def METHOD_NAME(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    # pylint: disable=duplicate-code
    bsz, q_len, _ = hidden_states.size()
    query_states = (self.q_proj(hidden_states).view(bsz, q_len, self.num_heads,
                                                    self.head_dim).transpose(
                                                        1, 2))
    key_states = (self.k_proj(hidden_states).view(bsz, q_len, self.num_heads,
                                                  self.head_dim).transpose(
                                                      1, 2))
    value_states = (self.v_proj(hidden_states).view(bsz, q_len, self.num_heads,
                                                    self.head_dim).transpose(
                                                        1, 2))
    kv_seq_len = key_states.shape[-2]
    if past_key_value is not None:
        kv_seq_len += past_key_value[0].shape[-2]
    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
    (
        query_states,
        key_states,
    ) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(
        query_states, key_states, cos, sin, position_ids)
    # [bsz, nh, t, hd]
    if past_key_value is not None:
        # reuse k, v, self_attention
        key_states = torch.cat([past_key_value[0], key_states], dim=2)
        value_states = torch.cat([past_key_value[1], value_states], dim=2)
    past_key_value = (key_states, value_states) if use_cache else None
    # We only apply xformers optimizations if we don't need to output the whole attention matrix
    if not output_attentions:
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)
        # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
        # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
        if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
            # input and output should be of form (bsz, q_len, num_heads, head_dim)
            attn_output = xformers.ops.memory_efficient_attention(
                query_states, key_states, value_states, attn_bias=None)
        else:
            # input and output should be of form (bsz, q_len, num_heads, head_dim)
            attn_output = xformers.ops.memory_efficient_attention(
                query_states,
                key_states,
                value_states,
                attn_bias=xformers.ops.LowerTriangularMask(),
            )
        attn_weights = None
    else:
        attn_weights = torch.matmul(query_states, key_states.transpose(
            2, 3)) / math.sqrt(self.head_dim)
        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}")
        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + attention_mask
            attn_weights = torch.max(
                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights,
                                             dim=-1,
                                             dtype=torch.float32).to(
                                                 query_states.dtype)
        attn_output = torch.matmul(attn_weights, value_states)
        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}")
        attn_output = attn_output.transpose(1, 2)
    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
    attn_output = self.o_proj(attn_output)
    return attn_output, attn_weights, past_key_value