On-Device Execution
Overview
The backends supported by MindSpore include Ascend, GPU, and CPU. The device in the “On-Device” refers to the Ascend AI processor.
The Ascend AI processor integrates the AICORE, AICPU, and CPU. The AICORE is responsible for large Tensor Vector computing, the AI CPU is responsible for scalar computing, and the CPU is responsible for logic control and task distribution.
The CPU on the host side delivers graphs or operators to the Ascend AI processor. The Ascend AI processor has the functions of computing, logic control, and task distribution. Therefore, it does not need to frequently interact with the CPU on the host side. It only needs to return the final calculation result to the host. In this way, the entire graph is sunk to the device for execution, avoiding frequent interaction between the host and device and reducing overheads.
Computational Graphs Sinking
The entire graph is executed on the device to reduce the interaction overheads between the host and device. Multiple steps can be moved downwards together with cyclic sinking to further reduce the number of interactions between the host and device.
Cyclic sinking is optimized based on on-device execution to further reduce the number of interactions between the host and device. Generally, each step returns a result. Cyclic sinking is used to control the number of steps at which a result is returned.
By default, the result is returned for each epoch. In this way, the host and device need to exchange data only once in each epoch.
You can also use dataset_sink_mode and sink_size of the train interface to control the sunk data volume of each epoch.
Data Sinking
The train interface parameter dataset_sink_mode of Model can be used to control whether data sinks. If the value of dataset_sink_mode is True, data sinking is enabled. Otherwise, data sinking is disabled. Sinking means that data is directly transmitted to the device through a channel.
The dataset_sink_mode parameter can be used with sink_size to control the amount of data sunk by each epoch. When dataset_sink_mode is set to True, that is, the data sinking mode is used:
If sink_size is set to the default value –1, each epoch trains the whole dataset. Ideally, the speed of sinking data is faster than hardware calculation, so as to ensure that the time spent in processing data is hidden in the network calculation time.
If sink_size is greater than 0, the raw dataset can be traversed for an unlimited number of times, sinking data flow is still the same as sink_size = -1, except that each epoch only trains sink_size amount of data. If there is LossMonitor, it will train sink_size amount of data and print the loss value once, and the next epoch continues to traverse from the end position of the previous traversal.
The total sunk data volume is controlled by the epoch and sink_size variables. That is, the total data volume is calculated as follows: Total data volume = epoch x sink_size.
When using LossMonitor, TimeMonitor or other Callback interfaces, if the dataset_sink_mode is set to False, each step between the Host side and the Device side interacts once, so each step will return a result. If dataset_sink_mode is True, because the data is transmitted through the channel on the Device, there is one data interaction between the Host side and the Device side for each epoch, so each epoch only returns one result.
Currently dataset sink mode is not supported on CPU target. If
fault kernel_name=GetNextorGetNext... task errororoutputs = self.get_next()error info occurs, it may be that some sample processing in the data processing process is too time-consuming, resulting in the failure of the network computing side to get the data for a long time and report an error. At this time, you can setdataset_sink_modeto False to verify again, or usecreate_dict_iterator()interface separate cyclic dataset and refer to Optimizing the Data Processing optimize data processing to ensure high performance of data processing.
The following is a code example:
import os
import requests
import mindspore as ms
import mindspore.dataset as ds
import mindspore.dataset.transforms as transforms
import mindspore.dataset.vision as vision
import mindspore.nn as nn
from mindspore.common.initializer import TruncatedNormal
from mindspore.dataset.vision import Inter
import mindspore.ops as ops
requests.packages.urllib3.disable_warnings()
def create_dataset(data_path, batch_size=32, repeat_size=1,
num_parallel_workers=1):
"""
create dataset for train or test
"""
# define dataset
mnist_ds = ds.MnistDataset(data_path)
resize_height, resize_width = 32, 32
rescale = 1.0 / 255.0
shift = 0.0
rescale_nml = 1 / 0.3081
shift_nml = -1 * 0.1307 / 0.3081
# define map operations
resize_op = vision.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
rescale_nml_op = vision.Rescale(rescale_nml, shift_nml)
rescale_op = vision.Rescale(rescale, shift)
hwc2chw_op = vision.HWC2CHW()
type_cast_op = transforms.TypeCast(ms.int32)
# apply map operations on images
mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)
# apply DatasetOps
buffer_size = 10000
mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script
mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
mnist_ds = mnist_ds.repeat(repeat_size)
return mnist_ds
def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
"""weight initial for conv layer"""
weight = weight_variable()
return nn.Conv2d(in_channels, out_channels,
kernel_size=kernel_size, stride=stride, padding=padding,
weight_init=weight, has_bias=False, pad_mode="valid")
def fc_with_initialize(input_channels, out_channels):
"""weight initial for fc layer"""
weight = weight_variable()
bias = weight_variable()
return nn.Dense(input_channels, out_channels, weight, bias)
def weight_variable():
"""weight initial"""
return TruncatedNormal(0.02)
class LeNet5(nn.Cell):
"""
Lenet network
Args:
num_class (int): Num classes. Default: 10.
Returns:
Tensor, output tensor
Examples:
>>> LeNet(num_class=10)
"""
def __init__(self, num_class=10):
super(LeNet5, self).__init__()
self.num_class = num_class
self.batch_size = 32
self.conv1 = conv(1, 6, 5)
self.conv2 = conv(6, 16, 5)
self.fc1 = fc_with_initialize(16 * 5 * 5, 120)
self.fc2 = fc_with_initialize(120, 84)
self.fc3 = fc_with_initialize(84, self.num_class)
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
self.reshape = ops.Reshape()
def construct(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.reshape(x, (self.batch_size, -1))
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)
return x
def download_dataset(dataset_url, path):
filename = dataset_url.split("/")[-1]
save_path = os.path.join(path, filename)
if os.path.exists(save_path):
return
if not os.path.exists(path):
os.makedirs(path)
res = requests.get(dataset_url, stream=True, verify=False)
with open(save_path, "wb") as f:
for chunk in res.iter_content(chunk_size=512):
if chunk:
f.write(chunk)
print("The {} file is downloaded and saved in the path {} after processing".format(os.path.basename(dataset_url), path))
if __name__ == "__main__":
ms.set_context(mode=ms.GRAPH_MODE, device_target="GPU")
ds_train_path = "./datasets/MNIST_Data/train/"
download_dataset("https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/train-labels-idx1-ubyte", ds_train_path)
download_dataset("https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/train-images-idx3-ubyte", ds_train_path)
ds_train = create_dataset(ds_train_path, 32)
network = LeNet5(10)
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
model = ms.Model(network, net_loss, net_opt)
print("============== Starting Training ==============")
model.train(epoch=10, train_dataset=ds_train, callbacks=[ms.LossMonitor()], dataset_sink_mode=True, sink_size=1000)
The output is as follows:
============== Starting Training ==============
epoch: 1 step: 1000, loss is 0.110185064
epoch: 2 step: 1000, loss is 0.12088283
epoch: 3 step: 1000, loss is 0.15903473
epoch: 4 step: 1000, loss is 0.030054657
epoch: 5 step: 1000, loss is 0.013846226
epoch: 6 step: 1000, loss is 0.052161213
epoch: 7 step: 1000, loss is 0.0050197737
epoch: 8 step: 1000, loss is 0.17207858
epoch: 9 step: 1000, loss is 0.010310417
epoch: 10 step: 1000, loss is 0.000672762
When batch_size is 32, the size of the dataset is 1875. When sink_size is set to 1000, each epoch sinks 1000 batches of data, the number of sinks is epoch (=10), and the total sunk data volume is epoch x sink_size = 10000.
dataset_sink_mode is True, so every epoch returns a result.
DatasetHelper is a class to process the dataset and provide information of the dataset. In sink mode, mindspore.connect_network_with_dataset function is used to connect the current training network or evaluate network network and DatasetHelper, this function wraps the input network with GetNext so that the data can be fetched automatically from the data channel with the corresponding name queue_name on the device side during forward computation, and pass the data to the input network. In the no-sink mode, the data set is fetched at host side by iterating through the dataset.
When
dataset_sink_modeis set to False, thesink_sizeparameter is invalid.