# https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py
from dataclasses import dataclass
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Final, Optional, Callable, Union, Tuple, List, Set, Dict, Type, Literal, Sequence
import math
import warnings
from timm.layers import (
PatchEmbed, Mlp, DropPath,
AttentionPoolLatent, PatchDropout, resample_abs_pos_embed, LayerType
)
from timm.models._manipulate import named_apply, checkpoint_seq, adapt_input_conv
from transformers.modeling_utils import is_flash_attn_2_available
from xformers.ops import memory_efficient_attention
from functools import partial
if is_flash_attn_2_available():
from flash_attn import flash_attn_qkvpacked_func
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn(
"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2,
)
with torch.no_grad():
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std) # noqa: E741
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.0))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
# type: (torch.Tensor, float, float, float, float) -> torch.Tensor
r"""The original timm.models.layers.weight_init.trunc_normal_ can not handle bfloat16 yet, here we first
convert the tensor to float32, apply the trunc_normal_() in float32, and then convert it back to its orignal dtype.
Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn
from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
with torch.no_grad():
dtype = tensor.dtype
tensor_fp32 = tensor.float()
tensor_fp32 = _no_grad_trunc_normal_(tensor_fp32, mean, std, a, b)
tensor_dtype = tensor_fp32.to(dtype=dtype)
tensor.copy_(tensor_dtype)
def init_weights(self):
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
trunc_normal_(self.latent, std=self.latent_dim ** -0.5)
def init_weights_vit_timm(module: nn.Module, name: str = '') -> None:
""" ViT weight initialization, original timm impl (for reproducibility) """
if isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
module.init_weights()
class Attention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_norm: bool = False,
attn_drop: float = 0.,
proj_drop: float = 0.,
norm_layer: nn.Module = nn.LayerNorm,
deterministic: bool = False,
) -> None:
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.qk_norm = qk_norm
self.fused_attn = True
self.deterministic = deterministic
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0. else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim)
if not self.qk_norm:
if self.head_dim % 32 == 0 and is_flash_attn_2_available():
# flashattn must have head_dim as a multiple of 32
x = flash_attn_qkvpacked_func(qkv, dropout_p=self.attn_drop.p if self.training else 0.,
deterministic=self.deterministic)
else:
q, k, v = qkv.unbind(2)
x = memory_efficient_attention(q, k, v, p=self.attn_drop.p if self.training else 0.)
x = x.reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
qkv = qkv.permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)
if self.fused_attn:
with torch.backends.cuda.sdp_kernel(enable_math=False, enable_mem_efficient=False):
# 用上下文的方式强行使用fa
x = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p if self.training else 0.,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScale(nn.Module):
def __init__(
self,
dim: int,
init_values: float = 1e-5,
inplace: bool = False,
) -> None:
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class Block(nn.Module):
def __init__(
self,
dim: int,
num_heads: int,
mlp_ratio: float = 4.,
qkv_bias: bool = False,
qk_norm: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
init_values: Optional[float] = None,
drop_path: float = 0.,
act_layer: nn.Module = nn.GELU,
norm_layer: nn.Module = nn.LayerNorm,
mlp_layer: nn.Module = Mlp,
deterministic: bool = False,
) -> None:
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
attn_drop=attn_drop,
proj_drop=proj_drop,
norm_layer=norm_layer,
deterministic=deterministic,
)
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = mlp_layer(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class VisionTransformer(nn.Module):
""" Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
- https://arxiv.org/abs/2010.11929
"""
dynamic_img_size: Final[bool]
def __init__(
self,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: Literal['', 'avg', 'token', 'map'] = 'token',
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
qk_norm: bool = False,
init_values: Optional[float] = None,
class_token: bool = True,
no_embed_class: bool = False,
reg_tokens: int = 0,
pre_norm: bool = False,
fc_norm: Optional[bool] = None,
dynamic_img_size: bool = False,
dynamic_img_pad: bool = False,
drop_rate: float = 0.,
pos_drop_rate: float = 0.,
patch_drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
weight_init: Literal['skip', 'jax', 'jax_nlhb', 'moco', ''] = '',
embed_layer: Callable = PatchEmbed,
norm_layer: Optional[LayerType] = None,
act_layer: Optional[LayerType] = None,
block_fn: Type[nn.Module] = Block,
mlp_layer: Type[nn.Module] = Mlp,
ignore_head: bool = False,
deterministic: bool = False,
num_recomputing_layers: int = 0
) -> None:
"""
Args:
img_size: Input image size.
patch_size: Patch size.
in_chans: Number of image input channels.
num_classes: Mumber of classes for classification head.
global_pool: Type of global pooling for final sequence (default: 'token').
embed_dim: Transformer embedding dimension.
depth: Depth of transformer.
num_heads: Number of attention heads.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: Enable bias for qkv projections if True.
init_values: Layer-scale init values (layer-scale enabled if not None).
class_token: Use class token.
no_embed_class: Don't include position embeddings for class (or reg) tokens.
reg_tokens: Number of register tokens.
fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
drop_rate: Head dropout rate.
pos_drop_rate: Position embedding dropout rate.
attn_drop_rate: Attention dropout rate.
drop_path_rate: Stochastic depth rate.
weight_init: Weight initialization scheme.
embed_layer: Patch embedding layer.
norm_layer: Normalization layer.
act_layer: MLP activation layer.
block_fn: Transformer block layer.
"""
super().__init__()
assert global_pool in ('', 'avg', 'token', 'map')
assert class_token or global_pool != 'token'
use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
# norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
# act_layer = get_act_layer(act_layer) or nn.GELU
norm_layer = partial(nn.LayerNorm, eps=1e-6)
# siglip use PytorchGELUTanh() rather than the vanilla nn.GELU()
# https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/siglip/configuration_siglip.py#L191
act_layer = partial(nn.GELU, approximate='tanh')
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_prefix_tokens = 1 if class_token else 0
self.num_prefix_tokens += reg_tokens
self.num_reg_tokens = reg_tokens
self.has_class_token = class_token
self.no_embed_class = no_embed_class # don't embed prefix positions (includes reg)
self.dynamic_img_size = dynamic_img_size
self.grad_checkpointing = False
self.ignore_head = ignore_head
self.num_recomputing_layers = num_recomputing_layers
embed_args = {}
if dynamic_img_size:
# flatten deferred until after pos embed
embed_args.update(dict(strict_img_size=False, output_fmt='NHWC'))
self.patch_embed = embed_layer(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
bias=not pre_norm, # disable bias if pre-norm is used (e.g. CLIP)
dynamic_img_pad=dynamic_img_pad,
**embed_args,
)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
self.reg_token = nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
embed_len = num_patches if no_embed_class else num_patches + self.num_prefix_tokens
self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
self.pos_drop = nn.Dropout(p=pos_drop_rate)
if patch_drop_rate > 0:
self.patch_drop = PatchDropout(
patch_drop_rate,
num_prefix_tokens=self.num_prefix_tokens,
)
else:
self.patch_drop = nn.Identity()
self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.Sequential(*[
block_fn(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
init_values=init_values,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
mlp_layer=mlp_layer,
deterministic=deterministic,
)
for i in range(depth)])
self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
# Classifier Head
if global_pool == 'map':
AttentionPoolLatent.init_weights = init_weights
self.attn_pool = AttentionPoolLatent(
self.embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
norm_layer=norm_layer,
)
else:
self.attn_pool = None
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if weight_init != 'skip':
self.init_weights(weight_init)
def init_weights(self, mode: Literal['jax', 'jax_nlhb', 'moco', ''] = '') -> None:
assert mode in ('jax', 'jax_nlhb', 'moco', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
trunc_normal_(self.pos_embed, std=.02)
if self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
named_apply(init_weights_vit_timm, self)
@torch.jit.ignore
def no_weight_decay(self) -> Set:
return {'pos_embed', 'cls_token', 'dist_token'}
@torch.jit.ignore
def group_matcher(self, coarse: bool = False) -> Dict:
return dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable: bool = True) -> None:
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head
def reset_classifier(self, num_classes: int, global_pool=None) -> None:
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'avg', 'token', 'map')
if global_pool == 'map' and self.attn_pool is None:
assert False, "Cannot currently add attention pooling in reset_classifier()."
elif global_pool != 'map ' and self.attn_pool is not None:
self.attn_pool = None # remove attention pooling
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
if self.dynamic_img_size:
B, H, W, C = x.shape
pos_embed = resample_abs_pos_embed(
self.pos_embed,
(H, W),
num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
)
x = x.view(B, -1, C)
else:
pos_embed = self.pos_embed
to_cat = []
if self.cls_token is not None:
to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
if self.reg_token is not None:
to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
if self.no_embed_class:
# deit-3, updated JAX (big vision)
# position embedding does not overlap with class token, add then concat
x = x + pos_embed
if to_cat:
x = torch.cat(to_cat + [x], dim=1)
else:
# original timm, JAX, and deit vit impl
# pos_embed has entry for class token, concat then add
if to_cat:
x = torch.cat(to_cat + [x], dim=1)
x = x + pos_embed
return self.pos_drop(x)
def _intermediate_layers(
self,
x: torch.Tensor,
n: Union[int, Sequence] = 1,
) -> List[torch.Tensor]:
outputs, num_blocks = [], len(self.blocks)
take_indices = set(range(num_blocks - n, num_blocks) if isinstance(n, int) else n)
# forward pass
x = self.patch_embed(x)
x = self._pos_embed(x)
x = self.patch_drop(x)
x = self.norm_pre(x)
for i, blk in enumerate(self.blocks):
x = blk(x)
if i in take_indices:
outputs.append(x)
return outputs
def get_intermediate_layers(
self,
x: torch.Tensor,
n: Union[int, Sequence] = 1,
reshape: bool = False,
return_prefix_tokens: bool = False,
norm: bool = False,
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
""" Intermediate layer accessor (NOTE: This is a WIP experiment).
Inspired by DINO / DINOv2 interface
"""
# take last n blocks if n is an int, if in is a sequence, select by matching indices
outputs = self._intermediate_layers(x, n)
if norm:
outputs = [self.norm(out) for out in outputs]
prefix_tokens = [out[:, 0:self.num_prefix_tokens] for out in outputs]
outputs = [out[:, self.num_prefix_tokens:] for out in outputs]
if reshape:
grid_size = self.patch_embed.grid_size
outputs = [
out.reshape(x.shape[0], grid_size[0], grid_size[1], -1).permute(0, 3, 1, 2).contiguous()
for out in outputs
]
if return_prefix_tokens:
return tuple(zip(outputs, prefix_tokens))
return tuple(outputs)
def forward_features(self, x: torch.Tensor) -> torch.Tensor:
if getattr(self, "is_first_stage", True):
x = self.patch_embed(x)
x = self._pos_embed(x)
x = self.patch_drop(x)
x = self.norm_pre(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
skip_last = max(1, len(self.blocks) - self.num_recomputing_layers)
x = checkpoint_seq(self.blocks, x, skip_last=skip_last)
else:
x = self.blocks(x)
if getattr(self, "is_last_stage", True):
x = self.norm(x)
return x
def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
if not getattr(self, "is_last_stage", True):
return x
if self.attn_pool is not None:
x = self.attn_pool(x)
elif self.global_pool == 'avg':
x = x[:, self.num_prefix_tokens:].mean(dim=1)
elif self.global_pool:
x = x[:, 0] # class token
x = self.fc_norm(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.forward_features(x)
if not self.ignore_head:
x = self.forward_head(x)
return x
def to_pipeline(self, pp_size, pp_rank, pp_splits: Optional[List[int]] = None):
self.is_first_stage = pp_rank == 0
self.is_last_stage = pp_rank == pp_size - 1
if not self.is_first_stage and hasattr(self, "patch_embed"):
del self.patch_embed, self.cls_token, self.reg_token, self.pos_embed, self.pos_drop, self.patch_drop, self.norm_pre
if not self.is_last_stage and hasattr(self, "norm"):
del self.norm, self.attn_pool, self.fc_norm, self.head_drop, self.head
if pp_splits is not None:
assert len(self.blocks) == sum(pp_splits)
splits = np.cumsum([0] + pp_splits)
self.blocks = self.blocks[splits[pp_rank]:splits[pp_rank + 1]]
return self
@dataclass
class SigLIPVisionCfg:
width: int = 1152
layers: Union[Tuple[int, int, int, int], int] = 27
heads: int = 16
patch_size: int = 14
image_size: Union[Tuple[int, int], int] = 336
global_pool: str = "map"
mlp_ratio: float = 3.7362
class_token: bool = False
num_classes: int = 0
use_checkpoint: bool = False
SigLIP_MODEL_CONFIG = {
"siglip_so400m_patch14_384": {
"image_size": 384,
"patch_size": 14,
"width": 1152,
"layers": 27,
"heads": 16,
"mlp_ratio": 3.7362,
"global_pool": "map",
"use_checkpoint": False
},
"siglip_so400m_patch14_224": {
"image_size": 224,
"patch_size": 14,
"width": 1152,
"layers": 27,
"heads": 16,
"mlp_ratio": 3.7362,
"global_pool": "map",
"use_checkpoint": False
},
"siglip_large_patch16_384": {
"image_size": 384,
"patch_size": 16,
"width": 1024,
"layers": 24,
"heads": 16,
"mlp_ratio": 4,
"global_pool": "map",
"use_checkpoint": False
}
}
def create_siglip_vit(
model_name: str = "siglip_so400m_patch14_384",
image_size: int = 384,
select_layer: int = -1,
ckpt_path: str = "",
**kwargs
):
assert model_name in SigLIP_MODEL_CONFIG.keys(), f"model name should be in {SigLIP_MODEL_CONFIG.keys()}"
vision_cfg = SigLIPVisionCfg(**SigLIP_MODEL_CONFIG[model_name])
if select_layer <= 0:
layers = min(vision_cfg.layers, vision_cfg.layers + select_layer + 1)
else:
layers = min(vision_cfg.layers, select_layer)
model = VisionTransformer(
img_size=image_size,
patch_size=vision_cfg.patch_size,
embed_dim=vision_cfg.width,
depth=layers,
num_heads=vision_cfg.heads,
mlp_ratio=vision_cfg.mlp_ratio,
class_token=vision_cfg.class_token,
global_pool=vision_cfg.global_pool,
ignore_head=kwargs.get("ignore_head", True),
weight_init=kwargs.get("weight_init", "skip"),
num_classes=0,
deterministic=kwargs.get("deterministic", False),
num_recomputing_layers=kwargs.get("num_recomputing_layers", 0)
)
if ckpt_path:
state_dict = torch.load(ckpt_path, map_location="cpu")
incompatible_keys = model.load_state_dict(state_dict, strict=False)
print(f"SigLIP-ViT restores from {ckpt_path},\n"
f"\tincompatible_keys:', {incompatible_keys}.")
return model