This code snippet is likely from an implementation of the attention mechanism in a deep learning model. Specifically, it is used to mask out certain elements in the attention scores matrix, which may correspond to invalid connections between elements in the input sequence.

The code first creates two matrices, attnMaskMul and attnMaskAdd, both of size seqLen by seqLen and with data type FLOAT32. These matrices are filled with values that form a triangular mask, where the values above the diagonal are set to 1.0 for attnMaskMul and 0.0 for attnMaskAdd, and the values below the diagonal are set to 0.0 for attnMaskMul and negative infinity for attnMaskAdd.

When the attention scores matrix att is multiplied element-wise with attnMaskMul, the resulting scores for invalid connections are set to 0.0, effectively masking them out. The attnMaskAdd is then added to the result, which ensures that the scores for valid connections remain the same, while the scores for invalid connections are set to negative infinity, which will be interpreted as a very low probability during the softmax operation that follows. This ensures that the attention distribution does not allocate any attention weight to invalid connections.

In summary, this code creates two triangular matrices to mask out invalid attention connections in a deep learning model's attention mechanism, and applies them to the attention scores matrix att.
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