Source code for paddle.fluid.dygraph.parallel

# 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 jin compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import six
import numpy as np

from .. import core
from . import layers
from . import parallel_helper
from .. import framework
from ..layers import collective
from . import to_variable

__all__ = ["prepare_context"]

ParallelStrategy = core.ParallelStrategy

[docs]def prepare_context(strategy=None): if strategy is None: strategy = ParallelStrategy() strategy.nranks = Env().nranks strategy.local_rank = Env().local_rank strategy.trainer_endpoints = Env().trainer_endpoints strategy.current_endpoint = Env().current_endpoint if strategy.nranks < 2: return assert framework.in_dygraph_mode() is True,\ "dygraph.parallel.prepare_context should be used with dygrahp mode." place = framework._current_expected_place() assert place is not None, \ "dygraph.parallel.prepare_context should be used in fluid.dygraph.guard(place) guard." if isinstance(place, core.CUDAPlace): parallel_helper._set_parallel_ctx( core.NCCLParallelContext(strategy, place)) else: # TODO(Yancey1989): add Gloo Parallel Context to support CPU parallel computation assert ("Only support CUDAPlace for now.") parallel_helper._init_parallel_ctx() return strategy
class Env(object): def __init__(self): self._nranks = int(os.getenv("PADDLE_TRAINERS_NUM", "1")) self._local_rank = int(os.getenv("PADDLE_TRAINER_ID", "0")) self._dev_id = int(os.getenv("FLAGS_selected_gpus", "0")) self._trainer_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS", "").split(",") self._current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT", "") @property def nranks(self): return self._nranks @property def local_rank(self): return self._local_rank @property def dev_id(self): return self._dev_id @property def current_endpoint(self): return self._current_endpoint @property def trainer_endpoints(self): return self._trainer_endpoints class DataParallel(layers.Layer): """ Runs the module with data parallelism. Currently, DataParallel only supports to run the dynamic graph with multi-process. The usage is: `python -m paddle.distributed.launch --gpus 2`. And the content of `` is the code of examples. Examples: .. code-block:: python import numpy as np import paddle.fluid as fluid import paddle.fluid.dygraph as dygraph from paddle.fluid.optimizer import AdamOptimizer from paddle.fluid.dygraph.nn import FC from paddle.fluid.dygraph.base import to_variable place = fluid.CUDAPlace(0) with fluid.dygraph.guard(place=place): # prepare the data parallel context strategy=dygraph.parallel.prepare_context() fc_layer = FC("FC", 10, act="softmax") adam = fluid.optimizer.AdamOptimizer() # make the module become the data parallelism module fc_layer = dygraph.parallel.DataParallel(fc_layer, strategy) x_data = np.random.random(size=[10, 1]).astype(np.float32) data = to_variable(x_data) hidden = fc_layer(data) avg_loss = fluid.layers.mean(hidden) # scale the loss according to the number of trainers. avg_loss = fc_layer.scale_loss(avg_loss) avg_loss.backward() # collect the gradients of trainers. fc_layer.apply_collective_grads() adam.minimize(avg_loss) fc_layer.clear_gradients() Args: layers(Layer): The module that should be executed by data parallel. strategy(ParallelStrategy): The strategy of data parallelism. Returns: Layer: The data paralleled module. """ def __init__(self, layers, strategy): super(DataParallel, self).__init__(layers.full_name() + "_data_parallel") self._layers = layers self._strategy = strategy def forward(self, *inputs, **kwargs): return self._layers(*inputs, **kwargs) def scale_loss(self, loss): """ Scale the loss. In data parallel mode, the loss should be scale with the number of trainers. If not in data parallel mode, return the loss directly. Args: loss(Layer): The loss of the current Model. Returns: Layer: the scaled loss. """ if not self._is_data_parallel_mode(): return loss loss_scale = to_variable( np.array([self._strategy.nranks]).astype("float32")) loss_scale.stop_gradient = True loss = loss / loss_scale return loss def apply_collective_grads(self): """ AllReduce the Parameters' gradient. """ if not self._is_data_parallel_mode(): return for param in self._layers.parameters(): # NOTE(zcd): The grad_ivar maybe no generated. if param.trainable and param._ivar._grad_ivar(): g_var = framework.Variable( block=self._helper.main_program.current_block(), name=param._ivar._grad_name(), stop_gradient=True, ivar=param._ivar._grad_ivar()) collective._allreduce(g_var, g_var, sync_mode=True) def _is_data_parallel_mode(self): return self._strategy.nranks > 1