    def forward(
        self,
        query: Tensor,
        key: Optional[Tensor],
        value: Optional[Tensor],
        key_padding_mask: Optional[Tensor] = None,
        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
        need_weights: bool = True,
        static_kv: bool = False,
        attn_mask: Optional[Tensor] = None,
        before_softmax: bool = False,
        need_head_weights: bool = False,
    ) -> 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.
            need_weights (bool, optional): return the attention weights,
                averaged over heads (default: False).
            attn_mask (ByteTensor, optional): typically used to
                implement causal attention, where the mask prevents the
                attention from looking forward in time (default: None).
            before_softmax (bool, optional): return the raw attention
                weights and values before the attention softmax.
            need_head_weights (bool, optional): return the attention
                weights for each head. Implies *need_weights*. Default:
                return the average attention weights over all heads.
        """
        if need_head_weights:
            need_weights = True

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

        tgt_len, bsz, embed_dim = query.size()
        src_len = tgt_len
        if not self.skip_embed_dim_check:
            assert (
                embed_dim == self.embed_dim
            ), f"query dim {embed_dim} != {self.embed_dim}"
        assert list(query.size()) == [tgt_len, bsz, embed_dim]
        if key is not None:
            src_len, key_bsz, _ = key.size()
            if not torch.jit.is_scripting():
                assert value is not None
                assert src_len, key_bsz == value.shape[:2]

        if (
            not self.onnx_trace
            and not is_tpu  # don't use PyTorch version on TPUs
            and incremental_state is None
            and not static_kv
            # A workaround for quantization to work. Otherwise JIT compilation
            # treats bias in linear module as method.
            and not torch.jit.is_scripting()
            # The Multihead attention implemented in pytorch forces strong dimension check
            # for input embedding dimention and K,Q,V projection dimension.
            # Since pruning will break the dimension check and it is not easy to modify the pytorch API,
            # it is preferred to bypass the pytorch MHA when we need to skip embed_dim_check
            and not self.skip_embed_dim_check
        ):
            assert key is not None and value is not None

            if self.use_xformers:
                return self._xformers_attn_forward(
                    query, key, value, key_padding_mask, need_weights, attn_mask
                )

            else:
                return F.multi_head_attention_forward(
                    query,
                    key,
                    value,
                    self.embed_dim,
                    self.num_heads,
                    torch.empty([0]),
                    torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
                    self.bias_k,
                    self.bias_v,
                    self.add_zero_attn,
                    self.dropout_module.p,
                    self.out_proj.weight,
                    self.out_proj.bias,
                    self.training or self.dropout_module.apply_during_inference,
                    key_padding_mask.bool() if key_padding_mask is not None else None,
                    need_weights,
                    attn_mask,
                    use_separate_proj_weight=True,
                    q_proj_weight=self.q_proj.weight,
                    k_proj_weight=self.k_proj.weight,
                    v_proj_weight=self.v_proj.weight,
                )

        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:
                if self.beam_size > 1 and bsz == key.size(1):
                    # key is [T, bsz*beam_size, C], reduce to [T, bsz, C]
                    key = key.view(key.size(0), -1, self.beam_size, key.size(2))[
                        :, :, 0, :
                    ]
                    if key_padding_mask is not None:
                        key_padding_mask = key_padding_mask.view(
                            -1, self.beam_size, key_padding_mask.size(1)
                        )[:, 0, :]
                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

        if self.bias_k is not None:
            assert self.bias_v is not None
            k, v, attn_mask, key_padding_mask = self._add_bias(
                k, v, attn_mask, key_padding_mask, bsz
            )

        q = (
            q.contiguous()
            .view(tgt_len, bsz * self.num_heads, self.head_dim)
            .transpose(0, 1)
        )
        kv_bsz = bsz  # need default value for scripting
        if k is not None:
            kv_bsz = k.size(1)
            k = (
                k.contiguous()
                .view(-1, kv_bsz * self.num_heads, self.head_dim)
                .transpose(0, 1)
            )
        if v is not None:
            v = (
                v.contiguous()
                .view(-1, kv_bsz * self.num_heads, self.head_dim)
                .transpose(0, 1)
            )

        if saved_state is not None:
            # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
            if "prev_key" in saved_state:
                _prev_key = saved_state["prev_key"]
                assert _prev_key is not None
                kv_bsz = _prev_key.size(0)
                prev_key = _prev_key.view(kv_bsz * self.num_heads, -1, self.head_dim)
                if static_kv:
                    k = prev_key
                else:
                    assert k is not None
                    k = torch.cat([prev_key, k], dim=1)
                src_len = k.size(1)
            if "prev_value" in saved_state:
                _prev_value = saved_state["prev_value"]
                assert _prev_value is not None
                assert kv_bsz == _prev_value.size(0)
                prev_value = _prev_value.view(
                    kv_bsz * self.num_heads, -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 = MultiheadAttention._append_prev_key_padding_mask(
                key_padding_mask=key_padding_mask,
                prev_key_padding_mask=prev_key_padding_mask,
                batch_size=kv_bsz,
                src_len=k.size(1),
                static_kv=static_kv,
            )

            saved_state["prev_key"] = k.view(kv_bsz, self.num_heads, -1, self.head_dim)
            saved_state["prev_value"] = v.view(
                kv_bsz, self.num_heads, -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
        assert k.size(1) == src_len

        # 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) == kv_bsz
            assert key_padding_mask.size(1) == src_len

        if self.add_zero_attn:
            assert v is not None
            src_len += 1
            k, v, key_padding_mask, attn_mask = self._append_zero_attn(
                k=k, v=v, key_padding_mask=key_padding_mask, attn_mask=attn_mask
            )

        if self.encoder_decoder_attention and bsz != kv_bsz:
            attn_weights = torch.einsum(
                "bxhtd,bhsd->bxhts",
                q.view((kv_bsz, -1, self.num_heads) + q.size()[1:]),
                k.view((kv_bsz, self.num_heads) + k.size()[1:]),
            )
            attn_weights = attn_weights.reshape((-1,) + attn_weights.size()[-2:])
        else:
            attn_weights = torch.bmm(q, k.transpose(1, 2))
        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)

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

        if attn_mask is not None:
            attn_mask = attn_mask.unsqueeze(0)
            if self.onnx_trace:
                attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
            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, tgt_len, src_len)
            if not is_tpu:
                attn_weights = attn_weights.view(
                    kv_bsz, -1, self.num_heads, tgt_len, src_len
                )
                attn_weights = attn_weights.masked_fill(
                    key_padding_mask.unsqueeze(1)
                    .unsqueeze(2)
                    .unsqueeze(3)
                    .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, tgt_len, src_len)

        if before_softmax:
            return attn_weights, v

        attn_weights_float = utils.softmax(
            attn_weights, dim=-1, onnx_trace=self.onnx_trace
        )
        attn_weights = attn_weights_float.type_as(attn_weights)
        attn_probs = self.dropout_module(attn_weights)

        assert v is not None
        attn: Optional[Tensor] = None
        if self.encoder_decoder_attention and bsz != kv_bsz:
            attn = torch.einsum(
                "bxhts,bhsd->bxhtd",
                attn_probs.view(
                    (
                        kv_bsz,
                        -1,
                        self.num_heads,
                    )
                    + attn_probs.size()[1:]
                ),
                v.view(
                    (
                        kv_bsz,
                        self.num_heads,
                    )
                    + v.size()[1:]
                ),
            )
            attn = attn.reshape((-1,) + attn.size()[-2:])
        else:
            attn = torch.bmm(attn_probs, v)
        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
        if self.onnx_trace and attn.size(1) == 1:
            # when ONNX tracing a single decoder step (sequence length == 1)
            # the transpose is a no-op copy before view, thus unnecessary
            attn = attn.contiguous().view(tgt_len, bsz, self.embed_dim)
        else:
            attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
        attn = self.out_proj(attn)
        attn_weights: Optional[Tensor] = None
        if need_weights:
            attn_weights = attn_weights_float.view(
                bsz, self.num_heads, tgt_len, src_len
            ).transpose(1, 0)
            if not need_head_weights:
                # average attention weights over heads
                attn_weights = attn_weights.mean(dim=0)

        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.
need_weights (bool, optional): return the attention weights,
averaged over heads (default: False).
attn_mask (ByteTensor, optional): typically used to
implement causal attention, where the mask prevents the
attention from looking forward in time (default: None).
before_softmax (bool, optional): return the raw attention
weights and values before the attention softmax.
need_head_weights (bool, optional): return the attention
weights for each head. Implies *need_weights*. Default:
return the average attention weights over all heads.
fairseq/modules/multihead_attention.py