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未验证 提交 e9c3da9e 编写于 作者: K kangguangli 提交者： GitHub
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[Executor] remove api `paddle.static.ParallelExecutor` (#51701) 

* remove api `class ParallelExecutor`

* remove other references
上级 cdba7e36
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Showing with 1 addition and 438 deletion
python/paddle/distributed/fleet/runtime/parameter_server_runtime.py
浏览文件 @ e9c3da9e
......@@ -20,7 +20,6 @@ from paddle.framework import core
from paddle.static import (
CompiledProgram,
Executor,
ParallelExecutor,
Program,
Variable,
default_main_program,
......@@ -697,11 +696,6 @@ class ParameterServerRuntime(RuntimeBase):
single file, use `filename` to specify the file name.
"""
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save_persistables() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save_persistables() function, executor must be as Executor type"
......@@ -733,11 +727,6 @@ class ParameterServerRuntime(RuntimeBase):
and then save it and all related parameters to given `dirname` by the `executor`.
"""
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save_inference_model() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save_inference_model() function, executor must be as Executor type"
......
python/paddle/distributed/fleet/runtime/the_one_ps.py
浏览文件 @ e9c3da9e
......@@ -20,7 +20,6 @@ from paddle.fluid import core
from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program
from paddle.fluid.parallel_executor import ParallelExecutor
from ..base.private_helper_function import wait_server_ready
from .runtime_base import RuntimeBase
......@@ -1362,11 +1361,6 @@ class TheOnePSRuntime(RuntimeBase):
single file, use `filename` to specify the file name.
"""
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save() function, executor must be as Executor type"
......@@ -1400,11 +1394,6 @@ class TheOnePSRuntime(RuntimeBase):
and then save it and all related parameters to given `dirname` by the `executor`.
"""
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save() function, executor must be as Executor type"
......
python/paddle/distributed/ps/the_one_ps.py
浏览文件 @ e9c3da9e
......@@ -28,7 +28,7 @@ from paddle.distributed.fleet.runtime.runtime_base import RuntimeBase
from paddle.distributed.ps.coordinator import Coordinator
from paddle.distributed.ps.utils.public import * # noqa: F403
from paddle.framework import core
from paddle.static import CompiledProgram, Executor, ParallelExecutor, Program
from paddle.static import CompiledProgram, Executor, Program
__all__ = [
'Table',
......@@ -1491,11 +1491,6 @@ class TheOnePSRuntime(RuntimeBase):
single file, use `filename` to specify the file name.
"""
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save() function, executor must be as Executor type"
......@@ -1526,11 +1521,6 @@ class TheOnePSRuntime(RuntimeBase):
and then save it and all related parameters to given `dirname` by the `executor`.
"""
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save() function, executor must be as Executor type"
......
python/paddle/fluid/__init__.py
浏览文件 @ e9c3da9e
......@@ -79,8 +79,6 @@ from .core import (
from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
from . import profiler
from . import unique_name
from . import parallel_executor
from .parallel_executor import *
from . import compiler
from .compiler import *
from paddle.fluid.layers.math_op_patch import monkey_patch_variable
......@@ -105,7 +103,6 @@ __all__ = (
framework.__all__
+ executor.__all__
+ trainer_desc.__all__
+ parallel_executor.__all__
+ lod_tensor.__all__
+ data_feed_desc.__all__
+ compiler.__all__
......
python/paddle/fluid/parallel_executor.py 已删除 100644 → 0
浏览文件 @ cdba7e36
# 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 . import core
from . import framework
from . import executor
from . import compiler
from .data_feeder import check_type
import sys
__all__ = ['ParallelExecutor']
ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy
BuildStrategy = core.ParallelExecutor.BuildStrategy
class ParallelExecutor:
"""
:api_attr: Static Graph
The ParallelExecutor is an upgraded version of :code:`paddle.static.Executor` that supports multi-node model
training and testing based on the data-parallel mode. In data-parallel mode,
ParallelExecutor will broadcast the parameters from Node0 to other nodes during
construction and copy the input Program to other nodes from Node0 to make sure
that the initial state on each node is the same. Each node runs the model independently
and the parameters' gradient is aggregated between those nodes during backward
computation, and then each node independently updates its parameters. If you use
the GPU to run the model, i.e. use_cuda=True, the node refers to the GPU,
ParallelExecutor will automatically get the GPU resources available on the
current machine, users can also set the available GPU resources in the environment
variable, for example: want to use GPU0, GPU1, export CUDA_VISIBLEDEVICES=0,1;
If the operation is performed on the CPU, i.e. use_cuda=False, the node refers to the CPU.
**Note: At this time, the user needs to manually add CPU_NUM to the environment variable
and set the number of CPU devices. For example, export CPU_NUM=4, if the environment
variable is not set, the executor will add the variable to the environment variable
and set it to 1.**
Args:
use_cuda (bool): Whether to use CUDA or not.
loss_name (str): This parameter is the name of the loss Tensor of the
model. **Note: If it is data-parallel model training, you must set loss_name,
otherwise, the results may be wrong**. The default is None.
main_program (Program): This parameter represents the Program to be executed.
If this parameter is not provided, that parameter is None, the program will
be set to :code:`paddle.static.default_main_program()`. The default is None.
share_vars_from(ParallelExecutor): If share_vars_from is set, the current
ParallelExecutor will share the parameters with the ParallelExecutor
specified by share_vars_from. This parameter needs to be set when model testing
is required during model training, and the data parallel mode is used for
training and testing. Since ParallelExecutor will only distribute parameter
variables to other devices when it is first executed, the ParallelExecutor
specified by share_vars_from must be run before the current ParallelExecutor.
The default is None.
exec_strategy(ExecutionStrategy): exec_strategy specifies the options that can
be changed when running the current model, such as the thread pool size.
For more information about exec_strategy, please refer to :code:`paddle.static.ExecutionStrategy`.
The default is None.
build_strategy(BuildStrategy): By configuring build_strategy, we can
optimize the computational graph, such as operators' fusion in the
computational graph and memory optimization during the execution
of the computational graph. For more information about build_strategy,
please refer to :code:`paddle.static.BuildStrategy`. The default is None.
num_trainers(int): This parameter needs to be set in GPU distributed training.
If the parameter value is greater than 1, NCCL will be initialized by multi-level
nodes. Each node should have the same number of GPUs. The default is 1.
trainer_id(int): This parameter needs to be set when performing GPU distributed
training. This parameter must be used with the num_trainers parameter.
Trainer_id indicates the "rank" of the current node. The trainer_id starts
counting from 0. The default is 0.
scope(Scope): Specifies the scope in which the program is executed.
The default is paddle.static.global_scope().
Returns:
ParallelExecutor: The initialized ParallelExecutor object.
Raises:
TypeError: If share_vars_from is provided, but not ParallelExecutor object.
NOTES:
1. If you only use ParallelExecutor to do multi-card test, you don't need to set loss_name
and share_vars_from.
2. If you need to train and test the model with ParallelExecutor, the share_vars_from
must be set when building the ParallelExecutor corresponding to the model test.
Otherwise, the parameters used in the model test and the model training are inconsistent.
Examples:
.. code-block:: python
import paddle
import numpy
import os
use_cuda = True
paddle.enable_static()
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
# NOTE: If you use CPU to run the program, you need
# to specify the CPU_NUM, otherwise, PaddlePaddle will use
# all the number of the logic core as the CPU_NUM,
# in that case, the batch size of the input should be
# greater than CPU_NUM, if not, the process will be
# failed by an exception.
if not use_cuda:
os.environ['CPU_NUM'] = str(2)
exe = paddle.static.Executor(place)
train_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(train_program, startup_program):
data = paddle.static.data(name='X', shape=[None, 1], dtype='float32')
hidden = paddle.static.nn.fc(data, 10)
loss = paddle.mean(hidden)
test_program = paddle.static.default_main_program().clone(for_test=True)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
exe.run(startup_program)
train_exe = paddle.static.ParallelExecutor(use_cuda=use_cuda,
main_program=train_program,
loss_name=loss.name)
# Note: if share_vars_from is not set here, the test parameter is different to the train one
test_exe = paddle.static.ParallelExecutor(use_cuda=use_cuda,
main_program=test_program,
share_vars_from=train_exe)
x = numpy.random.random(size=(10, 1)).astype('float32')
loss_data, = train_exe.run(feed={"X": x},
fetch_list=[loss.name])
loss_data, = test_exe.run(feed={"X": x},
fetch_list=[loss.name])
"""
def __init__(
self,
use_cuda,
loss_name=None,
main_program=None,
share_vars_from=None,
exec_strategy=None,
build_strategy=None,
num_trainers=1,
trainer_id=0,
scope=None,
):
if build_strategy is None:
build_strategy = BuildStrategy()
# TODO(paddle-dev): trainer_id and num_trainers should be removed from parameter list.
if num_trainers != 1 and build_strategy.num_trainers != num_trainers:
sys.stderr.write(
'The value of build_strategy.num_trainers[%d] is overwritten '
'by the passed num_trainers[%d].\n'
% (build_strategy.num_trainers, num_trainers)
)
build_strategy.num_trainers = num_trainers
if trainer_id != 0 and build_strategy.trainer_id != trainer_id:
sys.stderr.write(
'The value of build_strategy.trainer_id[%d] is overwritten '
'by the passed trainer_id[%d].\n'
% (build_strategy.trainer_id, trainer_id)
)
build_strategy.trainer_id = trainer_id
self._places = (
framework.cuda_places() if use_cuda else framework.cpu_places()
)
self._scope = scope if scope is not None else executor.global_scope()
main_program = (
main_program
if main_program is not None
else framework.default_main_program()
)
self._compiled_program = compiler.CompiledProgram(
main_program, build_strategy=build_strategy
)
if share_vars_from:
assert isinstance(
share_vars_from, ParallelExecutor
), "The share_vars_from should be ParallelExecutor."
self._place = core.CUDAPlace(0) if use_cuda else core.CPUPlace()
self._exe = executor.Executor(self._place)
def run(self, fetch_list, feed=None, feed_dict=None, return_numpy=True):
"""
This interface is used to run the current model. It should be noted
that the executor will execute all the operators in the Program,
and will not prune some operators in the Program according to the
fetch_list.
Args:
fetch_list(list): This parameter represents the Tensors that need to be returned
after the model runs. The default is None.
feed(list|dict): This parameter represents the input Tensors of the model.
If it is single card training, the feed is dict type, and if it is multi-card
training, the parameter feed can be dict or list of Tensor. If the
parameter type is dict, the data in the feed will be split and sent to
multiple devices (CPU/GPU), that is to say, the input data will be evenly
sent to different devices, so you should make sure the number of samples of
the current mini-batch must be greater than the number of places;
if the parameter type is list, those data are copied directly to each device,
so the length of this list should be equal to the number of places.
The default is None.
feed_dict: Alias for feed parameter, for backward compatibility.
This parameter has been deprecated. Default None.
return_numpy(bool): This parameter indicates whether convert the fetched Tensors
(the Tensor specified in the fetch list) to numpy.ndarray. if it is False,
the type of the return value is a list of :code:`LoDTensor`. The default is True.
Returns:
List: The fetched result list.
Raises:
ValueError: If the feed is a list, but its length is not equal the
length of active places, or its element's is not dict.
NOTES:
1. If the feed parameter is dict type, the input data will be evenly distributed
to different cards. For example, using two GPUs to run the model, the input
sample number is 3, that is, [0, 1, 2], the sample number on GPU0 is 1,
that is, [0], and the sample number on GPU1 is 2, that is, [1, 2].
If the number of samples is less than the number of devices, the program will
throw an exception, so when running the model, you should make sure that the
number of samples of the last batch of the data set should be greater than the
number of CPU cores or GPU cards, if it is less than, it is recommended that
the batch be discarded.
2. If the number of CPU cores or GPU cards available is greater than 1, the fetch
results are spliced together in dimension 0 for the same Tensor values
(Tensors in fetch_list) on different devices.
Examples:
.. code-block:: python
import paddle
import numpy
import os
use_cuda = True
paddle.enable_static()
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
# NOTE: If you use CPU to run the program, you need
# to specify the CPU_NUM, otherwise, PaddlePaddle will use
# all the number of the logic core as the CPU_NUM,
# in that case, the batch size of the input should be
# greater than CPU_NUM, if not, the process will be
# failed by an exception.
if not use_cuda:
os.environ['CPU_NUM'] = str(2)
exe = paddle.static.Executor(place)
train_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(train_program, startup_program):
data = paddle.static.data(name='X', shape=[None, 1], dtype='float32')
hidden = paddle.static.nn.fc(data, 10)
loss = paddle.mean(hidden)
paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
exe.run(startup_program)
train_exe = paddle.static.ParallelExecutor(use_cuda=use_cuda,
main_program=train_program,
loss_name=loss.name)
# If the feed is a dict:
# the image will be split into devices. If there is two devices
# each device will process an image with shape (5, 1)
x = numpy.random.random(size=(10, 1)).astype('float32')
loss_data, = train_exe.run(feed={"X": x},
fetch_list=[loss.name])
# If the feed is a list:
# each device will process each element in the list.
# the 1st device will process an image with shape (10, 1)
# the 2nd device will process an image with shape (9, 1)
#
# you can use exe.device_count to get the device number.
x2 = numpy.random.random(size=(9, 1)).astype('float32')
loss_data, = train_exe.run(feed=[{"X": x}, {"X": x2}],
fetch_list=[loss.name])
"""
return self._exe.run(
program=self._compiled_program,
scope=self._scope,
feed=feed,
fetch_list=fetch_list,
return_numpy=return_numpy,
)
@property
def device_count(self):
return len(self._places)
def drop_local_exe_scopes(self):
"""
Drop the local execution scopes immediately. In order to avoid frequently
application and release of temporary variables, the strategy adopted by
ParallelExecutor is to drop the local execution scopes after several iterations.
ParallelExecutor provides the num_iteration_per_drop_scope option in
:code:`paddle.static.ExecutionStrategy`, which indicates how many iterations are intervened to
drop the local execution scopes. If the num_iteration_per_drop_scope value
is 100, but you want to drop the local execution scopes after 50 iterations,
you can call the interface manually.
Returns:
None
Examples:
.. code-block:: python
import paddle
import numpy
import os
use_cuda = True
# NOTE: If you use CPU to run the program, you need
# to specify the CPU_NUM, otherwise, PaddlePaddle will use
# all the number of the logic core as the CPU_NUM,
# in that case, the batch size of the input should be
# greater than CPU_NUM, if not, the process will be
# failed by an exception.
if not use_cuda:
os.environ['CPU_NUM'] = str(2)
paddle.enable_static()
train_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(train_program, startup_program):
data = paddle.static.data(name='X', shape=[None, 1], dtype='float32')
hidden = paddle.static.nn.fc(data, 10)
loss = paddle.mean(hidden)
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(startup_program)
parallel_exe = paddle.static.ParallelExecutor(use_cuda=use_cuda,
main_program=train_program,
loss_name=loss.name)
x = numpy.random.random(size=(10, 1)).astype('float32')
loss_data, = parallel_exe.run(feed={"X": x},
fetch_list=[loss.name])
parallel_exe.drop_local_exe_scopes()
"""
check_type(
self._compiled_program._executor,
"the Executor of compiled program",
core.ParallelExecutor,
"ParallelExecutor.drop_local_exe_scopes",
)
self._compiled_program._executor.drop_local_exe_scopes()
# This API is used to check whether DropLocalExeScopes can work.
def _need_create_local_exe_scopes(self):
check_type(
self._compiled_program._executor,
"the Executor of compiled program",
core.ParallelExecutor,
"ParallelExecutor._need_create_local_exe_scopes",
)
return self._compiled_program._executor._need_create_local_exe_scopes()
python/paddle/fluid/tests/unittests/test_directory_migration.py
浏览文件 @ e9c3da9e
......@@ -80,7 +80,6 @@ class TestDirectory(unittest.TestCase):
'paddle.static.program_guard',
'paddle.static.Print',
'paddle.static.py_func',
'paddle.static.ParallelExecutor',
'paddle.static.WeightNormParamAttr',
'paddle.static.nn.fc',
'paddle.static.nn.batch_norm',
......@@ -163,7 +162,6 @@ class TestDirectory(unittest.TestCase):
'paddle.program_guard',
'paddle.Print',
'paddle.py_func',
'paddle.ParallelExecutor',
'paddle.default_main_program',
'paddle.default_startup_program',
'paddle.Program',
......
python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/__init__.py
浏览文件 @ e9c3da9e
......@@ -28,7 +28,6 @@ from paddle.static import (
Executor,
)
from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.parallel_executor import ParallelExecutor
from paddle.fluid.optimizer import Optimizer
from paddle.distributed.transpiler.distribute_transpiler import (
......@@ -429,11 +428,6 @@ class FleetTranspiler(Fleet):
if self._inner_mode == PSMode.PSLIB:
raise NotImplementedError("add implement later")
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save_inference_model() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save_inference_model() function, executor must be as Executor type"
......@@ -766,11 +760,6 @@ class FleetTranspiler(Fleet):
if self._inner_mode == PSMode.PSLIB:
raise NotImplementedError("add implement later")
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save_persistables() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save_persistables() function, executor must be as Executor type"
......
python/paddle/static/__init__.py
浏览文件 @ e9c3da9e
......@@ -68,7 +68,6 @@ from ..fluid.framework import Parameter # noqa: F401
from ..fluid.framework import ipu_shard_guard # noqa: F401
from ..fluid.framework import set_ipu_shard # noqa: F401
from ..fluid.layers.control_flow import Print # noqa: F401
from ..fluid.parallel_executor import ParallelExecutor # noqa: F401
from ..fluid.param_attr import WeightNormParamAttr # noqa: F401
from ..fluid.optimizer import Optimizer # noqa: F401
from ..fluid.optimizer import Adam # noqa: F401
......@@ -96,7 +95,6 @@ __all__ = [ # noqa
'py_func',
'ExecutionStrategy',
'name_scope',
'ParallelExecutor',
'program_guard',
'WeightNormParamAttr',
'ExponentialMovingAverage',
......
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