src/transformers/models/mask2former/modeling_mask2former.py/Mask2FormerAttention/forward
class Mask2FormerAttention:    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_embeddings: Optional[torch.Tensor] = None,
        key_value_states: Optional[torch.Tensor] = None,
        key_value_position_embeddings: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        hidden_states = hidden_states.permute(1, 0, 2) if hidden_states is not None else None
        position_embeddings = position_embeddings.permute(1, 0, 2) if position_embeddings is not None else None
        key_value_states = key_value_states.permute(1, 0, 2) if key_value_states is not None else None
        key_value_position_embeddings = (
            key_value_position_embeddings.permute(1, 0, 2) if key_value_position_embeddings is not None else None
        )

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        batch_size, target_len, embed_dim = hidden_states.size()

        # add position embeddings to the hidden states before projecting to queries and keys
        if position_embeddings is not None:
            hidden_states_original = hidden_states
            hidden_states = self.with_pos_embed(hidden_states, position_embeddings)

        # add key-value position embeddings to the key value states
        if key_value_position_embeddings is not None:
            key_value_states_original = key_value_states
            key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings)

        # get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        # get key, value proj
        if is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(key_value_states), -1, batch_size)
            value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, batch_size)
            value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size)

        proj_shape = (batch_size * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape)
        key_states = key_states.view(*proj_shape)
        value_states = value_states.view(*proj_shape)

        source_len = key_states.size(1)

        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

        if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len):
            raise ValueError(
                f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (batch_size * self.num_heads, target_len, source_len):
                raise ValueError(
                    f"Attention mask should be of size {(target_len, batch_size * self.num_heads, source_len)}, but is"
                    f" {attention_mask.size()}"
                )
            attn_weights += attention_mask

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len)
            attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len)
        else:
            attn_weights_reshaped = None

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.bmm(attn_probs, value_states)

        if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(batch_size, target_len, embed_dim)

        attn_output = self.out_proj(attn_output).permute(1, 0, 2)

        return attn_output, attn_weights_reshaped