Merge branch 'develop' into high_level_api_machine_translation

CMakeLists.txt

@@ -59,7 +59,6 @@ option(USE_NNPACK       "Compile PaddlePaddle with NNPACK library"      OFF)
 option(WITH_DISTRIBUTE  "Compile with grpc distributed support"         OFF)
 option(USE_EIGEN_FOR_BLAS   "Use matrix multiplication in Eigen"        OFF)
 option(WITH_ARM_FP16    "Use half precision support on armv8.2-a cpu"   OFF)
-option(WITH_FAST_BUNDLE_TEST    "Bundle tests that can be run in a single process together to reduce launch overhead"   OFF)
 
 # CMAKE_BUILD_TYPE
 if(NOT CMAKE_BUILD_TYPE)
@@ -100,6 +99,9 @@ endif()
 set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
   "A path setting third party libraries download & build directories.")
 
+set(FLUID_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_install_dir" CACHE STRING
+  "A path setting fluid shared and static libraries")
+
 if (WITH_C_API AND WITH_PYTHON)
   message(WARNING "It is suggest not embedded a python interpreter in Paddle "
     "when using C-API. It will give an unpredictable behavior when using a "
@@ -117,13 +119,14 @@ else()
 endif()
 
 set(WITH_MKLML ${WITH_MKL})
-if (WITH_MKL AND AVX2_FOUND)
-    set(WITH_MKLDNN ON)
-else()
-    message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
-    set(WITH_MKLDNN OFF)
+if (NOT DEFINED WITH_MKLDNN)
+    if (WITH_MKL AND AVX2_FOUND)
+        set(WITH_MKLDNN ON)
+    else()
+        message(STATUS "Do not have AVX2 intrinsics and disabled MKL-DNN")
+        set(WITH_MKLDNN OFF)
+    endif()
 endif()
-
 ########################################################################################
 
 include(external/mklml)     # download mklml package

Dockerfile

@@ -70,7 +70,7 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
 # specify sphinx version as 1.5.6 and remove -U option for [pip install -U
 # sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
 # version(1.7.1 for now), which causes building documentation failed.
-RUN pip install --upgrade pip==9.0.3 && \
+RUN easy_install -U pip && \
     pip install -U wheel && \
     pip install -U docopt PyYAML sphinx==1.5.6 && \
     pip install sphinx-rtd-theme==0.1.9 recommonmark

benchmark/cluster/vgg16/vgg16_fluid.py

@@ -38,7 +38,7 @@ def str2bool(v):
 
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
-    '--batch_size', type=int, default=128, help="Batch size for training.")
+    '--batch_size', type=int, default=16, help="Batch size for training.")
 parser.add_argument(
     '--learning_rate',
     type=float,
@@ -61,7 +61,7 @@ parser.add_argument(
 parser.add_argument(
     '--data_set',
     type=str,
-    default='cifar10',
+    default='flowers',
     choices=['cifar10', 'flowers'],
     help='Optional dataset for benchmark.')
 parser.add_argument(
@@ -200,26 +200,30 @@ def main():
                     fetch_list=[avg_cost, batch_acc, batch_size])
                 return loss, acc, b_size
 
-            if args.profile and args.task_index == 0:
-                # warmup.
-                for batch_id, data in enumerate(train_reader()):
-                    if batch_id > 5: break
-                    run_step(batch_id, data)
-                with profiler.profiler('All', 'total', '/tmp/profile_vgg'):
+            if args.profile:
+                with profiler.profiler('All', 'total',
+                                       '/tmp/profile_vgg_%d' % args.task_index):
                     for batch_id, data in enumerate(train_reader()):
                         if batch_id > 5: break
                         run_step(batch_id, data)
 
+            total_time = 0.0
+            count = 0
             for batch_id, data in enumerate(train_reader()):
                 ts = time.time()
                 loss, acc, b_size = run_step(batch_id, data)
                 iters += 1
                 num_samples += len(data)
                 train_pass_acc.add(value=acc, weight=b_size)
+
+                duration = time.time() - ts
+                total_time += duration
+                count += len(data)
                 print(
                     "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, "
-                    "Speed = %.2f img/s" % (pass_id, iters, loss, acc,
-                                            len(data) / (time.time() - ts))
+                    "Speed = %.2f (%.2f) img/s" % (pass_id, iters, loss, acc,
+                                                   len(data) / duration,
+                                                   count / total_time)
                 )  # The accuracy is the accumulation of batches, but not the current batch.
 
             pass_elapsed = time.time() - start_time

benchmark/fluid/README.md

+# Fluid Benchmark
+
+This directory contains several models configurations and tools that used to run
+Fluid benchmarks for local and distributed training.
+
+
+## Run the Benchmark
+
+To start, run the following command to get the full help message:
+
+```bash
+python fluid_benchmark.py --help
+```
+
+Currently supported `--model` argument include:
+
+* mnist
+* resnet
+    * you can chose to use different dataset using `--data_set cifar10` or
+      `--data_set flowers`.
+* vgg
+* stacked_dynamic_lstm
+* machine_translation
+
+* Run the following command to start a benchmark job locally:
+    ```bash
+      python fluid_benchmark.py --model mnist --parallel 1 --device GPU --with_test
+    ```
+    You can choose to use GPU/CPU training. With GPU training, you can specify
+    `--parallel 1` to run multi GPU training.
+* Run distributed training with parameter servers:
+    * start parameter servers:
+        ```bash
+        PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method pserver
+        ```
+    * start trainers:
+        ```bash
+        PADDLE_TRAINING_ROLE=PSERVER PADDLE_PSERVER_PORT=7164 PADDLE_PSERVER_IPS=127.0.0.1 PADDLE_TRAINERS=1 PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method pserver
+        ```
+* Run distributed training using NCCL2
+    ```bash
+    PADDLE_PSERVER_PORT=7164 PADDLE_TRAINER_IPS=192.168.0.2,192.168.0.3  PADDLE_CURRENT_IP=127.0.0.1 PADDLE_TRAINER_ID=0 python fluid_benchmark.py --model mnist --parallel 0 --device GPU --update_method nccl2
+    ```
+
+## Run Distributed Benchmark on Kubernetes Cluster
+
+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 --with_test" --disttype pserver
+```
+
+Then the yaml files are generated under directory `myjob`, you can run:
+
+```bash
+kubectl create -f myjob/
+```
+
+The job shall start.

benchmark/fluid/fluid_benchmark.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 argparse
+import cProfile
+import time
+import os
+
+import numpy as np
+
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.fluid.profiler as profiler
+import paddle.fluid.transpiler.distribute_transpiler as distribute_transpiler
+
+BENCHMARK_MODELS = [
+    "machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
+]
+
+
+def parse_args():
+    parser = argparse.ArgumentParser('Fluid model benchmarks.')
+    parser.add_argument(
+        '--model',
+        type=str,
+        choices=BENCHMARK_MODELS,
+        default='resnet',
+        help='The model to run benchmark with.')
+    parser.add_argument(
+        '--batch_size', type=int, default=32, help='The minibatch size.')
+    parser.add_argument(
+        '--learning_rate',
+        type=float,
+        default=0.001,
+        help='The minibatch size.')
+    # TODO(wuyi): add "--use_fake_data" option back.
+    parser.add_argument(
+        '--skip_batch_num',
+        type=int,
+        default=5,
+        help='The first num of minibatch num to skip, for better performance test'
+    )
+    parser.add_argument(
+        '--iterations', type=int, default=80, help='The number of minibatches.')
+    parser.add_argument(
+        '--pass_num', type=int, default=100, help='The number of passes.')
+    parser.add_argument(
+        '--data_format',
+        type=str,
+        default='NCHW',
+        choices=['NCHW', 'NHWC'],
+        help='The data data_format, now only support NCHW.')
+    parser.add_argument(
+        '--device',
+        type=str,
+        default='GPU',
+        choices=['CPU', 'GPU'],
+        help='The device type.')
+    parser.add_argument(
+        '--gpus',
+        type=int,
+        default=1,
+        help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
+    parser.add_argument(
+        '--data_set',
+        type=str,
+        default='flowers',
+        choices=['cifar10', 'flowers'],
+        help='Optional dataset for benchmark.')
+    parser.add_argument(
+        '--infer_only', action='store_true', help='If set, run forward only.')
+    parser.add_argument(
+        '--use_cprof', action='store_true', help='If set, use cProfile.')
+    parser.add_argument(
+        '--use_nvprof',
+        action='store_true',
+        help='If set, use nvprof for CUDA.')
+    parser.add_argument(
+        '--no_test',
+        action='store_false',
+        help='If set, test the testset during training.')
+    parser.add_argument(
+        '--memory_optimize',
+        action='store_true',
+        help='If set, optimize runtime memory before start.')
+    parser.add_argument(
+        '--update_method',
+        type=str,
+        default='local',
+        choices=['local', 'pserver', 'nccl2'],
+        help='Choose parameter update method, can be local, pserver, nccl2.')
+    args = parser.parse_args()
+    return args
+
+
+def append_nccl2_prepare():
+    if os.getenv("PADDLE_TRAINER_ID", None) != None:
+        # append gen_nccl_id at the end of startup program
+        trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+        port = os.getenv("PADDLE_PSERVER_PORT")
+        worker_ips = os.getenv("PADDLE_TRAINER_IPS")
+        worker_endpoints = []
+        for ip in worker_ips.split(","):
+            worker_endpoints.append(':'.join([ip, port]))
+        num_trainers = len(worker_endpoints)
+        current_endpoint = os.getenv("PADDLE_CURRENT_IP") + ":" + port
+        worker_endpoints.remove(current_endpoint)
+
+        nccl_id_var = fluid.default_startup_program().global_block().create_var(
+            name="NCCLID",
+            persistable=True,
+            type=fluid.core.VarDesc.VarType.RAW)
+        fluid.default_startup_program().global_block().append_op(
+            type="gen_nccl_id",
+            inputs={},
+            outputs={"NCCLID": nccl_id_var},
+            attrs={
+                "endpoint": current_endpoint,
+                "endpoint_list": worker_endpoints,
+                "trainer_id": trainer_id
+            })
+        return nccl_id_var, num_trainers, trainer_id
+    else:
+        raise Exception(
+            "must set PADDLE_TRAINER_ID env variables for dist train.")
+
+
+def dist_transpile():
+    if "PADDLE_TRAINING_ROLE" not in os.environ:
+        return None, None
+
+    # the port of all pservers, needed by both trainer and pserver
+    port = os.getenv("PADDLE_PSERVER_PORT", "6174")
+    # comma separated ips of all pservers, needed by trainer and
+    # pserver
+    pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "")
+    eplist = []
+    for ip in pserver_ips.split(","):
+        eplist.append(':'.join([ip, port]))
+    pserver_endpoints = ",".join(eplist)
+    # total number of workers/trainers in the job, needed by
+    # trainer and pserver
+    trainers = int(os.getenv("PADDLE_TRAINERS"))
+    # the IP of the local machine, needed by pserver only
+    current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
+    # the unique trainer id, starting from 0, needed by trainer
+    # only
+    trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
+    # the role, should be either PSERVER or TRAINER
+    training_role = os.getenv("PADDLE_TRAINING_ROLE")
+
+    t = distribute_transpiler.DistributeTranspiler()
+    t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
+    if training_role == "PSERVER":
+        pserver_program = t.get_pserver_program(current_endpoint)
+        pserver_startup_program = t.get_startup_program(current_endpoint,
+                                                        pserver_program)
+        return pserver_program, pserver_startup_program
+    elif training_role == "TRAINER":
+        train_program = t.get_trainer_program()
+        return train_program, fluid.default_startup_program()
+    else:
+        raise ValueError(
+            'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
+        )
+
+
+def test(exe, inference_program, test_reader, feeder, batch_acc):
+    accuracy_evaluator = fluid.metrics.Accuracy()
+    for batch_id, data in enumerate(test_reader()):
+        acc = exe.run(inference_program,
+                      feed=feeder.feed(data),
+                      fetch_list=[batch_acc])
+        accuracy_evaluator.update(value=np.array(acc), weight=len(data))
+
+    return accuracy_evaluator.eval()
+
+
+# TODO(wuyi): replace train, train_parallel, test functions with new trainer
+# API once it is ready.
+def train(avg_loss, infer_prog, optimizer, train_reader, test_reader, batch_acc,
+          args, train_prog, startup_prog):
+    if os.getenv("PADDLE_TRAINING_ROLE") == "PSERVER":
+        place = core.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_prog)
+        exe.run(train_prog)
+        return
+
+    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
+    exe = fluid.Executor(place)
+    exe.run(startup_prog)
+    feed_var_list = [
+        var for var in train_prog.global_block().vars.itervalues()
+        if var.is_data
+    ]
+    feeder = fluid.DataFeeder(feed_var_list, place)
+
+    iters, num_samples, start_time = 0, 0, time.time()
+    for pass_id in range(args.pass_num):
+        train_losses = []
+        for batch_id, data in enumerate(train_reader()):
+            if iters == args.skip_batch_num:
+                start_time = time.time()
+                num_samples = 0
+            if iters == args.iterations:
+                break
+            loss = exe.run(train_prog,
+                           feed=feeder.feed(data),
+                           fetch_list=[avg_loss])
+            iters += 1
+            num_samples += len(data)
+            train_losses.append(loss)
+            print("Pass: %d, Iter: %d, Loss: %f\n" %
+                  (pass_id, iters, np.mean(train_losses)))
+        train_elapsed = time.time() - start_time
+        examples_per_sec = num_samples / train_elapsed
+        print('\nTotal examples: %d, total time: %.5f, %.5f examples/sec\n' %
+              (num_samples, train_elapsed, examples_per_sec))
+        print("Pass: %d, Loss: %f" % (pass_id, np.mean(train_losses)))
+        # evaluation
+        if not args.no_test and batch_acc != None:
+            pass_test_acc = test(exe, infer_prog, test_reader, feeder,
+                                 batch_acc)
+            print(", Test Accuracy: %f" % pass_test_acc)
+        print("\n")
+        # TODO(wuyi): add warmup passes to get better perf data.
+        exit(0)
+
+
+# TODO(wuyi): replace train, train_parallel, test functions with new trainer
+# API once it is ready.
+def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
+                   batch_acc, args, train_prog, startup_prog, nccl_id_var,
+                   num_trainers, trainer_id):
+    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
+    startup_exe = fluid.Executor(place)
+    startup_exe.run(startup_prog)
+    strategy = fluid.ExecutionStrategy()
+    strategy.num_threads = 1
+    strategy.allow_op_delay = False
+    exe = fluid.ParallelExecutor(
+        True,
+        avg_loss.name,
+        exec_strategy=strategy,
+        num_trainers=num_trainers,
+        trainer_id=trainer_id)
+    feed_var_list = [
+        var for var in train_prog.global_block().vars.itervalues()
+        if var.is_data
+    ]
+    feeder = fluid.DataFeeder(feed_var_list, place)
+    for pass_id in range(args.pass_num):
+        num_samples = 0
+        iters = 0
+        start_time = time.time()
+        for batch_id, data in enumerate(train_reader()):
+            if iters == args.skip_batch_num:
+                start_time = time.time()
+                num_samples = 0
+            if iters == args.iterations:
+                break
+            loss, = exe.run([avg_loss.name], feed=feeder.feed(data))
+            if args.update_method == "pserver":
+                exe.bcast_params()
+            num_samples += len(data)
+            iters += 1
+            if batch_id % 1 == 0:
+                print("Pass %d, batch %d, loss %s" %
+                      (pass_id, batch_id, np.array(loss)))
+        train_elapsed = time.time() - start_time
+        examples_per_sec = num_samples / train_elapsed
+        print('\nTotal examples: %d, total time: %.5f, %.5f examples/sed\n' %
+              (num_samples, train_elapsed, examples_per_sec))
+        if not args.no_test and batch_acc != None:
+            test_acc = test(startup_exe, infer_prog, test_reader, feeder,
+                            batch_acc)
+            print("Pass: %d, Test Accuracy: %f\n" % (pass_id, test_acc))
+        exit(0)
+
+
+def print_arguments(args):
+    vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
+                                vars(args)['device'] == 'GPU')
+    print('----------- resnet Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def main():
+    args = parse_args()
+    print_arguments(args)
+    nccl_id_var, num_trainers, trainer_id = None, 1, 0
+
+    if args.use_cprof:
+        pr = cProfile.Profile()
+        pr.enable()
+    model_def = __import__("models.%s" % args.model, fromlist=["models"])
+    train_args = list(model_def.get_model(args))
+    train_args.append(args)
+    # Run optimizer.minimize(avg_loss)
+    train_args[2].minimize(train_args[0])
+    if args.memory_optimize:
+        fluid.memory_optimize(fluid.default_main_program())
+
+    if args.update_method == "pserver":
+        train_prog, startup_prog = dist_transpile()
+        if not train_prog:
+            raise Exception(
+                "Must configure correct environments to run dist train.")
+        train_args.extend([train_prog, startup_prog])
+        if args.gpus > 1 and os.getenv("PADDLE_TRAINING_ROLE") == "TRAINER":
+            train_args.extend([nccl_id_var, num_trainers, trainer_id])
+            train_parallel(*train_args)
+        train(*train_args)
+        exit(0)
+
+    # for other update methods, use default programs
+    train_args.append(fluid.default_main_program())
+    train_args.append(fluid.default_startup_program())
+
+    if args.update_method == "nccl2":
+        nccl_id_var, num_trainers, trainer_id = append_nccl2_prepare()
+    if args.gpus == 1:
+        # NOTE: parallel executor use profiler interanlly
+        if args.use_nvprof and args.device == 'GPU':
+            with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
+                train(*train_args)
+        else:
+            train(*train_args)
+    else:
+        if args.device == "CPU":
+            raise Exception("Only support GPU perf with parallel exe")
+        train_args.extend([nccl_id_var, num_trainers, trainer_id])
+        train_parallel(*train_args)
+
+
+if __name__ == "__main__":
+    main()

benchmark/fluid/kube_gen_job.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 yaml
+import copy
+import argparse
+import random
+import os
+from kube_templates import pserver, trainer, envs
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Generate dist job yamls.')
+
+    parser.add_argument(
+        '--jobname', default="paddlejob", help='unique job name')
+    parser.add_argument(
+        '--cpu', default=1, type=int, help='CPU cores per trainer node')
+    parser.add_argument(
+        '--pscpu', default=1, type=int, help='CPU cores per pserver node')
+    parser.add_argument(
+        '--gpu', default=0, type=int, help='num of GPUs per node')
+    parser.add_argument(
+        '--image',
+        default="bootstrapper:5000/fluid_benchmark:gpu",
+        help='num of GPUs per node')
+    parser.add_argument(
+        '--pservers', default=1, type=int, help='num of pservers')
+    parser.add_argument(
+        '--trainers', default=1, type=int, help='num of trainers')
+    parser.add_argument('--memory', default=1, type=int, help='trainer memory')
+    parser.add_argument(
+        '--psmemory', default=1, type=int, help='pserver memory')
+    parser.add_argument(
+        '--port', default=30236, type=int, help='num of trainers')
+    parser.add_argument(
+        '--entry', default="python train.py", help='command to run')
+    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')
+    parser.add_argument(
+        '--disttype',
+        default="pserver",
+        type=str,
+        choices=['pserver', 'nccl2', 'local'],
+        help='pserver or nccl2 or local')
+
+    args = parser.parse_args()
+    return args
+
+
+def gen_job():
+    ps = pserver
+    tn = trainer
+    args = parse_args()
+
+    ps_container = ps["spec"]["template"]["spec"]["containers"][0]
+    tn_container = tn["spec"]["template"]["spec"]["containers"][0]
+
+    if args.fluid == 1:
+        ps_container["command"] = \
+            ["paddle_k8s", "start_fluid"]
+        tn_container["command"] = \
+            ["paddle_k8s", "start_fluid"]
+    ps["metadata"]["name"] = args.jobname + "-pserver"
+    ps["spec"]["template"]["metadata"]["labels"][
+        "paddle-job-pserver"] = args.jobname
+    tn["metadata"]["name"] = args.jobname + "-trainer"
+    tn["spec"]["template"]["metadata"]["labels"]["paddle-job"] = args.jobname
+
+    ps_container["image"] = args.image
+    tn_container["image"] = args.image
+
+    ps_container["resources"]["requests"]["cpu"] = str(args.pscpu)
+    ps_container["resources"]["requests"]["memory"] = str(args.psmemory) + "Gi"
+    ps_container["resources"]["limits"]["cpu"] = str(args.pscpu)
+    ps_container["resources"]["limits"]["memory"] = str(args.psmemory) + "Gi"
+
+    tn_container["resources"]["requests"]["cpu"] = str(args.cpu)
+    tn_container["resources"]["requests"]["memory"] = str(args.memory) + "Gi"
+    tn_container["resources"]["limits"]["cpu"] = str(args.cpu)
+    tn_container["resources"]["limits"]["memory"] = str(args.memory) + "Gi"
+    if args.gpu > 0:
+        tn_container["resources"]["requests"][
+            "alpha.kubernetes.io/nvidia-gpu"] = str(args.gpu)
+        tn_container["resources"]["limits"][
+            "alpha.kubernetes.io/nvidia-gpu"] = str(args.gpu)
+
+    ps["spec"]["replicas"] = int(args.pservers)
+    tn["spec"]["parallelism"] = int(args.trainers)
+    tn["spec"]["completions"] = int(args.trainers)
+    ps_container["ports"][0]["name"] = "jobport-" + str(args.port)
+    ps_container["ports"][0]["containerPort"] = args.port
+    spreadport = random.randint(40000, 60000)
+    tn_container["ports"][0]["name"] = "spr-" + str(spreadport)
+    tn_container["ports"][0]["containerPort"] = spreadport
+
+    envs.append({"name": "PADDLE_JOB_NAME", "value": args.jobname})
+    envs.append({"name": "TRAINERS", "value": str(args.trainers)})
+    envs.append({"name": "PSERVERS", "value": str(args.pservers)})
+    envs.append({"name": "ENTRY", "value": args.entry})
+    envs.append({"name": "PADDLE_INIT_PORT", "value": str(args.port)})
+    # NOTE: these directories below are cluster specific, please modify
+    # this settings before you run on your own cluster.
+    envs.append({
+        "name": "LD_LIBRARY_PATH",
+        "value":
+        "/usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind"
+    })
+
+    volumes = [{
+        "name": "nvidia-driver",
+        "hostPath": {
+            "path": "/usr/local/nvidia/lib64"
+        }
+    }]
+    volumeMounts = [{
+        "mountPath": "/usr/local/nvidia/lib64",
+        "name": "nvidia-driver"
+    }]
+
+    if args.rdma:
+        volumes.extend([{
+            "name": "ibetc",
+            "hostPath": {
+                "path": "/etc/libibverbs.d"
+            }
+        }, {
+            "name": "iblibs",
+            "hostPath": {
+                "path": "/usr/local/rdma"
+            }
+        }, {
+            "name": "valgrind",
+            "hostPath": {
+                "path": "/usr/lib64/mlnx_ofed/valgrind"
+            }
+        }])
+        volumeMounts.extend([{
+            "mountPath": "/etc/libibverbs.d",
+            "name": "ibetc"
+        }, {
+            "mountPath": "/usr/local/rdma",
+            "name": "iblibs"
+        }, {
+            "mountPath": "/usr/lib64/mlnx_ofed/valgrind",
+            "name": "valgrind"
+        }])
+        # append shm for NCCL2
+        volumes.append({"name": "dshm", "emptyDir": {"medium": "Memory"}})
+        volumeMounts.append({"mountPath": "/dev/shm", "name": "dshm"})
+
+    tn["spec"]["template"]["spec"]["volumes"] = volumes
+    tn_container["volumeMounts"] = volumeMounts
+
+    ps_container["env"] = envs
+    ps_container["env"].append({"name": "TRAINING_ROLE", "value": "PSERVER"})
+    tn_container["env"] = envs
+    if args.disttype == "pserver":
+        tn_container["env"].append({
+            "name": "TRAINING_ROLE",
+            "value": "TRAINER"
+        })
+    elif args.disttype == "nccl2" or args.disttype == "local":
+        # NCCL2 have no training role, set to plain WORKER
+        tn_container["env"].append({"name": "TRAINING_ROLE", "value": "WORKER"})
+
+    os.mkdir(args.jobname)
+    if args.disttype == "pserver":
+        with open("%s/pserver.yaml" % args.jobname, "w") as fn:
+            yaml.dump(ps, fn)
+
+    with open("%s/trainer.yaml" % args.jobname, "w") as fn:
+        yaml.dump(tn, fn)
+
+
+if __name__ == "__main__":
+    gen_job()

benchmark/fluid/kube_templates/__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 pserver import pserver
+from trainer import trainer
+
+__all__ = ["pserver", "trainer", "envs"]
+
+envs = [
+    # envs that don't need to change
+    {
+        "name": "GLOG_v",
+        "value": "0"
+    },
+    {
+        "name": "GLOG_logtostderr",
+        "value": "1"
+    },
+    {
+        "name": "TOPOLOGY",
+        "value": ""
+    },
+    {
+        "name": "TRAINER_PACKAGE",
+        "value": "/workspace"
+    },
+    {
+        "name": "PADDLE_INIT_NICS",
+        "value": "eth2"
+    },
+    {
+        "name": "NAMESPACE",
+        "valueFrom": {
+            "fieldRef": {
+                "fieldPath": "metadata.namespace"
+            }
+        }
+    },
+    {
+        "name": "POD_IP",
+        "valueFrom": {
+            "fieldRef": {
+                "fieldPath": "status.podIP"
+            }
+        }
+    }
+]

benchmark/fluid/kube_templates/pserver.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.
+
+pserver = {
+    "apiVersion": "extensions/v1beta1",
+    "kind": "ReplicaSet",
+    "metadata": {
+        "name": "jobname-pserver"
+    },
+    "spec": {
+        "replicas": 1,
+        "template": {
+            "metadata": {
+                "labels": {
+                    "paddle-job-pserver": "jobname"
+                }
+            },
+            "spec": {
+                "hostNetwork": True,
+                "imagePullSecrets": [{
+                    "name": "job-registry-secret"
+                }],
+                "containers": [{
+                    "name": "pserver",
+                    "image": "",
+                    "imagePullPolicy": "Always",
+                    "ports": [{
+                        "name": "jobport-1",
+                        "containerPort": 1
+                    }],
+                    "env": [],
+                    "command": ["paddle_k8s", "start_pserver"],
+                    "resources": {
+                        "requests": {
+                            "memory": "10Gi",
+                            "cpu": "4"
+                        },
+                        "limits": {
+                            "memory": "10Gi",
+                            "cpu": "4"
+                        }
+                    }
+                }]
+            }
+        }
+    }
+}

benchmark/fluid/kube_templates/trainer.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.
+
+trainer = {
+    "apiVersion": "batch/v1",
+    "kind": "Job",
+    "metadata": {
+        "name": "jobname-pserver"
+    },
+    "spec": {
+        "parallelism": 4,
+        "completions": 4,
+        "template": {
+            "metadata": {
+                "labels": {
+                    "paddle-job": "jobname"
+                }
+            },
+            "spec": {
+                "hostNetwork": True,
+                "imagePullSecrets": [{
+                    "name": "job-registry-secret"
+                }],
+                "restartPolicy": "Never",
+                "containers": [{
+                    "name": "trainer",
+                    "image": "",
+                    "imagePullPolicy": "Always",
+                    # to let container set rlimit
+                    "securityContext": {
+                        "privileged": True
+                        # TODO(wuyi): use below specific cap instead of privileged,
+                        # using privileged will cause all GPU device are visible
+                        # in the container.
+                        # "capabilities": {
+                        #     "add": ["SYS_RESOURCE"]
+                        # }
+                    },
+                    "ports": [{
+                        "name": "jobport-1",
+                        "containerPort": 1
+                    }],
+                    "env": [],
+                    "command": ["paddle_k8s", "start_trainer", "v2"],
+                    "resources": {
+                        "requests": {
+                            "memory": "10Gi",
+                            "cpu": "4",
+                        },
+                        "limits": {
+                            "memory": "10Gi",
+                            "cpu": "4",
+                        }
+                    }
+                }]
+            }
+        }
+    }
+}

benchmark/fluid/models/__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.
+
+__all__ = [
+    "machine_translation", "resnet", "vgg", "mnist", "stacked_dynamic_lstm"
+]

benchmark/fluid/machine_translation.py → benchmark/fluid/models/machine_translation.py

@@ -27,74 +27,6 @@ import paddle.fluid.core as core
 import paddle.fluid.framework as framework
 from paddle.fluid.executor import Executor
 
-parser = argparse.ArgumentParser(description=__doc__)
-parser.add_argument(
-    "--embedding_dim",
-    type=int,
-    default=512,
-    help="The dimension of embedding table. (default: %(default)d)")
-parser.add_argument(
-    "--encoder_size",
-    type=int,
-    default=512,
-    help="The size of encoder bi-rnn unit. (default: %(default)d)")
-parser.add_argument(
-    "--decoder_size",
-    type=int,
-    default=512,
-    help="The size of decoder rnn unit. (default: %(default)d)")
-parser.add_argument(
-    "--batch_size",
-    type=int,
-    default=16,
-    help="The sequence number of a mini-batch data. (default: %(default)d)")
-parser.add_argument(
-    '--skip_batch_num',
-    type=int,
-    default=5,
-    help='The first num of minibatch num to skip, for better performance test')
-parser.add_argument(
-    '--iterations', type=int, default=80, help='The number of minibatches.')
-parser.add_argument(
-    "--dict_size",
-    type=int,
-    default=30000,
-    help="The dictionary capacity. Dictionaries of source sequence and "
-    "target dictionary have same capacity. (default: %(default)d)")
-parser.add_argument(
-    "--pass_num",
-    type=int,
-    default=2,
-    help="The pass number to train. (default: %(default)d)")
-parser.add_argument(
-    "--learning_rate",
-    type=float,
-    default=0.0002,
-    help="Learning rate used to train the model. (default: %(default)f)")
-parser.add_argument(
-    "--infer_only", action='store_true', help="If set, run forward only.")
-parser.add_argument(
-    "--beam_size",
-    type=int,
-    default=3,
-    help="The width for beam searching. (default: %(default)d)")
-parser.add_argument(
-    '--device',
-    type=str,
-    default='GPU',
-    choices=['CPU', 'GPU'],
-    help="The device type.")
-parser.add_argument(
-    "--max_length",
-    type=int,
-    default=250,
-    help="The maximum length of sequence when doing generation. "
-    "(default: %(default)d)")
-parser.add_argument(
-    '--with_test',
-    action='store_true',
-    help='If set, test the testset during training.')
-
 
 def lstm_step(x_t, hidden_t_prev, cell_t_prev, size):
     def linear(inputs):
@@ -264,116 +196,37 @@ def lodtensor_to_ndarray(lod_tensor):
     return ndarray
 
 
-def train():
+def get_model(args):
+    embedding_dim = 512
+    encoder_size = 512
+    decoder_size = 512
+    dict_size = 30000
+    beam_size = 3
+    max_length = 250
     avg_cost, feeding_list = seq_to_seq_net(
-        args.embedding_dim,
-        args.encoder_size,
-        args.decoder_size,
-        args.dict_size,
-        args.dict_size,
+        embedding_dim,
+        encoder_size,
+        decoder_size,
+        dict_size,
+        dict_size,
         False,
-        beam_size=args.beam_size,
-        max_length=args.max_length)
+        beam_size=beam_size,
+        max_length=max_length)
 
     # clone from default main program
     inference_program = fluid.default_main_program().clone()
 
     optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
-    optimizer.minimize(avg_cost)
-
-    fluid.memory_optimize(fluid.default_main_program())
 
     train_batch_generator = paddle.batch(
         paddle.reader.shuffle(
-            paddle.dataset.wmt14.train(args.dict_size), buf_size=1000),
+            paddle.dataset.wmt14.train(dict_size), buf_size=1000),
         batch_size=args.batch_size)
 
     test_batch_generator = paddle.batch(
         paddle.reader.shuffle(
-            paddle.dataset.wmt14.test(args.dict_size), buf_size=1000),
+            paddle.dataset.wmt14.test(dict_size), buf_size=1000),
         batch_size=args.batch_size)
 
-    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
-    exe = Executor(place)
-    exe.run(framework.default_startup_program())
-
-    def do_validation():
-        total_loss = 0.0
-        count = 0
-        for batch_id, data in enumerate(test_batch_generator()):
-            src_seq = to_lodtensor(map(lambda x: x[0], data), place)[0]
-            trg_seq = to_lodtensor(map(lambda x: x[1], data), place)[0]
-            lbl_seq = to_lodtensor(map(lambda x: x[2], data), place)[0]
-
-            fetch_outs = exe.run(inference_program,
-                                 feed={
-                                     feeding_list[0]: src_seq,
-                                     feeding_list[1]: trg_seq,
-                                     feeding_list[2]: lbl_seq
-                                 },
-                                 fetch_list=[avg_cost],
-                                 return_numpy=False)
-
-            total_loss += lodtensor_to_ndarray(fetch_outs[0])[0]
-            count += 1
-
-        return total_loss / count
-
-    iters, num_samples, start_time = 0, 0, time.time()
-    for pass_id in xrange(args.pass_num):
-        train_accs = []
-        train_losses = []
-        for batch_id, data in enumerate(train_batch_generator()):
-            if iters == args.skip_batch_num:
-                start_time = time.time()
-                num_samples = 0
-            if iters == args.iterations:
-                break
-            src_seq, word_num = to_lodtensor(map(lambda x: x[0], data), place)
-            num_samples += word_num
-            trg_seq, word_num = to_lodtensor(map(lambda x: x[1], data), place)
-            num_samples += word_num
-            lbl_seq, _ = to_lodtensor(map(lambda x: x[2], data), place)
-
-            fetch_outs = exe.run(framework.default_main_program(),
-                                 feed={
-                                     feeding_list[0]: src_seq,
-                                     feeding_list[1]: trg_seq,
-                                     feeding_list[2]: lbl_seq
-                                 },
-                                 fetch_list=[avg_cost])
-
-            iters += 1
-            loss = np.array(fetch_outs[0])
-            print(
-                "Pass = %d, Iter = %d, Loss = %f" % (pass_id, iters, loss)
-            )  # The accuracy is the accumulation of batches, but not the current batch.
-
-        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))
-        # evaluation
-        if args.with_test:
-            test_loss = do_validation()
-        exit(0)
-
-
-def infer():
-    pass
-
-
-def print_arguments(args):
-    print('----------- seq2seq Configuration Arguments -----------')
-    for arg, value in sorted(vars(args).iteritems()):
-        print('%s: %s' % (arg, value))
-    print('------------------------------------------------')
-
-
-if __name__ == '__main__':
-    args = parser.parse_args()
-    print_arguments(args)
-    if args.infer_only:
-        infer()
-    else:
-        train()
+    return avg_cost, inference_program, optimizer, train_batch_generator, \
+           test_batch_generator, None

benchmark/fluid/mnist.py → benchmark/fluid/models/mnist.py

@@ -19,6 +19,7 @@ from __future__ import print_function
 import numpy as np
 import argparse
 import time
+import cProfile
 
 import paddle
 import paddle.fluid as fluid
@@ -31,42 +32,6 @@ DTYPE = "float32"
 # fluid.default_startup_program().random_seed = SEED
 
 
-def parse_args():
-    parser = argparse.ArgumentParser("mnist model benchmark.")
-    parser.add_argument(
-        '--batch_size', type=int, default=128, help='The minibatch size.')
-    parser.add_argument(
-        '--skip_batch_num',
-        type=int,
-        default=5,
-        help='The first num of minibatch num to skip, for better performance test'
-    )
-    parser.add_argument(
-        '--iterations', type=int, default=35, help='The number of minibatches.')
-    parser.add_argument(
-        '--pass_num', type=int, default=5, help='The number of passes.')
-    parser.add_argument(
-        '--device',
-        type=str,
-        default='GPU',
-        choices=['CPU', 'GPU'],
-        help='The device type.')
-    parser.add_argument(
-        '--infer_only', action='store_true', help='If set, run forward only.')
-    parser.add_argument(
-        '--use_cprof', action='store_true', help='If set, use cProfile.')
-    parser.add_argument(
-        '--use_nvprof',
-        action='store_true',
-        help='If set, use nvprof for CUDA.')
-    parser.add_argument(
-        '--with_test',
-        action='store_true',
-        help='If set, test the testset during training.')
-    args = parser.parse_args()
-    return args
-
-
 def cnn_model(data):
     conv_pool_1 = fluid.nets.simple_img_conv_pool(
         input=data,
@@ -99,36 +64,13 @@ def cnn_model(data):
     return predict
 
 
-def eval_test(exe, batch_acc, batch_size_tensor, inference_program):
-    test_reader = paddle.batch(
-        paddle.dataset.mnist.test(), batch_size=args.batch_size)
-    test_pass_acc = fluid.average.WeightedAverage()
-    for batch_id, data in enumerate(test_reader()):
-        img_data = np.array(map(lambda x: x[0].reshape([1, 28, 28]),
-                                data)).astype(DTYPE)
-        y_data = np.array(map(lambda x: x[1], data)).astype("int64")
-        y_data = y_data.reshape([len(y_data), 1])
-
-        acc, weight = exe.run(inference_program,
-                              feed={"pixel": img_data,
-                                    "label": y_data},
-                              fetch_list=[batch_acc, batch_size_tensor])
-        test_pass_acc.add(value=acc, weight=weight)
-        pass_acc = test_pass_acc.eval()
-    return pass_acc
-
-
-def run_benchmark(model, args):
-    if args.use_cprof:
-        pr = cProfile.Profile()
-        pr.enable()
-    start_time = time.time()
+def get_model(args):
     # Input data
     images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
     # Train program
-    predict = model(images)
+    predict = cnn_model(images)
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(x=cost)
 
@@ -143,86 +85,10 @@ def run_benchmark(model, args):
     # Optimization
     opt = fluid.optimizer.AdamOptimizer(
         learning_rate=0.001, beta1=0.9, beta2=0.999)
-    opt.minimize(avg_cost)
-
-    fluid.memory_optimize(fluid.default_main_program())
-
-    # Initialize executor
-    place = fluid.CPUPlace() if args.device == 'CPU' else fluid.CUDAPlace(0)
-    exe = fluid.Executor(place)
-
-    # Parameter initialization
-    exe.run(fluid.default_startup_program())
 
     # Reader
     train_reader = paddle.batch(
         paddle.dataset.mnist.train(), batch_size=args.batch_size)
-
-    accuracy = fluid.metrics.Accuracy()
-    train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
-    iters, num_samples, start_time = 0, 0, time.time()
-    for pass_id in range(args.pass_num):
-        accuracy.reset()
-        train_accs = []
-        train_losses = []
-        for batch_id, data in enumerate(train_reader()):
-            if iters == args.skip_batch_num:
-                start_time = time.time()
-                num_samples = 0
-            if iters == args.iterations:
-                break
-            img_data = np.array(
-                map(lambda x: x[0].reshape([1, 28, 28]), data)).astype(DTYPE)
-            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
-            y_data = y_data.reshape([len(y_data), 1])
-
-            outs = train_exe.run(
-                feed={"pixel": img_data,
-                      "label": y_data},
-                fetch_list=[
-                    avg_cost.name, batch_acc.name, batch_size_tensor.name
-                ]
-            )  # The accuracy is the accumulation of batches, but not the current batch.
-            accuracy.update(
-                value=np.array(np.mean(outs[1])),
-                weight=np.mean(np.array(outs[2])))
-            iters += 1
-            num_samples += len(y_data)
-            loss = np.mean(np.array(outs[0]))
-            acc = np.mean(np.array(outs[1]))
-            train_losses.append(loss)
-            train_accs.append(acc)
-            print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %
-                  (pass_id, iters, loss, acc))
-
-        print("Pass: %d, Loss: %f, Train Accuray: %f\n" %
-              (pass_id, np.mean(train_losses), np.mean(train_accs)))
-        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))
-        # evaluation
-        if args.with_test:
-            test_avg_acc = eval_test(exe, batch_acc, batch_size_tensor,
-                                     inference_program)
-        exit(0)
-
-
-def print_arguments(args):
-    vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
-                                vars(args)['device'] == 'GPU')
-    print('----------- mnist Configuration Arguments -----------')
-    for arg, value in sorted(vars(args).iteritems()):
-        print('%s: %s' % (arg, value))
-    print('------------------------------------------------')
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    print_arguments(args)
-    if args.use_nvprof and args.device == 'GPU':
-        with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
-            run_benchmark(cnn_model, args)
-    else:
-        run_benchmark(cnn_model, args)
+    test_reader = paddle.batch(
+        paddle.dataset.mnist.test(), batch_size=args.batch_size)
+    return avg_cost, inference_program, opt, train_reader, test_reader, batch_acc

benchmark/fluid/resnet.py → benchmark/fluid/models/resnet.py

@@ -16,7 +16,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import argparse
 import functools
 import numpy as np
 import time
@@ -29,64 +28,6 @@ import paddle.fluid.core as core
 import paddle.fluid.profiler as profiler
 
 
-def parse_args():
-    parser = argparse.ArgumentParser('Convolution model benchmark.')
-    parser.add_argument(
-        '--model',
-        type=str,
-        choices=['resnet_imagenet', 'resnet_cifar10'],
-        default='resnet_imagenet',
-        help='The model architecture.')
-    parser.add_argument(
-        '--batch_size', type=int, default=32, help='The minibatch size.')
-    parser.add_argument(
-        '--use_fake_data',
-        action='store_true',
-        help='use real data or fake data')
-    parser.add_argument(
-        '--skip_batch_num',
-        type=int,
-        default=5,
-        help='The first num of minibatch num to skip, for better performance test'
-    )
-    parser.add_argument(
-        '--iterations', type=int, default=80, help='The number of minibatches.')
-    parser.add_argument(
-        '--pass_num', type=int, default=100, help='The number of passes.')
-    parser.add_argument(
-        '--data_format',
-        type=str,
-        default='NCHW',
-        choices=['NCHW', 'NHWC'],
-        help='The data data_format, now only support NCHW.')
-    parser.add_argument(
-        '--device',
-        type=str,
-        default='GPU',
-        choices=['CPU', 'GPU'],
-        help='The device type.')
-    parser.add_argument(
-        '--data_set',
-        type=str,
-        default='flowers',
-        choices=['cifar10', 'flowers'],
-        help='Optional dataset for benchmark.')
-    parser.add_argument(
-        '--infer_only', action='store_true', help='If set, run forward only.')
-    parser.add_argument(
-        '--use_cprof', action='store_true', help='If set, use cProfile.')
-    parser.add_argument(
-        '--use_nvprof',
-        action='store_true',
-        help='If set, use nvprof for CUDA.')
-    parser.add_argument(
-        '--with_test',
-        action='store_true',
-        help='If set, test the testset during training.')
-    args = parser.parse_args()
-    return args
-
-
 def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
     conv1 = fluid.layers.conv2d(
         input=input,
@@ -100,7 +41,7 @@ def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
 
 
 def shortcut(input, ch_out, stride):
-    ch_in = input.shape[1] if args.data_format == 'NCHW' else input.shape[-1]
+    ch_in = input.shape[1]  # if args.data_format == 'NCHW' else input.shape[-1]
     if ch_in != ch_out:
         return conv_bn_layer(input, ch_out, 1, stride, 0, None)
     else:
@@ -172,23 +113,22 @@ def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
     return out
 
 
-def run_benchmark(model, args):
-    if args.use_cprof:
-        pr = cProfile.Profile()
-        pr.enable()
-
+def get_model(args):
+    model = resnet_cifar10
     if args.data_set == "cifar10":
         class_dim = 10
         if args.data_format == 'NCHW':
             dshape = [3, 32, 32]
         else:
             dshape = [32, 32, 3]
+        model = resnet_cifar10
     else:
         class_dim = 102
         if args.data_format == 'NCHW':
             dshape = [3, 224, 224]
         else:
             dshape = [224, 224, 3]
+        model = resnet_imagenet
 
     input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
@@ -206,9 +146,6 @@ def run_benchmark(model, args):
             target_vars=[batch_acc, batch_size_tensor])
 
     optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
-    opts = optimizer.minimize(avg_cost)
-
-    fluid.memory_optimize(fluid.default_main_program())
 
     train_reader = paddle.batch(
         paddle.reader.shuffle(
@@ -221,97 +158,4 @@ def run_benchmark(model, args):
         if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
         batch_size=args.batch_size)
 
-    def test(exe):
-        test_accuracy = fluid.average.WeightedAverage()
-        for batch_id, data in enumerate(test_reader()):
-            img_data = np.array(map(lambda x: x[0].reshape(dshape),
-                                    data)).astype("float32")
-            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
-            y_data = y_data.reshape([-1, 1])
-
-            acc, weight = exe.run(inference_program,
-                                  feed={"data": img_data,
-                                        "label": y_data},
-                                  fetch_list=[batch_acc, batch_size_tensor])
-            test_accuracy.add(value=acc, weight=weight)
-
-        return test_accuracy.eval()
-
-    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
-    accuracy = fluid.average.WeightedAverage()
-    train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
-    if args.use_fake_data:
-        data = train_reader().next()
-        image = np.array(map(lambda x: x[0].reshape(dshape), data)).astype(
-            'float32')
-        label = np.array(map(lambda x: x[1], data)).astype('int64')
-        label = label.reshape([-1, 1])
-
-    iters, num_samples, start_time = 0, 0, time.time()
-    for pass_id in range(args.pass_num):
-        accuracy.reset()
-        train_accs = []
-        train_losses = []
-        for batch_id, data in enumerate(train_reader()):
-            if iters == args.skip_batch_num:
-                start_time = time.time()
-                num_samples = 0
-            if iters == args.iterations:
-                break
-            if not args.use_fake_data:
-                image = np.array(map(lambda x: x[0].reshape(dshape),
-                                     data)).astype('float32')
-                label = np.array(map(lambda x: x[1], data)).astype('int64')
-                label = label.reshape([-1, 1])
-            loss, acc, weight = train_exe.run(
-                feed={'data': image,
-                      'label': label},
-                fetch_list=[
-                    avg_cost.name, batch_acc.name, batch_size_tensor.name
-                ])
-            iters += 1
-            num_samples += len(label)
-            accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
-            loss = np.mean(np.array(loss))
-            acc = np.mean(np.array(acc))
-            train_losses.append(loss)
-            train_accs.append(acc)
-            print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %
-                  (pass_id, iters, loss, acc))
-        print("Pass: %d, Loss: %f, Train Accuray: %f\n" %
-              (pass_id, np.mean(train_losses), np.mean(train_accs)))
-        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))
-        # evaluation
-        if args.with_test:
-            pass_test_acc = test(exe)
-        exit(0)
-
-
-def print_arguments(args):
-    vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
-                                vars(args)['device'] == 'GPU')
-    print('----------- resnet Configuration Arguments -----------')
-    for arg, value in sorted(vars(args).iteritems()):
-        print('%s: %s' % (arg, value))
-    print('------------------------------------------------')
-
-
-if __name__ == '__main__':
-    model_map = {
-        'resnet_imagenet': resnet_imagenet,
-        'resnet_cifar10': resnet_cifar10
-    }
-    args = parse_args()
-    print_arguments(args)
-    if args.data_format == 'NHWC':
-        raise ValueError('Only support NCHW data_format now.')
-    if args.use_nvprof and args.device == 'GPU':
-        with profiler.cuda_profiler("cuda_profiler.txt", 'csv') as nvprof:
-            run_benchmark(model_map[args.model], args)
-    else:
-        run_benchmark(model_map[args.model], args)
+    return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc

benchmark/fluid/stacked_dynamic_lstm.py → benchmark/fluid/models/stacked_dynamic_lstm.py

@@ -29,57 +29,6 @@ import paddle.fluid as fluid
 import paddle.batch as batch
 import paddle.fluid.profiler as profiler
 
-
-def parse_args():
-    parser = argparse.ArgumentParser("Understand Sentiment by Dynamic RNN.")
-    parser.add_argument(
-        '--batch_size',
-        type=int,
-        default=32,
-        help='The sequence number of a batch data. (default: %(default)d)')
-    parser.add_argument(
-        '--skip_batch_num',
-        type=int,
-        default=5,
-        help='The first num of minibatch num to skip, for better performance test'
-    )
-    parser.add_argument(
-        '--iterations', type=int, default=80, help='The number of minibatches.')
-    parser.add_argument(
-        '--emb_dim',
-        type=int,
-        default=512,
-        help='Dimension of embedding table. (default: %(default)d)')
-    parser.add_argument(
-        '--hidden_dim',
-        type=int,
-        default=512,
-        help='Hidden size of lstm unit. (default: %(default)d)')
-    parser.add_argument(
-        '--pass_num',
-        type=int,
-        default=100,
-        help='Epoch number to train. (default: %(default)d)')
-    parser.add_argument(
-        '--device',
-        type=str,
-        default='CPU',
-        choices=['CPU', 'GPU'],
-        help='The device type.')
-    parser.add_argument(
-        '--crop_size',
-        type=int,
-        default=int(os.environ.get('CROP_SIZE', '1500')),
-        help='The max sentence length of input. Since this model use plain RNN,'
-        ' Gradient could be explored if sentence is too long')
-    parser.add_argument(
-        '--with_test',
-        action='store_true',
-        help='If set, test the testset during training.')
-    args = parser.parse_args()
-    return args
-
-
 word_dict = imdb.word_dict()
 
 
@@ -94,14 +43,15 @@ def crop_sentence(reader, crop_size):
     return __impl__
 
 
-def main():
-    args = parse_args()
-    lstm_size = args.hidden_dim
+def get_model(args):
+    lstm_size = 512
+    emb_dim = 512
+    crop_size = 1500
 
     data = fluid.layers.data(
         name="words", shape=[1], lod_level=1, dtype='int64')
     sentence = fluid.layers.embedding(
-        input=data, size=[len(word_dict), args.emb_dim])
+        input=data, size=[len(word_dict), emb_dim])
 
     sentence = fluid.layers.fc(input=sentence, size=lstm_size, act='tanh')
 
@@ -161,51 +111,17 @@ def main():
             target_vars=[batch_acc, batch_size_tensor])
 
     adam = fluid.optimizer.Adam()
-    adam.minimize(loss)
-
-    fluid.memory_optimize(fluid.default_main_program())
-
-    place = fluid.CPUPlace() if args.device == 'CPU' else fluid.CUDAPlace(0)
-    exe = fluid.Executor(place)
-    exe.run(fluid.default_startup_program())
 
     train_reader = batch(
         paddle.reader.shuffle(
-            crop_sentence(imdb.train(word_dict), args.crop_size),
-            buf_size=25000),
+            crop_sentence(imdb.train(word_dict), crop_size), buf_size=25000),
+        batch_size=args.batch_size)
+    test_reader = batch(
+        paddle.reader.shuffle(
+            crop_sentence(imdb.test(word_dict), crop_size), buf_size=25000),
         batch_size=args.batch_size)
 
-    iters, num_samples, start_time = 0, 0, time.time()
-    for pass_id in range(args.pass_num):
-        train_accs = []
-        train_losses = []
-        for batch_id, data in enumerate(train_reader()):
-            if iters == args.skip_batch_num:
-                start_time = time.time()
-                num_samples = 0
-            if iters == args.iterations:
-                break
-            tensor_words = to_lodtensor([x[0] for x in data], place)
-            label = numpy.array([x[1] for x in data]).astype("int64")
-            label = label.reshape((-1, 1))
-            loss_np, acc, weight = exe.run(
-                fluid.default_main_program(),
-                feed={"words": tensor_words,
-                      "label": label},
-                fetch_list=[loss, batch_acc, batch_size_tensor])
-            iters += 1
-            for x in data:
-                num_samples += len(x[0])
-            print(
-                "Pass = %d, Iter = %d, Loss = %f, Accuracy = %f" %
-                (pass_id, iters, loss_np, acc)
-            )  # The accuracy is the accumulation of batches, but not the current batch.
-
-        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))
-        exit(0)
+    return loss, inference_program, adam, train_reader, test_reader, batch_acc
 
 
 def to_lodtensor(data, place):
@@ -221,16 +137,3 @@ def to_lodtensor(data, place):
     res.set(flattened_data, place)
     res.set_lod([lod])
     return res
-
-
-def print_arguments(args):
-    print('----------- lstm Configuration Arguments -----------')
-    for arg, value in sorted(vars(args).iteritems()):
-        print('%s: %s' % (arg, value))
-    print('------------------------------------------------')
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    print_arguments(args)
-    main()

benchmark/fluid/vgg.py → benchmark/fluid/models/vgg.py

@@ -23,46 +23,6 @@ import paddle.fluid.core as core
 import argparse
 import functools
 
-parser = argparse.ArgumentParser(description=__doc__)
-parser.add_argument(
-    '--batch_size', type=int, default=128, help="Batch size for training.")
-parser.add_argument(
-    '--skip_batch_num',
-    type=int,
-    default=5,
-    help='The first num of minibatch num to skip, for better performance test')
-parser.add_argument(
-    '--iterations', type=int, default=80, help='The number of minibatches.')
-parser.add_argument(
-    '--learning_rate',
-    type=float,
-    default=1e-3,
-    help="Learning rate for training.")
-parser.add_argument('--pass_num', type=int, default=50, help="No. of passes.")
-parser.add_argument(
-    '--device',
-    type=str,
-    default='GPU',
-    choices=['CPU', 'GPU'],
-    help="The device type.")
-parser.add_argument(
-    '--data_format',
-    type=str,
-    default='NCHW',
-    choices=['NCHW', 'NHWC'],
-    help='The data order, now only support NCHW.')
-parser.add_argument(
-    '--data_set',
-    type=str,
-    default='cifar10',
-    choices=['cifar10', 'flowers'],
-    help='Optional dataset for benchmark.')
-parser.add_argument(
-    '--with_test',
-    action='store_true',
-    help='If set, test the testset during training.')
-args = parser.parse_args()
-
 
 def vgg16_bn_drop(input):
     def conv_block(input, num_filter, groups, dropouts):
@@ -91,7 +51,7 @@ def vgg16_bn_drop(input):
     return fc2
 
 
-def main():
+def get_model(args):
     if args.data_set == "cifar10":
         classdim = 10
         if args.data_format == 'NCHW':
@@ -128,16 +88,6 @@ def main():
 
     # Optimization
     optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
-    opts = optimizer.minimize(avg_cost)
-
-    fluid.memory_optimize(fluid.default_main_program())
-
-    # Initialize executor
-    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
-    exe = fluid.Executor(place)
-
-    # Parameter initialization
-    exe.run(fluid.default_startup_program())
 
     # data reader
     train_reader = paddle.batch(
@@ -151,78 +101,4 @@ def main():
         if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
         batch_size=args.batch_size)
 
-    # test
-    def test(exe):
-        test_accuracy = fluid.average.WeightedAverage()
-        for batch_id, data in enumerate(test_reader()):
-            img_data = np.array(map(lambda x: x[0].reshape(data_shape),
-                                    data)).astype("float32")
-            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
-            y_data = y_data.reshape([-1, 1])
-
-            acc, weight = exe.run(inference_program,
-                                  feed={"pixel": img_data,
-                                        "label": y_data},
-                                  fetch_list=[batch_acc, batch_size_tensor])
-            test_accuracy.add(value=acc, weight=weight)
-        return test_accuracy.eval()
-
-    iters, num_samples, start_time = 0, 0, time.time()
-    accuracy = fluid.average.WeightedAverage()
-    train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=avg_cost.name)
-    for pass_id in range(args.pass_num):
-        accuracy.reset()
-        train_accs = []
-        train_losses = []
-        for batch_id, data in enumerate(train_reader()):
-            if iters == args.skip_batch_num:
-                start_time = time.time()
-                num_samples = 0
-            if iters == args.iterations:
-                break
-            img_data = np.array(map(lambda x: x[0].reshape(data_shape),
-                                    data)).astype("float32")
-            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
-            y_data = y_data.reshape([-1, 1])
-
-            loss, acc, weight = train_exe.run(
-                feed={"pixel": img_data,
-                      "label": y_data},
-                fetch_list=[
-                    avg_cost.name, batch_acc.name, batch_size_tensor.name
-                ])
-            accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
-            iters += 1
-            num_samples += len(y_data)
-            loss = np.mean(np.array(loss))
-            acc = np.mean(np.array(acc))
-            print(
-                "Pass = %d, Iter = %d, Loss = %f, Accuracy = %f" %
-                (pass_id, iters, loss, acc)
-            )  # The accuracy is the accumulation of batches, but not the current batch.
-
-        # pass_train_acc = accuracy.eval()
-        train_losses.append(loss)
-        train_accs.append(acc)
-        print("Pass: %d, Loss: %f, Train Accuray: %f\n" %
-              (pass_id, np.mean(train_losses), np.mean(train_accs)))
-        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))
-        # evaluation
-        if args.with_test:
-            pass_test_acc = test(exe)
-        exit(0)
-
-
-def print_arguments():
-    print('----------- vgg Configuration Arguments -----------')
-    for arg, value in sorted(vars(args).iteritems()):
-        print('%s: %s' % (arg, value))
-    print('------------------------------------------------')
-
-
-if __name__ == "__main__":
-    print_arguments()
-    main()
+    return avg_cost, inference_program, optimizer, train_reader, test_reader, batch_acc

cmake/external/boost.cmake

@@ -23,8 +23,12 @@ set(BOOST_PROJECT       "extern_boost")
 # checked that the devtools package of CentOS 6 installs boost 1.41.0.
 # So we use 1.41.0 here.
 set(BOOST_VER           "1.41.0")
-set(BOOST_TAR           "boost_1_41_0")
-set(BOOST_URL           "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz")
+if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
+    message(STATUS "use pre defined download url")
+    set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
+    set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
+endif()
+MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
 set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
 set(BOOST_DOWNLOAD_DIR  "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
 set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)

cmake/external/grpc.cmake

@@ -23,17 +23,20 @@ SET(GRPC_SOURCES_DIR ${THIRD_PARTY_PATH}/grpc)
 SET(GRPC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/grpc)
 SET(GRPC_INCLUDE_DIR "${GRPC_INSTALL_DIR}/include/" CACHE PATH "grpc include directory." FORCE)
 SET(GRPC_CPP_PLUGIN "${GRPC_INSTALL_DIR}/bin/grpc_cpp_plugin" CACHE FILEPATH "GRPC_CPP_PLUGIN" FORCE)
+
+include(ProcessorCount)
+ProcessorCount(NUM_OF_PROCESSOR)
+
 IF(APPLE)
-  SET(BUILD_CMD make -n HAS_SYSTEM_PROTOBUF=false -s -j static grpc_cpp_plugin | sed "s/-Werror//g" | sh)
+  SET(BUILD_CMD make -n HAS_SYSTEM_PROTOBUF=false -s -j ${NUM_OF_PROCESSOR} static grpc_cpp_plugin | sed "s/-Werror//g" | sh)
 ELSE()
-  SET(BUILD_CMD make HAS_SYSTEM_PROTOBUF=false -s -j static grpc_cpp_plugin)
+  SET(BUILD_CMD make HAS_SYSTEM_PROTOBUF=false -s -j ${NUM_OF_PROCESSOR} static grpc_cpp_plugin)
 ENDIF()
 
 ExternalProject_Add(
     extern_grpc
     DEPENDS protobuf zlib
-    GIT_REPOSITORY "https://github.com/grpc/grpc.git"
-    GIT_TAG "v1.10.x"
+    URL "http://paddlepaddledeps.bj.bcebos.com/grpc.tar.xz"
     PREFIX          ${GRPC_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CONFIGURE_COMMAND ""

cmake/external/mklml.cmake

@@ -27,8 +27,12 @@ ENDIF()
 INCLUDE(ExternalProject)
 
 SET(MKLML_PROJECT       "extern_mklml")
-SET(MKLML_VER           "mklml_lnx_2018.0.3.20180406")
-SET(MKLML_URL           "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz")
+IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
+  MESSAGE(STATUS "use pre defined download url")
+  SET(MKLML_VER "mklml_lnx_2018.0.3.20180406" CACHE STRING "" FORCE)
+  SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
+ENDIF()
+MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
 SET(MKLML_SOURCE_DIR    "${THIRD_PARTY_PATH}/mklml")
 SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
 SET(MKLML_DST_DIR       "mklml")

cmake/generic.cmake

@@ -231,7 +231,7 @@ endfunction(cc_binary)
 
 function(cc_test TARGET_NAME)
   if(WITH_TESTING)
-    set(options "")
+    set(options SERIAL)
     set(oneValueArgs "")
     set(multiValueArgs SRCS DEPS ARGS)
     cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
@@ -241,6 +241,9 @@ function(cc_test TARGET_NAME)
     add_test(NAME ${TARGET_NAME}
              COMMAND ${TARGET_NAME} ${cc_test_ARGS}
              WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
+    if (${cc_test_SERIAL})
+        set_property(TEST ${TARGET_NAME} PROPERTY SERIAL 1)
+    endif()
   endif()
 endfunction(cc_test)
 
@@ -295,7 +298,7 @@ endfunction(nv_binary)
 
 function(nv_test TARGET_NAME)
   if (WITH_GPU AND WITH_TESTING)
-    set(options "")
+    set(options SERIAL)
     set(oneValueArgs "")
     set(multiValueArgs SRCS DEPS)
     cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
@@ -303,6 +306,9 @@ function(nv_test TARGET_NAME)
     target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main memory gtest gflags glog)
     add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main memory gtest gflags glog)
     add_test(${TARGET_NAME} ${TARGET_NAME})
+    if (nv_test_SERIAL)
+        set_property(TEST ${TARGET_NAME} PROPERTY SERIAL 1)
+    endif()
   endif()
 endfunction(nv_test)
 

cmake/inference_lib.cmake

@@ -52,32 +52,32 @@ function(copy TARGET)
 endfunction()
 
 # third party
-set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/eigen3")
+set(dst_dir "${FLUID_INSTALL_DIR}/third_party/eigen3")
 copy(eigen3_lib
   SRCS ${EIGEN_INCLUDE_DIR}/Eigen/Core ${EIGEN_INCLUDE_DIR}/Eigen/src ${EIGEN_INCLUDE_DIR}/unsupported/Eigen
   DSTS ${dst_dir}/Eigen ${dst_dir}/Eigen ${dst_dir}/unsupported
 )
 
-set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/gflags")
+set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/gflags")
 copy(gflags_lib
   SRCS ${GFLAGS_INCLUDE_DIR} ${GFLAGS_LIBRARIES}
   DSTS ${dst_dir} ${dst_dir}/lib
 )
 
-set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/glog")
+set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/glog")
 copy(glog_lib
   SRCS ${GLOG_INCLUDE_DIR} ${GLOG_LIBRARIES}
   DSTS ${dst_dir} ${dst_dir}/lib
 )
 
-set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/boost/")
+set(dst_dir "${FLUID_INSTALL_DIR}/third_party/boost/")
 copy(boost_lib
   SRCS ${BOOST_INCLUDE_DIR}/boost
   DSTS ${dst_dir}
 )
 
 if(NOT PROTOBUF_FOUND)
-    set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
+    set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/protobuf")
     copy(protobuf_lib
       SRCS ${PROTOBUF_INCLUDE_DIR} ${PROTOBUF_LIBRARY}
       DSTS ${dst_dir} ${dst_dir}/lib
@@ -85,13 +85,13 @@ if(NOT PROTOBUF_FOUND)
 endif()
 
 if(NOT CBLAS_FOUND)
-    set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/openblas")
+    set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/openblas")
     copy(openblas_lib
       SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
       DSTS ${dst_dir} ${dst_dir}
     )
 elseif (WITH_MKLML)
-    set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/mklml")
+    set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mklml")
     copy(mklml_lib
       SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
       DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
@@ -99,7 +99,7 @@ elseif (WITH_MKLML)
 endif()
 
 if(WITH_MKLDNN)
-  set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/mkldnn")
+  set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mkldnn")
   copy(mkldnn_lib
     SRCS ${MKLDNN_INC_DIR} ${MKLDNN_SHARED_LIB}
     DSTS ${dst_dir} ${dst_dir}/lib
@@ -107,17 +107,17 @@ if(WITH_MKLDNN)
 endif()
 
 if(NOT MOBILE_INFERENCE AND NOT RPI)
-  set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/snappy")
+  set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
   copy(snappy_lib
     SRCS ${SNAPPY_INCLUDE_DIR} ${SNAPPY_LIBRARIES}
     DSTS ${dst_dir} ${dst_dir}/lib)
 
-  set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/snappystream")
+  set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappystream")
   copy(snappystream_lib
     SRCS ${SNAPPYSTREAM_INCLUDE_DIR} ${SNAPPYSTREAM_LIBRARIES}
     DSTS ${dst_dir} ${dst_dir}/lib)
 
-  set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/zlib")
+  set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
   copy(zlib_lib
     SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
     DSTS ${dst_dir} ${dst_dir}/lib)
@@ -125,7 +125,7 @@ endif()
 
 # paddle fluid module
 set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
-set(dst_dir "${CMAKE_INSTALL_PREFIX}/paddle/fluid")
+set(dst_dir "${FLUID_INSTALL_DIR}/paddle/fluid")
 set(module "framework")
 copy(framework_lib DEPS framework_py_proto 
   SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
@@ -162,4 +162,25 @@ copy(pybind_lib
   DSTS ${dst_dir}/${module}
 )
 
+# CMakeCache Info
+copy(cmake_cache
+  SRCS ${CMAKE_CURRENT_BINARY_DIR}/CMakeCache.txt
+  DSTS ${FLUID_INSTALL_DIR})
+
 add_custom_target(inference_lib_dist DEPENDS ${inference_lib_dist_dep}) 
+
+# paddle fluid version
+execute_process(
+  COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
+  WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}
+  OUTPUT_VARIABLE PADDLE_GIT_COMMIT)
+set(version_file ${FLUID_INSTALL_DIR}/version.txt)
+file(WRITE ${version_file}
+  "GIT COMMIT ID: ${PADDLE_GIT_COMMIT}\n"
+  "WITH_MKL: ${WITH_MKL}\n"
+  "WITH_GPU: ${WITH_GPU}\n")
+if(WITH_GPU)
+  file(APPEND ${version_file}
+    "CUDA version: ${CUDA_VERSION}\n"
+    "CUDNN version: v${CUDNN_MAJOR_VERSION}\n")
+endif()

doc/fluid/api/clip.rst

+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+====
+clip
+====
+
+ErrorClipByValue
+----------------
+
+..  autoclass:: paddle.fluid.clip.ErrorClipByValue
+    :members:
+    :noindex:
+
+GradientClipByValue
+-------------------
+
+..  autoclass:: paddle.fluid.clip.GradientClipByValue
+    :members:
+    :noindex:
+
+GradientClipByNorm
+------------------
+
+..  autoclass:: paddle.fluid.clip.GradientClipByNorm
+    :members:
+    :noindex:
+
+GradientClipByGlobalNorm
+------------------------
+
+..  autoclass:: paddle.fluid.clip.GradientClipByGlobalNorm
+    :members:
+    :noindex:
+
+append_gradient_clip_ops
+------------------------
+
+..  autofunction:: paddle.fluid.clip.append_gradient_clip_ops
+    :noindex:
+
+error_clip_callback
+-------------------
+
+..  autofunction:: paddle.fluid.clip.error_clip_callback
+    :noindex:
+

doc/fluid/api/evaluator.rst

@@ -5,24 +5,3 @@
 evaluator
 =========
 
-ChunkEvaluator
---------------
-
-..  autoclass:: paddle.fluid.evaluator.ChunkEvaluator
-    :members:
-    :noindex:
-
-EditDistance
---------------
-
-..  autoclass:: paddle.fluid.evaluator.EditDistance
-    :members:
-    :noindex:
-
-DetectionMAP
---------------
-
-..  autoclass:: paddle.fluid.evaluator.DetectionMAP
-    :members:
-    :noindex:
-  

doc/fluid/api/executor.rst

@@ -30,3 +30,9 @@ switch_scope
 ..  autofunction:: paddle.fluid.executor.switch_scope
     :noindex:
 
+fetch_var
+---------
+
+..  autofunction:: paddle.fluid.executor.fetch_var
+    :noindex:
+

doc/fluid/api/gen_doc.sh

 #!/bin/bash
 python gen_doc.py layers --submodules control_flow device io nn ops tensor > layers.rst
 
-for module in io data_feeder evaluator executor initializer io nets optimizer param_attr profiler regularizer
+for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer
 do
   python gen_doc.py ${module} > ${module}.rst
 done

doc/fluid/api/index_en.rst

@@ -9,8 +9,9 @@ Fluid
     data_feeder.rst
     executor.rst
     initializer.rst
-    evaluator.rst
+    metrics.rst
     nets.rst
+    clip.rst
     optimizer.rst
     param_attr.rst
     profiler.rst

doc/fluid/api/initializer.rst

@@ -33,11 +33,16 @@ Xavier
     :members:
     :noindex:
 
-MSRA
-------
+force_init_on_cpu
+-----------------
 
-..  autoclass:: paddle.fluid.initializer.MSRA
-    :members:
+..  autofunction:: paddle.fluid.initializer.force_init_on_cpu
+    :noindex:
+
+init_on_cpu
+-----------
+
+..  autofunction:: paddle.fluid.initializer.init_on_cpu
     :noindex:
 
 ConstantInitializer
@@ -68,9 +73,3 @@ XavierInitializer
     :members:
     :noindex:
 
-
-MSRAInitializer
------------------
-..  autoclass:: paddle.fluid.initializer.MSRAInitializer
-    :members:
-    :noindex:

doc/fluid/api/layers.rst

@@ -55,6 +55,13 @@ While
     :members:
     :noindex:
 
+Switch
+------
+
+..  autoclass:: paddle.fluid.layers.Switch
+    :members:
+    :noindex:
+
 lod_rank_table
 --------------
 
@@ -67,12 +74,6 @@ max_sequence_len
 ..  autofunction:: paddle.fluid.layers.max_sequence_len
     :noindex:
 
-topk
-----
-
-..  autofunction:: paddle.fluid.layers.topk
-    :noindex:
-
 lod_tensor_to_array
 -------------------
 
@@ -109,6 +110,12 @@ less_than
 ..  autofunction:: paddle.fluid.layers.less_than
     :noindex:
 
+equal
+-----
+
+..  autofunction:: paddle.fluid.layers.equal
+    :noindex:
+
 array_read
 ----------
 
@@ -212,6 +219,42 @@ Send
 ..  autofunction:: paddle.fluid.layers.Send
     :noindex:
 
+open_recordio_file
+------------------
+
+..  autofunction:: paddle.fluid.layers.open_recordio_file
+    :noindex:
+
+open_files
+----------
+
+..  autofunction:: paddle.fluid.layers.open_files
+    :noindex:
+
+read_file
+---------
+
+..  autofunction:: paddle.fluid.layers.read_file
+    :noindex:
+
+shuffle
+-------
+
+..  autofunction:: paddle.fluid.layers.shuffle
+    :noindex:
+
+batch
+-----
+
+..  autofunction:: paddle.fluid.layers.batch
+    :noindex:
+
+double_buffer
+-------------
+
+..  autofunction:: paddle.fluid.layers.double_buffer
+    :noindex:
+
 nn
 ==
 
@@ -281,12 +324,6 @@ square_error_cost
 ..  autofunction:: paddle.fluid.layers.square_error_cost
     :noindex:
 
-accuracy
---------
-
-..  autofunction:: paddle.fluid.layers.accuracy
-    :noindex:
-
 chunk_eval
 ----------
 
@@ -311,6 +348,18 @@ sequence_pool
 ..  autofunction:: paddle.fluid.layers.sequence_pool
     :noindex:
 
+sequence_softmax
+----------------
+
+..  autofunction:: paddle.fluid.layers.sequence_softmax
+    :noindex:
+
+softmax
+-------
+
+..  autofunction:: paddle.fluid.layers.softmax
+    :noindex:
+
 pool2d
 ------
 
@@ -323,12 +372,6 @@ batch_norm
 ..  autofunction:: paddle.fluid.layers.batch_norm
     :noindex:
 
-layer_norm
-----------
-
-..  autofunction:: paddle.fluid.layers.layer_norm
-    :noindex:
-
 beam_search_decode
 ------------------
 
@@ -377,6 +420,12 @@ reduce_min
 ..  autofunction:: paddle.fluid.layers.reduce_min
     :noindex:
 
+reduce_prod
+-----------
+
+..  autofunction:: paddle.fluid.layers.reduce_prod
+    :noindex:
+
 sequence_first_step
 -------------------
 
@@ -425,6 +474,12 @@ matmul
 ..  autofunction:: paddle.fluid.layers.matmul
     :noindex:
 
+topk
+----
+
+..  autofunction:: paddle.fluid.layers.topk
+    :noindex:
+
 warpctc
 -------
 
@@ -473,6 +528,60 @@ multiplex
 ..  autofunction:: paddle.fluid.layers.multiplex
     :noindex:
 
+layer_norm
+----------
+
+..  autofunction:: paddle.fluid.layers.layer_norm
+    :noindex:
+
+softmax_with_cross_entropy
+--------------------------
+
+..  autofunction:: paddle.fluid.layers.softmax_with_cross_entropy
+    :noindex:
+
+smooth_l1
+---------
+
+..  autofunction:: paddle.fluid.layers.smooth_l1
+    :noindex:
+
+one_hot
+-------
+
+..  autofunction:: paddle.fluid.layers.one_hot
+    :noindex:
+
+autoincreased_step_counter
+--------------------------
+
+..  autofunction:: paddle.fluid.layers.autoincreased_step_counter
+    :noindex:
+
+reshape
+-------
+
+..  autofunction:: paddle.fluid.layers.reshape
+    :noindex:
+
+lod_reset
+---------
+
+..  autofunction:: paddle.fluid.layers.lod_reset
+    :noindex:
+
+lrn
+---
+
+..  autofunction:: paddle.fluid.layers.lrn
+    :noindex:
+
+pad
+---
+
+..  autofunction:: paddle.fluid.layers.pad
+    :noindex:
+
 label_smooth
 ------------
 
@@ -480,12 +589,12 @@ label_smooth
     :noindex:
 
 roi_pool
----------
+--------
 
 ..  autofunction:: paddle.fluid.layers.roi_pool
     :noindex:
 
-    
+
 ops
 ===
 
@@ -501,18 +610,6 @@ mul
 ..  autofunction:: paddle.fluid.layers.mul
     :noindex:
 
-reshape
--------
-
-..  autofunction:: paddle.fluid.layers.reshape
-    :noindex:
-
-pad
----
-
-..  autofunction:: paddle.fluid.layers.pad
-    :noindex:
-
 scale
 -----
 
@@ -579,10 +676,70 @@ clip_by_norm
 ..  autofunction:: paddle.fluid.layers.clip_by_norm
     :noindex:
 
-sequence_softmax
-----------------
+logical_and
+-----------
 
-..  autofunction:: paddle.fluid.layers.sequence_softmax
+..  autofunction:: paddle.fluid.layers.logical_and
+    :noindex:
+
+logical_or
+----------
+
+..  autofunction:: paddle.fluid.layers.logical_or
+    :noindex:
+
+logical_xor
+-----------
+
+..  autofunction:: paddle.fluid.layers.logical_xor
+    :noindex:
+
+logical_not
+-----------
+
+..  autofunction:: paddle.fluid.layers.logical_not
+    :noindex:
+
+uniform_random
+--------------
+
+..  autofunction:: paddle.fluid.layers.uniform_random
+    :noindex:
+
+uniform_random_batch_size_like
+------------------------------
+
+..  autofunction:: paddle.fluid.layers.uniform_random_batch_size_like
+    :noindex:
+
+gaussian_random
+---------------
+
+..  autofunction:: paddle.fluid.layers.gaussian_random
+    :noindex:
+
+gaussian_random_batch_size_like
+-------------------------------
+
+..  autofunction:: paddle.fluid.layers.gaussian_random_batch_size_like
+    :noindex:
+
+cumsum
+------
+
+..  autofunction:: paddle.fluid.layers.cumsum
+    :noindex:
+
+scatter
+-------
+
+..  autofunction:: paddle.fluid.layers.scatter
+    :noindex:
+
+sum
+---
+
+..  autofunction:: paddle.fluid.layers.sum
     :noindex:
 
 sigmoid
@@ -651,6 +808,18 @@ floor
 ..  autofunction:: paddle.fluid.layers.floor
     :noindex:
 
+cos
+---
+
+..  autofunction:: paddle.fluid.layers.cos
+    :noindex:
+
+sin
+---
+
+..  autofunction:: paddle.fluid.layers.sin
+    :noindex:
+
 round
 -----
 
@@ -828,4 +997,15 @@ topk
 ..  autofunction:: paddle.fluid.layers.topk
     :noindex:
 
+dice_loss
+----
+
+..  autofunction:: paddle.fluid.layers.dice_loss
+    :noindex:
+
+bilinear_interp
+____
+
+..  autofunction:: paddle.fluid.layers.bilinear_interp
+    :noindex:
 

doc/fluid/api/metrics.rst

+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+=======
+metrics
+=======
+
+MetricBase
+----------
+
+..  autoclass:: paddle.fluid.metrics.MetricBase
+    :members:
+    :noindex:
+
+CompositeMetric
+---------------
+
+..  autoclass:: paddle.fluid.metrics.CompositeMetric
+    :members:
+    :noindex:
+
+Accuracy
+--------
+
+..  autoclass:: paddle.fluid.metrics.Accuracy
+    :members:
+    :noindex:
+
+ChunkEvaluator
+--------------
+
+..  autoclass:: paddle.fluid.metrics.ChunkEvaluator
+    :members:
+    :noindex:
+
+EditDistance
+------------
+
+..  autoclass:: paddle.fluid.metrics.EditDistance
+    :members:
+    :noindex:
+
+DetectionMAP
+------------
+
+..  autoclass:: paddle.fluid.metrics.DetectionMAP
+    :members:
+    :noindex:
+
+Auc
+---
+
+..  autoclass:: paddle.fluid.metrics.Auc
+    :members:
+    :noindex:
+

doc/fluid/api/optimizer.rst

@@ -47,6 +47,28 @@ 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
 ------------
 
@@ -89,9 +111,25 @@ DecayedAdagradOptimizer
     :members:
     :noindex:
 
-Adadelta
---------------
+
+AdadeltaOptimizer
+-----------------
 
 ..  autoclass:: paddle.fluid.optimizer.AdadeltaOptimizer
     :members:
     :noindex:
+
+
+RMSPropOptimizer
+-----------------
+
+..  autoclass:: paddle.fluid.optimizer.RMSPropOptimizer
+    :members:
+    :noindex:
+    
+Optimizer
+---------
+
+..  autoclass:: paddle.fluid.optimizer.Optimizer
+    :members:
+    :noindex:

doc/fluid/api/regularizer.rst

@@ -11,6 +11,13 @@ append_regularization_ops
 ..  autofunction:: paddle.fluid.regularizer.append_regularization_ops
     :noindex:
 
+WeightDecayRegularizer
+----------------------
+
+..  autoclass:: paddle.fluid.regularizer.WeightDecayRegularizer
+    :members:
+    :noindex:
+
 L1Decay
 -------
 
@@ -26,15 +33,16 @@ L2Decay
     :noindex:
 
 L1DecayRegularizer
----------------------
+------------------
 
 ..  autoclass:: paddle.fluid.regularizer.L1DecayRegularizer
     :members:
     :noindex:
 
 L2DecayRegularizer
----------------------
+------------------
 
 ..  autoclass:: paddle.fluid.regularizer.L2DecayRegularizer
     :members:
     :noindex:
+

doc/fluid/howto/index_cn.rst

@@ -3,5 +3,6 @@
 
 .. toctree::
   :maxdepth: 1
-  
+
   optimization/index_cn.rst
+  inference/inference_support_in_fluid.md

doc/fluid/howto/index_en.rst

@@ -5,3 +5,4 @@ HOW TO
   :maxdepth: 1
 
   optimization/index_en.rst
+  inference/inference_support_in_fluid.md

doc/fluid/howto/inference/inference_support_in_fluid.md

+# Fluid Inference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
+
+## Python Inference API **[改进中]**
+- 保存Inference模型 ([链接](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/io.py#L295))
+
+  ```python
+  def save_inference_model(dirname,
+                           feeded_var_names,
+                           target_vars,
+                           executor,
+                           main_program=None,
+                           model_filename=None,
+                           params_filename=None):
+  ```
+  Inference模型和参数将会保存到`dirname`目录下：
+  - 序列化的模型
+    - `model_filename`为`None`，保存到`dirname/__model__`
+    - `model_filename`非`None`，保存到`dirname/model_filename`
+  - 参数
+    - `params_filename`为`None`，单独保存到各个独立的文件，各文件以参数变量的名字命名
+    - `params_filename`非`None`，保存到`dirname/params_filename`
+
+- 两种存储格式
+  - 参数保存到各个独立的文件
+    - 如，设置`model_filename`为`None`、`params_filename`为`None`
+
+    ```bash
+    $ cd recognize_digits_conv.inference.model
+    $ ls
+    $ __model__ batch_norm_1.w_0 batch_norm_1.w_2 conv2d_2.w_0 conv2d_3.w_0 fc_1.w_0 batch_norm_1.b_0 batch_norm_1.w_1 conv2d_2.b_0 conv2d_3.b_0 fc_1.b_0
+    ```
+  - 参数保存到同一个文件
+    - 如，设置`model_filename`为`None`、`params_filename`为`__params__`
+
+    ```bash
+    $ cd recognize_digits_conv.inference.model
+    $ ls
+    $ __model__ __params__
+    ```
+- 加载Inference模型([链接](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/io.py#L380))
+  ```python
+  def load_inference_model(dirname,
+                           executor,
+                           model_filename=None,
+                           params_filename=None):
+    ...
+    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))
+
+  - GCC配置
+    ```bash
+    $ g++ -o a.out -std=c++11 main.cc \
+          -I${PADDLE_ROOT}/ \
+          -I${PADDLE_ROOT}/third_party/install/gflags/include \
+          -I${PADDLE_ROOT}/third_party/install/glog/include \
+          -I${PADDLE_ROOT}/third_party/install/protobuf/include \
+          -I${PADDLE_ROOT}/third_party/eigen3 \
+          -L${PADDLE_ROOT}/paddle/fluid/inference -lpaddle_fluid \
+          -lrt -ldl -lpthread
+    ```
+
+  - CMake配置
+    ```cmake
+    include_directories(${PADDLE_ROOT}/)
+    include_directories(${PADDLE_ROOT}/third_party/install/gflags/include)
+    include_directories(${PADDLE_ROOT}/third_party/install/glog/include)
+    include_directories(${PADDLE_ROOT}/third_party/install/protobuf/include)
+    include_directories(${PADDLE_ROOT}/third_party/eigen3)
+    target_link_libraries(${TARGET_NAME}
+                          ${PADDLE_ROOT}/paddle/fluid/inference/libpaddle_fluid.so
+                          -lrt -ldl -lpthread)
+    ```
+
+  - 设置环境变量：
+  `export LD_LIBRARY_PATH=${PADDLE_ROOT}/paddle/fluid/inference:$LD_LIBRARY_PATH`
+
+
+
+## C++ Inference API
+
+- 推断流程([链接](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/test_helper.h#L91))
+
+  - 1、 初始化设备
+    ```cpp
+    #include "paddle/fluid/framework/init.h"
+    paddle::framework::InitDevices(false);
+    ```
+
+  - 2、 定义place，executor，scope
+    ```cpp
+    auto place = paddle::platform::CPUPlace();
+    auto executor = paddle::framework::Executor(place);
+    auto* scope = new paddle::framework::Scope();
+    ```
+
+  - 3、 加载模型
+    ```cpp
+    #include "paddle/fluid/inference/io.h"
+    auto inference_program = paddle::inference::Load(executor, *scope, dirname);
+    // or
+    auto inference_program = paddle::inference::Load(executor,
+                                                     *scope,
+                                                     dirname + "/" + model_filename,
+                                                     dirname + "/" + params_filename);
+    ```
+
+  - 4、 获取`feed_target_names`和`fetch_target_names`
+    ```cpp
+    const std::vector<std::string>& feed_target_names = inference_program->GetFeedTargetNames();
+    const std::vector<std::string>& fetch_target_names = inference_program->GetFetchTargetNames();
+    ```
+
+  - 5、 准备`feed`数据
+    ```cpp
+    #include "paddle/fluid/framework/lod_tensor.h"
+    std::vector<paddle::framework::LoDTensor*> cpu_feeds;
+    ...
+    std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
+    for (size_t i = 0; i < feed_target_names.size(); ++i) {
+      // Please make sure that cpu_feeds[i] is right for feed_target_names[i]
+      feed_targets[feed_target_names[i]] = cpu_feeds[i];
+    }
+    ```
+
+  - 6、 定义`Tensor`来`fetch`结果
+    ```cpp
+    std::vector<paddle::framework::LoDTensor*> cpu_fetchs;
+    std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+    for (size_t i = 0; i < fetch_target_names.size(); ++i) {
+      fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
+    }
+    ```
+
+  - 7、 执行`inference_program`
+    ```cpp
+    executor.Run(*inference_program, scope, feed_targets, fetch_targets);
+    ```
+
+  - 8、 使用`fetch`数据
+    ```cpp
+    for (size_t i = 0; i < cpu_fetchs.size(); ++i) {
+      std::cout << "lod_i: " << cpu_fetchs[i]->lod();
+      std::cout << "dims_i: " << cpu_fetchs[i]->dims();
+      std::cout << "result:";
+      float* output_ptr = cpu_fetchs[i]->data<float>();
+      for (int j = 0; j < cpu_fetchs[i]->numel(); ++j) {
+        std::cout << " " << output_ptr[j];
+      }
+      std::cout << std::endl;
+    }
+    ```
+    针对不同的数据，4. - 8.可执行多次。
+
+  - 9、 释放内存
+    ```cpp
+    delete scope;
+    ```
+
+
+- 接口说明
+
+  ```cpp
+  void Run(const ProgramDesc& program, Scope* scope,
+           std::map<std::string, const LoDTensor*>& feed_targets,
+           std::map<std::string, LoDTensor*>& fetch_targets,
+           bool create_vars = true,
+           const std::string& feed_holder_name = "feed",
+           const std::string& fetch_holder_name = "fetch");
+  ```
+  - 使用Python API `save_inference_model`保存的`program`里面包含了`feed_op`和`fetch_op`，用户提供的`feed_targets`、`fetch_targets`必须和`inference_program`中的`feed_op`、`fetch_op`保持一致。
+  - 用户提供的`feed_holder_name`和`fetch_holder_name`也必须和`inference_program`中`feed_op`、`fetch_op`保持一致，可使用`SetFeedHolderName`和`SetFetchHolderName`接口重新设置`inferece_program`
+  - 默认情况下，除了`persistable`属性设置为`True`的`Variable`之外，每次执行`executor.Run`会创建一个局部`Scope`，并且在这个局部`Scope`中创建和销毁所有的`Variable`，以最小化空闲时的内存占用。
+  - `persistable`属性为`True`的`Variable`有：
+    - Operators的参数`w`、`b`等
+    - `feed_op`的输入变量
+    - `fetch_op`的输出变量
+
+
+- **不在每次执行时创建和销毁变量
+ ([PR](https://github.com/PaddlePaddle/Paddle/pull/9301))**
+  - 执行`inference_program`
+    ```cpp
+    // Call once
+    executor.CreateVariables(*inference_program, scope, 0);
+    // Call as many times as you like
+    executor.Run(
+        *inference_program, scope, feed_targets, fetch_targets, false);
+    ```
+  - **优点**
+    - 节省了频繁创建、销毁变量的时间（约占每次`Run`总时间的1% ~ 12%）
+    - 执行结束后可获取所有Operators的计算结果
+  - **缺点**
+    - 空闲时也会占用大量的内存
+    - 在同一个`Scope`中，相同的变量名是公用同一块内存的，容易引起意想不到的错误
+
+
+- **不在每次执行时创建Op([PR](https://github.com/PaddlePaddle/Paddle/pull/9630))**
+  - 执行`inference_program`
+    ```cpp
+    // Call once
+    auto ctx = executor.Prepare(*inference_program, 0);
+    // Call as many times as you like if you have no need to change the inference_program
+    executor.RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets);
+    ```
+  - **优点**
+    - 节省了频繁创建、销毁Op的时间
+  - **缺点**
+    - 一旦修改了`inference_program`，则需要重新创建`ctx`
+
+
+- **多线程共享Parameters([链接](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/test_multi_thread_helper.h))**
+  - 主线程
+    - 1、 初始化设备
+    - 2、 定义`place`，`executor`，`scope`
+    - 3、 加载模型，得到`inference_program`
+  - 从线程
+    - **复制`inference_program`得到`copy_program`，修改`copy_program`的`feed_holder_name`和`fetch_holder_name`**
+      ```cpp
+      auto copy_program = std::unique_ptr<paddle::framework::ProgramDesc>(
+                 new paddle::framework::ProgramDesc(*inference_program));
+      std::string feed_holder_name = "feed_" + paddle::string::to_string(thread_id);
+      std::string fetch_holder_name = "fetch_" + paddle::string::to_string(thread_id);
+      copy_program->SetFeedHolderName(feed_holder_name);
+      copy_program->SetFetchHolderName(fetch_holder_name);
+      ```
+    - 4、 获取`copy_program`的`feed_target_names`和`fetch_target_names`
+    - 5、 准备feed数据，定义Tensor来fetch结果
+    - 6、 执行`copy_program`
+      ```cpp
+      executor->Run(*copy_program, scope, feed_targets, fetch_targets, true, feed_holder_name, fetch_holder_name);
+      ```
+    - 7、 使用fetch数据
+  - 主线程
+    - 8、 释放资源
+
+
+- 基本概念
+  - 数据相关：
+    - [Tensor](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/tensor.md)，一个N维数组，数据可以是任意类型（int，float，double等）
+    - [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/lod_tensor.md)，带LoD(Level-of-Detail)即序列信息的Tensor
+    - [Scope](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md)，记录了变量Variable
+  - 执行相关：
+    - [Executor](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/executor.md)，无状态执行器，只跟设备相关
+    - Place
+      - CPUPlace，CPU设备
+      - CUDAPlace，CUDA GPU设备
+  - 神经网络表示：
+    - [Program](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/program.md).
+
+    详细介绍请参考[**Paddle Fluid开发者指南**](https://github.com/lcy-seso/learning_notes/blob/master/Fluid/developer's_guid_for_Fluid/Developer's_Guide_to_Paddle_Fluid.md)
+
+
+
+## Inference实例
+
+  1. fit a line: [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_fit_a_line.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/book/test_inference_fit_a_line.cc)
+  1. image classification: [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_image_classification.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/book/test_inference_image_classification.cc)
+  1. label semantic roles: [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_label_semantic_roles.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/book/test_inference_label_semantic_roles.cc)
+  1. recognize digits: [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_recognize_digits.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/book/test_inference_recognize_digits.cc)
+  1. recommender system: [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_recommender_system.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/book/test_inference_recommender_system.cc)
+  1. understand sentiment: [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_understand_sentiment.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/book/test_inference_understand_sentiment.cc)
+  1. word2vec: [Python](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_word2vec.py), [C++](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/inference/tests/book/test_inference_word2vec.cc)
+
+
+## Inference计算优化
+- 使用Python推理优化工具([inference_transpiler](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/inference_transpiler.py))
+  ```python
+  class InferenceTranspiler:
+    def transpile(self, program, place, scope=None):
+        ...
+        if scope is None:
+            scope = global_scope()
+        ...
+  ```
+  - 使用`InferenceTranspiler`将会直接修改`program`。
+  - 使用`InferenceTranspiler`会修改参数的值，请确保`program`的参数在`scope`内。
+- 支持的优化
+  - 融合batch_norm op的计算
+- 使用示例([链接](https://github.com/Xreki/Xreki.github.io/blob/master/fluid/inference/inference_transpiler.py))
+  ```python
+  import paddle.fluid as fluid
+  # NOTE: Applying the inference transpiler will change the inference_program.
+  t = fluid.InferenceTranspiler()
+  t.transpile(inference_program, place, inference_scope)
+  ```
+
+
+
+
+## 内存使用优化
+- 使用Python内存优化工具([memory_optimization_transipiler](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/memory_optimization_transpiler.py))
+  ```python
+  fluid.memory_optimize(inference_program)
+  ```

doc/fluid/howto/optimization/cpu_profiling_cn.md

+# CPU性能调优
+
 此教程会介绍如何使用Python的cProfile包、Python库yep、Google perftools来进行性能分析 (profiling) 与调优（performance tuning）。
 
 Profling 指发现性能瓶颈。系统中的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。Tuning 指消除瓶颈。性能优化的过程通常是不断重复地 profiling 和 tuning。
@@ -8,7 +10,7 @@ PaddlePaddle 用户一般通过调用 Python API 编写深度学习程序。大
 * Python 与 C++ 混合代码的性能分析
 
 
-# Python代码的性能分析
+## Python代码的性能分析
 
 ### 生成性能分析文件
 

doc/fluid/howto/optimization/cpu_profiling_en.md

+# Tune CPU performance
+
 This tutorial introduces techniques we use to profile and tune the
 CPU performance of PaddlePaddle.  We will use Python packages
 `cProfile` and `yep`, and Google's `perftools`.
@@ -14,7 +16,7 @@ the profiling and tuning of
 1. the Python code and
 1. the mixture of Python and C++ code.
 
-# Profiling the Python Code
+## Profiling the Python Code
 
 ### Generate the Performance Profiling File
 

doc/v2/build_and_install/build_from_source_cn.rst

@@ -35,7 +35,7 @@ PaddlePaddle需要使用Docker环境完成编译，这样可以免去单独安
    # 2. 可选步骤：源码中构建用于编译PaddlePaddle的Docker镜像
    docker build -t paddle:dev .
    # 3. 执行下面的命令编译CPU-Only的二进制
-   docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh
+   docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/paddle_build.sh build
    # 4. 或者也可以使用为上述可选步骤构建的镜像（必须先执行第2步）
    docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddle:dev
 

doc/v2/build_and_install/build_from_source_en.rst

@@ -34,7 +34,7 @@ Or you can build your own image from source as the optional step below:
    # 2. Optional: build development docker image from source
    docker build -t paddle:dev .
    # 3. Run the following command to build a CPU-Only binaries
-   docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh
+   docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/paddle_build.sh build
    # 4. Or, use your built Docker image to build PaddlePaddle (must run step 2)
    docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddle:dev
 

doc/v2/build_and_install/pip_install_cn.rst

@@ -10,20 +10,38 @@ PaddlePaddle可以使用常用的Python包管理工具
 使用pip安装
 ------------------------------
 
-
-执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件，版本为cpu_avx_openblas。
+执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件。
 
   .. code-block:: bash
 
      pip install paddlepaddle
 
+当前的默认版本为0.12.0，cpu_avx_openblas，您可以通过指定版本号来安装其它版本，例如:
+
+  .. code-block:: bash
+
+      pip install paddlepaddle==0.11.0
+
 
-如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
+如果需要安装支持GPU的版本（cuda8.0_cudnn5_avx_openblas），需要执行：
 
   .. code-block:: bash
 
      pip install paddlepaddle-gpu
 
+当前的默认版本也是0.12.0，PaddlePaddle针对不同需求提供了更多版本的安装包，部分列表如下：
+
+=================================   ========================================
+版本号                               版本说明
+=================================   ========================================
+paddlepaddle-gpu==0.12.0            使用CUDA 8.0和cuDNN 5编译的0.12.0版本
+paddlepaddle-gpu==0.11.0.post87     使用CUDA 8.0和cuDNN 7编译的0.11.0版本
+paddlepaddle-gpu==0.11.0.post8      使用CUDA 8.0和cuDNN 5编译的0.11.0版本
+paddlepaddle-gpu==0.11.0            使用CUDA 7.5和cuDNN 5编译的0.11.0版本
+=================================   ========================================
+
+您可以在 `Release History <https://pypi.org/project/paddlepaddle-gpu/#history>`_ 中找到paddlepaddle-gpu的各个发行版本。
+
 如果需要获取并安装最新的（开发分支）PaddlePaddle，可以从我们的CI系统中下载最新的whl安装包和c-api开发包并安装，
 您可以从下面的表格中找到需要的版本：
 
@@ -37,12 +55,11 @@ PaddlePaddle可以使用常用的Python包管理工具
     :header: "版本说明", "cp27-cp27mu", "cp27-cp27m"
     :widths: 1, 3, 3
 
-    "cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_avx_mkl", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_avx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
 
 .. _pip_dependency:
 
@@ -69,7 +86,7 @@ PaddlePaddle发布的安装包会尽量对齐 `manylinux1 <https://www.python.or
 ------------------------------
 
 - paddlepaddle*.whl is not a supported wheel on this platform.
-  
+
   出现这个问题的主要原因是，没有找到和当前系统匹配的paddlepaddle安装包。请检查Python版本是否为2.7系列。另外最新的pip官方源中的安装包默认是manylinux1标准，需要使用最新的pip (>9.0.0) 才可以安装。可以使用下面的命令更新您的pip：
 
     .. code-block:: bash

doc/v2/build_and_install/pip_install_en.rst

@@ -12,20 +12,38 @@ Install using pip
 ------------------------------
 
 Run the following command to install PaddlePaddle on the current
-machine, it will also download requirements, the version is cpu_avx_openblas.
+machine, it will also download requirements.
 
   .. code-block:: bash
 
      pip install paddlepaddle
 
+the default version is 0.12.0, cpu_avx_openblas, you can specify the versions to satisfy your demands, like:
 
-If you wish to install GPU version (cuda7.5_cudnn5_avx_openblas), just run:
+  .. code-block:: bash
+
+      pip install paddlepaddle==0.11.0
+
+If you need to install a GPU-enabled version (cuda8.0_cudnn5_avx_openblas), you need to run:
 
   .. code-block:: bash
 
      pip install paddlepaddle-gpu
 
-If you wish to install the latest develop branch PaddlePaddle, 
+The default version is also 0.12.0, PaddlePaddle provides several versions of packages for different needs, as shown in the table:
+
+=================================   ========================================
+版本号                               版本说明
+=================================   ========================================
+paddlepaddle-gpu==0.12.0            0.12.0 built with CUDA 8.0 and cuDNN 5
+paddlepaddle-gpu==0.11.0.post87     0.11.0 built with CUDA 8.0 and cuDNN 7
+paddlepaddle-gpu==0.11.0.post8      0.11.0 built with CUDA 8.0 and cuDNN 5
+paddlepaddle-gpu==0.11.0            0.11.0 built with CUDA 7.5 and cuDNN 5
+=================================   ========================================
+
+You can find all versions released of paddlepaddle-gpu in `Release History <https://pypi.org/project/paddlepaddle-gpu/#history>`_ .
+
+If you wish to install the latest develop branch PaddlePaddle,
 you can download the latest whl package from our CI system. Access
 the below links, log in as guest, then click at the "Artifact"
 tab, you'll find the download link of whl packages.
@@ -40,12 +58,11 @@ If the links below shows up the login form, just click "Log in as guest" to star
     :header: "version", "cp27-cp27mu", "cp27-cp27m"
     :widths: 1, 3, 3
 
-    "cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
-    "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_avx_mkl", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_avx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
+    "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
 
 .. _pip_dependency:
 
@@ -79,7 +96,7 @@ FAQ
 ------------------------------
 
 - paddlepaddle*.whl is not a supported wheel on this platform.
-  
+
   The main cause of this issue is that your current platform is
   not supported. Please check that you are using Python 2.7 series.
   Besides, pypi only supports manylinux1 standard, you'll need to

doc/v2/howto/capi/workflow_of_capi_cn.md

@@ -59,7 +59,7 @@
     代码示例如下：
 
     ```python
-    from paddle.utils.merge_model import merge_v2_modelss
+    from paddle.utils.merge_model import merge_v2_model
     from mnist_v2 import network
 
     net = network(is_infer=True)

paddle/fluid/framework/data_type_transform.cc

@@ -91,6 +91,12 @@ void TransDataType(const OpKernelType& kernel_type_for_var,
     case proto::VarType::BOOL:
       framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
       break;
+    case proto::VarType::INT16:
+      framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
+      break;
+    case proto::VarType::UINT8:
+      framework::VisitDataType(dst_type, CastDataType<bool>(in, out, ctx));
+      break;
     default:
       PADDLE_THROW("Not support type %d", src_type);
   }

paddle/fluid/framework/details/CMakeLists.txt

@@ -36,5 +36,5 @@ 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_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 )
+#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/multi_devices_graph_builder.cc

@@ -18,6 +18,7 @@
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
 #include "paddle/fluid/framework/details/send_op_handle.h"
+#include "paddle/fluid/framework/op_info.h"
 #include "paddle/fluid/framework/scope.h"
 
 #ifdef PADDLE_WITH_CUDA
@@ -98,7 +99,7 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(const OpDesc &op,
     return false;
   };
 
-  if (op.Type() == "split") {
+  if (op.Type() == "split" || op.Type() == "split_byref") {
     return checker(op.OutputArgumentNames(), send_op->InputArgumentNames());
   } else if (op.Type() == "concat") {
     return checker(op.InputArgumentNames(), send_op->OutputArgumentNames());
@@ -159,25 +160,39 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
       if (!is_forwarding && places_.size() > 1) {
         // Currently, we assume that once gradient is generated, it can be
         // broadcast, and each gradient is only broadcast once.
-        for (auto &og : op->OutputArgumentNames()) {
-          if (IsParameterGradientOnce(og, &og_has_been_broadcast)) {
-            switch (strategy_.reduce_) {
-              case BuildStrategy::ReduceStrategy::kReduce:
-                CreateReduceOp(&result, og, cur_device_id);
-                var_name_on_devices[cur_device_id].emplace(og);
-                bcast_var_name_set[cur_device_id].emplace(
-                    og.substr(0, og.size() - strlen(kGradVarSuffix)));
-                cur_device_id = (cur_device_id + 1) % places_.size();
-                break;
-              case BuildStrategy::ReduceStrategy::kAllReduce:
-                if (IsSparseGradient(var_types, og)) {
-                  CreateReduceOp(&result, og, 0);
-                  CreateBroadcastOp(&result, og, 0);
-                } else {
-                  InsertNCCLAllReduceOp(&result, og);
-                }
-                break;
+        if (static_cast<bool>(boost::get<int>(op->GetAttr(
+                                  OpProtoAndCheckerMaker::OpRoleAttrName())) &
+                              static_cast<int>(OpRole::kBackward))) {
+          try {
+            auto backward_vars =
+                boost::get<std::vector<std::string>>(op->GetNullableAttr(
+                    OpProtoAndCheckerMaker::OpRoleVarAttrName()));
+
+            PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);
+
+            for (size_t i = 0; i < backward_vars.size(); i += 2) {
+              auto &p_name = backward_vars[i];
+              auto &g_name = backward_vars[i + 1];
+              VLOG(10) << "Bcast " << g_name << " for parameter " << p_name;
+
+              switch (strategy_.reduce_) {
+                case BuildStrategy::ReduceStrategy::kReduce:
+                  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)) {
+                    CreateReduceOp(&result, g_name, 0);
+                    CreateBroadcastOp(&result, g_name, 0);
+                  } else {
+                    InsertNCCLAllReduceOp(&result, g_name);
+                  }
+                  break;
+              }
             }
+          } catch (boost::bad_get e) {
           }
         }
       }
@@ -398,11 +413,12 @@ void MultiDevSSAGraphBuilder::CreateSendOp(SSAGraph *result,
 }
 
 bool MultiDevSSAGraphBuilder::IsScaleLossOp(const OpDesc &op) const {
-  // FIXME(yy): Do not hard code like this
-  return op.OutputArgumentNames().size() == 1 &&
-         op.OutputArgumentNames()[0] == GradVarName(loss_var_name_);
+  return boost::get<int>(
+             op.GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
+             (static_cast<int>(OpRole::kBackward) |
+              static_cast<int>(OpRole::kLoss)) &&
+         !loss_var_name_.empty();  // If loss_var is empty. This is test mode
 }
-
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/op_registry.h

@@ -96,10 +96,7 @@ struct OpInfoFiller<T, kOpProtoAndCheckerMaker> {
     info->proto_ = new proto::OpProto;
     info->checker_ = new OpAttrChecker();
     T maker;
-    maker.SetProto(info->proto_);
-    maker.SetChecker(info->checker_);
-    maker.Make();
-    maker.Validate();
+    maker(info->proto_, info->checker_);
     info->proto_->set_type(op_type);
     PADDLE_ENFORCE(
         info->proto_->IsInitialized(),

paddle/fluid/framework/executor.cc

@@ -228,7 +228,8 @@ static bool has_fetch_operators(
 void Executor::Run(const ProgramDesc& program, Scope* scope,
                    std::map<std::string, const LoDTensor*>* feed_targets,
                    std::map<std::string, LoDTensor*>* fetch_targets,
-                   bool create_vars, const std::string& feed_holder_name,
+                   bool create_local_scope, bool create_vars,
+                   const std::string& feed_holder_name,
                    const std::string& fetch_holder_name) {
   platform::RecordBlock b(kProgramId);
   bool has_feed_ops =
@@ -290,8 +291,9 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
   }
 
   auto ctx = Prepare(*copy_program, 0);
-  RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets, create_vars,
-                     feed_holder_name, fetch_holder_name);
+  RunPreparedContext(ctx.get(), scope, feed_targets, fetch_targets,
+                     create_local_scope, create_vars, feed_holder_name,
+                     fetch_holder_name);
 }
 
 std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
@@ -366,8 +368,9 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
 void Executor::RunPreparedContext(
     ExecutorPrepareContext* ctx, Scope* scope,
     std::map<std::string, const LoDTensor*>* feed_targets,
-    std::map<std::string, LoDTensor*>* fetch_targets, bool create_vars,
-    const std::string& feed_holder_name, const std::string& fetch_holder_name) {
+    std::map<std::string, LoDTensor*>* fetch_targets, bool create_local_scope,
+    bool create_vars, const std::string& feed_holder_name,
+    const std::string& fetch_holder_name) {
   auto& global_block = ctx->prog_.Block(ctx->block_id_);
 
   PADDLE_ENFORCE(
@@ -387,7 +390,7 @@ void Executor::RunPreparedContext(
     }
   }
 
-  RunPreparedContext(ctx, scope, create_vars, create_vars);
+  RunPreparedContext(ctx, scope, create_local_scope, create_vars);
 
   // obtain the data of fetch_targets from fetch_holder
   for (auto* op : global_block.AllOps()) {

paddle/fluid/framework/executor.h

@@ -57,7 +57,7 @@ class Executor {
   void Run(const ProgramDesc& program, Scope* scope,
            std::map<std::string, const LoDTensor*>* feed_targets,
            std::map<std::string, LoDTensor*>* fetch_targets,
-           bool create_vars = true,
+           bool create_local_scope = true, bool create_vars = true,
            const std::string& feed_holder_name = "feed",
            const std::string& fetch_holder_name = "fetch");
 
@@ -76,6 +76,7 @@ class Executor {
   void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
                           std::map<std::string, const LoDTensor*>* feed_targets,
                           std::map<std::string, LoDTensor*>* fetch_targets,
+                          bool create_local_scope = true,
                           bool create_vars = true,
                           const std::string& feed_holder_name = "feed",
                           const std::string& fetch_holder_name = "fetch");

paddle/fluid/framework/op_desc.cc

@@ -20,6 +20,7 @@ limitations under the License. */
 #include <unordered_map>
 #include "glog/logging.h"
 #include "paddle/fluid/framework/block_desc.h"
+#include "paddle/fluid/framework/op_proto_maker.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/shape_inference.h"
@@ -222,6 +223,15 @@ Attribute OpDesc::GetAttr(const std::string &name) const {
   return it->second;
 }
 
+Attribute OpDesc::GetNullableAttr(const std::string &name) const {
+  auto it = attrs_.find(name);
+  if (it != attrs_.end()) {
+    return it->second;
+  } else {
+    return Attribute();
+  }
+}
+
 int OpDesc::GetBlockAttr(const std::string &name) const {
   auto it = attrs_.find(name);
   PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
@@ -249,6 +259,13 @@ void OpDesc::RenameOutput(const std::string &old_name,
     std::replace(output.second.begin(), output.second.end(), old_name,
                  new_name);
   }
+
+  auto it = attrs_.find(framework::OpProtoAndCheckerMaker::OpRoleVarAttrName());
+  if (it != attrs_.end()) {
+    auto &op_vars = boost::get<std::vector<std::string>>(it->second);
+    std::replace(op_vars.begin(), op_vars.end(), old_name, new_name);
+  }
+
   need_update_ = true;
 }
 

paddle/fluid/framework/op_desc.h

@@ -78,6 +78,8 @@ class OpDesc {
 
   Attribute GetAttr(const std::string &name) const;
 
+  Attribute GetNullableAttr(const std::string &name) const;
+
   int GetBlockAttr(const std::string &name) const;
 
   void Rename(const std::string &old_name, const std::string &new_name);

paddle/fluid/framework/op_proto_maker.cc

@@ -13,6 +13,7 @@ limitations under the License. */
 
 #include "paddle/fluid/framework/op_proto_maker.h"
 #include <string>
+#include <vector>
 
 namespace paddle {
 namespace framework {
@@ -55,5 +56,28 @@ void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
   }
 }
 
+void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
+                                        OpAttrChecker* attr_checker) {
+  proto_ = proto;
+  op_checker_ = attr_checker;
+  Make();
+
+  AddAttr<int>(OpRoleAttrName(), "The role of this operator")
+      .InEnum(
+          {static_cast<int>(OpRole::kForward),
+           static_cast<int>(OpRole::kBackward),
+           static_cast<int>(OpRole::kOptimize),
+           static_cast<int>(OpRole::kLoss) | static_cast<int>(OpRole::kForward),
+           static_cast<int>(OpRole::kLoss) |
+               static_cast<int>(OpRole::kBackward),
+           static_cast<int>(OpRole::kNotSpecified)})
+      .SetDefault(static_cast<int>(OpRole::kNotSpecified));
+  AddAttr<std::vector<std::string>>(OpRoleVarAttrName(),
+                                    "Optimized for variable")
+      .SetDefault({});
+
+  Validate();
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/op_proto_maker.h

@@ -20,21 +20,31 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+enum class OpRole {
+  kForward = 0x0000,
+  kBackward = 0x0001,
+  kOptimize = 0x0002,
+
+  kLoss = 0x0100,
+  // The default value of op's role. This should be only used for unittests and
+  // CreateOp inside a operator.
+  kNotSpecified = 0x1000,
+};
+
 // this class not only make proto but also init attribute checkers.
 class OpProtoAndCheckerMaker {
  public:
+  static const char *OpRoleAttrName() { return "op_role"; }
+  static const char *OpRoleVarAttrName() { return "op_role_var"; }
+
+  void operator()(proto::OpProto *proto, OpAttrChecker *attr_checker);
+
   virtual void Make() = 0;
 
   virtual ~OpProtoAndCheckerMaker() {
     CHECK(validated_) << "should call Validate after build";
   }
 
-  void SetProto(proto::OpProto *proto) { proto_ = proto; }
-
-  void SetChecker(OpAttrChecker *attr_checker) { op_checker_ = attr_checker; }
-
-  void Validate();
-
  protected:
   struct VariableBuilder {
     proto::OpProto::Var *var_;
@@ -76,6 +86,7 @@ class OpProtoAndCheckerMaker {
 
  private:
   void CheckNoDuplicatedInOutAttrs();
+  void Validate();
 
   proto::OpProto *proto_;
   OpAttrChecker *op_checker_;

paddle/fluid/framework/op_proto_maker_test.cc

@@ -28,10 +28,8 @@ TEST(ProtoMaker, DuplicatedAttr) {
   paddle::framework::proto::OpProto op_proto;
   paddle::framework::OpAttrChecker op_checker;
   TestAttrProtoMaker proto_maker;
-  proto_maker.SetProto(&op_proto);
-  proto_maker.SetChecker(&op_checker);
-  proto_maker.Make();
-  ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
+  ASSERT_THROW(proto_maker(&op_proto, &op_checker),
+               paddle::platform::EnforceNotMet);
 }
 
 class TestInOutProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
@@ -46,8 +44,6 @@ TEST(ProtoMaker, DuplicatedInOut) {
   paddle::framework::proto::OpProto op_proto;
   paddle::framework::OpAttrChecker op_checker;
   TestAttrProtoMaker proto_maker;
-  proto_maker.SetProto(&op_proto);
-  proto_maker.SetChecker(&op_checker);
-  proto_maker.Make();
-  ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet);
+  ASSERT_THROW(proto_maker(&op_proto, &op_checker),
+               paddle::platform::EnforceNotMet);
 }

paddle/fluid/inference/analysis/CMakeLists.txt

-cc_library(analysis SRCS dot.cc node.cc node.h)
+set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor init)
+cc_library(analysis SRCS dot.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc fluid_to_data_flow_graph_pass.cc
+  DEPS paddle_fluid)
 cc_test(test_node SRCS node_tester.cc DEPS analysis)
+cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
+
+set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
+
+cc_test(test_data_flow_graph SRCS data_flow_graph_tester.cc DEPS analysis ${FLUID_CORE_MODULES} paddle_fluid
+  ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
+set_tests_properties(test_data_flow_graph PROPERTIES DEPENDS test_word2vec)
+
+cc_test(test_subgraph_splitter
+        SRCS subgraph_splitter_tester.cc
+        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)

paddle/fluid/inference/analysis/data_flow_graph.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/data_flow_graph.h"
+#include "paddle/fluid/inference/analysis/dot.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+// It is a better idea that the inputs and outputs of this graph is set manully
+// before, but there must be a Pass that helps to prune the unnecessary ops that
+// do not contribute to the given targets, so in this pass, analysis and get the
+// inputs and outputs is OK.
+void DataFlowGraph::Build() {
+  inputs.clear();
+  outputs.clear();
+  std::unordered_set<Node *> ins;
+  std::unordered_set<Node *> outs;
+  for (auto &node : nodes.nodes()) {
+    for (auto *in : node->inlinks) {
+      ins.insert(in);
+    }
+    for (auto *out : node->outlinks) {
+      outs.insert(out);
+    }
+  }
+
+  // The nodes that in ins but not in outs is the graph's inputs
+  // similarly, the nodes that in outs but not in ins is the graphs' outputs
+  for (auto *in : ins) {
+    if (!outs.count(in)) {
+      inputs.push_back(in);
+    }
+  }
+  for (auto *out : outs) {
+    if (!outs.count(out)) {
+      outputs.push_back(out);
+    }
+  }
+}
+
+std::string DataFlowGraph::DotString() const {
+  Dot dot;
+
+  // Add nodes
+  for (size_t i = 0; i < nodes.size(); i++) {
+    const Node &node = nodes.Get(i);
+    switch (node.type()) {
+      case Node::Type::kValue:
+        dot.AddNode(node.repr(), node.dot_attrs());
+        break;
+      case Node::Type::kFunction:
+        dot.AddNode(node.repr(), node.dot_attrs());
+        break;
+      case Node::Type::kFunctionBlock:
+        dot.AddNode(node.repr(), node.dot_attrs());
+        break;
+      default:
+        PADDLE_THROW("unsupported Node type %d", static_cast<int>(node.type()));
+    }
+  }
+
+  // Add edges
+  for (size_t i = 0; i < nodes.size(); i++) {
+    const Node &node = nodes.Get(i);
+    for (auto &in : node.inlinks) {
+      dot.AddEdge(in->repr(), node.repr(), {});
+    }
+  }
+  return dot.Build();
+}
+
+//
+// NodesBFSIterator
+//
+
+GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
+    const std::vector<Node *> &source)
+    : queue_(source.begin(), source.end()) {}
+
+// GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
+//     GraphTraits<DataFlowGraph>::NodesBFSIterator &&other) noexcept
+//     : queue_(std::move(other.queue_)),
+//       visited_(std::move(other.visited_)) {}
+
+GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
+    const GraphTraits<DataFlowGraph>::NodesBFSIterator &other)
+    : queue_(other.queue_), visited_(other.visited_) {}
+
+Node &GraphTraits<DataFlowGraph>::NodesBFSIterator::operator*() {
+  PADDLE_ENFORCE(!queue_.empty());
+  return *queue_.front();
+}
+
+Node *GraphTraits<DataFlowGraph>::NodesBFSIterator::operator->() {
+  PADDLE_ENFORCE(!queue_.empty());
+  return queue_.front();
+}
+
+GraphTraits<DataFlowGraph>::NodesBFSIterator &
+GraphTraits<DataFlowGraph>::NodesBFSIterator::operator=(
+    const GraphTraits<DataFlowGraph>::NodesBFSIterator &other) {
+  queue_ = other.queue_;
+  visited_ = other.visited_;
+  return *this;
+}
+
+GraphTraits<DataFlowGraph>::NodesBFSIterator
+    &GraphTraits<DataFlowGraph>::NodesBFSIterator::operator++() {
+  PADDLE_ENFORCE(!queue_.empty());
+  auto *cur = queue_.front();
+  visited_.insert(cur);
+  queue_.pop_front();
+  for (auto *output : cur->outlinks) {
+    if (!visited_.count(output)) {
+      queue_.push_back(output);
+      visited_.insert(output);
+    }
+  }
+  return *this;
+}
+
+bool GraphTraits<DataFlowGraph>::NodesBFSIterator::operator==(
+    const GraphTraits<DataFlowGraph>::NodesBFSIterator &other) {
+  if (queue_.empty()) return other.queue_.empty();
+  if ((!queue_.empty()) && (!other.queue_.empty())) {
+    return queue_.front() == other.queue_.front() &&
+           visited_.size() == other.visited_.size();  // here need to check the
+                                                      // equality of queue and
+    // visited. Just a light but week implementation.
+  }
+  return false;
+}
+
+//
+// NodesDFSIterator
+//
+GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
+    const std::vector<Node *> &source) {
+  for (auto *x : source) stack_.push(x);
+}
+
+// GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
+//     GraphTraits<DataFlowGraph>::NodesDFSIterator &&other) noexcept
+//     : stack_(std::move(other.stack_)),
+//       visited_(std::move(other.visited_)) {}
+
+GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
+    const GraphTraits<DataFlowGraph>::NodesDFSIterator &other)
+    : stack_(other.stack_), visited_(other.visited_) {}
+
+Node &GraphTraits<DataFlowGraph>::NodesDFSIterator::operator*() {
+  PADDLE_ENFORCE(!stack_.empty());
+  return *stack_.top();
+}
+
+GraphTraits<DataFlowGraph>::NodesDFSIterator
+    &GraphTraits<DataFlowGraph>::NodesDFSIterator::operator++() {
+  if (stack_.empty()) return *this;
+  visited_.insert(stack_.top());
+  auto *cur = stack_.top();
+  stack_.pop();
+  for (auto *x : cur->outlinks) {
+    if (!visited_.count(x)) {
+      stack_.push(x);
+      visited_.insert(x);
+    }
+  }
+  return *this;
+}
+bool GraphTraits<DataFlowGraph>::NodesDFSIterator::operator==(
+    const GraphTraits<DataFlowGraph>::NodesDFSIterator &other) {
+  if (stack_.empty()) return other.stack_.empty();
+  if ((!stack_.empty()) && (!other.stack_.empty())) {
+    return stack_.top() == other.stack_.top();
+  }
+  return false;
+}
+
+GraphTraits<DataFlowGraph>::NodesDFSIterator &
+GraphTraits<DataFlowGraph>::NodesDFSIterator::operator=(
+    const GraphTraits<DataFlowGraph>::NodesDFSIterator &other) {
+  stack_ = other.stack_;
+  visited_ = other.visited_;
+  return *this;
+}
+Node *GraphTraits<DataFlowGraph>::NodesDFSIterator::operator->() {
+  return stack_.top();
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph.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. */
+
+/*
+ * Data flow graph is an pass that build the basic graph. It contains a graph
+ * and the iterators that enable the iteration over the graph.
+ */
+
+#pragma once
+
+#include <deque>
+#include <stack>
+#include <unordered_set>
+
+#include "paddle/fluid/inference/analysis/graph_traits.h"
+#include "paddle/fluid/inference/analysis/node.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+/*
+ * DataFlowGraph - A container of Value and Function Nodes.
+ */
+struct DataFlowGraph {
+  NodeMap nodes;
+  std::vector<Node *> inputs;
+  std::vector<Node *> outputs;
+
+  // Extract inputs and outputs of the graph.
+  void Build();
+
+  // Output a DOT graph file for debug.
+  std::string DotString() const;
+};
+
+/*
+ * An graph trait help to traverse the graph using BFS.
+ * The BFS start from a graph's inputs, the graph should be fully-connected, so
+ * that the iterator can reach the end.
+ */
+template <>
+struct GraphTraits<DataFlowGraph> {
+  // BFS iterator on nodes.
+  struct NodesBFSIterator
+      : public std::iterator<std::forward_iterator_tag, Node *> {
+    NodesBFSIterator() = default;
+    explicit NodesBFSIterator(const std::vector<Node *> &source);
+    // NodesBFSIterator(NodesBFSIterator &&other) noexcept;
+    // NOTE Heavy to use.
+    NodesBFSIterator(const NodesBFSIterator &other);
+
+    Node &operator*();
+    NodesBFSIterator &operator++();
+    Node *operator->();
+    // TODO(Superjomn) current implementation just compare the first
+    // element, need to compare the graph and all the elements in the queue and
+    // set.
+    NodesBFSIterator &operator=(const NodesBFSIterator &other);
+    bool operator==(const NodesBFSIterator &other);
+    bool operator!=(const NodesBFSIterator &other) { return !(*this == other); }
+
+   private:
+    std::deque<Node *> queue_;
+    std::unordered_set<Node *> visited_;
+  };
+
+  // DFS iterator on nodes.
+  struct NodesDFSIterator
+      : public std::iterator<std::forward_iterator_tag, Node *> {
+    NodesDFSIterator() = default;
+    explicit NodesDFSIterator(const std::vector<Node *> &source);
+    // NodesDFSIterator(NodesDFSIterator &&other) noexcept;
+    NodesDFSIterator(const NodesDFSIterator &other);
+
+    Node &operator*();
+    NodesDFSIterator &operator++();
+    // TODO(Superjomn) current implementation just compare the first
+    // element, need to compare the graph and all the elements in the queue and
+    // set.
+    NodesDFSIterator &operator=(const NodesDFSIterator &other);
+    bool operator==(const NodesDFSIterator &other);
+    bool operator!=(const NodesDFSIterator &other) { return !(*this == other); }
+    Node *operator->();
+
+   private:
+    std::stack<Node *> stack_;
+    std::unordered_set<Node *> visited_;
+  };
+
+  explicit GraphTraits(DataFlowGraph *graph) : graph_(graph) {}
+
+  // default use BFS to visit the nodes.
+  iterator_range<NodesBFSIterator> nodes() {
+    return iterator_range<NodesBFSIterator>(nodes_bfs_begin(), nodes_bfs_end());
+  }
+  iterator_range<NodesBFSIterator> nodes_in_BFS() {
+    return iterator_range<NodesBFSIterator>(nodes_bfs_begin(), nodes_bfs_end());
+  }
+  iterator_range<NodesDFSIterator> nodes_in_DFS() {
+    return iterator_range<NodesDFSIterator>(nodes_dfs_begin(), nodes_dfs_end());
+  }
+
+ private:
+  NodesBFSIterator nodes_bfs_begin() {
+    return NodesBFSIterator(graph_->inputs);
+  }
+  NodesBFSIterator nodes_bfs_end() { return NodesBFSIterator(); }
+  NodesDFSIterator nodes_dfs_begin() {
+    return NodesDFSIterator(graph_->inputs);
+  }
+  NodesDFSIterator nodes_dfs_end() { return NodesDFSIterator(); }
+
+ private:
+  DataFlowGraph *graph_;
+};
+
+// Extract the inputs and outputs of a graph. The inputs and outputs of a
+// sub-graph is the inputs nodes and output nodes that doesn't inside the
+// sub-graph.
+std::pair<
+    std::vector<Node *>,
+    std::vector<
+        Node *>> static ExtractInputAndOutputOfSubGraph(std::vector<Node *>
+                                                            &graph) {
+  std::unordered_set<Node *> nodes(graph.begin(), graph.end());
+  std::unordered_set<Node *> inputs;
+  std::unordered_set<Node *> outputs;
+  for (auto &node : graph) {
+    for (auto *in : node->inlinks) {
+      if (!nodes.count(in) && in->type() == Node::Type::kValue) {
+        inputs.insert(in);
+      }
+    }
+    for (auto *out : node->outlinks) {
+      if (!nodes.count(out) && out->type() == Node::Type::kValue) {
+        outputs.insert(out);
+      }
+    }
+  }
+  return std::make_pair(std::vector<Node *>(inputs.begin(), inputs.end()),
+                        std::vector<Node *>(outputs.begin(), outputs.end()));
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph_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/data_flow_graph.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+TEST(DataFlowGraph, BFS) {
+  auto desc = LoadProgramDesc();
+  auto dfg = ProgramDescToDFG(desc);
+  dfg.Build();
+
+  for (auto* in : dfg.inputs) {
+    LOG(INFO) << "inputs: " << in->name() << " "
+              << static_cast<int>(in->type());
+  }
+  for (auto* out : dfg.outputs) {
+    LOG(INFO) << "outputs: " << out->name() << " "
+              << static_cast<int>(out->type());
+  }
+
+  GraphTraits<DataFlowGraph> trait(&dfg);
+  auto nodes = trait.nodes();
+  int count = 0;
+  for (auto it = nodes.begin(); it != nodes.end(); ++it) {
+    LOG(INFO) << "visiting " << it->name();
+    ++count;
+  }
+  ASSERT_EQ(count, dfg.nodes.size());
+}
+
+TEST(DataFlowGraph, DFS) {
+  auto desc = LoadProgramDesc();
+  auto dfg = ProgramDescToDFG(desc);
+  dfg.Build();
+  GraphTraits<DataFlowGraph> trait(&dfg);
+  auto nodes = trait.nodes_in_DFS();
+  int count = 0;
+  for (auto it = nodes.begin(); it != nodes.end(); ++it) {
+    LOG(INFO) << "visiting " << it->name();
+    ++count;
+  }
+  ASSERT_EQ(count, dfg.nodes.size());
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_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/data_flow_graph_to_fluid_pass.h"
+
+#include <glog/logging.h>
+#include <google/protobuf/text_format.h>
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/io.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+TEST_F(DFG_Tester, Test) {
+  framework::proto::ProgramDesc new_desc;
+  DataFlowGraph graph;
+
+  FluidToDataFlowGraphPass pass0;
+  DataFlowGraphToFluidPass pass1;
+  pass0.Initialize(desc);
+  pass1.Initialize(&new_desc);
+
+  pass0.Run(&graph);
+  pass1.Run(&graph);
+
+  pass0.Finalize();
+  pass1.Finalize();
+
+  LOG(INFO) << graph.nodes.size();
+}
+
+}  // analysis
+}  // inference
+}  // paddle

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.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/fluid_to_data_flow_graph_pass.h"
+#include <vector>
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+FluidToDataFlowGraphPass::FluidToDataFlowGraphPass() {}
+
+bool FluidToDataFlowGraphPass::Initialize() { return Pass::Initialize(); }
+
+bool FluidToDataFlowGraphPass::Initialize(
+    const framework::proto::ProgramDesc &desc) {
+  desc_ = &desc;
+  return true;
+}
+
+bool FluidToDataFlowGraphPass::Finalize() { return Pass::Finalize(); }
+
+void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
+  // insert vars
+  std::unordered_map<std::string, size_t> var2id;
+  auto &main_block = desc_->blocks(framework::kRootBlockIndex);
+  for (int i = 0; i < main_block.vars_size(); i++) {
+    const auto &var = main_block.vars(i);
+    auto *v = graph->nodes.Create(Node::Type::kValue);
+    v->SetName(var.name());
+    v->SetExtraInfo(const_cast<void *>(static_cast<const void *>(&var)));
+    var2id[var.name()] = v->id();
+  }
+  for (int i = 0; i < main_block.ops_size(); i++) {
+    const auto &op = main_block.ops(i);
+    auto *o = graph->nodes.Create(Node::Type::kFunction);
+    o->SetName(op.type());
+    static_cast<Function *>(o)->SetFuncType(op.type());
+    // Link to the original protobuf message's memory, make it easier to
+    // generate from a data flow graph to fluid ProgramDesc.
+    o->SetExtraInfo(const_cast<void *>(static_cast<const void *>(&op)));
+    // set inputs and outputs
+    // TODO(Superjomn) make sure the InputNames is the real variable name.
+    for (int j = 0; j < op.inputs_size(); j++) {
+      auto &in_var = op.inputs(j);
+      for (int k = 0; k < in_var.arguments_size(); k++) {
+        auto *in = graph->nodes.GetMutable(var2id.at(in_var.arguments(k)));
+        in->outlinks.push_back(o);
+        o->inlinks.push_back(in);
+      }
+    }
+    for (int j = 0; j < op.outputs_size(); j++) {
+      auto &out_var = op.outputs(j);
+      for (int k = 0; k < out_var.arguments_size(); k++) {
+        auto *out = graph->nodes.GetMutable(var2id[out_var.arguments(k)]);
+        out->inlinks.push_back(o);
+        o->outlinks.push_back(out);
+      }
+    }
+  }
+  // Analysis and extract the inputs and outputs of this graph.
+  graph->Build();
+}
+
+Pass *FluidToDataFlowGraphPass::CreatePrinterPass(
+    std::ostream &os, const std::string &banner) const {
+  return nullptr;
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_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 implements the transformation from data flow graph to fluid
+ * ProgramDesc.
+ */
+
+#pragma once
+
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/inference/analysis/data_flow_graph.h"
+#include "paddle/fluid/inference/analysis/pass.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+/*
+ * Transform a FluidDesc to a data flow graph.
+ */
+class FluidToDataFlowGraphPass final : public DataFlowGraphPass {
+ public:
+  FluidToDataFlowGraphPass();
+  bool Initialize() override;
+  bool Initialize(const framework::proto::ProgramDesc &desc) override;
+  bool Finalize() override;
+
+  void Run(DataFlowGraph *graph) override;
+
+  Pass *CreatePrinterPass(std::ostream &os,
+                          const std::string &banner) const override;
+
+ private:
+  framework::proto::ProgramDesc const *desc_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_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/fluid_to_data_flow_graph_pass.h"
+
+#include <gtest/gtest.h>
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+TEST_F(DFG_Tester, Init) {
+  FluidToDataFlowGraphPass pass;
+  pass.Initialize();
+  pass.Initialize(desc);
+  DataFlowGraph graph;
+  pass.Run(&graph);
+  ASSERT_GT(graph.nodes.size(), 0);
+  pass.Finalize();
+  LOG(INFO) << '\n' << graph.DotString();
+}
+
+}  // analysis
+}  // inference
+}  // paddle

paddle/fluid/inference/analysis/graph_traits.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/graph_traits.h"

paddle/fluid/inference/analysis/graph_traits.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 defines the GraphTraits<X> template class that should be specified
+ * by classes that want to be iteratable by generic graph iterators.
+ *
+ * This file also defines the marker class Inverse that is used to iterate over
+ * graphs in a graph defined, inverse ordering...
+ */
+
+#pragma once
+
+#include "paddle/fluid/inference/analysis/helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+/*
+ * This class should be specialized by different graph types...
+ * That's why the base class is empty.
+ */
+template <typename GraphType>
+struct GraphTraits {
+  // using NodesBFSIterator = xxx
+
+  // NodesBFSIterator nodes_begin();
+  // NodesBFSIterator nodes_end();
+};
+
+/*
+ * Inverse - This class is used as a marker class to tell the graph iterator to
+ * iterate in a graph defined Inverse order.
+ */
+template <typename GraphType>
+struct Inverse {
+  const GraphType &graph;
+
+  explicit Inverse(const GraphType &graph) : graph(graph) {}
+};
+
+/*
+ * Provide a partial specialization of GraphTraits so that the inverse of an
+ * inverse turns into the original graph.
+ */
+template <typename GraphType>
+struct GraphTraits<Inverse<Inverse<GraphType>>> : GraphTraits<GraphType> {};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/helper.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 <string>
-#include <unordered_map>
-#include <vector>
-
-#include "paddle/fluid/platform/enforce.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-template <typename IteratorT>
-class iterator_range {
-  IteratorT begin_, end_;
-
- public:
-  template <typename Container>
-  explicit iterator_range(Container &&c) : begin_(c.begin()), end_(c.end()) {}
-
-  iterator_range(const IteratorT &begin, const IteratorT &end)
-      : begin_(begin), end_(end) {}
-
-  const IteratorT &begin() const { return begin_; }
-  const IteratorT &end() const { return end_; }
-};
-
-/*
- * An registry helper class, with its records keeps the order they registers.
- */
-template <typename T>
-class OrderedRegistry {
- public:
-  T *Register(const std::string &name, T *x) {
-    PADDLE_ENFORCE(!dic_.count(name));
-    dic_[name] = data_.size();
-    data_.emplace_back(std::unique_ptr<T>(x));
-    return data_.back().get();
-  }
-
-  T *Lookup(const std::string &name) {
-    auto it = dic_.find(name);
-    if (it == dic_.end()) return nullptr;
-    return data_[it->second].get();
-  }
-
- protected:
-  std::unordered_map<std::string, int> dic_;
-  std::vector<std::unique_ptr<T>> data_;
-};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle
-
-#define PADDLE_DISALLOW_COPY_AND_ASSIGN(type__) \
-                                                \
-  type__(const type__ &) = delete;              \
-                                                \
-  void operator=(const type__ &) = delete;
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+#define SET_TYPE(type__) dic_[typeid(type__).hash_code()] = #type__;
+/*
+ * Map typeid to representation.
+ */
+struct DataTypeNamer {
+  static const DataTypeNamer &Global() {
+    static auto *x = new DataTypeNamer();
+    return *x;
+  }
+
+  template <typename T>
+  const std::string &repr() const {
+    auto x = typeid(T).hash_code();
+    PADDLE_ENFORCE(dic_.count(x), "unknown type for representation");
+    return dic_.at(x);
+  }
+
+  const std::string &repr(size_t &hash) const {
+    PADDLE_ENFORCE(dic_.count(hash), "unknown type for representation");
+    return dic_.at(hash);
+  }
+
+ private:
+  DataTypeNamer() {
+    SET_TYPE(int);
+    SET_TYPE(bool);
+    SET_TYPE(float);
+  }
+
+  std::unordered_map<decltype(typeid(int).hash_code()), std::string> dic_;
+};
+#undef SET_TYPE
+
+template <typename IteratorT>
+class iterator_range {
+  IteratorT begin_, end_;
+
+ public:
+  template <typename Container>
+  explicit iterator_range(Container &&c) : begin_(c.begin()), end_(c.end()) {}
+
+  iterator_range(const IteratorT &begin, const IteratorT &end)
+      : begin_(begin), end_(end) {}
+
+  const IteratorT &begin() const { return begin_; }
+  const IteratorT &end() const { return end_; }
+};
+
+/*
+ * An registry helper class, with its records keeps the order they registers.
+ */
+template <typename T>
+class OrderedRegistry {
+ public:
+  T *Register(const std::string &name, T *x) {
+    PADDLE_ENFORCE(!dic_.count(name));
+    dic_[name] = data_.size();
+    data_.emplace_back(std::unique_ptr<T>(x));
+    return data_.back().get();
+  }
+
+  T *Lookup(const std::string &name) {
+    auto it = dic_.find(name);
+    if (it == dic_.end()) return nullptr;
+    return data_[it->second].get();
+  }
+
+ protected:
+  std::unordered_map<std::string, int> dic_;
+  std::vector<std::unique_ptr<T>> data_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle
+
+#define PADDLE_DISALLOW_COPY_AND_ASSIGN(type__) \
+                                                \
+  type__(const type__ &) = delete;              \
+                                                \
+  void operator=(const type__ &) = delete;

paddle/fluid/inference/analysis/node.h

@@ -117,7 +117,10 @@ class Node {
         type_hash_ = typeid(T).hash_code();
         data_.resize(sizeof(T));
       }
-      PADDLE_ENFORCE(type_hash_ == typeid(T).hash_code(), "type not matched");
+      PADDLE_ENFORCE(type_hash_ == typeid(T).hash_code(),
+                     "type not matched, origin is %s, want %s",
+                     DataTypeNamer::Global().repr(type_hash_),
+                     DataTypeNamer::Global().repr<T>());
       PADDLE_ENFORCE_EQ(data_.size(), sizeof(T), "Node attr type recast error");
       return *reinterpret_cast<T *>(&data_[0]);
     }
@@ -127,6 +130,10 @@ class Node {
     size_t type_hash_{std::numeric_limits<size_t>::max()};
   };
 
+  bool IsFunction() const { return type_ == Node::Type::kFunction; }
+  bool IsValue() const { return type_ == Node::Type::kValue; }
+  bool IsFunctionBlock() const { return type_ == Node::Type::kFunctionBlock; }
+
   virtual ~Node() {}
 
   friend class NodeMap;

paddle/fluid/inference/analysis/node_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
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
 
-   http://www.apache.org/licenses/LICENSE-2.0
+http://www.apache.org/licenses/LICENSE-2.0
 
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License. */
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #include "paddle/fluid/inference/analysis/node.h"
 

paddle/fluid/inference/analysis/pass.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/pass.h"

paddle/fluid/inference/analysis/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. */
+
+#pragma once
+
+#include <glog/logging.h>
+#include <iosfwd>
+
+#include "paddle/fluid/framework/framework.pb.h"
+#include "paddle/fluid/inference/analysis/data_flow_graph.h"
+#include "paddle/fluid/inference/analysis/helper.h"
+#include "paddle/fluid/inference/analysis/node.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+class Pass {
+ public:
+  Pass() = default;
+  virtual ~Pass() {}
+  // Virtual method overridden by subclasses to do only necessary initialization
+  // before any pass is run.
+  virtual bool Initialize() { return false; }
+  // There is some passes such as FlowToDataFlowGraphPass that needs a
+  // ProgramDesc. Here use the native ProgramDesc ProtoBuf message, so that it
+  // only couple with the proto file.
+  virtual bool Initialize(const framework::proto::ProgramDesc &desc) {
+    return false;
+  }
+  // There are some Passes such as DataFlowGraphToFluidPass that will output a
+  // ProgramDesc.
+  virtual bool Initialize(framework::proto::ProgramDesc *desc) { return false; }
+
+  // Virtual method overriden by subclasses to do any necessary clean up after
+  // all passes have run.
+  virtual bool Finalize() { return false; }
+
+  // Get a Pass appropriate to print the Node this pass operates on.
+  virtual Pass *CreatePrinterPass(std::ostream &os,
+                                  const std::string &banner) const = 0;
+
+  // Run on a single Node.
+  virtual void Run(Node *x) { LOG(FATAL) << "not valid"; }
+  // Run on a single Function.
+  virtual void Run(Function *x) { LOG(FATAL) << "not valid"; }
+  // Run on a single FunctionBlock.
+  virtual void Run(FunctionBlock *x) { LOG(FATAL) << "not valid"; }
+  // Run on a single DataFlowGraph.
+  virtual void Run(DataFlowGraph *x) { LOG(FATAL) << "not valid"; }
+};
+
+// NodePass process on any Node types.
+class NodePass : public Pass {
+ public:
+  virtual void Run(Node *node) = 0;
+};
+
+// NodePass process on any Function node types.
+class FunctionPass : public Pass {
+ public:
+  virtual void Run(Function *node) = 0;
+};
+
+// NodePass process on any FunctionBlock node types.
+class FunctionBlockPass : public Pass {
+ public:
+  virtual void Run(FunctionBlock *node) = 0;
+};
+
+// GraphPass processes on any GraphType.
+class DataFlowGraphPass : public Pass {
+ public:
+  virtual void Run(DataFlowGraph *graph) = 0;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/subgraph_splitter.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/subgraph_splitter.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+const char *SubGraphSplitter::kMarkerAttrName =
+    "_sub_graph_splitter_inside_sub_graph";
+
+std::vector<std::vector<Node *>> SubGraphSplitter::operator()() {
+  MarkNodesInsideSubGraph();
+  return ExtractSubGraphs();
+}
+
+// Mark the output variables inside a subgraph with the func.
+inline void MarkOutLinksInSubGraph(const Function *func) {
+  for (auto *var : func->outlinks) {
+    var->attr(SubGraphSplitter::kMarkerAttrName).Bool() = true;
+  }
+}
+
+void SubGraphSplitter::MarkNodesInsideSubGraph() {
+  for (auto &node : GraphTraits<DataFlowGraph>(graph_).nodes()) {
+    if (node_inside_subgraph_teller_(&node)) {
+      node.attr(kMarkerAttrName).Bool() = true;
+      if (node.type() == Node::Type::kFunction) {
+        // If a function is inside the sub-graph, mark all the output variables
+        // to be inside too, so that two marked functions will be inside a same
+        // sub-graph, lets take a example:  A_function->var->B_function, if
+        // A_function is marked, var should also be marked, so that B_function
+        // will be in the same sub-graph with A_function if B_function is
+        // marked.
+        MarkOutLinksInSubGraph(static_cast<const Function *>(&node));
+      }
+    }
+  }
+}
+
+const char *kUnionFindParent = "_sub_graph_splitter_union_find_parent_";
+
+// Use the Union Find(UF) algorithm to find fully connected sub-graphs, if node
+// a's output is node b, that is a and b is in the same sub-graph. The UF
+// algorithm will group them to the same cluster.
+using node_map_t = std::unordered_map<int, Node *>;
+// Find the ancestor id of a node.
+int UnionFindGetAncestor(const node_map_t &node_map, size_t id) {
+  int tmp = id;
+  do {
+    tmp = node_map.at(tmp)->attr(kUnionFindParent).Int32();
+  } while (node_map.at(tmp)->attr(kUnionFindParent).Int32() != tmp);
+  return tmp;
+}
+// Make this two node share the same ancestor.
+// TODO(Superjom) bad performance, make a balanced tree latter.
+void UnionFindCombine(const node_map_t &node_map, size_t a, size_t b) {
+  int a_ancestor = UnionFindGetAncestor(node_map, a);
+  int b_ancestor = UnionFindGetAncestor(node_map, b);
+  node_map.at(b_ancestor)->attr(kUnionFindParent).Int32() = a_ancestor;
+  node_map.at(a)->attr(kUnionFindParent).Int32() = a_ancestor;
+  node_map.at(b)->attr(kUnionFindParent).Int32() = a_ancestor;
+}
+
+std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
+  std::vector<Node *> marked_nodes;
+  for (auto &node : GraphTraits<DataFlowGraph>(graph_).nodes()) {
+    if (node.attr(kMarkerAttrName).Bool()) {
+      marked_nodes.push_back(&node);
+    }
+  }
+  // extract sub-graphs in the marked node set, use Union Find algorithm.
+  node_map_t node_map;  // id to ptr
+  for (auto *n : marked_nodes) {
+    // n's parent == n.id means it is the ancestor
+    n->attr(kUnionFindParent).Int32() = n->id();
+    node_map[n->id()] = n;
+  }
+  std::unordered_set<Node *> visited;
+  for (auto *n : marked_nodes) {
+    for (auto *out : n->outlinks) {
+      if (node_map.count(out->id())) {
+        UnionFindCombine(node_map, n->id(), out->id());
+      }
+    }
+  }
+
+  std::unordered_map<int /*ancestor*/, std::vector<Node *>> clusters;
+  for (auto *n : marked_nodes) {
+    if (n->type() == Node::Type::kFunction) {
+      clusters[UnionFindGetAncestor(node_map,
+                                    n->attr(kUnionFindParent).Int32())]
+          .push_back(n);
+    }
+  }
+  std::vector<std::vector<Node *>> result;
+  std::for_each(clusters.begin(), clusters.end(),
+                [&](const decltype(clusters)::value_type &it) {
+                  result.push_back(it.second);
+                });
+
+  return result;
+}
+
+void SubGraphFuse::operator()() { ReplaceNodesWithSubGraphs(); }
+
+void SubGraphFuse::ReplaceNodesWithSubGraphs() {
+  auto subgraphs = SubGraphSplitter(graph_, node_inside_subgraph_teller_)();
+  for (auto &subgraph : subgraphs) {
+    // replace this sub-graph with the first node. Two steps: 1. Create a Block
+    // Node that contains this subgraph 2. Mark the nodes inside the sub-graph
+    // as deleted. 3. Replace the deleted node with the new Block Node.
+    auto *block_node = graph_->nodes.Create(Node::Type::kFunctionBlock);
+    auto io = ExtractInputAndOutputOfSubGraph(subgraph);
+    block_node->inlinks = std::move(io.first);
+    block_node->outlinks = std::move(io.second);
+    for (auto *node : subgraph) {
+      // TODO(Superjomn) need a unified mechanism to treat deleted node in each
+      // pass.
+      node->SetDeleted();
+    }
+
+    std::unordered_map<Node *, Node *>
+        delelte_node_map;  // deleted node to BlockNode
+    for (auto *n : block_node->inlinks) {
+      n->inlinks.clear();
+    }
+    for (auto *n : block_node->outlinks) {
+      n->outlinks.clear();
+    }
+    for (auto *n : block_node->inlinks) {
+      n->outlinks.push_back(block_node);
+    }
+    for (auto *n : block_node->outlinks) {
+      n->inlinks.push_back(n);
+    }
+  }
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/subgraph_splitter.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 defines the the class to partition a graph.
+ */
+
+#pragma once
+
+#include "paddle/fluid/inference/analysis/data_flow_graph.h"
+#include "paddle/fluid/inference/analysis/node.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+/*
+ * Detect the nodes in a sub-graph that meet some conditions. This class doesn't
+ * modify the graph.
+ */
+class SubGraphSplitter {
+ public:
+  static const char *kMarkerAttrName;
+  // Tell whether a node is inside a sub-graph.
+  using NodeInsideSubgraphTeller = std::function<bool(const Node *)>;
+
+  SubGraphSplitter(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller)
+      : graph_(graph), node_inside_subgraph_teller_(teller) {}
+
+  std::vector<std::vector<Node *>> operator()();
+
+ protected:
+  // Mark the nodes inside the accepted sub-graph using
+  // node_inside_subgraph_teller.
+  void MarkNodesInsideSubGraph();
+
+  // Merge the marked nodes into sub-graphs and return the sub-graphs.
+  std::vector<std::vector<Node *>> ExtractSubGraphs();
+
+ private:
+  DataFlowGraph *graph_;
+  NodeInsideSubgraphTeller node_inside_subgraph_teller_;
+};
+
+/*
+ * SubGraphFuse - Replace some nodes with the sub-graph node they are inside. To
+ * some extent, the TensorRT engine is just a fusion op for a model.
+ */
+class SubGraphFuse {
+ public:
+  using NodeInsideSubgraphTeller = SubGraphSplitter::NodeInsideSubgraphTeller;
+
+  SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller)
+      : graph_(graph), node_inside_subgraph_teller_(teller) {}
+
+  // The main method which run all the logic.
+  void operator()();
+
+ protected:
+  // Remove the nodes inside sub-graphs and replace with the SubGraphNode.
+  void ReplaceNodesWithSubGraphs();
+
+ private:
+  DataFlowGraph *graph_;
+  NodeInsideSubgraphTeller node_inside_subgraph_teller_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/subgraph_splitter_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/subgraph_splitter.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+TEST_F(DFG_Tester, Split) {
+  auto desc = LoadProgramDesc();
+  auto dfg = ProgramDescToDFG(desc);
+  LOG(INFO) << "spliter\n" << dfg.DotString();
+
+  SubGraphSplitter::NodeInsideSubgraphTeller teller = [](const Node* node) {
+    if (node->type() != Node::Type::kFunction) return false;
+    const auto* func = static_cast<const Function*>(node);
+    if (func->func_type() == "elementwise_add" || func->func_type() == "relu" ||
+        func->func_type() == "conv2d" || func->func_type() == "mul" ||
+        func->func_type() == "sigmoid" || func->func_type() == "softmax") {
+      LOG(INFO) << "sub-graph marked " << node->repr();
+      return true;
+    }
+    return false;
+  };
+  ASSERT_GT(dfg.nodes.size(), 5UL);
+
+  auto subgraphs = SubGraphSplitter(&dfg, teller)();
+
+  // Check the number of the marked nodes.
+  int marked_nodes = 0;
+  for (auto& node : dfg.nodes.nodes()) {
+    if (node->IsFunction() &&
+        node->attr(SubGraphSplitter::kMarkerAttrName).Bool()) {
+      ++marked_nodes;
+    }
+  }
+  EXPECT_EQ(marked_nodes, 6);
+
+  // For human debug.
+  for (auto& subgraph : subgraphs) {
+    LOG(INFO) << "subgraph size " << subgraph.size();
+    for (auto* node : subgraph) {
+      LOG(INFO) << "node " << node->repr();
+    }
+  }
+
+  ASSERT_EQ(subgraphs.size(), 1UL);
+  // The last sub-graph has 5 Functions.
+  ASSERT_EQ(subgraphs.back().size(), 6UL);
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/ut_helper.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 <gflags/gflags.h>
+#include <gtest/gtest.h>
+#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"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/io.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+DEFINE_string(inference_model_dir, "", "inference test model dir");
+
+static framework::proto::ProgramDesc LoadProgramDesc(
+    const std::string& model_dir = FLAGS_inference_model_dir) {
+  paddle::platform::CPUPlace place;
+  paddle::framework::Executor executor(place);
+  paddle::framework::Scope scope;
+  auto program = Load(&executor, &scope, model_dir);
+  return *program->Proto();
+}
+
+static DataFlowGraph ProgramDescToDFG(
+    const framework::proto::ProgramDesc& desc) {
+  DataFlowGraph graph;
+  FluidToDataFlowGraphPass pass;
+  pass.Initialize(desc);
+  pass.Run(&graph);
+  pass.Finalize();
+  return graph;
+}
+
+class DFG_Tester : public ::testing::Test {
+ protected:
+  void SetUp() override { desc = LoadProgramDesc(FLAGS_inference_model_dir); }
+
+  framework::proto::ProgramDesc desc;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

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

 nv_test(test_op_converter SRCS test_op_converter.cc mul_op.cc conv2d_op.cc DEPS ${FLUID_CORE_MODULES})
 nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc io_converter.cc
-  DEPS ${FLUID_CORE_MODULES} activation_op tensorrt_engine)
+    DEPS ${FLUID_CORE_MODULES} activation_op tensorrt_engine
+    SERIAL)
 nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)

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

@@ -21,6 +21,7 @@ DEFINE_string(fp16_dirname, "", "Directory of the float16 inference model.");
 DEFINE_int32(batch_size, 1, "Batch size of input data");
 DEFINE_int32(repeat, 1, "Running the inference program repeat times");
 DEFINE_bool(skip_cpu, false, "Skip the cpu test");
+DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run inference");
 
 TEST(inference, image_classification) {
   if (FLAGS_dirname.empty() || FLAGS_batch_size < 1 || FLAGS_repeat < 1) {
@@ -58,8 +59,10 @@ TEST(inference, image_classification) {
     // Run inference on CPU
     LOG(INFO) << "--- CPU Runs: ---";
     LOG(INFO) << "Batch size is " << FLAGS_batch_size;
+    LOG(INFO) << "FLAGS_use_mkldnn: " << FLAGS_use_mkldnn;
     TestInference<paddle::platform::CPUPlace, false, true>(
-        dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined);
+        dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined,
+        FLAGS_use_mkldnn);
     LOG(INFO) << output1.dims();
   }
 

paddle/fluid/operators/CMakeLists.txt

@@ -201,10 +201,13 @@ if(WITH_DISTRIBUTE)
     set_source_files_properties(send_vars_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     op_library(send_barrier_op DEPS ${DISTRIBUTE_DEPS})
     set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
-    set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
-    cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor)
+    #set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+    #cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op
+    #        listen_and_serv_op sum_op executor SERIAL)
     if(WITH_GPU)
-        cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op listen_and_serv_op executor)
+        set_source_files_properties(test_send_nccl_id.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+        cc_test(test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op
+                listen_and_serv_op executor SERIAL)
         op_library(gen_nccl_id_op DEPS nccl_common sendrecvop_grpc)
         set_source_files_properties(gen_nccl_id_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     else()

paddle/fluid/operators/beam_search_op.h

@@ -14,10 +14,6 @@ limitations under the License. */
 
 #pragma once
 
-#ifdef PADDLE_WITH_TESTING
-#include "gtest/gtest.h"
-#endif
-
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor.h"

paddle/fluid/operators/detail/CMakeLists.txt

@@ -4,6 +4,8 @@ if(WITH_DISTRIBUTE)
   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
-      cares zlib protobuf sendrecvop_grpc)
-  cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_table_op)
+          cares zlib protobuf sendrecvop_grpc SERIAL)
+  cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc
+          grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor
+          proto_desc lookup_table_op SERIAL)
 endif()

paddle/fluid/operators/detail/grpc_client.cc

@@ -19,6 +19,7 @@ limitations under the License. */
 #include <limits>
 
 #include "paddle/fluid/framework/threadpool.h"
+#include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
 namespace operators {
@@ -196,9 +197,14 @@ bool RPCClient::Wait() {
   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, this] { a[i] = Proceed(); });
+    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++) {