/
fleet_base.py
1252 lines (936 loc) · 41.5 KB
/
fleet_base.py
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# Copyright (c) 2020 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
import copy
import warnings
import paddle
import os
from paddle.fluid.framework import dygraph_only
from paddle.fluid import compiler
from .role_maker import UserDefinedRoleMaker, PaddleCloudRoleMaker, RoleMakerBase
from .strategy_compiler import StrategyCompiler
from .distributed_strategy import DistributedStrategy
from .meta_optimizer_factory import MetaOptimizerFactory
from .runtime_factory import RuntimeFactory
from paddle.fluid.wrapped_decorator import wrap_decorator
from paddle.fluid.dygraph import parallel_helper
def _inited_runtime_handler_(func):
def __impl__(*args, **kwargs):
cls = args[0]
if cls._runtime_handle is None:
raise ValueError("Fleet can not find suitable runtime handler")
return func(*args, **kwargs)
return __impl__
def _is_non_distributed_check_(func):
def __impl__(*args, **kwargs):
cls = args[0]
if cls._role_maker is not None and cls._role_maker._is_non_distributed(
) is True:
warnings.warn(
"%s() function doesn't work when use non_distributed fleet." %
(func.__name__))
return
return func(*args, **kwargs)
return __impl__
inited_runtime_handler = wrap_decorator(_inited_runtime_handler_)
is_non_distributed_check = wrap_decorator(_is_non_distributed_check_)
class Fleet(object):
"""
Unified API for distributed training of PaddlePaddle
Please reference the https://github.com/PaddlePaddle/FleetX for details
Returns:
Fleet: A Fleet instance
Example for collective training:
.. code-block:: python
import paddle
paddle.enable_static()
import paddle.distributed.fleet as fleet
fleet.init(is_collective=True)
strategy = fleet.DistributedStrategy()
optimizer = paddle.optimizer.SGD(learning_rate=0.001)
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
# do distributed training
Example for parameter server training:
.. code-block:: python
import paddle
paddle.enable_static()
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
fleet.init(strategy=strategy)
optimizer = paddle.optimizer.SGD(learning_rate=0.001)
optimizer = fleet.distributed_optimizer(optimizer)
if fleet.is_first_worker():
print("this is first worker")
print("current node index: {}".format(fleet.worker_index()))
print("total number of worker num: {}".format(fleet.worker_num()))
if fleet.is_worker():
print("this is worker")
print("worker endpoints: {}".format(fleet.worker_endpoints(to_string=True)))
print("server num: {}".format(fleet.server_num()))
print("server endpoints: {}".format(fleet.server_endpoints(to_string=True)))
if fleet.is_server():
print("this is server")
fleet.stop_worker()
"""
def __init__(self):
self._role_maker = None
self.strategy_compiler = None
self._is_collective = False
self._runtime_handle = None
self._util = None
self._context = {}
def init(self, role_maker=None, is_collective=False, strategy=None):
"""
Initialize role_maker in Fleet.
This function is responsible for the distributed architecture
what you want to run your code behind.
Args:
role_maker (RoleMakerBase, optional): A ``RoleMakerBase`` containing the configuration
of environment variables related to distributed training.If you did not initialize
the rolemaker by yourself, it will be automatically initialized to PaddleRoleMaker.
The default value is None.
is_collective (Boolean, optional): A ``Boolean`` variable determines whether the program
runs on the CPU or GPU. False means set distributed training using CPU, and True means
GPU.The default value is False.The default value is False.
strategy (DistributedStrategy): Extra properties for distributed training.
For details, please refer to paddle.distributed.fleet.DistributedStrategy. Default: None.
Returns:
None
Examples1:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
Examples2:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init(is_collective=True)
Examples3:
.. code-block:: python
import paddle.distributed.fleet as fleet
role = fleet.PaddleCloudRoleMaker()
fleet.init(role)
Examples4:
.. code-block:: python
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
fleet.init(strategy=strategy)
"""
if strategy is None:
strategy = DistributedStrategy()
self._user_defined_strategy = copy.deepcopy(strategy)
if role_maker is None:
if isinstance(is_collective, bool):
self._is_collective = is_collective
self._role_maker = PaddleCloudRoleMaker(
is_collective=self._is_collective)
else:
raise ValueError(
"`is_collective` should be instance of `bool`, but got {}".
format(type(is_collective)))
else:
if isinstance(role_maker, RoleMakerBase):
self._role_maker = role_maker
self._is_collective = role_maker._is_collective
else:
raise ValueError(
"`role_maker` should be subclass of `RoleMakerBase`, but got {}".
format(type(role_maker)))
self._role_maker._generate_role()
import paddle.distributed.fleet as fleet
fleet.util._set_role_maker(self._role_maker)
self.strategy_compiler = StrategyCompiler()
if self._role_maker._is_non_distributed() and self._is_collective:
if paddle.fluid.core.is_compiled_with_cuda():
gpus_num = paddle.fluid.core.get_cuda_device_count()
if gpus_num != 1:
raise ValueError(
"CUDA_VISIBLE_DEVICES shoule be set only 1 card if you use `python` to launch fleet program."
)
if paddle.fluid.framework.in_dygraph_mode():
if self.worker_num() == 1:
return
if parallel_helper._is_parallel_ctx_initialized():
warnings.warn(
"The dygraph parallel environment has been initialized.")
else:
# FLAGS_nccl_nrings is used for dynamic graph multi-stream communication
if "FLAGS_nccl_nrings" in os.environ:
warnings.warn(
"You have set the environment variable FLAGS_nccl_nrings "
"outside the program, so the nccl_comm_num in "
"DistributedStrategy will not take effect here.")
else:
os.environ["FLAGS_nccl_nrings"] = str(
self._user_defined_strategy.nccl_comm_num)
paddle.distributed.init_parallel_env()
def is_first_worker(self):
"""
Check whether the node is the first instance of worker.
Returns:
bool: True if this is the first node of worker,
False if not.
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.is_first_worker()
"""
return self._role_maker._is_first_worker()
def worker_index(self):
"""
Get current worker index.
Returns:
int: node id
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.worker_index()
"""
return self._role_maker._worker_index()
def worker_num(self):
"""
Get current total worker number.
Returns:
int: worker numbers
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.worker_num()
"""
return self._role_maker._worker_num()
def is_worker(self):
"""
Check whether the node is an instance of worker.
Returns:
bool: True if this is a node of worker,
False if not.
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.is_worker()
"""
return self._role_maker._is_worker()
def worker_endpoints(self, to_string=False):
"""
Get current worker endpoints, such as ["127.0.0.1:1001", "127.0.0.1:1002"].
Returns:
list/string: server endpoints
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.worker_endpoints()
"""
if to_string:
return ",".join(self._role_maker._get_trainer_endpoints())
else:
return self._role_maker._get_trainer_endpoints()
def server_num(self):
"""
Get current total worker number.
Returns:
int: server number
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.server_num()
"""
return len(self._role_maker._get_pserver_endpoints())
def server_index(self):
"""
Get current server index.
Returns:
int: node id
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.server_index()
"""
return self._role_maker._server_index()
def server_endpoints(self, to_string=False):
"""
Get current server endpoints, such as ["127.0.0.1:1001", "127.0.0.1:1002"].
Returns:
list/string: server endpoints
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.server_endpoints()
"""
if to_string:
return ",".join(self._role_maker._get_pserver_endpoints())
else:
return self._role_maker._get_pserver_endpoints()
def is_server(self):
"""
Check whether the node is an instance of server.
Returns:
bool: True if this is a node of server,
False if not.
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
fleet.is_server()
"""
return self._role_maker._is_server(
) or self._role_maker._is_heter_worker()
def barrier_worker(self):
"""
barrier all workers
Returns:
None
"""
self._role_maker._barrier("worker")
@is_non_distributed_check
@inited_runtime_handler
def init_worker(self):
"""
initialize `Communicator` for parameter server training.
Returns:
None
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
# build net
# fleet.distributed_optimizer(...)
fleet.init_worker()
"""
self._runtime_handle._init_worker()
@is_non_distributed_check
@inited_runtime_handler
def init_server(self, *args, **kwargs):
"""
init_server executor to initialize startup program,
if the `args` is not empty, it will run load_persistables for increment training.
Returns:
None
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
# build net
# fleet.distributed_optimizer(...)
fleet.init_server()
"""
self._runtime_handle._init_server(*args, **kwargs)
@is_non_distributed_check
@inited_runtime_handler
def run_server(self):
"""
run server will run pserver main program with executor.
Returns:
None
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
# build net
# fleet.distributed_optimizer(...)
if fleet.is_server():
fleet.init_server()
"""
self._runtime_handle._run_server()
@is_non_distributed_check
@inited_runtime_handler
def stop_worker(self):
"""
stop `Communicator` and give training complete notice to parameter server.
Returns:
None
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
# build net
# fleet.distributed_optimizer(...)
fleet.init_server()
"""
self._runtime_handle._stop_worker()
def save_inference_model(self,
executor,
dirname,
feeded_var_names,
target_vars,
main_program=None,
export_for_deployment=True,
mode=0):
"""
save inference model for inference.
Returns:
None
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
fleet.init()
# build net
# fleet.distributed_optimizer(...)
fleet.init_server()
"""
self._runtime_handle._save_inference_model(
executor, dirname, feeded_var_names, target_vars, main_program,
export_for_deployment, mode)
def save_persistables(self, executor, dirname, main_program=None, mode=0):
"""
saves all persistable tensors from :code:`main_program` to
the folder :code:`dirname`. You can refer to
The :code:`dirname` is used to specify the folder where persistable tensors
are going to be saved. If you would like to save tensors in separate
files, set :code:`filename` None.
Args:
executor(Executor): The executor to run for saving persistable tensors.
You can refer to :ref:`api_guide_executor_en` for
more details.
dirname(str, optional): The saving directory path.
When you need to save the parameter to the memory, set it to None.
main_program(Program, optional): The program whose persistbale tensors will
be saved. Default: None.
Returns:
None
Examples:
.. code-block:: text
import paddle
paddle.enable_static()
import paddle.distributed.fleet as fleet
fleet.init()
# build net
# fleet.distributed_optimizer(...)
exe = paddle.static.Executor(paddle.CPUPlace())
fleet.save_persistables(exe, "dirname", paddle.static.default_main_program())
"""
self._runtime_handle._save_persistables(executor, dirname, main_program,
mode)
def shrink(self, threshold):
self._runtime_handle._shrink(threshold)
def distributed_optimizer(self, optimizer, strategy=None):
"""
Optimizer for distributed training.
For the distributed training, this method would rebuild a new instance of DistributedOptimizer.
Which has basic Optimizer function and special features for distributed training.
Args:
optimizer(Optimizer): The executor to run for init server.
strategy(DistributedStrategy): Extra properties for distributed optimizer.
It is recommended to use DistributedStrategy in fleet.init(). The strategy
here is for compatibility. If the strategy in fleet.distributed_optimizer()
is not None, then it will overwrite the DistributedStrategy in fleet.init(),
which will take effect in distributed training.
Returns:
Fleet: instance of fleet.
Examples:
.. code-block:: python
import paddle
import paddle.distributed.fleet as fleet
fleet.init(is_collective=True)
strategy = fleet.DistributedStrategy()
optimizer = paddle.optimizer.SGD(learning_rate=0.001)
optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
"""
self.user_defined_optimizer = optimizer
if strategy is not None:
if self._is_collective:
warnings.warn(
"It is recommended to use DistributedStrategy "
"in fleet.init(). The strategy here is only for compatibility. "
"If the strategy in fleet.distributed_optimizer() is "
"not None, then it will overwrite the DistributedStrategy in fleet.init(), "
"which will take effect in distributed training.")
self._user_defined_strategy = copy.deepcopy(strategy)
self._context = {}
return self
@dygraph_only
def distributed_model(self, model):
"""
Return distributed data parallel model (Only work in dygraph mode)
Args:
model (Layer): the user-defind model which inherits Layer.
Returns:
distributed data parallel model which inherits Layer.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
# 1. initialize fleet environment
fleet.init(is_collective=True)
# 2. create layer & optimizer
layer = LinearNet()
loss_fn = nn.MSELoss()
adam = paddle.optimizer.Adam(
learning_rate=0.001, parameters=layer.parameters())
# 3. get data_parallel model using fleet
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
# 4. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
print("loss:", loss.numpy())
loss.backward()
adam.step()
adam.clear_grad()
"""
assert model is not None
self.model = paddle.DataParallel(
model,
comm_buffer_size=self._user_defined_strategy.fuse_grad_size_in_MB,
last_comm_buffer_size=self._user_defined_strategy.
last_comm_group_size_MB)
return self.model
@dygraph_only
def state_dict(self):
"""
Get state dict information from optimizer.
(Only work in dygraph mode)
Returns:
state_dict(dict) : dict contains all the Tensor used by optimizer
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.to_tensor(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.state_dict()
@dygraph_only
def set_state_dict(self, state_dict):
"""
Load optimizer state dict.
(Only work in dygraph mode)
Args:
state_dict(dict) : Dict contains all the Tensor needed by optimizer
Returns:
None
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.to_tensor(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
paddle.save(state_dict, "paddle_dy")
para_state_dict = paddle.load("paddle_dy")
adam.set_state_dict(para_state_dict)
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.set_state_dict(state_dict)
@dygraph_only
def set_lr(self, value):
"""
Set the value of the learning rate manually in the optimizer.
(Only work in dygraph mode)
Args:
value (float|Tensor): the value of learning rate
Returns:
None
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.to_tensor(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
for i in range(5):
adam.set_lr(lr_list[i])
lr = adam.get_lr()
print("current lr is {}".format(lr))
# Print:
# current lr is 0.2
# current lr is 0.3
# current lr is 0.4
# current lr is 0.5
# current lr is 0.6
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.set_lr(value)
@dygraph_only
def get_lr(self):
"""
Get current step learning rate.
(Only work in dygraph mode)
Returns:
float: The learning rate of the current step.
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.to_tensor(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
lr = adam.get_lr()
print(lr) # 0.01
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.get_lr()
@dygraph_only
def step(self):
"""
Execute the optimizer once.
(Only work in dygraph mode)
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
# 1. initialize fleet environment
fleet.init(is_collective=True)
# 2. create layer & optimizer
layer = LinearNet()
loss_fn = nn.MSELoss()
adam = paddle.optimizer.Adam(
learning_rate=0.001, parameters=layer.parameters())
# 3. get data_parallel model using fleet
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
# 4. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
print("loss:", loss.numpy())
loss.backward()
adam.step()
adam.clear_grad()
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.step()
@dygraph_only
def clear_grad(self):
"""
Clear the gradients of all optimized parameters for model.
(Only work in dygraph mode)
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
# 1. initialize fleet environment
fleet.init(is_collective=True)
# 2. create layer & optimizer
layer = LinearNet()
loss_fn = nn.MSELoss()
adam = paddle.optimizer.Adam(
learning_rate=0.001, parameters=layer.parameters())
# 3. get data_parallel model using fleet
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
# 4. run layer
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
print("loss:", loss.numpy())
loss.backward()
adam.step()
adam.clear_grad()
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.clear_grad()
def amp_init(self,
place,
scope=None,
test_program=None,
use_fp16_test=False):
"""
Init the amp training, such as cast fp32 parameters to fp16 type.
Args:
place(CUDAPlace): place is used to initialize
fp16 parameters with fp32 values.
scope(Scope): The scope is used to find fp32 parameters.
test_program(Program): The program is used for testing.
use_fp16_test(bool): Whether to use fp16 testing.
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.nn.functional as F
paddle.enable_static()
def run_example_code():
place = paddle.CUDAPlace(0)
exe = paddle.static.Executor(place)
data = paddle.static.data(name='X', shape=[None, 1, 28, 28], dtype='float32')
conv2d = paddle.static.nn.conv2d(input=data, num_filters=6, filter_size=3)
# 1) Use fp16_guard to control the range of fp16 kernels used.
with paddle.static.amp.fp16_guard():
bn = paddle.static.nn.batch_norm(input=conv2d, act="relu")
pool = F.max_pool2d(bn, kernel_size=2, stride=2)
hidden = paddle.static.nn.fc(pool, size=10)
loss = paddle.mean(hidden)