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# 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:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# --------------------------------------------------------
from functools import partial
from typing import Tuple
import timm.models.vision_transformer
from safetensors import safe_open
from safetensors.torch import save_file
import torch
import torch.nn as nn
import math
from mmocr.registry import MODELS
@MODELS.register_module()
class VisionTransformer(timm.models.vision_transformer.VisionTransformer):
""" Vision Transformer.
Args:
global_pool (bool): If True, apply global pooling to the output
of the last stage. Default: False.
patch_size (int): Patch token size. Default: 8.
img_size (tuple[int]): Input image size. Default: (32, 128).
embed_dim (int): Number of linear projection output channels.
Default: 192.
depth (int): Number of blocks. Default: 12.
num_heads (int): Number of attention heads. Default: 3.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
Default: 4.
qkv_bias (bool): If True, add a learnable bias to query, key,
value. Default: True.
norm_layer (nn.Module): Normalization layer. Default:
partial(nn.LayerNorm, eps=1e-6).
pretrained (str): Path to pre-trained checkpoint. Default: None.
"""
def __init__(self,
global_pool: bool = False,
patch_size: int = 8,
img_size: Tuple[int, int] = (32, 128),
embed_dim: int = 192,
depth: int = 12,
num_heads: int = 3,
mlp_ratio: int = 4.,
qkv_bias: bool = True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
pretrained: bool = None,
**kwargs):
super(VisionTransformer, self).__init__(
patch_size=patch_size,
img_size=img_size,
embed_dim=embed_dim,
depth=depth,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
norm_layer=norm_layer,
**kwargs)
self.global_pool = global_pool
if self.global_pool:
norm_layer = kwargs['norm_layer']
embed_dim = kwargs['embed_dim']
self.fc_norm = norm_layer(embed_dim)
del self.norm # remove the original norm
self.reset_classifier(0)
if pretrained:
checkpoint = torch.load(pretrained, map_location='cpu')
print("Load pre-trained checkpoint from: %s" % pretrained)
checkpoint_model = checkpoint['model']
state_dict = self.state_dict()
for k in ['head.weight', 'head.bias']:
if k in checkpoint_model and checkpoint_model[
k].shape != state_dict[k].shape:
print(f"Removing key {k} from pretrained checkpoint")
del checkpoint_model[k]
# remove key with decoder
for k in list(checkpoint_model.keys()):
if 'decoder' in k:
del checkpoint_model[k]
msg = self.load_state_dict(checkpoint_model, strict=False)
print(msg)
def forward_features(self, x: torch.Tensor):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(
B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
x = x + self.pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
def forward(self, x):
return self.forward_features(x)
class _LoRA_qkv_timm(nn.Module):
"""LoRA layer for query and value projection in Vision Transformer of timm.
Args:
qkv (nn.Module): qkv projection layer in Vision Transformer of timm.
linear_a_q (nn.Module): Linear layer for query projection.
linear_b_q (nn.Module): Linear layer for query projection.
linear_a_v (nn.Module): Linear layer for value projection.
linear_b_v (nn.Module): Linear layer for value projection.
"""
def __init__(
self,
qkv: nn.Module,
linear_a_q: nn.Module,
linear_b_q: nn.Module,
linear_a_v: nn.Module,
linear_b_v: nn.Module,
):
super().__init__()
self.qkv = qkv
self.linear_a_q = linear_a_q
self.linear_b_q = linear_b_q
self.linear_a_v = linear_a_v
self.linear_b_v = linear_b_v
self.dim = qkv.in_features
def forward(self, x):
qkv = self.qkv(x) # B, N, 3*dim
new_q = self.linear_b_q(self.linear_a_q(x))
new_v = self.linear_b_v(self.linear_a_v(x))
qkv[:, :, :self.dim] += new_q
qkv[:, :, -self.dim:] += new_v
return qkv
@MODELS.register_module()
class VisionTransformer_LoRA(nn.Module):
"""Vision Transformer with LoRA. For each block, we add a LoRA layer for
the linear projection of query and value.
Args:
vit_config (dict): Config dict for VisionTransformer.
rank (int): Rank of LoRA layer. Default: 4.
lora_layers (int): Stages to add LoRA layer. Defaults None means
add LoRA layer to all stages.
pretrained_lora (str): Path to pre-trained checkpoint of LoRA layer.
"""
def __init__(self,
vit_config: dict,
rank: int = 4,
lora_layers: int = None,
pretrained_lora: str = None):
super(VisionTransformer_LoRA, self).__init__()
self.vit = MODELS.build(vit_config)
assert rank > 0
if lora_layers:
self.lora_layers = lora_layers
else:
self.lora_layers = list(range(len(self.vit.blocks)))
# creat list of LoRA layers
self.query_As = nn.Sequential() # matrix A for query linear projection
self.query_Bs = nn.Sequential()
self.value_As = nn.Sequential() # matrix B for value linear projection
self.value_Bs = nn.Sequential()
# freeze the original vit
for param in self.vit.parameters():
param.requires_grad = False
# compose LoRA layers
for block_idx, block in enumerate(self.vit.blocks):
if block_idx not in self.lora_layers:
continue
# create LoRA layer
w_qkv_linear = block.attn.qkv
self.dim = w_qkv_linear.in_features
w_a_linear_q = nn.Linear(self.dim, rank, bias=False)
w_b_linear_q = nn.Linear(rank, self.dim, bias=False)
w_a_linear_v = nn.Linear(self.dim, rank, bias=False)
w_b_linear_v = nn.Linear(rank, self.dim, bias=False)
self.query_As.append(w_a_linear_q)
self.query_Bs.append(w_b_linear_q)
self.value_As.append(w_a_linear_v)
self.value_Bs.append(w_b_linear_v)
# replace the original qkv layer with LoRA layer
block.attn.qkv = _LoRA_qkv_timm(
w_qkv_linear,
w_a_linear_q,
w_b_linear_q,
w_a_linear_v,
w_b_linear_v,
)
self._init_lora()
if pretrained_lora is not None:
self._load_lora(pretrained_lora)
def _init_lora(self):
"""Initialize the LoRA layers to be identity mapping."""
for query_A, query_B, value_A, value_B in zip(self.query_As,
self.query_Bs,
self.value_As,
self.value_Bs):
nn.init.kaiming_uniform_(query_A.weight, a=math.sqrt(5))
nn.init.kaiming_uniform_(value_A.weight, a=math.sqrt(5))
nn.init.zeros_(query_B.weight)
nn.init.zeros_(value_B.weight)
def _load_lora(self, checkpoint_lora: str):
"""Load pre-trained LoRA checkpoint.
Args:
checkpoint_lora (str): Path to pre-trained LoRA checkpoint.
"""
assert checkpoint_lora.endswith(".safetensors")
with safe_open(checkpoint_lora, framework="pt") as f:
for i, q_A, q_B, v_A, v_B in zip(
range(len(self.query_As)),
self.query_As,
self.query_Bs,
self.value_As,
self.value_Bs,
):
q_A.weight = nn.Parameter(f.get_tensor(f"q_a_{i:03d}"))
q_B.weight = nn.Parameter(f.get_tensor(f"q_b_{i:03d}"))
v_A.weight = nn.Parameter(f.get_tensor(f"v_a_{i:03d}"))
v_B.weight = nn.Parameter(f.get_tensor(f"v_b_{i:03d}"))
def forward(self, x):
x = self.vit(x)
return x
def extract_lora_from_vit(checkpoint_path: str,
save_path: str,
ckpt_key: str = None):
"""Given a checkpoint of VisionTransformer_LoRA, extract the LoRA weights
and save them to a new checkpoint.
Args:
checkpoint_path (str): Path to checkpoint of VisionTransformer_LoRA.
ckpt_key (str): Key of model in the checkpoint.
save_path (str): Path to save the extracted LoRA checkpoint.
"""
assert save_path.endswith(".safetensors")
ckpt = torch.load(checkpoint_path, map_location="cpu")
# travel throung the ckpt to find the LoRA layers
query_As = []
query_Bs = []
value_As = []
value_Bs = []
ckpt = ckpt if ckpt_key is None else ckpt[ckpt_key]
for k, v in ckpt.items():
if k.startswith("query_As"):
query_As.append(v)
elif k.startswith("query_Bs"):
query_Bs.append(v)
elif k.startswith("value_As"):
value_As.append(v)
elif k.startswith("value_Bs"):
value_Bs.append(v)
# save the LoRA layers to a new checkpoint
ckpt_dict = {}
for i in range(len(query_As)):
ckpt_dict[f"q_a_{i:03d}"] = query_As[i]
ckpt_dict[f"q_b_{i:03d}"] = query_Bs[i]
ckpt_dict[f"v_a_{i:03d}"] = value_As[i]
ckpt_dict[f"v_b_{i:03d}"] = value_Bs[i]
save_file(ckpt_dict, save_path)
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