247203f29b7e841204c76d922c1ea5b2680c3663,reagent/models/seq2slate.py,DecoderPyTorch,forward,#DecoderPyTorch#Any#Any#Any#Any#,213

Before Change

        // (2) True -> item should be ignored in attention
        tgt_mask = tgt_tgt_mask[0, :, :] == 0
        memory_mask = tgt_src_mask[0, :, :] == 0
        output = super(DecoderPyTorch, self).forward(tgt, memory, tgt_mask, memory_mask)
        return output.transpose(0, 1)


class MultiHeadedAttention(nn.Module):
    def __init__(self, num_heads, dim_model):

After Change

        tgt_embed = tgt_embed.transpose(0, 1)
        memory = memory.transpose(0, 1)

        output = tgt_embed

        for mod in self.layers:
            output = mod(
                output,
                memory,
                tgt_mask=tgt_tgt_mask,
                memory_mask=tgt_src_mask,
            )

        batch_size, tgt_seq_len, _ = output.shape
        probs_for_placeholders = torch.zeros(
            batch_size, tgt_seq_len, 2, device=output.device
        )
        probs = torch.cat((probs_for_placeholders, output), dim=2)
        return probs


class MultiHeadedAttention(nn.Module):
    def __init__(self, num_heads, dim_model):

Instances