157 lines
5.1 KiB
Python
157 lines
5.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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#
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# This source code is licensed under the Apache License, Version 2.0
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# found in the LICENSE file in the root directory of this source tree.
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# References:
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# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
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# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
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import os
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import warnings
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from torch import Tensor
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from torch import nn
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XFORMERS_ENABLED = os.environ.get("XFORMERS_DISABLED") is None
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try:
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if XFORMERS_ENABLED:
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from xformers.ops import memory_efficient_attention, unbind
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XFORMERS_AVAILABLE = True
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warnings.warn("xFormers is available (Attention)")
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else:
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warnings.warn("xFormers is disabled (Attention)")
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raise ImportError
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except ImportError:
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XFORMERS_AVAILABLE = False
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warnings.warn("xFormers is not available (Attention)")
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class Attention(nn.Module):
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def __init__(
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self,
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dim: int,
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num_heads: int = 8,
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qkv_bias: bool = False,
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proj_bias: bool = True,
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attn_drop: float = 0.0,
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proj_drop: float = 0.0,
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) -> None:
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim**-0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim, bias=proj_bias)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x: Tensor) -> Tensor:
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
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attn = q @ k.transpose(-2, -1)
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class MemEffAttention(Attention):
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def forward(self, x: Tensor, attn_bias=None) -> Tensor:
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if not XFORMERS_AVAILABLE:
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if attn_bias is not None:
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raise AssertionError("xFormers is required for using nested tensors")
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return super().forward(x)
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
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q, k, v = unbind(qkv, 2)
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x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
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x = x.reshape([B, N, C])
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class CrossAttention(nn.Module):
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def __init__(
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self,
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dim: int,
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dim_q: int,
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dim_k: int,
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dim_v: int,
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num_heads: int = 8,
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qkv_bias: bool = False,
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proj_bias: bool = True,
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attn_drop: float = 0.0,
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proj_drop: float = 0.0,
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) -> None:
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim**-0.5
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self.to_q = nn.Linear(dim_q, dim, bias=qkv_bias)
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self.to_k = nn.Linear(dim_k, dim, bias=qkv_bias)
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self.to_v = nn.Linear(dim_v, dim, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim, bias=proj_bias)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
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# q: [B, N, Cq]
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# k: [B, M, Ck]
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# v: [B, M, Cv]
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# return: [B, N, C]
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B, N, _ = q.shape
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M = k.shape[1]
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q = self.scale * self.to_q(q).reshape(B, N, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, N, C/nh]
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k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, M, C/nh]
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v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads).permute(0, 2, 1, 3) # [B, nh, M, C/nh]
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attn = q @ k.transpose(-2, -1) # [B, nh, N, M]
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attn = attn.softmax(dim=-1) # [B, nh, N, M]
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, -1) # [B, nh, N, M] @ [B, nh, M, C/nh] --> [B, nh, N, C/nh] --> [B, N, nh, C/nh] --> [B, N, C]
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class MemEffCrossAttention(CrossAttention):
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def forward(self, q: Tensor, k: Tensor, v: Tensor, attn_bias=None) -> Tensor:
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if not XFORMERS_AVAILABLE:
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if attn_bias is not None:
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raise AssertionError("xFormers is required for using nested tensors")
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return super().forward(x)
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B, N, _ = q.shape
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M = k.shape[1]
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q = self.scale * self.to_q(q).reshape(B, N, self.num_heads, self.dim // self.num_heads) # [B, N, nh, C/nh]
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k = self.to_k(k).reshape(B, M, self.num_heads, self.dim // self.num_heads) # [B, M, nh, C/nh]
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v = self.to_v(v).reshape(B, M, self.num_heads, self.dim // self.num_heads) # [B, M, nh, C/nh]
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x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
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x = x.reshape(B, N, -1)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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