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Running
on
Zero
| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # References: | |
| # https://github.com/facebookresearch/dino/blob/master/vision_transformer.py | |
| # https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py | |
| import logging | |
| from torch import Tensor | |
| from torch import nn | |
| logger = logging.getLogger("dinov2") | |
| try: | |
| from xformers.ops import memory_efficient_attention, unbind, fmha | |
| XFORMERS_AVAILABLE = True | |
| except ImportError: | |
| logger.warning("xFormers not available") | |
| XFORMERS_AVAILABLE = False | |
| class Attention(nn.Module): | |
| def __init__( | |
| self, | |
| dim: int, | |
| num_heads: int = 8, | |
| qkv_bias: bool = False, | |
| proj_bias: bool = True, | |
| attn_drop: float = 0.0, | |
| proj_drop: float = 0.0, | |
| ) -> None: | |
| super().__init__() | |
| self.num_heads = num_heads | |
| head_dim = dim // num_heads | |
| self.scale = head_dim**-0.5 | |
| self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) | |
| self.attn_drop = nn.Dropout(attn_drop) | |
| self.proj = nn.Linear(dim, dim, bias=proj_bias) | |
| self.proj_drop = nn.Dropout(proj_drop) | |
| def forward(self, x: Tensor) -> Tensor: | |
| B, N, C = x.shape | |
| qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) | |
| q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] | |
| attn = q @ k.transpose(-2, -1) | |
| attn = attn.softmax(dim=-1) | |
| attn = self.attn_drop(attn) | |
| x = (attn @ v).transpose(1, 2).reshape(B, N, C) | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| return x | |
| class MemEffAttention(Attention): | |
| def forward(self, x: Tensor, attn_bias=None) -> Tensor: | |
| if not XFORMERS_AVAILABLE: | |
| assert attn_bias is None, "xFormers is required for nested tensors usage" | |
| return super().forward(x) | |
| B, N, C = x.shape | |
| qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) | |
| q, k, v = unbind(qkv, 2) | |
| x = memory_efficient_attention(q, k, v, attn_bias=attn_bias) | |
| x = x.reshape([B, N, C]) | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| return x | |