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234 lines
10 KiB
234 lines
10 KiB
""" Halo Self Attention
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Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
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- https://arxiv.org/abs/2103.12731
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@misc{2103.12731,
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Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
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Jonathon Shlens},
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Title = {Scaling Local Self-Attention for Parameter Efficient Visual Backbones},
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Year = {2021},
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}
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Status:
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This impl is a WIP, there is no official ref impl and some details in paper weren't clear to me.
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The attention mechanism works but it's slow as implemented.
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Hacked together by / Copyright 2021 Ross Wightman
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"""
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from typing import List
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import torch
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from torch import nn
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import torch.nn.functional as F
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from .helpers import make_divisible
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from .weight_init import trunc_normal_
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from .trace_utils import _assert
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def rel_logits_1d(q, rel_k, permute_mask: List[int]):
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""" Compute relative logits along one dimension
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As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
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Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
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Args:
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q: (batch, height, width, dim)
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rel_k: (2 * window - 1, dim)
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permute_mask: permute output dim according to this
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"""
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B, H, W, dim = q.shape
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rel_size = rel_k.shape[0]
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win_size = (rel_size + 1) // 2
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x = (q @ rel_k.transpose(-1, -2))
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x = x.reshape(-1, W, rel_size)
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# pad to shift from relative to absolute indexing
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x_pad = F.pad(x, [0, 1]).flatten(1)
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x_pad = F.pad(x_pad, [0, rel_size - W])
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# reshape and slice out the padded elements
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x_pad = x_pad.reshape(-1, W + 1, rel_size)
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x = x_pad[:, :W, win_size - 1:]
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# reshape and tile
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x = x.reshape(B, H, 1, W, win_size).expand(-1, -1, win_size, -1, -1)
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return x.permute(permute_mask)
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class PosEmbedRel(nn.Module):
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""" Relative Position Embedding
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As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
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Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
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"""
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def __init__(self, block_size, win_size, dim_head, scale):
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"""
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Args:
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block_size (int): block size
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win_size (int): neighbourhood window size
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dim_head (int): attention head dim
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scale (float): scale factor (for init)
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"""
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super().__init__()
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self.block_size = block_size
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self.dim_head = dim_head
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self.height_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
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self.width_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
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def forward(self, q):
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B, BB, HW, _ = q.shape
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# relative logits in width dimension.
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q = q.reshape(-1, self.block_size, self.block_size, self.dim_head)
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rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
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# relative logits in height dimension.
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q = q.transpose(1, 2)
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rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2))
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rel_logits = rel_logits_h + rel_logits_w
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rel_logits = rel_logits.reshape(B, BB, HW, -1)
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return rel_logits
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class HaloAttn(nn.Module):
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""" Halo Attention
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Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
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- https://arxiv.org/abs/2103.12731
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The internal dimensions of the attention module are controlled by the interaction of several arguments.
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* the output dimension of the module is specified by dim_out, which falls back to input dim if not set
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* the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
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* the query and key (qk) dimensions are determined by
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* num_heads * dim_head if dim_head is not None
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* num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None
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* as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used
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Args:
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dim (int): input dimension to the module
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dim_out (int): output dimension of the module, same as dim if not set
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feat_size (Tuple[int, int]): size of input feature_map (not used, for arg compat with bottle/lambda)
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stride: output stride of the module, query downscaled if > 1 (default: 1).
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num_heads: parallel attention heads (default: 8).
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dim_head: dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
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block_size (int): size of blocks. (default: 8)
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halo_size (int): size of halo overlap. (default: 3)
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qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
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qkv_bias (bool) : add bias to q, k, and v projections
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avg_down (bool): use average pool downsample instead of strided query blocks
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scale_pos_embed (bool): scale the position embedding as well as Q @ K
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"""
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def __init__(
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self, dim, dim_out=None, feat_size=None, stride=1, num_heads=8, dim_head=None, block_size=8, halo_size=3,
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qk_ratio=1.0, qkv_bias=False, avg_down=False, scale_pos_embed=False):
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super().__init__()
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dim_out = dim_out or dim
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assert dim_out % num_heads == 0
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assert stride in (1, 2)
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self.num_heads = num_heads
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self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
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self.dim_head_v = dim_out // self.num_heads
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self.dim_out_qk = num_heads * self.dim_head_qk
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self.dim_out_v = num_heads * self.dim_head_v
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self.scale = self.dim_head_qk ** -0.5
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self.scale_pos_embed = scale_pos_embed
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self.block_size = self.block_size_ds = block_size
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self.halo_size = halo_size
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self.win_size = block_size + halo_size * 2 # neighbourhood window size
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self.block_stride = 1
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use_avg_pool = False
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if stride > 1:
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use_avg_pool = avg_down or block_size % stride != 0
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self.block_stride = 1 if use_avg_pool else stride
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self.block_size_ds = self.block_size // self.block_stride
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# FIXME not clear if this stride behaviour is what the paper intended
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# Also, the paper mentions using a 3D conv for dealing with the blocking/gather, and leaving
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# data in unfolded block form. I haven't wrapped my head around how that'd look.
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self.q = nn.Conv2d(dim, self.dim_out_qk, 1, stride=self.block_stride, bias=qkv_bias)
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self.kv = nn.Conv2d(dim, self.dim_out_qk + self.dim_out_v, 1, bias=qkv_bias)
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self.pos_embed = PosEmbedRel(
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block_size=self.block_size_ds, win_size=self.win_size, dim_head=self.dim_head_qk, scale=self.scale)
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self.pool = nn.AvgPool2d(2, 2) if use_avg_pool else nn.Identity()
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self.reset_parameters()
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def reset_parameters(self):
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std = self.q.weight.shape[1] ** -0.5 # fan-in
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trunc_normal_(self.q.weight, std=std)
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trunc_normal_(self.kv.weight, std=std)
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trunc_normal_(self.pos_embed.height_rel, std=self.scale)
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trunc_normal_(self.pos_embed.width_rel, std=self.scale)
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def forward(self, x):
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B, C, H, W = x.shape
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_assert(H % self.block_size == 0, '')
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_assert(W % self.block_size == 0, '')
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num_h_blocks = H // self.block_size
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num_w_blocks = W // self.block_size
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num_blocks = num_h_blocks * num_w_blocks
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q = self.q(x)
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# unfold
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q = q.reshape(
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-1, self.dim_head_qk,
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num_h_blocks, self.block_size_ds, num_w_blocks, self.block_size_ds).permute(0, 1, 3, 5, 2, 4)
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# B, num_heads * dim_head * block_size ** 2, num_blocks
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q = q.reshape(B * self.num_heads, self.dim_head_qk, -1, num_blocks).transpose(1, 3)
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# B * num_heads, num_blocks, block_size ** 2, dim_head
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kv = self.kv(x)
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# Generate overlapping windows for kv. This approach is good for GPU and CPU. However, unfold() is not
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# lowered for PyTorch XLA so it will be very slow. See code at bottom of file for XLA friendly approach.
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# FIXME figure out how to switch impl between this and conv2d if XLA being used.
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kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size])
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kv = kv.unfold(2, self.win_size, self.block_size).unfold(3, self.win_size, self.block_size).reshape(
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B * self.num_heads, self.dim_head_qk + self.dim_head_v, num_blocks, -1).permute(0, 2, 3, 1)
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k, v = torch.split(kv, [self.dim_head_qk, self.dim_head_v], dim=-1)
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# B * num_heads, num_blocks, win_size ** 2, dim_head_qk or dim_head_v
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if self.scale_pos_embed:
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attn = (q @ k.transpose(-1, -2) + self.pos_embed(q)) * self.scale
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else:
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attn = (q @ k.transpose(-1, -2)) * self.scale + self.pos_embed(q)
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# B * num_heads, num_blocks, block_size ** 2, win_size ** 2
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attn = attn.softmax(dim=-1)
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out = (attn @ v).transpose(1, 3) # B * num_heads, dim_head_v, block_size ** 2, num_blocks
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# fold
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out = out.reshape(-1, self.block_size_ds, self.block_size_ds, num_h_blocks, num_w_blocks)
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out = out.permute(0, 3, 1, 4, 2).contiguous().view(
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B, self.dim_out_v, H // self.block_stride, W // self.block_stride)
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# B, dim_out, H // block_stride, W // block_stride
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out = self.pool(out)
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return out
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""" Three alternatives for overlapping windows.
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`.unfold().unfold()` is same speed as stride tricks with similar clarity as F.unfold()
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if is_xla:
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# This code achieves haloing on PyTorch XLA with reasonable runtime trade-off, it is
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# EXTREMELY slow for backward on a GPU though so I need a way of selecting based on environment.
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WW = self.win_size ** 2
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pw = torch.eye(WW, dtype=x.dtype, device=x.device).reshape(WW, 1, self.win_size, self.win_size)
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kv = F.conv2d(kv.reshape(-1, 1, H, W), pw, stride=self.block_size, padding=self.halo_size)
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elif self.stride_tricks:
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kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]).contiguous()
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kv = kv.as_strided((
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B, self.dim_out_qk + self.dim_out_v, self.win_size, self.win_size, num_h_blocks, num_w_blocks),
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stride=(kv.stride(0), kv.stride(1), kv.shape[-1], 1, self.block_size * kv.shape[-1], self.block_size))
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else:
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kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
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kv = kv.reshape(
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B * self.num_heads, self.dim_head_qk + self.dim_head_v, -1, num_blocks).transpose(1, 3)
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"""
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