } else {
-        VLOG(3) << "received grad: " << recv_var_name;
-        recv_var_cnt++;
-        auto var = v.second->GetVar();
-        if (var == nullptr) {
-          LOG(ERROR) << "Can not find server side var: " << recv_var_name;
-          PADDLE_THROW("Can not find server side var");
-        }
-        if (var->IsType<framework::SelectedRows>()) {
-          sparse_vars.push_back(var);
-        }
-      }
-    }
-    if (exit_flag) {
-      rpc_service_->SetCond(1);
-      rpc_service_->ShutDown();
+    rpc_service_->SetCond(detail::kRequestSend);
+    rpc_service_->WaitBarrier(detail::kRequestSend);
+
+    if (rpc_service_->IsExit()) {
+      LOG(WARNING) << "get exit!rpc_processor break!";
+      rpc_service_->SetCond(detail::kRequestGet);
       break;
     }
 
     // NOTE: if is_gpu_place, CUDA kernels are launched by multiple threads
     // and this will still work.
-
     // The optimize blocks which have the same parent ID would run parallel
     // TODO(Yancey1989): need to use ParallelExecutor for future
     int32_t last_parent_blkid = program->Block(1).Parent();
@@ -194,52 +155,18 @@ void ListenAndServOp::RunSyncLoop(framework::Executor *executor,
       var->GetMutable<framework::SelectedRows>()->mutable_rows()->clear();
     }
 
-    rpc_service_->SetCond(1);
-    // FIXME(typhoonzero): use another condition to sync wait clients get.
-    rpc_service_->WaitClientGet(fan_in);
-    sparse_vars.clear();
+    rpc_service_->SetCond(detail::kRequestGet);
+    rpc_service_->WaitBarrier(detail::kRequestGet);
+    rpc_service_->ResetBarrierCounter();
   }  // while(true)
 }
 
-static void AsyncUpdateThread(
-    const std::string &var_name, const bool &exit_flag,
-    const std::shared_ptr<detail::ReceivedQueue> &queue,
-    framework::Executor *executor,
-    framework::ExecutorPrepareContext *prepared) {
-  VLOG(3) << "update thread for " << var_name << " started";
-  while (!exit_flag && !SignalHandler::IsProgramExit()) {
-    const detail::ReceivedMessage v = queue->Pop();
-    if (SignalHandler::IsProgramExit()) {
-      VLOG(3) << "update thread for " << var_name << " exit";
-      break;
-    }
-    auto recv_var_name = v.first;
-    VLOG(4) << "async update " << recv_var_name;
-    auto var = v.second->GetVar();
-    if (var == nullptr) {
-      LOG(ERROR) << "Can not find server side var: " << recv_var_name;
-      PADDLE_THROW("Can not find server side var");
-    }
-    auto fs = framework::Async([var_name, &executor, &v, prepared] {
-      try {
-        executor->RunPreparedContext(prepared,
-                                     v.second->GetMutableLocalScope());
-      } catch (const std::exception &e) {
-        LOG(ERROR) << "run sub program error " << e.what();
-      }
-    });
-    fs.wait();
-  }
-}
-
 void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
                                    framework::ProgramDesc *program) const {
   VLOG(3) << "RunAsyncLoop in";
   // grad name to block id
   std::unordered_map<std::string, int32_t> grad_to_block_id;
   std::unordered_map<int32_t, std::string> id_to_grad;
-  std::unordered_map<std::string, std::shared_ptr<detail::ReceivedQueue>>
-      grad_to_queue;
 
   auto grad_to_block_id_str =
       Attr<std::vector<std::string>>("grad_to_block_id");
@@ -249,13 +176,9 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
     VLOG(3) << "after split, grad = " << pieces[0] << ", id=" << pieces[1];
     PADDLE_ENFORCE_EQ(pieces.size(), 2);
     PADDLE_ENFORCE_EQ(grad_to_block_id.count(pieces[0]), 0);
+
     int block_id = std::stoi(pieces[1]);
     grad_to_block_id[pieces[0]] = block_id;
-    std::shared_ptr<detail::ReceivedQueue> queue =
-        std::make_shared<detail::ReceivedQueue>();
-    grad_to_queue[pieces[0]] = queue;
-    // record blocking queue in SignalHandler
-    SignalHandler::RegisterBlockingQueue(queue);
     id_to_grad[block_id] = pieces[0];
   }
   size_t num_blocks = program->Size();
@@ -274,39 +197,36 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
     grad_to_prepared_ctx[id_to_grad[block_list[i]]] = optimize_prepared[i];
   }
 
-  bool exit_flag = false;
+  request_send_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
+  request_get_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
+  request_prefetch_handler_->SetGradToPreparedCtx(&grad_to_prepared_ctx);
 
-  VLOG(3) << "start async optimize threads";
-  std::vector<std::future<void>> fs;
-  for (auto iter = grad_to_queue.begin(); iter != grad_to_queue.end(); iter++) {
-    std::string grad_name = iter->first;
-    VLOG(3) << "create async update thread for " << grad_name;
-    fs.push_back(framework::AsyncIO([grad_name, &exit_flag, &executor,
-                                     &grad_to_queue, &grad_to_prepared_ctx]() {
-      AsyncUpdateThread(grad_name, exit_flag, grad_to_queue[grad_name],
-                        executor, grad_to_prepared_ctx[grad_name].get());
-    }));
-  }
   VLOG(3) << "RunAsyncLoop into while";
-  while (!exit_flag && !SignalHandler::IsProgramExit()) {
-    const detail::ReceivedMessage v = rpc_service_->Get();
-    auto recv_var_name = v.first;
-    if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
-      LOG(INFO) << "received terminate message and exit";
-      exit_flag = true;
+  while (true) {
+    if (rpc_service_->IsExit()) {
+      LOG(INFO) << "get exit!rpc_processor break!";
       break;
-    } else {
-      VLOG(3) << "received grad: " << recv_var_name;
-      grad_to_queue[recv_var_name]->Push(v);
     }
 
-    if (exit_flag) {
-      rpc_service_->ShutDown();
-      break;
-    }
+    sleep(1);
   }  // while(true)
 }
 
+static void FillRequestCtx(detail::RequestHandler *h, framework::Scope *scope,
+                           platform::DeviceContext *dev_ctx,
+                           framework::Executor *executor,
+                           framework::ProgramDesc *program,
+                           framework::ExecutorPrepareContext *prefetch_ctx,
+                           detail::RPCServer *rpc_server) {
+  h->SetScope(scope);
+  h->SetDevCtx(dev_ctx);
+  h->SetExecutor(executor);
+  h->SetProgram(program);
+  h->SetPrefetchPreparedCtx(
+      std::unique_ptr<framework::ExecutorPrepareContext>(prefetch_ctx));
+  h->SetRPCServer(rpc_server);
+}
+
 void ListenAndServOp::RunImpl(const framework::Scope &scope,
                               const platform::Place &dev_place) const {
   // Mark this as PS that it should decide profiling by listening from trainer.
@@ -316,27 +236,42 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
   framework::Scope &recv_scope = scope.NewScope();
 
   bool sync_mode = Attr<bool>("sync_mode");
+  auto fan_in = Attr<int>("Fanin");
 
   PADDLE_ENFORCE(!rpc_service_);
   std::string endpoint = Attr<std::string>("endpoint");
 
-  rpc_service_.reset(new detail::AsyncGRPCServer(endpoint, sync_mode));
+  LOG(INFO) << "sync_mode:" << sync_mode << ", fan_in:" << fan_in
+            << ", end_point:" << endpoint;
+
+  // request_handler_.reset(new detail::GRPCRequestSendHandler(sync_mode));
+  rpc_service_.reset(new detail::AsyncGRPCServer(endpoint, fan_in));
+  request_send_handler_.reset(new detail::RequestSendHandler(sync_mode));
+  request_get_handler_.reset(new detail::RequestGetHandler(sync_mode));
+  request_prefetch_handler_.reset(
+      new detail::RequestPrefetchHandler(sync_mode));
+
+  rpc_service_->RegisterRPC(detail::kRequestSend, request_send_handler_.get());
+  rpc_service_->RegisterRPC(detail::kRequestGet, request_get_handler_.get());
+  rpc_service_->RegisterRPC(detail::kRequestPrefetch,
+                            request_prefetch_handler_.get());
 
   auto *optimize_block = Attr<framework::BlockDesc *>(kOptimizeBlock);
   auto *prefetch_block = Attr<framework::BlockDesc *>(kPrefetchBlock);
   auto *program = optimize_block->Program();
   framework::Executor executor(dev_place);
 
-  // prepare rpc_service
-  rpc_service_->SetScope(&recv_scope);
-  rpc_service_->SetDevCtx(&dev_ctx);
-  rpc_service_->SetProgram(program);
-  rpc_service_->SetExecutor(&executor);
-
   // prepare for prefetch
   VLOG(3) << "prefetch block id is " << prefetch_block->ID();
   auto prefetch_prepared = executor.Prepare(*program, prefetch_block->ID());
-  rpc_service_->SetPrefetchPreparedCtx(std::move(prefetch_prepared));
+
+  auto f = std::bind(FillRequestCtx, std::placeholders::_1, &recv_scope,
+                     &dev_ctx, &executor, program, prefetch_prepared.release(),
+                     rpc_service_.get());
+
+  f(request_send_handler_.get());
+  f(request_get_handler_.get());
+  f(request_prefetch_handler_.get());
 
   // start the server listening after all member initialized.
   server_thread_.reset(new std::thread(RunServer, rpc_service_));
@@ -348,8 +283,6 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
   signal(SIGTERM, SignalHandler::StopAndExit);
 
   // Write to a file of server selected port for python use.
-  std::string file_path = string::Sprintf("/tmp/paddle.%d.selected_port",
-                                          static_cast<int>(::getpid()));
   SavePort();
   if (sync_mode) {
     RunSyncLoop(&executor, program, &recv_scope, prefetch_block);
@@ -385,27 +318,9 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
-bool SignalHandler::program_exit_flag_ = false;
-
-SignalHandler::BlockingQueueSet SignalHandler::blocking_queue_set_{};
-
 void SignalHandler::StopAndExit(int signal_num) {
   VLOG(3) << "Catch interrupt signal: " << signal_num << ", program will exit";
-
-  program_exit_flag_ = true;
-
-  // awake all blocking queues
-  for (BlockingQueueSet::iterator iter = blocking_queue_set_.begin();
-       iter != blocking_queue_set_.end(); iter++) {
-    iter->get()->Push(
-        std::make_pair(std::string(LISTEN_TERMINATE_MESSAGE), nullptr));
-  }
-
-  exit(EXIT_SUCCESS);
-}
-
-void SignalHandler::RegisterBlockingQueue(BlockingQueue &queue) {
-  blocking_queue_set_.insert(queue);
+  exit(0);
 }
 
 }  // namespace operators

paddle/fluid/operators/listen_and_serv_op.h

@@ -23,7 +23,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/threadpool.h"
-#include "paddle/fluid/operators/detail/grpc_server.h"
+#include "paddle/fluid/operators/detail/request_handler.h"
+#include "paddle/fluid/operators/detail/rpc_server.h"
 
 namespace paddle {
 namespace operators {
@@ -31,7 +32,7 @@ namespace operators {
 constexpr char kOptimizeBlock[] = "OptimizeBlock";
 constexpr char kPrefetchBlock[] = "PrefetchBlock";
 
-void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service);
+void RunServer(std::shared_ptr<detail::RPCServer> service);
 
 class ListenAndServOp : public framework::OperatorBase {
  public:
@@ -52,41 +53,27 @@ class ListenAndServOp : public framework::OperatorBase {
 
   void SavePort() const;
 
-  void WaitServerReady();
-
-  int GetSelectedPort() { return selected_port_; }
+  int GetSelectedPort() { return rpc_service_->GetSelectedPort(); }
 
   void Stop() override;
 
   void RunImpl(const framework::Scope& scope,
                const platform::Place& dev_place) const override;
 
-  static void ResetPort() { selected_port_ = 0; }
-
  protected:
-  mutable std::shared_ptr<detail::AsyncGRPCServer> rpc_service_;
+  mutable std::shared_ptr<detail::RPCServer> rpc_service_;
+  mutable std::shared_ptr<detail::RequestHandler> request_send_handler_;
+  mutable std::shared_ptr<detail::RequestHandler> request_get_handler_;
+  mutable std::shared_ptr<detail::RequestHandler> request_prefetch_handler_;
+
   mutable std::shared_ptr<std::thread> server_thread_;
-  // FIXME(wuyi): it's static so that the operator can be cloned.
-  static std::atomic_int selected_port_;
 };
 
 class SignalHandler {
- public:
-  typedef std::shared_ptr<detail::ReceivedQueue> BlockingQueue;
-  typedef std::unordered_set<BlockingQueue> BlockingQueueSet;
-
  public:
   static void StopAndExit(int signal_num);
 
-  static void RegisterBlockingQueue(BlockingQueue&);
-
-  static inline bool IsProgramExit() { return program_exit_flag_; }
-
  private:
-  static bool program_exit_flag_;
-
-  static BlockingQueueSet blocking_queue_set_;
-
   DISABLE_COPY_AND_ASSIGN(SignalHandler);
 };
 

paddle/fluid/operators/pool_cudnn_op.cu.cc

@@ -135,7 +135,11 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
 
     PoolingMode pooling_mode;
     if (pooling_type == "max") {
+      if (FLAGS_cudnn_deterministic) {
+        pooling_mode = PoolingMode::kMaximumDeterministic;
+      } else {
         pooling_mode = PoolingMode::kMaximum;
+      }
     } else {
       pooling_mode = PoolingMode::kAverage;
     }

paddle/fluid/operators/prefetch_op.cc

@@ -53,7 +53,7 @@ class PrefetchOp : public framework::OperatorBase {
         VLOG(3) << "don't send no-initialied variable: " << ins[i];
       }
     }
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
   }
 };
 

paddle/fluid/operators/reader/open_files_op.cc

@@ -26,7 +26,11 @@ class MultiFileReader : public framework::ReaderBase {
   MultiFileReader(const std::vector<std::string>& file_names,
                   const std::vector<framework::DDim>& dims, size_t thread_num,
                   size_t buffer_size)
-      : file_names_(file_names), dims_(dims), buffer_size_(buffer_size) {
+      : buffer_size_(buffer_size) {
+    readers_.reserve(file_names.size());
+    for (const std::string& f_name : file_names) {
+      readers_.emplace_back(CreateReaderByFileName(f_name, dims));
+    }
     prefetchers_.resize(thread_num);
     StartNewScheduler();
   }
@@ -40,14 +44,13 @@ class MultiFileReader : public framework::ReaderBase {
   void StartNewScheduler();
   void EndScheduler();
   void ScheduleThreadFunc();
-  void PrefetchThreadFunc(std::string file_name, size_t thread_idx);
+  void PrefetchThreadFunc(size_t reader_idx, size_t thread_idx);
 
-  std::vector<std::string> file_names_;
-  std::vector<framework::DDim> dims_;
+  std::vector<std::unique_ptr<framework::ReaderBase>> readers_;
   std::thread scheduler_;
   std::vector<std::thread> prefetchers_;
   size_t buffer_size_;
-  reader::BlockingQueue<size_t>* waiting_file_idx_;
+  reader::BlockingQueue<size_t>* waiting_reader_idx_;
   reader::BlockingQueue<size_t>* available_thread_idx_;
   reader::BlockingQueue<std::vector<framework::LoDTensor>>* buffer_;
 };
@@ -65,15 +68,15 @@ void MultiFileReader::ReInit() {
 
 void MultiFileReader::StartNewScheduler() {
   size_t thread_num = prefetchers_.size();
-  waiting_file_idx_ = new reader::BlockingQueue<size_t>(file_names_.size());
+  waiting_reader_idx_ = new reader::BlockingQueue<size_t>(readers_.size());
   available_thread_idx_ = new reader::BlockingQueue<size_t>(thread_num);
   buffer_ = new reader::BlockingQueue<std::vector<framework::LoDTensor>>(
       buffer_size_);
 
-  for (size_t i = 0; i < file_names_.size(); ++i) {
-    waiting_file_idx_->Send(i);
+  for (size_t i = 0; i < readers_.size(); ++i) {
+    waiting_reader_idx_->Send(i);
   }
-  waiting_file_idx_->Close();
+  waiting_reader_idx_->Close();
   for (size_t i = 0; i < thread_num; ++i) {
     available_thread_idx_->Send(i);
   }
@@ -84,13 +87,13 @@ void MultiFileReader::StartNewScheduler() {
 void MultiFileReader::EndScheduler() {
   available_thread_idx_->Close();
   buffer_->Close();
-  waiting_file_idx_->Close();
+  waiting_reader_idx_->Close();
   if (scheduler_.joinable()) {
     scheduler_.join();
   }
   delete buffer_;
   delete available_thread_idx_;
-  delete waiting_file_idx_;
+  delete waiting_reader_idx_;
 }
 
 void MultiFileReader::ScheduleThreadFunc() {
@@ -102,12 +105,11 @@ void MultiFileReader::ScheduleThreadFunc() {
     if (prefetcher.joinable()) {
       prefetcher.join();
     }
-    size_t file_idx;
-    if (waiting_file_idx_->Receive(&file_idx)) {
+    size_t reader_idx;
+    if (waiting_reader_idx_->Receive(&reader_idx)) {
       // Still have files to read. Start a new prefetch thread.
-      std::string file_name = file_names_[file_idx];
-      prefetcher = std::thread([this, file_name, thread_idx] {
-        PrefetchThreadFunc(file_name, thread_idx);
+      prefetcher = std::thread([this, reader_idx, thread_idx] {
+        PrefetchThreadFunc(reader_idx, thread_idx);
       });
     } else {
       // No more file to read.
@@ -129,23 +131,22 @@ void MultiFileReader::ScheduleThreadFunc() {
   VLOG(5) << "MultiFileReader schedule thread terminates.";
 }
 
-void MultiFileReader::PrefetchThreadFunc(std::string file_name,
-                                         size_t thread_idx) {
-  VLOG(5) << "The prefetch thread of file '" << file_name << "' starts.";
-  std::unique_ptr<framework::ReaderBase> reader =
-      CreateReaderByFileName(file_name, dims_);
+void MultiFileReader::PrefetchThreadFunc(size_t reader_idx, size_t thread_idx) {
+  VLOG(5) << "The prefetch thread of file idx '" << reader_idx << "' starts.";
+  std::unique_ptr<framework::ReaderBase>& reader = readers_[reader_idx];
   while (true) {
     std::vector<framework::LoDTensor> ins;
     reader->ReadNext(&ins);
     if (ins.empty()) {
+      reader->ReInit();
       break;
     }
     try {
       buffer_->Send(std::move(ins));
     } catch (paddle::platform::EnforceNotMet e) {
       VLOG(5) << "WARNING: The buffer channel has been closed. The prefetch "
-                 "thread of file '"
-              << file_name << "' will terminate.";
+                 "thread of file idx '"
+              << reader_idx << "' will terminate.";
       break;
     }
   }
@@ -154,7 +155,8 @@ void MultiFileReader::PrefetchThreadFunc(std::string file_name,
     VLOG(5) << "WARNING: The available_thread_idx_ channel has been closed. "
                "Fail to send thread_idx.";
   }
-  VLOG(5) << "The prefetch thread of file '" << file_name << "' terminates.";
+  VLOG(5) << "The prefetch thread of file idx '" << reader_idx
+          << "' terminates.";
 }
 
 class OpenFilesOp : public framework::OperatorBase {

paddle/fluid/operators/recv_op.cc

@@ -51,7 +51,7 @@ class RecvOp : public framework::OperatorBase {
       rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
     }
     if (sync_mode) {
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
     }
   }
 };

paddle/fluid/operators/reduce_op.h

@@ -135,15 +135,16 @@ class ReduceKernel : public framework::OpKernel<T> {
     } else {
       int ndim = context.Input<Tensor>("X")->dims().size();
       int rdim = context.Attr<std::vector<int>>("dim").size();
-      HANDLE_DIM(6, 5);
-      HANDLE_DIM(6, 4);
-      HANDLE_DIM(6, 3);
-      HANDLE_DIM(6, 2);
-      HANDLE_DIM(6, 1);
-      HANDLE_DIM(5, 4);
-      HANDLE_DIM(5, 3);
-      HANDLE_DIM(5, 2);
-      HANDLE_DIM(5, 1);
+      // comments for accelerating compiling temporarily.
+      //      HANDLE_DIM(6, 5);
+      //      HANDLE_DIM(6, 4);
+      //      HANDLE_DIM(6, 3);
+      //      HANDLE_DIM(6, 2);
+      //      HANDLE_DIM(6, 1);
+      //      HANDLE_DIM(5, 4);
+      //      HANDLE_DIM(5, 3);
+      //      HANDLE_DIM(5, 2);
+      //      HANDLE_DIM(5, 1);
       HANDLE_DIM(4, 3);
       HANDLE_DIM(4, 2);
       HANDLE_DIM(4, 1);

paddle/fluid/operators/send_barrier_op.cc

@@ -46,14 +46,16 @@ class SendBarrierOp : public framework::OperatorBase {
 
     auto rpc_client = detail::RPCClient::GetInstance();
 
+    VLOG(3) << "SendBarrierOp sync_mode:" << sync_mode;
+
     // need to wait before sending send_barrier message
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
     if (sync_mode) {
       for (auto& ep : eps) {
         VLOG(3) << "send barrier, ep: " << ep;
         rpc_client->AsyncSendBatchBarrier(ep);
       }
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
     }
   }
 };

paddle/fluid/operators/send_op.cc

@@ -59,14 +59,14 @@ class SendOp : public framework::OperatorBase {
         VLOG(3) << "don't send no-initialied variable: " << ins[i];
       }
     }
-    PADDLE_ENFORCE(rpc_client->Wait());
+    rpc_client->Wait();
 
     if (sync_mode) {
       for (auto& ep : endpoints) {
         VLOG(3) << "batch barrier, ep: " << ep;
         rpc_client->AsyncSendBatchBarrier(ep);
       }
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
     }
 
     if (outs.size() > 0) {
@@ -74,13 +74,13 @@ class SendOp : public framework::OperatorBase {
         VLOG(2) << "getting " << outs[i] << " from " << epmap[i];
         rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
       }
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
       // tell pservers that current trainer have called fetch
       for (auto& ep : endpoints) {
         VLOG(2) << "send fetch barrier, ep: " << ep;
         rpc_client->AsyncSendFetchBarrier(ep);
       }
-      PADDLE_ENFORCE(rpc_client->Wait());
+      rpc_client->Wait();
     }
   }
 };

paddle/fluid/operators/sgd_op.h

@@ -114,7 +114,7 @@ class SGDOpKernel : public framework::OpKernel<T> {
         int64_t id_index = param.Index(grad.rows()[i]);
         PADDLE_ENFORCE_GE(id_index, static_cast<int64_t>(0),
                           "id should be in the table");
-        for (size_t j = 0; j < grad_row_width; j++) {
+        for (int64_t j = 0; j < grad_row_width; j++) {
           out_data[id_index * grad_row_width + j] -=
               lr[0] * grad_data[i * grad_row_width + j];
         }

paddle/fluid/operators/shape_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/shape_op.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+class ShapeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input (Input) of get_shape op should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output (Out) of get_shape op should not be null.");
+    auto in_dim = ctx->GetInputDim("Input");
+    ctx->SetOutputDim("Out", {in_dim.size()});
+  }
+};
+
+class ShapeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Input", "(Tensor), The input tensor.");
+    AddOutput("Out", "(Tensor), The shape of input tensor.");
+    AddComment(R"DOC(
+Shape Operator. 
+Get the shape of input tensor.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(shape, ops::ShapeOp, ops::ShapeOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<int>, ops::ShapeKernel<int64_t>,
+                       ops::ShapeKernel<float>, ops::ShapeKernel<double>);

paddle/fluid/operators/shape_op.cu

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/fluid/operators/shape_op.h"
+
+REGISTER_OP_CUDA_KERNEL(shape, paddle::operators::ShapeKernel<int>,
+                        paddle::operators::ShapeKernel<int64_t>,
+                        paddle::operators::ShapeKernel<float>,
+                        paddle::operators::ShapeKernel<double>);

paddle/fluid/operators/shape_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include <algorithm>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class ShapeKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in_t = ctx.Input<Tensor>("Input");
+    auto* out_t = ctx.Output<Tensor>("Out");
+    auto out_data = out_t->mutable_data<int64_t>(platform::CPUPlace());
+    auto in_dims = in_t->dims();
+    for (int i = 0; i < in_dims.size(); ++i) {
+      out_data[i] = in_dims[i];
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/tensorrt_engine_op.cc

@@ -31,8 +31,9 @@ void paddle::operators::TensorRTEngineKernel<DeviceContext, T>::Prepare(
   auto max_workspace = context.Attr<int>("max_workspace");
   engine_.reset(new inference::tensorrt::TensorRTEngine(
       max_batch_, max_workspace, nullptr));
+  // TODO(Superjomn) parameters should be passed after analysised from outside.
   inference::Singleton<inference::tensorrt::OpConverter>::Global().ConvertBlock(
-      block, engine_.get());
+      block, {}, context.scope(), engine_.get());
   engine_->FreezeNetwork();
 }
 

paddle/fluid/operators/test_send_nccl_id.cc

@@ -21,6 +21,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/operators/detail/grpc_client.h"
+#include "paddle/fluid/operators/detail/grpc_server.h"
+#include "paddle/fluid/operators/detail/request_handler_impl.h"
 #include "paddle/fluid/operators/listen_and_serv_op.h"
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/operators/math/selected_rows_functor.h"
@@ -35,42 +37,43 @@ namespace m = paddle::operators::math;
 namespace detail = paddle::operators::detail;
 namespace string = paddle::string;
 
-std::unique_ptr<detail::AsyncGRPCServer> rpc_service;
+std::unique_ptr<detail::AsyncGRPCServer> g_rpc_service;
+std::unique_ptr<detail::RequestHandler> g_req_handler;
 
-void StartServer(std::atomic<bool>* initialized) {
+void StartServer() {
   f::Scope scope;
   p::CPUPlace place;
   scope.Var(NCCL_ID_VARNAME);
   p::DeviceContextPool& pool = p::DeviceContextPool::Instance();
   auto& dev_ctx = *pool.Get(p::CPUPlace());
 
-  rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", true));
-
   f::ProgramDesc empty_program;
   f::Executor executor(dev_ctx.GetPlace());
-  rpc_service->SetScope(&scope);
-  rpc_service->SetDevCtx(&dev_ctx);
-  rpc_service->SetProgram(&empty_program);
-  rpc_service->SetExecutor(&executor);
+  g_req_handler->SetScope(&scope);
+  g_req_handler->SetDevCtx(&dev_ctx);
+  g_req_handler->SetProgram(&empty_program);
+  g_req_handler->SetExecutor(&executor);
+
+  g_rpc_service->RegisterRPC(detail::kRequestSend, g_req_handler.get());
+  g_req_handler->SetRPCServer(g_rpc_service.get());
 
   std::thread server_thread(
-      std::bind(&detail::AsyncGRPCServer::RunSyncUpdate, rpc_service.get()));
-  *initialized = true;
-  rpc_service->SetCond(0);
-  auto recv = rpc_service->Get();
+      std::bind(&detail::AsyncGRPCServer::StartServer, g_rpc_service.get()));
+
+  g_rpc_service->SetCond(detail::kRequestSend);
+  g_rpc_service->WaitBarrier(detail::kRequestSend);
+
   LOG(INFO) << "got nccl id and stop server...";
-  rpc_service->ShutDown();
+  g_rpc_service->ShutDown();
   server_thread.join();
 }
 
-TEST(SendNcclId, DISABLED_Normal) {
-  std::atomic<bool> initialized{false};
-  std::thread server_thread(StartServer, &initialized);
-  while (!initialized) {
-  }
-  // wait server to start
-  // sleep(2);
-  rpc_service->WaitServerReady();
+TEST(SendNcclId, GrpcServer) {
+  g_req_handler.reset(new detail::RequestSendHandler(true));
+  g_rpc_service.reset(new detail::AsyncGRPCServer("127.0.0.1:0", 1));
+
+  std::thread server_thread(StartServer);
+  g_rpc_service->WaitServerReady();
 
   f::Scope scope;
   p::CPUPlace place;
@@ -78,17 +81,20 @@ TEST(SendNcclId, DISABLED_Normal) {
   auto& dev_ctx = *pool.Get(p::CPUPlace());
 
   auto var = scope.Var(NCCL_ID_VARNAME);
-  // var->SetType(f::proto::VarType_Type_RAW);
   auto id = var->GetMutable<ncclUniqueId>();
   p::dynload::ncclGetUniqueId(id);
 
-  int port = rpc_service->GetSelectedPort();
+  int port = g_rpc_service->GetSelectedPort();
+
   std::string ep = string::Sprintf("127.0.0.1:%d", port);
-  detail::RPCClient client;
+  detail::RPCClient* client = detail::RPCClient::GetInstance();
+  LOG(INFO) << "connect to server " << ep;
+  client->AsyncSendVariable(ep, dev_ctx, scope, NCCL_ID_VARNAME);
+  client->Wait();
+  client->AsyncSendBatchBarrier(ep);
+  client->Wait();
 
-  client.AsyncSendVariable(ep, dev_ctx, scope, NCCL_ID_VARNAME);
-  client.Wait();
   server_thread.join();
-  auto* ptr = rpc_service.release();
-  delete ptr;
+  g_rpc_service.reset(nullptr);
+  g_req_handler.reset(nullptr);
 }

paddle/fluid/platform/cudnn_helper.h

@@ -22,6 +22,8 @@ limitations under the License. */
 #include "paddle/fluid/platform/float16.h"
 #include "paddle/fluid/platform/macros.h"
 
+DECLARE_bool(cudnn_deterministic);
+
 namespace paddle {
 namespace platform {
 
@@ -76,8 +78,22 @@ enum class DataLayout {  // Not use
 enum class PoolingMode {
   kMaximum,
   kAverage,
+  kMaximumDeterministic,
 };
 
+inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
+  switch (mode) {
+    case PoolingMode::kMaximumDeterministic:
+      return CUDNN_POOLING_MAX_DETERMINISTIC;
+    case PoolingMode::kAverage:
+      return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
+    case PoolingMode::kMaximum:
+      return CUDNN_POOLING_MAX;
+    default:
+      PADDLE_THROW("Unexpected pooling mode.");
+  }
+}
+
 template <typename T>
 class CudnnDataType;
 
@@ -293,9 +309,7 @@ class ScopedPoolingDescriptor {
     PADDLE_ENFORCE_EQ(kernel.size(), pads.size());
     PADDLE_ENFORCE_EQ(kernel.size(), strides.size());
     PADDLE_ENFORCE(dynload::cudnnSetPoolingNdDescriptor(
-        desc_, (mode == PoolingMode::kMaximum
-                    ? CUDNN_POOLING_MAX
-                    : CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING),
+        desc_, (GetPoolingMode(mode)),
         CUDNN_PROPAGATE_NAN,  // Always propagate nans.
         kernel.size(), kernel.data(), pads.data(), strides.data()));
     return desc_;

paddle/fluid/platform/device_context.cc

@@ -175,7 +175,6 @@ CUDADeviceContext::~CUDADeviceContext() {
 Place CUDADeviceContext::GetPlace() const { return place_; }
 
 void CUDADeviceContext::Wait() const {
-  std::lock_guard<std::recursive_mutex> guard(mutex_);
   PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
   PADDLE_ENFORCE(cudaGetLastError());
 }

paddle/fluid/platform/device_context.h

@@ -100,7 +100,6 @@ class CUDADeviceContext : public DeviceContext {
 
   template <typename Callback>
   void RecordEvent(cudaEvent_t ev, Callback callback) {
-    std::lock_guard<std::recursive_mutex> guard(mutex_);
     callback();
     PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
   }
@@ -110,8 +109,6 @@ class CUDADeviceContext : public DeviceContext {
 
   std::unique_ptr<Eigen::GpuDevice> eigen_device_;
   std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
-
-  mutable std::recursive_mutex mutex_;
   cudaStream_t stream_;
   cudnnHandle_t cudnn_handle_;
   cublasHandle_t cublas_handle_;

paddle/fluid/platform/dynload/cublas.h

@@ -45,7 +45,7 @@ extern void *cublas_dso_handle;
       std::call_once(cublas_dso_flag, []() {                                 \
         cublas_dso_handle = paddle::platform::dynload::GetCublasDsoHandle(); \
       });                                                                    \
-      void *p_##__name = dlsym(cublas_dso_handle, #__name);                  \
+      static void *p_##__name = dlsym(cublas_dso_handle, #__name);           \
       return reinterpret_cast<FUNC_TYPE>(p_##__name)(args...);               \
     }                                                                        \
   };                                                                         \

paddle/fluid/platform/dynload/cudnn.h

@@ -39,7 +39,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
         cudnn_dso_handle = paddle::platform::dynload::GetCUDNNDsoHandle(); \
       });                                                                  \
       EnforceCUDNNLoaded(#__name);                                         \
-      void* p_##__name = dlsym(cudnn_dso_handle, #__name);                 \
+      static void* p_##__name = dlsym(cudnn_dso_handle, #__name);          \
       return reinterpret_cast<cudnn_func>(p_##__name)(args...);            \
     }                                                                      \
   };                                                                       \

paddle/fluid/platform/dynload/cupti.h

@@ -45,7 +45,7 @@ extern void *cupti_dso_handle;
       std::call_once(cupti_dso_flag, []() {                                \
         cupti_dso_handle = paddle::platform::dynload::GetCUPTIDsoHandle(); \
       });                                                                  \
-      void *p_##__name = dlsym(cupti_dso_handle, #__name);                 \
+      static void *p_##__name = dlsym(cupti_dso_handle, #__name);          \
       return reinterpret_cast<cuptiFunc>(p_##__name)(args...);             \
     }                                                                      \
   };                                                                       \

paddle/fluid/platform/dynload/curand.h

@@ -34,7 +34,7 @@ extern void *curand_dso_handle;
       std::call_once(curand_dso_flag, []() {                                 \
         curand_dso_handle = paddle::platform::dynload::GetCurandDsoHandle(); \
       });                                                                    \
-      void *p_##__name = dlsym(curand_dso_handle, #__name);                  \
+      static void *p_##__name = dlsym(curand_dso_handle, #__name);           \
       return reinterpret_cast<curandFunc>(p_##__name)(args...);              \
     }                                                                        \
   };                                                                         \

paddle/fluid/platform/dynload/nccl.h

@@ -37,7 +37,7 @@ extern void* nccl_dso_handle;
       std::call_once(nccl_dso_flag, []() {                               \
         nccl_dso_handle = paddle::platform::dynload::GetNCCLDsoHandle(); \
       });                                                                \
-      void* p_##__name = dlsym(nccl_dso_handle, #__name);                \
+      static void* p_##__name = dlsym(nccl_dso_handle, #__name);         \
       return reinterpret_cast<nccl_func>(p_##__name)(args...);           \
     }                                                                    \
   };                                                                     \

paddle/fluid/platform/dynload/tensorrt.h

@@ -40,7 +40,7 @@ extern void* tensorrt_dso_handle;
             paddle::platform::dynload::GetTensorRtDsoHandle();          \
         PADDLE_ENFORCE(tensorrt_dso_handle, "load tensorrt so failed"); \
       });                                                               \
-      void* p_##__name = dlsym(tensorrt_dso_handle, #__name);           \
+      static void* p_##__name = dlsym(tensorrt_dso_handle, #__name);    \
       PADDLE_ENFORCE(p_##__name, "load %s failed", #__name);            \
       return reinterpret_cast<tensorrt_func>(p_##__name)(args...);      \
     }                                                                   \

paddle/fluid/platform/dynload/warpctc.h

@@ -40,7 +40,7 @@ extern void* warpctc_dso_handle;
       std::call_once(warpctc_dso_flag, []() {                                  \
         warpctc_dso_handle = paddle::platform::dynload::GetWarpCTCDsoHandle(); \
       });                                                                      \
-      void* p_##_name = dlsym(warpctc_dso_handle, #__name);                    \
+      static void* p_##_name = dlsym(warpctc_dso_handle, #__name);             \
       return reinterpret_cast<warpctcFunc>(p_##_name)(args...);                \
     }                                                                          \
   };                                                                           \

paddle/fluid/platform/nccl_helper.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include <stdio.h>
+#include <string>
 #include <thread>  // NOLINT
 #include <typeindex>
 #include <vector>

paddle/fluid/platform/profiler.cc

@@ -127,6 +127,7 @@ double Event::CpuElapsedMs(const Event& e) const {
 
 double Event::CudaElapsedMs(const Event& e) const {
 #ifdef PADDLE_WITH_CUDA
+  if (!has_cuda_) return 0.0;
   PADDLE_ENFORCE(e.has_cuda() && has_cuda());
   PADDLE_ENFORCE(e.device() == device());
   PADDLE_ENFORCE(cudaEventSynchronize(event_));

paddle/fluid/pybind/pybind.cc

@@ -519,6 +519,14 @@ All parameter, weight, gradient are variables in Paddle.
           [](const ExecutionStrategy &self) { return self.allow_op_delay_; },
           [](ExecutionStrategy &self, bool allow_op_delay) {
             self.allow_op_delay_ = allow_op_delay;
+          })
+      .def_property(
+          "num_iteration_per_drop_scope",
+          [](const ExecutionStrategy &self) {
+            return self.num_iteration_per_drop_scope_;
+          },
+          [](ExecutionStrategy &self, size_t num_iteration_per_drop_scope) {
+            self.num_iteration_per_drop_scope_ = num_iteration_per_drop_scope;
           });
   py::class_<BuildStrategy> build_strategy(pe, "BuildStrategy");
 

paddle/fluid/recordio/chunk.cc

@@ -119,40 +119,56 @@ bool Chunk::Write(std::ostream& os, Compressor ct) const {
 }
 
 bool Chunk::Parse(std::istream& sin) {
-  Header hdr;
-  bool ok = hdr.Parse(sin);
+  ChunkParser parser(sin);
+  if (!parser.Init()) {
+    return false;
+  }
+  Clear();
+  while (parser.HasNext()) {
+    Add(parser.Next());
+  }
+  return true;
+}
+
+ChunkParser::ChunkParser(std::istream& sin) : in_(sin) {}
+bool ChunkParser::Init() {
+  pos_ = 0;
+  bool ok = header_.Parse(in_);
   if (!ok) {
     return ok;
   }
-  auto beg_pos = sin.tellg();
-  uint32_t crc = Crc32Stream(sin, hdr.CompressSize());
-  PADDLE_ENFORCE_EQ(hdr.Checksum(), crc);
-  Clear();
-  sin.seekg(beg_pos, sin.beg);
-  std::unique_ptr<std::istream> compressed_stream;
-  switch (hdr.CompressType()) {
+  auto beg_pos = in_.tellg();
+  uint32_t crc = Crc32Stream(in_, header_.CompressSize());
+  PADDLE_ENFORCE_EQ(header_.Checksum(), crc);
+  in_.seekg(beg_pos, in_.beg);
+
+  switch (header_.CompressType()) {
     case Compressor::kNoCompress:
       break;
     case Compressor::kSnappy:
-      compressed_stream.reset(new snappy::iSnappyStream(sin));
+      compressed_stream_.reset(new snappy::iSnappyStream(in_));
       break;
     default:
       PADDLE_THROW("Not implemented");
   }
+  return true;
+}
 
-  std::istream& stream = compressed_stream ? *compressed_stream : sin;
+bool ChunkParser::HasNext() const { return pos_ < header_.NumRecords(); }
 
-  for (uint32_t i = 0; i < hdr.NumRecords(); ++i) {
+std::string ChunkParser::Next() {
+  if (!HasNext()) {
+    return "";
+  }
+  ++pos_;
+  std::istream& stream = compressed_stream_ ? *compressed_stream_ : in_;
   uint32_t rec_len;
   stream.read(reinterpret_cast<char*>(&rec_len), sizeof(uint32_t));
   std::string buf;
   buf.resize(rec_len);
   stream.read(&buf[0], rec_len);
   PADDLE_ENFORCE_EQ(rec_len, stream.gcount());
-    Add(buf);
-  }
-  return true;
+  return buf;
 }
-
 }  // namespace recordio
 }  // namespace paddle

paddle/fluid/recordio/chunk.h

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #pragma once
+#include <memory>
 #include <string>
 #include <vector>
 
@@ -53,9 +54,20 @@ class Chunk {
   DISABLE_COPY_AND_ASSIGN(Chunk);
 };
 
-size_t CompressData(const char* in, size_t in_length, Compressor ct, char* out);
+class ChunkParser {
+ public:
+  explicit ChunkParser(std::istream& sin);
+
+  bool Init();
+  std::string Next();
+  bool HasNext() const;
 
-void DeflateData(const char* in, size_t in_length, Compressor ct, char* out);
+ private:
+  Header header_;
+  uint32_t pos_{0};
+  std::istream& in_;
+  std::unique_ptr<std::istream> compressed_stream_;
+};
 
 }  // namespace recordio
 }  // namespace paddle

paddle/fluid/recordio/scanner.cc

@@ -22,35 +22,33 @@ namespace paddle {
 namespace recordio {
 
 Scanner::Scanner(std::unique_ptr<std::istream> &&stream)
-    : stream_(std::move(stream)) {
+    : stream_(std::move(stream)), parser_(*stream_) {
   Reset();
 }
 
-Scanner::Scanner(const std::string &filename) {
-  stream_.reset(new std::ifstream(filename));
+Scanner::Scanner(const std::string &filename)
+    : stream_(new std::ifstream(filename)), parser_(*stream_) {
   Reset();
 }
 
 void Scanner::Reset() {
   stream_->clear();
   stream_->seekg(0, std::ios::beg);
-  ParseNextChunk();
+  parser_.Init();
 }
 
 std::string Scanner::Next() {
-  PADDLE_ENFORCE(!eof_, "StopIteration");
-  auto rec = cur_chunk_.Record(offset_++);
-  if (offset_ == cur_chunk_.NumRecords()) {
-    ParseNextChunk();
+  if (stream_->eof()) {
+    return "";
   }
-  return rec;
-}
 
-void Scanner::ParseNextChunk() {
-  eof_ = !cur_chunk_.Parse(*stream_);
-  offset_ = 0;
+  auto res = parser_.Next();
+  if (!parser_.HasNext() && HasNext()) {
+    parser_.Init();
+  }
+  return res;
 }
 
-bool Scanner::HasNext() const { return !eof_; }
+bool Scanner::HasNext() const { return !stream_->eof(); }
 }  // namespace recordio
 }  // namespace paddle

paddle/fluid/recordio/scanner.h

@@ -37,11 +37,7 @@ class Scanner {
 
  private:
   std::unique_ptr<std::istream> stream_;
-  Chunk cur_chunk_;
-  size_t offset_;
-  bool eof_;
-
-  void ParseNextChunk();
+  ChunkParser parser_;
 };
 }  // namespace recordio
 }  // namespace paddle

paddle/scripts/paddle_build.sh

@@ -145,19 +145,17 @@ function check_style() {
     trap 'abort' 0
     set -e
 
-    # install glide
-    curl https://glide.sh/get | bash
+    if [ -x "$(command -v gimme)" ]; then
     	eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
+    fi
 
     # set up go environment for running gometalinter
     mkdir -p $GOPATH/src/github.com/PaddlePaddle/
     ln -sf ${PADDLE_ROOT} $GOPATH/src/github.com/PaddlePaddle/Paddle
-    cd  $GOPATH/src/github.com/PaddlePaddle/Paddle/go; glide install; cd -
-
-    go get github.com/alecthomas/gometalinter
-    gometalinter --install
+    mkdir -p ./build/go
+    cp go/glide.* build/go
+    cd build/go; glide install; cd -
 
-    cd ${PADDLE_ROOT}
     export PATH=/usr/bin:$PATH
     pre-commit install
     clang-format --version

python/paddle/batch.py

@@ -15,7 +15,7 @@
 __all__ = ['batch']
 
 
-def batch(reader, batch_size):
+def batch(reader, batch_size, drop_last=False):
     """
     Create a batched reader.
 
@@ -23,6 +23,8 @@ def batch(reader, batch_size):
     :type reader: callable
     :param batch_size: size of each mini-batch
     :type batch_size: int
+    :param drop_last: drop the last batch, if the size of last batch is not equal to batch_size.
+    :type drop_last: bool
     :return: the batched reader.
     :rtype: callable
     """
@@ -35,7 +37,7 @@ def batch(reader, batch_size):
             if len(b) == batch_size:
                 yield b
                 b = []
-        if b:
+        if drop_last == False and len(b) != 0:
             yield b
 
     return batch_reader

python/paddle/fluid/__init__.py

@@ -44,8 +44,8 @@ import transpiler
 from param_attr import ParamAttr, WeightNormParamAttr
 from data_feeder import DataFeeder
 from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace
-from transpiler import DistributeTranspiler, SimpleDistributeTranspiler, \
-    InferenceTranspiler, memory_optimize, release_memory
+from transpiler import DistributeTranspiler, InferenceTranspiler, \
+    memory_optimize, release_memory
 from concurrency import (Go, make_channel, channel_send, channel_recv,
                          channel_close, Select)
 from lod_tensor import create_lod_tensor, create_random_int_lodtensor
@@ -120,7 +120,7 @@ def __bootstrap__():
     ]
     if core.is_compiled_with_cuda():
         read_env_flags += [
-            'fraction_of_gpu_memory_to_use', 'cudnn_algo_use_autotune'
+            'fraction_of_gpu_memory_to_use', 'cudnn_deterministic'
         ]
     core.init_gflags([sys.argv[0]] +
                      ["--tryfromenv=" + ",".join(read_env_flags)])

python/paddle/fluid/layers/nn.py

@@ -82,7 +82,10 @@ __all__ = [
     'label_smooth',
     'roi_pool',
     'dice_loss',
-    'upsampling_bilinear2d',
+    'image_resize',
+    'image_resize_short',
+    'resize_bilinear',
+    'gather',
     'random_crop',
 ]
 
@@ -3917,7 +3920,6 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
 
 def dice_loss(input, label, epsilon=0.00001):
     """
-    **Dice loss Layer**
     Dice loss for comparing the similarity of two batch of data,
     usually is used for binary image segmentation i.e. labels are binary.
     The dice loss can be defined as below equation:
@@ -3957,30 +3959,35 @@ def dice_loss(input, label, epsilon=0.00001):
     return reduce_mean(dice_score)
 
 
-def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
+def image_resize(input,
+                 out_shape=None,
+                 scale=None,
+                 name=None,
+                 resample='BILINEAR'):
     """
-    The mathematical meaning of upsampling_bilinear2d is also called
-    Bilinear interpolation.
-    Bilinear interpolation is an extension of linear interpolation for
-    interpolating functions of two variables (e.g. H-direction and
-    W-direction in this layer) on a rectilinear 2D grid.
+    Resize a batch of images.
 
-    For details, please refer to Wikipedia:
-    https://en.wikipedia.org/wiki/Bilinear_interpolation
+    The input must be a tensor of the shape (num_batches, channels, in_h, in_w), 
+    and the resizing only applies on the last two dimensions(hight and width).
+
+    Supporting resample methods:
+        'BILINEAR' : Bilinear interpolation
 
     Args:
-        input (Variable): The input tensor of bilinear interpolation,
+        input (Variable): The input tensor of image resize layer,
                           This is a 4-D tensor of the shape
                           (num_batches, channels, in_h, in_w).
-        out_shape(list|tuple|None): Output shape of bilinear interpolation
+        out_shape(list|tuple|Variable|None): Output shape of image resize
                                     layer, the shape is (out_h, out_w).
                                     Default: None
-        scale(int|None): The multiplier for the input height or width.
+        scale(float|None): The multiplier for the input height or width.
                          At least one of out_shape or scale must be set.
                          And out_shape has a higher priority than scale.
                          Default: None
         name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
+        resample(str): The resample method. It can only be 'BILINEAR' currently.
+                       Default: 'BILINEAR'
 
     Returns:
         out (Variable): The output is a 4-D tensor of the shape
@@ -3989,8 +3996,12 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     Examples:
         .. code-block:: python
 
-            out = fluid.layers.bilinear_interp(input, out_shape=[12, 12])
+            out = fluid.layers.image_resize(input, out_shape=[12, 12])
     """
+    resample_methods = {'BILINEAR': 'bilinear_interp'}
+    if resample not in resample_methods:
+        raise ValueError(
+            "The 'resample' of image_resize can only be 'BILINEAR' currently.")
     if out_shape is None and scale is None:
         raise ValueError("One of out_shape and scale must not be None")
     helper = LayerHelper('bilinear_interp', **locals())
@@ -3999,27 +4010,131 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     def _is_list_or_turple_(data):
         return (isinstance(data, list) or isinstance(data, tuple))
 
+    out_h = 0
+    out_w = 0
+    inputs = {"X": input}
     if out_shape is not None:
-        if not (_is_list_or_turple_(out_shape) and len(out_shape) == 2):
-            raise ValueError('out_shape should be a list or tuple ',
-                             'with length 2, (out_h, out_w).')
+        if not (_is_list_or_turple_(out_shape) and
+                len(out_shape) == 2) and not isinstance(out_shape, Variable):
+            raise ValueError('out_shape should be a list or tuple or variable')
+        if _is_list_or_turple_(out_shape):
             out_shape = list(map(int, out_shape))
             out_h = out_shape[0]
             out_w = out_shape[1]
+        else:
+            inputs['OutSize'] = out_shape
     else:
         out_h = int(input.shape[2] * scale)
         out_w = int(input.shape[3] * scale)
 
     out = helper.create_tmp_variable(dtype)
     helper.append_op(
-        type="bilinear_interp",
-        inputs={"X": input},
+        type=resample_methods[resample],
+        inputs=inputs,
         outputs={"Out": out},
         attrs={"out_h": out_h,
                "out_w": out_w})
     return out
 
 
+def resize_bilinear(input, out_shape=None, scale=None, name=None):
+    """
+    This is an alias of layer 'image_resize' with bilinear interpolation.
+
+    The mathematical meaning of resize bilinear layer is
+    Bilinear interpolation.
+    Bilinear interpolation is an extension of linear interpolation for
+    interpolating functions of two variables (e.g. H-direction and
+    W-direction in this layer) on a rectilinear 2D grid.
+
+    For details, please refer to Wikipedia:
+    https://en.wikipedia.org/wiki/Bilinear_interpolation
+    """
+
+    return image_resize(input, out_shape, scale, name, 'BILINEAR')
+
+
+def image_resize_short(input, out_short_len, resample='BILINEAR'):
+    """
+    Resize a batch of images. The short edge of input images will be 
+    resized to the given 'out_short_len'. The long edge of input images 
+    will be resized proportionately to make images' length-width ratio 
+    constant.
+
+    Args:
+        input (Variable): The input tensor of image resize layer,
+                          This is a 4-D tensor of the shape
+                          (num_batches, channels, in_h, in_w).
+        out_short_len(int): The length of output images' short edge.
+
+    Returns:
+        out (Variable): The output is a 4-D tensor of the shape
+                        (num_batches, channls, out_h, out_w).
+    """
+    in_shape = input.shape
+    if len(in_shape) != 4:
+        raise ValueError(
+            "The rank of input must be 4 (num_batches, channels, in_h, in_w).")
+    hw = in_shape[2:4]
+    short_idx = hw.index(min(hw))
+    long_idx = 1 - short_idx
+    out_shape = list(hw)
+    out_shape[short_idx] = out_short_len
+    out_shape[long_idx] = int(
+        float(out_shape[long_idx]) * (float(out_short_len) / float(hw[
+            short_idx])) + 0.5)
+    return image_resize(input=input, out_shape=out_shape, resample=resample)
+
+
+def gather(input, index):
+    """
+    Output is obtained by gathering entries of the outer-most dimension 
+    of X indexed by `index` and concatenate them together.
+
+    .. math::
+
+        Out = X[Index]
+
+
+    .. code-block:: text
+
+
+                Given:
+
+                X = [[1, 2],
+                     [3, 4],
+                     [5, 6]]
+
+                Index = [1, 2]
+
+                Then:
+
+                Out = [[3, 4],
+                       [5, 6]]
+
+    Args:
+        input (Variable): The source input with rank>=1. 
+        index (Variable): The index input with rank=1.
+
+    Returns:
+        output (Variable): The output is a tensor with the same rank as input.
+
+    Examples:
+        .. code-block:: python
+
+            output = fluid.layers.gather(x, index)
+    """
+    helper = LayerHelper('gather', **locals())
+    dtype = helper.input_dtype()
+    out = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type="gather",
+        inputs={"X": input,
+                "Index": index},
+        outputs={"Out": out})
+    return out
+
+
 @templatedoc()
 def random_crop(x, shape, seed=None):
     """
@@ -4039,7 +4154,6 @@ def random_crop(x, shape, seed=None):
         ${out_comment}
 
     """
-
     helper = LayerHelper("random_crop", **locals())
     dtype = helper.input_dtype()
     out = helper.create_tmp_variable(dtype)

python/paddle/fluid/layers/ops.py

@@ -71,6 +71,8 @@ __all__ = [
     'cumsum',
     'scatter',
     'sum',
+    'polygon_box_transform',
+    'shape',
 ] + __activations__
 
 for _OP in set(__all__):

python/paddle/fluid/recordio_writer.py

@@ -12,10 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import core
 import contextlib
-
-__all__ = ['convert_reader_to_recordio_file']
+__all__ = [
+    'convert_reader_to_recordio_file', 'convert_reader_to_recordio_files'
+]
 
 
 @contextlib.contextmanager
@@ -46,3 +48,36 @@ def convert_reader_to_recordio_file(
             writer.complete_append_tensor()
             counter += 1
     return counter
+
+
+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 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
+    return counter

python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py

@@ -48,13 +48,15 @@ def linear():
     return avg_loss
 
 
+def optimizer_func():
+    return fluid.optimizer.SGD(learning_rate=0.001)
+
+
 def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     trainer = fluid.Trainer(
-        train_func=train_program,
-        place=place,
-        optimizer=fluid.optimizer.SGD(learning_rate=0.001))
+        train_func=train_program, place=place, optimizer_func=optimizer_func)
 
     def event_handler(event):
         if isinstance(event, fluid.EndStepEvent):

python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py

@@ -85,6 +85,10 @@ def train_network():
     return [avg_cost, accuracy]
 
 
+def optimizer_func():
+    return fluid.optimizer.Adam(learning_rate=0.001)
+
+
 def train(use_cuda, train_program, params_dirname):
     BATCH_SIZE = 128
     EPOCH_NUM = 1
@@ -111,9 +115,7 @@ def train(use_cuda, train_program, params_dirname):
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     trainer = fluid.Trainer(
-        train_func=train_program,
-        optimizer=fluid.optimizer.Adam(learning_rate=0.001),
-        place=place)
+        train_func=train_program, optimizer_func=optimizer_func, place=place)
 
     trainer.train(
         reader=train_reader,

python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py

@@ -64,6 +64,10 @@ def train_network():
     return [avg_cost, accuracy]
 
 
+def optimizer_func():
+    return fluid.optimizer.Adam(learning_rate=0.001)
+
+
 def train(use_cuda, train_program, params_dirname):
     BATCH_SIZE = 128
     train_reader = paddle.batch(
@@ -88,9 +92,7 @@ def train(use_cuda, train_program, params_dirname):
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     trainer = fluid.Trainer(
-        train_func=train_program,
-        place=place,
-        optimizer=fluid.optimizer.Adam(learning_rate=0.001))
+        train_func=train_program, place=place, optimizer_func=optimizer_func)
 
     trainer.train(
         reader=train_reader,

python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py

@@ -141,12 +141,16 @@ def train_program():
     return [avg_cost]
 
 
+def optimize_func():
+    return fluid.optimizer.SGD(learning_rate=fluid.layers.exponential_decay(
+        learning_rate=0.01, decay_steps=100000, decay_rate=0.5, staircase=True))
+
+
 def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    optimizer = fluid.optimizer.SGD(learning_rate=0.01)
 
     trainer = fluid.Trainer(
-        train_func=train_program, place=place, optimizer=optimizer)
+        train_func=train_program, place=place, optimizer_func=optimize_func)
 
     feed_order = [
         'word_data', 'ctx_n2_data', 'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
@@ -245,7 +249,7 @@ def infer(use_cuda, inference_program, params_dirname):
         },
         return_numpy=False)
 
-    print("infer results: ", np.array(results[0]))
+    print("infer results: ", np.array(results[0]).shape)
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py

@@ -158,6 +158,13 @@ def train_program(is_sparse):
     return avg_cost
 
 
+def optimizer_func():
+    return fluid.optimizer.Adagrad(
+        learning_rate=1e-4,
+        regularization=fluid.regularizer.L2DecayRegularizer(
+            regularization_coeff=0.1))
+
+
 def train(use_cuda, is_sparse, is_local=True):
     EPOCH_NUM = 1
 
@@ -182,11 +189,8 @@ def train(use_cuda, is_sparse, is_local=True):
 
     trainer = fluid.Trainer(
         train_func=partial(train_program, is_sparse),
-        optimizer=fluid.optimizer.Adagrad(
-            learning_rate=1e-4,
-            regularization=fluid.regularizer.L2DecayRegularizer(
-                regularization_coeff=0.1)),
-        place=place)
+        place=place,
+        optimizer_func=optimizer_func)
 
     trainer.train(
         reader=train_reader,

python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py

@@ -57,14 +57,17 @@ def train_program():
     return [avg_cost, acc]
 
 
+def optimizer_func():
+    return fluid.optimizer.Adam(learning_rate=0.001)
+
+
 def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
 
     trainer = fluid.Trainer(
         train_func=train_program,
         place=place,
-        optimizer=optimizer,
+        optimizer_func=optimizer_func,
         parallel=True)
 
     def event_handler(event):

python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py

@@ -44,12 +44,15 @@ def train_program():
     return [avg_cost, acc]
 
 
+def optimizer_func():
+    return fluid.optimizer.Adam(learning_rate=0.001)
+
+
 def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
 
     trainer = fluid.Trainer(
-        train_func=train_program, place=place, optimizer=optimizer)
+        train_func=train_program, place=place, optimizer_func=optimizer_func)
 
     def event_handler(event):
         if isinstance(event, fluid.EndEpochEvent):

python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py

@@ -155,12 +155,15 @@ def train_program():
     return [avg_cost, scale_infer]
 
 
+def optimizer_func():
+    return fluid.optimizer.SGD(learning_rate=0.2)
+
+
 def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    optimizer = fluid.optimizer.SGD(learning_rate=0.2)
 
     trainer = fluid.Trainer(
-        train_func=train_program, place=place, optimizer=optimizer)
+        train_func=train_program, place=place, optimizer_func=optimizer_func)
 
     feed_order = [
         'user_id', 'gender_id', 'age_id', 'job_id', 'movie_id', 'category_id',

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py

@@ -64,15 +64,18 @@ def train_program(word_dict):
     return [avg_cost, accuracy]
 
 
+def optimizer_func():
+    return fluid.optimizer.Adagrad(learning_rate=0.002)
+
+
 def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    optimizer = fluid.optimizer.Adagrad(learning_rate=0.002)
 
     word_dict = paddle.dataset.imdb.word_dict()
     trainer = fluid.Trainer(
         train_func=partial(train_program, word_dict),
         place=place,
-        optimizer=optimizer)
+        optimizer_func=optimizer_func)
 
     def event_handler(event):
         if isinstance(event, fluid.EndEpochEvent):

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py

@@ -79,15 +79,18 @@ def train_program(word_dict):
     return [avg_cost, accuracy]
 
 
+def optimizer_func():
+    return fluid.optimizer.Adagrad(learning_rate=0.002)
+
+
 def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    optimizer = fluid.optimizer.Adagrad(learning_rate=0.002)
 
     word_dict = paddle.dataset.imdb.word_dict()
     trainer = fluid.Trainer(
         train_func=partial(train_program, word_dict),
         place=place,
-        optimizer=optimizer)
+        optimizer_func=optimizer_func)
 
     def event_handler(event):
         if isinstance(event, fluid.EndEpochEvent):

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py

@@ -71,15 +71,18 @@ def train_program(word_dict):
     return [avg_cost, accuracy]
 
 
+def optimizer_func():
+    return fluid.optimizer.Adagrad(learning_rate=0.002)
+
+
 def train(use_cuda, train_program, params_dirname):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    optimizer = fluid.optimizer.Adagrad(learning_rate=0.002)
 
     word_dict = paddle.dataset.imdb.word_dict()
     trainer = fluid.Trainer(
         train_func=partial(train_program, word_dict),
         place=place,
-        optimizer=optimizer)
+        optimizer_func=optimizer_func)
 
     def event_handler(event):
         if isinstance(event, fluid.EndEpochEvent):

python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py

@@ -80,6 +80,10 @@ def train_program(is_sparse):
     return avg_cost
 
 
+def optimizer_func():
+    return fluid.optimizer.SGD(learning_rate=0.001)
+
+
 def train(use_cuda, train_program, params_dirname):
     train_reader = paddle.batch(
         paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
@@ -104,9 +108,7 @@ def train(use_cuda, train_program, params_dirname):
                 sys.exit("got NaN loss, training failed.")
 
     trainer = fluid.Trainer(
-        train_func=train_program,
-        optimizer=fluid.optimizer.SGD(learning_rate=0.001),
-        place=place)
+        train_func=train_program, optimizer_func=optimizer_func, place=place)
 
     trainer.train(
         reader=train_reader,

python/paddle/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py

@@ -80,21 +80,6 @@ def encoder_decoder():
     return rnn()
 
 
-def to_lodtensor(data, place):
-    seq_lens = [len(seq) for seq in data]
-    cur_len = 0
-    lod = [cur_len]
-    for l in seq_lens:
-        cur_len += l
-        lod.append(cur_len)
-    flattened_data = np.concatenate(data, axis=0).astype("int64")
-    flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = core.LoDTensor()
-    res.set(flattened_data, place)
-    res.set_lod([lod])
-    return res
-
-
 def main():
     rnn_out = encoder_decoder()
     label = layers.data(
@@ -122,18 +107,21 @@ def main():
 
     exe.run(framework.default_startup_program())
 
+    feed_order = [
+        'src_word_id', 'target_language_word', 'target_language_next_word'
+    ]
+
+    feed_list = [
+        fluid.default_main_program().global_block().var(var_name)
+        for var_name in feed_order
+    ]
+    feeder = fluid.DataFeeder(feed_list, place)
+
     batch_id = 0
     for pass_id in xrange(10):
         for data in train_data():
-            word_data = to_lodtensor(map(lambda x: x[0], data), place)
-            trg_word = to_lodtensor(map(lambda x: x[1], data), place)
-            trg_word_next = to_lodtensor(map(lambda x: x[2], data), place)
             outs = exe.run(fluid.default_main_program(),
-                           feed={
-                               'src_word_id': word_data,
-                               'target_language_word': trg_word,
-                               'target_language_next_word': trg_word_next
-                           },
+                           feed=feeder.feed(data),
                            fetch_list=[avg_cost])
             avg_cost_val = np.array(outs[0])
             print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -48,5 +48,7 @@ foreach(TEST_OP ${TEST_OPS})
 endforeach(TEST_OP)
 py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
 py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
-# tests that need to be done in fixed timeout
-set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)
+# FIXME(Yancey1989): this test would cost much more time on CUDAPlace
+# since load cudnn libraries, so we use a longer timeout to make this
+# unit test stability.
+set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 30)

python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py

@@ -17,7 +17,10 @@ import numpy as np
 from op_test import OpTest
 
 
-def bilinear_interp_np(input, out_h, out_w):
+def bilinear_interp_np(input, out_h, out_w, out_size):
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
     batch_size, channel, in_h, in_w = input.shape
     if out_h > 1:
         ratio_h = (in_h - 1.0) / (out_h - 1.0)
@@ -49,12 +52,15 @@ def bilinear_interp_np(input, out_h, out_w):
 
 class TestBilinearInterpOp(OpTest):
     def setUp(self):
+        self.out_size = None
         self.init_test_case()
         self.op_type = "bilinear_interp"
         input_np = np.random.random(self.input_shape).astype("float32")
-        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w)
-
+        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
+                                       self.out_size)
         self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
         self.attrs = {'out_h': self.out_h, 'out_w': self.out_w}
         self.outputs = {'Out': output_np}
 
@@ -68,6 +74,7 @@ class TestBilinearInterpOp(OpTest):
         self.input_shape = [2, 3, 4, 4]
         self.out_h = 2
         self.out_w = 2
+        self.out_size = np.array([3, 3]).astype("int32")
 
 
 class TestCase1(TestBilinearInterpOp):
@@ -91,5 +98,29 @@ class TestCase3(TestBilinearInterpOp):
         self.out_w = 128
 
 
+class TestCase4(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.out_size = np.array([2, 2]).astype("int32")
+
+
+class TestCase5(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.out_size = np.array([11, 11]).astype("int32")
+
+
+class TestCase6(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 128, 64]
+        self.out_h = 64
+        self.out_w = 128
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_box_coder_op.py

@@ -19,7 +19,8 @@ import math
 from op_test import OpTest
 
 
-def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
+def box_coder(target_box, prior_box, prior_box_var, output_box, code_type,
+              box_normalized):
     prior_box_x = (
         (prior_box[:, 2] + prior_box[:, 0]) / 2).reshape(1, prior_box.shape[0])
     prior_box_y = (
@@ -30,6 +31,9 @@ def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
         (prior_box[:, 3] - prior_box[:, 1])).reshape(1, prior_box.shape[0])
     prior_box_var = prior_box_var.reshape(1, prior_box_var.shape[0],
                                           prior_box_var.shape[1])
+    if not box_normalized:
+        prior_box_height = prior_box_height + 1
+        prior_box_width = prior_box_width + 1
 
     if (code_type == "EncodeCenterSize"):
         target_box_x = ((target_box[:, 2] + target_box[:, 0]) / 2).reshape(
@@ -40,6 +44,9 @@ def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
             target_box.shape[0], 1)
         target_box_height = ((target_box[:, 3] - target_box[:, 1])).reshape(
             target_box.shape[0], 1)
+        if not box_normalized:
+            target_box_height = target_box_height + 1
+            target_box_width = target_box_width + 1
 
         output_box[:,:,0] = (target_box_x - prior_box_x) / prior_box_width / \
                 prior_box_var[:,:,0]
@@ -64,9 +71,13 @@ def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
         output_box[:, :, 1] = target_box_y - target_box_height / 2
         output_box[:, :, 2] = target_box_x + target_box_width / 2
         output_box[:, :, 3] = target_box_y + target_box_height / 2
+        if not box_normalized:
+            output_box[:, :, 2] = output_box[:, :, 2] - 1
+            output_box[:, :, 3] = output_box[:, :, 3] - 1
 
 
-def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type):
+def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type,
+                    box_normalized):
     n = target_box.shape[0]
     m = prior_box.shape[0]
     output_box = np.zeros((n, m, 4), dtype=np.float32)
@@ -74,11 +85,11 @@ def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type):
         if (code_type == "EncodeCenterSize"):
             box_coder(target_box[lod[i]:lod[i + 1], :], prior_box,
                       prior_box_var, output_box[lod[i]:lod[i + 1], :, :],
-                      code_type)
+                      code_type, box_normalized)
         elif (code_type == "DecodeCenterSize"):
             box_coder(target_box[lod[i]:lod[i + 1], :, :], prior_box,
                       prior_box_var, output_box[lod[i]:lod[i + 1], :, :],
-                      code_type)
+                      code_type, box_normalized)
     return output_box
 
 
@@ -93,15 +104,45 @@ class TestBoxCoderOp(OpTest):
         prior_box_var = np.random.random((10, 4)).astype('float32')
         target_box = np.random.random((5, 10, 4)).astype('float32')
         code_type = "DecodeCenterSize"
+        box_normalized = False
         output_box = batch_box_coder(prior_box, prior_box_var, target_box,
-                                     lod[0], code_type)
+                                     lod[0], code_type, box_normalized)
 
         self.inputs = {
             'PriorBox': prior_box,
             'PriorBoxVar': prior_box_var,
             'TargetBox': target_box,
         }
-        self.attrs = {'code_type': 'decode_center_size'}
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False
+        }
+        self.outputs = {'OutputBox': output_box}
+
+
+class TestBoxCoderOpWithoutBoxVar(OpTest):
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.op_type = "box_coder"
+        lod = [[0, 1, 2, 3, 4, 5]]
+        prior_box = np.random.random((10, 4)).astype('float32')
+        prior_box_var = np.ones((10, 4)).astype('float32')
+        target_box = np.random.random((5, 10, 4)).astype('float32')
+        code_type = "DecodeCenterSize"
+        box_normalized = False
+        output_box = batch_box_coder(prior_box, prior_box_var, target_box,
+                                     lod[0], code_type, box_normalized)
+
+        self.inputs = {
+            'PriorBox': prior_box,
+            'TargetBox': target_box,
+        }
+        self.attrs = {
+            'code_type': 'decode_center_size',
+            'box_normalized': False
+        }
         self.outputs = {'OutputBox': output_box}
 
 
@@ -116,15 +157,16 @@ class TestBoxCoderOpWithLoD(OpTest):
         prior_box_var = np.random.random((10, 4)).astype('float32')
         target_box = np.random.random((20, 4)).astype('float32')
         code_type = "EncodeCenterSize"
+        box_normalized = True
         output_box = batch_box_coder(prior_box, prior_box_var, target_box,
-                                     lod[0], code_type)
+                                     lod[0], code_type, box_normalized)
 
         self.inputs = {
             'PriorBox': prior_box,
             'PriorBoxVar': prior_box_var,
             'TargetBox': (target_box, lod),
         }
-        self.attrs = {'code_type': 'encode_center_size'}
+        self.attrs = {'code_type': 'encode_center_size', 'box_normalized': True}
         self.outputs = {'OutputBox': output_box}
 
 

python/paddle/fluid/tests/unittests/test_dist_transpiler.py

@@ -12,40 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import paddle.fluid as fluid
-import paddle.fluid.core as core
-import paddle.fluid.layers as layers
 from paddle.fluid.transpiler.distribute_transpiler import delete_ops
-import numpy
+
+from transpiler_test import TranspilerTest
 
 
-class TestDistTranspiler(unittest.TestCase):
+class TestDistTranspiler(TranspilerTest):
     def setUp(self):
-        self.trainer_id = 0
-        self.trainers = 2
-        self.pservers = 2
-        self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
         self.current_pserver_ep = "127.0.0.1:6174"
 
-    def net_conf(self):
-        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
-
-        y_predict = fluid.layers.fc(input=x,
-                                    size=1000,
-                                    act=None,
-                                    param_attr=fluid.ParamAttr(name='fc_w'))
-
-        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
-
-        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
-        avg_cost = fluid.layers.mean(cost)
-        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
-
-        optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
-        return optimize_ops, params_grads
-
     def test_transpiler(self):
         trainer = self.get_trainer()
         pserver, startup = self.get_pserver(self.current_pserver_ep)
@@ -70,14 +46,6 @@ class TestDistTranspiler(unittest.TestCase):
         fc_w_var = startup.global_block().var("fc_w.block1")
         self.assertEqual(fc_w_var.shape, (500, 1000))
 
-    def get_main_program(self):
-        main = fluid.Program()
-
-        with fluid.program_guard(main):
-            self.net_conf()
-
-        return main
-
     def get_expect_trainer_ops(self):
         trainer = fluid.Program()
 
@@ -92,25 +60,6 @@ class TestDistTranspiler(unittest.TestCase):
         ops.insert(ops.index("elementwise_add_grad") + 1, "send_vars")
         return ops
 
-    def get_trainer(self):
-        return self._transpiler_instance().get_trainer_program()
-
-    def get_pserver(self, ep):
-        t = self._transpiler_instance()
-        pserver = t.get_pserver_program(ep)
-        startup = t.get_startup_program(ep, pserver)
-        return pserver, startup
-
-    def _transpiler_instance(self):
-        main = self.get_main_program()
-        t = fluid.DistributeTranspiler()
-        t.transpile(
-            self.trainer_id,
-            program=main,
-            pservers=self.pserver_eps,
-            trainers=self.trainers)
-        return t
-
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_gather_op.py

@@ -20,8 +20,9 @@ from op_test import OpTest
 class TestGatherOp(OpTest):
     def setUp(self):
         self.op_type = "gather"
-        xnp = np.random.random((10, 20)).astype("float32")
-        self.inputs = {'X': xnp, 'Index': np.array([1, 3, 5]).astype("int32")}
+        self.config()
+        xnp = np.random.random(self.x_shape).astype("float32")
+        self.inputs = {'X': xnp, 'Index': np.array(self.index).astype("int32")}
         self.outputs = {'Out': self.inputs["X"][self.inputs["Index"]]}
 
     def test_check_output(self):
@@ -30,6 +31,16 @@ class TestGatherOp(OpTest):
     def test_check_grad(self):
         self.check_grad(['X'], 'Out')
 
+    def config(self):
+        self.x_shape = (10, 20)
+        self.index = [1, 3, 5]
+
+
+class TestCase1(TestGatherOp):
+    def config(self):
+        self.x_shape = (10)
+        self.index = [1, 3, 5]
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_layers.py

@@ -369,13 +369,21 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
-    def test_upsampling_bilinear2d(self):
+    def test_resize_bilinear(self):
         program = Program()
         with program_guard(program):
             x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
-            output = layers.upsampling_bilinear2d(x, out_shape=[12, 12])
+            output = layers.resize_bilinear(x, out_shape=[12, 12])
             self.assertIsNotNone(output)
-            output = layers.upsampling_bilinear2d(x, scale=3)
+            output = layers.resize_bilinear(x, scale=3)
+            self.assertIsNotNone(output)
+        print(str(program))
+
+    def test_polygon_box_transform(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[8, 4, 4], dtype="float32")
+            output = layers.polygon_box_transform(input=x)
             self.assertIsNotNone(output)
         print(str(program))
 

python/paddle/fluid/tests/unittests/test_listen_and_serv_op.py

@@ -23,7 +23,7 @@ from multiprocessing import Process
 from op_test import OpTest
 
 
-def run_pserver(use_cuda, sync_mode, ip, port, trainer_count, trainer_id):
+def run_pserver(use_cuda, sync_mode, ip, port, trainers, trainer_id):
     x = fluid.layers.data(name='x', shape=[1], dtype='float32')
     y_predict = fluid.layers.fc(input=x, size=1, act=None)
     y = fluid.layers.data(name='y', shape=[1], dtype='float32')
@@ -39,15 +39,8 @@ def run_pserver(use_cuda, sync_mode, ip, port, trainer_count, trainer_id):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
-    port = os.getenv("PADDLE_INIT_PORT", port)
-    pserver_ips = os.getenv("PADDLE_INIT_PSERVERS", ip)  # ip,ip...
-    eplist = []
-    for ip in pserver_ips.split(","):
-        eplist.append(':'.join([ip, port]))
-    pserver_endpoints = ",".join(eplist)  # ip:port,ip:port...
-    trainers = int(os.getenv("TRAINERS", trainer_count))
-    current_endpoint = os.getenv("POD_IP", ip) + ":" + port
-    trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID", trainer_id))
+    pserver_endpoints = ip + ":" + port
+    current_endpoint = ip + ":" + port
     t = fluid.DistributeTranspiler()
     t.transpile(
         trainer_id,
@@ -62,47 +55,51 @@ def run_pserver(use_cuda, sync_mode, ip, port, trainer_count, trainer_id):
 
 class TestListenAndServOp(OpTest):
     def setUp(self):
-        self.sleep_time = 5
+        self.ps_timeout = 5
         self.ip = "127.0.0.1"
         self.port = "6173"
-        self.trainer_count = 1
+        self.trainers = 1
         self.trainer_id = 1
 
-    def _raise_signal(self, parent_pid, raised_signal):
-        time.sleep(self.sleep_time)
-        ps_command = subprocess.Popen(
-            "ps -o pid --ppid %d --noheaders" % parent_pid,
-            shell=True,
-            stdout=subprocess.PIPE)
-        ps_output = ps_command.stdout.read()
-        retcode = ps_command.wait()
-        assert retcode == 0, "ps command returned %d" % retcode
-
-        for pid_str in ps_output.split("\n")[:-1]:
-            try:
-                os.kill(int(pid_str), raised_signal)
-            except Exception:
-                continue
-
     def _start_pserver(self, use_cuda, sync_mode):
         p = Process(
             target=run_pserver,
-            args=(use_cuda, sync_mode, self.ip, self.port, self.trainer_count,
+            args=(use_cuda, sync_mode, self.ip, self.port, self.trainers,
                   self.trainer_id))
         p.start()
+        return p.pid
+
+    def _wait_ps_ready(self, pid):
+        retry_times = self.ps_timeout
+        while True:
+            assert retry_times >= 0, "wait ps ready failed"
+            time.sleep(0.5)
+            try:
+                # the listen_and_serv_op would touch a file which contains the listen port
+                # on the /tmp directory until it was ready to process all the RPC call.
+                os.stat("/tmp/paddle.%d.port" % pid)
+                return
+            except os.error:
+                retry_times -= 1
+
+    def test_rpc_interfaces(self):
+        # TODO(Yancey1989): need to make sure the rpc interface correctly.
+        pass
 
     def test_handle_signal_in_serv_op(self):
         # run pserver on CPU in sync mode
-        self._start_pserver(False, True)
+        pid = self._start_pserver(False, True)
+        self._wait_ps_ready(pid)
 
-        # raise SIGINT to pserver
-        self._raise_signal(os.getpid(), signal.SIGINT)
+        # raise SIGTERM to pserver
+        os.kill(pid, signal.SIGTERM)
 
         # run pserver on CPU in async mode
-        self._start_pserver(False, False)
+        pid = self._start_pserver(False, False)
+        self._wait_ps_ready(pid)
 
         # raise SIGTERM to pserver
-        self._raise_signal(os.getpid(), signal.SIGTERM)
+        os.kill(pid, signal.SIGTERM)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/test_shape_op.py

+#   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 unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestShapeOp(OpTest):
+    def setUp(self):
+        self.op_type = "shape"
+        self.config()
+        self.shape = [2, 3]
+        input = np.zeros(self.shape)
+        self.inputs = {'Input': input}
+        self.outputs = {'Out': np.array(self.shape)}
+
+    def config(self):
+        self.shape = [2, 3]
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class case1(TestShapeOp):
+    def config(self):
+        self.shape = [2]
+
+
+class case2(TestShapeOp):
+    def config(self):
+        self.shape = [1, 2, 3]
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_simple_dist_transpiler.py

+#   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 numpy as np
+
+import paddle.fluid as fluid
+from paddle.fluid.transpiler.distribute_transpiler import delete_ops
+
+from transpiler_test import TranspilerTest
+
+
+class TestSimpleDistTranspiler(TranspilerTest):
+    def setUp(self):
+        self.current_pserver_ep = "127.0.0.1:6175"
+
+    def test_simple_transpiler(self):
+        np.random.seed(1)
+
+        trainer = self.get_trainer()
+        pserver, startup = self.get_pserver(self.current_pserver_ep)
+        self.assertEqual([op.type for op in trainer.global_block().ops],
+                         self.get_expect_trainer_ops())
+
+        self.assertEqual(len(pserver.blocks), 2)
+        # block0: listen_and_serv
+        self.assertEqual([op.type for op in pserver.blocks[0].ops],
+                         ["listen_and_serv"])
+        # block1: optimize pass
+        self.assertEqual([op.type for op in pserver.blocks[1].ops],
+                         ["sum", "scale", "sgd"])
+
+        # confirm startup program
+        self.assertEqual([op.type for op in startup.global_block().ops],
+                         ["fill_constant", "uniform_random", "uniform_random"])
+
+        # the variable #fc_w will NOT be splited
+        fc_w_var = startup.global_block().var("fc_w@GRAD")
+        self.assertEqual(fc_w_var.shape, (1000, 1000))
+
+        fc_w_var = startup.global_block().var("fc_w@GRAD.trainer_0")
+        self.assertEqual(fc_w_var.shape, (1000, 1000))
+
+    def get_expect_trainer_ops(self):
+        trainer = fluid.Program()
+
+        with fluid.program_guard(trainer):
+            optimize_ops, params_grads = self.net_conf()
+
+        delete_ops(trainer.global_block(), optimize_ops)
+        ops = [op.type for op in trainer.global_block().ops] + [
+            "send_vars", "send_barrier", "recv", "recv", "fetch_barrier"
+        ]
+        ops.insert(ops.index("elementwise_add_grad") + 1, "send_vars")
+        return ops
+
+    def _transpiler_instance(self):
+        main = self.get_main_program()
+        t = fluid.DistributeTranspiler()
+        t.transpile(
+            self.trainer_id,
+            program=main,
+            pservers=self.pserver_eps,
+            trainers=self.trainers,
+            slice_var_up=False)
+        return t
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_split_var.py → python/paddle/fluid/tests/unittests/test_slice_var.py

@@ -14,14 +14,14 @@
 
 import math
 import unittest
-from paddle.fluid.transpiler.distribute_transpiler import split_dense_variable
+from paddle.fluid.transpiler.distribute_transpiler import slice_variable
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import random
 
 
-class TestSplitVar(unittest.TestCase):
-    def check_split_output(self, shapes, expected_sizes, min_size):
+class TestSliceVar(unittest.TestCase):
+    def check_slice_output(self, shapes, expected_sizes, min_size):
         var_list = []
         program = fluid.Program()
         for shape in shapes:
@@ -31,7 +31,7 @@ class TestSplitVar(unittest.TestCase):
                 # dtype=core.VarDesc.VarType.LOD_TENSOR,
                 shape=shape)
             var_list.append(var)
-        blocks = split_dense_variable(var_list, 10, min_size)
+        blocks = slice_variable(var_list, 10, min_size)
         all_sizes = []
         for s in expected_sizes:
             for s2 in s:
@@ -49,7 +49,7 @@ class TestSplitVar(unittest.TestCase):
             [1150, 1150, 1150, 1150, 1150, 1150, 1100]
         ]
 
-        self.check_split_output(shapes, expected_sizes, 1024)
+        self.check_slice_output(shapes, expected_sizes, 1024)
 
     def test_check_output_8k(self):
         shapes = [[3, 5], [1024], [28, 784], [8, 1020], [800, 10],
@@ -57,7 +57,7 @@ class TestSplitVar(unittest.TestCase):
         expected_sizes = [[15], [1024], [10976, 10976], [8160], [8000],
                           [35937, 35937, 35937, 35937, 35937, 35937]]
 
-        self.check_split_output(shapes, expected_sizes, 8192)
+        self.check_slice_output(shapes, expected_sizes, 8192)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/unittests/transpiler_test.py

+#   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 unittest
+import numpy as np
+
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.fluid.layers as layers
+
+
+class TranspilerTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(self):
+        self.trainer_id = 0
+        self.trainers = 2
+        self.pservers = 2
+        self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
+
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+
+        y_predict = fluid.layers.fc(input=x,
+                                    size=1000,
+                                    act=None,
+                                    param_attr=fluid.ParamAttr(name='fc_w'))
+
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
+
+        optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
+        return optimize_ops, params_grads
+
+    def get_main_program(self):
+        main = fluid.Program()
+
+        with fluid.program_guard(main):
+            self.net_conf()
+
+        return main
+
+    def get_trainer(self):
+        return self._transpiler_instance().get_trainer_program()
+
+    def get_pserver(self, ep):
+        t = self._transpiler_instance()
+        pserver = t.get_pserver_program(ep)
+        startup = t.get_startup_program(ep, pserver)
+        return pserver, startup
+
+    def _transpiler_instance(self):
+        main = self.get_main_program()
+        t = fluid.DistributeTranspiler()
+        t.transpile(
+            self.trainer_id,
+            program=main,
+            pservers=self.pserver_eps,
+            trainers=self.trainers)
+        return t

python/paddle/fluid/trainer.py

@@ -90,13 +90,13 @@ class Trainer(object):
 
     Args:
         train_func(callable): A function which will return loss. The loss must be a scalar.
-        optimizer(optimizer.Optimizer): The optimizer should be an instance of Optimizer
+        optimizer_func(callable): A function that returns an Optimizer object.
         place: The device place of this trainer.
     """
 
     def __init__(self,
                  train_func,
-                 optimizer,
+                 optimizer_func,
                  param_path=None,
                  place=None,
                  parallel=False):
@@ -105,8 +105,6 @@ class Trainer(object):
         # 1. we need to generate a framework.Program by calling
         # program_func. Reference: fluid.program_guard in
         # test_word2vec.py
-        if not isinstance(optimizer, opt_module.Optimizer):
-            raise TypeError("The optimizer should be an instance of Optimizer")
 
         self.scope = core.Scope()
 
@@ -118,11 +116,14 @@ class Trainer(object):
             self.train_func_outputs = program_func_outs if isinstance(
                 program_func_outs, list) else [program_func_outs]
             self.test_program = self.train_program.clone()
+
+            # The fisrt element of program_func_outs is loss.
+            loss = self.train_func_outputs[0]
+
+            optimizer = optimizer_func()
             if not isinstance(optimizer, opt_module.Optimizer):
                 raise TypeError(
                     "The optimizer should be an instance of Optimizer")
-            # The fisrt element of program_func_outs is loss.
-            loss = self.train_func_outputs[0]
             optimize_ops, params_grads = optimizer.minimize(loss)
 
         self.place = check_and_get_place(place)

python/paddle/fluid/transpiler/__init__.py

@@ -15,10 +15,9 @@
 from distribute_transpiler import DistributeTranspiler
 from inference_transpiler import InferenceTranspiler
 from memory_optimization_transpiler import memory_optimize, release_memory
-from distribute_transpiler_simple import SimpleDistributeTranspiler
 from ps_dispatcher import HashName, RoundRobin
 
 __all__ = [
-    "DistributeTranspiler", "InferenceTranspiler", "SimpleDistributeTranspiler",
-    "memory_optimize", "release_memory", "HashName", "RoundRobin"
+    "DistributeTranspiler", "InferenceTranspiler", "memory_optimize",
+    "release_memory", "HashName", "RoundRobin"
 ]

python/paddle/fluid/transpiler/details/__init__.py

+# 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.
+
+from program_utils import *
+from ufind import *

python/paddle/fluid/transpiler/details/program_utils.py

+# 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.
+
+
+def delete_ops(block, ops):
+    try:
+        start = list(block.ops).index(ops[0])
+        end = list(block.ops).index(ops[-1])
+        [block.remove_op(start) for _ in xrange(end - start + 1)]
+    except Exception, e:
+        raise e
+    block.program.sync_with_cpp()
+
+
+def find_op_by_input_arg(block, arg_name):
+    for index, op in enumerate(block.ops):
+        if arg_name in op.input_arg_names:
+            return index
+    return -1
+
+
+def find_op_by_output_arg(block, arg_name):
+    for index, op in enumerate(block.ops):
+        if arg_name in op.output_arg_names:
+            return index
+    return -1

python/paddle/fluid/transpiler/details/ufind.py

+# 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.
+
+
+class UnionFind(object):
+    """ Union-find data structure.
+
+    Union-find is a data structure that keeps track of a set of elements partitioned
+    into a number of disjoint (non-overlapping) subsets.
+
+    Reference:
+    https://en.wikipedia.org/wiki/Disjoint-set_data_structure
+
+    Args:
+      elements(list): The initialize element list.
+    """
+
+    def __init__(self, elementes=None):
+        self._parents = []  # index -> parent index
+        self._index = {}  # element -> index
+        self._curr_idx = 0
+        if not elementes:
+            elementes = []
+        for ele in elementes:
+            self._parents.append(self._curr_idx)
+            self._index.update({ele: self._curr_idx})
+            self._curr_idx += 1
+
+    def find(self, x):
+        # Find the root index of given element x,
+        # execute the path compress while findind the root index
+        if not x in self._index:
+            return -1
+        idx = self._index[x]
+        while idx != self._parents[idx]:
+            t = self._parents[idx]
+            self._parents[idx] = self._parents[t]
+            idx = t
+        return idx
+
+    def union(self, x, y):
+        # Union two given element
+        x_root = self.find(x)
+        y_root = self.find(y)
+
+        if x_root == y_root:
+            return
+        self._parents[x_root] = y_root
+
+    def is_connected(self, x, y):
+        # If two given elements have the same root index,
+        # then they are connected.
+        return self.find(x) == self.find(y)

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -11,19 +11,46 @@
 # 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.
+"""
+Transpile the program to distributed data-parallelism programs.
+The main_program will be transformed to use a remote parameter server
+to do parameter optimization. And the optimization graph will be put
+into a parameter server program.
+
+Use different methods to split trainable variables to different
+parameter servers.
+
+Steps to transpile trainer:
+1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
+2. rename splited grad variables to add trainer_id suffix ".trainer_%d".
+3. modify trainer program add split_op to each grad variable.
+4. append send_op to send splited variables to server and fetch
+    params(splited blocks or origin param) from server.
+5. append concat_op to merge splited blocks to update local weights.
+
+Steps to transpile pserver:
+1. create new program for parameter server.
+2. create params and grad variables that assigned to current server instance.
+3. create a sub-block in the server side program
+4. append ops that should run on current server instance.
+5. add listen_and_serv op
+"""
 
 from __future__ import print_function
 
 import math
+import numpy as np
 
 from ps_dispatcher import RoundRobin, HashName, PSDispatcher
 from .. import core, framework
 from ..framework import Program, default_main_program, \
                         default_startup_program, \
                         Variable, Parameter, grad_var_name
+from details import *
 
 LOOKUP_TABLE_TYPE = "lookup_table"
 LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
+OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
 RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName(
 )
 RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC
@@ -40,62 +67,11 @@ class VarBlock:
         return "%s:%d:%d" % (self.varname, self.offset, self.size)
 
 
-class UnionFind(object):
-    """ Union-find data structure.
-
-    Union-find is a data structure that keeps track of a set of elements partitioned
-    into a number of disjoint (non-overlapping) subsets.
-
-    Reference:
-    https://en.wikipedia.org/wiki/Disjoint-set_data_structure
-
-    Args:
-      elements(list): The initialize element list.
-    """
-
-    def __init__(self, elementes=None):
-        self._parents = []  # index -> parent index
-        self._index = {}  # element -> index
-        self._curr_idx = 0
-        if not elementes:
-            elementes = []
-        for ele in elementes:
-            self._parents.append(self._curr_idx)
-            self._index.update({ele: self._curr_idx})
-            self._curr_idx += 1
-
-    def find(self, x):
-        # Find the root index of given element x,
-        # execute the path compress while findind the root index
-        if not x in self._index:
-            return -1
-        idx = self._index[x]
-        while idx != self._parents[idx]:
-            t = self._parents[idx]
-            self._parents[idx] = self._parents[t]
-            idx = t
-        return idx
-
-    def union(self, x, y):
-        # Union two given element
-        x_root = self.find(x)
-        y_root = self.find(y)
-
-        if x_root == y_root:
-            return
-        self._parents[x_root] = y_root
-
-    def is_connected(self, x, y):
-        # If two given elements have the same root index,
-        # then they are connected.
-        return self.find(x) == self.find(y)
-
-
 def same_or_split_var(p_name, var_name):
     return p_name == var_name or p_name.startswith(var_name + ".block")
 
 
-def split_dense_variable(var_list, service_count, min_block_size=8192):
+def slice_variable(var_list, slice_count, min_block_size=8192):
     """
     We may need to split dense tensor to one or more blocks and put
     them equally onto parameter server. One block is a sub-tensor
@@ -107,8 +83,8 @@ def split_dense_variable(var_list, service_count, min_block_size=8192):
 
     Args:
         var_list (list): List of variables.
-        service_count (int): Numel of pserver services. A pserver may have two
-            or more listening ports.
+        slice_count (int): Numel of count that variables will be sliced, which
+            could be the pserver services' count.
         min_block_size (int): Minimum splitted block size.
     Returns:
         blocks (list[(varname, block_id, current_block_size)]): A list
@@ -116,12 +92,12 @@ def split_dense_variable(var_list, service_count, min_block_size=8192):
     """
     blocks = []
     for var in var_list:
-        split_count = service_count
+        split_count = slice_count
         var_numel = reduce(lambda x, y: x * y, var.shape)
         max_pserver_count = int(math.floor(var_numel / float(min_block_size)))
         if max_pserver_count == 0:
             max_pserver_count = 1
-        if max_pserver_count < service_count:
+        if max_pserver_count < slice_count:
             split_count = max_pserver_count
         block_size = int(math.ceil(var_numel / float(split_count)))
 
@@ -141,99 +117,15 @@ def split_dense_variable(var_list, service_count, min_block_size=8192):
     return blocks
 
 
-def delete_ops(block, ops):
-    try:
-        start = list(block.ops).index(ops[0])
-        end = list(block.ops).index(ops[-1])
-        [block.remove_op(start) for _ in xrange(end - start + 1)]
-    except Exception, e:
-        raise e
-    block.program.sync_with_cpp()
-
-
-def find_op_by_input_arg(block, arg_name):
-    for index, op in enumerate(block.ops):
-        if arg_name in op.input_arg_names:
-            return index
-    return -1
-
-
-def find_op_by_output_arg(block, arg_name):
-    for index, op in enumerate(block.ops):
-        if arg_name in op.output_arg_names:
-            return index
-    return -1
-
-
 class DistributeTranspiler:
-    def transpile(self,
-                  trainer_id,
-                  program=None,
-                  pservers="127.0.0.1:6174",
-                  trainers=1,
-                  split_method=RoundRobin,
-                  sync_mode=True):
-        """
-        Transpile the program to distributed data-parallelism programs.
-        The main_program will be transformed to use a remote parameter server
-        to do parameter optimization. And the optimization graph will be put
-        into a parameter server program.
-
-        Use different methods to split trainable variables to different
-        parameter servers.
-
-        Steps to transpile trainer:
-        1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
-        2. rename splited grad variables to add trainer_id suffix ".trainer_%d".
-        3. modify trainer program add split_op to each grad variable.
-        4. append send_op to send splited variables to server and fetch
-            params(splited blocks or origin param) from server.
-        5. append concat_op to merge splited blocks to update local weights.
-
-        Steps to transpile pserver:
-        1. create new program for parameter server.
-        2. create params and grad variables that assigned to current server instance.
-        3. create a sub-block in the server side program
-        4. append ops that should run on current server instance.
-        5. add listen_and_serv op
-
-        :param trainer_id: one unique id for each trainer in a job.
-        :type trainer_id: int
-        :param program: program to transpile, default is default_main_program
-        :type program: Program
-        :param pservers: parameter server endpoints like "m1:6174,m2:6174"
-        :type pservers: string
-        :param trainers: total number of workers/trainers in the job
-        :type trainers: int
-        :param split_method: A function to determin how to split variables
-            to different servers equally.
-        :type split_method: function
-        :type sync_mode: boolean default True
-        :param sync_mode: if sync_mode is set True, it means that dist transpiler
-        will transpile the program into sync_mode pserver and trainer program.
-        """
-        assert (split_method.__bases__[0] == PSDispatcher)
-        if program is None:
-            program = default_main_program()
-        self.origin_program = program
-        self.trainer_num = trainers
-        self.sync_mode = sync_mode
-        # TODO(typhoonzero): currently trainer_id is fetched from cluster system
-        # like Kubernetes, we should port this to use etcd later when developing
-        # fluid distributed training with fault-tolerance.
-        self.trainer_id = trainer_id
-        pserver_endpoints = pservers.split(",")
-        self.pserver_endpoints = pserver_endpoints
-        self.optimize_ops, params_grads = self._get_optimize_pass()
-        ps_dispatcher = split_method(pserver_endpoints)
-
+    def _has_distributed_lookup_table(self):
         # process lookup_table_op
         # 1. check all lookup_table_op is distributed
         # 2. check all lookup_table_op share the same table.
         distributed_lookup_table_ops = []
         # support only one distributed_lookup_table now
         self.table_name = None
-        for op in program.global_block().ops:
+        for op in self.origin_program.global_block().ops:
             if op.type == LOOKUP_TABLE_TYPE:
                 if op.attrs['is_distributed'] is True:
                     if self.table_name is None:
@@ -246,20 +138,13 @@ class DistributeTranspiler:
                     if self.table_name is not None:
                         assert op.input("W")[0] != self.table_name
 
-        self.has_distributed_lookup_table = len(
-            distributed_lookup_table_ops) > 0
-
-        # step1: For large parameters and gradients, split them into smaller
-        # blocks.
-        param_list = []
-        grad_list = []
-        for p, g in params_grads:
-            # skip parameter marked not trainable
-            if type(p) == Parameter and p.trainable == False:
-                continue
-            param_list.append(p)
-            grad_list.append(g)
+        return len(distributed_lookup_table_ops) > 0
 
+    def _update_dist_lookup_table_vars(self, param_list, grad_list,
+                                       params_grads):
+        # TODO(wuyi): put find a way to put dist lookup table stuff all together.
+        # update self.table_param_grad and self.trainer_side_table_grad_list
+        program = self.origin_program
         if self.has_distributed_lookup_table:
             param_list = [
                 param for param in param_list if param.name != self.table_name
@@ -277,7 +162,7 @@ class DistributeTranspiler:
                 self.trainer_side_table_grad_list = [
                     program.global_block().create_var(
                         name="%s.trainer_%d.pserver_%d" %
-                        (table_grad_var.name, trainer_id, index),
+                        (table_grad_var.name, self.trainer_id, index),
                         type=table_grad_var.type,
                         shape=table_grad_var.shape,
                         dtype=table_grad_var.dtype)
@@ -293,23 +178,56 @@ class DistributeTranspiler:
                     for index in range(len(self.pserver_endpoints))
                 ]
 
-        grad_blocks = split_dense_variable(grad_list, len(pserver_endpoints))
-        param_blocks = split_dense_variable(param_list, len(pserver_endpoints))
+    def _init_splited_vars(self, slice_var_up):
+        # update these mappings for further transpile:
+        # 1. param_var_mapping: param var name -> [splited params vars]
+        # 2. grad_var_mapping: grad var name -> [splited grads vars]
+        # 3. grad_param_mapping: grad.blockx -> param.blockx
+        # 4. param_grad_ep_mapping: ep -> {"params": [], "grads": []}
+
+        param_list = []
+        grad_list = []
+        param_grad_set = set()
+        for p, g in self.params_grads:
+            # skip parameter marked not trainable
+            if type(p) == Parameter and p.trainable == False:
+                continue
+            if p.name not in param_grad_set:
+                param_list.append(p)
+                param_grad_set.add(p.name)
+            if g.name not in param_grad_set:
+                grad_list.append(g)
+                param_grad_set.add(g.name)
+
+        self._update_dist_lookup_table_vars(param_list, grad_list,
+                                            self.params_grads)
+
+        if slice_var_up:
+            # when we slice var up into blocks, we will slice the var according to
+            # pserver services' count. A pserver may have two or more listening ports.
+            grad_blocks = slice_variable(grad_list, len(self.pserver_endpoints))
+            param_blocks = slice_variable(param_list,
+                                          len(self.pserver_endpoints))
+        else:
+            # when we do NOT slice var up into blocks, we will always slice params
+            # grads into one block.
+            grad_blocks = slice_variable(grad_list, 1)
+            param_blocks = slice_variable(param_list, 1)
         assert (len(grad_blocks) == len(param_blocks))
-        # step2: Create new vars for the parameters and gradients blocks and
-        # add ops to do the split.
-        param_var_mapping = self._create_vars_from_blocklist(program,
-                                                             param_blocks)
-        grad_var_mapping = self._create_vars_from_blocklist(
-            program, grad_blocks, add_trainer_suffix=self.trainer_num > 1)
-        grad_param_mapping = dict()
+
+        # origin_varname -> [splited_var]
+        self.param_var_mapping = self._create_vars_from_blocklist(
+            self.origin_program, param_blocks)
+        self.grad_var_mapping = self._create_vars_from_blocklist(
+            self.origin_program,
+            grad_blocks,
+            add_trainer_suffix=self.trainer_num > 1)
+        self.grad_param_mapping = dict()
         for g, p in zip(grad_blocks, param_blocks):
             g_name, g_bid, _ = g.split(":")
             p_name, p_bid, _ = p.split(":")
-            grad_param_mapping[grad_var_mapping[g_name][int(g_bid)]] =  \
-                    param_var_mapping[p_name][int(p_bid)]
-
-        # step 3: transpile trainer side program, insert recv op and send op.
+            self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] =  \
+                    self.param_var_mapping[p_name][int(p_bid)]
 
         # create mapping of endpoint -> split var to create pserver side program
         self.param_grad_ep_mapping = dict()
@@ -322,11 +240,66 @@ class DistributeTranspiler:
             }) for ep in self.pserver_endpoints
         ]
 
+    def transpile(self,
+                  trainer_id,
+                  program=None,
+                  pservers="127.0.0.1:6174",
+                  trainers=1,
+                  slice_var_up=True,
+                  split_method=RoundRobin,
+                  sync_mode=True):
+        """
+        :param trainer_id: one unique id for each trainer in a job.
+        :type trainer_id: int
+        :param program: program to transpile, default is default_main_program
+        :type program: Program
+        :param pservers: parameter server endpoints like "m1:6174,m2:6174"
+        :type pservers: string
+        :param trainers: total number of workers/trainers in the job
+        :type trainers: int
+        :param split_method: A function to determin how to split variables
+            to different servers equally.
+        :type split_method: function
+        :type sync_mode: boolean default True
+        :param sync_mode: if sync_mode is set True, it means that dist transpiler
+        will transpile the program into sync_mode pserver and trainer program.
+        """
+        assert (split_method.__bases__[0] == PSDispatcher)
+        if program is None:
+            program = default_main_program()
+        self.origin_program = program
+        self.trainer_num = trainers
+        self.sync_mode = sync_mode
+        self.trainer_id = trainer_id
+        pserver_endpoints = pservers.split(",")
+        self.pserver_endpoints = pserver_endpoints
+        self.optimize_ops, self.params_grads = self._get_optimize_pass()
+
+        ps_dispatcher = split_method(self.pserver_endpoints)
+        self.has_distributed_lookup_table = self._has_distributed_lookup_table()
+
+        # split and create vars, then put splited vars in dicts for later use.
+        self._init_splited_vars(slice_var_up)
+
         # step 3.1: insert send op to send gradient vars to parameter servers
         ps_dispatcher.reset()
         send_vars = []
-        for orig_varname, splited_vars in grad_var_mapping.items():
+
+        # in general cases, the number of pservers is times of 2, and this
+        # will lead to uneven distribution among weights and bias:
+        #       fc_w@GRAD_trainer_0, fc_w@GRAD_trainer_1 --> pserver1
+        #       fc_b@GRAD_trainer_0, fc_b@GRAD_trainer_1 --> pserver2
+        # shuffle the map will avoid the uneven distribution above
+        grad_var_mapping_items = self.grad_var_mapping.items()
+        if not slice_var_up:
+            np.random.shuffle(grad_var_mapping_items)
+
+        for orig_varname, splited_vars in grad_var_mapping_items:
             eplist = ps_dispatcher.dispatch(splited_vars)
+
+            if not slice_var_up:
+                assert (len(splited_vars) == 1)
+
             if len(splited_vars) == 1:
                 orig_varname = splited_vars[0].name
                 index = find_op_by_output_arg(program.global_block(),
@@ -367,15 +340,16 @@ class DistributeTranspiler:
         # step 3.2: insert recv op to receive parameters from parameter server
         recv_vars = []
         for _, var in enumerate(send_vars):
-            recv_vars.append(grad_param_mapping[var])
+            recv_vars.append(self.grad_param_mapping[var])
         ps_dispatcher.reset()
         eplist = ps_dispatcher.dispatch(recv_vars)
 
         for i, ep in enumerate(eplist):
             self.param_grad_ep_mapping[ep]["params"].append(recv_vars[i])
             self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])
+
         # step4: Concat the parameters splits together after recv.
-        for varname, splited_var in param_var_mapping.iteritems():
+        for varname, splited_var in self.param_var_mapping.iteritems():
             eps = []
             for var in splited_var:
                 index = [v.name for v in recv_vars].index(var.name)
@@ -399,7 +373,7 @@ class DistributeTranspiler:
                 RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
             })
 
-        for varname, splited_var in param_var_mapping.iteritems():
+        for varname, splited_var in self.param_var_mapping.iteritems():
             if len(splited_var) <= 1:
                 continue
             orig_param = program.global_block().vars[varname]
@@ -440,7 +414,6 @@ class DistributeTranspiler:
             # we don't need to create them when grad arrives.
             # change client side var name to origin name by
             # removing ".trainer_%d" suffix
-
             suff_idx = v.name.find(".trainer_")
             if suff_idx >= 0:
                 orig_var_name = v.name[:suff_idx]
@@ -477,24 +450,14 @@ class DistributeTranspiler:
         # located on current pserver
         opt_op_on_pserver = []
         for _, op in enumerate(self.optimize_ops):
-            if self._is_opt_op(op) and self._is_opt_op_on_pserver(endpoint, op):
+            if self._is_optimizer_op(op) and self._is_opt_op_on_pserver(
+                    endpoint, op):
                 opt_op_on_pserver.append(op)
         # step 3.3
         # Iterate through the ops, and if an op and the optimize ops
         # which located on current pserver are in one set, then
         # append it into the sub program.
 
-        # We try to put optimization program run parallelly, assume
-        # optimization program always looks like:
-        #
-        # prevop -> prevop -> opt op -> following op -> following op; ->
-        # prevop -> prevop -> opt op -> following op -> following op; ->
-        # global op -> global op
-        #
-        # we put operators that can run parallelly to many program blocks.
-        # in above example, we seperate ops by the ";". Global ops must run
-        # after all the optimize ops finished.
-
         global_ops = []
         # HACK: optimization global ops only used to scale beta1 and beta2
         # replace it with dependency engine.
@@ -502,12 +465,18 @@ class DistributeTranspiler:
             if self._is_adam_connected_op(op):
                 global_ops.append(op)
 
-        def __append_optimize_op__(op, block, grad_to_block_id):
-            if self._is_opt_op(op):
+        def __append_optimize_op__(op, block, grad_to_block_id, merged_var):
+            if self._is_optimizer_op(op):
                 self._append_pserver_ops(block, op, endpoint, grad_to_block_id,
-                                         self.origin_program)
+                                         self.origin_program, merged_var)
             else:
-                self._append_pserver_non_opt_ops(block, op)
+                self._append_pserver_non_opt_ops(block, op, endpoint)
+
+        def __op_have_grad_input__(op):
+            for varname in op.input_arg_names:
+                if varname.find("@GRAD") >= 0:
+                    return varname
+            return ""
 
         # append lr decay ops to the child block if exists
         lr_ops = self._get_lr_ops()
@@ -515,17 +484,26 @@ class DistributeTranspiler:
             lr_decay_block = pserver_program.create_block(
                 pserver_program.num_blocks - 1)
             for _, op in enumerate(lr_ops):
-                self._append_pserver_non_opt_ops(lr_decay_block, op)
+                self._append_pserver_non_opt_ops(lr_decay_block, op, endpoint)
 
         # append op to the current block
         grad_to_block_id = []
         pre_block_idx = pserver_program.num_blocks - 1
         for idx, opt_op in enumerate(opt_op_on_pserver):
             per_opt_block = pserver_program.create_block(pre_block_idx)
+            # append grad merging ops before clip and weight decay
+            for _, op in enumerate(self.optimize_ops):
+                # find the origin @GRAD var before clipping
+                grad_varname_for_block = __op_have_grad_input__(op)
+                if ufind.is_connected(op, opt_op) and grad_varname_for_block:
+                    merged_var = self._append_pserver_grad_merge_ops(
+                        per_opt_block, grad_varname_for_block, endpoint,
+                        grad_to_block_id, self.origin_program)
             for _, op in enumerate(self.optimize_ops):
                 # optimizer is connected to itself
                 if ufind.is_connected(op, opt_op) and op not in global_ops:
-                    __append_optimize_op__(op, per_opt_block, grad_to_block_id)
+                    __append_optimize_op__(op, per_opt_block, grad_to_block_id,
+                                           merged_var)
 
         # append global ops
         if global_ops:
@@ -533,15 +511,7 @@ class DistributeTranspiler:
                 pserver_program.num_blocks - 1)
             for glb_op in global_ops:
                 __append_optimize_op__(glb_op, opt_state_block,
-                                       grad_to_block_id)
-
-        # NOT USED: single block version:
-        #
-        # for _, op in enumerate(self.optimize_ops):
-        #     for _, opt_op in enumerate(opt_op_on_pserver):
-        #         if ufind.is_connected(op, opt_op):
-        #             __append_optimize_op__(glb_op, optimize_block)
-        #             break
+                                       grad_to_block_id, None)
 
         # process distributed lookup_table
         prefetch_block = None
@@ -631,6 +601,8 @@ class DistributeTranspiler:
                     attrs=op.attrs)
         return s_prog
 
+    # ====================== private transpiler functions =====================
+
     # transpiler function for dis lookup_table
     def _replace_lookup_table_op_with_prefetch(self, program,
                                                pserver_endpoints):
@@ -836,7 +808,6 @@ class DistributeTranspiler:
 
         return table_opt_block
 
-    # ====================== private transpiler functions =====================
     def _create_vars_from_blocklist(self,
                                     program,
                                     block_list,
@@ -863,6 +834,9 @@ class DistributeTranspiler:
             if not block_map.has_key(varname):
                 block_map[varname] = []
             block_map[varname].append((long(offset), long(size)))
+        # Do not remove this important debug message:
+        print("block map: %s" % block_map)
+
         for varname, splited in block_map.iteritems():
             orig_var = program.global_block().var(varname)
             if len(splited) == 1:
@@ -979,44 +953,57 @@ class DistributeTranspiler:
             pass
         return orig_shape
 
-    def _orig_varname(self, varname):
-        suff_idx = varname.find(".trainer_")
+    def _get_varname_parts(self, varname):
+        # returns origin, blockid, trainerid
         orig_var_name = ""
-        if suff_idx >= 0:
-            orig_var_name = varname[:suff_idx]
+        trainer_part = ""
+        block_part = ""
+        trainer_idx = varname.find(".trainer_")
+        if trainer_idx >= 0:
+            trainer_part = varname[trainer_idx + 1:]
         else:
-            orig_var_name = varname
-        return orig_var_name
+            trainer_idx = len(varname)
+        block_index = varname.find(".block")
+        if block_index >= 0:
+            block_part = varname[block_index + 1:trainer_idx]
+        else:
+            block_index = len(varname)
+        orig_var_name = varname[0:min(block_index, trainer_idx)]
+        return orig_var_name, block_part, trainer_part
 
-    def _append_pserver_ops(self, optimize_block, opt_op, endpoint,
+    def _orig_varname(self, varname):
+        orig, _, _ = self._get_varname_parts(varname)
+        return orig
+
+    def _append_pserver_grad_merge_ops(self, optimize_block,
+                                       grad_varname_for_block, endpoint,
                                        grad_to_block_id, origin_program):
         program = optimize_block.program
         pserver_block = program.global_block()
-        new_inputs = dict()
-        # update param/grad shape first, then other inputs like
-        # moment can use the updated shape
-        for key in opt_op.input_names:
-            if key == "Grad":
         grad_block = None
         for g in self.param_grad_ep_mapping[endpoint]["grads"]:
-                    if same_or_split_var(
-                            self._orig_varname(g.name),
-                            self._orig_varname(opt_op.input(key)[0])):
+            if self._orig_varname(g.name) == \
+                    self._orig_varname(grad_varname_for_block):
                 grad_block = g
                 break
         if not grad_block:
             # do not append this op if current endpoint
             # is not dealing with this grad block
             return
+        orig_varname, block_name, trainer_name = self._get_varname_parts(
+            grad_block.name)
+        if block_name:
+            merged_var_name = '.'.join([orig_varname, block_name])
+        else:
+            merged_var_name = orig_varname
         merged_var = \
-                    pserver_block.vars[self._orig_varname(grad_block.name)]
-                grad_to_block_id.append(merged_var.name + ":" + str(
-                    optimize_block.idx))
+            pserver_block.vars[merged_var_name]
+        grad_to_block_id.append(merged_var.name + ":" + str(optimize_block.idx))
         if self.sync_mode and self.trainer_num > 1:
             vars2merge = []
             for i in xrange(self.trainer_num):
                 per_trainer_name = "%s.trainer_%d" % \
-                        (self._orig_varname(grad_block.name), i)
+                (merged_var_name, i)
                 vars2merge.append(pserver_block.vars[per_trainer_name])
 
             optimize_block.append_op(
@@ -1030,7 +1017,17 @@ class DistributeTranspiler:
                     inputs={"X": merged_var},
                     outputs={"Out": merged_var},
                     attrs={"scale": 1.0 / float(self.trainer_num)})
+        return merged_var
 
+    def _append_pserver_ops(self, optimize_block, opt_op, endpoint,
+                            grad_to_block_id, origin_program, merged_var):
+        program = optimize_block.program
+        pserver_block = program.global_block()
+        new_inputs = dict()
+        # update param/grad shape first, then other inputs like
+        # moment can use the updated shape
+        for key in opt_op.input_names:
+            if key == "Grad":
                 new_inputs[key] = merged_var
             elif key == "Param":
                 # param is already created on global program
@@ -1089,17 +1086,31 @@ class DistributeTranspiler:
             outputs=outputs,
             attrs=opt_op.attrs)
 
-    def _append_pserver_non_opt_ops(self, optimize_block, opt_op):
+    def _is_splited_grad_var(self, var, var_dict):
+        grad_block = None
+        for _, g in var_dict.iteritems():
+            if self._orig_varname(g.name) == self._orig_varname(var.name):
+                if g.name.find(".trainer_") == -1:
+                    grad_block = g
+                    break
+        return grad_block
+
+    def _append_pserver_non_opt_ops(self, optimize_block, opt_op, endpoint):
         program = optimize_block.program
         # Append the ops for parameters that do not need to be optimized/updated
         inputs = self._get_input_map_from_op(
             self.origin_program.global_block().vars, opt_op)
-        for varlist in inputs.itervalues():
+        for key, varlist in inputs.iteritems():
             if not isinstance(varlist, list):
                 varlist = [varlist]
-
             for var in varlist:
-                if not program.global_block().vars.has_key(var.name):
+                # for ops like clipping and weight decay, get the splited var
+                # for inputs/outputs
+                grad_block = self._is_splited_grad_var(
+                    var, program.global_block().vars)
+                if grad_block:
+                    inputs[key] = grad_block
+                elif not program.global_block().vars.has_key(var.name):
                     program.global_block().create_var(
                         name=var.name,
                         persistable=var.persistable,
@@ -1108,12 +1119,15 @@ class DistributeTranspiler:
 
         outputs = self._get_output_map_from_op(
             self.origin_program.global_block().vars, opt_op)
-
-        for varlist in outputs.itervalues():
+        for key, varlist in outputs.iteritems():
             if not isinstance(varlist, list):
                 varlist = [varlist]
-
             for var in varlist:
+                grad_block = self._is_splited_grad_var(
+                    var, program.global_block().vars)
+                if grad_block:
+                    outputs[key] = grad_block
+                elif not program.global_block().vars.has_key(var.name):
                     program.global_block().clone_variable(var)
 
         optimize_block.append_op(
@@ -1160,9 +1174,17 @@ class DistributeTranspiler:
                     ufind.union(op1, op2)
         return ufind
 
-    def _is_opt_op(self, op):
-        # NOTE: It's a HACK implement.
-        # optimize op: SGDOptimize, MomentumOptimizer, AdamOptimizer and etc...
+    def _is_opt_role_op(self, op):
+        # NOTE: depend on oprole to find out whether this op is for
+        # optimize
+        op_maker = core.op_proto_and_checker_maker
+        optimize_role = core.op_proto_and_checker_maker.OpRole.Optimize
+        if op_maker.kOpRoleAttrName() in op.attrs and \
+            int(op.attrs[op_maker.kOpRoleAttrName()]) == int(optimize_role):
+            return True
+        return False
+
+    def _is_optimizer_op(self, op):
         if "Param" in op.input_names and \
             "LearningRate" in op.input_names:
             return True
@@ -1212,7 +1234,7 @@ class DistributeTranspiler:
         # find learning rate variables by optimize op
         lr_vars = set()
         for op in self.optimize_ops:
-            if self._is_opt_op(op):
+            if self._is_optimizer_op(op):
                 lr_vars.add(op.input("LearningRate")[0])
 
         find_ops = []
@@ -1229,7 +1251,7 @@ class DistributeTranspiler:
                 # NOTE: we need to skip all optimize ops, since it is connected
                 # with forward/backward ops and lr ops, we only need the lr ops.
                 if op1 != op2 and self._is_op_connected(op1, op2) and \
-                    not self._is_opt_op(op1) and not self._is_opt_op(op2):
+                    not self._is_optimizer_op(op1) and not self._is_optimizer_op(op2):
                     ufind.union(op1, op2)
         # find all ops which is related with lr var
         for op1 in block.ops:
@@ -1250,13 +1272,21 @@ class DistributeTranspiler:
         block = self.origin_program.global_block()
         opt_ops = []
         params_grads = []
+        origin_var_dict = self.origin_program.global_block().vars
         for op in block.ops:
-            if self._is_opt_op(op):
+            if self._is_opt_role_op(op):
                 opt_ops.append(op)
-                params_grads.append((self.origin_program.global_block().var(
-                    op.input("Param")[0]),
-                                     self.origin_program.global_block().var(
-                                         op.input("Grad")[0])))
+                # HACK(wuyi): if we find grad vars from input of optimize
+                # ops, we may get the output of clip op. Use syntax "@GRAD"
+                # and op_role_var to get the pair.
+                for input_name in op.input_arg_names:
+                    if input_name.find("@GRAD") != -1 and \
+                        op.attrs[RPC_OP_ROLE_ATTR_NAME]:
+                        param_name = op.attrs[OP_ROLE_VAR_ATTR_NAME][0]
+                        params_grads.append([
+                            origin_var_dict[param_name],
+                            origin_var_dict[input_name]
+                        ])
             elif self._is_adam_connected_op(op):
                 opt_ops.append(op)
             else:

python/paddle/fluid/transpiler/distribute_transpiler_simple.py

-#   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.
-
-from ..framework import Program, default_main_program, Parameter, Variable
-from ..layer_helper import LayerHelper
-
-
-def hash_name_to_server(params_grads, pserver_endpoints):
-    """
-    :param param_grads:
-    :return: a map of pserver endpoint -> 
-                    params -> [param list]
-                    grads  -> [grad list]
-    """
-
-    def _hash_param(param_name, total):
-        return hash(param_name) % total
-
-    param_grad_map = dict()
-    for param, grad in params_grads:
-        if param.trainable is True and grad is not None:
-            server_id = _hash_param(param.name, len(pserver_endpoints))
-            server_for_param = pserver_endpoints[server_id]
-            if not param_grad_map.has_key(server_for_param):
-                param_grad_map[server_for_param] = {"params": [], "grads": []}
-            param_grad_map[server_for_param]["params"].append(param)
-            param_grad_map[server_for_param]["grads"].append(grad)
-
-    return param_grad_map
-
-
-def round_robin(params_grads, pserver_endpoints):
-    assert (len(params_grads) > len(pserver_endpoints))
-
-    param_grad_map = dict()
-    pserver_idx = 0
-    for param, grad in params_grads:
-        if param.trainable is True:
-            server_for_param = pserver_endpoints[pserver_idx]
-            if not param_grad_map.has_key(server_for_param):
-                param_grad_map[server_for_param] = {"params": [], "grads": []}
-
-            param_grad_map[server_for_param]["params"].append(param)
-            param_grad_map[server_for_param]["grads"].append(grad)
-
-            pserver_idx += 1
-            if pserver_idx >= len(pserver_endpoints):
-                pserver_idx = 0
-    return param_grad_map
-
-
-class SimpleDistributeTranspiler:
-    def transpile(self,
-                  optimize_ops,
-                  params_grads,
-                  program=None,
-                  pservers="127.0.0.1:6174",
-                  trainers=1,
-                  split_method=round_robin):
-        """
-            Transpile the program to a distributed data-parallelism programs.
-
-            The main_program will be transform to use a remote parameter server
-            to do parameter optimization. And the optimization graph will be put
-            in to a parameter server program.
-
-            Use different methods to split trainable varialbles to different
-            parameter servers.
-
-            Example to run:
-
-            exe = fluid.Executor(place)
-            t = fluid.DistributeTranspiler()
-            t.transpile(optimize_ops, params_grads, pservers="127.0.0.1:6174", trainers=1)
-
-            pserver_endpoint = os.getenv("PSERVER")
-            if pserver_endpoint:
-                pserver_prog = t.get_pserver_program(pserver_endpoint, optimize_ops)
-                exe.run(fluid.default_startup_program())
-                exe.run(pserver_prog)
-            else:
-                feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
-                exe.run(fluid.default_startup_program())
-
-                for pass_id in range(PASS_NUM):
-                    ...
-
-            :param optimize_ops: op list of optimization, should be the
-                                 return value of Optimizer.minimize
-            :type optimize_ops: list
-            :param program: program to optimize, default default_main_program
-            :param pservers: parameter server endpoints like "m1:6174,m2:6174"
-            :type pservers: string
-
-            :return: return a list of programs
-        """
-        if program is None:
-            program = default_main_program()
-        self.program = program
-        self.trainers = trainers
-        self.optimize_ops = optimize_ops
-        self._optimize_distributed(
-            optimize_ops,
-            program,
-            params_grads,
-            pservers=pservers,
-            trainers=trainers,
-            split_method=split_method)
-
-    def _clone_param(self, block, v):
-        assert isinstance(v, Parameter)
-        new_p = Parameter(
-            block=block,
-            shape=v.shape,
-            dtype=v.dtype,
-            type=v.type,
-            lod_level=v.lod_level,
-            stop_gradient=v.stop_gradient,
-            trainable=v.trainable,
-            optimize_attr=v.optimize_attr,
-            regularizer=v.regularizer,
-            name=v.name)
-        block.vars[new_p.name] = new_p
-
-    def _clone_var(self, block, var):
-        assert isinstance(var, Variable)
-        return block.create_var(
-            name=var.name,
-            shape=var.shape,
-            dtype=var.dtype,
-            type=var.type,
-            lod_level=var.lod_level,
-            persistable=var.persistable)
-
-    def _optimize_distributed(self, optimize_ops, program, params_and_grads,
-                              **kwargs):
-        if kwargs.has_key("split_method"):
-            split_method = kwargs["split_method"]
-        else:
-            split_method = round_robin
-
-        assert (callable(split_method))
-        pserver_endpoints = kwargs["pservers"].split(",")
-        self.param_grad_map = split_method(params_and_grads, pserver_endpoints)
-
-        send_op_ordered_inputs = []
-        send_op_ordered_outputs = []
-        epmap = []
-        for ep, v in self.param_grad_map.iteritems():
-            send_op_ordered_inputs.extend(v["grads"])
-            send_op_ordered_outputs.extend(v["params"])
-            for i in v["grads"]:
-                epmap.append(ep)
-        send_op = program.global_block().append_op(
-            type="send",
-            inputs={"X": send_op_ordered_inputs
-                    },  # inputs is a list of tensors to be send
-            outputs={"Out": send_op_ordered_outputs},
-            attrs={"endpoints": pserver_endpoints,
-                   "epmap": epmap})
-
-    def get_trainer_program(self):
-        # remove optimize ops and add a send op to main_program
-        self.program.global_block().delete_ops(self.optimize_ops)
-        return self.program
-
-    def _create_var_for_trainers(self, block, var, trainers):
-        var_list = []
-        for i in xrange(trainers):
-            var_each = block.create_var(
-                name="%s.trainer_%d" % (var.name, i),
-                psersistable=var.persistable,
-                dtype=var.dtype,
-                shape=var.shape)
-            var_list.append(var_each)
-        return var_list
-
-    def get_pserver_program(self, endpoint, optimize_ops):
-        pserver_program = Program()
-        for v in self.param_grad_map[endpoint]["params"]:
-            self._clone_param(pserver_program.global_block(), v)
-
-        optimize_sub_program = Program()
-        grad_var_names = [
-            var.name for var in self.param_grad_map[endpoint]["grads"]
-        ]
-        for opt_op in optimize_ops:
-            for _, var in opt_op.inputs.iteritems():
-                # NOTE: append operators to merge gradients from multiple
-                # trainers. If trainers == 1, this is not needed.
-                if self.trainers > 1 and var.name in grad_var_names:
-                    vars2merge = self._create_var_for_trainers(
-                        optimize_sub_program.global_block(), var, self.trainers)
-                    merged_var = optimize_sub_program.global_block().create_var(
-                        name=var.name,
-                        persistable=var.persistable,
-                        dtype=var.dtype,
-                        shape=var.shape)
-                    optimize_sub_program.global_block().append_op(
-                        type="sum",
-                        inputs={"X": vars2merge},
-                        outputs={"Out": merged_var})
-                    optimize_sub_program.global_block().append_op(
-                        type="scale",
-                        inputs={"X": merged_var},
-                        outputs={"Out": merged_var},
-                        attrs={"scale": 1.0 / float(self.trainers)})
-                else:
-                    optimize_sub_program.global_block().create_var(
-                        name=var.name,
-                        persistable=var.persistable,
-                        dtype=var.dtype,
-                        shape=var.shape)
-
-            if opt_op.inputs.has_key("Grad"):
-                if opt_op.inputs["Grad"].name in grad_var_names:
-                    optimize_sub_program.global_block().append_op(
-                        type=opt_op.type,
-                        inputs=opt_op.inputs,
-                        outputs=opt_op.outputs,
-                        attrs=opt_op.attrs)
-            else:
-                optimize_sub_program.global_block().append_op(
-                    type=opt_op.type,
-                    inputs=opt_op.inputs,
-                    outputs=opt_op.outputs,
-                    attrs=opt_op.attrs)
-        pserver_program.global_block().append_op(
-            type="recv",
-            inputs={"RX":
-                    self.param_grad_map[endpoint]["grads"]},  # grads to recv
-            outputs={},
-            attrs={
-                "OptimizeBlock": optimize_sub_program.global_block(),
-                "endpoint": endpoint,
-                "ParamList":
-                [p.name for p in self.param_grad_map[endpoint]["params"]],
-                "GradList":
-                [p.name for p in self.param_grad_map[endpoint]["grads"]],
-                "Trainers": self.trainers
-            })
-        pserver_program.sync_with_cpp()
-        return pserver_program

python/paddle/v2/minibatch.py

@@ -15,7 +15,7 @@
 __all__ = ['batch']
 
 
-def batch(reader, batch_size):
+def batch(reader, batch_size, drop_last=False):
     """
     Create a batched reader.
 
@@ -23,6 +23,8 @@ def batch(reader, batch_size):
     :type reader: callable
     :param batch_size: size of each mini-batch
     :type batch_size: int
+    :param drop_last: drop the last batch, if the size of last batch is not equal to batch_size.
+    :type drop_last: bool
     :return: the batched reader.
     :rtype: callable
     """
@@ -35,7 +37,7 @@ def batch(reader, batch_size):
             if len(b) == batch_size:
                 yield b
                 b = []
-        if b:
+        if drop_last == False and len(b) != 0:
             yield b
 
     return batch_reader

python/setup.py.in

@@ -69,7 +69,8 @@ packages=['paddle',
           'paddle.fluid.proto',
           'paddle.fluid.proto.profiler',
           'paddle.fluid.layers',
-          'paddle.fluid.transpiler']
+          'paddle.fluid.transpiler',
+          'paddle.fluid.transpiler.details']
 
 if '${WITH_FLUID_ONLY}'== 'OFF':
     packages+=['paddle.proto',

tools/codestyle/.gitignore

+*.pyc