Source code for mmpretrain.models.backbones.revvit
# Copyright (c) OpenMMLab. All rights reserved.
import sys
import numpy as np
import torch
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
from mmengine.model import BaseModule, ModuleList
from mmengine.model.weight_init import trunc_normal_
from torch import nn
from torch.autograd import Function as Function
from mmpretrain.models.backbones.base_backbone import BaseBackbone
from mmpretrain.registry import MODELS
from ..utils import (MultiheadAttention, build_norm_layer, resize_pos_embed,
to_2tuple)
class RevBackProp(Function):
"""Custom Backpropagation function to allow (A) flushing memory in forward
and (B) activation recomputation reversibly in backward for gradient
calculation.
Inspired by
https://github.com/RobinBruegger/RevTorch/blob/master/revtorch/revtorch.py
"""
@staticmethod
def forward(
ctx,
x,
layers,
buffer_layers, # List of layer ids for int activation to buffer
):
"""Reversible Forward pass.
Any intermediate activations from `buffer_layers` are cached in ctx for
forward pass. This is not necessary for standard usecases. Each
reversible layer implements its own forward pass logic.
"""
buffer_layers.sort()
x1, x2 = torch.chunk(x, 2, dim=-1)
intermediate = []
for layer in layers:
x1, x2 = layer(x1, x2)
if layer.layer_id in buffer_layers:
intermediate.extend([x1.detach(), x2.detach()])
if len(buffer_layers) == 0:
all_tensors = [x1.detach(), x2.detach()]
else:
intermediate = [torch.LongTensor(buffer_layers), *intermediate]
all_tensors = [x1.detach(), x2.detach(), *intermediate]
ctx.save_for_backward(*all_tensors)
ctx.layers = layers
return torch.cat([x1, x2], dim=-1)
@staticmethod
def backward(ctx, dx):
"""Reversible Backward pass.
Any intermediate activations from `buffer_layers` are recovered from
ctx. Each layer implements its own loic for backward pass (both
activation recomputation and grad calculation).
"""
d_x1, d_x2 = torch.chunk(dx, 2, dim=-1)
# retrieve params from ctx for backward
x1, x2, *int_tensors = ctx.saved_tensors
# no buffering
if len(int_tensors) != 0:
buffer_layers = int_tensors[0].tolist()
else:
buffer_layers = []
layers = ctx.layers
for _, layer in enumerate(layers[::-1]):
if layer.layer_id in buffer_layers:
x1, x2, d_x1, d_x2 = layer.backward_pass(
y1=int_tensors[buffer_layers.index(layer.layer_id) * 2 +
1],
y2=int_tensors[buffer_layers.index(layer.layer_id) * 2 +
2],
d_y1=d_x1,
d_y2=d_x2,
)
else:
x1, x2, d_x1, d_x2 = layer.backward_pass(
y1=x1,
y2=x2,
d_y1=d_x1,
d_y2=d_x2,
)
dx = torch.cat([d_x1, d_x2], dim=-1)
del int_tensors
del d_x1, d_x2, x1, x2
return dx, None, None
class RevTransformerEncoderLayer(BaseModule):
"""Reversible Transformer Encoder Layer.
This module is a building block of Reversible Transformer Encoder,
which support backpropagation without storing activations.
The residual connection is not applied to the FFN layer.
Args:
embed_dims (int): The feature dimension.
num_heads (int): Parallel attention heads.
feedforward_channels (int): The hidden dimension for FFNs.
drop_rate (float): Probability of an element to be zeroed.
Default: 0.0
attn_drop_rate (float): The drop out rate for attention layer.
Default: 0.0
drop_path_rate (float): stochastic depth rate.
Default 0.0
num_fcs (int): The number of linear in FFN
Default: 2
qkv_bias (bool): enable bias for qkv if True.
Default: True
act_cfg (dict): The activation config for FFNs.
Default: dict(type='GELU')
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='LN')
layer_id (int): The layer id of current layer. Used in RevBackProp.
Default: 0
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self,
embed_dims: int,
num_heads: int,
feedforward_channels: int,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
num_fcs: int = 2,
qkv_bias: bool = True,
act_cfg: dict = dict(type='GELU'),
norm_cfg: dict = dict(type='LN'),
layer_id: int = 0,
init_cfg=None):
super(RevTransformerEncoderLayer, self).__init__(init_cfg=init_cfg)
self.drop_path_cfg = dict(type='DropPath', drop_prob=drop_path_rate)
self.embed_dims = embed_dims
self.ln1 = build_norm_layer(norm_cfg, self.embed_dims)
self.attn = MultiheadAttention(
embed_dims=embed_dims,
num_heads=num_heads,
attn_drop=attn_drop_rate,
proj_drop=drop_rate,
qkv_bias=qkv_bias)
self.ln2 = build_norm_layer(norm_cfg, self.embed_dims)
self.ffn = FFN(
embed_dims=embed_dims,
feedforward_channels=feedforward_channels,
num_fcs=num_fcs,
ffn_drop=drop_rate,
act_cfg=act_cfg,
add_identity=False)
self.layer_id = layer_id
self.seeds = {}
def init_weights(self):
super(RevTransformerEncoderLayer, self).init_weights()
for m in self.ffn.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
nn.init.normal_(m.bias, std=1e-6)
def seed_cuda(self, key):
"""Fix seeds to allow for stochastic elements such as dropout to be
reproduced exactly in activation recomputation in the backward pass."""
# randomize seeds
# use cuda generator if available
if (hasattr(torch.cuda, 'default_generators')
and len(torch.cuda.default_generators) > 0):
# GPU
device_idx = torch.cuda.current_device()
seed = torch.cuda.default_generators[device_idx].seed()
else:
# CPU
seed = int(torch.seed() % sys.maxsize)
self.seeds[key] = seed
torch.manual_seed(self.seeds[key])
def forward(self, x1, x2):
"""
Implementation of Reversible TransformerEncoderLayer
`
x = x + self.attn(self.ln1(x))
x = self.ffn(self.ln2(x), identity=x)
`
"""
self.seed_cuda('attn')
# attention output
f_x2 = self.attn(self.ln1(x2))
# apply droppath on attention output
self.seed_cuda('droppath')
f_x2_dropped = build_dropout(self.drop_path_cfg)(f_x2)
y1 = x1 + f_x2_dropped
# free memory
if self.training:
del x1
# ffn output
self.seed_cuda('ffn')
g_y1 = self.ffn(self.ln2(y1))
# apply droppath on ffn output
torch.manual_seed(self.seeds['droppath'])
g_y1_dropped = build_dropout(self.drop_path_cfg)(g_y1)
# final output
y2 = x2 + g_y1_dropped
# free memory
if self.training:
del x2
return y1, y2
def backward_pass(self, y1, y2, d_y1, d_y2):
"""Activation re-compute with the following equation.
x2 = y2 - g(y1), g = FFN
x1 = y1 - f(x2), f = MSHA
"""
# temporarily record intermediate activation for G
# and use them for gradient calculation of G
with torch.enable_grad():
y1.requires_grad = True
torch.manual_seed(self.seeds['ffn'])
g_y1 = self.ffn(self.ln2(y1))
torch.manual_seed(self.seeds['droppath'])
g_y1 = build_dropout(self.drop_path_cfg)(g_y1)
g_y1.backward(d_y2, retain_graph=True)
# activate recomputation is by design and not part of
# the computation graph in forward pass
with torch.no_grad():
x2 = y2 - g_y1
del g_y1
d_y1 = d_y1 + y1.grad
y1.grad = None
# record F activation and calculate gradients on F
with torch.enable_grad():
x2.requires_grad = True
torch.manual_seed(self.seeds['attn'])
f_x2 = self.attn(self.ln1(x2))
torch.manual_seed(self.seeds['droppath'])
f_x2 = build_dropout(self.drop_path_cfg)(f_x2)
f_x2.backward(d_y1, retain_graph=True)
# propagate reverse computed activations at the
# start of the previous block
with torch.no_grad():
x1 = y1 - f_x2
del f_x2, y1
d_y2 = d_y2 + x2.grad
x2.grad = None
x2 = x2.detach()
return x1, x2, d_y1, d_y2
class TwoStreamFusion(nn.Module):
"""A general constructor for neural modules fusing two equal sized tensors
in forward.
Args:
mode (str): The mode of fusion. Options are 'add', 'max', 'min',
'avg', 'concat'.
"""
def __init__(self, mode: str):
super().__init__()
self.mode = mode
if mode == 'add':
self.fuse_fn = lambda x: torch.stack(x).sum(dim=0)
elif mode == 'max':
self.fuse_fn = lambda x: torch.stack(x).max(dim=0).values
elif mode == 'min':
self.fuse_fn = lambda x: torch.stack(x).min(dim=0).values
elif mode == 'avg':
self.fuse_fn = lambda x: torch.stack(x).mean(dim=0)
elif mode == 'concat':
self.fuse_fn = lambda x: torch.cat(x, dim=-1)
else:
raise NotImplementedError
def forward(self, x):
# split the tensor into two halves in the channel dimension
x = torch.chunk(x, 2, dim=2)
return self.fuse_fn(x)
[docs]@MODELS.register_module()
class RevVisionTransformer(BaseBackbone):
"""Reversible Vision Transformer.
A PyTorch implementation of : `Reversible Vision Transformers
<https://openaccess.thecvf.com/content/CVPR2022/html/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.html>`_ # noqa: E501
Args:
arch (str | dict): Vision Transformer architecture. If use string,
choose from 'small', 'base', 'large', 'deit-tiny', 'deit-small'
and 'deit-base'. If use dict, it should have below keys:
- **embed_dims** (int): The dimensions of embedding.
- **num_layers** (int): The number of transformer encoder layers.
- **num_heads** (int): The number of heads in attention modules.
- **feedforward_channels** (int): The hidden dimensions in
feedforward modules.
Defaults to 'base'.
img_size (int | tuple): The expected input image shape. Because we
support dynamic input shape, just set the argument to the most
common input image shape. Defaults to 224.
patch_size (int | tuple): The patch size in patch embedding.
Defaults to 16.
in_channels (int): The num of input channels. Defaults to 3.
drop_rate (float): Probability of an element to be zeroed.
Defaults to 0.
drop_path_rate (float): stochastic depth rate. Defaults to 0.
qkv_bias (bool): Whether to add bias for qkv in attention modules.
Defaults to True.
norm_cfg (dict): Config dict for normalization layer.
Defaults to ``dict(type='LN')``.
final_norm (bool): Whether to add a additional layer to normalize
final feature map. Defaults to True.
out_type (str): The type of output features. Please choose from
- ``"cls_token"``: The class token tensor with shape (B, C).
- ``"featmap"``: The feature map tensor from the patch tokens
with shape (B, C, H, W).
- ``"avg_featmap"``: The global averaged feature map tensor
with shape (B, C).
- ``"raw"``: The raw feature tensor includes patch tokens and
class tokens with shape (B, L, C).
Defaults to ``"avg_featmap"``.
with_cls_token (bool): Whether concatenating class token into image
tokens as transformer input. Defaults to False.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Defaults to -1.
interpolate_mode (str): Select the interpolate mode for position
embeding vector resize. Defaults to "bicubic".
patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict.
layer_cfgs (Sequence | dict): Configs of each transformer layer in
encoder. Defaults to an empty dict.
fusion_mode (str): The fusion mode of transformer layers.
Defaults to 'concat'.
no_custom_backward (bool): Whether to use custom backward.
Defaults to False.
init_cfg (dict, optional): Initialization config dict.
Defaults to None.
"""
arch_zoo = {
**dict.fromkeys(
['s', 'small'], {
'embed_dims': 768,
'num_layers': 8,
'num_heads': 8,
'feedforward_channels': 768 * 3,
}),
**dict.fromkeys(
['b', 'base'], {
'embed_dims': 768,
'num_layers': 12,
'num_heads': 12,
'feedforward_channels': 3072
}),
**dict.fromkeys(
['l', 'large'], {
'embed_dims': 1024,
'num_layers': 24,
'num_heads': 16,
'feedforward_channels': 4096
}),
**dict.fromkeys(
['h', 'huge'],
{
# The same as the implementation in MAE
# <https://arxiv.org/abs/2111.06377>
'embed_dims': 1280,
'num_layers': 32,
'num_heads': 16,
'feedforward_channels': 5120
}),
**dict.fromkeys(
['deit-t', 'deit-tiny'], {
'embed_dims': 192,
'num_layers': 12,
'num_heads': 3,
'feedforward_channels': 192 * 4
}),
**dict.fromkeys(
['deit-s', 'deit-small'], {
'embed_dims': 384,
'num_layers': 12,
'num_heads': 6,
'feedforward_channels': 384 * 4
}),
**dict.fromkeys(
['deit-b', 'deit-base'], {
'embed_dims': 768,
'num_layers': 12,
'num_heads': 12,
'feedforward_channels': 768 * 4
}),
}
num_extra_tokens = 0 # The official RevViT doesn't have class token
OUT_TYPES = {'raw', 'cls_token', 'featmap', 'avg_featmap'}
def __init__(self,
arch='base',
img_size=224,
patch_size=16,
in_channels=3,
drop_rate=0.,
drop_path_rate=0.,
qkv_bias=True,
norm_cfg=dict(type='LN', eps=1e-6),
final_norm=True,
out_type='avg_featmap',
with_cls_token=False,
frozen_stages=-1,
interpolate_mode='bicubic',
patch_cfg=dict(),
layer_cfgs=dict(),
fusion_mode='concat',
no_custom_backward=False,
init_cfg=None):
super(RevVisionTransformer, self).__init__(init_cfg)
if isinstance(arch, str):
arch = arch.lower()
assert arch in set(self.arch_zoo), \
f'Arch {arch} is not in default archs {set(self.arch_zoo)}'
self.arch_settings = self.arch_zoo[arch]
else:
essential_keys = {
'embed_dims', 'num_layers', 'num_heads', 'feedforward_channels'
}
assert isinstance(arch, dict) and essential_keys <= set(arch), \
f'Custom arch needs a dict with keys {essential_keys}'
self.arch_settings = arch
self.embed_dims = self.arch_settings['embed_dims']
self.num_layers = self.arch_settings['num_layers']
self.img_size = to_2tuple(img_size)
self.no_custom_backward = no_custom_backward
# Set patch embedding
_patch_cfg = dict(
in_channels=in_channels,
input_size=img_size,
embed_dims=self.embed_dims,
conv_type='Conv2d',
kernel_size=patch_size,
stride=patch_size,
)
_patch_cfg.update(patch_cfg)
self.patch_embed = PatchEmbed(**_patch_cfg)
self.patch_resolution = self.patch_embed.init_out_size
num_patches = self.patch_resolution[0] * self.patch_resolution[1]
# Set out type
if out_type not in self.OUT_TYPES:
raise ValueError(f'Unsupported `out_type` {out_type}, please '
f'choose from {self.OUT_TYPES}')
self.out_type = out_type
# Set cls token
if with_cls_token:
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dims))
self.num_extra_tokens = 1
elif out_type != 'cls_token':
self.cls_token = None
self.num_extra_tokens = 0
else:
raise ValueError(
'with_cls_token must be True when `out_type="cls_token"`.')
# Set position embedding
self.interpolate_mode = interpolate_mode
self.pos_embed = nn.Parameter(
torch.zeros(1, num_patches + self.num_extra_tokens,
self.embed_dims))
self._register_load_state_dict_pre_hook(self._prepare_pos_embed)
self.drop_after_pos = nn.Dropout(p=drop_rate)
# stochastic depth decay rule
dpr = np.linspace(0, drop_path_rate, self.num_layers)
self.layers = ModuleList()
if isinstance(layer_cfgs, dict):
layer_cfgs = [layer_cfgs] * self.num_layers
for i in range(self.num_layers):
_layer_cfg = dict(
embed_dims=self.embed_dims,
num_heads=self.arch_settings['num_heads'],
feedforward_channels=self.
arch_settings['feedforward_channels'],
drop_rate=drop_rate,
drop_path_rate=dpr[i],
qkv_bias=qkv_bias,
layer_id=i,
norm_cfg=norm_cfg)
_layer_cfg.update(layer_cfgs[i])
self.layers.append(RevTransformerEncoderLayer(**_layer_cfg))
# fusion operation for the final output
self.fusion_layer = TwoStreamFusion(mode=fusion_mode)
self.frozen_stages = frozen_stages
self.final_norm = final_norm
if final_norm:
self.ln1 = build_norm_layer(norm_cfg, self.embed_dims * 2)
# freeze stages only when self.frozen_stages > 0
if self.frozen_stages > 0:
self._freeze_stages()
def init_weights(self):
super(RevVisionTransformer, self).init_weights()
if not (isinstance(self.init_cfg, dict)
and self.init_cfg['type'] == 'Pretrained'):
trunc_normal_(self.pos_embed, std=0.02)
def _prepare_pos_embed(self, state_dict, prefix, *args, **kwargs):
name = prefix + 'pos_embed'
if name not in state_dict.keys():
return
ckpt_pos_embed_shape = state_dict[name].shape
if self.pos_embed.shape != ckpt_pos_embed_shape:
from mmengine.logging import MMLogger
logger = MMLogger.get_current_instance()
logger.info(
f'Resize the pos_embed shape from {ckpt_pos_embed_shape} '
f'to {self.pos_embed.shape}.')
ckpt_pos_embed_shape = to_2tuple(
int(np.sqrt(ckpt_pos_embed_shape[1] - self.num_extra_tokens)))
pos_embed_shape = self.patch_embed.init_out_size
state_dict[name] = resize_pos_embed(state_dict[name],
ckpt_pos_embed_shape,
pos_embed_shape,
self.interpolate_mode,
self.num_extra_tokens)
[docs] @staticmethod
def resize_pos_embed(*args, **kwargs):
"""Interface for backward-compatibility."""
return resize_pos_embed(*args, **kwargs)
def _freeze_stages(self):
# freeze position embedding
self.pos_embed.requires_grad = False
# set dropout to eval model
self.drop_after_pos.eval()
# freeze patch embedding
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.requires_grad = False
# freeze cls_token
if self.cls_token is not None:
self.cls_token.requires_grad = False
# freeze layers
for i in range(1, self.frozen_stages + 1):
m = self.layers[i - 1]
m.eval()
for param in m.parameters():
param.requires_grad = False
# freeze the last layer norm
if self.frozen_stages == len(self.layers) and self.final_norm:
self.ln1.eval()
for param in self.ln1.parameters():
param.requires_grad = False
def forward(self, x):
B = x.shape[0]
x, patch_resolution = self.patch_embed(x)
if self.cls_token is not None:
cls_token = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_token, x), dim=1)
x = x + resize_pos_embed(
self.pos_embed,
self.patch_resolution,
patch_resolution,
mode=self.interpolate_mode,
num_extra_tokens=self.num_extra_tokens)
x = self.drop_after_pos(x)
x = torch.cat([x, x], dim=-1)
# forward with different conditions
if not self.training or self.no_custom_backward:
# in eval/inference model
executing_fn = RevVisionTransformer._forward_vanilla_bp
else:
# use custom backward when self.training=True.
executing_fn = RevBackProp.apply
x = executing_fn(x, self.layers, [])
if self.final_norm:
x = self.ln1(x)
x = self.fusion_layer(x)
return (self._format_output(x, patch_resolution), )
@staticmethod
def _forward_vanilla_bp(hidden_state, layers, buffer=[]):
"""Using reversible layers without reversible backpropagation.
Debugging purpose only. Activated with self.no_custom_backward
"""
# split into ffn state(ffn_out) and attention output(attn_out)
ffn_out, attn_out = torch.chunk(hidden_state, 2, dim=-1)
del hidden_state
for _, layer in enumerate(layers):
attn_out, ffn_out = layer(attn_out, ffn_out)
return torch.cat([attn_out, ffn_out], dim=-1)
def _format_output(self, x, hw):
if self.out_type == 'raw':
return x
if self.out_type == 'cls_token':
return x[:, 0]
patch_token = x[:, self.num_extra_tokens:]
if self.out_type == 'featmap':
B = x.size(0)
# (B, N, C) -> (B, H, W, C) -> (B, C, H, W)
return patch_token.reshape(B, *hw, -1).permute(0, 3, 1, 2)
if self.out_type == 'avg_featmap':
return patch_token.mean(dim=1)