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| """ Halo Self Attention | |
| Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones` | |
| - https://arxiv.org/abs/2103.12731 | |
| @misc{2103.12731, | |
| Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and | |
| Jonathon Shlens}, | |
| Title = {Scaling Local Self-Attention for Parameter Efficient Visual Backbones}, | |
| Year = {2021}, | |
| } | |
| Status: | |
| This impl is a WIP, there is no official ref impl and some details in paper weren't clear to me. | |
| The attention mechanism works but it's slow as implemented. | |
| Hacked together by / Copyright 2021 Ross Wightman | |
| """ | |
| from typing import List | |
| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from .helpers import 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, height, width, dim) | |
| rel_k: (2 * window - 1, dim) | |
| permute_mask: permute output dim according to this | |
| """ | |
| B, H, W, dim = q.shape | |
| rel_size = rel_k.shape[0] | |
| win_size = (rel_size + 1) // 2 | |
| x = (q @ rel_k.transpose(-1, -2)) | |
| x = x.reshape(-1, W, rel_size) | |
| # pad to shift from relative to absolute indexing | |
| x_pad = F.pad(x, [0, 1]).flatten(1) | |
| x_pad = F.pad(x_pad, [0, rel_size - W]) | |
| # reshape and slice out the padded elements | |
| x_pad = x_pad.reshape(-1, W + 1, rel_size) | |
| x = x_pad[:, :W, win_size - 1:] | |
| # reshape and tile | |
| x = x.reshape(B, H, 1, W, win_size).expand(-1, -1, win_size, -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, block_size, win_size, dim_head, scale): | |
| """ | |
| Args: | |
| block_size (int): block size | |
| win_size (int): neighbourhood window size | |
| dim_head (int): attention head dim | |
| scale (float): scale factor (for init) | |
| """ | |
| super().__init__() | |
| self.block_size = block_size | |
| self.dim_head = dim_head | |
| self.height_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale) | |
| self.width_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale) | |
| def forward(self, q): | |
| B, BB, HW, _ = q.shape | |
| # relative logits in width dimension. | |
| q = q.reshape(-1, self.block_size, self.block_size, self.dim_head) | |
| 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, BB, HW, -1) | |
| return rel_logits | |
| class HaloAttn(nn.Module): | |
| """ Halo Attention | |
| Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones` | |
| - https://arxiv.org/abs/2103.12731 | |
| 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 | |
| feat_size (Tuple[int, int]): size of input feature_map (not used, for arg compat with bottle/lambda) | |
| stride: output stride of the module, query downscaled if > 1 (default: 1). | |
| num_heads: parallel attention heads (default: 8). | |
| dim_head: dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set | |
| block_size (int): size of blocks. (default: 8) | |
| halo_size (int): size of halo overlap. (default: 3) | |
| 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 | |
| avg_down (bool): use average pool downsample instead of strided query blocks | |
| 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=8, dim_head=None, block_size=8, halo_size=3, | |
| qk_ratio=1.0, qkv_bias=False, avg_down=False, scale_pos_embed=False): | |
| super().__init__() | |
| dim_out = dim_out or dim | |
| assert dim_out % num_heads == 0 | |
| assert stride in (1, 2) | |
| 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.block_size = self.block_size_ds = block_size | |
| self.halo_size = halo_size | |
| self.win_size = block_size + halo_size * 2 # neighbourhood window size | |
| self.block_stride = 1 | |
| use_avg_pool = False | |
| if stride > 1: | |
| use_avg_pool = avg_down or block_size % stride != 0 | |
| self.block_stride = 1 if use_avg_pool else stride | |
| self.block_size_ds = self.block_size // self.block_stride | |
| # FIXME not clear if this stride behaviour is what the paper intended | |
| # Also, the paper mentions using a 3D conv for dealing with the blocking/gather, and leaving | |
| # data in unfolded block form. I haven't wrapped my head around how that'd look. | |
| self.q = nn.Conv2d(dim, self.dim_out_qk, 1, stride=self.block_stride, bias=qkv_bias) | |
| self.kv = nn.Conv2d(dim, self.dim_out_qk + self.dim_out_v, 1, bias=qkv_bias) | |
| self.pos_embed = PosEmbedRel( | |
| block_size=self.block_size_ds, win_size=self.win_size, dim_head=self.dim_head_qk, scale=self.scale) | |
| self.pool = nn.AvgPool2d(2, 2) if use_avg_pool else nn.Identity() | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| std = self.q.weight.shape[1] ** -0.5 # fan-in | |
| trunc_normal_(self.q.weight, std=std) | |
| trunc_normal_(self.kv.weight, std=std) | |
| 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.block_size == 0, '') | |
| _assert(W % self.block_size == 0, '') | |
| num_h_blocks = H // self.block_size | |
| num_w_blocks = W // self.block_size | |
| num_blocks = num_h_blocks * num_w_blocks | |
| q = self.q(x) | |
| # unfold | |
| q = q.reshape( | |
| -1, self.dim_head_qk, | |
| num_h_blocks, self.block_size_ds, num_w_blocks, self.block_size_ds).permute(0, 1, 3, 5, 2, 4) | |
| # B, num_heads * dim_head * block_size ** 2, num_blocks | |
| q = q.reshape(B * self.num_heads, self.dim_head_qk, -1, num_blocks).transpose(1, 3) | |
| # B * num_heads, num_blocks, block_size ** 2, dim_head | |
| kv = self.kv(x) | |
| # Generate overlapping windows for kv. This approach is good for GPU and CPU. However, unfold() is not | |
| # lowered for PyTorch XLA so it will be very slow. See code at bottom of file for XLA friendly approach. | |
| # FIXME figure out how to switch impl between this and conv2d if XLA being used. | |
| kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]) | |
| kv = kv.unfold(2, self.win_size, self.block_size).unfold(3, self.win_size, self.block_size).reshape( | |
| B * self.num_heads, self.dim_head_qk + self.dim_head_v, num_blocks, -1).permute(0, 2, 3, 1) | |
| k, v = torch.split(kv, [self.dim_head_qk, self.dim_head_v], dim=-1) | |
| # B * num_heads, num_blocks, win_size ** 2, dim_head_qk or dim_head_v | |
| if self.scale_pos_embed: | |
| attn = (q @ k.transpose(-1, -2) + self.pos_embed(q)) * self.scale | |
| else: | |
| attn = (q @ k.transpose(-1, -2)) * self.scale + self.pos_embed(q) | |
| # B * num_heads, num_blocks, block_size ** 2, win_size ** 2 | |
| attn = attn.softmax(dim=-1) | |
| out = (attn @ v).transpose(1, 3) # B * num_heads, dim_head_v, block_size ** 2, num_blocks | |
| # fold | |
| out = out.reshape(-1, self.block_size_ds, self.block_size_ds, num_h_blocks, num_w_blocks) | |
| out = out.permute(0, 3, 1, 4, 2).contiguous().view( | |
| B, self.dim_out_v, H // self.block_stride, W // self.block_stride) | |
| # B, dim_out, H // block_stride, W // block_stride | |
| out = self.pool(out) | |
| return out | |
| """ Three alternatives for overlapping windows. | |
| `.unfold().unfold()` is same speed as stride tricks with similar clarity as F.unfold() | |
| if is_xla: | |
| # This code achieves haloing on PyTorch XLA with reasonable runtime trade-off, it is | |
| # EXTREMELY slow for backward on a GPU though so I need a way of selecting based on environment. | |
| WW = self.win_size ** 2 | |
| pw = torch.eye(WW, dtype=x.dtype, device=x.device).reshape(WW, 1, self.win_size, self.win_size) | |
| kv = F.conv2d(kv.reshape(-1, 1, H, W), pw, stride=self.block_size, padding=self.halo_size) | |
| elif self.stride_tricks: | |
| kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]).contiguous() | |
| kv = kv.as_strided(( | |
| B, self.dim_out_qk + self.dim_out_v, self.win_size, self.win_size, num_h_blocks, num_w_blocks), | |
| stride=(kv.stride(0), kv.stride(1), kv.shape[-1], 1, self.block_size * kv.shape[-1], self.block_size)) | |
| else: | |
| kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size) | |
| kv = kv.reshape( | |
| B * self.num_heads, self.dim_head_qk + self.dim_head_v, -1, num_blocks).transpose(1, 3) | |
| """ | |