    def forward(
        self,
        query,
        key: Optional[Tensor],
        value: Optional[Tensor],
        key_padding_mask: Optional[Tensor] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        static_kv: bool = False,
        attn_mask: Optional[Tensor] = None,
        **unused_kwargs,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        """Input shape: Time x Batch x Channel

        Args:
            key_padding_mask (ByteTensor, optional): mask to exclude
                keys that are pads, of shape `(batch, src_len)`, where
                padding elements are indicated by 1s.
            attn_mask (ByteTensor, optional): typically used to
                implement causal attention, where the mask prevents the
                attention from looking forward in time (default: None).
        """
        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]

        is_tpu = query.device.type == "xla"

        if incremental_state is not None:
            saved_state = self._get_input_buffer(incremental_state)
            if saved_state is not None and "prev_key" in saved_state:
                # previous time steps are cached - no need to recompute
                # key and value if they are static
                if static_kv:
                    assert self.encoder_decoder_attention and not self.self_attention
                    key = value = None
        else:
            saved_state = None

        if self.self_attention:
            q = self.q_proj(query)
            k = self.k_proj(query)
            v = self.v_proj(query)
        elif self.encoder_decoder_attention:
            # encoder-decoder attention
            q = self.q_proj(query)
            if key is None:
                assert value is None
                k = v = None
            else:
                k = self.k_proj(key)
                v = self.v_proj(key)

        else:
            assert key is not None and value is not None
            q = self.q_proj(query)
            k = self.k_proj(key)
            v = self.v_proj(value)
        q *= self.scaling

        q = (
            q.contiguous()
            .view(tgt_len, bsz * self.num_heads_partition, self.head_dim)
            .transpose(0, 1)
        )
        if k is not None:
            k = (
                k.contiguous()
                .view(-1, bsz * self.num_heads_partition, self.head_dim)
                .transpose(0, 1)
            )
        if v is not None:
            v = (
                v.contiguous()
                .view(-1, bsz * self.num_heads_partition, self.head_dim)
                .transpose(0, 1)
            )

        if saved_state is not None:
            # saved states are stored with shape (bsz, num_heads_partition, seq_len, head_dim)
            if "prev_key" in saved_state:
                _prev_key = saved_state["prev_key"]
                assert _prev_key is not None
                prev_key = _prev_key.view(
                    bsz * self.num_heads_partition, -1, self.head_dim
                )
                if static_kv:
                    k = prev_key
                else:
                    assert k is not None
                    k = torch.cat([prev_key, k], dim=1)
            if "prev_value" in saved_state:
                _prev_value = saved_state["prev_value"]
                assert _prev_value is not None
                prev_value = _prev_value.view(
                    bsz * self.num_heads_partition, -1, self.head_dim
                )
                if static_kv:
                    v = prev_value
                else:
                    assert v is not None
                    v = torch.cat([prev_value, v], dim=1)
            prev_key_padding_mask: Optional[Tensor] = None
            if "prev_key_padding_mask" in saved_state:
                prev_key_padding_mask = saved_state["prev_key_padding_mask"]
            assert k is not None and v is not None
            key_padding_mask = (
                ModelParallelMultiheadAttention._append_prev_key_padding_mask(
                    key_padding_mask=key_padding_mask,
                    prev_key_padding_mask=prev_key_padding_mask,
                    batch_size=bsz,
                    src_len=k.size(1),
                    static_kv=static_kv,
                )
            )

            saved_state["prev_key"] = k.view(
                bsz, self.num_heads_partition, -1, self.head_dim
            )
            saved_state["prev_value"] = v.view(
                bsz, self.num_heads_partition, -1, self.head_dim
            )
            saved_state["prev_key_padding_mask"] = key_padding_mask
            # In this branch incremental_state is never None
            assert incremental_state is not None
            incremental_state = self._set_input_buffer(incremental_state, saved_state)
        assert k is not None
        src_len = k.size(1)

        # This is part of a workaround to get around fork/join parallelism
        # not supporting Optional types.
        if key_padding_mask is not None and key_padding_mask.dim() == 0:
            key_padding_mask = None

        if key_padding_mask is not None:
            assert key_padding_mask.size(0) == bsz
            assert key_padding_mask.size(1) == src_len

        attn_weights = torch.bmm(q, k.transpose(1, 2))

        assert list(attn_weights.size()) == [
            bsz * self.num_heads_partition,
            tgt_len,
            src_len,
        ]

        if attn_mask is not None:
            attn_mask = attn_mask.unsqueeze(0)
            attn_weights += attn_mask

        if key_padding_mask is not None:
            # don't attend to padding symbols
            attn_weights = attn_weights.view(
                bsz, self.num_heads_partition, tgt_len, src_len
            )
            if not is_tpu:
                attn_weights = attn_weights.masked_fill(
                    key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
                    float("-inf"),
                )
            else:
                attn_weights = attn_weights.transpose(0, 2)
                attn_weights = attn_weights.masked_fill(key_padding_mask, float("-inf"))
                attn_weights = attn_weights.transpose(0, 2)
            attn_weights = attn_weights.view(
                bsz * self.num_heads_partition, tgt_len, src_len
            )

        attn_weights_float = utils.softmax(attn_weights, dim=-1)
        attn_weights = attn_weights_float.type_as(attn_weights)

        with get_cuda_rng_tracker().fork():
            attn_probs = self.dropout_module(attn_weights)

        assert v is not None
        attn = torch.bmm(attn_probs, v)
        assert list(attn.size()) == [
            bsz * self.num_heads_partition,
            tgt_len,
            self.head_dim,
        ]
        embed_dim_partition = embed_dim // self.model_parallel_size
        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim_partition)
        attn = self.out_proj(attn)
        # return attn_weights None to keep the return type same as single gpu multihead attention
        # This will be deprecated.
        attn_weights: Optional[Tensor] = None

        return attn, attn_weights
Input shape: Time x Batch x Channel

Args:
key_padding_mask (ByteTensor, optional): mask to exclude
keys that are pads, of shape `(batch, src_len)`, where
padding elements are indicated by 1s.
attn_mask (ByteTensor, optional): typically used to
implement causal attention, where the mask prevents the
attention from looking forward in time (default: None).
fairseq/model_parallel/modules/multihead_attention.py