Merge branch 'develop' into core_inference_multi_thread

.pre-commit-config.yaml

+repos:
 -   repo: https://github.com/Lucas-C/pre-commit-hooks.git
     sha: v1.0.1
     hooks:
@@ -25,6 +26,14 @@
         entry: bash ./.clang_format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
+-   repo: local
+    hooks:
+    -   id: cpplint-cpp-source
+        name: cpplint
+        description: Check C++ code style using cpplint.py.
+        entry: bash ./tools/codestyle/cpplint_pre_commit.hook
+        language: system
+        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx)$
 -   repo: https://github.com/PaddlePaddle/pre-commit-golang
     sha: 8337620115c25ff8333f1b1a493bd031049bd7c0
     hooks:

.travis.yml

@@ -34,7 +34,7 @@ addons:
       - automake
       - libtool
       - ccache
-  ssh_known_hosts: 52.76.173.135
+  ssh_known_hosts: 13.229.163.131
 before_install:
   - if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
   # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python

CMakeLists.txt

@@ -36,6 +36,7 @@ include(simd)
 
 ################################ Configurations #######################################
 option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_FOUND})
+option(WITH_AMD_GPU     "Compile PaddlePaddle with AMD GPU"             OFF)
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
 option(WITH_MKL         "Compile PaddlePaddle with MKL support."        ${AVX_FOUND})
 option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
@@ -52,8 +53,7 @@ option(WITH_COVERAGE    "Compile PaddlePaddle with code coverage"       OFF)
 option(COVERALLS_UPLOAD "Package code coverage data to coveralls"       OFF)
 option(ON_TRAVIS        "Exclude special unit test on Travis CI"        OFF)
 option(WITH_C_API       "Compile PaddlePaddle with C-API(Prediction)"   OFF)
-# TODO: Only compile PaddlePaddle fluid version by WITH_FLUID option. 
-option(WITH_FLUID       "Compile PaddlePaddle fluid only(TODO)"         OFF)
+option(WITH_FLUID_ONLY  "Compile PaddlePaddle fluid only"               OFF)
 option(WITH_GOLANG      "Compile PaddlePaddle with GOLANG"              OFF)
 option(GLIDE_INSTALL    "Download and install go dependencies "         ON)
 option(USE_NNPACK       "Compile PaddlePaddle with NNPACK library"      OFF)
@@ -108,7 +108,7 @@ if (WITH_C_API AND WITH_PYTHON)
 endif()
 
 if (WITH_C_API)
-  set(WITH_FLUID OFF CACHE STRING "Disable install fluid when compile the C_API" FORCE)
+  set(WITH_FLUID_ONLY OFF CACHE STRING "Disable install fluid when compile the C_API" FORCE)
 endif()
 
 if(MOBILE_INFERENCE)
@@ -146,6 +146,7 @@ include(external/cares)
 include(external/grpc)
 include(external/snappy)    # download snappy
 include(external/snappystream)
+include(external/threadpool)
 
 include(cudnn)              # set cudnn libraries, must before configure
 include(cupti)
@@ -180,6 +181,11 @@ if(WITH_GPU)
     include(cuda)
 endif(WITH_GPU)
 
+if(WITH_AMD_GPU)
+    find_package(HIP)
+    include(hip)
+endif(WITH_AMD_GPU)
+
 if(WITH_MKLML)
     list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
 endif()

benchmark/cluster/vgg16/vgg16_fluid.py

@@ -18,12 +18,13 @@ import sys
 import time
 import numpy as np
 import paddle.v2 as paddle
-import paddle.v2.fluid as fluid
-import paddle.v2.fluid.core as core
-import paddle.v2.fluid.profiler as profiler
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import paddle.fluid.profiler as profiler
 import argparse
 import functools
 import os
+from paddle.fluid import debuger
 
 
 def str2bool(v):
@@ -182,7 +183,6 @@ def main():
             start_time = time.time()
             num_samples = 0
             train_pass_acc.reset()
-            with profiler.profiler("CPU", 'total') as prof:
             for batch_id, data in enumerate(train_reader()):
                 ts = time.time()
                 img_data = np.array(
@@ -254,9 +254,7 @@ def main():
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,
                                                     pserver_prog)
-            print("starting server side startup")
             exe.run(pserver_startup)
-            print("starting parameter server...")
             exe.run(pserver_prog)
         elif training_role == "TRAINER":
             # Parameter initialization

benchmark/cluster/vgg16/vgg16_tf.py

@@ -292,14 +292,18 @@ def run_benchmark(cluster_spec, server):
         return np.mean(test_accs)
 
     config = tf.ConfigProto(
-        intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
+        intra_op_parallelism_threads=1,
+        inter_op_parallelism_threads=1,
+        log_device_placement=True)
     config.gpu_options.allow_growth = True
 
     hooks = [tf.train.StopAtStepHook(last_step=1000000)]
 
     with tf.train.MonitoredTrainingSession(
-            master=server.target, is_chief=(args.task_index == 0),
-            hooks=hooks) as sess:
+            master=server.target,
+            is_chief=(args.task_index == 0),
+            hooks=hooks,
+            config=config) as sess:
         iters, num_samples, start_time = 0, 0, 0.0
         for pass_id in range(args.num_passes):
             # train

benchmark/fluid/machine_translation.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.
+"""seq2seq model for fluid."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+import distutils.util
+
+import paddle.v2 as paddle
+import paddle.fluid as fluid
+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(
+    "--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(
+    "--use_gpu",
+    type=distutils.util.strtobool,
+    default=True,
+    help="Whether to use gpu. (default: %(default)d)")
+parser.add_argument(
+    "--max_length",
+    type=int,
+    default=250,
+    help="The maximum length of sequence when doing generation. "
+    "(default: %(default)d)")
+
+
+def lstm_step(x_t, hidden_t_prev, cell_t_prev, size):
+    def linear(inputs):
+        return fluid.layers.fc(input=inputs, size=size, bias_attr=True)
+
+    forget_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
+    input_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
+    output_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
+    cell_tilde = fluid.layers.tanh(x=linear([hidden_t_prev, x_t]))
+
+    cell_t = fluid.layers.sums(input=[
+        fluid.layers.elementwise_mul(
+            x=forget_gate, y=cell_t_prev), fluid.layers.elementwise_mul(
+                x=input_gate, y=cell_tilde)
+    ])
+
+    hidden_t = fluid.layers.elementwise_mul(
+        x=output_gate, y=fluid.layers.tanh(x=cell_t))
+
+    return hidden_t, cell_t
+
+
+def seq_to_seq_net(embedding_dim, encoder_size, decoder_size, source_dict_dim,
+                   target_dict_dim, is_generating, beam_size, max_length):
+    """Construct a seq2seq network."""
+
+    def bi_lstm_encoder(input_seq, gate_size):
+        # Linear transformation part for input gate, output gate, forget gate
+        # and cell activation vectors need be done outside of dynamic_lstm.
+        # So the output size is 4 times of gate_size.
+        input_forward_proj = fluid.layers.fc(input=input_seq,
+                                             size=gate_size * 4,
+                                             act=None,
+                                             bias_attr=False)
+        forward, _ = fluid.layers.dynamic_lstm(
+            input=input_forward_proj, size=gate_size * 4, use_peepholes=False)
+        input_reversed_proj = fluid.layers.fc(input=input_seq,
+                                              size=gate_size * 4,
+                                              act=None,
+                                              bias_attr=False)
+        reversed, _ = fluid.layers.dynamic_lstm(
+            input=input_reversed_proj,
+            size=gate_size * 4,
+            is_reverse=True,
+            use_peepholes=False)
+        return forward, reversed
+
+    src_word_idx = fluid.layers.data(
+        name='source_sequence', shape=[1], dtype='int64', lod_level=1)
+
+    src_embedding = fluid.layers.embedding(
+        input=src_word_idx,
+        size=[source_dict_dim, embedding_dim],
+        dtype='float32')
+
+    src_forward, src_reversed = bi_lstm_encoder(
+        input_seq=src_embedding, gate_size=encoder_size)
+
+    encoded_vector = fluid.layers.concat(
+        input=[src_forward, src_reversed], axis=1)
+
+    encoded_proj = fluid.layers.fc(input=encoded_vector,
+                                   size=decoder_size,
+                                   bias_attr=False)
+
+    backward_first = fluid.layers.sequence_pool(
+        input=src_reversed, pool_type='first')
+
+    decoder_boot = fluid.layers.fc(input=backward_first,
+                                   size=decoder_size,
+                                   bias_attr=False,
+                                   act='tanh')
+
+    def lstm_decoder_with_attention(target_embedding, encoder_vec, encoder_proj,
+                                    decoder_boot, decoder_size):
+        def simple_attention(encoder_vec, encoder_proj, decoder_state):
+            decoder_state_proj = fluid.layers.fc(input=decoder_state,
+                                                 size=decoder_size,
+                                                 bias_attr=False)
+            decoder_state_expand = fluid.layers.sequence_expand(
+                x=decoder_state_proj, y=encoder_proj)
+            concated = fluid.layers.concat(
+                input=[encoder_proj, decoder_state_expand], axis=1)
+            attention_weights = fluid.layers.fc(input=concated,
+                                                size=1,
+                                                act='tanh',
+                                                bias_attr=False)
+            attention_weights = fluid.layers.sequence_softmax(
+                input=attention_weights)
+            weigths_reshape = fluid.layers.reshape(
+                x=attention_weights, shape=[-1])
+            scaled = fluid.layers.elementwise_mul(
+                x=encoder_vec, y=weigths_reshape, axis=0)
+            context = fluid.layers.sequence_pool(input=scaled, pool_type='sum')
+            return context
+
+        rnn = fluid.layers.DynamicRNN()
+
+        cell_init = fluid.layers.fill_constant_batch_size_like(
+            input=decoder_boot,
+            value=0.0,
+            shape=[-1, decoder_size],
+            dtype='float32')
+        cell_init.stop_gradient = False
+
+        with rnn.block():
+            current_word = rnn.step_input(target_embedding)
+            encoder_vec = rnn.static_input(encoder_vec)
+            encoder_proj = rnn.static_input(encoder_proj)
+            hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True)
+            cell_mem = rnn.memory(init=cell_init)
+            context = simple_attention(encoder_vec, encoder_proj, hidden_mem)
+            decoder_inputs = fluid.layers.concat(
+                input=[context, current_word], axis=1)
+            h, c = lstm_step(decoder_inputs, hidden_mem, cell_mem, decoder_size)
+            rnn.update_memory(hidden_mem, h)
+            rnn.update_memory(cell_mem, c)
+            out = fluid.layers.fc(input=h,
+                                  size=target_dict_dim,
+                                  bias_attr=True,
+                                  act='softmax')
+            rnn.output(out)
+        return rnn()
+
+    if not is_generating:
+        trg_word_idx = fluid.layers.data(
+            name='target_sequence', shape=[1], dtype='int64', lod_level=1)
+
+        trg_embedding = fluid.layers.embedding(
+            input=trg_word_idx,
+            size=[target_dict_dim, embedding_dim],
+            dtype='float32')
+
+        prediction = lstm_decoder_with_attention(trg_embedding, encoded_vector,
+                                                 encoded_proj, decoder_boot,
+                                                 decoder_size)
+        label = fluid.layers.data(
+            name='label_sequence', shape=[1], dtype='int64', lod_level=1)
+        cost = fluid.layers.cross_entropy(input=prediction, label=label)
+        avg_cost = fluid.layers.mean(x=cost)
+
+        feeding_list = ["source_sequence", "target_sequence", "label_sequence"]
+
+        return avg_cost, feeding_list
+
+
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    lod_t = core.LoDTensor()
+    lod_t.set(flattened_data, place)
+    lod_t.set_lod([lod])
+    return lod_t, lod[-1]
+
+
+def lodtensor_to_ndarray(lod_tensor):
+    dims = lod_tensor.get_dims()
+    ndarray = np.zeros(shape=dims).astype('float32')
+    for i in xrange(np.product(dims)):
+        ndarray.ravel()[i] = lod_tensor.get_float_element(i)
+    return ndarray
+
+
+def train():
+    avg_cost, feeding_list = seq_to_seq_net(
+        args.embedding_dim,
+        args.encoder_size,
+        args.decoder_size,
+        args.dict_size,
+        args.dict_size,
+        False,
+        beam_size=args.beam_size,
+        max_length=args.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),
+        batch_size=args.batch_size)
+
+    test_batch_generator = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.wmt14.test(args.dict_size), buf_size=1000),
+        batch_size=args.batch_size)
+
+    place = core.CUDAPlace(0) if args.use_gpu else core.CPUPlace()
+    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
+
+    for pass_id in xrange(args.pass_num):
+        pass_start_time = time.time()
+        words_seen = 0
+        for batch_id, data in enumerate(train_batch_generator()):
+            src_seq, word_num = to_lodtensor(map(lambda x: x[0], data), place)
+            words_seen += word_num
+            trg_seq, word_num = to_lodtensor(map(lambda x: x[1], data), place)
+            words_seen += 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])
+
+            avg_cost_val = np.array(fetch_outs[0])
+            print('pass_id=%d, batch_id=%d, train_loss: %f' %
+                  (pass_id, batch_id, avg_cost_val))
+
+        pass_end_time = time.time()
+        test_loss = do_validation()
+        time_consumed = pass_end_time - pass_start_time
+        words_per_sec = words_seen / time_consumed
+        print("pass_id=%d, test_loss: %f, words/s: %f, sec/pass: %f" %
+              (pass_id, test_loss, words_per_sec, time_consumed))
+
+
+def infer():
+    pass
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    if args.infer_only:
+        infer()
+    else:
+        train()

benchmark/fluid/mnist.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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import argparse
+import time
+
+import paddle.v2 as paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+
+SEED = 1
+DTYPE = "float32"
+
+# random seed must set before configuring the network.
+# 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(
+        '--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.')
+    args = parser.parse_args()
+    return args
+
+
+def print_arguments(args):
+    vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
+                                vars(args)['device'] == 'GPU')
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def cnn_model(data):
+    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+        input=data,
+        filter_size=5,
+        num_filters=20,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+        input=conv_pool_1,
+        filter_size=5,
+        num_filters=50,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+
+    # TODO(dzhwinter) : refine the initializer and random seed settting
+    SIZE = 10
+    input_shape = conv_pool_2.shape
+    param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
+    scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
+
+    predict = fluid.layers.fc(
+        input=conv_pool_2,
+        size=SIZE,
+        act="softmax",
+        param_attr=fluid.param_attr.ParamAttr(
+            initializer=fluid.initializer.NormalInitializer(
+                loc=0.0, scale=scale)))
+    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()
+    # 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)
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    # Evaluator
+    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+    batch_acc = fluid.layers.accuracy(
+        input=predict, label=label, total=batch_size_tensor)
+
+    # inference program
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        inference_program = fluid.io.get_inference_program(
+            target_vars=[batch_acc, batch_size_tensor])
+
+    # 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.average.WeightedAverage()
+    for pass_id in range(args.pass_num):
+        accuracy.reset()
+        pass_start = time.time()
+        for batch_id, data in enumerate(train_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])
+
+            start = time.time()
+            outs = exe.run(
+                fluid.default_main_program(),
+                feed={"pixel": img_data,
+                      "label": y_data},
+                fetch_list=[avg_cost, batch_acc, batch_size_tensor]
+            )  # The accuracy is the accumulation of batches, but not the current batch.
+            accuracy.add(value=outs[1], weight=outs[2])
+            end = time.time()
+            loss = np.array(outs[0])
+            acc = np.array(outs[1])
+            print("pass=%d, batch=%d, loss=%f, error=%f, elapse=%f" %
+                  (pass_id, batch_id, loss, 1 - acc, (end - start) / 1000))
+
+        pass_end = time.time()
+
+        train_avg_acc = accuracy.eval()
+        test_avg_acc = eval_test(exe, batch_acc, batch_size_tensor,
+                                 inference_program)
+
+        print("pass=%d, train_avg_acc=%f, test_avg_acc=%f, elapse=%f" %
+              (pass_id, train_avg_acc, test_avg_acc,
+               (pass_end - pass_start) / 1000))
+
+
+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)

benchmark/fluid/resnet.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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import functools
+import numpy as np
+import time
+
+import cProfile, pstats, StringIO
+
+import paddle.v2 as paddle
+import paddle.fluid as fluid
+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 print_arguments(args):
+    vars(args)['use_nvprof'] = (vars(args)['use_nvprof'] and
+                                vars(args)['device'] == 'GPU')
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
+    conv1 = fluid.layers.conv2d(
+        input=input,
+        filter_size=filter_size,
+        num_filters=ch_out,
+        stride=stride,
+        padding=padding,
+        act=None,
+        bias_attr=False)
+    return fluid.layers.batch_norm(input=conv1, act=act)
+
+
+def shortcut(input, ch_out, stride):
+    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:
+        return input
+
+
+def basicblock(input, ch_out, stride):
+    short = shortcut(input, ch_out, stride)
+    conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
+    conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, act=None)
+    return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
+
+
+def bottleneck(input, ch_out, stride):
+    short = shortcut(input, ch_out * 4, stride)
+    conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
+    conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
+    conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0, act=None)
+    return fluid.layers.elementwise_add(x=short, y=conv3, act='relu')
+
+
+def layer_warp(block_func, input, ch_out, count, stride):
+    res_out = block_func(input, ch_out, stride)
+    for i in range(1, count):
+        res_out = block_func(res_out, ch_out, 1)
+    return res_out
+
+
+def resnet_imagenet(input, class_dim, depth=50, data_format='NCHW'):
+
+    cfg = {
+        18: ([2, 2, 2, 1], basicblock),
+        34: ([3, 4, 6, 3], basicblock),
+        50: ([3, 4, 6, 3], bottleneck),
+        101: ([3, 4, 23, 3], bottleneck),
+        152: ([3, 8, 36, 3], bottleneck)
+    }
+    stages, block_func = cfg[depth]
+    conv1 = conv_bn_layer(input, ch_out=64, filter_size=7, stride=2, padding=3)
+    pool1 = fluid.layers.pool2d(
+        input=conv1, pool_type='avg', pool_size=3, pool_stride=2)
+    res1 = layer_warp(block_func, pool1, 64, stages[0], 1)
+    res2 = layer_warp(block_func, res1, 128, stages[1], 2)
+    res3 = layer_warp(block_func, res2, 256, stages[2], 2)
+    res4 = layer_warp(block_func, res3, 512, stages[3], 2)
+    pool2 = fluid.layers.pool2d(
+        input=res4,
+        pool_size=7,
+        pool_type='avg',
+        pool_stride=1,
+        global_pooling=True)
+    out = fluid.layers.fc(input=pool2, size=class_dim, act='softmax')
+    return out
+
+
+def resnet_cifar10(input, class_dim, depth=32, data_format='NCHW'):
+    assert (depth - 2) % 6 == 0
+
+    n = (depth - 2) // 6
+
+    conv1 = conv_bn_layer(
+        input=input, ch_out=16, filter_size=3, stride=1, padding=1)
+    res1 = layer_warp(basicblock, conv1, 16, n, 1)
+    res2 = layer_warp(basicblock, res1, 32, n, 2)
+    res3 = layer_warp(basicblock, res2, 64, n, 2)
+    pool = fluid.layers.pool2d(
+        input=res3, pool_size=8, pool_type='avg', pool_stride=1)
+    out = fluid.layers.fc(input=pool, size=class_dim, act='softmax')
+    return out
+
+
+def run_benchmark(model, args):
+    if args.use_cprof:
+        pr = cProfile.Profile()
+        pr.enable()
+
+    if args.data_set == "cifar10":
+        class_dim = 10
+        if args.data_format == 'NCHW':
+            dshape = [3, 32, 32]
+        else:
+            dshape = [32, 32, 3]
+    else:
+        class_dim = 102
+        if args.data_format == 'NCHW':
+            dshape = [3, 224, 224]
+        else:
+            dshape = [224, 224, 3]
+
+    input = fluid.layers.data(name='data', shape=dshape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    predict = model(input, class_dim)
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+    batch_acc = fluid.layers.accuracy(
+        input=predict, label=label, total=batch_size_tensor)
+
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        inference_program = fluid.io.get_inference_program(
+            target_vars=[batch_acc, batch_size_tensor])
+
+    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(
+            paddle.dataset.cifar.train10()
+            if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
+            buf_size=5120),
+        batch_size=args.batch_size)
+    test_reader = paddle.batch(
+        paddle.dataset.cifar.test10()
+        if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
+        batch_size=args.batch_size)
+
+    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()
+    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 = exe.run(
+                fluid.default_main_program(),
+                feed={'data': image,
+                      'label': label},
+                fetch_list=[avg_cost, batch_acc, batch_size_tensor])
+            iters += 1
+            num_samples += label[0]
+            accuracy.add(value=acc, weight=weight)
+            train_losses.append(loss)
+            train_accs.append(acc)
+            print("Pass: %d, Iter: %d, Loss: %f, Accuracy: %f" %
+                  (pass_id, iters, loss, acc))
+        pass_train_acc = accuracy.eval()
+        # evaluation
+        if args.with_test:
+            pass_test_acc = test(exe)
+        train_elapsed = time.time() - start_time
+        print("Pass: %d, Loss: %f, Train Accuray: %f\n" %
+              (pass_id, np.mean(train_losses), np.mean(train_accs)))
+
+        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 args.use_cprof:
+        pr.disable()
+        s = StringIO.StringIO()
+        sortby = 'cumulative'
+        ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
+        ps.print_stats()
+        print(s.getvalue())
+
+
+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)

benchmark/fluid/run.sh

+#!/bin/bash
+# This script benchmarking the PaddlePaddle Fluid on
+# single thread single GPU.
+export CUDNN_PATH=/paddle/cudnn_v5/cuda/lib
+
+# disable openmp and mkl parallel
+#https://github.com/PaddlePaddle/Paddle/issues/7199
+export MKL_NUM_THREADS=1
+export OMP_NUM_THREADS=1
+ht=`lscpu |grep "per core"|awk -F':' '{print $2}'|xargs`
+if [ $ht -eq 1 ]; then # HT is OFF
+    if [ -z "$KMP_AFFINITY" ]; then
+        export KMP_AFFINITY="granularity=fine,compact,0,0"
+    fi
+    if [ -z "$OMP_DYNAMIC" ]; then
+        export OMP_DYNAMIC="FALSE"
+    fi
+else # HT is ON
+    if [ -z "$KMP_AFFINITY" ]; then
+        export KMP_AFFINITY="granularity=fine,compact,1,0"
+    fi
+fi
+# disable multi-gpu if have more than one
+export CUDA_VISIBLE_DEVICES=0
+export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
+export LD_LIBRARY_PATH=$CUDNN_PATH:$LD_LIBRARY_PATH
+
+
+# vgg16
+# cifar10 gpu cifar10 128
+FLAGS_benchmark=true python fluid/vgg.py \
+               --device=GPU \
+               --batch_size=128 \
+               --skip_batch_num=5 \
+               --iterations=30  \
+               2>&1 > vgg16_gpu_128.log
+
+# resnet50
+# resnet50 gpu cifar10 128
+FLAGS_benchmark=true python fluid/resnet.py \
+               --device=GPU \
+               --batch_size=128 \
+               --data_set=cifar10 \
+               --model=resnet_cifar10 \
+               --skip_batch_num=5 \
+               --iterations=30 \
+               2>&1 > resnet50_gpu_128.log
+
+# lstm

benchmark/fluid/stacked_dynamic_lstm.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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import cPickle
+import os
+import random
+import time
+
+import numpy
+import paddle.v2 as paddle
+import paddle.v2.dataset.imdb as imdb
+import paddle.fluid as fluid
+from paddle.v2 import 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(
+        '--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')
+    args = parser.parse_args()
+    return args
+
+
+word_dict = imdb.word_dict()
+
+
+def crop_sentence(reader, crop_size):
+    unk_value = word_dict['<unk>']
+
+    def __impl__():
+        for item in reader():
+            if len([x for x in item[0] if x != unk_value]) < crop_size:
+                yield item
+
+    return __impl__
+
+
+def main():
+    args = parse_args()
+    lstm_size = args.hidden_dim
+
+    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])
+
+    sentence = fluid.layers.fc(input=sentence, size=lstm_size, act='tanh')
+
+    rnn = fluid.layers.DynamicRNN()
+    with rnn.block():
+        word = rnn.step_input(sentence)
+        prev_hidden = rnn.memory(value=0.0, shape=[lstm_size])
+        prev_cell = rnn.memory(value=0.0, shape=[lstm_size])
+
+        def gate_common(
+                ipt,
+                hidden,
+                size, ):
+            gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True)
+            gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
+            gate = fluid.layers.sums(input=[gate0, gate1])
+            return gate
+
+        forget_gate = fluid.layers.sigmoid(
+            x=gate_common(word, prev_hidden, lstm_size))
+        input_gate = fluid.layers.sigmoid(
+            x=gate_common(word, prev_hidden, lstm_size))
+        output_gate = fluid.layers.sigmoid(
+            x=gate_common(word, prev_hidden, lstm_size))
+        cell_gate = fluid.layers.tanh(
+            x=gate_common(word, prev_hidden, lstm_size))
+
+        cell = fluid.layers.sums(input=[
+            fluid.layers.elementwise_mul(
+                x=forget_gate, y=prev_cell), fluid.layers.elementwise_mul(
+                    x=input_gate, y=cell_gate)
+        ])
+
+        hidden = fluid.layers.elementwise_mul(
+            x=output_gate, y=fluid.layers.tanh(x=cell))
+
+        rnn.update_memory(prev_cell, cell)
+        rnn.update_memory(prev_hidden, hidden)
+        rnn.output(hidden)
+
+    last = fluid.layers.sequence_pool(rnn(), 'last')
+    logit = fluid.layers.fc(input=last, size=2, act='softmax')
+    loss = fluid.layers.cross_entropy(
+        input=logit,
+        label=fluid.layers.data(
+            name='label', shape=[1], dtype='int64'))
+    loss = fluid.layers.mean(x=loss)
+
+    # add acc
+    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+    batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
+                shape=[1], dtype='int64'), total=batch_size_tensor)
+
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        inference_program = fluid.io.get_inference_program(
+            target_vars=[batch_acc, batch_size_tensor])
+
+    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())
+
+    def train_loop(pass_num, crop_size):
+        with profiler.profiler(args.device, 'total') as prof:
+            for pass_id in range(pass_num):
+                train_reader = batch(
+                    paddle.reader.shuffle(
+                        crop_sentence(imdb.train(word_dict), crop_size),
+                        buf_size=25000),
+                    batch_size=args.batch_size)
+                word_nums = 0
+                pass_start_time = time.time()
+                for batch_id, data in enumerate(train_reader()):
+                    tensor_words = to_lodtensor([x[0] for x in data], place)
+                    for x in data:
+                        word_nums += len(x[0])
+                    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])
+                    print("pass_id=%d, batch_id=%d, loss=%f, acc=%f" %
+                          (pass_id, batch_id, loss_np, acc))
+
+                pass_end_time = time.time()
+                time_consumed = pass_end_time - pass_start_time
+                words_per_sec = word_nums / time_consumed
+                print("pass_id=%d, sec/pass: %f, words/s: %f" %
+                      (pass_id, time_consumed, words_per_sec))
+
+    train_loop(args.pass_num, args.crop_size)
+
+
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = numpy.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = fluid.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res
+
+
+if __name__ == '__main__':
+    main()

benchmark/fluid/vgg.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.
+"""VGG16 benchmark in Fluid"""
+from __future__ import print_function
+
+import sys
+import time
+import numpy as np
+import paddle.v2 as paddle
+import paddle.fluid as fluid
+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):
+        return fluid.nets.img_conv_group(
+            input=input,
+            pool_size=2,
+            pool_stride=2,
+            conv_num_filter=[num_filter] * groups,
+            conv_filter_size=3,
+            conv_act='relu',
+            conv_with_batchnorm=True,
+            conv_batchnorm_drop_rate=dropouts,
+            pool_type='max')
+
+    conv1 = conv_block(input, 64, 2, [0.3, 0])
+    conv2 = conv_block(conv1, 128, 2, [0.4, 0])
+    conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
+    conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
+    conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
+
+    drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
+    fc1 = fluid.layers.fc(input=drop, size=512, act=None)
+    bn = fluid.layers.batch_norm(input=fc1, act='relu')
+    drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
+    fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
+    return fc2
+
+
+def main():
+    if args.data_set == "cifar10":
+        classdim = 10
+        if args.data_format == 'NCHW':
+            data_shape = [3, 32, 32]
+        else:
+            data_shape = [32, 32, 3]
+    else:
+        classdim = 102
+        if args.data_format == 'NCHW':
+            data_shape = [3, 224, 224]
+        else:
+            data_shape = [224, 224, 3]
+
+    # Input data
+    images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    # Train program
+    net = vgg16_bn_drop(images)
+    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    # Evaluator
+    batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
+    batch_acc = fluid.layers.accuracy(
+        input=predict, label=label, total=batch_size_tensor)
+
+    # inference program
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        inference_program = fluid.io.get_inference_program(
+            target_vars=[batch_acc, batch_size_tensor])
+
+    # 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(
+        paddle.reader.shuffle(
+            paddle.dataset.cifar.train10()
+            if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
+            buf_size=5120),
+        batch_size=args.batch_size)
+    test_reader = paddle.batch(
+        paddle.dataset.cifar.test10()
+        if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
+        batch_size=args.batch_size)
+
+    # 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()
+    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 = exe.run(
+                fluid.default_main_program(),
+                feed={"pixel": img_data,
+                      "label": y_data},
+                fetch_list=[avg_cost, batch_acc, batch_size_tensor])
+            accuracy.add(value=acc, weight=weight)
+            iters += 1
+            num_samples += len(data)
+            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)
+        # evaluation
+        if args.with_test:
+            pass_test_acc = test(exe)
+        train_elapsed = time.time() - start_time
+        print("Pass: %d, Loss: %f, Train Accuray: %f\n" %
+              (pass_id, np.mean(train_losses), np.mean(train_accs)))
+
+
+def print_arguments():
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+if __name__ == "__main__":
+    print_arguments()
+    main()

cmake/configure.cmake

@@ -57,11 +57,7 @@ if(NOT WITH_GOLANG)
     add_definitions(-DPADDLE_WITHOUT_GOLANG)
 endif(NOT WITH_GOLANG)
 
-if(NOT WITH_GPU)
-    add_definitions(-DHPPL_STUB_FUNC)
-
-    list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu)
-else()
+if(WITH_GPU)
     add_definitions(-DPADDLE_WITH_CUDA)
 
     FIND_PACKAGE(CUDA REQUIRED)
@@ -84,7 +80,14 @@ else()
     # Include cuda and cudnn
     include_directories(${CUDNN_INCLUDE_DIR})
     include_directories(${CUDA_TOOLKIT_INCLUDE})
-endif(NOT WITH_GPU)
+elseif(WITH_AMD_GPU)
+    add_definitions(-DPADDLE_WITH_HIP)
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__HIP_PLATFORM_HCC__")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_HCC__")
+else()
+    add_definitions(-DHPPL_STUB_FUNC)
+    list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu)
+endif()
 
 if (WITH_MKLML AND MKLML_IOMP_LIB)
     message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")

cmake/external/boost.cmake

@@ -24,7 +24,7 @@ set(BOOST_PROJECT       "extern_boost")
 # 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.s3-website-us-west-1.amazonaws.com/${BOOST_TAR}.tar.gz")
+set(BOOST_URL           "http://paddlepaddledeps.bj.bcebos.com/${BOOST_TAR}.tar.gz")
 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/eigen.cmake

@@ -4,7 +4,21 @@ SET(EIGEN_SOURCE_DIR ${THIRD_PARTY_PATH}/eigen3)
 SET(EIGEN_INCLUDE_DIR ${EIGEN_SOURCE_DIR}/src/extern_eigen3)
 INCLUDE_DIRECTORIES(${EIGEN_INCLUDE_DIR})
 
-ExternalProject_Add(
+if(WITH_AMD_GPU)
+    ExternalProject_Add(
+        extern_eigen3
+        ${EXTERNAL_PROJECT_LOG_ARGS}
+        GIT_REPOSITORY  "https://github.com/sabreshao/hipeigen.git"
+        GIT_TAG         0cba03ff9f8f9f70bbd92ac5857b031aa8fed6f9
+        PREFIX          ${EIGEN_SOURCE_DIR}
+        UPDATE_COMMAND  ""
+        CONFIGURE_COMMAND ""
+        BUILD_COMMAND     ""
+        INSTALL_COMMAND   ""
+        TEST_COMMAND      ""
+    )
+else()
+    ExternalProject_Add(
         extern_eigen3
         ${EXTERNAL_PROJECT_LOG_ARGS}
         GIT_REPOSITORY  "https://github.com/RLovelett/eigen.git"
@@ -15,7 +29,8 @@ ExternalProject_Add(
         BUILD_COMMAND     ""
         INSTALL_COMMAND   ""
         TEST_COMMAND      ""
-)
+    )
+endif()
 
 if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
     set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/eigen3_dummy.c)

cmake/external/mklml.cmake

@@ -34,7 +34,7 @@ SET(MKLML_DOWNLOAD_DIR  "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
 SET(MKLML_DST_DIR       "mklml")
 SET(MKLML_INSTALL_ROOT  "${THIRD_PARTY_PATH}/install")
 SET(MKLML_INSTALL_DIR   ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
-SET(MKLML_ROOT          ${MKLML_INSTALL_DIR}/${MKLML_VER})
+SET(MKLML_ROOT          ${MKLML_INSTALL_DIR})
 SET(MKLML_INC_DIR       ${MKLML_ROOT}/include)
 SET(MKLML_LIB_DIR       ${MKLML_ROOT}/lib)
 SET(MKLML_LIB           ${MKLML_LIB_DIR}/libmklml_intel.so)
@@ -46,7 +46,7 @@ INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
 FILE(WRITE ${MKLML_DOWNLOAD_DIR}/CMakeLists.txt
   "PROJECT(MKLML)\n"
   "cmake_minimum_required(VERSION 3.0)\n"
-  "install(DIRECTORY ${MKLML_VER}\n"
+  "install(DIRECTORY ${MKLML_VER}/include ${MKLML_VER}/lib \n"
   "        DESTINATION ${MKLML_DST_DIR})\n")
 
 ExternalProject_Add(

cmake/external/threadpool.cmake

+INCLUDE(ExternalProject)
+
+SET(THREADPOOL_SOURCE_DIR ${THIRD_PARTY_PATH}/threadpool)
+SET(THREADPOOL_INCLUDE_DIR ${THREADPOOL_SOURCE_DIR}/src/extern_threadpool)
+INCLUDE_DIRECTORIES(${THREADPOOL_INCLUDE_DIR})
+
+ExternalProject_Add(
+    extern_threadpool
+    ${EXTERNAL_PROJECT_LOG_ARGS}
+    GIT_REPOSITORY  "https://github.com/progschj/ThreadPool.git"
+    GIT_TAG         9a42ec1329f259a5f4881a291db1dcb8f2ad9040
+    PREFIX          ${THREADPOOL_SOURCE_DIR}
+    UPDATE_COMMAND  ""
+    CONFIGURE_COMMAND ""
+    BUILD_COMMAND     ""
+    INSTALL_COMMAND   ""
+    TEST_COMMAND      ""
+)
+
+if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
+    set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/threadpool_dummy.c)
+    file(WRITE ${dummyfile} "const char *dummy_threadpool = \"${dummyfile}\";")
+    add_library(simple_threadpool STATIC ${dummyfile})
+else()
+    add_library(simple_threadpool INTERFACE)
+endif()
+
+add_dependencies(simple_threadpool extern_threadpool)
+
+LIST(APPEND external_project_dependencies simple_threadpool)

cmake/generic.cmake

@@ -317,6 +317,82 @@ function(nv_test TARGET_NAME)
   endif()
 endfunction(nv_test)
 
+function(hip_library TARGET_NAME)
+  if (WITH_AMD_GPU)
+    set(options STATIC static SHARED shared)
+    set(oneValueArgs "")
+    set(multiValueArgs SRCS DEPS)
+    cmake_parse_arguments(hip_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+    set(_sources ${hip_library_SRCS})
+    HIP_PREPARE_TARGET_COMMANDS(${TARGET_NAME} OBJ _generated_files _source_files ${_sources} HIPCC_OPTIONS ${_hipcc_options} HCC_OPTIONS ${_hcc_options} NVCC_OPTIONS ${_nvcc_options})
+    if(_source_files)
+      list(REMOVE_ITEM _sources ${_source_files})
+    endif()
+    if(hip_library_SRCS)
+      if (hip_library_SHARED OR hip_library_shared) # build *.so
+        add_library(${TARGET_NAME} SHARED ${_cmake_options} ${_generated_files} ${_sources})
+        set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE HIP)
+      else()
+        add_library(${TARGET_NAME} STATIC ${_cmake_options} ${_generated_files} ${_sources})
+        set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE CXX)
+        target_link_libraries(${TARGET_NAME} /opt/rocm/hip/lib/libhip_hcc.so /opt/rocm/hip/lib/libhip_device.a)
+	find_fluid_modules(${TARGET_NAME})
+      endif()
+      if (hip_library_DEPS)
+	add_dependencies(${TARGET_NAME} ${hip_library_DEPS})
+	target_link_libraries(${TARGET_NAME} ${hip_library_DEPS})
+      endif()
+      # cpplint code style
+      foreach(source_file ${hip_library_SRCS})
+	string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
+	if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
+	  list(APPEND hip_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
+	endif()
+      endforeach()
+      add_style_check_target(${TARGET_NAME} ${hip_library_SRCS} ${hip_library_HEADERS})
+    else(hip_library_SRCS)
+      if (hip_library_DEPS)
+	merge_static_libs(${TARGET_NAME} ${hip_library_DEPS})
+      else()
+	message(FATAL "Please specify source file or library in nv_library.")
+      endif()
+    endif(hip_library_SRCS)
+  endif()
+endfunction(hip_library)
+
+function(hip_binary TARGET_NAME)
+  if (WITH_AMD_GPU)
+    set(options "")
+    set(oneValueArgs "")
+    set(multiValueArgs SRCS DEPS)
+    cmake_parse_arguments(hip_binary "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+    hip_add_executable(${TARGET_NAME} ${hip_binary_SRCS})
+    if(hip_binary_DEPS)
+      target_link_libraries(${TARGET_NAME} ${hip_binary_DEPS})
+      add_dependencies(${TARGET_NAME} ${hip_binary_DEPS})
+    endif()
+  endif()
+endfunction(hip_binary)
+
+function(hip_test TARGET_NAME)
+  if (WITH_AMD_GPU AND WITH_TESTING)
+    set(options "")
+    set(oneValueArgs "")
+    set(multiValueArgs SRCS DEPS)
+    cmake_parse_arguments(hip_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+    set(_sources ${hip_test_SRCS})
+    HIP_PREPARE_TARGET_COMMANDS(${TARGET_NAME} OBJ _generated_files _source_files ${_sources} HIPCC_OPTIONS ${_hipcc_options} HCC_OPTIONS ${_hcc_options} NVCC_OPTIONS ${_nvcc_options})
+    if(_source_files)
+      list(REMOVE_ITEM _sources ${_source_files})
+    endif()
+    add_executable(${TARGET_NAME} ${_cmake_options} ${_generated_files} ${_sources})
+    set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE HIP)
+    target_link_libraries(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
+    add_dependencies(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
+    add_test(${TARGET_NAME} ${TARGET_NAME})
+  endif()
+endfunction(hip_test)
+
 function(go_library TARGET_NAME)
   set(options STATIC static SHARED shared)
   set(oneValueArgs "")
@@ -511,6 +587,9 @@ function(grpc_library TARGET_NAME)
   get_filename_component(PROTO_WE ${grpc_library_PROTO} NAME_WE)
   get_filename_component(PROTO_PATH ${ABS_PROTO} PATH)
 
+  #FIXME(putcn): the follwoing line is supposed to generate *.pb.h and cc, but
+  # somehow it didn't. line 602 to 604 is to patching this. Leaving this here 
+  # for now to enable dist CI.
   protobuf_generate_cpp(grpc_proto_srcs grpc_proto_hdrs "${ABS_PROTO}")
   set(grpc_grpc_srcs "${CMAKE_CURRENT_BINARY_DIR}/${PROTO_WE}.grpc.pb.cc")
   set(grpc_grpc_hdrs "${CMAKE_CURRENT_BINARY_DIR}/${PROTO_WE}.grpc.pb.h")
@@ -521,6 +600,9 @@ function(grpc_library TARGET_NAME)
           COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
           ARGS --grpc_out "${CMAKE_CURRENT_BINARY_DIR}" -I "${PROTO_PATH}"
           --plugin=protoc-gen-grpc="${GRPC_CPP_PLUGIN}" "${ABS_PROTO}"
+          COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
+          ARGS --cpp_out "${CMAKE_CURRENT_BINARY_DIR}" -I "${PROTO_PATH}"
+          "${ABS_PROTO}"
           DEPENDS "${ABS_PROTO}" ${PROTOBUF_PROTOC_EXECUTABLE} extern_grpc)
 
   # FIXME(typhoonzero): grpc generated code do not generate virtual-dtor, mark it

cmake/hip.cmake

+if(NOT WITH_AMD_GPU)
+    return()
+endif()
+
+include_directories("/opt/rocm/include")
+include_directories("/opt/rocm/hipblas/include")
+include_directories("/opt/rocm/hiprand/include")
+include_directories("/opt/rocm/rocrand/include")
+include_directories("/opt/rocm/rccl/include")
+include_directories("/opt/rocm/thrust")
+
+list(APPEND EXTERNAL_LIBS "-L/opt/rocm/lib/ -lhip_hcc")
+
+set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -fPIC -DPADDLE_WITH_HIP -std=c++14" )
+
+if(WITH_DSO)
+  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_USE_DSO")
+endif(WITH_DSO)
+
+if(WITH_DOUBLE)
+  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_TYPE_DOUBLE")
+endif(WITH_DOUBLE)
+
+if(WITH_TESTING)
+  set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_TESTING")
+endif(WITH_TESTING)
+
+if(CMAKE_BUILD_TYPE  STREQUAL "Debug")
+    list(APPEND HIP_HCC_FLAGS  ${CMAKE_CXX_FLAGS_DEBUG})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "RelWithDebInfo")
+    list(APPEND HIP_HCC_FLAGS  ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
+elseif(CMAKE_BUILD_TYPE  STREQUAL "MinSizeRel")
+    list(APPEND HIP_HCC_FLAGS  ${CMAKE_CXX_FLAGS_MINSIZEREL})
+endif()
+
+if("x${HCC_HOME}" STREQUAL "x")
+  set(HCC_HOME "/opt/rocm/hcc")
+endif()
+
+set(CMAKE_HIP_LINK_EXECUTABLE "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
+set(CMAKE_HIP_CREATE_SHARED_LIBRARY "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES> -shared")
+set(CMAKE_HIP_CREATE_SHARED_MODULE "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES> -shared")
+

cmake/inference_lib.cmake

@@ -69,6 +69,12 @@ if(NOT CBLAS_FOUND)
       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")
+    copy(mklml_lib
+      SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
+      DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
+    )
 endif()
 
 # paddle fluid module

doc/CMakeLists.txt

+add_custom_target(paddle_apis ALL
+                  DEPENDS paddle_v2_apis paddle_fluid_apis)
+
+add_custom_target(paddle_docs ALL
+                  DEPENDS paddle_v2_docs paddle_v2_docs_cn
+                  paddle_fluid_docs paddle_fluid_docs_cn)
+
 add_subdirectory(v2)
+add_subdirectory(fluid)

doc/design/images/parallel_executor_overview.dot

+digraph G {
+  subgraph cluster_init {
+    label="Initialization"
+    startup_program [label="startup", shape=box]
+    node_w_g0 [label="W\nGPU0"]
+    startup_program -> node_w_g0 [label="Initialize"]
+    node_w_g1 [label="W\nGPU1"]
+    node_w_g0 -> node_w_g1 [label="broadcast"]
+  }
+
+  subgraph cluster_train {
+    label="forward_backward"
+
+    subgraph cluster_gpu0 {
+      label="GPU0"
+      fc_0 [label="fc\nGPU0", shape=box]
+      hidden_0 [label="hidden\nGPU0"]
+      node_w_g0 -> fc_0
+      fc_0 -> hidden_0
+      loss0 [label="loss\nGPU0"]
+      hidden_0 -> loss0 [label="many ops omitted"]
+      scale_loss_0 [label="scale_loss_gradient\nGPU0", shape=box]
+      loss_g0 [label="loss_grad\nGPU0"]
+      scale_loss_0->loss_g0
+      
+      fc_g_0 [label="w_grad\nGPU0", shape=box]
+      loss0 -> fc_g_0
+      loss_g0 -> fc_g_0
+      hidden_0 -> fc_g_0
+    }
+
+    subgraph cluster_gpu1 {
+      label="GPU1"
+      fc_1 [label="fc\nGPU1", shape=box]
+      hidden_1 [label="hidden\nGPU1"]
+      node_w_g1 -> fc_1
+      fc_1 -> hidden_1
+      loss1 [label="loss\nGPU1"]
+      hidden_1 -> loss1 [label="many ops omitted"]
+      scale_loss_1 [label="scale_loss_gradient\nGPU1", shape=box]
+      loss_g1 [label="loss_grad\nGPU1"]
+      scale_loss_1->loss_g1
+      
+      fc_g_1 [label="w_grad\nGPU1", shape=box]
+      loss1 -> fc_g_1
+      loss_g1 -> fc_g_1
+      hidden_1 -> fc_g_1
+    }
+  }
+
+  all_reduce_w [label="Merge Gradients(AllReduce)", shape=box]
+  fc_g_0 -> all_reduce_w
+  fc_g_1 -> all_reduce_w
+
+  fc_g_0_merged [label="w_grad\nMerged\nGPU0"]
+  fc_g_1_merged [label="w_grad\nMerged\nGPU1"]
+  all_reduce_w -> fc_g_0_merged
+  all_reduce_w -> fc_g_1_merged
+
+  subgraph cluster_optimization {
+    label="Optimization"
+    subgraph cluster_opt_gpu0 {
+      label="GPU0"
+      sgd_0 [label="SGD Op\nGPU0", shape=box]
+
+      fc_g_0_merged -> sgd_0
+      node_w_g0 -> sgd_0
+      optimized_w_0 [label="Optimized W\nGPU0"]
+      sgd_0 -> optimized_w_0
+    }
+    subgraph cluster_opt_gpu1 {
+      label="GPU1"
+      sgd_1 [label="SGD Op\nGPU1", shape=box]
+
+      fc_g_1_merged -> sgd_1
+      node_w_g1 -> sgd_1
+      optimized_w_1 [label="Optimized W\nGPU0"]
+      sgd_1 -> optimized_w_1
+    }
+  }
+
+
+}

doc/design/parallel_executor.md

+# ParallelExecutor
+
+## Background
+
+Neural network models are defined as a `ProgramDesc` in Fluid. The `ProgramDesc` can be executed by an interpreter(i.e. the `executor` concept in Fluid). The instructions or operators in a `Program` will be executed, and the results will be fetched in Python side.
+
+The executor is a very naive interpreter. It runs operators one by one. We can use `Parallel.Do` to support data parallelism, however, lacking device information in `ProgramDesc`; it is not possible to optimize the performance of `Parallel.Do`.
+
+We want a `ProgramDesc` can be run on different nodes. It is better not to contain device information in `ProgramDesc`. However, we can write a high-performance interpreter, which can hold an alternative intermediate representation of `ProgramDesc`, to take full usage of Multi-GPUs. 
+
+ParallelExecutor is an interpreter of `ProgramDesc` which will [out-of-order execute](https://en.wikipedia.org/wiki/Out-of-order_execution) `Program` in data parallelism mode and maximise the utility of Multi-GPUs.
+
+
+## Overview of MultiGPUs logic
+
+The ParallelExecutor takes the startup program and main program as inputs. The parameters will be initialised on `GPU0` by startup program and will broadcast to multi-GPUs. The main program will be duplicated into multi-GPUs. The gradient will be merged during each iteration, and each device will optimize parameters independently. Since the gradients on each device will be merged before parameter optimization, the parameters will be the same on each device and it does not need to be broadcast the parameters.
+
+![alt](images/parallel_executor_overview.png)
+
+There are several optimizations for this logic.
+
+1. We use an alternate representation in ParallelExecutor. It because the device information is critical for performance optimization.
+2. The execution is out-of-order, i.e., an operator will be executed whenever the inputs of the operator are ready. 
+   * GPU is a high-performance device; only one CPU thread cannot fulfil one GPU. So there is a thread pool to execute operators.
+   * Out-of-order also helps transpilers to generate `ProgramDesc`. It is no need to concern about the best order of performance when implementing a transpiler.
+3. The streams of computation, merge gradients and fetch data are different.
+
+The performance of `ResNeXt152` on `TitanX` which `batch_size=12` is shown below.
+
+| Number of GPUs | 1 | 2 | 3 | 4|
+| --- | --- | --- | --- | --- |
+| Image/Sec | 17.9906 | 25.771 | 36.911 | 48.8428 |
+| Speed Up | N/A | 1.43247029 | 2.05168255 | 2.71490667 |
+
+
+## Static single assignment Graph
+
+[Static single assignment form](https://en.wikipedia.org/wiki/Static_single_assignment_form)(`SSA` for short) is a common form for compiler optimization. To implement concurrent execution, we uses an `SSA` graph as an intermedia representation of `ProgramDesc`.
+
+The `Program` is a directed acyclic graph, since a variable can be assigned multiple times. We enforce a variable will be assigned once, by adding version number to varaibles. We parsing the `Program` into a `SSA` graph. Also, ProgramExecutor duplicate `Program` into multi-devices. We also add a device number to varaibles and insert `NCCLAllReduce` into Graph.
+
+The data structure of `SSA` graph is:
+
+```c++
+struct VarHandleBase {
+  OpHandleBase* generated_op_;
+  vector<OpHandleBase*> pending_ops_;
+  
+  string name;
+  Place place;
+  size_t version;
+};
+
+struct OpHandleBase {
+  vector<OpHandleBase*> inputs_;
+  vector<OpHnadleBase*> outputs_;
+};
+
+struct SSAGraph {
+  // vars on each devices. 
+  //   * the vars in each map in vector is on different device.
+  //   * the map is mapping a variable name to variable handles
+  //   with different versions
+  vector<std::unordered_map<string, vector<VarHandleBase>>> vars_;
+  
+  // All ops
+  vector<OpHandleBase> ops_;
+};
+```
+The variable handles are the wrapper of `Variables`. The operator handles are the wrapper of `OperatorBase`. Some `OpHandle` is not an `OperatorBase`, such as `NCCLAllReduceOpHandle`, because `AllReduceOpHandle` will use new device contexts.
+
+When the `ProgramDesc` converted into an `SSA` Graph, the [data hazard](https://en.wikipedia.org/wiki/Hazard_(computer_architecture)) problem is also need to be taken care. The dummy variables, which represent the dependency between operators, will be manually inserted into SSA graph to resolve the [data hazard](https://en.wikipedia.org/wiki/Hazard_(computer_architecture)) problem.
+
+## Execute SSA Graph
+
+The SSA graph can be out-of-order executed by an approximate [topological sorting](https://en.wikipedia.org/wiki/Topological_sorting) algorithm. The algorithm is
+
+1. Maintaining a map of an operator and its needed input number.
+2. If a variable is not generated by an operator, i.e., `var.generated_op == nullptr`, decrease the needed input number of its pending operators.
+3. If there is an operator which needed input number is decreased to zero, just run this operator.
+4. After run this operator, just mark the variables are generated and repeat step 2 until all variables are generated.
+
+Running an operator can be asynchronized. There is a thread pool to execute an `SSA` graph.
+
+## Synchronize GPU Kernels
+
+The GPU is a non-blocking device. The different streams need be synchronized when switing streams. In current implementation, the synchronization based on the following algorithm:
+
+1. `OpHandle` will record `DeviceContext` that it is used.
+2. In `OpHandle::Run`, if the `DeviceContext` of current operator is different from `DeviceContext` of any input variable, just wait the generate operator of this input variable.
+
+The `wait` are implemented by two strategies:
+
+1. Invoke `DeviceContext->Wait()`, It will wait all operators on this device contexts complete.
+2. Uses `cudaStreamWaitEvent` to sending a event to the stream. It is a non-blocking call. The wait operators will be executed in GPU.
+
+Generally, the `cudaStreamWaitEvent` will have a better perforamnce. However, `DeviceContext->Wait()` strategy is easier to debug. The strategy can be changed in runtime.
+
+## What's next?
+
+* Merging gradient of dense parameters has been done. However, the merging of sparse parameters has not been done.
+* The CPU version of Parallel Executor has not been implemented. The out-of-order logic will make CPU compuatation faster, too.
+* A better strategy to merge gradients can be introduced. We can shrink the gradients from `float32` to `int8` or `int4` while merging. It will significantly speed up multi-GPUs training without much loss of precision.
+* Combine multi-Nodes implementation. By the benifit of out-of-order, sending and recving operator can be an blocking operator, and the transpiler does not need to concern about the best position of operator.

doc/fluid/CMakeLists.txt

+if(NOT DEFINED SPHINX_THEME)
+    set(SPHINX_THEME default)
+endif()
+
+if(NOT DEFINED SPHINX_THEME_DIR)
+    set(SPHINX_THEME_DIR)
+endif()
+
+# configured documentation tools and intermediate build results
+set(BINARY_BUILD_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_build")
+
+# Sphinx cache with pickled ReST documents
+set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees")
+
+# HTML output director
+set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html")
+
+configure_file(
+    "${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.en.in"
+    "${BINARY_BUILD_DIR_EN}/conf.py"
+    @ONLY)
+
+sphinx_add_target(paddle_fluid_docs
+                  html
+                  ${BINARY_BUILD_DIR_EN}
+                  ${SPHINX_CACHE_DIR_EN}
+                  ${CMAKE_CURRENT_SOURCE_DIR}
+                  ${SPHINX_HTML_DIR_EN})
+
+add_dependencies(paddle_fluid_docs gen_proto_py)
+
+# configured documentation tools and intermediate build results
+set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build")
+
+# Sphinx cache with pickled ReST documents
+set(SPHINX_CACHE_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_doctrees")
+
+# HTML output directory
+set(SPHINX_HTML_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/html")
+
+configure_file(
+    "${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.cn.in"
+    "${BINARY_BUILD_DIR_CN}/conf.py"
+    @ONLY)
+
+sphinx_add_target(paddle_fluid_docs_cn
+                  html
+                  ${BINARY_BUILD_DIR_CN}
+                  ${SPHINX_CACHE_DIR_CN}
+                  ${CMAKE_CURRENT_SOURCE_DIR}
+                  ${SPHINX_HTML_DIR_CN})
+
+add_dependencies(paddle_fluid_docs_cn gen_proto_py)
+
+add_subdirectory(api)

doc/fluid/api/CMakeLists.txt

+# configured documentation tools and intermediate build results
+set(BINARY_BUILD_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_build")
+
+# Sphinx cache with pickled ReST documents
+set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees")
+
+# HTML output director
+set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html")
+
+configure_file(
+    "${CMAKE_CURRENT_SOURCE_DIR}/../../templates/conf.py.en.in"
+    "${BINARY_BUILD_DIR_EN}/conf.py"
+    @ONLY)
+
+sphinx_add_target(paddle_fluid_apis
+                  html
+                  ${BINARY_BUILD_DIR_EN}
+                  ${SPHINX_CACHE_DIR_EN}
+                  ${CMAKE_CURRENT_SOURCE_DIR}
+                  ${SPHINX_HTML_DIR_EN})
+
+add_dependencies(paddle_fluid_apis  gen_proto_py framework_py_proto copy_paddle_pybind)

doc/fluid/build_and_install/build_from_source_cn.rst

+../../v2/build_and_install/build_from_source_cn.rst
\ No newline at end of file

doc/fluid/build_and_install/build_from_source_en.rst

+../../v2/build_and_install/build_from_source_en.rst
\ No newline at end of file

doc/fluid/build_and_install/docker_install_cn.rst

+../../v2/build_and_install/docker_install_cn.rst
\ No newline at end of file

doc/fluid/build_and_install/docker_install_en.rst

+../../v2/build_and_install/docker_install_en.rst
\ No newline at end of file

doc/fluid/build_and_install/index_cn.rst

+../../v2/build_and_install/index_cn.rst
\ No newline at end of file

doc/fluid/build_and_install/index_en.rst

+../../v2/build_and_install/index_en.rst
\ No newline at end of file

doc/fluid/build_and_install/pip_install_cn.rst

+../../v2/build_and_install/pip_install_cn.rst
\ No newline at end of file

doc/fluid/build_and_install/pip_install_en.rst

+../../v2/build_and_install/pip_install_en.rst
\ No newline at end of file

doc/fluid/design/algorithm/index_cn.rst

+梯度更新算法
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  parameter_average.md

doc/fluid/design/algorithm/index_en.rst

+Gradient Update Algorithm
+--------------------------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  parameter_average.md

doc/fluid/design/concepts/README.md

@@ -2,7 +2,7 @@ A few months ago when we were trying to replace CMake with Bazel, @emailweixu su
 
 Here are some initial thoughts. Your comments are welcome!
 
-### Required CMake Function
+# Required CMake Function
 
 I think we need only the following few CMake functions to make a project description mean and clean:
 
@@ -25,7 +25,7 @@ Also,
 - to describe external dependencies, we need `external_library`.
 - to build shared libraries, we need `shared_library`.
 
-### An Example Project
+## An Example Project
 
 Suppose that we have aforementioned functions defined in our `/cmake` directory.  The following example `CMakeLists.txt` describes a project including the following source files:
 
@@ -102,11 +102,11 @@ shared_library(api
 
 ```
 
-### Implementation
+## Implementation
 
 As above example CMakeLists.txt executes, each function invocation adds "nodes" to a dependency graph.  It also use this graph to generate CMake commands including `add_executable`, `add_dependencies`, `target_link_libraries`, and `add_test`.
 
-### Using Package Manager For Go
+## Using Package Manager For Go
 
 Building Go binaries and libraries need to satisfy their dependencies, generally
 we can do `go get ./...` to download and compile all external dependencies. The
@@ -122,7 +122,7 @@ problems are:
    at many cloud file hosting, so users what to compile paddle by themselves can
    download this "vendor" package from a mirror site.
 
-#### Choose A Suitable Tool
+### Choose A Suitable Tool
 
 As mentioned by @wangkuiyi, [Here](https://github.com/golang/go/wiki/PackageManagementTools)
 list dozens of Go package managers. We choose the tool using following principles:
@@ -140,7 +140,7 @@ management tool has been started at: https://github.com/golang/dep to resolve
 such problems, but it's currently at Alpha stage. So the best choice now is
 glide obviously.
 
-#### Manage Go Packages
+### Manage Go Packages
 
 - Dependencies: `go/glide.yaml` will store the dependencies and their versions which
   is directly imported by paddle. `go/glide.lock` will store all dependencies recursively

doc/fluid/design/concepts/cpp_data_feeding.md

@@ -113,7 +113,7 @@ To solve this problem, we introduce `ReaderHolder` as a wrapper. It acts as an e
 
 To create and invoke readers, some new ops are introduced:
 
-### CreateReaderOp
+### Operators That Create Readers
 
 Each reader has its creation op. File readers' creation ops have no input and yield the created file reader as its output. Decorated readers' creation ops take the underlying readers as inputs and then yield new decorated readers.
 
@@ -153,19 +153,52 @@ double_buffer_reader = create_double_buffer_op(batch_reader)
 The forwarding ops of the corresponding `main_program` would be like this:
 
 ```
-while_op {
+not_completed = true
+pass_count = 0
+while_op(not_completed) {
     has_next = has_next_op(double_buffer_reader)
     if_else_op(has_next) {
         batch_data = read_op(double_buffer_reader)
         ... (subsequent training ops)
     } else {
         reset_op(double_buffer_reader)
+        increase_op(pass_count)
+        not_completed = less_than_op(pass_count, reqiured_pass_num)
     }
 }
 ```
 
-Two important considerations for these programs are as follows:
+A few important considerations for these programs are as follows:
 
-1. The multiple\_reader is the batch\_reader's underlying reader, and the batch\_reader is the double\_buffer\_reader's underlying reader. `read_op`, `has_next_op` and other reader related ops will only invoke the top-most reader. In this case, it's the double\_buffer\_reader.
+1. `not_completed`, `pass_count` and other variables shown above are all Fluid Variables.
 
-2. All readers exist in both `startup_program` and `main_program`. And they are persistable.
+2. The multiple\_reader is the batch\_reader's underlying reader, and the batch\_reader is the double\_buffer\_reader's underlying reader. `read_op`, `has_next_op` and other reader related ops will only invoke the top-most reader. In this case, it's the double\_buffer\_reader.
+
+3. All readers exist in both `startup_program` and `main_program`. And they are persistable.
+
+### Simplify Configuration by MultiPassReader
+
+The Program configuration mentioned above is complicated. Users need to be very familiar to concepts of Program and Block to prevent making mistakes in their code. To make the usage of C++ readers more friendly to new users, we introduce `MultiPassReader`.
+
+`MultiPassReader` is a decorated reader. A multi-pass reader is used to continuously yield data for several training passes. It takes the number of passes to run as one of its attributes('pass_num') and maintains a counter to record how many passes it has completed. Each time its underlying reader reaches the EOF, the multi-pass reader checks whether it has completed the training of given number of pass. If not, the underlying reader will be re-initialized and starts a new pass automatically. Before completing the whole training, the return of MultiPassReader's `HasNext()` will always be `true`.
+
+With `MultiPassReader`, the startup program would be like this:
+
+```
+multiple_reader = open_files_op(...)
+batch_reader = create_batch_reader_op(multiple_reader)
+multi_pass_reader = create_multi_pass_reader_op(batch_reader)
+double_buffer_reader = create_double_buffer_op(multi_pass_reader)
+... (other initializers)
+```
+
+The forwarding part of the corresponding `main_program` would be like this:
+
+```
+not_completed = true
+while_op(not_completed) {
+    batch_data = read_op(double_buffer_reader)
+    ... (subsequent training ops)
+    not_completed = has_next_op(double_buffer_reader)
+}
+```

doc/fluid/design/concepts/index_cn.rst

+核心概念
+-------------
+
+.. toctree::
+  :maxdepth: 1
+
+  README.md
+  cpp_data_feeding.md
+  functions_operators_layers.md
+  program.md
+  variable.md
+  var_desc.md
+  tensor.md
+  tensor_array.md
+  lod_tensor.md
+  block.md
+  scope.md
+  executor.md

doc/fluid/design/concepts/index_en.rst

+Core Concepts
+--------------------------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  README.md
+  cpp_data_feeding.md
+  functions_operators_layers.md
+  program.md
+  variable.md
+  var_desc.md
+  tensor.md
+  tensor_array.md
+  lod_tensor.md
+  block.md
+  scope.md
+  executor.md

doc/fluid/design/concepts/scope.md

@@ -78,7 +78,7 @@ In `Scope` class, there is a private data member called `parent_`. `parent_` is
 
 A local scope is very useful when we implement Recurrent Neural Network. Each timestep of an RNN should be a `Net`. Each `Net` of timestep (`StepNet` for short) should use an independent local scope. Just like variables in a while loop is inside a local scope in programming languages. By using a single `StepNet` and changing local scope, we can implement an RNN easily.
 
-# Interface Design
+## Interface Design
 
 ```cpp
 class Variable {

doc/fluid/design/concepts/var_desc.md

+# Design Doc: Var_desc
+
 ## Background
 PaddlePaddle divides the description of neural network computation into two stages: compile time and runtime. At compile time, the neural network computation is described as a `ProgramDesc` whereas at runtime an `Executor` interprets the `ProgramDesc` to compute the operations.
 

doc/fluid/design/concurrent/channel.md

+# Channel Design
+
+## Introduction
+
+A Channel is a data structure that allows for synchronous interprocess 
+communication via message passing.  It is a fundemental component of CSP
+(communicating sequential processes), and allows for users to pass data
+between threads without having to worry about synchronization.
+
+## How to use it
+
+Paddle offers python APIs to open and close channels, along with sending
+and receiving data to/from a channel.
+
+### Create a channel
+
+Creates a new channel that takes in variables of a specific dtype.
+
+- **fluid.make_channel(dtype, capacity=0)**
+  - **dtype**: The data type of variables being sent/received through channel
+  - **capacity**: The capacity of the channel.  A capacity of 0 represents 
+    an unbuffered channel.  Capacity > 0 represents a buffered channel
+
+```
+ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR, 10)
+```
+
+### Close a channel
+
+Closes a channel.  Any pending senders and receivers will be awoken during
+this time.  Receivers can still receive from a closed channel, but senders
+are not allowed to send any additional data to the channel (Paddle will
+raise an exception if users try to send to a closed channel.)
+
+- **fluid.channel_close(channel)**
+
+```
+fluid.channel_close(ch)
+```
+
+### Send data to a channel
+
+Sends a variable to a channel.  Currently, variables of dtype `LoDTensor`, 
+`LoDRankTable`, `LoDTensorArray`, `SelectedRows`, `ReaderHolder`, and 
+`ChannelHolder` are supported.
+
+By default, the data of the Variable is moved from the sender to the receiver,
+however the user can optionally copy the data before performing the send.
+
+- **channel_send(channel, variable, is_copy=False)**
+  - **channel**: The channel to send the variable to
+  - **variable**: The variable to send to the channel
+  - **is_copy**: If set to True, channel_send will perform a variable assign
+  to copy the source variable to a new variable to be sent.
+  
+```
+ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
+var = fill_constant(shape=[1],dtype=core.VarDesc.VarType.INT32, value=100)
+fluid.channel_send(ch, var, True)
+```
+
+### Receive data from a channel
+
+Receives a variable from a channel.  The data of the variable is moved to the
+receiving variable.
+
+- **channel_recv(channel, return_variable)**
+  - **channel**: The channel to receive the variable from
+  - **return_variable**: The destination variable used to store the data of the
+  variable received from the channel
+  
+```
+ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
+var = fill_constant(shape=[1],dtype=core.VarDesc.VarType.INT32, value=-1)
+fluid.channel_recv(ch, var)
+```
+
+## How it Works
+
+Channels provides a simple interface for different threads to share data.
+To support the synchronization requirements, channels utilizes a series of
+internal queues, locks, and conditional variables.
+
+### QueueMessage
+
+QueueMessage encapsulates the state of the channel send/receive operation to be
+put in the **sendq/recvq**.  It contains a condition variable used to lock the 
+thread (when there are no available sends/receives).  In addition, it contains
+a callback function to notify a thread when the QueueMessage is being 
+processed by the channel.
+
+### Queues
+
+- **buff_**: This queue holds the data buffer in a buffered channel.  The
+capacity is set to the capacity of the channel.  This data buffer is not
+used in an unbuffered channel.
+
+- **sendq**: This queue holds the QueueMessage of any pending senders of a
+channel.  When a thread performs a channel_send operation on the channel, the
+channel_send operation will put a new QueueMessage on the sendq and block the
+current thread under two conditions:
+  1. The channel is buffered and is full
+  2. The channel is unbuffered and does not have a receiver
+
+- **recvq**:  This queue holds the QueueMessage of any pending receivers of a
+channel.  When a thread performs a channel_recv operation on the channel, the
+channel_recv operation will put a new QueueMessage on the recvq and block the
+current thread under two conditions:
+  1. The channel is buffered and there is no data on the buff_
+  2. The channel is unbuffered and does not have a sender
+  
+### State diagram
+
+#### Channel Send
+
+<p align="center">
+<img src="./images/channel_send.png"/><br/>
+</p>
+  
+#### Channel Receive
+
+<p align="center">
+<img src="./images/channel_recv.png"/><br/>
+</p>
+  
+## Limitations and Considerations
+
+### Variable Copy
+
+In golang, variables in channels are copied from the sender to the receiver.
+In Paddle, the data from our variables are **moved** from sender to receiver.
+As a result, these variables should not be used after they are sent.  We
+provide a flag in channel_send method to allow users to copy the variable to
+be sent before it is sent.  
+
+Please note that this is acheived by adding an **assign** operator and creating
+a temporary variable that is sent in place of the original variable.  Please
+note that **assign** operator has limited support for only certain variables 
+datatypes.

doc/fluid/design/concurrent/go_op.md

+# go_op Design
+
+## Introduction
+
+The **go_op** allows user's of PaddlePaddle to run program blocks on a detached
+thread.  It works in conjuction with CSP operators (channel_send, 
+channel_receive, channel_open, channel_close, and select) to allow users to
+concurrently process data and communicate easily between different threads.
+
+## How to use it
+
+```
+channel = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
+
+with fluid.Go():
+    # Send a tensor of value 99 to "channel" on a detached thread
+    tensor = fill_constant(shape=[1], dtype='int', value=99)
+    tensor.stop_gradient = True
+    fluid.channel_send(channel, tensor)
+    
+# Receive sent tensor from "channel" on the main thread
+result = fill_constant(shape=[1], dtype='int', value=-1)    
+fluid.channel_recv(ch, result)  
+```
+
+The go operator can be accessed by using the fluid.Go() control flow.  This
+will create a new sub block, where the user can add additional operators
+to be ran on the thread.
+
+**Note:** Since back propegation is currently not support in the go_op, users
+should ensure that operators in the go block does not require gradient 
+calculations.
+
+## How it Works
+
+Similar to other control blocks, go_op will create a sub block and add it
+as a child to the current block.  Operators and variables defined in this
+block will be added to the go sub_block.
+
+In addition, the go operator will create a new child scope whose parent is
+the global scope.  Please refer to [block captures](#block-captures) for more
+information.
+
+When Paddle executor runs go_op, go_op will take the sub_block and pass it to
+the executor.run method (along with a newly created local scope) on a detached
+thread.
+
+An example of the generated program description is shown below.  Take note of
+the **go_op** in particular.  It is added as an operator in the current 
+block (in this example, block0).  The **go_op** contains a `sub_block`
+attribute, which points to the id of the block that will be executed in a 
+detached thread.
+
+```
+blocks {
+  idx: 0
+  parent_idx: -1
+  vars {
+    name: "return_value"
+    type {
+      type: LOD_TENSOR
+      lod_tensor {
+        tensor {
+          data_type: INT64
+        }
+      }
+    }
+  }
+  vars {
+    name: "status_recv"
+    type {
+      type: LOD_TENSOR
+      lod_tensor {
+        tensor {
+          data_type: BOOL
+        }
+      }
+    }
+  }
+  ...
+  ops {
+    outputs {
+      parameter: "Out"
+      arguments: "channel"
+    }
+    type: "channel_create"
+    attrs {
+      name: "data_type"
+      type: INT
+      i: 7
+    }
+    attrs {
+      name: "capacity"
+      type: INT
+      i: 0
+    }
+  }
+  ops {
+    inputs {
+      parameter: "X"
+      arguments: "channel"
+    }
+    type: "go"
+    attrs {
+      name: "sub_block"
+      type: BLOCK
+      block_idx: 1
+    }
+  }
+  ops {
+    inputs {
+      parameter: "Channel"
+      arguments: "channel"
+    }
+    outputs {
+      parameter: "Out"
+      arguments: "return_value"
+    }
+    outputs {
+      parameter: "Status"
+      arguments: "status_recv"
+    }
+    type: "channel_recv"
+  }
+  ...
+}
+
+blocks {
+  idx: 1
+  parent_idx: 0
+  vars {
+    name: "status"
+    type {
+      type: LOD_TENSOR
+      lod_tensor {
+        tensor {
+          data_type: BOOL
+        }
+      }
+    }
+  }
+  ...
+  
+  ops {
+    outputs {
+      parameter: "Out"
+      arguments: "fill_constant_1.tmp_0"
+    }
+    type: "fill_constant"
+    attrs {
+      name: "force_cpu"
+      type: BOOLEAN
+      b: false
+    }
+    attrs {
+      name: "value"
+      type: FLOAT
+      f: 99.0
+    }
+    attrs {
+      name: "shape"
+      type: INTS
+      ints: 1
+    }
+    attrs {
+      name: "dtype"
+      type: INT
+      i: 3
+    }
+  }
+  ops {
+    inputs {
+      parameter: "Channel"
+      arguments: "channel"
+    }
+    inputs {
+      parameter: "X"
+      arguments: "fill_constant_1.tmp_0"
+    }
+    outputs {
+      parameter: "Status"
+      arguments: "status"
+    }
+    type: "channel_send"
+    attrs {
+      name: "copy"
+      type: BOOLEAN
+      b: false
+    }
+  }
+```
+
+## Current Limitations
+
+#### <a name="block-captures"></a>Scopes and block captures:
+
+Paddle utilizes [scopes](./../concepts/scope.md) to store variables used in a
+block.  When a block is executed, a new local scope is created from the parent
+scope (ie: scope derived from the parent block) and associated with the new 
+child block.  After the block finishes executing, then the local scope and
+all associated variables in the scope is deleted.
+
+This works well in a single threaded scenario, however with introduction of
+go_op, a child block may continue to execute even after the parent block has
+exited.  If the go_op tries to access variables located in the parent block's
+scope, it may receive a segmentation fault because the parent scope may have
+been deleted.
+
+We need to implement block closures in order to prevent access to parent
+scope variables from causing a segmentation fault.  As a temporary workaround,
+please ensure that all variables accessed in the go block is not destructed
+before it is being accessed.  Currently, the go_op will explicitly enforce 
+this requirement and raise an exception if a variable could not be found in 
+the scope.
+
+Please refer to [Closure issue](https://github.com/PaddlePaddle/Paddle/issues/8502)
+for more details.
+
+#### Green Threads
+
+Golang utilizes `green threads`, which is a mechnism for the runtime library to 
+manage multiple threads (instead of natively by the OS).  Green threads usually
+allows for faster thread creation and switching, as there is less overhead
+when spawning these threads.  For the first version of CSP, we only support
+OS threads.
+
+
+#### Backward Propegation:
+
+go_op currently does not support backwards propagation.  Please use go_op with
+non training operators.

doc/fluid/design/concurrent/index_cn.rst

+并发编程
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  concurrent_programming.md
+  parallel_do.md

doc/fluid/design/concurrent/index_en.rst

+Concurrent Programming
+-------------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  concurrent_programming.md
+  parallel_do.md

doc/fluid/design/data_type/index_cn.rst

+数据类型
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  float16.md

doc/fluid/design/data_type/index_en.rst

+Data Type
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  float16.md

doc/fluid/design/dist_train/distributed_lookup_table_design.md

-## Design Doc: Distributed Lookup Table Operator
+# Design Doc: Distributed Lookup Table Operator
 
 A lookup table operator in PaddlePaddle where the table could be out
 of the memory of a computer.

doc/fluid/design/dist_train/index_cn.rst

+分布式训练
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  distributed_architecture.md
+  distributed_lookup_table_design.md
+  parameter_server.md

doc/fluid/design/dist_train/index_en.rst

+Distributed Training
+---------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  distributed_architecture.md
+  distributed_lookup_table_design.md
+  parameter_server.md

doc/fluid/design/dynamic_rnn/index_cn.rst

+动态RNN
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  rnn.md
+  rnn_design.md

doc/fluid/design/dynamic_rnn/index_en.rst

+Dynamic RNN
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  rnn.md
+  rnn_design.md

doc/fluid/design/dynamic_rnn/rnn_design.md

@@ -233,7 +233,10 @@ x    x
 - 将每个序列concat 为规则的mini-batch表示
 
 ## 参考文献
-1. [Tensorflow Bucketing](https://www.tensorflow.org/versions/r0.12/api_docs/python/contrib.training/bucketing)
-2. [mxnet Bucketing](http://mxnet.io/how_to/bucketing.html)
-3. [variable length input in RNN scenario](https://discuss.pytorch.org/t/about-the-variable-length-input-in-rnn-scenario/345/5)
-4. [Level of details](https://en.wikipedia.org/wiki/Level_of_detail)
+[Tensorflow Bucketing](https://www.tensorflow.org/versions/r0.12/api_docs/python/contrib.training/bucketing)
+
+[mxnet Bucketing](http://mxnet.io/how_to/bucketing.html)
+
+[variable length input in RNN scenario](https://discuss.pytorch.org/t/about-the-variable-length-input-in-rnn-scenario/345/5)
+
+[Level of details](https://en.wikipedia.org/wiki/Level_of_detail)

doc/fluid/design/execution/index_cn.rst

+执行流程
+-------------
+
+.. toctree::
+  :maxdepth: 1
+
+  switch.md
+  if_else_op.md

doc/fluid/design/execution/index_en.rst

+Execution Process
+--------------------------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  switch.md
+  if_else_op.md

doc/fluid/design/execution/switch.md

-### Design Doc: Switch
+# Design Doc: Switch
 
-### Background
+## Background
 
 Many programming languages provide `switch` as a generalization of `if-elif-else`.  We want to add it to Fluid.
 
@@ -19,7 +19,7 @@ with switch() as switch:
         fluid.print("Case 3")
 ```
 
-### The Semantics
+## The Semantics
 
 1. A `switch` control-flow checks cases one-by-one.
 1. The condition of each case is a boolean value, which is a scalar, and differs from the `fluid.if_else` control-flow, which condition could be a vector of boolean values.

doc/fluid/design/index_cn.rst

+设计思想
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  motivation/index_cn.rst
+  execution/index_cn.rst
+  concepts/index_cn.rst
+  data_type/index_cn.rst
+  memory/index_cn.rst
+  muti_devices/index_cn.rst
+  dynamic_rnn/index_cn.rst
+  concurrent/index_cn.rst
+  algorithm/index_cn.rst
+  network/index_cn.rst
+  modules/index_cn.rst
+  interface/index_cn.rst
+  dist_train/index_cn.rst

doc/fluid/design/index_en.rst

+Design
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  motivation/index_en.rst
+  execution/index_en.rst
+  concepts/index_en.rst
+  data_type/index_en.rst
+  memory/index_en.rst
+  muti_devices/index_en.rst
+  dynamic_rnn/index_en.rst
+  concurrent/index_en.rst
+  algorithm/index_en.rst
+  network/index_en.rst
+  modules/index_en.rst
+  interface/index_en.rst
+  dist_train/index_en.rst

doc/fluid/design/interface/index_cn.rst

+多语言接口
+------------
+
+TBD

doc/fluid/design/interface/index_en.rst

+Multi-Language Interface
+-----------------------
+
+TBD

doc/fluid/design/memory/index_cn.rst

+内存管理
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  memory_optimization.md

doc/fluid/design/memory/index_en.rst

+Memory Management
+-------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  memory_optimization.md

doc/fluid/design/modules/evaluator.md

-## Evaluator Design
+# Evaluator Design
 
-### Problem Statement
+## Problem Statement
 
 During training or inference, we provide an evaluation function to measure the model performance, for example, accuracy, precision, etc. In the operator based framework design, the data passes through the network pipeline batch by batch. As a result, inside the operator, we only calculate the metrics for one minibatch. Thus, we need to provide a mechanism to calculate the metrics for each N pass/batch the user wants.
 
-### Evaluator Design
+## Evaluator Design
 Currently, every operation is expressed in the graph. We divide the evaluator process into three steps.
 
 1. Initialize the metric state and add it into the block.
@@ -14,7 +14,7 @@ Currently, every operation is expressed in the graph. We divide the evaluator pr
 
 3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
 
-### Implementation
+## Implementation
 This design is shown in the Python API.
 Each metric operator needs to caculate the metric statistic and return the batch-aware states. Python side is responsible for accumulating the states for each pass.
 

doc/fluid/design/modules/index_cn.rst

+代码结构和重要模块
+-----------------
+
+.. toctree::
+  :maxdepth: 1
+
+  backward.md
+  python_api.md
+  regularization.md
+  infer_var_type.md
+  optimizer.md
+  prune.md
+  register_grad_op.md
+  net_op_design.md

doc/fluid/design/modules/index_en.rst

+Code Structure and Important Modules
+-------------------------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  backward.md
+  python_api.md
+  regularization.md
+  infer_var_type.md
+  optimizer.md
+  prune.md
+  register_grad_op.md
+  net_op_design.md

doc/fluid/design/modules/net_op_design.md

@@ -8,9 +8,9 @@ A network object knows all Operators belonging to this network. Variables,
 which are inputs and outputs of these operators,
 are created and managed by a hierarchy of Scope objects.
 
-# API
+## API
 
-## Net
+### Net
 To make the `Network` extendable, a base class is defined like this
 
 ```c++
@@ -80,7 +80,7 @@ if (net) {
 }
 ```
 
-## `PlainNet` as a simple implementation of `BaseNet`
+### `PlainNet` as a simple implementation of `BaseNet`
 
 A very basic implementation is as follows. All it does is simply to run every operators in sequence.
 
@@ -211,7 +211,7 @@ class NetBuilder final {
 }
 ```
 
-## Compatibility with RNN
+### Compatibility with RNN
 
 Benefitting from the decoupling of `PlainNet.Run` and `Scope`, `PlainNet` is compatible with future RNN design,
 for example we can implement a simple recurrent neural network as follows

doc/fluid/design/modules/optimizer.md

-## Optimizer Design
+# Optimizer Design
 
-### The Problem
+## The Problem
 
 A PaddlePaddle program, or a block, is a sequence of operators operating variables.  A training program needs to do three kinds of works:
 
@@ -19,7 +19,7 @@ It's true that users should be able to create all these operators manually by ca
 In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass.
 
 
-### High-level Python API to describe the training process
+## High-level Python API to describe the training process
 
 1. User write code to describe the network:
 
@@ -54,7 +54,7 @@ In this design, we propose a high-level API that automatically derives the optim
 	sess.run(target= opt_op_list, ...)
 	```
 
-#### Optimizer Python interface:
+### Optimizer Python interface:
 
 ```python
 class Optimizer(object):

doc/fluid/design/motivation/index_cn.rst

+设计动机和目标
+-------------
+
+.. toctree::
+  :maxdepth: 1
+
+  api.md
+  refactorization.md
+  fluid.md
+  fluid_compiler.md

doc/fluid/design/motivation/index_en.rst

+Design Motivations and Goals
+--------------------------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  api.md
+  refactorization.md
+  fluid.md
+  fluid_compiler.md

doc/fluid/design/motivation/refactorization.md

@@ -97,13 +97,13 @@ Compile Time -> IR -> Runtime
 
 ---
 
-# Operator/OpWithKernel/OpKernel
+## Operator/OpWithKernel/OpKernel
 
 ![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/49caf1fb70820fb4a6c217634317c9306f361f36/op_op_with_kern_class_diagram.dot)
 
 ---
 
-# Operator
+## Operator
 ![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot)
 
 * `Operator` is the fundamental building block of the user interface.
@@ -113,7 +113,7 @@ Compile Time -> IR -> Runtime
 
 ---
 
-# OpWithKernel/Kernel
+## OpWithKernel/Kernel
 
 ![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/9d7f4eba185cf41c8e2fbfb40ae21890dbddcd39/op_with_kernel.dot)
 
@@ -124,7 +124,7 @@ Compile Time -> IR -> Runtime
 
 ---
 
-# Why separate Kernel and Operator
+## Why separate Kernel and Operator
 
 * Separate GPU and CPU code.
     * Make Paddle capable of running without GPU.
@@ -132,7 +132,7 @@ Compile Time -> IR -> Runtime
     * For example, same multiplication op can have different implementations kernels such as FP16 kernel, FP32 kernel, MKL, eigen kernel.
 ---
 
-# Libraries for Kernel development
+## Libraries for Kernel development
 
 * `Eigen::Tensor` contains basic math and element-wise functions.
     * Note that `Eigen::Tensor` has broadcast implementation.
@@ -143,16 +143,16 @@ Compile Time -> IR -> Runtime
 * Hand-writing `GPUKernel` and `CPU` code
     * Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.)
 ---
-# Operator Registration
+## Operator Registration
 
-## Why is registration necessary?
+### Why is registration necessary?
 We need a method to build mappings between Op type names and Op classes.
 
-## How is registration implemented?
+### How is registration implemented?
 Maintaining a map, whose key is the type name and the value is the corresponding Op constructor.
 
 ---
-# The Registry Map
+## The Registry Map
 
 ### `OpInfoMap`
 
@@ -166,7 +166,7 @@ Maintaining a map, whose key is the type name and the value is the corresponding
 - **`checker`**: Used to check attributes.
 
 ---
-# Related Concepts
+## Related Concepts
 
 ### Op_Maker
 It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37))
@@ -178,7 +178,7 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
 ```
 
 ---
-# Registration Process
+## Registration Process
 1. Write an Op class and its gradient Op class, if required.
 2. Write an Op maker class. In the constructor of this class, describe the inputs, outputs and attributes of the operator.
 3. Invoke the macro `REGISTER_OP`. This macro will
@@ -186,13 +186,13 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
 	2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap`
 
 ---
-# Backward Module (1/2)
+## Backward Module (1/2)
 ### Create Backward Operator
 - Mapping from forward Op to backward Op
 ![backward](https://gist.githubusercontent.com/dzhwinter/a6fbd4623ee76c459f7f94591fd1abf0/raw/61026ab6e518e66bde66a889bc42557a1fccff33/backward.png)
 
 ---
-# Backward Module (2/2)
+## Backward Module (2/2)
 ### Build Backward Network
 - **Input**: a graph of forward operators
 - **Output**: a graph of backward operators
@@ -205,7 +205,7 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
 
 
 ---
-# Scope, Variable, Tensor
+## Scope, Variable, Tensor
 
 * `Tensor` is an n-dimension array with type.
 	* Only dims and data pointers are stored in `Tensor`.
@@ -218,8 +218,8 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
 	* `Scope` has a hierarchical structure. The local scope can get variables from its parent scope.
 
 ---
-# Block (in design)
-## the difference between original RNNOp and Block
+## Block (in design)
+### the difference between original RNNOp and Block
 - As an operator is more intuitive than `RNNOp`,
 - Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`,
 - Fits the compile-time/ runtime separation design paradigm.
@@ -227,7 +227,7 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
   - When graph executes, a Block with `BlockDesc` is passed. It then creates `Op` and `Var` instances and then invokes `Run`.
 
 ---
-# Milestone
+## Milestone
 - Take Paddle/books as the main line, the requirement of the models motivates framework refactoring,
 - Model migration
   - Framework development gives **priority support** to model migration, for example,
@@ -240,7 +240,7 @@ REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class)
 - Accept imperfection, concentrate on solving the specific problem at the right price.
 
 ---
-# Control the migration quality
+## Control the migration quality
 - Compare the performance of migrated models with old ones.
 - Follow the google C++ style guide.
 - Build the automatic workflow of generating Python/C++ documentations.

doc/fluid/design/muti_devices/index_cn.rst

+多设备支持
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  operator_kernel_type.md
+  kernel_selection.md
+  kernel_hint_design.md

doc/fluid/design/muti_devices/index_en.rst

+Multi-Device Support
+----------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  operator_kernel_type.md
+  kernel_selection.md
+  kernel_hint_design.md

doc/fluid/design/muti_devices/kernel_hint_design.md

-## Problem
+# Problem
 In PaddlePaddle's [Design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/switch_kernel.md), one Operator may have multiple kernels. Users may have some personal preference to choose a certain type of kernel for an operator, such as `force_cpu` to choose a CPU kernel, `use_cudnn` to choose a CUDNN kernel, we need to provide a way for users to do this.
 
 In the current design, we use KernelType to describe one kernel.

doc/fluid/design/muti_devices/kernel_selection.md

-## Background
+# Background
 Every operator has many kernels because there are multiple data types, places, data layout, library type that Fluid supports. We use the `OpKernelType ` to describe kernel types that operators can hold.
 
 The `OpKernelType ` is as follows:

doc/fluid/design/network/index_cn.rst

+复杂网络设计
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  sequence_decoder.md

doc/fluid/design/network/index_en.rst

+Complex Network Design
+------------------------
+
+.. toctree::
+  :maxdepth: 1
+
+  sequence_decoder.md

doc/fluid/dev/api_doc_std_en.md

+# API Doc Standard
+
+- [API Doc Structure](#API Doc Structure)
+- [Format and Examples](#Format and Examples)
+- [Complete Example](#Complete Example)
+
+
+## API Doc Structure
+
+API Doc should contain the following parts(please write them in order):
+
+- Python API Definition
+
+  The definition of API
+
+- Function Description
+
+  Description of API's function. 
+  The description includes: meaning, purpose and operation on input of API, reference and corresponding link(if any), formula(if necessary) and explanations of key variables in the formula.
+
+- Args Description
+
+  Description of API parameters.
+  Introduce parameters one by one according to the order in API definition.
+  The introduction includes: data type, default value(if any), meaning, etc.
+
+- Returns
+
+  Introduction of API returned value.
+  Introduce meaning of returned value, provide correspoding format if necessary.
+  If returned value is a tuple containing multiple parameters, then introduce parameters one by one in order.
+
+- Raises（if any）
+
+   Abnormality, error that may occur, and possible reasons. If there are more than one possible abnormity or error, they should be listed in order. 
+
+- Note（if any）
+
+  Matters needing attention. If there are more than one matters, they should be listed in order. 
+
+- Examples
+
+  Examples of how to use API.
+
+
+## Format and Examples
+
+API documentation must obey reStructuredText format, please refer to [here](http://sphinx-doc-zh.readthedocs.io/en/latest/rest.html).
+Format and examples of each part of API documantation are as follows: (take fc for example)
+
+- Python API Definition
+
+  - Format
+
+      [Python API Definition]
+
+  - Example
+
+      ```
+      fc(input,
+         size,
+         num_flatten_dims=1,
+         param_attr=None,
+         bias_attr=None,
+         act=None,
+         name=None,
+         main_program=None,
+         startup_program=None)
+      ```
+
+- Function Description
+
+  - Format
+
+      This part contains (please write them in order):
+
+      [Function Description]
+
+      [Formula]
+
+      [Symbols' Descriptions if necessary]
+
+      [References if necessary]
+
+  - Example
+
+      [Function Description]
+
+       ```
+       **Fully Connected Layer**
+
+       The fully connected layer can take multiple tensors as its inputs. It
+       creates a variable called weights for each input tensor, which represents
+       a fully connected weight matrix from each input unit to each output unit.
+       The fully connected layer multiplies each input tensor with its coresponding
+       weight to produce an output Tensor. If multiple input tensors are given,
+       the results of multiple multiplications will be sumed up. If bias_attr is
+       not None, a bias variable will be created and added to the output. Finally,
+       if activation is not None, it will be applied to the output as well.
+       ```
+
+      [Formula]
+
+      ```
+      This process can be formulated as follows:
+
+      .. math::
+
+           Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
+      ```
+
+      [Symbols' Descriptions if necessary]
+
+      ```
+      In the above equation:
+
+      * :math:`N`: Number of the input.
+      * :math:`X_i`: The input tensor.
+      * :math:`W`: The weights created by this layer.
+      * :math:`b`: The bias parameter created by this layer (if needed).
+      * :math:`Act`: The activation function.
+      * :math:`Out`: The output tensor.
+      ```
+
+      [References if necessary]
+
+      Since there is no need for reference of fc, we omit them here. Under other circumstances, please provide explicit reference and link, take layer_norm for example: 
+
+      ```
+      Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_ for more details.
+      ```
+
+
+- Args Description
+
+  - Format
+
+      \[Arg's Name\][(Data Type, Default Value)][Description]
+
+  - Example
+
+      part of fc parameters are as follows:
+
+      ```
+      Args:
+          input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
+              the input tensor(s) is at least 2.
+          param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
+              parameters/weights of this layer.
+          name (str, default None): The name of this layer.
+      ```
+
+- Returns
+
+  - Format
+
+      [Name][Shape]
+
+  - Example
+
+      ```
+      Returns:
+          A tensor variable storing the transformation result.
+      ```
+
+      when returned value contain more than one tuple, please introduce every parameter in order, take dynamic_lstm for example:
+
+      ```
+      Returns:
+          A tuple containing:
+            The hidden state of LSTM whose shape is (T X D).
+            The cell state of LSTM whose shape is (T X D).
+      ```
+
+- Raises
+
+  - Format
+
+      [Exception Type][Condition]
+
+  - Example
+
+      ```
+      Raises:
+          ValueError: If the rank of the input is less than 2.
+      ```
+
+- Note
+
+  - Format
+
+     [Note]
+
+  - Example
+
+      there is no Note in fc, so we omit this part. If there is any note, please write clearly. If there are more than one notes, please list them in order. Take scaled\_dot\_product\_attention for example:
+
+      ```
+      Note:
+          1. When num_heads > 1, three linear projections are learned respectively
+             to map input queries, keys and values into queries', keys' and values'.
+             queries', keys' and values' have the same shapes with queries, keys
+             and values.
+          2. When num_heads == 1, scaled_dot_product_attention has no learnable
+             parameters.
+      ```
+
+- Examples
+
+  - Format
+
+      \[Python Code Snipper]
+
+  - Example
+
+      ```
+      Examples:
+          .. code-block:: python
+
+            data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+            fc = fluid.layers.fc(input=data, size=1000, act="tanh")
+      ```
+
+## Complete Example
+
+Complete Example of fc please see [here](src/fc.py)。

doc/fluid/dev/index_cn.rst

+开发标准
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  new_op_en.md
+  new_op_kernel_en.md
+  use_eigen_en.md
+  name_convention.md
+  support_new_device.md
+  releasing_process.md
+  op_markdown_format.md

doc/fluid/dev/index_en.rst

+Development
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  new_op_en.md
+  new_op_kernel_en.md
+  use_eigen_en.md
+  name_convention.md
+  support_new_device.md
+  releasing_process.md
+  op_markdown_format.md

doc/fluid/dev/name_convention.md

-## Operator's Parameter Name Convention
+# Operator's Parameter Name Convention
 
 To make the operator document itself more clear, we recommend operator names obey the listing conventions.
 
-### OpProtoMaker names
+## OpProtoMaker names
 
 When defining an operator in Paddle, a corresponding [OpProtoMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L170) (TODO: OpProtoMaker Doc)need to be defined. All the Input/Output and Attributes will write into the [OpProto](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L61) , and will be used in client language to create operator.
 
@@ -20,7 +20,7 @@ When defining an operator in Paddle, a corresponding [OpProtoMaker](https://gith
 - Order.
   - Follow the order of Input/Output, then Attribute, then Comments. See the example in best practice.
 
-### Best Practice
+## Best Practice
 
 Here we give some examples to show how these rules will be used.
 

doc/fluid/dev/new_op_kernel_en.md

-## Add Kernels for a New Device
+# Add Kernels for a New Device
 
-### Background
+## Background
 
 PaddlePaddle Fluid have hundreds of operators.  Each operator could have one or more kernels.  A kernel is an implementation of the operator for a certain device, which could be a hardware device, e.g., the CUDA GPU, or a library that utilizes a device, e.g., Intel MKL that makes full use of the Xeon CPU.
 
 [This document](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_en.md) explains how to add an operator, and its kernels.  The kernels of an operator are indexed by a C++ type [`OpKernelType`](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/operator_kernel_type.md).  An operator chooses the right kernel at runtime.  This choosing mechanism is described [here](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/switch_kernel.md).
 
-### Write Kernels for A New Device 
+## Write Kernels for A New Device
 
-#### Add A New Device
+### Add A New Device
 
   For some historical reaons, we misuse the word *library* for *device*.  For example, we call the deivce type by *library type*.  An example is the header file [`library_type.h`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/library_type.h#L24).  We will correct this ASAP.
 
@@ -23,7 +23,7 @@ enum class LibraryType {
 ```
 
 
-#### Add A New [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L53)
+### Add A New [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L53)
 
 If you have a new kind of Device, firstly you need to add a new kind of [`Place`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L53). For example `CUDAPlace`:
 
@@ -45,7 +45,7 @@ struct CUDAPlace {
 typedef boost::variant<CUDAPlace, CPUPlace> Place;
 ```
 
-#### Add [device context]((https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/device_context.h#L37))
+### Add [device context]((https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/device_context.h#L37))
 After a new kind of Device is added, you should add a corresponding [DeviceContext](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/device_context.h#L37) for it.
 
 ```cpp
@@ -58,7 +58,7 @@ class DeviceContext {
 };
 ```
 
-#### Implement new [OpKernel](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L351) for your Device.
+### Implement new [OpKernel](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L351) for your Device.
 
 A detailed documentation can be found in [`new_op_and_kernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/new_op_en.md)
 
@@ -85,7 +85,7 @@ class OpKernel : public OpKernelBase {
 ```
 
 
-#### Register the OpKernel to framework
+### Register the OpKernel to framework
 
 After writing the components described above, we should register the kernel to the framework.
 

doc/fluid/dev/op_markdown_format.md

@@ -15,26 +15,26 @@ The signature of the operator.
 
 Each section mentioned above has been covered in further detail in the rest of the document.
 
-# PaddlePaddle Operator Name
+## PaddlePaddle Operator Name
 This should be in all small letters, in case of multiple words, we separate them with an underscore. For example:
 `array to lod tensor` should be written as `array_to_lod_tensor`.
 
 This naming convention should be standard across all PaddlePaddle operators.
 
-# Standard Operator Name
+## Standard Operator Name
 This is the standard name of the operator as used in the community. The general standard is usually:
 - Standard abbreviations like `SGD` are written in all capital letters.
 - Operator names that have multiple words inside a single word use `camelCase` (capitalize word boundaries inside of a word).
 - Keep numbers inside a word as is, with no boundary delimiters.
 - Follow the name of the operator with the keyword: `Activation Operator.`
 
-# Operator description
+## Operator description
 This section should contain the description of what the operator does, including the operation performed, the literature from where it comes and was introduced first, and other important details. The relevant paper/article including the hyperlink should be cited in this section.
 
-# LaTeX equation
+## LaTeX equation
 This section should contain an overall equation of the update or operation that the operator performs. The variables used in the equation should follow the naming convention of operators as described [here](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/name_convention.md). Two words in the same word should be separated by an underscore (`_`).
 
-# The signature
+## The signature
 This section describes the signature of the operator. A list of Inputs and Outputs, each of which have a small description of what the variable represents and the type of variable. The variable names follow the `CamelCase` naming convention. The proposed format for this is:
 `Section :
 VariableName : (VariableType) VariableDescription

doc/fluid/dev/use_eigen_cn.md

-## 在Paddle中如何使用Eigen
+# 在Paddle中如何使用Eigen
 
 神经网络本质上是一个计算图，计算需要的数据存放在`Tensor`中，而计算过程是由`Operartor`来描述的。在执行时，`Operator`调用对应`OpKernel`中的`Compute`接口，实现对`Tensor`的操作。
 
 
-### Eigen Tensor模块
+## Eigen Tensor模块
 
 Eigen Tensor模块对element-wise计算提供了强大的支持，并且书写一份代码，可以同时在CPU、GPU执行。但Eigen Tensor是一个正在开发中的模块，因此可能测试不够完备，文档较少。
 
 关于Eigen Tensor模块的详细介绍请参考[文档1](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md) 和[文档2](https://bitbucket.org/eigen/eigen/src/default/unsupported/Eigen/CXX11/src/Tensor/README.md)
 
 
-### paddle::framework::Tensor
+## paddle::framework::Tensor
 
 Paddle Tensor定义在framework目录下，其主要接口如下：
 
@@ -129,7 +129,7 @@ From是EigenTensor模板提供的一个接口，可以实现从paddle::framework
 
 
 
-### 实现计算
+## 实现计算
 
 当需要完成计算时，我们需要等式左边的EigenTensor调用device接口。在这里需要注意的是，这里的EigenTensor之间的运算只是改变了原有Tensor中的数据，而不会改变原有Tensor的shape信息。