modules.py

import os, sys

sys.path.insert(1, os.getcwd())
import torch
from utils import init_weights
import warnings


class Block_UNET(torch.nn.Module):
    """
    A down block of U-net
    """

    def __init__(
        self,
        channels_in,
        channels_out,
        channels_mid=None,
        activation=torch.nn.ReLU,
        batchnorm=True,
        **kwargs,
    ):
        super(Block_UNET, self).__init__(**kwargs)
        if channels_mid is None:
            channels_mid = channels_out
        else:
            assert isinstance(channels_mid, int)
        self.layers = torch.nn.Sequential(
            torch.nn.Conv2d(
                channels_in,
                channels_mid,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=not batchnorm,
            ),
            torch.nn.BatchNorm2d(channels_mid, track_running_stats=False) if batchnorm else torch.nn.Identity(),
            activation(inplace=True),
            torch.nn.Conv2d(
                channels_mid,
                channels_out,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=not batchnorm,
            ),
            torch.nn.BatchNorm2d(channels_out, track_running_stats=False) if batchnorm else torch.nn.Identity(),
            activation(inplace=True),
        )

    def to(self, device):
        super().to(device)
        self.layers.to(device)

    def parameters(self):
        return list(self.layers.parameters())

    def forward(self, input):
        return self.layers(input)


class Block_UNET_simple(torch.nn.Module):
    """
    A down block of U-net
    """

    def __init__(
        self,
        channels_in,
        channels_out,
        channels_mid=None,
        activation=torch.nn.ReLU,
        batchnorm=True,
        **kwargs,
    ):
        super(Block_UNET_simple, self).__init__(**kwargs)
        self.layers = torch.nn.Sequential(
            torch.nn.Conv2d(
                channels_in,
                channels_out,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=not batchnorm,
            ),
            torch.nn.BatchNorm2d(channels_out, track_running_stats=False) if batchnorm else torch.nn.Identity(),
            activation(inplace=True),
        )

    def to(self, device):
        super().to(device)
        self.layers.to(device)

    def parameters(self):
        return list(self.layers.parameters())

    def forward(self, input):
        return self.layers(input)


class ResidualBlock(torch.nn.Module):
    def __init__(self, len_in, width=None, depth=2, kernel_size=3, stride=1, padding=0, activation=torch.nn.ReLU):
        super(ResidualBlock, self).__init__()
        if width is None:
            width = 2 * len_in
        layers = []
        for idx_layer in range(depth):
            if idx_layer == 0:
                dim_in = len_in
            else:
                dim_in = width
            if idx_layer == depth - 1:
                dim_out = len_in
            else:
                dim_out = width
            layers.append(activation(inplace=idx_layer > 0))
            layers.append(torch.nn.Conv2d(dim_in, dim_out, kernel_size, stride=stride, padding=padding))
        self.layers = torch.nn.Sequential(*layers)
        init_weights(self.layers)

    def parameters(self):
        parameters = []
        parameters += list(self.layers.parameters())
        return parameters

    def forward(self, input_tensor):
        return input_tensor + self.layers(input_tensor)


class ResidualBlock_Original(torch.nn.Module):
    def __init__(self, len_in, width=None, depth=2, kernel_size=3, stride=1, padding=0, activation=torch.nn.ReLU):
        super(ResidualBlock_Original, self).__init__()
        self.len_in = len_in
        if width is None:
            width = 2 * len_in
        layers = []
        for idx_layer in range(depth):
            if idx_layer == 0:
                dim_in = len_in
            else:
                dim_in = width
            if idx_layer == depth - 1:
                dim_out = len_in
            else:
                dim_out = width
            layers.append(torch.nn.Conv2d(dim_in, dim_out, kernel_size, stride=stride, padding=padding))
            if idx_layer != depth - 1:
                layers.append(activation(inplace=True))
        self.layers = torch.nn.Sequential(*layers)
        self.activation = activation

    def parameters(self):
        parameters = []
        for layer in self.layers:
            parameters += list(layer.parameters())
        return parameters

    def forward(self, input_tensor):
        return self.activation()(input_tensor + self.layers(input_tensor))


import warnings
from typing import Optional, Tuple

import torch
from torch import Tensor
import math
import warnings

from torch.nn.functional import _mha_shape_check, _in_projection_packed, _in_projection, pad, softmax, linear, dropout


class TopKMultiheadAttention(torch.nn.MultiheadAttention):
    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,
        device=None,
        dtype=None,
        size_bottleneck=4,
        no_out_proj=False,
    ) -> None:
        super(TopKMultiheadAttention, self).__init__(
            embed_dim,
            num_heads,
            dropout=dropout,
            bias=bias,
            add_bias_kv=add_bias_kv,
            add_zero_attn=add_zero_attn,
            kdim=kdim,
            vdim=vdim,
            batch_first=batch_first,
            device=device,
            dtype=dtype,
        )
        self.size_bottleneck = size_bottleneck
        self.no_out_proj = no_out_proj
        if self.no_out_proj:
            self.out_proj = None

    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,
        set_q_proj_weight_zero: bool = False,
    ) -> 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")

        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 self.no_out_proj:
            out_proj_weight = None
            out_proj_bias = None
        else:
            out_proj_weight = self.out_proj.weight
            out_proj_bias = self.out_proj.bias

        if set_q_proj_weight_zero and self.in_proj_weight is not None:
            with torch.no_grad():
                self.in_proj_weight[: self.in_proj_weight.shape[0] // 3, :] = 0.0

        if not self._qkv_same_embed_dim:
            (
                attn_output,
                attn_output_weights,
            ) = bottlenecked_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,
                out_proj_weight,
                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,
                size_bottleneck=self.size_bottleneck,
                no_out_proj=self.no_out_proj,
            )
        else:
            (
                attn_output,
                attn_output_weights,
            ) = bottlenecked_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,
                out_proj_weight,
                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,
                size_bottleneck=self.size_bottleneck,
                no_out_proj=self.no_out_proj,
            )
        if self.batch_first and is_batched:
            return attn_output.transpose(1, 0), attn_output_weights
        else:
            return attn_output, attn_output_weights


def bottlenecked_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: Optional[Tensor],
    out_proj_bias: Optional[Tensor],
    no_out_proj: bool = False,
    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,
    size_bottleneck: int = 8,
) -> 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 FloatTensor is provided, it will be directly added to the value.
          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_attn_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)`.
    """

    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
    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")
    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 = _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 = _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")

    # 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 = pad(attn_mask, (0, 1))
        if key_padding_mask is not None:
            key_padding_mask = 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.view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
    if static_k is None:
        k = k.view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
    else:
        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.view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1)
    else:
        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 = pad(attn_mask, (0, 1))
        if key_padding_mask is not None:
            key_padding_mask = 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

    B, Nt, E = q.shape
    q_scaled = q / math.sqrt(E)
    if attn_mask is not None:
        attn_output_logits = torch.baddbmm(attn_mask, q_scaled, k.transpose(-2, -1))
    else:
        attn_output_logits = torch.bmm(q_scaled, k.transpose(-2, -1))
    if size_bottleneck < attn_output_logits.shape[-1]:
        logits_topk, indices_topk = torch.topk(attn_output_logits, dim=-1, k=size_bottleneck, sorted=False)
        attn_weights_topk = softmax(logits_topk, dim=-1)
        attn_output_weights = torch.zeros_like(attn_output_logits).scatter_(-1, indices_topk, attn_weights_topk)
    else:
        attn_output_weights = softmax(attn_output_logits, 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)
    if not no_out_proj:
        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:
            attn_output = attn_output.squeeze(1)  # squeeze the output if input was unbatched
            attn_output_weights = attn_output_weights.squeeze(0)
        return attn_output, attn_output_weights
    else:
        if not is_batched:
            attn_output = attn_output.squeeze(1)  # squeeze the output if input was unbatched
        return attn_output, None