mmclassification/mmpretrain/models/utils/attention.py

# Copyright (c) OpenMMLab. All rights reserved.
import itertools
import warnings
from functools import partial
from typing import List, Optional, Union

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.drop import build_dropout
from mmengine.model import BaseModule
from mmengine.model.weight_init import trunc_normal_
from mmengine.utils import digit_version

from mmpretrain.registry import MODELS
from .helpers import to_2tuple
from .layer_scale import LayerScale

# After pytorch v1.10.0, use torch.meshgrid without indexing
# will raise extra warning. For more details,
# refers to https://github.com/pytorch/pytorch/issues/50276
if digit_version(torch.__version__) >= digit_version('1.10.0'):
    torch_meshgrid = partial(torch.meshgrid, indexing='ij')
else:
    torch_meshgrid = torch.meshgrid


def scaled_dot_product_attention_pyimpl(query,
                                        key,
                                        value,
                                        attn_mask=None,
                                        dropout_p=0.,
                                        scale=None,
                                        is_causal=False):
    scale = scale or query.size(-1)**0.5
    if is_causal and attn_mask is not None:
        attn_mask = torch.ones(
            query.size(-2), key.size(-2), dtype=torch.bool).tril(diagonal=0)
    if attn_mask is not None and attn_mask.dtype == torch.bool:
        attn_mask = attn_mask.masked_fill(not attn_mask, -float('inf'))

    attn_weight = query @ key.transpose(-2, -1) / scale
    if attn_mask is not None:
        attn_weight += attn_mask
    attn_weight = torch.softmax(attn_weight, dim=-1)
    attn_weight = torch.dropout(attn_weight, dropout_p, True)
    return attn_weight @ value


if digit_version(torch.__version__) >= digit_version('2.0.0'):
    scaled_dot_product_attention = F.scaled_dot_product_attention
else:
    scaled_dot_product_attention = scaled_dot_product_attention_pyimpl


class WindowMSA(BaseModule):
    """Window based multi-head self-attention (W-MSA) module with relative
    position bias.

    Args:
        embed_dims (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
            Defaults to True.
        qk_scale (float, optional): Override default qk scale of
            ``head_dim ** -0.5`` if set. Defaults to None.
        attn_drop (float, optional): Dropout ratio of attention weight.
            Defaults to 0.
        proj_drop (float, optional): Dropout ratio of output. Defaults to 0.
        init_cfg (dict, optional): The extra config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 window_size,
                 num_heads,
                 qkv_bias=True,
                 qk_scale=None,
                 attn_drop=0.,
                 proj_drop=0.,
                 init_cfg=None):

        super().__init__(init_cfg)
        self.embed_dims = embed_dims
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_embed_dims = embed_dims // num_heads
        self.scale = qk_scale or head_embed_dims**-0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # About 2x faster than original impl
        Wh, Ww = self.window_size
        rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
        rel_position_index = rel_index_coords + rel_index_coords.T
        rel_position_index = rel_position_index.flip(1).contiguous()
        self.register_buffer('relative_position_index', rel_position_index)

        self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(embed_dims, embed_dims)
        self.proj_drop = nn.Dropout(proj_drop)

        self.softmax = nn.Softmax(dim=-1)

    def init_weights(self):
        super(WindowMSA, self).init_weights()

        trunc_normal_(self.relative_position_bias_table, std=0.02)

    def forward(self, x, mask=None):
        """
        Args:

            x (tensor): input features with shape of (num_windows*B, N, C)
            mask (tensor, Optional): mask with shape of (num_windows, Wh*Ww,
                Wh*Ww), value should be between (-inf, 0].
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads,
                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[
            2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[
            self.relative_position_index.view(-1)].view(
                self.window_size[0] * self.window_size[1],
                self.window_size[0] * self.window_size[1],
                -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(
            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N,
                             N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    @staticmethod
    def double_step_seq(step1, len1, step2, len2):
        seq1 = torch.arange(0, step1 * len1, step1)
        seq2 = torch.arange(0, step2 * len2, step2)
        return (seq1[:, None] + seq2[None, :]).reshape(1, -1)


class WindowMSAV2(BaseModule):
    """Window based multi-head self-attention (W-MSA) module with relative
    position bias.

    Based on implementation on Swin Transformer V2 original repo. Refers to
    https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer_v2.py
    for more details.

    Args:
        embed_dims (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool): If True, add a learnable bias to q, k, v.
            Defaults to True.
        attn_drop (float): Dropout ratio of attention weight.
            Defaults to 0.
        proj_drop (float): Dropout ratio of output. Defaults to 0.
        cpb_mlp_hidden_dims (int): The hidden dimensions of the continuous
            relative position bias network. Defaults to 512.
        pretrained_window_size (tuple(int)): The height and width of the window
            in pre-training. Defaults to (0, 0), which means not load
            pretrained model.
        init_cfg (dict, optional): The extra config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 window_size,
                 num_heads,
                 qkv_bias=True,
                 attn_drop=0.,
                 proj_drop=0.,
                 cpb_mlp_hidden_dims=512,
                 pretrained_window_size=(0, 0),
                 init_cfg=None):

        super().__init__(init_cfg)
        self.embed_dims = embed_dims
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads

        # Use small network for continuous relative position bias
        self.cpb_mlp = nn.Sequential(
            nn.Linear(
                in_features=2, out_features=cpb_mlp_hidden_dims, bias=True),
            nn.ReLU(inplace=True),
            nn.Linear(
                in_features=cpb_mlp_hidden_dims,
                out_features=num_heads,
                bias=False))

        # Add learnable scalar for cosine attention
        self.logit_scale = nn.Parameter(
            torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)

        # get relative_coords_table
        relative_coords_h = torch.arange(
            -(self.window_size[0] - 1),
            self.window_size[0],
            dtype=torch.float32)
        relative_coords_w = torch.arange(
            -(self.window_size[1] - 1),
            self.window_size[1],
            dtype=torch.float32)
        relative_coords_table = torch.stack(
            torch_meshgrid([relative_coords_h, relative_coords_w])).permute(
                1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
        if pretrained_window_size[0] > 0:
            relative_coords_table[:, :, :, 0] /= (
                pretrained_window_size[0] - 1)
            relative_coords_table[:, :, :, 1] /= (
                pretrained_window_size[1] - 1)
        else:
            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
        relative_coords_table *= 8  # normalize to -8, 8
        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
        self.register_buffer('relative_coords_table', relative_coords_table)

        # get pair-wise relative position index
        # for each token inside the window
        indexes_h = torch.arange(self.window_size[0])
        indexes_w = torch.arange(self.window_size[1])
        coordinates = torch.stack(
            torch_meshgrid([indexes_h, indexes_w]), dim=0)  # 2, Wh, Ww
        coordinates = torch.flatten(coordinates, start_dim=1)  # 2, Wh*Ww
        # 2, Wh*Ww, Wh*Ww
        relative_coordinates = coordinates[:, :, None] - coordinates[:,
                                                                     None, :]
        relative_coordinates = relative_coordinates.permute(
            1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2

        relative_coordinates[:, :, 0] += self.window_size[
            0] - 1  # shift to start from 0
        relative_coordinates[:, :, 1] += self.window_size[1] - 1
        relative_coordinates[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coordinates.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer('relative_position_index',
                             relative_position_index)

        self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=False)
        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(embed_dims))
            self.v_bias = nn.Parameter(torch.zeros(embed_dims))
        else:
            self.q_bias = None
            self.v_bias = None
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(embed_dims, embed_dims)
        self.proj_drop = nn.Dropout(proj_drop)

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        Args:

            x (tensor): input features with shape of (num_windows*B, N, C)
            mask (tensor, Optional): mask with shape of (num_windows, Wh*Ww,
                Wh*Ww), value should be between (-inf, 0].
        """
        B_, N, C = x.shape
        qkv_bias = None
        if self.q_bias is not None:
            qkv_bias = torch.cat(
                (self.q_bias,
                 torch.zeros_like(self.v_bias,
                                  requires_grad=False), self.v_bias))
        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
        qkv = qkv.reshape(B_, N, 3, self.num_heads,
                          C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[
            2]  # make torchscript happy (cannot use tensor as tuple)

        # cosine attention
        attn = (
            F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
        logit_scale = torch.clamp(
            self.logit_scale, max=np.log(1. / 0.01)).exp()
        attn = attn * logit_scale

        relative_position_bias_table = self.cpb_mlp(
            self.relative_coords_table).view(-1, self.num_heads)
        relative_position_bias = relative_position_bias_table[
            self.relative_position_index.view(-1)].view(
                self.window_size[0] * self.window_size[1],
                self.window_size[0] * self.window_size[1],
                -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(
            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N,
                             N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


@MODELS.register_module()
class ShiftWindowMSA(BaseModule):
    """Shift Window Multihead Self-Attention Module.

    Args:
        embed_dims (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (int): The height and width of the window.
        shift_size (int, optional): The shift step of each window towards
            right-bottom. If zero, act as regular window-msa. Defaults to 0.
        dropout_layer (dict, optional): The dropout_layer used before output.
            Defaults to dict(type='DropPath', drop_prob=0.).
        pad_small_map (bool): If True, pad the small feature map to the window
            size, which is common used in detection and segmentation. If False,
            avoid shifting window and shrink the window size to the size of
            feature map, which is common used in classification.
            Defaults to False.
        window_msa (Callable): To build a window multi-head attention module.
            Defaults to :class:`WindowMSA`.
        init_cfg (dict, optional): The extra config for initialization.
            Defaults to None.
        **kwargs: Other keyword arguments to build the window multi-head
            attention module.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 window_size,
                 shift_size=0,
                 dropout_layer=dict(type='DropPath', drop_prob=0.),
                 pad_small_map=False,
                 window_msa=WindowMSA,
                 init_cfg=None,
                 **kwargs):
        super().__init__(init_cfg)

        self.shift_size = shift_size
        self.window_size = window_size
        assert 0 <= self.shift_size < self.window_size

        self.w_msa = window_msa(
            embed_dims=embed_dims,
            num_heads=num_heads,
            window_size=to_2tuple(self.window_size),
            **kwargs,
        )

        self.drop = build_dropout(dropout_layer)
        self.pad_small_map = pad_small_map

    def forward(self, query, hw_shape):
        B, L, C = query.shape
        H, W = hw_shape
        assert L == H * W, f"The query length {L} doesn't match the input "\
            f'shape ({H}, {W}).'
        query = query.view(B, H, W, C)

        window_size = self.window_size
        shift_size = self.shift_size

        if min(H, W) == window_size:
            # If not pad small feature map, avoid shifting when the window size
            # is equal to the size of feature map. It's to align with the
            # behavior of the original implementation.
            shift_size = shift_size if self.pad_small_map else 0
        elif min(H, W) < window_size:
            # In the original implementation, the window size will be shrunk
            # to the size of feature map. The behavior is different with
            # swin-transformer for downstream tasks. To support dynamic input
            # shape, we don't allow this feature.
            assert self.pad_small_map, \
                f'The input shape ({H}, {W}) is smaller than the window ' \
                f'size ({window_size}). Please set `pad_small_map=True`, or ' \
                'decrease the `window_size`.'

        pad_r = (window_size - W % window_size) % window_size
        pad_b = (window_size - H % window_size) % window_size
        query = F.pad(query, (0, 0, 0, pad_r, 0, pad_b))

        H_pad, W_pad = query.shape[1], query.shape[2]

        # cyclic shift
        if shift_size > 0:
            query = torch.roll(
                query, shifts=(-shift_size, -shift_size), dims=(1, 2))

        attn_mask = self.get_attn_mask((H_pad, W_pad),
                                       window_size=window_size,
                                       shift_size=shift_size,
                                       device=query.device)

        # nW*B, window_size, window_size, C
        query_windows = self.window_partition(query, window_size)
        # nW*B, window_size*window_size, C
        query_windows = query_windows.view(-1, window_size**2, C)

        # W-MSA/SW-MSA (nW*B, window_size*window_size, C)
        attn_windows = self.w_msa(query_windows, mask=attn_mask)

        # merge windows
        attn_windows = attn_windows.view(-1, window_size, window_size, C)

        # B H' W' C
        shifted_x = self.window_reverse(attn_windows, H_pad, W_pad,
                                        window_size)
        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(
                shifted_x, shifts=(shift_size, shift_size), dims=(1, 2))
        else:
            x = shifted_x

        if H != H_pad or W != W_pad:
            x = x[:, :H, :W, :].contiguous()

        x = x.view(B, H * W, C)

        x = self.drop(x)

        return x

    @staticmethod
    def window_reverse(windows, H, W, window_size):
        B = int(windows.shape[0] / (H * W / window_size / window_size))
        x = windows.view(B, H // window_size, W // window_size, window_size,
                         window_size, -1)
        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
        return x

    @staticmethod
    def window_partition(x, window_size):
        B, H, W, C = x.shape
        x = x.view(B, H // window_size, window_size, W // window_size,
                   window_size, C)
        windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
        windows = windows.view(-1, window_size, window_size, C)
        return windows

    @staticmethod
    def get_attn_mask(hw_shape, window_size, shift_size, device=None):
        if shift_size > 0:
            img_mask = torch.zeros(1, *hw_shape, 1, device=device)
            h_slices = (slice(0, -window_size), slice(-window_size,
                                                      -shift_size),
                        slice(-shift_size, None))
            w_slices = (slice(0, -window_size), slice(-window_size,
                                                      -shift_size),
                        slice(-shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            # nW, window_size, window_size, 1
            mask_windows = ShiftWindowMSA.window_partition(
                img_mask, window_size)
            mask_windows = mask_windows.view(-1, window_size * window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, -100.0)
            attn_mask = attn_mask.masked_fill(attn_mask == 0, 0.0)
        else:
            attn_mask = None
        return attn_mask


class MultiheadAttention(BaseModule):
    """Multi-head Attention Module.

    This module implements multi-head attention that supports different input
    dims and embed dims. And it also supports a shortcut from ``value``, which
    is useful if input dims is not the same with embed dims.

    Args:
        embed_dims (int): The embedding dimension.
        num_heads (int): Parallel attention heads.
        input_dims (int, optional): The input dimension, and if None,
            use ``embed_dims``. Defaults to None.
        attn_drop (float): Dropout rate of the dropout layer after the
            attention calculation of query and key. Defaults to 0.
        proj_drop (float): Dropout rate of the dropout layer after the
            output projection. Defaults to 0.
        dropout_layer (dict): The dropout config before adding the shortcut.
            Defaults to ``dict(type='Dropout', drop_prob=0.)``.
        qkv_bias (bool): If True, add a learnable bias to q, k, v.
            Defaults to True.
        qk_scale (float, optional): Override default qk scale of
            ``head_dim ** -0.5`` if set. Defaults to None.
        proj_bias (bool) If True, add a learnable bias to output projection.
            Defaults to True.
        v_shortcut (bool): Add a shortcut from value to output. It's usually
            used if ``input_dims`` is different from ``embed_dims``.
            Defaults to False.
        use_layer_scale (bool): Whether to use layer scale. Defaults to False.
        layer_scale_init_value (float or torch.Tensor): Init value of layer
            scale. Defaults to 0.
        init_cfg (dict, optional): The Config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 input_dims=None,
                 attn_drop=0.,
                 proj_drop=0.,
                 dropout_layer=dict(type='Dropout', drop_prob=0.),
                 qkv_bias=True,
                 qk_scale=None,
                 proj_bias=True,
                 v_shortcut=False,
                 use_layer_scale=False,
                 layer_scale_init_value=0.,
                 init_cfg=None):
        super(MultiheadAttention, self).__init__(init_cfg=init_cfg)

        self.input_dims = input_dims or embed_dims
        self.embed_dims = embed_dims
        self.num_heads = num_heads
        self.v_shortcut = v_shortcut

        self.head_dims = embed_dims // num_heads
        if qk_scale is not None:
            self.scaled_dot_product_attention = partial(
                scaled_dot_product_attention_pyimpl,
                scale=self.head_dims**-0.5)
        else:
            self.scaled_dot_product_attention = scaled_dot_product_attention

        self.qkv = nn.Linear(self.input_dims, embed_dims * 3, bias=qkv_bias)
        self.attn_drop = attn_drop
        self.proj = nn.Linear(embed_dims, embed_dims, bias=proj_bias)
        self.proj_drop = nn.Dropout(proj_drop)

        self.out_drop = build_dropout(dropout_layer)

        if use_layer_scale:
            warnings.warn('The `use_layer_scale` in `MultiheadAttention` will '
                          'be deprecated. Please use `layer_scale_init_value` '
                          'to control whether using layer scale or not.')

        if use_layer_scale or (layer_scale_init_value > 0):
            layer_scale_init_value = layer_scale_init_value or 1e-5
            self.gamma1 = LayerScale(
                embed_dims, layer_scale_init_value=layer_scale_init_value)
        else:
            self.gamma1 = nn.Identity()

    def forward(self, x):
        B, N, _ = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
                                  self.head_dims).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn_drop = self.attn_drop if self.training else 0.
        x = self.scaled_dot_product_attention(q, k, v, dropout_p=attn_drop)
        x = x.transpose(1, 2).reshape(B, N, self.embed_dims)

        x = self.proj(x)
        x = self.out_drop(self.gamma1(self.proj_drop(x)))

        if self.v_shortcut:
            x = v.squeeze(1) + x
        return x


class BEiTAttention(BaseModule):
    """Window based multi-head self-attention (W-MSA) module with relative
    position bias.

    The initial implementation is in MMSegmentation.

    Args:
        embed_dims (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (tuple[int, int]): The height and width of the window.
        use_rel_pos_bias (bool): Whether to use unique relative position bias,
            if False, use shared relative position bias defined in backbone.
        bias (str): The option to add leanable bias for q, k, v. If bias is
            True, it will add leanable bias. If bias is 'qv_bias', it will only
            add leanable bias for q, v. If bias is False, it will not add bias
            for q, k, v. Default to 'qv_bias'.
        qk_scale (float | None, optional): Override default qk scale of
            head_dim ** -0.5 if set. Default: None.
        attn_drop_rate (float): Dropout ratio of attention weight.
            Default: 0.0
        proj_drop_rate (float): Dropout ratio of output. Default: 0.
        init_cfg (dict | None, optional): The Config for initialization.
            Default: None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads,
                 window_size,
                 use_rel_pos_bias,
                 bias='qv_bias',
                 qk_scale=None,
                 attn_drop_rate=0.,
                 proj_drop_rate=0.,
                 init_cfg=None,
                 **kwargs):
        super().__init__(init_cfg=init_cfg)
        self.embed_dims = embed_dims
        self.num_heads = num_heads
        head_embed_dims = embed_dims // num_heads
        self.bias = bias
        self.scale = qk_scale or head_embed_dims**-0.5

        qkv_bias = bias
        if bias == 'qv_bias':
            self._init_qv_bias()
            qkv_bias = False

        if window_size is None:
            assert not use_rel_pos_bias
        else:
            assert isinstance(window_size, tuple)
        self.window_size = window_size
        self.use_rel_pos_bias = use_rel_pos_bias
        self._init_rel_pos_embedding()

        self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop_rate)
        self.proj = nn.Linear(embed_dims, embed_dims)
        self.proj_drop = nn.Dropout(proj_drop_rate)

    def _init_qv_bias(self):
        self.q_bias = nn.Parameter(torch.zeros(self.embed_dims))
        self.v_bias = nn.Parameter(torch.zeros(self.embed_dims))

    def _init_rel_pos_embedding(self):
        if self.use_rel_pos_bias:
            Wh, Ww = self.window_size
            # cls to token & token 2 cls & cls to cls
            self.num_relative_distance = (2 * Wh - 1) * (2 * Ww - 1) + 3
            # relative_position_bias_table shape is (2*Wh-1 * 2*Ww-1 + 3, nH)
            self.relative_position_bias_table = nn.Parameter(
                torch.zeros(self.num_relative_distance, self.num_heads))

            # get pair-wise relative position index for
            # each token inside the window
            coords_h = torch.arange(Wh)
            coords_w = torch.arange(Ww)
            # coords shape is (2, Wh, Ww)
            coords = torch.stack(torch_meshgrid([coords_h, coords_w]))
            # coords_flatten shape is (2, Wh*Ww)
            coords_flatten = torch.flatten(coords, 1)
            relative_coords = (
                coords_flatten[:, :, None] - coords_flatten[:, None, :])
            # relative_coords shape is (Wh*Ww, Wh*Ww, 2)
            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
            # shift to start from 0
            relative_coords[:, :, 0] += Wh - 1
            relative_coords[:, :, 1] += Ww - 1
            relative_coords[:, :, 0] *= 2 * Ww - 1
            relative_position_index = torch.zeros(
                size=(Wh * Ww + 1, ) * 2, dtype=relative_coords.dtype)
            # relative_position_index shape is (Wh*Ww, Wh*Ww)
            relative_position_index[1:, 1:] = relative_coords.sum(-1)
            relative_position_index[0, 0:] = self.num_relative_distance - 3
            relative_position_index[0:, 0] = self.num_relative_distance - 2
            relative_position_index[0, 0] = self.num_relative_distance - 1

            self.register_buffer('relative_position_index',
                                 relative_position_index)
        else:
            self.window_size = None
            self.relative_position_bias_table = None
            self.relative_position_index = None

    def init_weights(self):
        super().init_weights()
        if self.use_rel_pos_bias:
            trunc_normal_(self.relative_position_bias_table, std=0.02)

    def forward(self, x, rel_pos_bias=None):
        """
        Args:
            x (tensor): input features with shape of (num_windows*B, N, C).
            rel_pos_bias (tensor): input relative position bias with shape of
                (num_heads, N, N).
        """
        B, N, C = x.shape

        if self.bias == 'qv_bias':
            k_bias = torch.zeros_like(self.v_bias, requires_grad=False)
            qkv_bias = torch.cat((self.q_bias, k_bias, self.v_bias))
            qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
        else:
            qkv = self.qkv(x)

        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        if self.relative_position_bias_table is not None:
            Wh = self.window_size[0]
            Ww = self.window_size[1]
            relative_position_bias = self.relative_position_bias_table[
                self.relative_position_index.view(-1)].view(
                    Wh * Ww + 1, Wh * Ww + 1, -1)
            relative_position_bias = relative_position_bias.permute(
                2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
            attn = attn + relative_position_bias.unsqueeze(0)

        if rel_pos_bias is not None:
            # use shared relative position bias
            attn = attn + rel_pos_bias

        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class ChannelMultiheadAttention(BaseModule):
    """Channel Multihead Self-attention Module.

    This module implements channel multi-head attention that supports different
    input dims and embed dims.
    Args:
        embed_dims (int): The embedding dimension.
        num_heads (int): Parallel attention heads.
        input_dims (int, optional): The input dimension, and if None,
            use ``embed_dims``. Defaults to None.
        attn_drop (float): Dropout rate of the dropout layer after the
            attention calculation of query and key. Defaults to 0.
        proj_drop (float): Dropout rate of the dropout layer after the
            output projection. Defaults to 0.
        dropout_layer (dict): The dropout config before adding the shoutcut.
            Defaults to ``dict(type='Dropout', drop_prob=0.)``.
        qkv_bias (bool): If True, add a learnable bias to q, k, v.
            Defaults to False.
        proj_bias (bool) If True, add a learnable bias to output projection.
            Defaults to True.
        qk_scale_type (str): The scale type of qk scale.
            Defaults to 'learnable'. It can be 'learnable', 'fixed' or 'none'.
        qk_scale (float, optional): If set qk_scale_type to 'none', this
            should be specified with valid float number. Defaults to None.
        v_shortcut (bool): Add a shortcut from value to output. It's usually
            used if ``input_dims`` is different from ``embed_dims``.
            Defaults to False.
        init_cfg (dict, optional): The Config for initialization.
            Defaults to None.
    """

    def __init__(self,
                 embed_dims,
                 num_heads=8,
                 input_dims=None,
                 attn_drop=0.,
                 proj_drop=0.,
                 dropout_layer=dict(type='Dropout', drop_prob=0.),
                 qkv_bias=False,
                 proj_bias=True,
                 qk_scale_type='learnable',
                 qk_scale=None,
                 v_shortcut=False,
                 init_cfg=None):
        super().__init__(init_cfg)

        self.input_dims = input_dims or embed_dims
        self.embed_dims = embed_dims
        self.num_heads = num_heads
        self.v_shortcut = v_shortcut

        self.head_dims = embed_dims // num_heads
        if qk_scale_type == 'learnable':
            self.scale = nn.Parameter(torch.ones(num_heads, 1, 1))
        elif qk_scale_type == 'fixed':
            self.scale = self.head_dims**-0.5
        elif qk_scale_type == 'none':
            assert qk_scale is not None
            self.scale = qk_scale

        self.qkv = nn.Linear(self.input_dims, embed_dims * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(embed_dims, embed_dims, bias=proj_bias)
        self.proj_drop = nn.Dropout(proj_drop)

        self.out_drop = build_dropout(dropout_layer)

    def forward(self, x):
        B, N, _ = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
                                  self.head_dims).permute(2, 0, 3, 1, 4)

        q, k, v = [item.transpose(-2, -1) for item in [qkv[0], qkv[1], qkv[2]]]

        q, k = F.normalize(q, dim=-1), F.normalize(k, dim=-1)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)

        x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, self.embed_dims)
        x = self.proj(x)
        x = self.out_drop(self.proj_drop(x))

        if self.v_shortcut:
            x = qkv[2].squeeze(1) + x
        return x


class LeAttention(BaseModule):
    """LeViT Attention. Multi-head attention with attention bias,  which is
    proposed in `LeViT: a Vision Transformer in ConvNet’s Clothing for Faster
    Inference<https://arxiv.org/abs/2104.01136>`_

    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads. Default: 8.
        key_dim (int): Dimension of key. Default: None.
        attn_ratio (int): Ratio of attention heads. Default: 8.
        resolution (tuple[int]): Input resolution. Default: (16, 16).
        init_cfg (dict, optional): The Config for initialization.
    """

    def __init__(self,
                 dim,
                 key_dim,
                 num_heads=8,
                 attn_ratio=4,
                 resolution=(14, 14),
                 init_cfg=None):
        super().__init__(init_cfg=init_cfg)
        # (h, w)
        assert isinstance(resolution, tuple) and len(resolution) == 2
        self.num_heads = num_heads
        self.scale = key_dim**-0.5
        self.key_dim = key_dim
        self.nh_kd = nh_kd = key_dim * num_heads
        self.d = int(attn_ratio * key_dim)
        self.dh = int(attn_ratio * key_dim) * num_heads
        self.attn_ratio = attn_ratio
        h = self.dh + nh_kd * 2

        self.norm = nn.LayerNorm(dim)
        self.qkv = nn.Linear(dim, h)
        self.proj = nn.Linear(self.dh, dim)

        points = list(
            itertools.product(range(resolution[0]), range(resolution[1])))
        N = len(points)
        attention_offsets = {}
        idxs = []
        for p1 in points:
            for p2 in points:
                offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
                if offset not in attention_offsets:
                    attention_offsets[offset] = len(attention_offsets)
                idxs.append(attention_offsets[offset])
        self.attention_biases = torch.nn.Parameter(
            torch.zeros(num_heads, len(attention_offsets)))
        self.register_buffer(
            'attention_bias_idxs',
            torch.LongTensor(idxs).view(N, N),
            persistent=False)

    @torch.no_grad()
    def train(self, mode=True):
        super().train(mode)
        if mode and hasattr(self, 'ab'):
            del self.ab
        else:
            self.ab = self.attention_biases[:, self.attention_bias_idxs]

    def forward(self, x):  # x (B,N,C)
        B, N, _ = x.shape

        # Normalization
        x = self.norm(x)

        qkv = self.qkv(x)
        # (B, N, num_heads, d)
        q, k, v = qkv.view(B, N, self.num_heads,
                           -1).split([self.key_dim, self.key_dim, self.d],
                                     dim=3)
        # (B, num_heads, N, d)
        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)
        v = v.permute(0, 2, 1, 3)

        attn = ((q @ k.transpose(-2, -1)) * self.scale +
                (self.attention_biases[:, self.attention_bias_idxs]
                 if self.training else self.ab))
        attn = attn.softmax(dim=-1)
        x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
        x = self.proj(x)
        return x


class CrossMultiheadAttention(BaseModule):
    """Cross attention between queries and the union of keys and values.

    This module is different from ``MultiheadAttention``, for the attention
    is computed between queries and the union of keys and values.

    Args:
        embed_dims (int): The embedding dimension.
        num_heads (int): Parallel attention heads.
        qkv_bias (bool): If True, add a learnable bias to q, k, v.
            Defaults to True.
        qk_scale (float, optional): Override default qk scale of
            ``head_dim ** -0.5`` if set. Defaults to None.
        attn_drop (float): Dropout rate of the dropout layer after the
            attention calculation of query and key. Defaults to 0.
        proj_drop (float): Dropout rate of the dropout layer after the
            output projection. Defaults to 0.
    """

    def __init__(self,
                 embed_dims: int,
                 num_heads: int = 8,
                 qkv_bias: bool = False,
                 qk_scale: float = None,
                 attn_drop: float = 0.,
                 proj_drop: float = 0.) -> None:
        super().__init__()
        self.num_heads = num_heads
        head_dim = embed_dims // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.q = nn.Linear(embed_dims, embed_dims, bias=False)
        self.k = nn.Linear(embed_dims, embed_dims, bias=False)
        self.v = nn.Linear(embed_dims, embed_dims, bias=False)

        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(embed_dims))
            self.v_bias = nn.Parameter(torch.zeros(embed_dims))
        else:
            self.q_bias = None
            self.k_bias = None
            self.v_bias = None

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(embed_dims, embed_dims)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self,
                x: torch.Tensor,
                k: torch.Tensor = None,
                v: torch.Tensor = None) -> None:
        """Forward function."""
        B, N, _ = x.shape

        N_k = k.shape[1]
        N_v = v.shape[1]

        q_bias, k_bias, v_bias = None, None, None
        if self.q_bias is not None:
            q_bias = self.q_bias
            k_bias = torch.zeros_like(self.v_bias, requires_grad=False)
            v_bias = self.v_bias

        q = F.linear(
            input=x, weight=self.q.weight, bias=q_bias)  # (B, N_q, dim)
        k = F.linear(
            input=k, weight=self.k.weight, bias=k_bias)  # (B, N_k, dim)
        v = F.linear(input=v, weight=self.v.weight, bias=v_bias)

        q = q.reshape(B, N, 1, self.num_heads,
                      -1).permute(2, 0, 3, 1,
                                  4).squeeze(0)  # (B, num_heads, N_q, dim)
        k = k.reshape(B, N_k, 1, self.num_heads,
                      -1).permute(2, 0, 3, 1,
                                  4).squeeze(0)  # (B, num_heads, N_k, dim)
        v = v.reshape(B, N_v, 1, self.num_heads,
                      -1).permute(2, 0, 3, 1,
                                  4).squeeze(0)  # (B, num_heads, N_v, dim)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))  # (B, N_head, N_q, N_k)

        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class PromptMultiheadAttention(MultiheadAttention):
    """Prompt Multihead Attention for MILAN.

    This module is specific for the prompt encoder in MILAN. It will not update
    the visible tokens from the encoder.

    Args:
        embed_dims (int): The embedding dimension.
        num_heads (int): Parallel attention heads.
        input_dims (int, optional): The input dimension, and if None,
            use ``embed_dims``. Defaults to None.
        attn_drop (float): Dropout rate of the dropout layer after the
            attention calculation of query and key. Defaults to 0.
        proj_drop (float): Dropout rate of the dropout layer after the
            output projection. Defaults to 0.
        dropout_layer (dict): The dropout config before adding the shortcut.
            Defaults to ``dict(type='Dropout', drop_prob=0.)``.
        qkv_bias (bool): If True, add a learnable bias to q, k, v.
            Defaults to True.
        qk_scale (float, optional): Override default qk scale of
            ``head_dim ** -0.5`` if set. Defaults to None.
        proj_bias (bool) If True, add a learnable bias to output projection.
            Defaults to True.
        v_shortcut (bool): Add a shortcut from value to output. It's usually
            used if ``input_dims`` is different from ``embed_dims``.
            Defaults to False.
        return_attention (bool): If True, return the attention map, computed by
            the cross attention between the class token and all other tokens.
            Defaults to False.
        init_cfg (Union[List[dict], dict], optional): The Config for
            initialization. Defaults to None.
    """

    def __init__(self,
                 embed_dims: int,
                 num_heads: int,
                 input_dims: Optional[int] = None,
                 attn_drop: float = 0,
                 proj_drop: float = 0,
                 dropout_layer: dict = dict(type='Dropout', drop_prob=0.),
                 qkv_bias: bool = True,
                 qk_scale: Optional[float] = None,
                 proj_bias: bool = True,
                 v_shortcut: bool = False,
                 use_layer_scale: bool = False,
                 init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
        super().__init__(
            embed_dims=embed_dims,
            num_heads=num_heads,
            input_dims=input_dims,
            attn_drop=attn_drop,
            proj_drop=proj_drop,
            dropout_layer=dropout_layer,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            proj_bias=proj_bias,
            v_shortcut=v_shortcut,
            use_layer_scale=use_layer_scale,
            init_cfg=init_cfg)
        # no longer need qkv
        del self.qkv

        # to project the mask tokens
        self.q = nn.Linear(embed_dims, embed_dims, bias=qkv_bias)
        # to project al the tokens
        self.kv = nn.Linear(embed_dims, embed_dims * 2, bias=qkv_bias)

    def forward(self, x: torch.Tensor, visible_tokens: torch.Tensor,
                ids_restore: torch.Tensor) -> torch.Tensor:
        """Forward function for `PromptMultiheadAttention`.

        Args:
            x (torch.Tensor): Mask token features with shape N x L_m x C.
            visible_tokens (torch.Tensor): The visible tokens features from
                encoder with shape N x L_v x C.
            ids_restore (torch.Tensor): The ids of all tokens in the original
                image with shape N x L.

        Returns:
            torch Tensor: Output features with shape N x L x C.
        """
        x_ = torch.cat([visible_tokens[:, 1:, :], x], dim=1)
        assert x_.shape[1] == ids_restore.shape[1]
        x_ = torch.gather(
            x_,
            dim=1,
            index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[-1]))
        x_ = torch.cat([visible_tokens[:, :1, :], x_], dim=1)

        # full sequence shape
        B, _, _ = x_.shape
        q = self.q(x).reshape(B, x.shape[1], self.num_heads,
                              self.head_dims).permute(0, 2, 1, 3)
        kv = self.kv(x_).reshape(B, x_.shape[1], 2, self.num_heads,
                                 self.head_dims).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]

        attn_drop = self.attn_drop if self.training else 0.
        attn = self.scaled_dot_product_attention(q, k, v, dropout_p=attn_drop)
        x = attn.transpose(1, 2).reshape(B, x.shape[1], self.embed_dims)

        x = self.proj(x)
        x = self.out_drop(self.gamma1(self.proj_drop(x)))
        return x