94 lines
2.8 KiB
Python
94 lines
2.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# 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 logging
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from torch import Tensor
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from torch import nn
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import comfy.ops
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ops = comfy.ops.manual_cast
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from comfy.ldm.modules.attention import optimized_attention
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logger = logging.getLogger("dinov2")
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try:
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from xformers.ops import memory_efficient_attention, unbind
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XFORMERS_AVAILABLE = True
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except ImportError:
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logger.warning("xFormers not available")
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XFORMERS_AVAILABLE = False
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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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self.head_dim = dim // num_heads
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self.scale = self.head_dim**-0.5
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self.qkv = ops.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = ops.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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# print("x shape: ", x.shape)
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B, N, C = x.shape
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q, k, v = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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out = optimized_attention(q, k, v, self.num_heads, skip_reshape=True)
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out= self.proj(out)
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out = self.proj_drop(out)
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return out
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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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assert attn_bias is None, "xFormers is required for nested tensors usage"
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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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