def METHOD_NAME(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.Tensor] = 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]]]:
    """Input shape: Batch x Time x Channel
    attention_mask: [bsz, q_len]
    """
    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))
    # [bsz, q_len, nh, hd]
    # [bsz, nh, q_len, hd]
    kv_seq_len = key_states.shape[-2]
    assert past_key_value is None, "past_key_value is not supported"
    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
                                                    cos, sin, position_ids)
    # [bsz, nh, t, hd]
    assert not output_attentions, "output_attentions is not supported"
    assert not use_cache, "use_cache is not supported"
    # Flash attention codes from
    # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
    # transform the data into the format required by flash attention
    qkv = torch.stack([query_states, key_states, value_states],
                      dim=2)  # [bsz, nh, 3, q_len, hd]
    qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
    # We have disabled _prepare_decoder_attention_mask in LlamaModel
    # the attention_mask should be the same as the key_padding_mask
    key_padding_mask = attention_mask
    if key_padding_mask is None:
        qkv = rearrange(qkv, "b s ... -> (b s) ...")
        max_s = q_len
        cu_q_lens = torch.arange(0, (bsz + 1) * q_len,
                                 step=q_len,
                                 dtype=torch.int32,
                                 device=qkv.device)
        output = flash_attn_varlen_qkvpacked_func(qkv,
                                                  cu_q_lens,
                                                  max_s,
                                                  0.0,
                                                  softmax_scale=None,
                                                  causal=True)
        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
    else:
        nheads = qkv.shape[-2]
        x = rearrange(qkv, "b s three h d -> b s (three h d)")
        x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
        x_unpad = rearrange(x_unpad,
                            "nnz (three h d) -> nnz three h d",
                            three=3,
                            h=nheads)
        output_unpad = flash_attn_varlen_qkvpacked_func(x_unpad,
                                                        cu_q_lens,
                                                        max_s,
                                                        0.0,
                                                        softmax_scale=None,
                                                        causal=True)
        output = rearrange(
            pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices,
                      bsz, q_len),
            "b s (h d) -> b s h d",
            h=nheads,
        )
    return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None