models/modules/activation.py

# cp from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py, modified by Puyuan Peng, 2024
from typing import Optional, Tuple

import torch
from torch import Tensor
from torch.nn import Linear, Module
from torch.nn import functional as F
from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
from torch.nn.parameter import Parameter
import logging
from typing import Callable, List, Optional, Tuple, Union
from typing import TYPE_CHECKING
if TYPE_CHECKING:
    from torch.types import _dtype as DType
else:
    # The JIT doesn't understand Union, nor torch.dtype here
    DType = int

def _canonical_mask(
        mask: Optional[Tensor],
        mask_name: str,
        other_type: Optional[DType],
        other_name: str,
        target_type: DType,
        check_other: bool = True,
) -> Optional[Tensor]:

    if mask is not None:
        _mask_dtype = mask.dtype
        _mask_is_float = torch.is_floating_point(mask)
        if _mask_dtype != torch.bool and not _mask_is_float:
            raise AssertionError(
                f"only bool and floating types of {mask_name} are supported")
        if check_other and other_type is not None:
            if _mask_dtype != other_type:
                warnings.warn(
                    f"Support for mismatched {mask_name} and {other_name} "
                    "is deprecated. Use same type for both instead."
                )
        if not _mask_is_float:
            mask = (
                torch.zeros_like(mask, dtype=target_type)
                .masked_fill_(mask, float("-inf"))
            )
    return mask

def _in_projection_packed(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    w: Tensor,
    b: Optional[Tensor] = None,
) -> List[Tensor]:
    r"""
    Performs the in-projection step of the attention operation, using packed weights.
    Output is a triple containing projection tensors for query, key and value.

    Args:
        q, k, v: query, key and value tensors to be projected. For self-attention,
            these are typically the same tensor; for encoder-decoder attention,
            k and v are typically the same tensor. (We take advantage of these
            identities for performance if they are present.) Regardless, q, k and v
            must share a common embedding dimension; otherwise their shapes may vary.
        w: projection weights for q, k and v, packed into a single tensor. Weights
            are packed along dimension 0, in q, k, v order.
        b: optional projection biases for q, k and v, packed into a single tensor
            in q, k, v order.

    Shape:
        Inputs:
        - q: :math:`(..., E)` where E is the embedding dimension
        - k: :math:`(..., E)` where E is the embedding dimension
        - v: :math:`(..., E)` where E is the embedding dimension
        - w: :math:`(E * 3, E)` where E is the embedding dimension
        - b: :math:`E * 3` where E is the embedding dimension

        Output:
        - in output list :math:`[q', k', v']`, each output tensor will have the
            same shape as the corresponding input tensor.
    """
    E = q.size(-1)
    if k is v:
        if q is k:
            # self-attention
            proj = F.linear(q, w, b)
            # reshape to 3, E and not E, 3 is deliberate for better memory coalescing and keeping same order as chunk()
            proj = proj.unflatten(-1, (3, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
            return proj[0], proj[1], proj[2]
        else:
            # encoder-decoder attention
            w_q, w_kv = w.split([E, E * 2])
            if b is None:
                b_q = b_kv = None
            else:
                b_q, b_kv = b.split([E, E * 2])
            q_proj = F.linear(q, w_q, b_q)
            kv_proj = F.linear(k, w_kv, b_kv)
            # reshape to 2, E and not E, 2 is deliberate for better memory coalescing and keeping same order as chunk()
            kv_proj = kv_proj.unflatten(-1, (2, E)).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
            return (q_proj, kv_proj[0], kv_proj[1])
    else:
        w_q, w_k, w_v = w.chunk(3)
        if b is None:
            b_q = b_k = b_v = None
        else:
            b_q, b_k, b_v = b.chunk(3)
        return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v)

def _none_or_dtype(input: Optional[Tensor]) -> Optional[DType]:
    if input is None:
        return None
    elif isinstance(input, torch.Tensor):
        return input.dtype
    raise RuntimeError("input to _none_or_dtype() must be None or torch.Tensor")
class MultiheadAttention(Module):
    r"""Allows the model to jointly attend to information
    from different representation subspaces as described in the paper:
    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.

    Multi-Head Attention is defined as:

    .. math::
        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O

    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.

    ``forward()`` will use a special optimized implementation if all of the following
    conditions are met:

    - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This
      restriction will be loosened in the future.)
    - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad``
    - training is disabled (using ``.eval()``)
    - dropout is 0
    - ``add_bias_kv`` is ``False``
    - ``add_zero_attn`` is ``False``
    - ``batch_first`` is ``True`` and the input is batched
    - ``kdim`` and ``vdim`` are equal to ``embed_dim``
    - at most one of ``key_padding_mask`` or ``attn_mask`` is passed
    - if a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
      nor ``attn_mask`` is passed

    If the optimized implementation is in use, a
    `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
    ``query``/``key``/``value`` to represent padding more efficiently than using a
    padding mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_
    will be returned, and an additional speedup proportional to the fraction of the input
    that is padding can be expected.

    Args:
        embed_dim: Total dimension of the model.
        num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
            across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
        dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
        bias: If specified, adds bias to input / output projection layers. Default: ``True``.
        add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
        add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
            Default: ``False``.
        kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
        vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
        batch_first: If ``True``, then the input and output tensors are provided
            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).

    Examples::

        >>> # xdoctest: +SKIP
        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)

    """
    __constants__ = ["batch_first"]
    bias_k: Optional[torch.Tensor]
    bias_v: Optional[torch.Tensor]

    def __init__(
        self,
        embed_dim,
        num_heads,
        dropout=0.0,
        bias=True,
        add_bias_kv=False,
        add_zero_attn=False,
        kdim=None,
        vdim=None,
        batch_first=False,
        linear1_cls=Linear,
        linear2_cls=Linear,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super(MultiheadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self._qkv_same_embed_dim = (
            self.kdim == embed_dim and self.vdim == embed_dim
        )

        self.num_heads = num_heads
        self.dropout = dropout
        self.batch_first = batch_first
        self.head_dim = embed_dim // num_heads
        assert (
            self.head_dim * num_heads == self.embed_dim
        ), "embed_dim must be divisible by num_heads"

        if add_bias_kv:
            self.bias_k = Parameter(
                torch.empty((1, 1, embed_dim), **factory_kwargs)
            )
            self.bias_v = Parameter(
                torch.empty((1, 1, embed_dim), **factory_kwargs)
            )
        else:
            self.bias_k = self.bias_v = None

        if linear1_cls == Linear:
            if not self._qkv_same_embed_dim:
                self.q_proj_weight = Parameter(
                    torch.empty((embed_dim, embed_dim), **factory_kwargs)
                )
                self.k_proj_weight = Parameter(
                    torch.empty((embed_dim, self.kdim), **factory_kwargs)
                )
                self.v_proj_weight = Parameter(
                    torch.empty((embed_dim, self.vdim), **factory_kwargs)
                )
                self.register_parameter("in_proj_weight", None)
            else:
                # go down this route with voicecraft
                self.in_proj_weight = Parameter(
                    torch.empty((3 * embed_dim, embed_dim), **factory_kwargs)
                )
                self.register_parameter("q_proj_weight", None)
                self.register_parameter("k_proj_weight", None)
                self.register_parameter("v_proj_weight", None)

            if bias: # True by default
                self.in_proj_bias = Parameter(
                    torch.empty(3 * embed_dim, **factory_kwargs)
                )
            else:
                self.register_parameter("in_proj_bias", None)
            self.out_proj = NonDynamicallyQuantizableLinear(
                embed_dim, embed_dim, bias=bias, **factory_kwargs
            )

            self._reset_parameters()
        else:
            if not self._qkv_same_embed_dim:
                raise NotImplementedError
            else:
                self.in_proj_linear = linear1_cls(
                    embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs
                )
                self.in_proj_weight = self.in_proj_linear.weight

                self.register_parameter("q_proj_weight", None)
                self.register_parameter("k_proj_weight", None)
                self.register_parameter("v_proj_weight", None)

                if bias:
                    self.in_proj_bias = self.in_proj_linear.bias
                else:
                    self.register_parameter("in_proj_bias", None)

            self.out_proj = linear2_cls(
                embed_dim, embed_dim, bias=bias, **factory_kwargs
            )

            if self.bias_k is not None:
                xavier_normal_(self.bias_k)
            if self.bias_v is not None:
                xavier_normal_(self.bias_v)

        self.add_zero_attn = add_zero_attn

    def _reset_parameters(self):
        if self._qkv_same_embed_dim:
            xavier_uniform_(self.in_proj_weight)
        else:
            xavier_uniform_(self.q_proj_weight)
            xavier_uniform_(self.k_proj_weight)
            xavier_uniform_(self.v_proj_weight)

        if self.in_proj_bias is not None:
            constant_(self.in_proj_bias, 0.0)
            constant_(self.out_proj.bias, 0.0)

        if self.bias_k is not None:
            xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            xavier_normal_(self.bias_v)

    def __setstate__(self, state):
        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
        if "_qkv_same_embed_dim" not in state:
            state["_qkv_same_embed_dim"] = True

        super(MultiheadAttention, self).__setstate__(state)

    def forward(
        self,
        query: Tensor,
        key: Tensor,
        value: Tensor,
        key_padding_mask: Optional[Tensor] = None,
        need_weights: bool = True,
        attn_mask: Optional[Tensor] = None,
        average_attn_weights: bool = True,
        past: Optional[Tensor] = None,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Args:
            query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
                or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
                :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
                Queries are compared against key-value pairs to produce the output.
                See "Attention Is All You Need" for more details.
            key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
                or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
                :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
                See "Attention Is All You Need" for more details.
            value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
                ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
                sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
                See "Attention Is All You Need" for more details.
            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
                to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
                Binary and byte masks are supported.
                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
                the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
                Default: ``True``.
            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
                the attention weight.
            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
                heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
                effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)

        Outputs:
            - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
              :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
              where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
              embedding dimension ``embed_dim``.
            - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
              returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
              head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.

            .. note::
                `batch_first` argument is ignored for unbatched inputs.
        """
        is_batched = query.dim() == 3
        if key_padding_mask is not None:
            _kpm_dtype = key_padding_mask.dtype
            if _kpm_dtype != torch.bool and not torch.is_floating_point(
                key_padding_mask
            ):
                raise AssertionError(
                    "only bool and floating types of key_padding_mask are supported"
                )
        why_not_fast_path = ""
        if not is_batched:
            why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
        elif query is not key or key is not value:
            # When lifting this restriction, don't forget to either
            # enforce that the dtypes all match or test cases where
            # they don't!
            why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
        elif (
            self.in_proj_bias is not None
            and query.dtype != self.in_proj_bias.dtype
        ):
            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
        elif (
            self.in_proj_weight is not None
            and query.dtype != self.in_proj_weight.dtype
        ):
            # this case will fail anyway, but at least they'll get a useful error message.
            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
        elif self.training:
            why_not_fast_path = "training is enabled"
        elif not self.batch_first:
            why_not_fast_path = "batch_first was not True"
        elif self.bias_k is not None:
            why_not_fast_path = "self.bias_k was not None"
        elif self.bias_v is not None:
            why_not_fast_path = "self.bias_v was not None"
        elif self.dropout:
            why_not_fast_path = f"dropout was {self.dropout}, required zero"
        elif self.add_zero_attn:
            why_not_fast_path = "add_zero_attn was enabled"
        elif not self._qkv_same_embed_dim:
            why_not_fast_path = "_qkv_same_embed_dim was not True"
        elif attn_mask is not None:
            why_not_fast_path = "attn_mask was not None"
        elif query.is_nested and key_padding_mask is not None:
            why_not_fast_path = (
                "key_padding_mask is not supported with NestedTensor input"
            )
        elif self.num_heads % 2 == 1:
            why_not_fast_path = "num_heads is odd"
        elif torch.is_autocast_enabled():
            why_not_fast_path = "autocast is enabled"

        if not why_not_fast_path:
            tensor_args = (
                query,
                key,
                value,
                self.in_proj_weight,
                self.in_proj_bias,
                self.out_proj.weight,
                self.out_proj.bias,
            )
            # We have to use list comprehensions below because TorchScript does not support
            # generator expressions.
            if torch.overrides.has_torch_function(tensor_args):
                why_not_fast_path = "some Tensor argument has_torch_function"
            elif not all(
                [
                    (x is None or x.is_cuda or "cpu" in str(x.device))
                    for x in tensor_args
                ]
            ):
                why_not_fast_path = (
                    "some Tensor argument is neither CUDA nor CPU"
                )
            elif torch.is_grad_enabled() and any(
                [x is not None and x.requires_grad for x in tensor_args]
            ):
                why_not_fast_path = (
                    "grad is enabled and at least one of query or the "
                    "input/output projection weights or biases requires_grad"
                )
            if not why_not_fast_path:
                return torch._native_multi_head_attention(
                    query,
                    key,
                    value,
                    self.embed_dim,
                    self.num_heads,
                    self.in_proj_weight,
                    self.in_proj_bias,
                    self.out_proj.weight,
                    self.out_proj.bias,
                    key_padding_mask
                    if key_padding_mask is not None
                    else attn_mask,
                    need_weights,
                    average_attn_weights,
                    1
                    if key_padding_mask is not None
                    else 0
                    if attn_mask is not None
                    else None,
                )

        any_nested = query.is_nested or key.is_nested or value.is_nested
        assert not any_nested, (
            "MultiheadAttention does not support NestedTensor outside of its fast path. "
            + f"The fast path was not hit because {why_not_fast_path}"
        )

        if self.batch_first and is_batched:
            # make sure that the transpose op does not affect the "is" property
            if key is value:
                if query is key:
                    query = key = value = query.transpose(1, 0)
                else:
                    query, key = [x.transpose(1, 0) for x in (query, key)]
                    value = key
            else:
                query, key, value = [
                    x.transpose(1, 0) for x in (query, key, value)
                ]

        if not self._qkv_same_embed_dim:
            attn_output, attn_output_weights = F.multi_head_attention_forward(
                query,
                key,
                value,
                self.embed_dim,
                self.num_heads,
                self.in_proj_weight,
                self.in_proj_bias,
                self.bias_k,
                self.bias_v,
                self.add_zero_attn,
                self.dropout,
                self.out_proj.weight,
                self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=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,
                average_attn_weights=average_attn_weights,
            )
        else:
            # re-write the self.attention here, to get k, v cache
            tgt_len, bsz, embed_dim = query.shape
            src_len, _, _ = key.shape
            num_heads = self.num_heads
            key_padding_mask = _canonical_mask(
                mask=key_padding_mask,
                mask_name="key_padding_mask",
                other_type=_none_or_dtype(attn_mask),
                other_name="attn_mask",
                target_type=query.dtype
            )
            attn_mask = _canonical_mask(
                            mask=attn_mask,
                            mask_name="attn_mask",
                            other_type=None,
                            other_name="",
                            target_type=query.dtype,
                            check_other=False,
                            )
            head_dim = self.embed_dim // self.num_heads
            assert head_dim * self.num_heads == self.embed_dim, f"embed_dim {self.embed_dim} not divisible by num_heads {self.num_heads}"
            assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"
            q, k, v = _in_projection_packed(query, key, value, self.in_proj_weight, self.in_proj_bias)
            # k_present, v_present = k, v
            
            #
            # reshape q, k, v for multihead attention and make em batch first
            #
            
            q = q.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
            k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
            v = v.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1) # (bsz * num_heads, src_len, head_dim)
            src_len = k.size(1)
            if past is not None and past.ndim > 2:
                expected_src_len = src_len + past[0].shape[-2]
            else:
                expected_src_len = src_len


            # ensure attn_mask's dim is 3
            if attn_mask.dim() == 2:
                correct_2d_size = (tgt_len, expected_src_len)
                if attn_mask.shape != correct_2d_size:
                    raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
                attn_mask = attn_mask.unsqueeze(0)
            elif attn_mask.dim() == 3:
                correct_3d_size = (bsz * num_heads, tgt_len, expected_src_len)
                if attn_mask.shape != correct_3d_size:
                    raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
            else:
                raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
            
            if key_padding_mask is not None:
                assert key_padding_mask.shape == (bsz, expected_src_len), \
                    f"expecting key_padding_mask shape of {(bsz, expected_src_len)}, but got {key_padding_mask.shape}"
                key_padding_mask = key_padding_mask.view(bsz, 1, 1, expected_src_len).   \
                    expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, expected_src_len)
                if attn_mask is None:
                    attn_mask = key_padding_mask
                else:
                    attn_mask = attn_mask + key_padding_mask
            
            if not self.training:
                dropout_p = 0.0
            else:
                dropout_p = self.dropout

            if need_weights:
                raise NotImplementedError("need_weights not implemented for voicecraft")
                # B, Nt, E = q.shape
                # q_scaled = q / math.sqrt(E)

                # assert not (is_causal and attn_mask is None), "FIXME: is_causal not implemented for need_weights"

                # if attn_mask is not None:
                #     attn_output_weights = torch.baddbmm(attn_mask, q_scaled, k.transpose(-2, -1))
                # else:
                #     attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1))
                # attn_output_weights = softmax(attn_output_weights, dim=-1)
                # if dropout_p > 0.0:
                #     attn_output_weights = dropout(attn_output_weights, p=dropout_p)

                # attn_output = torch.bmm(attn_output_weights, v)

                # attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
                # attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
                # attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))

                # # optionally average attention weights over heads
                # attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
                # if average_attn_weights:
                #     attn_output_weights = attn_output_weights.mean(dim=1)

                # if not is_batched:
                #     # squeeze the output if input was unbatched
                #     attn_output = attn_output.squeeze(1)
                #     attn_output_weights = attn_output_weights.squeeze(0)
                # return attn_output, attn_output_weights
            else:
                # attn_mask can be either (L,S) or (N*num_heads, L, S)
                # if attn_mask's shape is (1, L, S) we need to unsqueeze to (1, 1, L, S)
                # in order to match the input for SDPA of (N, num_heads, L, S)
                if attn_mask is not None:
                    if attn_mask.size(0) == 1 and attn_mask.dim() == 3:
                        attn_mask = attn_mask.unsqueeze(0)
                    else:
                        attn_mask = attn_mask.view(bsz, num_heads, -1, expected_src_len)

                q = q.view(bsz, num_heads, tgt_len, head_dim)
                k = k.view(bsz, num_heads, src_len, head_dim)
                v = v.view(bsz, num_heads, src_len, head_dim)
                # logging.info(f"shape of past: {past.shape}")
                if past is not None:
                    present = torch.stack([k, v], dim=0) # (2, bsz, num_heads, src_len, head_dim)
                    if past.ndim > 2: # this means we use kvcache, otherwise we just pass in a placeholder, but not actually using kvcache
                        pk, pv = past
                        k = torch.cat([pk, k], dim=-2)
                        v = torch.cat([pv, v], dim=-2)
                else:
                    present = None
                attn_output = F.scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal=False)
                attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)

                attn_output = F.linear(attn_output, self.out_proj.weight, self.out_proj.bias)
                attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
                if not is_batched:
                    # squeeze the output if input was unbatched
                    attn_output = attn_output.squeeze(1)
                # if self.training:
                #     return attn_output, None
                # else:
                #     return (attn_output, present), None

        # harded coded, the code do not support returning attn weigths yet
        attn_output_weights=None
        if self.batch_first and is_batched:
            return attn_output.transpose(1, 0), present
        else:
            return attn_output, present