[Fluidclean]move fluid.transpiler to distributed.transpiler (#51025)

* remove transpiler

* Revert "remove transpiler"

This reverts commit 46044ccd52011d45d7026786d331f264a6a8f645.

* Revert "Revert "remove transpiler""

This reverts commit 80ad0945401b5b5efebac4baee0ec50a793d4405.

* codestyle

* fix setup

* fix

* fix

python/paddle/distributed/io.py

@@ -48,7 +48,7 @@ def _save_distributed_persistables(executor, dirname, main_program):
             paddle.enable_static()
             exe = paddle.static.Executor(paddle.CPUPlace())
             param_path = "./my_paddle_model"
-            t = distribute_transpiler.DistributeTranspiler()
+            t = paddle.distributed.transpiler.DistributeTranspiler()
             t.transpile(...)
             train_program = t.get_trainer_program()
             _save_distributed_persistables(executor=exe, dirname=param_path, main_program=train_program)

python/paddle/distributed/transpiler/__init__.py

@@ -11,3 +11,9 @@
 # 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 .distribute_transpiler import (
+    DistributeTranspiler,
+    DistributeTranspilerConfig,
+)
+from .memory_optimization_transpiler import memory_optimize, release_memory

python/paddle/fluid/transpiler/distribute_transpiler.py → python/paddle/distributed/transpiler/distribute_transpiler.py

@@ -28,26 +28,29 @@ Steps to transpile pserver:
 5. add listen_and_serv op
 """
 
+import collections
+import logging
+import math
 import os
 import sys
-import math
 from functools import reduce
 
-import collections
-import logging
-
 import numpy as np
 
-from .ps_dispatcher import RoundRobin, PSDispatcher
-from .. import core, framework, unique_name
-from ..framework import (
-    Program,
+from paddle import framework
+from paddle.fluid.framework import grad_var_name
+from paddle.framework import Block, Program, core
+from paddle.incubate.distributed.fleet.parameter_server.ir.ps_dispatcher import (
+    PSDispatcher,
+    RoundRobin,
+)
+from paddle.nn.initializer import Constant
+from paddle.static import (
+    Parameter,
     default_main_program,
     default_startup_program,
-    Block,
-    Parameter,
-    grad_var_name,
 )
+from paddle.utils import unique_name
 
 LOOKUP_TABLE_TYPE = ["lookup_table", "lookup_table_v2"]
 LOOKUP_TABLE_GRAD_TYPE = ["lookup_table_grad", "lookup_table_v2_grad"]
@@ -172,10 +175,10 @@ class DistributeTranspilerConfig:
     Examples:
         .. code-block:: python
 
-            from paddle.fluid.transpiler.ps_dispatcher import RoundRobin
-            import paddle.fluid as fluid
+            from paddle.distributed.transpiler.ps_dispatcher import RoundRobin
+            import paddle.distributed.transpiler as transpiler
 
-            config = fluid.DistributeTranspilerConfig()
+            config = transpiler.DistributeTranspilerConfig()
             config.slice_var_up = True
             config.split_method = RoundRobin
             config.min_block_size = 81920
@@ -281,11 +284,12 @@ class DistributeTranspiler:
 
             import paddle
             import paddle.fluid as fluid
+            import paddle.distributed.transpiler as transpiler
 
             paddle.enable_static()
 
-            x = fluid.data(name='x', shape=[1,13], dtype='float32')
-            y = fluid.data(name='y', shape=[1], dtype='float32')
+            x = paddle.static.data(name='x', shape=[1,13], dtype='float32')
+            y = paddle.static.data(name='y', shape=[1], dtype='float32')
             y_predict = paddle.static.nn.fc(x, size=1, activation=None)
 
             cost =paddle.nn.functional.square_error_cost(input=y_predict, label=y)
@@ -301,7 +305,7 @@ class DistributeTranspiler:
             trainer_id = 0
             trainers = 4
             role = "PSERVER"
-            t = fluid.DistributeTranspiler()
+            t = transpiler.DistributeTranspiler()
             t.transpile(
                  trainer_id, pservers=pserver_endpoints, trainers=trainers)
             if role == "PSERVER":
@@ -314,12 +318,12 @@ class DistributeTranspiler:
             # for nccl2 mode
             trainer_num = 2
             trainer_id = 0
-            config = fluid.DistributeTranspilerConfig()
+            config = transpiler.DistributeTranspilerConfig()
             config.mode = "nccl2"
             trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
-            t = fluid.DistributeTranspiler(config=config)
+            t = transpiler.DistributeTranspiler(config=config)
             t.transpile(trainer_id=trainer_id, trainers=trainer_endpoints, current_endpoint="192.168.0.1:6174")
-            exe = fluid.ParallelExecutor(
+            exe = paddle.static.ParallelExecutor(
                 use_cuda=True,
                 loss_name=avg_loss.name,
                 num_trainers=trainer_num,
@@ -588,9 +592,9 @@ class DistributeTranspiler:
             trainer_id (int): id for current trainer worker, if you have
                 n workers, the id may range from 0 ~ n-1
             program (Program|None): program to transpile,
-                default is fluid.default_main_program().
+                default is paddle.static.default_main_program().
             startup_program (Program|None): startup_program to transpile,
-                default is fluid.default_startup_program().
+                default is paddle.static.default_startup_program().
             pservers (str): comma separated ip:port string for the pserver
                 list.
             trainers (int|str): in pserver mode this is the number of
@@ -598,7 +602,7 @@ class DistributeTranspiler:
                 endpoints.
             sync_mode (bool): Do sync training or not, default is True.
             startup_program (Program|None): startup_program to transpile,
-                default is fluid.default_main_program().
+                default is paddle.static.default_main_program().
             current_endpoint (str): need pass current endpoint when
                 transpile as nccl2 distributed mode. In pserver mode
                 this argument is not used.
@@ -606,7 +610,7 @@ class DistributeTranspiler:
         Examples:
             .. code-block:: python
 
-                transpiler = fluid.DistributeTranspiler()
+                transpiler = paddle.distributed.transpiler.DistributeTranspiler()
                 t.transpile(
                     trainer_id=0,
                     pservers="127.0.0.1:7000,127.0.0.1:7001",
@@ -1124,12 +1128,12 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
+              import paddle.distributed.transpiler as transpiler
               #this is an example, find available endpoints in your case
               pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
               trainer_id = 0
               trainers = 4
-              t = fluid.DistributeTranspiler()
+              t = transpiler.DistributeTranspiler()
               t.transpile(trainer_id, trainers=trainers, pservers=pserver_endpoints)
               trainer_program = t.get_trainer_program()
         """
@@ -1270,13 +1274,13 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
+              import paddle.distributed.transpiler as transpiler
               #this is an example, find available endpoints in your case
               pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
               current_endpoint = "192.168.0.1:6174"
               trainer_id = 0
               trainers = 4
-              t = fluid.DistributeTranspiler()
+              t = transpiler.DistributeTranspiler()
               t.transpile(
                    trainer_id, pservers=pserver_endpoints, trainers=trainers)
               pserver_program = t.get_pserver_program(current_endpoint)
@@ -1349,8 +1353,8 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
                 opt_op_on_pserver.append(op)
         # step 3.3
         # prepare if dc asgd is enabled
-        if self.config.enable_dc_asgd == True:
-            assert self.sync_mode == False
+        if self.config.enable_dc_asgd is True:
+            assert self.sync_mode is False
             self.param_bak_list = []
             # add param_bak for each trainer
             for p in self.param_grad_ep_mapping[endpoint]["params"]:
@@ -1579,13 +1583,13 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
         Examples:
             .. code-block:: python
 
-              import paddle.fluid as fluid
+              import paddle.distributed.transpiler as transpiler
               #this is an example, find available endpoints in your case
               pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
               current_endpoint = "192.168.0.1:6174"
               trainer_id = 0
               trainers = 4
-              t = fluid.DistributeTranspiler()
+              t = transpiler.DistributeTranspiler()
               t.transpile(
                    trainer_id, pservers=pserver_endpoints, trainers=trainers)
               pserver_program, pserver_startup_program = t.get_pserver_programs(current_endpoint)
@@ -1624,7 +1628,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
                 trainer_id = 0
                 trainers = 4
 
-                t = fluid.DistributeTranspiler()
+                t = paddle.distributed.transpiler.DistributeTranspiler()
                 t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
                 pserver_program = t.get_pserver_program(current_endpoint)
                 pserver_startup_program = t.get_startup_program(current_endpoint,
@@ -1851,7 +1855,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
         param_grad_set = set()
         for p, g in self.params_grads:
             # skip parameter marked not trainable
-            if type(p) == Parameter and p.trainable == False:
+            if type(p) == Parameter and p.trainable is False:
                 continue
             if p.name not in param_grad_set:
                 param_list.append(p)
@@ -2834,7 +2838,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
             if role_id == int(LR_SCHED_OP_ROLE_ATTR_VALUE) or role_id == int(
                 LR_SCHED_OP_ROLE_ATTR_VALUE
             ) | int(OPT_OP_ROLE_ATTR_VALUE):
-                if self.sync_mode == False and op.type == 'increment':
+                if self.sync_mode is False and op.type == 'increment':
                     inputs = self._get_input_map_from_op(
                         self.origin_program.global_block().vars, op
                     )
@@ -2879,7 +2883,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
                             dtype=var.dtype,
                             shape=var.shape,
                             persistable=var.persistable,
-                            initializer=paddle.nn.initializer.Constant(1),
+                            initializer=Constant(1),
                         )
                     op_role_attr_name = (
                         core.op_proto_and_checker_maker.kOpRoleAttrName()
@@ -2978,7 +2982,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
                 if op.attr(OP_ROLE_VAR_ATTR_NAME):
                     param_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[0]
                     grad_name = op.attr(OP_ROLE_VAR_ATTR_NAME)[1]
-                    if not param_name in optimize_params:
+                    if param_name not in optimize_params:
                         optimize_params.add(param_name)
                         log("adding param_grad pair: ", param_name, grad_name)
                         params_grads.append(

python/paddle/fluid/transpiler/geo_sgd_transpiler.py → python/paddle/distributed/transpiler/geo_sgd_transpiler.py

@@ -24,36 +24,34 @@ Steps to transpile pserver:
 4. append sum ops that should run on current server instance.
 5. add listen_and_serv op
 """
-import sys
 import collections
-import numpy as np
 
-from .ps_dispatcher import RoundRobin, PSDispatcher
-from .. import core, framework
-from ..framework import (
-    Program,
-    default_main_program,
-    default_startup_program,
-    Block,
-    Parameter,
+from paddle import framework
+from paddle.distributed.distribute_lookup_table import (
+    find_distributed_lookup_table,
 )
 from paddle.distributed.transpiler.details import (
-    wait_server_ready,
     VarsDistributed,
+    wait_server_ready,
 )
-from paddle.distributed.transpiler.details import delete_ops
-from .distribute_transpiler import (
-    DistributeTranspiler,
-    DistributeTranspilerConfig,
-    slice_variable,
-    same_or_split_var,
-    ServerRuntimeConfig,
+from paddle.framework import Program, core
+from paddle.incubate.distributed.fleet.parameter_server.ir.ps_dispatcher import (
+    PSDispatcher,
+    RoundRobin,
 )
 from paddle.incubate.distributed.fleet.parameter_server.mode import (
     DistributedMode,
 )
-from paddle.distributed.distribute_lookup_table import (
-    find_distributed_lookup_table,
+from paddle.static import (
+    Parameter,
+    default_main_program,
+    default_startup_program,
+)
+
+from .distribute_transpiler import (
+    DistributeTranspiler,
+    DistributeTranspilerConfig,
+    slice_variable,
 )
 
 RPC_OP_ROLE_ATTR_NAME = (
@@ -311,7 +309,7 @@ class GeoSgdTranspiler(DistributeTranspiler):
         param_grad_set = set()
         # step 1. create param_list
         for p, g in self.params_grads:
-            if type(p) == Parameter and p.trainable == False:
+            if type(p) == Parameter and p.trainable is False:
                 continue
             if p.name not in param_grad_set:
                 param_list.append(p)

python/paddle/fluid/transpiler/memory_optimization_transpiler.py → python/paddle/distributed/transpiler/memory_optimization_transpiler.py

@@ -25,7 +25,7 @@ def memory_optimize(
     memory optimization strategies are enabled by default.
     """
     logging.warn(
-        'Caution! paddle.fluid.memory_optimize() is deprecated '
+        'Caution! paddle.distributed.transpiler.memory_optimize() is deprecated '
         'and not maintained any more, since it is not stable!\n'
         'This API would not take any memory optimizations on your Program '
         'now, since we have provided default strategies for you.\n'
@@ -48,6 +48,6 @@ def release_memory(input_program, skip_opt_set=None):
     memory optimization strategies are enabled by default.
     """
     logging.warn(
-        'paddle.fluid.release_memory() is deprecated, it would not'
+        'paddle.distributed.transpiler.release_memory() is deprecated, it would not'
         ' take any memory release on your program'
     )

python/paddle/fluid/__init__.py

@@ -64,7 +64,6 @@ from .backward import gradients
 from . import regularizer
 from . import average
 from . import metrics
-from . import transpiler
 from . import incubate
 from .param_attr import ParamAttr, WeightNormParamAttr
 from .data_feeder import DataFeeder
@@ -80,12 +79,6 @@ from .core import (
     MLUPlace,
     CustomPlace,
 )
-from .transpiler import (
-    DistributeTranspiler,
-    memory_optimize,
-    release_memory,
-    DistributeTranspilerConfig,
-)
 from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
 from . import profiler
 from . import unique_name
@@ -107,7 +100,6 @@ from .trainer_desc import (
     MultiTrainer,
     HeterXpuTrainer,
 )
-from .transpiler import HashName, RoundRobin
 from .backward import append_backward
 
 Tensor = LoDTensor
@@ -118,7 +110,6 @@ __all__ = (
     framework.__all__
     + executor.__all__
     + trainer_desc.__all__
-    + transpiler.__all__
     + parallel_executor.__all__
     + lod_tensor.__all__
     + data_feed_desc.__all__
@@ -135,7 +126,6 @@ __all__ = (
         'disable_dygraph',
         'enable_imperative',
         'disable_imperative',
-        'transpiler',
         'nets',
         'optimizer',
         'backward',

python/paddle/fluid/contrib/tests/test_image_classification_fp16.py

@@ -235,7 +235,7 @@ def train(net_type, use_cuda, save_dirname, is_local):
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
-        t = fluid.DistributeTranspiler()
+        t = paddle.distributed.transpiler.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)

python/paddle/fluid/io.py

@@ -563,7 +563,7 @@ def _save_distributed_persistables(executor, dirname, main_program):
             paddle.enable_static()
             exe = fluid.Executor(fluid.CPUPlace())
             param_path = "./my_paddle_model"
-            t = distribute_transpiler.DistributeTranspiler()
+            t = paddle.distributed.transpiler.DistributeTranspiler()
             t.transpile(...)
             train_program = t.get_trainer_program()
             _save_distributed_persistables(executor=exe, dirname=param_path, main_program=train_program)
@@ -1179,7 +1179,7 @@ def _load_distributed_persistables(executor, dirname, main_program=None):
             paddle.enable_static()
             exe = fluid.Executor(fluid.CPUPlace())
             param_path = "./my_paddle_model"
-            t = distribute_transpiler.DistributeTranspiler()
+            t = paddle.distributed.transpiler.DistributeTranspiler()
             t.transpile(...)
             pserver_prog = t.get_pserver_program(...)
             _load_distributed_persistables(executor=exe, dirname=param_path, main_program=pserver_prog)

python/paddle/fluid/tests/book/notest_understand_sentiment.py

@@ -129,7 +129,7 @@ def train(
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
-        t = fluid.DistributeTranspiler()
+        t = paddle.distributed.transpiler.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)

python/paddle/fluid/tests/book/test_fit_a_line.py

@@ -144,7 +144,7 @@ def train(use_cuda, save_dirname, is_local, use_bf16, pure_bf16):
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
-        t = fluid.DistributeTranspiler()
+        t = paddle.distributed.transpiler.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)

python/paddle/fluid/tests/book/test_image_classification.py

@@ -202,7 +202,7 @@ def train(net_type, use_cuda, save_dirname, is_local):
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
-        t = fluid.DistributeTranspiler()
+        t = paddle.distributed.transpiler.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -178,7 +178,7 @@ def train(
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
-        t = fluid.DistributeTranspiler()
+        t = paddle.distributed.transpiler.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)

python/paddle/fluid/tests/book/test_recommender_system.py

@@ -279,7 +279,7 @@ def train(use_cuda, save_dirname, is_local=True):
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
-        t = fluid.DistributeTranspiler()
+        t = paddle.distributed.transpiler.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)

python/paddle/fluid/tests/book/test_word2vec_book.py

@@ -184,7 +184,7 @@ def train(
         current_endpoint = os.getenv("POD_IP") + ":" + port
         trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
         training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
-        t = fluid.DistributeTranspiler()
+        t = paddle.distributed.transpiler.DistributeTranspiler()
         t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)

python/paddle/fluid/tests/unittests/collective/fleet/test_distributed_strategy.py

@@ -18,7 +18,7 @@ import unittest
 import paddle
 import paddle.fluid as fluid
 import paddle.incubate.distributed.fleet.role_maker as role_maker
-from paddle.fluid.transpiler.distribute_transpiler import (
+from paddle.distributed.transpiler.distribute_transpiler import (
     DistributeTranspilerConfig,
     ServerRuntimeConfig,
 )

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

@@ -25,10 +25,10 @@ class TestTranspile(unittest.TestCase):
     """TestCases for BoxPS Preload"""
 
     def get_transpile(self, mode, trainers="127.0.0.1:6174"):
-        config = fluid.DistributeTranspilerConfig()
+        config = paddle.distributed.transpiler.DistributeTranspilerConfig()
         config.mode = 'collective'
         config.collective_mode = mode
-        t = fluid.DistributeTranspiler(config=config)
+        t = paddle.distributed.transpiler.DistributeTranspiler(config=config)
         return t
 
     def test_transpile(self):

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

@@ -16,12 +16,13 @@ import unittest
 
 from simple_nets import simple_fc_net
 
+import paddle.distributed.transpiler as transpiler
 import paddle.fluid as fluid
 
 
 class DeprecatedMemoryOptimizationInterfaceTest(unittest.TestCase):
     def setUp(self):
-        self.method = fluid.memory_optimize
+        self.method = transpiler.memory_optimize
 
     def build_network(self, call_interface):
         startup_prog = fluid.Program()
@@ -63,7 +64,7 @@ class DeprecatedMemoryOptimizationInterfaceTest(unittest.TestCase):
 
 class ReleaseMemoryTest(DeprecatedMemoryOptimizationInterfaceTest):
     def setUp(self):
-        self.method = fluid.release_memory
+        self.method = transpiler.release_memory
 
 
 if __name__ == '__main__':

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

@@ -80,7 +80,7 @@ class TestDistRunnerBase:
         hogwild_mode=False,
     ):
         # NOTE: import fluid until runtime, or else forking processes will cause error.
-        config = fluid.DistributeTranspilerConfig()
+        config = paddle.distributed.transpiler.DistributeTranspilerConfig()
         config.enable_dc_asgd = dc_asgd
         config.sync_mode = sync_mode
         config.runtime_split_send_recv = hogwild_mode
@@ -88,7 +88,7 @@ class TestDistRunnerBase:
         if nccl_comm_num > 1:
             config.nccl_comm_num = nccl_comm_num
         # config.runtime_split_send_recv = True
-        t = fluid.DistributeTranspiler(config=config)
+        t = paddle.distributed.transpiler.DistributeTranspiler(config=config)
         t.transpile(
             trainer_id=trainer_id,
             program=main_program,
@@ -454,7 +454,7 @@ class TestDistRunnerBase:
             or args.update_method == "nccl2_reduce_layer"
         ):
             # transpile for nccl2
-            config = fluid.DistributeTranspilerConfig()
+            config = paddle.distributed.transpiler.DistributeTranspilerConfig()
             config.mode = "nccl2"
             config.nccl_comm_num = args.nccl_comm_num
             if args.use_hallreduce:
@@ -466,7 +466,9 @@ class TestDistRunnerBase:
                 type(self).__name__,
                 "begin to run transpile on trainer with nccl2 mode",
             )
-            nccl2_t = fluid.DistributeTranspiler(config=config)
+            nccl2_t = paddle.distributed.transpiler.DistributeTranspiler(
+                config=config
+            )
             nccl2_t.transpile(
                 args.trainer_id,
                 program=fluid.default_main_program(),

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

@@ -81,7 +81,11 @@ class TranspilerTest(unittest.TestCase):
     def _transpiler_instance(self, config=None, sync_mode=True):
         if not self.transpiler:
             main = self.get_main_program()
-            self.transpiler = fluid.DistributeTranspiler(config=config)
+            self.transpiler = (
+                paddle.distributed.transpiler.DistributeTranspiler(
+                    config=config
+                )
+            )
             self.transpiler.transpile(
                 self.trainer_id,
                 program=main,
@@ -202,7 +206,7 @@ class TestBasicModel(TranspilerTest):
 
 class TestBasicModelWithLargeBlockSize(TranspilerTest):
     def transpiler_test_impl(self):
-        config = fluid.DistributeTranspilerConfig()
+        config = paddle.distributed.transpiler.DistributeTranspilerConfig()
         config.min_block_size = 1048576
 
         pserver, startup = self.get_pserver(self.pserver1_ep, config)
@@ -276,7 +280,7 @@ class TestNoSliceVar(TranspilerTest):
         super().setUp()
 
     def transpiler_test_impl(self):
-        config = fluid.DistributeTranspilerConfig()
+        config = paddle.distributed.transpiler.DistributeTranspilerConfig()
         config.slice_var_up = False
 
         _, startup = self.get_pserver(self.pserver1_ep, config)
@@ -692,7 +696,7 @@ class TestEmptyPserverOptimizeBlocks(TranspilerTest):
         sgd_optimizer.minimize(avg_cost)
 
     def transpiler_test_impl(self):
-        config = fluid.DistributeTranspilerConfig()
+        config = paddle.distributed.transpiler.DistributeTranspilerConfig()
         config.slice_var_up = False
 
         pserver, startup = self.get_pserver(ep=self.pserver2_ep, config=config)
@@ -924,7 +928,7 @@ class TestAsyncLocalLookupTable(TestDistLookupTableBase):
         self.network_with_table(is_sparse=True, is_distributed=False)
 
     def transpiler_test_impl(self):
-        config = fluid.DistributeTranspilerConfig()
+        config = paddle.distributed.transpiler.DistributeTranspilerConfig()
         pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
 
         self.assertEqual(len(pserver1.blocks), 4)
@@ -991,7 +995,7 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
         self.network_with_table(is_sparse=True, is_distributed=True)
 
     def transpiler_test_impl(self):
-        config = fluid.DistributeTranspilerConfig()
+        config = paddle.distributed.transpiler.DistributeTranspilerConfig()
 
         pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
 
@@ -1089,7 +1093,7 @@ class TestDistLookupTableSliceSize(TestDistLookupTableBase):
         self.network_with_table(is_sparse=True, is_distributed=True)
 
     def transpiler_test_impl(self):
-        config = fluid.DistributeTranspilerConfig()
+        config = paddle.distributed.transpiler.DistributeTranspilerConfig()
         pserver1, _ = self.get_pserver(self.pserver1_ep, config)
 
         self.assertTrue(self.transpiler.has_distributed_lookup_table)
@@ -1220,10 +1224,12 @@ class TestNCCL2Transpile(TranspilerTest):
             with fluid.program_guard(main, startup):
                 self.net_conf()
 
-            config = fluid.DistributeTranspilerConfig()
+            config = paddle.distributed.transpiler.DistributeTranspilerConfig()
             config.mode = "nccl2"
             config.wait_port = False
-            t = fluid.DistributeTranspiler(config=config)
+            t = paddle.distributed.transpiler.DistributeTranspiler(
+                config=config
+            )
             t.transpile(
                 0,
                 trainers="127.0.0.1:6174,127.0.0.1:6175",

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

@@ -19,7 +19,7 @@ from dist_fleet_simnet_bow import train_network
 import paddle
 import paddle.fluid as fluid
 import paddle.incubate.distributed.fleet.role_maker as role_maker
-from paddle.fluid.transpiler.distribute_transpiler import (
+from paddle.distributed.transpiler.distribute_transpiler import (
     DistributeTranspilerConfig,
 )
 from paddle.incubate.distributed.fleet.collective import CollectiveOptimizer

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

@@ -49,9 +49,9 @@ def run_pserver(use_cuda, sync_mode, ip, port, trainers, trainer_id):
     pserver_endpoints = ip + ":" + port
     current_endpoint = ip + ":" + port
 
-    config = fluid.DistributeTranspilerConfig()
+    config = paddle.distributed.transpiler.DistributeTranspilerConfig()
     config.sync_mode = sync_mode
-    t = fluid.DistributeTranspiler(config=config)
+    t = paddle.distributed.transpiler.DistributeTranspiler(config=config)
     t.transpile(
         trainer_id,
         pservers=pserver_endpoints,
@@ -87,11 +87,11 @@ def run_pserver_with_empty_block(
     ps2 = ip + ":" + port
     pserver_endpoints = ps1 + "," + ps2
 
-    config = fluid.DistributeTranspilerConfig()
+    config = paddle.distributed.transpiler.DistributeTranspilerConfig()
     config.sync_mode = sync_mode
     config.slice_var_up = False
 
-    t = fluid.DistributeTranspiler(config=config)
+    t = paddle.distributed.transpiler.DistributeTranspiler(config=config)
     t.transpile(
         trainer_id,
         pservers=pserver_endpoints,

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

@@ -16,7 +16,7 @@ import random
 import unittest
 
 import paddle.fluid as fluid
-from paddle.fluid.transpiler.distribute_transpiler import slice_variable
+from paddle.distributed.transpiler.distribute_transpiler import slice_variable
 
 
 class TestSliceVar(unittest.TestCase):

python/paddle/fluid/transpiler/__init__.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .distribute_transpiler import (
-    DistributeTranspiler,
-    DistributeTranspilerConfig,
-)
-from .memory_optimization_transpiler import memory_optimize, release_memory
-from .ps_dispatcher import HashName, RoundRobin
-
-__all__ = [
-    "DistributeTranspiler",
-    "memory_optimize",
-    "release_memory",
-    "HashName",
-    "RoundRobin",
-    "DistributeTranspilerConfig",
-]

python/paddle/fluid/transpiler/ps_dispatcher.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-class PSDispatcher:
-    """
-    PSDispatcher is the base class for dispatching vars
-    into different pserver instance.
-    You need to implement the `dispatch` interface.
-    """
-
-    def __init__(self, pserver_endpoints):
-        self._eps = pserver_endpoints
-        self._step = 0
-
-    @property
-    def eps(self):
-        return self._eps
-
-    def reset(self):
-        """
-        reset the step counter, set it zero.
-        """
-        self._step = 0
-
-    def dispatch(self, varlist):
-        """
-        Args:
-            varlist(list): a list of Variables
-        Returns:
-            a map of pserver endpoint -> varname
-        """
-        AssertionError("Interface has not been implemented.")
-
-
-class HashName(PSDispatcher):
-    """
-        :api_attr: Static Graph
-
-    Hash variable names to several endpoints using python
-    "hash()" function.
-
-    Args:
-        pserver_endpoints (list): list of endpoint(ip:port).
-
-    Examples:
-        .. code-block:: python
-
-        pserver_endpoints = ["127.0.0.1:6007", "127.0.0.1:6008"]
-        vars = ["var1","var2","var3","var4","var5"]
-
-        rr = RoundRobin(pserver_endpoints)
-        rr.dispatch(vars)
-
-    """
-
-    def __init__(self, pserver_endpoints):
-        super().__init__(pserver_endpoints)
-
-    def _hash_block(self, block_str, total):
-        return hash(block_str) % total
-
-    def dispatch(self, varlist):
-        """
-        use `HashName` method to dispatch variables with each parameter server.
-        Args:
-            varlist (list): a list of Variables
-
-        """
-        eplist = []
-        for var in varlist:
-            server_id = self._hash_block(var.name(), len(self._eps))
-            server_for_param = self._eps[server_id]
-            eplist.append(server_for_param)
-        return eplist
-
-
-class RoundRobin(PSDispatcher):
-    """
-        :api_attr: Static Graph
-
-    Distribute variables to several endpoints using
-    RondRobin<https://en.wikipedia.org/wiki/Round-robin_scheduling> method.
-
-    Args:
-        pserver_endpoints (list): list of endpoint(ip:port).
-
-    Examples:
-        .. code-block:: python
-
-        pserver_endpoints = ["127.0.0.1:6007", "127.0.0.1:6008"]
-        vars = ["var1","var2","var3","var4","var5"]
-
-        rr = RoundRobin(pserver_endpoints)
-        rr.dispatch(vars)
-
-    """
-
-    def __init__(self, pserver_endpoints):
-        super().__init__(pserver_endpoints)
-
-    def dispatch(self, varlist):
-        """
-        use `RoundRobin` method to dispatch variables with each parameter server.
-        Args:
-            varlist (list): a list of Variables
-
-        """
-        eplist = []
-        for var in varlist:
-            server_for_param = self._eps[self._step]
-            eplist.append(server_for_param)
-            self._step += 1
-            if self._step >= len(self._eps):
-                self._step = 0
-        return eplist

python/paddle/incubate/distributed/fleet/collective.py

@@ -15,9 +15,9 @@ import logging
 import os
 
 import paddle
+import paddle.distributed.transpiler.distribute_transpiler as dist_transpiler
 import paddle.fluid as fluid
 import paddle.fluid.io as io
-import paddle.fluid.transpiler.distribute_transpiler as dist_transpiler
 from paddle.fluid.compiler import CompiledProgram
 from paddle.fluid.executor import Executor
 from paddle.fluid.framework import Program

python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/__init__.py

@@ -31,7 +31,7 @@ from paddle.fluid.compiler import CompiledProgram
 from paddle.fluid.parallel_executor import ParallelExecutor
 from paddle.fluid.optimizer import Optimizer
 
-from paddle.fluid.transpiler.distribute_transpiler import (
+from paddle.distributed.transpiler.distribute_transpiler import (
     DistributeTranspilerConfig,
 )
 
@@ -78,7 +78,7 @@ from paddle.incubate.distributed.fleet.parameter_server.ir import (
 
 class FleetTranspiler(Fleet):
     """
-    A subclass for compatibility with fluid.transpiler.DistributeTranspiler.
+    A subclass for compatibility with distributed.transpiler.DistributeTranspiler.
     """
 
     def __init__(self):

python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/distributed_strategy.py

@@ -25,7 +25,7 @@ __all__ = [
 import os
 
 import paddle.fluid as fluid
-from paddle.fluid.transpiler.distribute_transpiler import (
+from paddle.distributed.transpiler.distribute_transpiler import (
     DistributeTranspilerConfig,
     ServerRuntimeConfig,
 )

python/setup.py.in

@@ -400,7 +400,6 @@ packages=['paddle',
           'paddle.fluid.contrib',
           'paddle.fluid.contrib.extend_optimizer',
           'paddle.fluid.contrib.layers',
-          'paddle.fluid.transpiler',
           'paddle.fluid.incubate',
           'paddle.incubate.distributed.fleet',
           'paddle.fluid.incubate.checkpoint',

setup.py

@@ -1300,7 +1300,6 @@ def get_setup_parameters():
         'paddle.fluid.contrib',
         'paddle.fluid.contrib.extend_optimizer',
         'paddle.fluid.contrib.layers',
-        'paddle.fluid.transpiler',
         'paddle.fluid.incubate',
         'paddle.incubate.distributed.fleet',
         'paddle.fluid.incubate.checkpoint',