Gradient Accumulation Algorithm
GPU Model Optimization
Overview
This tutorial describes the gradient accumulation training methods to solve the problem that some large-scale networks cannot train large batch_size due to insufficient memory.
In a traditional training method, after a loss and a gradient are computed each time, a parameter is directly updated by using the obtained gradient.
Compared to the traditional training method, mini-batch is introduced to the gradient accumulation. The loss and gradient are computed for each mini-batch data, but the model parameters are not updated immediately. Instead, the obtained gradients are accumulated first, and then after a specified number (N) of mini-batches, the accumulated gradient is used to update the network parameters. Before the next training, the accumulated gradients are cleared and re-accumulated. The ultimate objective is to achieve the same effect as training with N x Mini-batch data.
This tutorial describes how to implement gradient accumulation training in standalone mode and parallel mode, respectively.
Standalone Mode
In standalone mode, the training process consists of three parts: forward and backward training, parameter update, and accumulated gradient clearance. MNIST is used as an example dataset. To customize a simple model to implement gradient accumulation, perform the following steps:
Download the main training sample code: https://gitee.com/mindspore/docs/tree/r1.6/docs/sample_code/gradient_accumulation
auto_parallelandsemi_auto_parallelmode don’t support gradient accumulation now.
Since you need to use the lenet network in the models repository, please execute the following command to pull the code of the models repository:
git clone https://gitee.com/mindspore/models.git -b r1.6
If the models repository is not in the system path, it needs to be in train.py add the following two pieces of code at the beginning of the code.
import sys
sys.path.append(path to models repository)
Importing Library Files
The following are the required public modules and MindSpore modules and library files.
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 models.official.cv.lenet.src.dataset import create_dataset
from models.official.cv.lenet.src.lenet import LeNet5
Loading the Dataset
Use the MnistDataset API provided by dataset of MindSpore to load the MNIST dataset. The code is imported from dataset.py in the lenet directory of models.
Defining the Network
LeNet is used as an example network. You can also use other networks, such as ResNet-50 and BERT. The code is imported from lenet.py in the lenet directory of models.
Defining the Training Process
The training process consists of three parts: forward and backward training, parameter update, and accumulated gradient clearance.
TrainForwardBackwardcalculates the loss and gradient, and uses grad_sum to implement gradient accumulation.TrainOptimupdates parameters.TrainClearclears the gradient accumulation variable grad_sum.
_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
Defining the Training Model
Each mini-batch computes the loss and gradient through forward and backward training, and uses mini_steps to control the accumulated times before each parameter update. After the number of accumulation times is reached, the parameter is updated and the accumulated gradient variable is cleared.
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", )
Training and Saving the Model
Call the network, optimizer, and loss function, and then customize the train_process API of GradientAccumulation to train the model.
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)
Experiment Result
After 10 epochs, the accuracy on the test set is about 96.31%.
Start training.
Run the training code and view the running result.
python train.py --data_path=./MNIST_Data
The output is as follows. You can see that the loss value decreases with the training.
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
Check the saved checkpoint files.
The checkpoint file
gradient_accumulation.ckpt, that is, the model file, is saved during training.
Validate the model.
Use the saved checkpoint file to load the validation dataset through eval.py in the lenet directory of models.
python eval.py --data_path=./MNIST_Data --ckpt_path=./gradient_accumulation.ckpt --device_target=GPU
The output is as follows. The accuracy of the validation dataset is about 96.31%, which is the same as the result when the value of batch_size is 32.
============== Starting Testing ==============
============== {'Accuracy': 0.9631730769230769} ==============