From 90df7ff3789869bd4d9161c2914eedc8521c4703 Mon Sep 17 00:00:00 2001
From: tangwei12 <tangwei12@baidu.com>
Date: Wed, 30 Jan 2019 14:36:35 +0800
Subject: [PATCH] transpiler.py code clean (#15555)

* move var strusted to vars_distributed.py, add optimizer's block name, test=develop

* rename optimzier's seems complex, revert it, test=develop

* replace * with details, test=develop
---
 .../fluid/transpiler/details/__init__.py      |   1 +
 .../transpiler/details/vars_distributed.py    | 269 ++++++++++++++++++
 .../fluid/transpiler/distribute_transpiler.py | 268 +----------------
 3 files changed, 279 insertions(+), 259 deletions(-)
 create mode 100644 python/paddle/fluid/transpiler/details/vars_distributed.py

diff --git a/python/paddle/fluid/transpiler/details/__init__.py b/python/paddle/fluid/transpiler/details/__init__.py
index f33c05ed2f4..82d0d336e52 100644
--- a/python/paddle/fluid/transpiler/details/__init__.py
+++ b/python/paddle/fluid/transpiler/details/__init__.py
@@ -17,3 +17,4 @@ from __future__ import print_function
 from .program_utils import *
 from .ufind import *
 from .checkport import *
+from .vars_distributed import *
diff --git a/python/paddle/fluid/transpiler/details/vars_distributed.py b/python/paddle/fluid/transpiler/details/vars_distributed.py
new file mode 100644
index 00000000000..05e7f6e3e70
--- /dev/null
+++ b/python/paddle/fluid/transpiler/details/vars_distributed.py
@@ -0,0 +1,269 @@
+# 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 print_function
+from paddle.fluid.framework import Variable
+
+
+class VarStruct(object):
+    """
+    record part properties of a Variable in python.
+    """
+
+    def __init__(self, name, shape, dtype, type, lod_level, persistable):
+        self.name = name
+        self.shape = shape
+        self.dtype = dtype
+        self.type = type
+        self.lod_level = lod_level
+        self.persistable = persistable
+
+
+class VarDistributed(object):
+    """
+    a class to record the var distributed on parameter servers.
+    the class will record the relationship between origin var and slice var.
+    the slice var's properties, such as type/shape/offset/endpoint.
+    """
+
+    def __init__(self,
+                 origin_var,
+                 slice_var,
+                 is_slice=None,
+                 block_id=None,
+                 offset=None,
+                 vtype=None,
+                 endpoint=None):
+        """
+        Args:
+            origin_var(Variable|VarStruct): origin var properties
+            slice_var(Variable|VarStruct): slice var properties
+            is_slice(bool|None): slice or not, slice_var=True/False and its block size > 8192 are the judgement standard.
+            block_id(int|None): the number about the slice var.
+            offset(int|None): if the slice var is sliced, offset is the numel before the var.
+            vtype(str|None): a tag, such as Optimizer/Param/RemoteProfetch.
+            endpoint(str|None): which parameter the slice var on, such as "127.0.0.1:1001"
+        """
+
+        if isinstance(origin_var, Variable):
+            self.origin = self.__create_var_struct(origin_var)
+        else:
+            self.origin = origin_var
+
+        if isinstance(slice_var, Variable):
+            self.slice = self.__create_var_struct(slice_var)
+        else:
+            self.slice = slice_var
+
+        if self.equal(self.origin, self.slice):
+            self.is_slice = False
+            self.block_id = 0
+            self.offset = 0
+        else:
+            self.is_slice = True
+            self.block_id = 0
+            self.offset = 0
+
+        if is_slice is not None:
+            self.is_slice = is_slice
+        if block_id is not None:
+            self.block_id = block_id
+        if offset is not None:
+            self.offset = offset
+
+        self.vtype = vtype
+        self.endpoint = endpoint
+
+    @staticmethod
+    def __create_var_struct(var):
+        return VarStruct(var.name, var.shape, var.dtype, var.type,
+                         var.lod_level, var.persistable)
+
+    @staticmethod
+    def equal(var1, var2):
+        """
+        the two var is equal or not.
+        Returns:
+            bool: equal will return True else False
+        """
+        assert isinstance(var1, VarStruct) and isinstance(var2, VarStruct)
+
+        return var1.name == var2.name and \
+               var1.type == var2.type and \
+               var1.shape == var2.shape and \
+               var1.dtype == var2.dtype and \
+               var1.lod_level == var2.lod_level and \
+               var1.persistable == var2.persistable
+
+    def __str__(self):
+        origin_var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})". \
+            format(i="{", e="}", name=self.origin.name, type=self.origin.type,
+                   shape=self.origin.shape, dtype=self.origin.dtype)
+
+        slice_var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})" \
+                        ".slice({is_slice}).block({block_id}).offset({offset})". \
+            format(i="{", e="}", name=self.slice.name, type=self.slice.type,
+                   shape=self.slice.shape, dtype=self.slice.dtype,
+                   is_slice=self.is_slice, block_id=self.block_id, offset=self.offset)
+
+        return "var owned: {}, origin var: ( {} ), slice var: ( {} ), endpoint: {} ".format(
+            self.vtype, origin_var_str, slice_var_str, self.endpoint)
+
+
+class VarsDistributed(object):
+    """
+    a gather about VarDistributed with many methods to find distributed vars.
+    through the class, we can get overview about the distributed parameters on parameter servers.
+    this class may centralized and convenient for developer to manage and get variable's distribute.
+    other module can also use this to find variables such io.py.
+    """
+
+    def __init__(self):
+        self.distributed_vars = []
+
+    def add_distributed_var(self,
+                            origin_var,
+                            slice_var,
+                            is_slice=None,
+                            block_id=None,
+                            offset=None,
+                            vtype=None,
+                            endpoint=None):
+        """
+        add distributed var in this.
+
+        Args:
+            origin_var(Variable|VarStruct): origin var properties
+            slice_var(Variable|VarStruct): slice var properties
+            is_slice(bool|None): slice or not, slice_var=True/False and its block size > 8192 are the judgement standard.
+            block_id(int|None): the number about the slice var.
+            offset(int|None): if the slice var is sliced, offset is the numel before the var.
+            vtype(str|None): a tag, such as Optimizer/Param/RemoteProfetch.
+            endpoint(str|None): which parameter the slice var on, such as "127.0.0.1:1001"
+        Returns:
+            None
+        """
+        self.distributed_vars.append(
+            VarDistributed(origin_var, slice_var, is_slice, block_id, offset,
+                           vtype, endpoint))
+
+    def get_distributed_var_by_slice(self, var_name):
+        """
+        get distributed var by conditions.
+
+        Args:
+            var_name(str): slice var name, such as "w.traier0.block1"
+        Returns:
+            VarDistributed: distributed var.
+        """
+        for dist_var in self.distributed_vars:
+            if dist_var.slice.name == var_name:
+                return dist_var
+        return None
+
+    @staticmethod
+    def equal(var1, var2):
+        """
+        the two var is equal or not.
+        Returns:
+            bool: equal will return True else False
+        """
+        return var1.name == var2.name and \
+               var1.type == var2.type and \
+               var1.shape == var2.shape and \
+               var1.dtype == var2.dtype and \
+               var1.lod_level == var2.lod_level and \
+               var1.persistable == var2.persistable
+
+    def get_distributed_var_by_origin_and_ep(self, origin_var_name, endpoint):
+        """
+        get distributed var by conditions.
+
+        Args:
+            origin_var_name(str):
+            endpoint(str): the parameter endpoint, such as "127.0.0.1:1001"
+        Returns:
+            VarDistributed: distributed var.
+        """
+        for dist_var in self.distributed_vars:
+            if dist_var.origin.name == origin_var_name and dist_var.endpoint == endpoint:
+                return dist_var
+        return None
+
+    def get_distributed_vars_by_vtypes(self, vtypes, groupby=False):
+        """
+        get distributed vars by conditions.
+
+        Args:
+            vtype(str|None): distributed var's vtype, such as "Optimizer", "RemotePrefetch"
+            groupby(bool|False): group by origin var or not.
+
+        Returns:
+            list: distributed var list.
+            dict: distributed var map when groupby=True
+        """
+        vtype_vars = []
+        for var in self.distributed_vars:
+            if var.vtype in vtypes:
+                vtype_vars.append(var)
+        if not groupby:
+            return vtype_vars
+
+        params_map = {}
+        for var in vtype_vars:
+            origin_var_name = var.origin.name
+
+            if origin_var_name in params_map.keys():
+                optimizers = params_map.get(origin_var_name)
+            else:
+                optimizers = []
+            optimizers.append(var)
+            params_map[origin_var_name] = optimizers
+        return params_map
+
+    def get_distributed_vars_by_ep(self, endpoint, vtype=None):
+        """
+        get distributed vars by conditions.
+
+        Args:
+            endpoint(str): the parameter server endpoint, such as "127.0.0.1:2001"
+            vtype(str|None): distributed var's vtype, such as "Optimizer", "RemotePrefetch"
+
+        Returns:
+            list: distributed var list.
+        """
+        endpoint_vars = []
+        for var in self.distributed_vars:
+            if var.endpoint == endpoint:
+                endpoint_vars.append(var)
+        if not vtype:
+            return endpoint_vars
+
+        vtype_vars = []
+        for var in endpoint_vars:
+            if var.vtype == vtype:
+                vtype_vars.append(var)
+        return vtype_vars
+
+    def overview(self):
+        """
+        get the overview string about all params on all parameter servers.
+
+        Returns:
+            Str: overview string.
+
+        """
+        vars_str = []
+        for var in self.distributed_vars:
+            vars_str.append(str(var))
+        return "\n".join(vars_str)
diff --git a/python/paddle/fluid/transpiler/distribute_transpiler.py b/python/paddle/fluid/transpiler/distribute_transpiler.py
index e58f34e3750..a3293afbbd7 100644
--- a/python/paddle/fluid/transpiler/distribute_transpiler.py
+++ b/python/paddle/fluid/transpiler/distribute_transpiler.py
@@ -30,19 +30,23 @@ Steps to transpile pserver:
 5. add listen_and_serv op
 """
 
+import sys
 import math
-import numpy as np
+from functools import reduce
+
 import collections
+import six
 import logging
 
+import numpy as np
+
 from .ps_dispatcher import RoundRobin, PSDispatcher
 from .. import core, framework, unique_name
 from ..framework import Program, default_main_program, \
-    default_startup_program, Block, \
-    Parameter, Variable, grad_var_name
-from .details import *
+    default_startup_program, Block, Parameter, grad_var_name
+from .details import wait_server_ready, UnionFind, VarStruct, VarsDistributed
+from .details import delete_ops, find_op_by_output_arg
 from ..distribute_lookup_table import find_distributed_lookup_table
-from functools import reduce
 
 LOOKUP_TABLE_TYPE = "lookup_table"
 LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
@@ -62,260 +66,6 @@ def log(*args):
         print(args)
 
 
-class VarStruct(object):
-    """
-    record part properties of a Variable in python.
-    """
-
-    def __init__(self, name, shape, dtype, type, lod_level, persistable):
-        self.name = name
-        self.shape = shape
-        self.dtype = dtype
-        self.type = type
-        self.lod_level = lod_level
-        self.persistable = persistable
-
-
-class VarDistributed(object):
-    """
-    a class to record the var distributed on parameter servers.
-    the class will record the relationship between origin var and slice var.
-    the slice var's properties, such as type/shape/offset/endpoint.
-    """
-
-    def __init__(self,
-                 origin_var,
-                 slice_var,
-                 is_slice=None,
-                 block_id=None,
-                 offset=None,
-                 vtype=None,
-                 endpoint=None):
-        """
-        Args:
-            origin_var(Variable|VarStruct): origin var properties
-            slice_var(Variable|VarStruct): slice var properties
-            is_slice(bool|None): slice or not, slice_var=True/False and its block size > 8192 are the judgement standard.
-            block_id(int|None): the number about the slice var.
-            offset(int|None): if the slice var is sliced, offset is the numel before the var.
-            vtype(str|None): a tag, such as Optimizer/Param/RemoteProfetch.
-            endpoint(str|None): which parameter the slice var on, such as "127.0.0.1:1001"
-        """
-
-        if isinstance(origin_var, Variable):
-            self.origin = self.__create_var_struct(origin_var)
-        else:
-            self.origin = origin_var
-
-        if isinstance(slice_var, Variable):
-            self.slice = self.__create_var_struct(slice_var)
-        else:
-            self.slice = slice_var
-
-        if self.equal(self.origin, self.slice):
-            self.is_slice = False
-            self.block_id = 0
-            self.offset = 0
-        else:
-            self.is_slice = True
-            self.block_id = 0
-            self.offset = 0
-
-        if is_slice is not None:
-            self.is_slice = is_slice
-        if block_id is not None:
-            self.block_id = block_id
-        if offset is not None:
-            self.offset = offset
-
-        self.vtype = vtype
-        self.endpoint = endpoint
-
-    @staticmethod
-    def __create_var_struct(var):
-        return VarStruct(var.name, var.shape, var.dtype, var.type,
-                         var.lod_level, var.persistable)
-
-    @staticmethod
-    def equal(var1, var2):
-        """
-        the two var is equal or not.
-        Returns:
-            bool: equal will return True else False
-        """
-        assert isinstance(var1, VarStruct) and isinstance(var2, VarStruct)
-
-        return var1.name == var2.name and \
-               var1.type == var2.type and \
-               var1.shape == var2.shape and \
-               var1.dtype == var2.dtype and \
-               var1.lod_level == var2.lod_level and \
-               var1.persistable == var2.persistable
-
-    def __str__(self):
-        origin_var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})". \
-            format(i="{", e="}", name=self.origin.name, type=self.origin.type,
-                   shape=self.origin.shape, dtype=self.origin.dtype)
-
-        slice_var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})" \
-                        ".slice({is_slice}).block({block_id}).offset({offset})". \
-            format(i="{", e="}", name=self.slice.name, type=self.slice.type,
-                   shape=self.slice.shape, dtype=self.slice.dtype,
-                   is_slice=self.is_slice, block_id=self.block_id, offset=self.offset)
-
-        return "var owned: {}, origin var: ( {} ), slice var: ( {} ), endpoint: {} ".format(
-            self.vtype, origin_var_str, slice_var_str, self.endpoint)
-
-
-class VarsDistributed(object):
-    """
-    a gather about VarDistributed with many methods to find distributed vars.
-    through the class, we can get overview about the distributed parameters on parameter servers.
-    this class may centralized and convenient for developer to manage and get variable's distribute.
-    other module can also use this to find variables such io.py.
-    """
-
-    def __init__(self):
-        self.distributed_vars = []
-
-    def add_distributed_var(self,
-                            origin_var,
-                            slice_var,
-                            is_slice=None,
-                            block_id=None,
-                            offset=None,
-                            vtype=None,
-                            endpoint=None):
-        """
-        add distributed var in this.
-
-        Args:
-            origin_var(Variable|VarStruct): origin var properties
-            slice_var(Variable|VarStruct): slice var properties
-            is_slice(bool|None): slice or not, slice_var=True/False and its block size > 8192 are the judgement standard.
-            block_id(int|None): the number about the slice var.
-            offset(int|None): if the slice var is sliced, offset is the numel before the var.
-            vtype(str|None): a tag, such as Optimizer/Param/RemoteProfetch.
-            endpoint(str|None): which parameter the slice var on, such as "127.0.0.1:1001"
-        Returns:
-            None
-        """
-        self.distributed_vars.append(
-            VarDistributed(origin_var, slice_var, is_slice, block_id, offset,
-                           vtype, endpoint))
-
-    def get_distributed_var_by_slice(self, var_name):
-        """
-        get distributed var by conditions.
-
-        Args:
-            var_name(str): slice var name, such as "w.traier0.block1"
-        Returns:
-            VarDistributed: distributed var.
-        """
-        for dist_var in self.distributed_vars:
-            if dist_var.slice.name == var_name:
-                return dist_var
-        return None
-
-    @staticmethod
-    def equal(var1, var2):
-        """
-        the two var is equal or not.
-        Returns:
-            bool: equal will return True else False
-        """
-        return var1.name == var2.name and \
-               var1.type == var2.type and \
-               var1.shape == var2.shape and \
-               var1.dtype == var2.dtype and \
-               var1.lod_level == var2.lod_level and \
-               var1.persistable == var2.persistable
-
-    def get_distributed_var_by_origin_and_ep(self, origin_var_name, endpoint):
-        """
-        get distributed var by conditions.
-
-        Args:
-            origin_var_name(str):
-            endpoint(str): the parameter endpoint, such as "127.0.0.1:1001"
-        Returns:
-            VarDistributed: distributed var.
-        """
-        for dist_var in self.distributed_vars:
-            if dist_var.origin.name == origin_var_name and dist_var.endpoint == endpoint:
-                return dist_var
-        return None
-
-    def get_distributed_vars_by_vtypes(self, vtypes, groupby=False):
-        """
-        get distributed vars by conditions.
-
-        Args:
-            vtype(str|None): distributed var's vtype, such as "Optimizer", "RemotePrefetch"
-            groupby(bool|False): group by origin var or not.
-
-        Returns:
-            list: distributed var list.
-            dict: distributed var map when groupby=True
-        """
-        vtype_vars = []
-        for var in self.distributed_vars:
-            if var.vtype in vtypes:
-                vtype_vars.append(var)
-        if not groupby:
-            return vtype_vars
-
-        params_map = {}
-        for var in vtype_vars:
-            origin_var_name = var.origin.name
-
-            if origin_var_name in params_map.keys():
-                optimizers = params_map.get(origin_var_name)
-            else:
-                optimizers = []
-            optimizers.append(var)
-            params_map[origin_var_name] = optimizers
-        return params_map
-
-    def get_distributed_vars_by_ep(self, endpoint, vtype=None):
-        """
-        get distributed vars by conditions.
-
-        Args:
-            endpoint(str): the parameter server endpoint, such as "127.0.0.1:2001"
-            vtype(str|None): distributed var's vtype, such as "Optimizer", "RemotePrefetch"
-
-        Returns:
-            list: distributed var list.
-        """
-        endpoint_vars = []
-        for var in self.distributed_vars:
-            if var.endpoint == endpoint:
-                endpoint_vars.append(var)
-        if not vtype:
-            return endpoint_vars
-
-        vtype_vars = []
-        for var in endpoint_vars:
-            if var.vtype == vtype:
-                vtype_vars.append(var)
-        return vtype_vars
-
-    def overview(self):
-        """
-        get the overview string about all params on all parameter servers.
-
-        Returns:
-            Str: overview string.
-
-        """
-        vars_str = []
-        for var in self.distributed_vars:
-            vars_str.append(str(var))
-        return "\n".join(vars_str)
-
-
 class VarBlock:
     def __init__(self, varname, offset, size):
         self.varname = varname
-- 
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