Padding and Masking a batch dataset - numpy

Question

When representing multiple strings of natural language, the number of characters in each string may not be equal. Then, the return result could be placed in a tf.RaggedTensor, where the length of the innermost dimension varies depending on the number of characters in each string:
rtensor = tf.ragged.constant([
[1, 2],
[3, 4, 5],
[6]
])
rtensor
#<tf.RaggedTensor [[1, 2], [3, 4, 5], [6]]>
In turn, applying to_tensor method, converts that RaggedTensor into a regular tf.Tensor and consequently apply the padding operation:
batch_size=3
max_length=8
tensor = rtensor.to_tensor(default_value=0, shape=(batch_size, max_length))
#<tf.Tensor: shape=(3, 8), dtype=int32, numpy=
#array([[1, 2, 0, 0, 0, 0, 0, 0],
# [3, 4, 5, 0, 0, 0, 0, 0],
# [6, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>
Now, is there an approach to generate also an adjunct tensor showing what is original data and what is padding? For the example above it would be:
<tf.Tensor: shape=(3, 8), dtype=int32, numpy=
array([[1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>

Answer 1

As thusv89 suggests, you can simply check for non-zero values. It can be as simple as converting to boolean and back.
import tensorflow as tf
rtensor = tf.ragged.constant([[1, 2],
[3, 4, 5],
[6]])
batch_size = 3
max_length = 8
tensor = rtensor.to_tensor(default_value=0, shape=(batch_size, max_length))
mask = tf.dtypes.cast(tf.dtypes.cast(tensor, tf.bool), tensor.dtype)
print(mask.numpy())
# [[1 1 0 0 0 0 0 0]
# [1 1 1 0 0 0 0 0]
# [1 0 0 0 0 0 0 0]]
The only possible drawback is that you might have had 0 values originally. You could use some other value as default value when converting to a tensor, for example -1, if you know that your data is always going to be non-negative:
tensor = rtensor.to_tensor(default_value=-1, shape=(batch_size, max_length))
mask = tf.dtypes.cast(tensor >= 0, tensor.dtype)
But if you want your mask to work for whatever values you have, you can also just use tf.ones_like with the ragged tensor:
rtensor_ones = tf.ones_like(rtensor)
mask = rtensor_ones.to_tensor(default_value=0, shape=(batch_size, max_length))
This way mask will always be one exactly where rtensor has a value.