att_utils.py

import torch.nn as nn
from typing import Optional, Tuple
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
from torch import Tensor
import torch.nn.functional as F
from torch._torch_docs import reproducibility_notes
from torch._C import _add_docstr
from torch.overrides import has_torch_function, handle_torch_function
import math
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
import warnings

linear = torch._C._nn.linear

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")

sparse_support_notes = {
    "sparse_beta_warning": """
.. warning::
    Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported,
    or may not have autograd support. If you notice missing functionality please
    open a feature request.""",
}

def scaled_dot_product_attention(
    q: Tensor,
    k: Tensor,
    v: Tensor,
    attn_mask: Optional[Tensor] = None,
    dropout_p: float = 0.0,
) -> Tuple[Tensor, Tensor]:
    r"""
    Computes scaled dot product attention on query, key and value tensors, using
    an optional attention mask if passed, and applying dropout if a probability
    greater than 0.0 is specified.
    Returns a tensor pair containing attended values and attention weights.

    Args:
        q, k, v: query, key and value tensors. See Shape section for shape details.
        attn_mask: optional tensor containing mask values to be added to calculated
            attention. May be 2D or 3D; see Shape section for details.
        dropout_p: dropout probability. If greater than 0.0, dropout is applied.

    Shape:
        - q: :math:`(B, Nt, E)` where B is batch size, Nt is the target sequence length,
            and E is embedding dimension.
        - key: :math:`(B, Ns, E)` where B is batch size, Ns is the source sequence length,
            and E is embedding dimension.
        - value: :math:`(B, Ns, E)` where B is batch size, Ns is the source sequence length,
            and E is embedding dimension.
        - attn_mask: either a 3D tensor of shape :math:`(B, Nt, Ns)` or a 2D tensor of
            shape :math:`(Nt, Ns)`.

        - Output: attention values have shape :math:`(B, Nt, E)`; attention weights
            have shape :math:`(B, Nt, Ns)`
    """
    B, Nt, E = q.shape
    q = q / math.sqrt(E)
    # (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns)
    if attn_mask is not None:
        attn = torch.baddbmm(attn_mask, q, k.transpose(-2, -1))
    else:
        attn = torch.bmm(q, k.transpose(-2, -1))
    
    attn = F.softmax(attn, dim=-1)

    if dropout_p > 0.0:
        attn = F.dropout(attn, p=dropout_p)
    # (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E)
    output = torch.bmm(attn, v)
    return output, attn

def _mha_shape_check(query: Tensor, key: Tensor, value: Tensor,
                     key_padding_mask: Optional[Tensor], attn_mask: Optional[Tensor], num_heads: int):
    # Verifies the expected shape for `query, `key`, `value`, `key_padding_mask` and `attn_mask`
    # and returns if the input is batched or not.
    # Raises an error if `query` is not 2-D (unbatched) or 3-D (batched) tensor.

    # Shape check.
    if query.dim() == 3:
        # Batched Inputs
        is_batched = True
        assert key.dim() == 3 and value.dim() == 3, \
            ("For batched (3-D) `query`, expected `key` and `value` to be 3-D"
             f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")
        if key_padding_mask is not None:
            assert key_padding_mask.dim() == 2, \
                ("For batched (3-D) `query`, expected `key_padding_mask` to be `None` or 2-D"
                 f" but found {key_padding_mask.dim()}-D tensor instead")
        if attn_mask is not None:
            assert attn_mask.dim() in (2, 3), \
                ("For batched (3-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
                 f" but found {attn_mask.dim()}-D tensor instead")
    elif query.dim() == 2:
        # Unbatched Inputs
        is_batched = False
        assert key.dim() == 2 and value.dim() == 2, \
            ("For unbatched (2-D) `query`, expected `key` and `value` to be 2-D"
             f" but found {key.dim()}-D and {value.dim()}-D tensors respectively")

        if key_padding_mask is not None:
            assert key_padding_mask.dim() == 1, \
                ("For unbatched (2-D) `query`, expected `key_padding_mask` to be `None` or 1-D"
                 f" but found {key_padding_mask.dim()}-D tensor instead")

        if attn_mask is not None:
            assert attn_mask.dim() in (2, 3), \
                ("For unbatched (2-D) `query`, expected `attn_mask` to be `None`, 2-D or 3-D"
                 f" but found {attn_mask.dim()}-D tensor instead")
            if attn_mask.dim() == 3:
                expected_shape = (num_heads, query.shape[0], key.shape[0])
                assert attn_mask.shape == expected_shape, \
                    (f"Expected `attn_mask` shape to be {expected_shape} but got {attn_mask.shape}")
    else:
        raise AssertionError(
            f"query should be unbatched 2D or batched 3D tensor but received {query.dim()}-D query tensor")

    return is_batched

def multi_head_attention_forward(
    query: Tensor,
    key: Tensor,
    value: Tensor,
    embed_dim_to_check: int,
    num_heads: int,
    in_proj_weight: Optional[Tensor],
    in_proj_bias: Optional[Tensor],
    bias_k: Optional[Tensor],
    bias_v: Optional[Tensor],
    add_zero_attn: bool,
    dropout_p: float,
    out_proj_weight: Tensor,
    out_proj_bias: Optional[Tensor],
    training: bool = True,
    key_padding_mask: Optional[Tensor] = None,
    need_weights: bool = True,
    attn_mask: Optional[Tensor] = None,
    use_separate_proj_weight: bool = False,
    q_proj_weight: Optional[Tensor] = None,
    k_proj_weight: Optional[Tensor] = None,
    v_proj_weight: Optional[Tensor] = None,
    static_k: Optional[Tensor] = None,
    static_v: Optional[Tensor] = None,
    average_attn_weights: bool = True,
) -> Tuple[Tensor, Optional[Tensor]]:
    r"""
    Args:
        query, key, value: map a query and a set of key-value pairs to an output.
            See "Attention Is All You Need" for more details.
        embed_dim_to_check: total dimension of the model.
        num_heads: parallel attention heads.
        in_proj_weight, in_proj_bias: input projection weight and bias.
        bias_k, bias_v: bias of the key and value sequences to be added at dim=0.
        add_zero_attn: add a new batch of zeros to the key and
                       value sequences at dim=1.
        dropout_p: probability of an element to be zeroed.
        out_proj_weight, out_proj_bias: the output projection weight and bias.
        training: apply dropout if is ``True``.
        key_padding_mask: if provided, specified padding elements in the key will
            be ignored by the attention. This is an binary mask. When the value is True,
            the corresponding value on the attention layer will be filled with -inf.
        need_weights: output attn_output_weights.
        attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
        use_separate_proj_weight: the function accept the proj. weights for query, key,
            and value in different forms. If false, in_proj_weight will be used, which is
            a combination of q_proj_weight, k_proj_weight, v_proj_weight.
        q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.
        static_k, static_v: static key and value used for attention operators.
        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


    Shape:
        Inputs:
        - query: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
          the embedding dimension.
        - key: :math:`(S, E)` or :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
          the embedding dimension.
        - value: :math:`(S, E)` or :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
          the embedding dimension.
        - key_padding_mask: :math:`(S)` or :math:`(N, S)` where N is the batch size, S is the source sequence length.
          If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions
          will be unchanged. If a BoolTensor is provided, the positions with the
          value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
          S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked
          positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
          while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
          are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
          is provided, it will be added to the attention weight.
        - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
        - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.

        Outputs:
        - attn_output: :math:`(L, E)` or :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
          E is the embedding dimension.
        - 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_weights=False``, returns attention weights per
          head of shape :math:`(num_heads, L, S)` when input is unbatched or :math:`(N, num_heads, L, S)`.
    """
    tens_ops = (query, key, value, in_proj_weight, in_proj_bias, bias_k, bias_v, out_proj_weight, out_proj_bias)
    if has_torch_function(tens_ops):
        return handle_torch_function(
            multi_head_attention_forward,
            tens_ops,
            query,
            key,
            value,
            embed_dim_to_check,
            num_heads,
            in_proj_weight,
            in_proj_bias,
            bias_k,
            bias_v,
            add_zero_attn,
            dropout_p,
            out_proj_weight,
            out_proj_bias,
            training=training,
            key_padding_mask=key_padding_mask,
            need_weights=need_weights,
            attn_mask=attn_mask,
            use_separate_proj_weight=use_separate_proj_weight,
            q_proj_weight=q_proj_weight,
            k_proj_weight=k_proj_weight,
            v_proj_weight=v_proj_weight,
            static_k=static_k,
            static_v=static_v,
            average_attn_weights=average_attn_weights,
        )

    is_batched = _mha_shape_check(query, key, value, key_padding_mask, attn_mask, num_heads)

    # For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
    # is batched, run the computation and before returning squeeze the
    # batch dimension so that the output doesn't carry this temporary batch dimension.
    if not is_batched:
        # unsqueeze if the input is unbatched
        query = query.unsqueeze(1)
        key = key.unsqueeze(1)
        value = value.unsqueeze(1)
        if key_padding_mask is not None:
            key_padding_mask = key_padding_mask.unsqueeze(0)

    # set up shape vars
    tgt_len, bsz, embed_dim = query.shape
    src_len, _, _ = key.shape
    assert embed_dim == embed_dim_to_check, \
        f"was expecting embedding dimension of {embed_dim_to_check}, but got {embed_dim}"
    if isinstance(embed_dim, torch.Tensor):
        # embed_dim can be a tensor when JIT tracing
        head_dim = embed_dim.div(num_heads, rounding_mode='trunc')
    else:
        head_dim = embed_dim // num_heads
    assert head_dim * num_heads == embed_dim, f"embed_dim {embed_dim} not divisible by num_heads {num_heads}"
    if use_separate_proj_weight:
        # allow MHA to have different embedding dimensions when separate projection weights are used
        assert key.shape[:2] == value.shape[:2], \
            f"key's sequence and batch dims {key.shape[:2]} do not match value's {value.shape[:2]}"
    else:
        assert key.shape == value.shape, f"key shape {key.shape} does not match value shape {value.shape}"

    #
    # compute in-projection
    #
    if not use_separate_proj_weight:
        assert in_proj_weight is not None, "use_separate_proj_weight is False but in_proj_weight is None"
        q, k, v = F._in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
    else:
        assert q_proj_weight is not None, "use_separate_proj_weight is True but q_proj_weight is None"
        assert k_proj_weight is not None, "use_separate_proj_weight is True but k_proj_weight is None"
        assert v_proj_weight is not None, "use_separate_proj_weight is True but v_proj_weight is None"
        if in_proj_bias is None:
            b_q = b_k = b_v = None
        else:
            b_q, b_k, b_v = in_proj_bias.chunk(3)
        q, k, v = F._in_projection(query, key, value, q_proj_weight, k_proj_weight, v_proj_weight, b_q, b_k, b_v)

    # prep attention mask
    if attn_mask is not None:
        if attn_mask.dtype == torch.uint8:
            warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
            attn_mask = attn_mask.to(torch.bool)
        else:
            assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \
                f"Only float, byte, and bool types are supported for attn_mask, not {attn_mask.dtype}"
        # ensure attn_mask's dim is 3
        if attn_mask.dim() == 2:
            correct_2d_size = (tgt_len, 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, 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")

    # prep key padding mask
    if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
        warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
        key_padding_mask = key_padding_mask.to(torch.bool)

    # add bias along batch dimension (currently second)
    if bias_k is not None and bias_v is not None:
        assert static_k is None, "bias cannot be added to static key."
        assert static_v is None, "bias cannot be added to static value."
        k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
        v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
        if attn_mask is not None:
            attn_mask = F.pad(attn_mask, (0, 1))
        if key_padding_mask is not None:
            key_padding_mask = F.pad(key_padding_mask, (0, 1))
    else:
        assert bias_k is None
        assert bias_v is None

    #
    # reshape q, k, v for multihead attention and make em batch first
    #
    q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
    if static_k is None:
        k = k.contiguous().view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
    else:
        # TODO finish disentangling control flow so we don't do in-projections when statics are passed
        assert static_k.size(0) == bsz * num_heads, \
            f"expecting static_k.size(0) of {bsz * num_heads}, but got {static_k.size(0)}"
        assert static_k.size(2) == head_dim, \
            f"expecting static_k.size(2) of {head_dim}, but got {static_k.size(2)}"
        k = static_k
    if static_v is None:
        v = v.contiguous().view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
    else:
        # TODO finish disentangling control flow so we don't do in-projections when statics are passed
        assert static_v.size(0) == bsz * num_heads, \
            f"expecting static_v.size(0) of {bsz * num_heads}, but got {static_v.size(0)}"
        assert static_v.size(2) == head_dim, \
            f"expecting static_v.size(2) of {head_dim}, but got {static_v.size(2)}"
        v = static_v

    # add zero attention along batch dimension (now first)
    if add_zero_attn:
        zero_attn_shape = (bsz * num_heads, 1, head_dim)
        k = torch.cat([k, torch.zeros(zero_attn_shape, dtype=k.dtype, device=k.device)], dim=1)
        v = torch.cat([v, torch.zeros(zero_attn_shape, dtype=v.dtype, device=v.device)], dim=1)
        if attn_mask is not None:
            attn_mask = F.pad(attn_mask, (0, 1))
        if key_padding_mask is not None:
            key_padding_mask = F.pad(key_padding_mask, (0, 1))

    # update source sequence length after adjustments
    src_len = k.size(1)

    # merge key padding and attention masks
    if key_padding_mask is not None:
        assert key_padding_mask.shape == (bsz, src_len), \
            f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
        key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len).   \
            expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
        if attn_mask is None:
            attn_mask = key_padding_mask
        elif attn_mask.dtype == torch.bool:
            attn_mask = attn_mask.logical_or(key_padding_mask)
        else:
            attn_mask = attn_mask.masked_fill(key_padding_mask, float("-inf"))

    # convert mask to float
    if attn_mask is not None and attn_mask.dtype == torch.bool:
        new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
        new_attn_mask.masked_fill_(attn_mask, float("-inf"))
        attn_mask = new_attn_mask

    # adjust dropout probability
    if not training:
        dropout_p = 0.0

    #
    # (deep breath) calculate attention and out projection
    #
    attn_output, attn_output_weights = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p)
    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))

    if need_weights:
        # 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.sum(dim=1) / num_heads

        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:
        if not is_batched:
            # squeeze the output if input was unbatched
            attn_output = attn_output.squeeze(1)
        return attn_output, None
    
class MultiheadAttention(nn.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::

        >>> 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., bias=True, add_bias_kv=False, add_zero_attn=False,
                 kdim=None, vdim=None, batch_first=False, 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 self._qkv_same_embed_dim is False:
            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:
            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:
            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)

        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

        self.add_zero_attn = add_zero_attn

        self._reset_parameters()

    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.)
            constant_(self.out_proj.bias, 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) -> 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 byte mask, a non-zero value indicates that the corresponding ``key``
            value will be ignored.
        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_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 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 = 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:
            attn_output, attn_output_weights = 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, average_attn_weights=average_attn_weights)
        if self.batch_first and is_batched:
            return attn_output.transpose(1, 0), attn_output_weights
        else:
            return attn_output, attn_output_weights