mindspore.utils

mindspore.utils.stress_detect()

Inspect the hardware to determine if there are any faults affecting its accuracy and precision.Common use cases include invoking this interface at each step or when saving checkpoints, allowing users to check if any hardware issues could impact precision.

Returns

int, the return value represents the error type: zero indicates normal operation; non-zero values indicate a hardware failure.

Supported Platforms:

Ascend

Examples

>>> from mindspore.utils import stress_detect
>>> ret = stress_detect()
>>> print(ret)
0

mindspore.utils.dryrun.set_simulation()

This interface is used to enable the dryrun function. The dryrun function is mainly used to simulate the actual operation of the large model. After it is enabled, the memory usage, compilation information, etc. can be simulated without occupying device card. In the PyNative mode, once it is enabled, if values are fetched from the device to the host, the Python call stack log will be printed to inform users that these values are inaccurate.

Supported Platforms:

Ascend

Examples

>>> import mindspore as ms
>>> from mindspore.utils import dryrun
>>> import numpy as np
>>> dryrun.set_simulation()
>>> print(os.environ.get('MS_SIMULATION_LEVEL'))
1

mindspore.utils.dryrun.mock(mock_val, *args)

In the network, if some if branch need to use the actual execution values and the virtual execution cannot obtain them, this interface can be used to return simulated values. During actual execution, the correct results can be obtained and the execution values can be returned.

Parameters

• mock_val (Union[Value, Tensor]) – The value you want to return.

• args (Union[Value, function]) – The content you want to mock, it can be values, function and so on.

Returns

If dryrun is enabled, mock_val will be returned; otherwise, the actual execution value of args will be returned.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> from mindspore.utils import dryrun
>>> import numpy as np
>>> dryrun.set_simulation()
>>> a = ms.Tensor(np.random.rand(3, 3).astype(np.float32))
>>> if dryrun.mock(True, a[0, 0] > 0.5):
...     print("return mock_val: True.")
return mock_val: True
>>>
>>> import mindspore as ms
>>> from mindspore.utils import dryrun
>>> import numpy as np
>>> a = ms.Tensor(np.ones((3, 3)).astype(np.float32))
>>> if dryrun.mock(False, a[0, 0] > 0.5):
...     print("return real execution: True.")
return real execution: True.
>>>
>>> import mindspore as ms
>>> from mindspore.utils import dryrun
>>> import numpy as np
>>> a = ms.Tensor(np.ones((3, 3)).astype(np.float32))
>>> if dryrun.mock(False, (a > 0.5).any):
...     print("return real execution: True.")
return real execution: True.