8e84bd1c5a4cd0e61ee67abc532ab692e5335914,src/gluonnlp/attention_cell.py,,masked_softmax,#Any#Any#Any#Any#,248

Before Change

            except ImportError:
                pass

        att_score = np.where(mask, att_score, neg)
        logits = npx.softmax(att_score, axis=axis) * mask
    else:
        logits = npx.softmax(att_score, axis=axis)
    return logits


// TODO(sxjscience) Directly implement a kernel for masked logsoftmax
def masked_logsoftmax(att_score, mask, dtype=np.float32, axis: int = -1):

After Change

        Shape (..., length, ...)
    
    if mask is None:
        return npx.softmax(att_score, axis=axis, temperature=temperature)
    else:
        return npx.masked_softmax(att_score, mask=mask.astype(np.bool),
                                  axis=axis, temperature=temperature)

Instances