Merge remote-tracking branch 'ups/develop' into refine/jit

doc/fluid/design/dist_train/dist_train_nccl2.md

 # Distributed Training with NCCL2
 
 We design a pattern that can enable training with `ParallelExecutor` and
-using [NCCL2](https://developer.nvidia.com/nccl) as it's collective
+use [NCCL2](https://developer.nvidia.com/nccl) as it's collective
 communication library.
 
 In `ParallelExecutor` we can use `AllReduce` or `Reduce` and `Broadcast`
@@ -9,14 +9,14 @@ to do multi GPU training. And if we initialize NCCL2 communicators as
 ranks in a distributed environment, we can simply run the `ParallelExecutor`
 as a distributed program! The only thing that may be different than in
 the single node version is that we need to broadcast the NCCL unique ID
-to all the nodes, and initialize communicators using that ID, so NCCL2
-will know each other as ranks.
+to all the nodes and initialize communicators using that ID, so NCCL2
+can know each other as ranks.
 
 To achieve this feature, we introduce a new operator: `gen_nccl_id` op,
 so we are ***not*** "bind to" running NCCL2 with MPI, we can run it in
-what ever platform you like.
+whatever platform you like.
 
-It have two running modes:
+It has two running modes:
 
 1. Generate and broadcast mode, which should be used on trainer 0;
 1. Listen and fetch mode, which should be used on trainers other than 0.
@@ -29,7 +29,7 @@ initialize NCCL communicator objects.
 <img src="src/ncc2_design.png">
 
 The above figure indicates the general process when training with NCCL2
-distributed. Each trainer have the number of communicators equal to the
+distributed. Each trainer has the number of communicators equal to the
 number of GPUs, but the ranks should match the global ranks number: here
 we have total 8 GPUs, so `nranks==8`, for each trainer, the ranks should
 be from 0 ~ 3 on trainer 0 and 4 ~ 7 on trainer 1.

doc/fluid/howto/cluster/nccl2_rdma_training.md

 # Distributed Training with NCCL2 and RDMA
 
-When doing distributed multi-GPU training, network bandwith often becomes the
-bottle neck. We introduce a way to use NCCL2 to do such training job to
-achieve best performace.
+When doing distributed multi-GPU training, network bandwidth often becomes the
+bottleneck. We introduce a way to use NCCL2 to do such training job to
+achieve best performance.
 
-## Prepare Hardwares with RDMA and Multiple GPUs
+## Prepare Hardware with RDMA and Multiple GPUs
 
-I'm using two Linux servers each of them is installed with 8 GPUs and
+I'm using two Linux servers each of them installed with 8 GPUs and
 one 100Gb RDMA card.
 Base environment is:
 
@@ -25,7 +25,7 @@ In general, the steps including:
 1. Use docker to run tests and make sure GPUs and RDMA can work inside
    the container.
 
-I'll ommit section "Install GPU drivers" because we can find it easily
+I'll omit the section "Install GPU drivers" because we can find it easily
 somewhere else.
 
 ### Install RDMA drivers
@@ -33,7 +33,7 @@ somewhere else.
 For my case, I've got two machines with device
 "Mellanox Technologies MT27700 Family [ConnectX-4]" installed. The OS was
 "CentOS 7.4" and I updated the kernel to version 4.4 so that docker can
-work with latest overlay2 filesystem.
+work with the latest overlay2 filesystem.
 
 ***NOTE: before you start, make sure you have a way to get a console
 of the server other than ssh because we may need to re-configure the
@@ -45,14 +45,14 @@ network device.***
 1. Run `./mlnxofedinstall --add-kernel-support` in the software package.
 1. Run `/etc/init.d/openibd restart` to make everything work, note that
    this operation may cause the network goes down if you are using this
-   RDMA device as default network device and use ssh to login the server.
+   RDMA device as default network device and use ssh to log in the server.
 1. Re-configure the network interface, for example:
    `ifconfig eth2 192.168.16.30/20 up`, then add routes if needed:
    `ip route add default via 192.168.16.1 dev eth2`.
 1. Do the same thing on the other node.
 1. Use `ping` to test if the two nodes have typical ICMP connection.
 1. Use either `udaddy` or `ib_write_bw` to test the network connection is
-   ready and have the desired bandwith.
+   ready and have the desired bandwidth.
 
 ### Prepare Docker Image to Run RDMA Programs
 
@@ -60,7 +60,7 @@ network device.***
    package in it.
 1. Start a docker container and mount GPU driver libs into it (you can
    skip this step if you are using nvidia-docker).
-1. Mount RDMA dirvers and libs into the docker image (see below section),
+1. Mount RDMA drivers and libs into the docker image (see below section),
    also `udaddy` and `ib_write_bw` if needed.
 1. Mount GPU devices and RDMA devices into the container using `--device`
    or just use privileged mode `--privileged`.

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import time
+import unittest
+from multiprocessing import Process
+import signal
+
+import numpy
+
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from paddle.fluid.layers.io import ListenAndServ
+from paddle.fluid.layers.io import Recv
+from paddle.fluid.layers.io import Send
+
+from paddle.fluid.transpiler.details import program_to_code
+
+
+class TestProgram2Code(unittest.TestCase):
+    def test_print(self):
+        place = fluid.CPUPlace()
+        self.init_serv(place)
+        self.init_client(place, 9123)
+
+    def init_serv(self, place):
+        main = fluid.Program()
+
+        with fluid.program_guard(main):
+            serv = ListenAndServ("127.0.0.1:0", ["X"], optimizer_mode=False)
+            with serv.do():
+                out_var = main.global_block().create_var(
+                    name="scale_0.tmp_0",
+                    psersistable=True,
+                    dtype="float32",
+                    shape=[32, 32])
+                x = layers.data(
+                    shape=[32, 32],
+                    dtype='float32',
+                    name="X",
+                    append_batch_size=False)
+                fluid.initializer.Constant(value=1.0)(x, main.global_block())
+                layers.scale(x=x, scale=10.0, out=out_var)
+
+        program_to_code(main)
+
+    def init_client(self, place, port):
+        main = fluid.Program()
+        with fluid.program_guard(main):
+            x = layers.data(
+                shape=[32, 32],
+                dtype='float32',
+                name='X',
+                append_batch_size=False)
+            fluid.initializer.Constant(value=2.3)(x, main.global_block())
+            get_var = main.global_block().create_var(
+                name="scale_0.tmp_0",  # server side var
+                dtype="float32",
+                persistable=False,
+                shape=[32, 32])
+            fluid.initializer.Constant(value=2.3)(get_var, main.global_block())
+            Send("127.0.0.1:%d" % port, [x])
+            o = Recv("127.0.0.1:%d" % port, [get_var])
+
+        program_to_code(main)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -16,6 +16,9 @@ from __future__ import print_function
 
 import six
 
+from paddle.fluid import core
+import paddle
+
 
 def delete_ops(block, ops):
     try:
@@ -39,3 +42,133 @@ def find_op_by_output_arg(block, arg_name):
         if arg_name in op.output_arg_names:
             return index
     return -1
+
+
+def get_indent_space(indent, space_num=4):
+    ret = ""
+    for i in range(0, indent * space_num):
+        ret += " "
+
+    return ret
+
+
+def variable_to_code(var):
+    """
+    Get readable codes of fluid variable.
+
+    Args:
+        var: A fluid operator.
+
+    Returns:
+        string: The formatted string.
+    """
+
+    var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})".\
+        format(i="{", e="}", name=var.name, type=var.type, shape=var.shape, dtype=var.dtype)
+
+    if type(var) == paddle.fluid.framework.Parameter:
+        if var.trainable:
+            var_str = "trainable parameter " + var_str
+        else:
+            var_str = "parameter " + var_str
+    else:
+        var_str = "var " + var_str
+
+    if var.persistable:
+        var_str = "persist " + var_str
+
+    return var_str
+
+
+def op_to_code(op):
+    """
+    Get readable codes of fluid operator.
+
+    Args:
+        op: A fluid operator.
+
+    Returns:
+        string: The foramtted string.
+    """
+
+    outputs_str = "{"
+    for i in range(0, len(op.output_names)):
+        outputs_str += "{name}=".format(name=op.output_names[i])
+        o = op.output(op.output_names[i])
+        outputs_str += "{value}".format(value=o)
+        if i != len(op.output_names) - 1:
+            outputs_str += ", "
+    outputs_str += "}"
+
+    inputs_str = "{"
+    for i in range(0, len(op.input_names)):
+        inputs_str += "{name}=".format(name=op.input_names[i])
+        o = op.input(op.input_names[i])
+        inputs_str += "{value}".format(value=o)
+
+        if i != len(op.input_names) - 1:
+            inputs_str += ", "
+    inputs_str += "}"
+
+    attrs_str = ""
+    for i in range(0, len(op.attr_names)):
+        name = op.attr_names[i]
+
+        attr_type = op.desc.attr_type(name)
+        if attr_type == core.AttrType.BLOCK:
+            a = "{name} = block[{value}]".format(
+                name=name, type=attr_type, value=op.block_attr_id(name))
+            attrs_str += a
+            continue
+
+        if attr_type == core.AttrType.BLOCKS:
+            a = "{name} = blocks{value}".format(
+                name=name, type=attr_type, value=op.blocks_attr_ids(name))
+            attrs_str += a
+            continue
+
+        a = "{name} = {value}".format(
+            name=name, type=attr_type, value=op.desc.attr(name))
+        attrs_str += a
+        if i != len(op.attr_names) - 1:
+            attrs_str += ", "
+
+    if outputs_str != "{}":
+        op_str = "{outputs} = {op_type}(inputs={inputs}, {attrs})".\
+            format(outputs = outputs_str, op_type=op.type, inputs=inputs_str, attrs=attrs_str)
+    else:
+        op_str = "{op_type}(inputs={inputs}, {attrs})".\
+            format(op_type=op.type, inputs=inputs_str, attrs=attrs_str)
+    return op_str
+
+
+def program_to_code(prog):
+    """
+    Print readable codes of fluid program.
+
+    Args:
+        prog : A fluid program.
+
+    An example result like bellow:
+    https://github.com/PaddlePaddle/Paddle/pull/12673
+    """
+    indent = 0
+    block_idx = 0
+    for block in prog.blocks:
+        print("{0}{1} // block {2}".format(
+            get_indent_space(indent), '{', block_idx))
+        indent += 1
+        # sort all vars
+        all_vars = sorted(block.vars.iteritems(), key=lambda x: x[0])
+        for var in all_vars:
+            print("{}{}".format(
+                get_indent_space(indent), variable_to_code(var[1])))
+
+        if len(all_vars) > 0:
+            print("")
+
+        for op in block.ops:
+            print("{}{}".format(get_indent_space(indent), op_to_code(op)))
+        indent -= 1
+        print("{0}{1}".format(get_indent_space(indent), '}'))
+        block_idx += 1