ab9e353c928218d12a977112d241c152ebcf06a9,src/gluonnlp/model/bert.py,BERTEncoder,hybrid_forward,#BERTEncoder#Any#Any#Any#Any#Any#,352

Before Change

            // valid_length for attention, shape = (batch_size, seq_length)
            attn_valid_len = F.broadcast_add(F.reshape(valid_length, shape=(-1, 1)),
                                             F.reshape(zeros, shape=(1, -1)))
            attn_valid_len = F.cast(attn_valid_len, dtype="int32")
            if states is None:
                states = [attn_valid_len]
            else:
                states.append(attn_valid_len)

After Change

            ones = F.ones_like(steps)
            mask = F.broadcast_lesser(F.reshape(steps, shape=(1, -1)),
                                      F.reshape(valid_length, shape=(-1, 1)))
            mask = F.broadcast_mul(F.expand_dims(mask, axis=1),
                                   F.broadcast_mul(ones, F.reshape(ones, shape=(-1, 1))))
            if states is None:
                states = [mask]
            else:
                states.append(mask)

Instances