Spaces:
Running on Zero
Running on Zero
| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # | |
| # This source code is licensed under the Apache License, Version 2.0 | |
| # 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/layers/patch_embed.py | |
| from typing import Callable, List | |
| import torch | |
| from torch import nn, Tensor | |
| from .attention import Attention, MemEffAttention | |
| from .drop_path import DropPath | |
| from .layer_scale import LayerScale | |
| from .mlp import Mlp | |
| class Block(nn.Module): | |
| def __init__( | |
| self, | |
| dim: int, | |
| num_heads: int, | |
| mlp_ratio: float = 4.0, | |
| qkv_bias: bool = False, | |
| proj_bias: bool = True, | |
| ffn_bias: bool = True, | |
| drop: float = 0.0, | |
| attn_drop: float = 0.0, | |
| init_values=None, | |
| drop_path: float = 0.0, | |
| act_layer: Callable[..., nn.Module] = nn.GELU, | |
| norm_layer: Callable[..., nn.Module] = nn.LayerNorm, | |
| attn_class: Callable[..., nn.Module] = MemEffAttention, | |
| ffn_layer: Callable[..., nn.Module] = Mlp, | |
| no_attn: bool = False | |
| ) -> None: | |
| super().__init__() | |
| self.norm1 = norm_layer(dim) | |
| if no_attn: | |
| self.attn = nn.Identity() | |
| else: | |
| self.attn = attn_class( | |
| dim, | |
| num_heads=num_heads, | |
| qkv_bias=qkv_bias, | |
| proj_bias=proj_bias, | |
| attn_drop=attn_drop, | |
| proj_drop=drop, | |
| ) | |
| self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() | |
| self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() | |
| self.norm2 = norm_layer(dim) | |
| mlp_hidden_dim = int(dim * mlp_ratio) | |
| self.mlp = ffn_layer( | |
| in_features=dim, | |
| hidden_features=mlp_hidden_dim, | |
| act_layer=act_layer, | |
| drop=drop, | |
| bias=ffn_bias, | |
| ) | |
| self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() | |
| self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() | |
| self.sample_drop_ratio = drop_path | |
| def forward(self, x: Tensor, y: Tensor | None = None) -> Tensor: | |
| def attn_residual_func(x: Tensor) -> Tensor: | |
| return self.ls1(self.attn(self.norm1(x), y=y)) | |
| def ffn_residual_func(x: Tensor) -> Tensor: | |
| return self.ls2(self.mlp(self.norm2(x))) | |
| if self.training and self.sample_drop_ratio > 0.1: | |
| x = drop_add_residual_stochastic_depth( | |
| x, | |
| residual_func=attn_residual_func, | |
| sample_drop_ratio=self.sample_drop_ratio, | |
| ) | |
| x = drop_add_residual_stochastic_depth( | |
| x, | |
| residual_func=ffn_residual_func, | |
| sample_drop_ratio=self.sample_drop_ratio, | |
| ) | |
| elif self.training and self.sample_drop_ratio > 0.0: | |
| x = x + self.drop_path1(attn_residual_func(x)) | |
| x = x + self.drop_path2(ffn_residual_func(x)) | |
| else: | |
| x = x + attn_residual_func(x) | |
| x = x + ffn_residual_func(x) | |
| return x | |
| def drop_add_residual_stochastic_depth( | |
| x: Tensor, | |
| residual_func: Callable[[Tensor], Tensor], | |
| sample_drop_ratio: float = 0.0, | |
| ) -> Tensor: | |
| b, n, d = x.shape | |
| sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1) | |
| brange = (torch.randperm(b, device=x.device))[:sample_subset_size] | |
| x_subset = x[brange] | |
| residual = residual_func(x_subset) | |
| x_flat = x.flatten(1) | |
| residual = residual.flatten(1) | |
| residual_scale_factor = b / sample_subset_size | |
| x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor) | |
| return x_plus_residual.view_as(x) | |
| class NestedTensorBlock(Block): | |
| def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: | |
| raise NotImplementedError("Nested tensor support requires xformers, which has been removed") | |
| def forward(self, x_or_x_list): | |
| if isinstance(x_or_x_list, Tensor): | |
| return super().forward(x_or_x_list) | |
| elif isinstance(x_or_x_list, list): | |
| raise NotImplementedError("Nested tensor support requires xformers, which has been removed") | |
| else: | |
| raise AssertionError | |