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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

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- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

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- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

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- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

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Differences with torch.nn.GELU

torch.nn.GELU

class torch.nn.GELU()(input) -> Tensor

For more information, see torch.nn.GELU.

mindspore.nn.GELU

class mindspore.nn.GELU(approximate=True)(x) -> Tensor

For more information, see mindspore.nn.GELU.

Differences

PyTorch: This function represents the Gaussian error linear unit function $GELU(X)=X\times \mathrm{\Phi }(x)$, where $\mathrm{\Phi }(x)$ is the cumulative distribution function of the Gaussian distribution. The input x denotes an arbitrary number of dimensions.

MindSpore: MindSpore API implements basically the same function as PyTorch.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameter	Parameter 1	-	approximate	Determines whether approximation is enabled or not, and the default value is True. After testing, the output is more similar to Pytorch when approximate is False
Input	Single input	input	x	Same function, different parameter names

Code Example 1

The two APIs achieve the same function and have the same usage.

# PyTorch
import torch
input_x = torch.Tensor([[2, 4], [1, 2]])
output = torch.nn.GELU()(input_x)
print(output.detach().numpy())
# [[1.9544997 3.9998734]
#  [0.8413447 1.9544997]]

# MindSpore
import mindspore
import numpy as np
x = mindspore.Tensor(np.array([[2, 4], [1, 2]]), mindspore.float32)
output = mindspore.nn.GELU(approximate=False)(x)
print(output)
# [[1.9544997 3.9998732]
#  [0.8413447 1.9544997]]