""" Bottleneck Self Attention (Bottleneck Transformers) Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605 @misc{2101.11605, Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani}, Title = {Bottleneck Transformers for Visual Recognition}, Year = {2021}, } Based on ref gist at: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2 This impl is a WIP but given that it is based on the ref gist likely not too far off. Hacked together by / Copyright 2021 Ross Wightman """ from typing import List import torch import torch.nn as nn import torch.nn.functional as F from .helpers import to_2tuple, make_divisible from .weight_init import trunc_normal_ from .trace_utils import _assert def rel_logits_1d(q, rel_k, permute_mask: List[int]): """ Compute relative logits along one dimension As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2 Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925 Args: q: (batch, heads, height, width, dim) rel_k: (2 * width - 1, dim) permute_mask: permute output dim according to this """ B, H, W, dim = q.shape x = (q @ rel_k.transpose(-1, -2)) x = x.reshape(-1, W, 2 * W -1) # pad to shift from relative to absolute indexing x_pad = F.pad(x, [0, 1]).flatten(1) x_pad = F.pad(x_pad, [0, W - 1]) # reshape and slice out the padded elements x_pad = x_pad.reshape(-1, W + 1, 2 * W - 1) x = x_pad[:, :W, W - 1:] # reshape and tile x = x.reshape(B, H, 1, W, W).expand(-1, -1, H, -1, -1) return x.permute(permute_mask) class PosEmbedRel(nn.Module): """ Relative Position Embedding As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2 Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925 """ def __init__(self, feat_size, dim_head, scale): super().__init__() self.height, self.width = to_2tuple(feat_size) self.dim_head = dim_head self.height_rel = nn.Parameter(torch.randn(self.height * 2 - 1, dim_head) * scale) self.width_rel = nn.Parameter(torch.randn(self.width * 2 - 1, dim_head) * scale) def forward(self, q): B, HW, _ = q.shape # relative logits in width dimension. q = q.reshape(B, self.height, self.width, -1) rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4)) # relative logits in height dimension. q = q.transpose(1, 2) rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2)) rel_logits = rel_logits_h + rel_logits_w rel_logits = rel_logits.reshape(B, HW, HW) return rel_logits class BottleneckAttn(nn.Module): """ Bottleneck Attention Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605 The internal dimensions of the attention module are controlled by the interaction of several arguments. * the output dimension of the module is specified by dim_out, which falls back to input dim if not set * the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim * the query and key (qk) dimensions are determined by * num_heads * dim_head if dim_head is not None * num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None * as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used Args: dim (int): input dimension to the module dim_out (int): output dimension of the module, same as dim if not set stride (int): output stride of the module, avg pool used if stride == 2 (default: 1). num_heads (int): parallel attention heads (default: 4) dim_head (int): dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0) qkv_bias (bool): add bias to q, k, and v projections scale_pos_embed (bool): scale the position embedding as well as Q @ K """ def __init__( self, dim, dim_out=None, feat_size=None, stride=1, num_heads=4, dim_head=None, qk_ratio=1.0, qkv_bias=False, scale_pos_embed=False): super().__init__() assert feat_size is not None, 'A concrete feature size matching expected input (H, W) is required' dim_out = dim_out or dim assert dim_out % num_heads == 0 self.num_heads = num_heads self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads self.dim_head_v = dim_out // self.num_heads self.dim_out_qk = num_heads * self.dim_head_qk self.dim_out_v = num_heads * self.dim_head_v self.scale = self.dim_head_qk ** -0.5 self.scale_pos_embed = scale_pos_embed self.qkv = nn.Conv2d(dim, self.dim_out_qk * 2 + self.dim_out_v, 1, bias=qkv_bias) # NOTE I'm only supporting relative pos embedding for now self.pos_embed = PosEmbedRel(feat_size, dim_head=self.dim_head_qk, scale=self.scale) self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity() self.reset_parameters() def reset_parameters(self): trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5) # fan-in trunc_normal_(self.pos_embed.height_rel, std=self.scale) trunc_normal_(self.pos_embed.width_rel, std=self.scale) def forward(self, x): B, C, H, W = x.shape _assert(H == self.pos_embed.height, '') _assert(W == self.pos_embed.width, '') x = self.qkv(x) # B, (2 * dim_head_qk + dim_head_v) * num_heads, H, W # NOTE head vs channel split ordering in qkv projection was decided before I allowed qk to differ from v # So, this is more verbose than if heads were before qkv splits, but throughput is not impacted. q, k, v = torch.split(x, [self.dim_out_qk, self.dim_out_qk, self.dim_out_v], dim=1) q = q.reshape(B * self.num_heads, self.dim_head_qk, -1).transpose(-1, -2) k = k.reshape(B * self.num_heads, self.dim_head_qk, -1) # no transpose, for q @ k v = v.reshape(B * self.num_heads, self.dim_head_v, -1).transpose(-1, -2) if self.scale_pos_embed: attn = (q @ k + self.pos_embed(q)) * self.scale # B * num_heads, H * W, H * W else: attn = (q @ k) * self.scale + self.pos_embed(q) attn = attn.softmax(dim=-1) out = (attn @ v).transpose(-1, -2).reshape(B, self.dim_out_v, H, W) # B, dim_out, H, W out = self.pool(out) return out