""" Relative position embedding modules and functions Hacked together by / Copyright 2022 Ross Wightman """ import math from typing import Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from .mlp import Mlp from .weight_init import trunc_normal_ def gen_relative_position_index( q_size: Tuple[int, int], k_size: Tuple[int, int] = None, class_token: bool = False) -> torch.Tensor: # Adapted with significant modifications from Swin / BeiT codebases # get pair-wise relative position index for each token inside the window q_coords = torch.stack(torch.meshgrid([torch.arange(q_size[0]), torch.arange(q_size[1])])).flatten(1) # 2, Wh, Ww if k_size is None: k_coords = q_coords k_size = q_size else: # different q vs k sizes is a WIP k_coords = torch.stack(torch.meshgrid([torch.arange(k_size[0]), torch.arange(k_size[1])])).flatten(1) relative_coords = q_coords[:, :, None] - k_coords[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute(1, 2, 0) # Wh*Ww, Wh*Ww, 2 _, relative_position_index = torch.unique(relative_coords.view(-1, 2), return_inverse=True, dim=0) if class_token: # handle cls to token & token 2 cls & cls to cls as per beit for rel pos bias # NOTE not intended or tested with MLP log-coords max_size = (max(q_size[0], k_size[0]), max(q_size[1], k_size[1])) num_relative_distance = (2 * max_size[0] - 1) * (2 * max_size[1] - 1) + 3 relative_position_index = F.pad(relative_position_index, [1, 0, 1, 0]) relative_position_index[0, 0:] = num_relative_distance - 3 relative_position_index[0:, 0] = num_relative_distance - 2 relative_position_index[0, 0] = num_relative_distance - 1 return relative_position_index.contiguous() class RelPosBias(nn.Module): """ Relative Position Bias Adapted from Swin-V1 relative position bias impl, modularized. """ def __init__(self, window_size, num_heads, prefix_tokens=0): super().__init__() assert prefix_tokens <= 1 self.window_size = window_size self.window_area = window_size[0] * window_size[1] self.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,) num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * prefix_tokens self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads)) self.register_buffer( "relative_position_index", gen_relative_position_index(self.window_size, class_token=prefix_tokens > 0), persistent=False, ) self.init_weights() def init_weights(self): trunc_normal_(self.relative_position_bias_table, std=.02) def get_bias(self) -> torch.Tensor: relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)] # win_h * win_w, win_h * win_w, num_heads relative_position_bias = relative_position_bias.view(self.bias_shape).permute(2, 0, 1) return relative_position_bias.unsqueeze(0).contiguous() def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): return attn + self.get_bias() def gen_relative_log_coords( win_size: Tuple[int, int], pretrained_win_size: Tuple[int, int] = (0, 0), mode='swin', ): assert mode in ('swin', 'cr', 'rw') # as per official swin-v2 impl, supporting timm specific 'cr' and 'rw' log coords as well relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32) relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32) relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous() # 2*Wh-1, 2*Ww-1, 2 if mode == 'swin': if pretrained_win_size[0] > 0: relative_coords_table[:, :, 0] /= (pretrained_win_size[0] - 1) relative_coords_table[:, :, 1] /= (pretrained_win_size[1] - 1) else: relative_coords_table[:, :, 0] /= (win_size[0] - 1) relative_coords_table[:, :, 1] /= (win_size[1] - 1) relative_coords_table *= 8 # normalize to -8, 8 relative_coords_table = torch.sign(relative_coords_table) * torch.log2( 1.0 + relative_coords_table.abs()) / math.log2(8) else: if mode == 'rw': # cr w/ window size normalization -> [-1,1] log coords relative_coords_table[:, :, 0] /= (win_size[0] - 1) relative_coords_table[:, :, 1] /= (win_size[1] - 1) relative_coords_table *= 8 # scale to -8, 8 relative_coords_table = torch.sign(relative_coords_table) * torch.log2( 1.0 + relative_coords_table.abs()) relative_coords_table /= math.log2(9) # -> [-1, 1] else: # mode == 'cr' relative_coords_table = torch.sign(relative_coords_table) * torch.log( 1.0 + relative_coords_table.abs()) return relative_coords_table class RelPosMlp(nn.Module): """ Log-Coordinate Relative Position MLP Based on ideas presented in Swin-V2 paper (https://arxiv.org/abs/2111.09883) This impl covers the 'swin' implementation as well as two timm specific modes ('cr', and 'rw') """ def __init__( self, window_size, num_heads=8, hidden_dim=128, prefix_tokens=0, mode='cr', pretrained_window_size=(0, 0) ): super().__init__() self.window_size = window_size self.window_area = self.window_size[0] * self.window_size[1] self.prefix_tokens = prefix_tokens self.num_heads = num_heads self.bias_shape = (self.window_area,) * 2 + (num_heads,) if mode == 'swin': self.bias_act = nn.Sigmoid() self.bias_gain = 16 mlp_bias = (True, False) elif mode == 'rw': self.bias_act = nn.Tanh() self.bias_gain = 4 mlp_bias = True else: self.bias_act = nn.Identity() self.bias_gain = None mlp_bias = True self.mlp = Mlp( 2, # x, y hidden_features=hidden_dim, out_features=num_heads, act_layer=nn.ReLU, bias=mlp_bias, drop=(0.125, 0.) ) self.register_buffer( "relative_position_index", gen_relative_position_index(window_size), persistent=False) # get relative_coords_table self.register_buffer( "rel_coords_log", gen_relative_log_coords(window_size, pretrained_window_size, mode=mode), persistent=False) def get_bias(self) -> torch.Tensor: relative_position_bias = self.mlp(self.rel_coords_log) if self.relative_position_index is not None: relative_position_bias = relative_position_bias.view(-1, self.num_heads)[ self.relative_position_index.view(-1)] # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.view(self.bias_shape) relative_position_bias = relative_position_bias.permute(2, 0, 1) relative_position_bias = self.bias_act(relative_position_bias) if self.bias_gain is not None: relative_position_bias = self.bias_gain * relative_position_bias if self.prefix_tokens: relative_position_bias = F.pad(relative_position_bias, [self.prefix_tokens, 0, self.prefix_tokens, 0]) return relative_position_bias.unsqueeze(0).contiguous() def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): return attn + self.get_bias() def generate_lookup_tensor( length: int, max_relative_position: Optional[int] = None, ): """Generate a one_hot lookup tensor to reindex embeddings along one dimension. Args: length: the length to reindex to. max_relative_position: the maximum relative position to consider. Relative position embeddings for distances above this threshold are zeroed out. Returns: a lookup Tensor of size [length, length, vocab_size] that satisfies ret[n,m,v] = 1{m - n + max_relative_position = v}. """ if max_relative_position is None: max_relative_position = length - 1 # Return the cached lookup tensor, otherwise compute it and cache it. vocab_size = 2 * max_relative_position + 1 ret = torch.zeros(length, length, vocab_size) for i in range(length): for x in range(length): v = x - i + max_relative_position if abs(x - i) > max_relative_position: continue ret[i, x, v] = 1 return ret def reindex_2d_einsum_lookup( relative_position_tensor, height: int, width: int, height_lookup: torch.Tensor, width_lookup: torch.Tensor, ) -> torch.Tensor: """Reindex 2d relative position bias with 2 independent einsum lookups. Adapted from: https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py Args: relative_position_tensor: tensor of shape [..., vocab_height, vocab_width, ...]. height: height to reindex to. width: width to reindex to. height_lookup: one-hot height lookup width_lookup: one-hot width lookup Returns: reindexed_tensor: a Tensor of shape [..., height * width, height * width, ...] """ reindexed_tensor = torch.einsum('nhw,ixh->nixw', relative_position_tensor, height_lookup) reindexed_tensor = torch.einsum('nixw,jyw->nijxy', reindexed_tensor, width_lookup) area = height * width return reindexed_tensor.reshape(relative_position_tensor.shape[0], area, area) class RelPosBiasTf(nn.Module): """ Relative Position Bias Impl (Compatible with Tensorflow MaxViT models) Adapted from: https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py """ def __init__(self, window_size, num_heads, prefix_tokens=0): super().__init__() assert prefix_tokens <= 1 self.window_size = window_size self.window_area = window_size[0] * window_size[1] self.num_heads = num_heads vocab_height = 2 * window_size[0] - 1 vocab_width = 2 * window_size[1] - 1 self.bias_shape = (self.num_heads, vocab_height, vocab_width) self.relative_position_bias_table = nn.Parameter(torch.zeros(self.bias_shape)) self.register_buffer('height_lookup', generate_lookup_tensor(window_size[0]), persistent=False) self.register_buffer('width_lookup', generate_lookup_tensor(window_size[1]), persistent=False) self.init_weights() def init_weights(self): nn.init.normal_(self.relative_position_bias_table, std=.02) def get_bias(self) -> torch.Tensor: # FIXME change to not use one-hot/einsum? return reindex_2d_einsum_lookup( self.relative_position_bias_table, self.window_size[0], self.window_size[1], self.height_lookup, self.width_lookup ) def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): return attn + self.get_bias()