org.sonar.l10n.py.rules.python.S6929.html Maven / Gradle / Ivy
This rule raises an issue when the axis/dim` argument is not provided to reduction operations.
Why is this an issue?
TensorFlow
The result of reduction operations (i.e. tf.math.reduce_sum, tf.math.reduce_std, torch.sum,
torch.mean, etc…), highly depends on the shape of the Tensor provided.
import tensorflow as tf
x = tf.constant([[1, 1, 1], [1, 1, 1]])
tf.math.reduce_sum(x)
In the example above the reduction of the 2 dimensional array will return the value 6 as all the elements are added together. By
default TensorFlow’s reduction operations are applied across all axis. When specifying an axis the result will be completely different.
import tensorflow as tf
x = tf.constant([[1, 1, 1], [1, 1, 1]])
tf.math.reduce_sum(x, axis=0)
Here the result will be [2,2,2] as the reduction is applied only on the axis 0.
TensorFlow’s default behavior can be confusing, especially when the reducing array of different shapes.
Considering the following example:
import tensorflow as tf
x = tf.constant([[1], [2]])
y = tf.constant([1, 2])
tf.math.reduce_sum(x + y)
Here the result will be 12 instead of the 6 that could be expected. This is because the implicit broadcasting reshapes
the first array to [[1,1], [2,2]] which is then added to the y array [1,2] resulting in [[2,3],
[3,4]]. As the reduction happen across all dimensions the result is then 2 + 3 + 3 + 4 = 12. It is not clear by looking at the
example if this was intentional or if the user made a mistake.
This is why a good practice is to always specify the axis on which to perform the reduction.
For example:
import tensorflow as tf
x = tf.constant([[1], [2]])
y = tf.constant([1, 2])
tf.math.reduce_sum(x + y, axis=0)
In the example above, specifying the axis clarifies the intent, as the result now is [5, 7]. If the intent was to effectively reduce
across all dimensions the user should provide the list of axis axis=[0,1] or clearly state the default behavior should be applied with
axis=None.
The PyTorch equivalent
The same behavior occurs in PyTorch, but the argument is called dim instead of axis.
How to fix it in TensorFlow
To fix this issue provide the axis argument when using a TensorFlow reduction operation such as tf.math.reduce_sum,
tf.math.reduce_prod, tf.math.reduce_mean, etc…
Code examples
Noncompliant code example
import tensorflow as tf
x = tf.constant([[1, 1, 1], [1, 1, 1]])
tf.math.reduce_sum(x) # Noncompliant: the axis arguments defaults to None
Compliant solution
import tensorflow as tf
x = tf.constant([[1, 1, 1], [1, 1, 1]])
tf.math.reduce_sum(x, axis=0) # Compliant: the reduction will happen only on the axis 0, resulting in `[2,2,2]`
How to fix it in PyTorch
To fix this issue provide the dim argument when using a PyTorch reduction operation such as torch.sum, torch.prod,
torch.mean, etc…
Code examples
Noncompliant code example
import torch
x = torch.tensor([[1, 1, 1], [1, 1, 1]])
torch.sum(x) # Noncompliant: the dim argument defaults to None
Compliant solution
import torch
x = torch.tensor([[1, 1, 1], [1, 1, 1]])
torch.sum(x, dim=None) # Compliant: all dimensions will be reduced
Resources
Documentation
- TensorFlow Documentation - tf.math.reduce_max reference
- TensorFlow Documentation - tf.math.reduce_mean reference
- TensorFlow Documentation - tf.math.reduce_min reference
- TensorFlow Documentation - tf.math.reduce_prod reference
- TensorFlow Documentation - tf.math.reduce_std reference
- TensorFlow Documentation - tf.math.reduce_sum reference
- TensorFlow Documentation - tf.math.reduce_variance reference
- PyTorch Documentation - Reduction operations
Articles & blog posts
- Vahidk Developers Guide - Broadcasting the good and the ugly