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from collections import OrderedDict
from typing import Tuple, Union
from itertools import repeat
import collections.abc
import math
import logging
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.checkpoint import checkpoint
import importlib.util
if importlib.util.find_spec('flash_attn'):
FlashMHA = importlib.import_module('flash_attn.flash_attention').FlashMHA
from clip import _tokenizer
from clip.configuration_bert import BertConfig
from clip.modeling_bert import BertModel
try:
from transformers import CLIPTextModelWithProjection
except:
pass
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super().__init__()
# all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = None
self.stride = stride
if stride > 1 or inplanes != planes * Bottleneck.expansion:
# downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
self.downsample = nn.Sequential(OrderedDict([
("-1", nn.AvgPool2d(stride)),
("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
("1", nn.BatchNorm2d(planes * self.expansion))
]))
def forward(self, x: torch.Tensor):
identity = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.relu(self.bn2(self.conv2(out)))
out = self.avgpool(out)
out = self.bn3(self.conv3(out))
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class AttentionPool2d(nn.Module):
def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
super().__init__()
self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
self.k_proj = nn.Linear(embed_dim, embed_dim)
self.q_proj = nn.Linear(embed_dim, embed_dim)
self.v_proj = nn.Linear(embed_dim, embed_dim)
self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
self.num_heads = num_heads
def forward(self, x):
x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC
x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
x, _ = F.multi_head_attention_forward(
query=x, key=x, value=x,
embed_dim_to_check=x.shape[-1],
num_heads=self.num_heads,
q_proj_weight=self.q_proj.weight,
k_proj_weight=self.k_proj.weight,
v_proj_weight=self.v_proj.weight,
in_proj_weight=None,
in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
bias_k=None,
bias_v=None,
add_zero_attn=False,
dropout_p=0,
out_proj_weight=self.c_proj.weight,
out_proj_bias=self.c_proj.bias,
use_separate_proj_weight=True,
training=self.training,
need_weights=False
)
return x[0]
class ModifiedResNet(nn.Module):
"""
A ResNet class that is similar to torchvision's but contains the following changes:
- There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
- Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
- The final pooling layer is a QKV attention instead of an average pool
"""
def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
super().__init__()
self.output_dim = output_dim
self.input_resolution = input_resolution
# the 3-layer stem
self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(width // 2)
self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(width // 2)
self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(width)
self.avgpool = nn.AvgPool2d(2)
self.relu = nn.ReLU(inplace=True)
# residual layers
self._inplanes = width # this is a *mutable* variable used during construction
self.layer1 = self._make_layer(width, layers[0])
self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
embed_dim = width * 32 # the ResNet feature dimension
self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
def _make_layer(self, planes, blocks, stride=1):
layers = [Bottleneck(self._inplanes, planes, stride)]
self._inplanes = planes * Bottleneck.expansion
for _ in range(1, blocks):
layers.append(Bottleneck(self._inplanes, planes))
return nn.Sequential(*layers)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
# FIXME support for non-transformer
pass
def forward(self, x):
def stem(x):
for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
x = self.relu(bn(conv(x)))
x = self.avgpool(x)
return x
x = x.type(self.conv1.weight.dtype)
x = stem(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.attnpool(x)
return x
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, use_flash_attention: bool = False):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head) if not use_flash_attention else FlashMHA(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(OrderedDict([
("c_fc", nn.Linear(d_model, d_model * 4)),
("gelu", QuickGELU()),
("c_proj", nn.Linear(d_model * 4, d_model))
]))
self.ln_2 = LayerNorm(d_model)
self.attn_mask = attn_mask
self.use_flash_attention = use_flash_attention
def attention(self, x: torch.Tensor):
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
if self.use_flash_attention:
# Batch first is needed for FlashAttention. See https://github.com/HazyResearch/flash-attention/issues/84 for more information.
return self.attn(x.transpose(1, 0))[0].transpose(1, 0)
else:
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
def forward(self, x: torch.Tensor):
x = x + self.attention(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class Transformer(nn.Module):
def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_flash_attention: bool = False):
super().__init__()
self.width = width
self.layers = layers
self.grad_checkpointing = False
self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask, use_flash_attention) for _ in range(layers)])
def forward(self, x: torch.Tensor):
if self.grad_checkpointing and not torch.jit.is_scripting():
for r in self.resblocks:
x = checkpoint(r, x)
return x
return self.resblocks(x)
class VisualTransformer(nn.Module):
def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int, use_flash_attention: bool = False):
super().__init__()
self.input_resolution = input_resolution
self.grid_size = (self.input_resolution // patch_size, self.input_resolution // patch_size)
self.output_dim = output_dim
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
scale = width ** -0.5
self.class_embedding = nn.Parameter(scale * torch.randn(width))
self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
self.ln_pre = LayerNorm(width)
self.transformer = Transformer(width, layers, heads, use_flash_attention=use_flash_attention)
self.ln_post = LayerNorm(width)
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.transformer.grad_checkpointing = enable
def random_masking(self, x, mask_ratio):
N, L, D = x.shape # batch, length, dim
len_keep = int((L - 1) * (1 - mask_ratio))
noise = torch.rand(N, L - 1, device=x.device)
ids_shuffle = torch.argsort(noise, dim=1) + torch.ones(N, L - 1, device=x.device,
dtype=int)
ids_keep = ids_shuffle[:, :len_keep]
x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
x0 = x[:, 0, :]
x0 = x0.reshape(N, 1, D)
x_masked_add = torch.cat([x0, x_masked], axis=1)
return x_masked_add
def forward(self, x: torch.Tensor, mask_ratio: float = 0.0):
x = self.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
x = x + self.positional_embedding.to(x.dtype)
if mask_ratio != 0:
x = self.random_masking(x, mask_ratio)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_post(x[:, 0, :])
if self.proj is not None:
x = x @ self.proj
return x
class CLIP(nn.Module):
def __init__(self,
embed_dim: int,
# vision
image_resolution: int,
vision_layers: Union[Tuple[int, int, int, int], int],
vision_width: int,
vision_patch_size: int,
# text
vocab_size: int,
text_attention_probs_dropout_prob: float,
text_hidden_act: str,
text_hidden_dropout_prob: float,
text_hidden_size: int,
text_initializer_range: float,
text_intermediate_size: int,
text_max_position_embeddings: int,
text_num_attention_heads: int,
text_num_hidden_layers: int,
text_type_vocab_size: int,
tokenizer = _tokenizer,
# vision head width, added this param for ViT-H
vision_head_width: int = 64,
use_flash_attention: bool = False,
):
super().__init__()
if isinstance(vision_layers, (tuple, list)):
vision_heads = vision_width * 32 // vision_head_width
self.visual = ModifiedResNet(
layers=vision_layers,
output_dim=embed_dim,
heads=vision_heads,
input_resolution=image_resolution,
width=vision_width
)
else:
vision_heads = vision_width // vision_head_width
self.visual = VisualTransformer(
input_resolution=image_resolution,
patch_size=vision_patch_size,
width=vision_width,
layers=vision_layers,
heads=vision_heads,
output_dim=embed_dim,
use_flash_attention=use_flash_attention
)
self.bert_config = BertConfig(
vocab_size_or_config_json_file=vocab_size,
hidden_size=text_hidden_size,
num_hidden_layers=text_num_hidden_layers,
num_attention_heads=text_num_attention_heads,
intermediate_size=text_intermediate_size,
hidden_act=text_hidden_act,
hidden_dropout_prob=text_hidden_dropout_prob,
attention_probs_dropout_prob=text_attention_probs_dropout_prob,
max_position_embeddings=text_max_position_embeddings,
type_vocab_size=text_type_vocab_size,
initializer_range=text_initializer_range,
layer_norm_eps=1e-12,
use_flash_attention=use_flash_attention
)
self.bert = BertModel(self.bert_config)
self.text_projection = nn.Parameter(torch.empty(text_hidden_size, embed_dim))
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
self.tokenizer = tokenizer
self.initialize_parameters()
def initialize_parameters(self):
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
if isinstance(self.visual, ModifiedResNet):
if self.visual.attnpool is not None:
std = self.visual.attnpool.c_proj.in_features ** -0.5
nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
for name, param in resnet_block.named_parameters():
if name.endswith("bn3.weight"):
nn.init.zeros_(param)
if self.text_projection is not None:
nn.init.normal_(self.text_projection, std=self.bert_config.hidden_size ** -0.5)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.visual.set_grad_checkpointing(enable)
self.bert.set_grad_checkpointing(enable)
@property
def dtype(self):
return self.visual.conv1.weight.dtype
def encode_image(self, image, mask_ratio=0):
if isinstance(self.visual, ModifiedResNet):
# mask_ratio > 0 (FLIP strategy) is currently only implemented for VisualTransformer.
return self.visual(image.type(self.dtype))
return self.visual(image.type(self.dtype), mask_ratio)
def encode_text(self, text):
pad_index = self.tokenizer.vocab['[PAD]']
attn_mask = text.ne(pad_index).type(self.dtype)
x = self.bert(text, attention_mask=attn_mask)[0].type(self.dtype) # [batch_size, seq_length, hidden_size]
return x[:, 0, :] @ self.text_projection
def forward(self, image, text, mask_ratio=0):
assert image is not None or text is not None, "text and image cannot both be None!"
if image is None:
return self.encode_text(text)
elif text is None:
return self.encode_image(image, mask_ratio)
image_features = self.encode_image(image, mask_ratio)
text_features = self.encode_text(text)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
return image_features, text_features, self.logit_scale.exp()
def get_similarity(self, image, text):
image_features = self.encode_image(image)
text_features = self.encode_text(text)
# normalized features
image_features = image_features / image_features.norm(dim=1, keepdim=True)
text_features = text_features / text_features.norm(dim=1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_image = logit_scale * image_features @ text_features.t()
logits_per_text = logits_per_image.t()
# shape = [global_batch_size, global_batch_size]
return logits_per_image, logits_per_text
class CLIPWithTwoTextEncoder(nn.Module):
def __init__(self,
embed_dim: int,
# vision
image_resolution: int,
vision_layers: Union[Tuple[int, int, int, int], int],
vision_width: int,
vision_patch_size: int,
# text
vocab_size: int,
text_attention_probs_dropout_prob: float,
text_hidden_act: str,
text_hidden_dropout_prob: float,
text_hidden_size: int,
text_initializer_range: float,
text_intermediate_size: int,
text_max_position_embeddings: int,
text_num_attention_heads: int,
text_num_hidden_layers: int,
text_type_vocab_size: int,
tokenizer = _tokenizer,
# vision head width, added this param for ViT-H
vision_head_width: int = 64,
use_flash_attention: bool = False,
openai_clip_path: str = "/group/30042/kunyi/CLIP/clip-vit-large-patch14/",
):
super().__init__()
if isinstance(vision_layers, (tuple, list)):
vision_heads = vision_width * 32 // vision_head_width
self.visual = ModifiedResNet(
layers=vision_layers,
output_dim=embed_dim,
heads=vision_heads,
input_resolution=image_resolution,
width=vision_width
)
else:
vision_heads = vision_width // vision_head_width
self.visual = VisualTransformer(
input_resolution=image_resolution,
patch_size=vision_patch_size,
width=vision_width,
layers=vision_layers,
heads=vision_heads,
output_dim=embed_dim,
use_flash_attention=use_flash_attention
)
self.bert_config = BertConfig(
vocab_size_or_config_json_file=vocab_size,
hidden_size=text_hidden_size,
num_hidden_layers=text_num_hidden_layers,
num_attention_heads=text_num_attention_heads,
intermediate_size=text_intermediate_size,
hidden_act=text_hidden_act,
hidden_dropout_prob=text_hidden_dropout_prob,
attention_probs_dropout_prob=text_attention_probs_dropout_prob,
max_position_embeddings=text_max_position_embeddings,
type_vocab_size=text_type_vocab_size,
initializer_range=text_initializer_range,
layer_norm_eps=1e-12,
use_flash_attention=use_flash_attention
)
self.bert = BertModel(self.bert_config)
self.text_projection = nn.Parameter(torch.empty(text_hidden_size, embed_dim))
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
self.tokenizer = tokenizer
print('loading openai clip text encoder')
self.openai_clip_text_encoder = CLIPTextModelWithProjection.from_pretrained(openai_clip_path)
self.initialize_parameters()
def initialize_parameters(self):
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
if isinstance(self.visual, ModifiedResNet):
if self.visual.attnpool is not None:
std = self.visual.attnpool.c_proj.in_features ** -0.5
nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
for name, param in resnet_block.named_parameters():
if name.endswith("bn3.weight"):
nn.init.zeros_(param)
if self.text_projection is not None:
nn.init.normal_(self.text_projection, std=self.bert_config.hidden_size ** -0.5)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.visual.set_grad_checkpointing(enable)
self.bert.set_grad_checkpointing(enable)
@property
def dtype(self):
return self.visual.conv1.weight.dtype
def encode_image(self, image, mask_ratio=0):
if isinstance(self.visual, ModifiedResNet):
# mask_ratio > 0 (FLIP strategy) is currently only implemented for VisualTransformer.
return self.visual(image.type(self.dtype))
return self.visual(image.type(self.dtype), mask_ratio)
def encode_text(self, text):
pad_index = self.tokenizer.vocab['[PAD]']
attn_mask = text.ne(pad_index).type(self.dtype)
x = self.bert(text, attention_mask=attn_mask)[0].type(self.dtype) # [batch_size, seq_length, hidden_size]
return x[:, 0, :] @ self.text_projection
def encode_text_ENG(self, text):
text_emb = self.openai_clip_text_encoder(text).text_embeds
return text_emb
def forward(self, image, text, is_ENG=False, mask_ratio=0):
assert image is not None or text is not None, "text and image cannot both be None!"
if image is None:
if not is_ENG:
return self.encode_text(text)
else:
return self.encode_text_ENG(text)
elif text is None:
return self.encode_image(image, mask_ratio)
image_features = self.encode_image(image, mask_ratio)
if not is_ENG:
text_features = self.encode_text(text)
else:
text_features = self.encode_text_ENG(text)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
return image_features, text_features, self.logit_scale.exp()
def get_similarity(self, image, text):
image_features = self.encode_image(image)
text_features = self.encode_text(text)
# normalized features
image_features = image_features / image_features.norm(dim=1, keepdim=True)
text_features = text_features / text_features.norm(dim=1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_image = logit_scale * image_features @ text_features.t()
logits_per_text = logits_per_image.t()
# shape = [global_batch_size, global_batch_size]
return logits_per_image, logits_per_text
class CLIP4SD(nn.Module):
def __init__(self,
embed_dim: int,
# vision
image_resolution: int,
vision_layers: Union[Tuple[int, int, int, int], int],
vision_width: int,
vision_patch_size: int,
# text
vocab_size: int,
text_attention_probs_dropout_prob: float,
text_hidden_act: str,
text_hidden_dropout_prob: float,
text_hidden_size: int,
text_initializer_range: float,
text_intermediate_size: int,
text_max_position_embeddings: int,
text_num_attention_heads: int,
text_num_hidden_layers: int,
text_type_vocab_size: int,
tokenizer = _tokenizer,
# vision head width, added this param for ViT-H
vision_head_width: int = 64,
use_flash_attention: bool = False,
):
super().__init__()
if isinstance(vision_layers, (tuple, list)):
vision_heads = vision_width * 32 // vision_head_width
self.visual = ModifiedResNet(
layers=vision_layers,
output_dim=embed_dim,
heads=vision_heads,
input_resolution=image_resolution,
width=vision_width
)
else:
vision_heads = vision_width // vision_head_width
self.visual = VisualTransformer(
input_resolution=image_resolution,
patch_size=vision_patch_size,
width=vision_width,
layers=vision_layers,
heads=vision_heads,
output_dim=embed_dim,
use_flash_attention=use_flash_attention
)
self.bert_config = BertConfig(
vocab_size_or_config_json_file=vocab_size,
hidden_size=text_hidden_size,
num_hidden_layers=text_num_hidden_layers,
num_attention_heads=text_num_attention_heads,
intermediate_size=text_intermediate_size,
hidden_act=text_hidden_act,
hidden_dropout_prob=text_hidden_dropout_prob,
attention_probs_dropout_prob=text_attention_probs_dropout_prob,
max_position_embeddings=text_max_position_embeddings,
type_vocab_size=text_type_vocab_size,
initializer_range=text_initializer_range,
layer_norm_eps=1e-12,
use_flash_attention=use_flash_attention
)
self.bert = BertModel(self.bert_config)
self.text_projection = nn.Parameter(torch.empty(text_hidden_size, embed_dim))
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
self.tokenizer = tokenizer
self.ln_final = LayerNorm(text_hidden_size)
self.initialize_parameters()
def initialize_parameters(self):
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
if isinstance(self.visual, ModifiedResNet):
if self.visual.attnpool is not None:
std = self.visual.attnpool.c_proj.in_features ** -0.5
nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
for name, param in resnet_block.named_parameters():
if name.endswith("bn3.weight"):
nn.init.zeros_(param)
if self.text_projection is not None:
nn.init.normal_(self.text_projection, std=self.bert_config.hidden_size ** -0.5)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.visual.set_grad_checkpointing(enable)
self.bert.set_grad_checkpointing(enable)
@property
def dtype(self):
return self.visual.conv1.weight.dtype
def encode_image(self, image, mask_ratio=0):
if isinstance(self.visual, ModifiedResNet):
# mask_ratio > 0 (FLIP strategy) is currently only implemented for VisualTransformer.
return self.visual(image.type(self.dtype))
return self.visual(image.type(self.dtype), mask_ratio)
# def encode_text(self, text):
# pad_index = self.tokenizer.vocab['[PAD]']
# attn_mask = text.ne(pad_index).type(self.dtype)
# x = self.bert(text, attention_mask=attn_mask)[0].type(self.dtype) # [batch_size, seq_length, hidden_size]
# return x[:, 0, :] @ self.text_projection
def encode_text(self, text):
pad_index = self.tokenizer.vocab['[PAD]']
attn_mask = text.ne(pad_index).type(self.dtype)
x = self.bert(text, attention_mask=attn_mask)[0].type(self.dtype) # [batch_size, seq_length, hidden_size]
x = self.ln_final(x).type(self.dtype)
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
return x
def forward(self, image, text, mask_ratio=0):
assert image is not None or text is not None, "text and image cannot both be None!"
if image is None:
return self.encode_text(text)
elif text is None:
return self.encode_image(image)
image_features = self.encode_image(image, mask_ratio)
text_features = self.encode_text(text)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
text_features = text_features / text_features.norm(dim=-1, keepdim=True)
return image_features, text_features, self.logit_scale.exp()
def get_similarity(self, image, text):
image_features = self.encode_image(image)
text_features = self.encode_text(text)
# normalized features
image_features = image_features / image_features.norm(dim=1, keepdim=True)
text_features = text_features / text_features.norm(dim=1, keepdim=True)
# cosine similarity as logits
logit_scale = self.logit_scale.exp()
logits_per_image = logit_scale * image_features @ text_features.t()
logits_per_text = logits_per_image.t()
# shape = [global_batch_size, global_batch_size]
return logits_per_image, logits_per_text
def convert_models_to_fp32(model):
for p in model.parameters():
p.data = p.data.float()
if p.grad:
p.grad.data = p.grad.data.float()
def convert_weights(model: nn.Module):
"""Convert applicable model parameters to fp16"""
def _convert_weights_to_fp16(l):
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
l.weight.data = l.weight.data.half()
if l.bias is not None:
l.bias.data = l.bias.data.half()
if isinstance(l, nn.MultiheadAttention):
for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
tensor = getattr(l, attr)
if tensor is not None:
tensor.data = tensor.data.half()
if isinstance(l, BertModel):
l.to(torch.half)
for name in ["text_projection", "proj"]:
try:
if hasattr(l, name):
attr = getattr(l, name)
if attr is not None:
attr.data = attr.data.half()
except:
print('name', name)
model.apply(_convert_weights_to_fp16)
def restore_model(model, clip_state_dict: dict, bert_state_dict: dict, use_flash_attention: bool):
merged_state_dict = {}
# use clip_state_dict to initialize the image encoder & logit scale
if clip_state_dict is not None:
for k, v in clip_state_dict.items():
if k.startswith("visual") or k == "logit_scale":
merged_state_dict[k] = v
# use bert_state_dict to initialize the text encoder
if bert_state_dict is not None:
for k, v in bert_state_dict.items():
if k.startswith("bert") and "bert.pooler" not in k:
merged_state_dict[k] = v
# adapt flash attention
if use_flash_attention:
merged_state_dict = convert_state_dict(merged_state_dict)
convert_weights(model)
resize_pos_embed(merged_state_dict, model)
model.load_state_dict(merged_state_dict, strict=False)
return model.eval()
def convert_state_dict(state_dict):
"""Adapt to Flash Attention"""
if not state_dict:
return state_dict
prefix = 'module.' if list(state_dict.keys())[0].startswith('module') else ''
if f'{prefix}visual.transformer.resblocks.0.attn.in_proj_weight' in state_dict:
for k in list(state_dict.keys()):
if 'attn.in_proj_weight' in k:
state_dict[k.replace('attn.in_proj_weight', 'attn.Wqkv.weight')] = state_dict.pop(k)
elif 'attn.in_proj_bias' in k:
state_dict[k.replace('attn.in_proj_bias', 'attn.Wqkv.bias')] = state_dict.pop(k)
elif f'{prefix}visual.transformer.resblocks.0.attn.Wqkv.weight' in state_dict:
for k in list(state_dict.keys()):
if 'attn.Wqkv.weight' in k:
state_dict[k.replace('attn.Wqkv.weight', 'attn.in_proj_weight')] = state_dict.pop(k)
elif 'attn.Wqkv.bias' in k:
state_dict[k.replace('attn.Wqkv.bias', 'attn.in_proj_bias')] = state_dict.pop(k)
if f'{prefix}bert.encoder.layer.0.attention.self.query.weight' in state_dict:
i = 0
while f'{prefix}bert.encoder.layer.{i}.attention.self.query.weight' in state_dict:
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.Wqkv.weight'] = torch.cat(
(state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.query.weight'),
state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.key.weight'),
state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.value.weight'))
)
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.Wqkv.bias'] = torch.cat(
(state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.query.bias'),
state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.key.bias'),
state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.value.bias'))
)
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.out_proj.weight'] = \
state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.output.dense.weight')
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.out_proj.bias'] = \
state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.output.dense.bias')
i += 1
elif f'{prefix}bert.encoder.layer.0.attention.self.Wqkv.weight' in state_dict:
i = 0
while f'{prefix}bert.encoder.layer.{i}.attention.self.Wqkv.weight' in state_dict:
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.query.weight'], \
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.key.weight'], \
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.value.weight'] = \
torch.chunk(state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.Wqkv.weight'), chunks=3)
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.query.bias'], \
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.key.bias'], \
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.self.value.bias'] = \
torch.chunk(state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.Wqkv.bias'), chunks=3)
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.output.dense.weight'] = \
state_dict.pop(f'{prefix}bert.encoder.layer.{i}.attention.self.out_proj.weight')
state_dict[f'{prefix}bert.encoder.layer.{i}.attention.output.dense.bias'] = \
state_dict.pop(f'module.bert.encoder.layer.{i}.attention.self.out_proj.bias')
i += 1
return state_dict
def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_dim=1, prefix=""):
# Rescale the grid of position embeddings when loading from state_dict
old_pos_embed = state_dict.get(prefix + 'visual.positional_embedding', None)
model = model.module if hasattr(model, 'module') else model
if old_pos_embed is None or not hasattr(model.visual, 'grid_size'):
return
grid_size = to_2tuple(model.visual.grid_size)
extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more)
new_seq_len = grid_size[0] * grid_size[1] + extra_tokens
if new_seq_len == old_pos_embed.shape[0]:
return
if extra_tokens:
pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]
else:
pos_emb_tok, pos_emb_img = None, old_pos_embed
old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))
logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)
pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)
pos_emb_img = F.interpolate(
pos_emb_img,
size=grid_size,
mode=interpolation,
align_corners=True,
)
pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]
if pos_emb_tok is not None:
new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)
else:
new_pos_embed = pos_emb_img
state_dict[prefix + 'visual.positional_embedding'] = new_pos_embed
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return x
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = lambda n, x: _ntuple(n)(x)
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