DataParallel

class paddle.fluid.dygraph. DataParallel ( layers, strategy ) [源代码]

API属性:命令式编程模式(动态图)

通过数据并行模式执行动态图模型。

目前,DataParallel 仅支持以多进程的方式执行动态图模型。使用方式如下:

python -m paddle.distributed.launch –selected_gpus=0,1 dynamic_graph_test.py

其中 dynamic_graph_test.py 脚本的代码可以是下面的示例代码。

参数

  • Layer (Layer) - 需要通过数据并行方式执行的模型。
  • strategy (ParallelStrategy) - 数据并行的策略,包括并行执行的环境配置。

返回

支持数据并行的 Layer

返回类型

Layer实例

代码示例

import numpy as np
import paddle.fluid as fluid

place = fluid.CUDAPlace(fluid.dygraph.ParallelEnv().dev_id)
with fluid.dygraph.guard(place):

    # prepare the data parallel context
    strategy = fluid.dygraph.prepare_context()

    linear = fluid.dygraph.Linear(1, 10, act="softmax")
    adam = fluid.optimizer.AdamOptimizer(
        learning_rate=0.001, parameter_list=linear.parameters())

    # make the module become the data parallelism module
    linear = fluid.dygraph.DataParallel(linear, strategy)

    x_data = np.random.random(size=[10, 1]).astype(np.float32)
    data = fluid.dygraph.to_variable(x_data)

    hidden = linear(data)
    avg_loss = fluid.layers.mean(hidden)

    # scale the loss according to the number of trainers.
    avg_loss = linear.scale_loss(avg_loss)

    avg_loss.backward()

    # collect the gradients of trainers.
    linear.apply_collective_grads()

    adam.minimize(avg_loss)
    linear.clear_gradients()
scale_loss ( loss )

缩放模型损失值 loss 。在数据并行模式中,损失值 loss 需要根据并行训练进程的数目进行缩放。

如果不在数据并行模式下,会直接返回原 loss

参数:
  • loss (Variable) - 当前模型的损失值。

返回:缩放后的损失值 loss

返回类型:Variable

代码示例

import numpy as np
import paddle.fluid as fluid

place = fluid.CUDAPlace(fluid.dygraph.ParallelEnv().dev_id)
with fluid.dygraph.guard(place):

    # prepare the data parallel context
    strategy = fluid.dygraph.prepare_context()

    linear = fluid.dygraph.Linear(1, 10, act="softmax")
    adam = fluid.optimizer.AdamOptimizer(
        learning_rate=0.001, parameter_list=linear.parameters())

    # make the module become the data parallelism module
    linear = fluid.dygraph.DataParallel(linear, strategy)

    x_data = np.random.random(size=[10, 1]).astype(np.float32)
    data = fluid.dygraph.to_variable(x_data)

    hidden = linear(data)
    avg_loss = fluid.layers.mean(hidden)

    # scale the loss according to the number of trainers.
    avg_loss = linear.scale_loss(avg_loss)

    avg_loss.backward()

    # collect the gradients of trainers.
    linear.apply_collective_grads()

    adam.minimize(avg_loss)
    linear.clear_gradients()
apply_collective_grads ( )

AllReduce(规约)参数的梯度值。

返回:无

代码示例

import numpy as np
import paddle.fluid as fluid

place = fluid.CUDAPlace(fluid.dygraph.ParallelEnv().dev_id)
with fluid.dygraph.guard(place):

    # prepare the data parallel context
    strategy = fluid.dygraph.prepare_context()

    linear = fluid.dygraph.Linear(1, 10, act="softmax")
    adam = fluid.optimizer.AdamOptimizer(
        learning_rate=0.001, parameter_list=linear.parameters())

    # make the module become the data parallelism module
    linear = fluid.dygraph.DataParallel(linear, strategy)

    x_data = np.random.random(size=[10, 1]).astype(np.float32)
    data = fluid.dygraph.to_variable(x_data)

    hidden = linear(data)
    avg_loss = fluid.layers.mean(hidden)

    # scale the loss according to the number of trainers.
    avg_loss = linear.scale_loss(avg_loss)

    avg_loss.backward()

    # collect the gradients of trainers.
    linear.apply_collective_grads()

    adam.minimize(avg_loss)
    linear.clear_gradients()