应用梯度累积算法

Linux GPU 模型调优 中级 高级

     

概述

本教程介绍梯度累积的训练方式,目的是为了解决由于内存不足导致某些大型网络无法训练大Batch_size的问题。

传统的训练方式是每次计算得到loss和梯度后,直接用所得梯度对参数进行更新。

与传统的训练方式不同,梯度累积引入Mini-batch的概念,首先对每个Mini-batch的数据计算loss和梯度,但不立即更新模型参数,而是先对所得梯度进行累加,然后在指定数量(N)个Mini-batch之后,用累积后的梯度更新网络参数。下次训练前清空过往累积梯度后重新累加,如此往复。

最终目的是为了达到跟直接用N*Mini-batch数据训练几乎同样的效果。

本教程用于GPU, 你可以在这里下载主要的训练样例代码:https://gitee.com/mindspore/docs/tree/r1.1/tutorials/tutorial_code/gradient_accumulation

创建梯度累积模型

以MNIST作为示范数据集,自定义简单模型实现梯度累积。

导入需要的库文件

下列是我们所需要的公共模块及MindSpore的模块及库文件。

import argparse
import os
from collections.abc import Iterable

import mindspore.nn as nn
from mindspore import ParameterTuple
from mindspore import context, DatasetHelper, save_checkpoint
from mindspore.nn import Cell
import mindspore.ops as ops
from model_zoo.official.cv.lenet.src.dataset import create_dataset
from model_zoo.official.cv.lenet.src.lenet import LeNet5

加载数据集

利用MindSpore的dataset提供的MnistDataset接口加载MNIST数据集,此部分代码由model_zoolenet目录下的dataset.py导入。

定义网络

这里以LeNet网络为例进行介绍,当然也可以使用其它的网络,如ResNet-50、BERT等, 此部分代码由model_zoolenet目录下的lenet.py导入。

定义训练模型

将训练流程拆分为正向反向训练、参数更新和累积梯度清理三个部分:

  • TrainForwardBackward计算loss和梯度,利用grad_sum实现梯度累加。

  • TrainOptim实现参数更新。

  • TrainClear实现对梯度累加变量grad_sum清零。

_sum_op = ops.MultitypeFuncGraph("grad_sum_op")
_clear_op = ops.MultitypeFuncGraph("clear_op")


@_sum_op.register("Tensor", "Tensor")
def _cumulative_grad(grad_sum, grad):
    """Apply grad sum to cumulative gradient."""
    add = ops.AssignAdd()
    return add(grad_sum, grad)


@_clear_op.register("Tensor", "Tensor")
def _clear_grad_sum(grad_sum, zero):
    """Apply zero to clear grad_sum."""
    success = True
    success = ops.depend(success, ops.assign(grad_sum, zero))
    return success


class TrainForwardBackward(Cell):
    def __init__(self, network, optimizer, grad_sum, sens=1.0):
        super(TrainForwardBackward, self).__init__(auto_prefix=False)
        self.network = network
        self.network.set_grad()
        self.network.add_flags(defer_inline=True)
        self.weights = ParameterTuple(network.trainable_params())
        self.optimizer = optimizer
        self.grad_sum = grad_sum
        self.grad = ops.GradOperation(get_by_list=True, sens_param=True)
        self.sens = sens
        self.hyper_map = ops.HyperMap()

    def construct(self, *inputs):
        weights = self.weights
        loss = self.network(*inputs)
        sens = ops.Fill()(ops.DType()(loss), ops.Shape()(loss), self.sens)
        grads = self.grad(self.network, weights)(*inputs, sens)
        return ops.depend(loss, self.hyper_map(ops.partial(_sum_op), self.grad_sum, grads))


class TrainOptim(Cell):
    def __init__(self, optimizer, grad_sum):
        super(TrainOptim, self).__init__(auto_prefix=False)
        self.optimizer = optimizer
        self.grad_sum = grad_sum

    def construct(self):
        return self.optimizer(self.grad_sum)


class TrainClear(Cell):
    def __init__(self, grad_sum, zeros):
        super(TrainClear, self).__init__(auto_prefix=False)
        self.grad_sum = grad_sum
        self.zeros = zeros
        self.hyper_map = ops.HyperMap()

    def construct(self):
        success = self.hyper_map(ops.partial(_clear_op), self.grad_sum, self.zeros)
        return success

定义训练过程

每个Mini-batch通过正反向训练计算loss和梯度,通过mini_steps控制每次更新参数前的累加次数。达到累加次数后进行参数更新和 累加梯度变量清零。

class GradientAccumulation:
    def __init__(self, network, loss_fn, optimizer):
        self._network = network
        self._loss_fn = loss_fn
        self._optimizer = optimizer

        params = self._optimizer.parameters
        self._grad_sum = params.clone(prefix="grad_sum", init='zeros')
        self._zeros = params.clone(prefix="zeros", init='zeros')
        self._train_forward_backward = self._build_train_forward_backward_network()
        self._train_optim = self._build_train_optim()
        self._train_clear = self._build_train_clear()

    @staticmethod
    def _transform_callbacks(callbacks):
        """Transform callback to a list."""
        if callbacks is None:
            return []

        if isinstance(callbacks, Iterable):
            return list(callbacks)

        return [callbacks]

    def _build_train_forward_backward_network(self):
        """Build forward and backward network"""
        network = self._network
        network = nn.WithLossCell(network, self._loss_fn)
        loss_scale = 1.0
        network = TrainForwardBackward(network, self._optimizer, self._grad_sum, loss_scale).set_train()
        return network

    def _build_train_optim(self):
        """Build optimizer network"""
        network = TrainOptim(self._optimizer, self._grad_sum).set_train()
        return network

    def _build_train_clear(self):
        """Build clear network"""
        network = TrainClear(self._grad_sum, self._zeros).set_train()
        return network

    def train_process(self, epoch, train_dataset, mini_steps=None):
        """
        Training process. The data would be passed to network directly.
        """
        dataset_helper = DatasetHelper(train_dataset, dataset_sink_mode=False, epoch_num=epoch)

        for i in range(epoch):
            step = 0
            for k, next_element in enumerate(dataset_helper):
                loss = self._train_forward_backward(*next_element)
                if (k + 1) % mini_steps == 0:
                    step += 1
                    print("epoch:", i + 1, "step:", step, "loss is ", loss)
                    self._train_optim()
                    self._train_clear()

            train_dataset.reset()

        save_checkpoint(self._train_forward_backward, "gradient_accumulation.ckpt", )

训练并保存模型

调用网络、优化器及损失函数,然后自定义GradientAccumulationtrain_process接口,进行模型训练。

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='MindSpore Grad Cumulative Example')
    parser.add_argument('--device_target', type=str, default="GPU", choices=['GPU'],
                        help='device where the code will be implemented (default: GPU)')
    parser.add_argument('--data_path', type=str, default="./Data",
                        help='path where the dataset is saved')
    args = parser.parse_args()

    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
    ds_train = create_dataset(os.path.join(args.data_path, "train"), 32)

    net = LeNet5(10)
    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
    net_opt = nn.Momentum(net.trainable_params(), 0.01, 0.9)
    model = GradientAccumulation(net, net_loss, net_opt)

    print("============== Starting Training ==============")
    model.train_process(10, ds_train, mini_steps=4)

实验结果

在经历了10轮epoch之后,在测试集上的精度约为96.31%。

执行训练:

  1. 运行训练代码,查看运行结果。

    python train.py --data_path=./MNIST_Data
    

    输出如下,可以看到loss值随着训练逐步降低:

    epoch: 1 step: 27 loss is  0.3660637
    epoch: 1 step: 28 loss is  0.25238192
    ...
    epoch: 3 step: 2 loss is  0.12296932
    epoch: 3 step: 3 loss is  0.15799297
    ...
    epoch: 10 step: 448 loss is  0.06443884
    epoch: 10 step: 449 loss is  0.0067842817
    
  2. 查看保存的CheckPoint文件。

    训练过程中保存了CheckPoint文件gradient_accumulation.ckpt,即模型文件。

验证模型:

通过model_zoolenet目录下的eval.py,使用保存的CheckPoint文件,加载验证数据集,进行验证。

python eval.py --data_path=./MNIST_Data --ckpt_path=./gradient_accumulation.ckpt --device_target=GPU

输出如下,可以看到使用验证的数据集,正确率在96.31%左右,与batch_size为32的验证结果一致。

============== Starting Testing ==============
============== {'Accuracy': 0.9631730769230769} ==============