File size: 17,425 Bytes
94f80f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 |
import torch
import torch.nn as nn
from torch.nn.functional import cross_entropy
from transformers import CLIPVisionModel, AutoModelForCausalLM, BitsAndBytesConfig
from peft import LoraConfig
from tqdm import tqdm
import os, peft
class CustomClipPhi2(nn.Module):
def __init__(self,tokenizer, phi2_model_name, clip_model_name, clip_embed=768, phi_embed=2560):
super().__init__()
self.tokenizer = tokenizer
# These two models are not finetuned
# pretrained Microsoft phi2 model
self.phi2_model = AutoModelForCausalLM.from_pretrained(phi2_model_name,torch_dtype=torch.float32, trust_remote_code=True)
# pretrained OpenAI clip model
self.clip_model = CLIPVisionModel.from_pretrained(clip_model_name)
self.EOS_TOKEN_ID = self.tokenizer.eos_token_id # 50256
self.IMAGE_TOKEN_ID = 23903 # token for Comments
self.clip_embed = clip_embed
self.phi_embed = phi_embed
# projection layers
# Trainable projection layer
self.projection_layer = torch.nn.Linear(clip_embed, phi_embed)
# Freeze Weights
for models in [self.phi2_model, self.clip_model]:
for param in models.parameters():
param.requires_grad_(False)
# load checkpoint weights
if os.path.exists('./ckpts/model_phase1.pth'):
self.projection_layer.load_state_dict(torch.load('./ckpts/model_phase1.pth', map_location='cpu'))
print("Loaded checkpoint weights for projection layer")
else:
print("No checkpoint weights for projection layer")
print("Initializing projection layer with random weights")
self.projection_layer.weight.data.normal_(mean=0.0, std=0.02)
self.projection_layer.bias.data.zero_()
def generate(self, images, tokenizer, config):
clip_outputs = self.clip_model(**images)
# remove cls token
images = clip_outputs.last_hidden_state[:, 1:, :]
image_embeddings = self.projection_layer(images).to(torch.float16)
batch_size = images.size()[0]
predicted_caption = torch.full((batch_size, config.get("max_tokens")), self.EOS_TOKEN_ID, dtype=torch.long, device=config.get('device'))
img_token_tensor = torch.tensor(self.IMAGE_TOKEN_ID).repeat(batch_size, 1)
img_token_embeds = self.phi2_model.model.embed_tokens(img_token_tensor.to(image_embeddings.device))
combined_embeds = torch.cat([image_embeddings, img_token_embeds], dim=1)
for pos in range(config.get("max_tokens") - 1):
model_output_logits = self.phi2_model.forward(inputs_embeds = combined_embeds)['logits']
predicted_word_token_logits = model_output_logits[:, -1, :].unsqueeze(1)
predicted_word_token = torch.argmax(predicted_word_token_logits, dim = -1)
predicted_caption[:, pos] = predicted_word_token.view(1,-1).to('cpu')
next_token_embeds = self.phi2_model.model.embed_tokens(predicted_word_token)
combined_embeds = torch.cat([combined_embeds, next_token_embeds], dim=1)
return predicted_caption
def forward(self, images, target_captions):
batch_size = target_captions.size()[0]
target_length = target_captions.size()[1]
print("---", target_length)
# clip model output for image
clip_outputs = self.clip_model(**images) # See this for loading https://huggingface.co/openai/clip-vit-base-patch36
images = clip_outputs.last_hidden_state[:, 1:, :] # remove CLS token
# projection layer
image_embeddings = self.projection_layer(images).to(torch.float16)
# add comment token from phi2
img_token_tensor = torch.tensor(self.IMAGE_TOKEN_ID).repeat(batch_size, 1)
img_token_embeds = self.phi2_model.model.embed_tokens(img_token_tensor.to(image_embeddings.device))
combined_embeds = torch.cat([image_embeddings, img_token_embeds], dim=1) # 4,49,2560
del clip_outputs
del image_embeddings
# for loss
loss = 0
for pos in range(target_length - 1):
model_output_logits = self.phi2_model.forward(inputs_embeds = combined_embeds)['logits']
predicted_word_token_logits = model_output_logits[:, -1, :].unsqueeze(1)
pos_loss = cross_entropy(predicted_word_token_logits.view(-1,predicted_word_token_logits.size(-1)), target_captions[:, pos].contiguous().view(-1), ignore_index=self.EOS_TOKEN_ID,label_smoothing=0.1)
loss += pos_loss
predicted_word_token = torch.argmax(predicted_word_token_logits, dim=-1)
next_token_embeds = self.phi2_model.model.embed_tokens(predicted_word_token)
combined_embeds = torch.cat([combined_embeds, next_token_embeds], dim=1)
loss = loss / target_length
# Delete variables to free up memory
del combined_embeds
del model_output_logits
torch.cuda.empty_cache()
return loss
def show_results_for_samples_phase1(model, val_dataloader, tokenizer, config, num_samples = 2):
model.eval()
with torch.no_grad():
for i in range(num_samples):
for images, target_captions in val_dataloader:
images = {'pixel_values': images.to(config.get('device'))}
target_captions = target_captions.to(config.get('device'))
target_captions_decoded = tokenizer.batch_decode(target_captions, ignore_index = tokenizer.eos_token_id)
predicted_captions = model.generate(images, tokenizer, config)
predicted_captions_decoded = tokenizer.batch_decode(predicted_captions,ignore_index = tokenizer.eos_token_id)
for idx, pc in enumerate(predicted_captions_decoded):
print(f"{idx} - Target captions: {target_captions_decoded[idx]} \n {'---------------------'*10} \n Predicted_captions:{pc} ")
break
def validate_model_phase1(model, val_dataloader, tokenizer, config):
model.eval()
total_loss = 0
with torch.no_grad():
try:
for images, target_captions in tqdm(val_dataloader):
images = {'pixel_values': images.to(config.get('device'))}
target_captions = target_captions.to(config.get('device'))
loss = model(images, target_captions)
total_loss+=loss.item()
print(f"Validation Loss: {total_loss/len(val_dataloader)}")
except Exception as e:
pass
model.train()
def train_model_phase1(model, train_loader, val_dataloader, optimizer, tokenizer, config):
model.train()
pbar = tqdm(train_loader)
for epoch in range(1, config.get("epochs")):
print(f"Epoch: {epoch}")
torch.cuda.empty_cache()
step = 1
try:
for idx, (images, target_captions) in enumerate(pbar):
try:
if target_captions.shape[1] >= config.get("max_tokens"):
# print(f"Skipping batch {idx} due to long caption")
continue
images = {'pixel_values': images.to(config.get('device'))}
target_captions = target_captions.to(config.get('device'))
optimizer.zero_grad()
loss = model(images, target_captions)
loss.backward()
optimizer.step()
pbar.set_description(f"Epoch: {epoch}: Training Loss = {loss.item()}")
torch.cuda.empty_cache()
step+=1
if (step%1000==0):
torch.save(model.projection_layer.state_dict(), './ckpts/model_phase1.pth')
except Exception as e:
print(e)
continue
# # save model
# if ((epoch % 2) == 0):
# Only save last checkpoint
validate_model_phase1(model, val_dataloader, tokenizer, config)
show_results_for_samples_phase1(model, val_dataloader, tokenizer, config)
torch.save(model.projection_layer.state_dict(), './ckpts/model_phase1.pth')
except Exception as e:
print(e)
continue
######################################## Phase 2 #########################################
class MainQLoraModel(nn.Module):
def __init__(self, tokenizer, config):
super().__init__()
self.tokenizer = tokenizer
self.config = config
self.clip_model = CLIPVisionModel.from_pretrained(config.get("clip_model_name"))
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
phi2_model = AutoModelForCausalLM.from_pretrained(
config.get("phi2_model_name"),
quantization_config=bnb_config,
trust_remote_code=True
)
phi2_model.config.use_cache = False
## 4 - LORA config
lora_alpha = 16
lora_dropout = 0.1
lora_r = 64
peft_config = LoraConfig(
lora_alpha = lora_alpha,
lora_dropout = lora_dropout,
r = lora_r,
bias="none",
task_type="CAUSAL_LM",
target_modules=[
"q_proj",
"k_proj",
"v_proj",
"dense",
"fc1",
"fc2"
]
)
self.phi2_model = peft.get_peft_model(phi2_model, peft_config).to(config.get("device"))
self.EOS_TOKEN_ID = self.tokenizer.eos_token_id
self.clip_embed = config.get("clip_embed")
self.phi_embed = config.get("phi_embed")
# projection layers
# Trainable projection layer
self.projection_layer = torch.nn.Linear(self.clip_embed, self.phi_embed)
# Freeze Weights
for models in [self.clip_model]:
for param in models.parameters():
param.requires_grad_(False)
# load checkpoint weights
if os.path.exists('./ckpts/model_phase2.pth'):
self.projection_layer.load_state_dict(torch.load('./ckpts/model_phase2.pth', map_location=config.get("device")))
self.phi2_model.from_pretrained(self.phi2_model,'./ckpts/Qlora_adaptor')
print("Loaded checkpoint weights for projection layer")
else:
# Load weights from phase 1
self.projection_layer.load_state_dict(torch.load('./ckpts/model_phase1.pth', map_location=config.get("device")))
def generate(self, tokenizer, config, images = None, ques = None, max_tokens = 100):
batch_size = 1
predicted_caption = torch.full((batch_size, max_tokens), self.EOS_TOKEN_ID, dtype=torch.long, device=self.config.get('device'))
start_iq = self.tokenizer.encode("<iQ>")
end_iq = self.tokenizer.encode("</iQ>")
start_iq_embeds = torch.tensor(start_iq).repeat(batch_size, 1)
end_iq_embeds = torch.tensor(end_iq).repeat(batch_size, 1)
start_iq_embeds = self.phi2_model.model.model.embed_tokens(start_iq_embeds.to(self.config.get("device")))
end_iq_embeds = self.phi2_model.model.model.embed_tokens(end_iq_embeds.to(self.config.get("device")))
questions_embed = self.phi2_model.model.model.embed_tokens(ques)
if images is not None:
clip_outputs = self.clip_model(**images)
# remove cls token
images = clip_outputs.last_hidden_state[:, 1:, :]
image_embeddings = self.projection_layer(images).to(torch.float16)
combined_embeds = torch.cat([start_iq_embeds, image_embeddings, questions_embed, end_iq_embeds], dim=1)
else:
combined_embeds = torch.cat([start_iq_embeds, questions_embed, end_iq_embeds], dim=1)
for pos in range(max_tokens - 1):
model_output_logits = self.phi2_model.forward(inputs_embeds = combined_embeds)['logits']
predicted_word_token_logits = model_output_logits[:, -1, :].unsqueeze(1)
predicted_word_token = torch.argmax(predicted_word_token_logits, dim = -1)
predicted_caption[:, pos] = predicted_word_token.view(1,-1).to('cpu')
next_token_embeds = self.phi2_model.model.embed_tokens(predicted_word_token)
combined_embeds = torch.cat([combined_embeds, next_token_embeds], dim=1)
return predicted_caption
def forward(self, images, ques, ans):
batch_size = ques.size()[0]
questions = ques.to(self.config.get("device"))
answers = ans.to(self.config.get("device"))
target_length = ans.size()[1]
start_iq = self.tokenizer.encode("<iQ>")
end_iq = self.tokenizer.encode("</iQ>")
start_iq_embeds = torch.tensor(start_iq).repeat(batch_size, 1)
end_iq_embeds = torch.tensor(end_iq).repeat(batch_size, 1)
start_iq_embeds = self.phi2_model.model.model.embed_tokens(start_iq_embeds.to(self.config.get("device")))
end_iq_embeds = self.phi2_model.model.model.embed_tokens(end_iq_embeds.to(self.config.get("device")))
questions_embed = self.phi2_model.model.model.embed_tokens(questions)
answers_embed = self.phi2_model.model.model.embed_tokens(answers)
are_all_zeros = torch.all(images == 0).item()
if are_all_zeros:
combined_embeds = torch.cat([start_iq_embeds, questions_embed, end_iq_embeds, answers_embed], dim=1)
else:
images = {'pixel_values': images.to(self.config.get("device"))}
clip_outputs = self.clip_model(**images)
images_embeds = clip_outputs.last_hidden_state[:,1:,:] # remove cls token
# projection
image_embeds = self.projection_layer(images_embeds).to(torch.float16)
combined_embeds = torch.cat([start_iq_embeds, image_embeds, questions_embed, end_iq_embeds, answers_embed], dim=1)
model_output_logits = self.phi2_model.forward(inputs_embeds = combined_embeds)['logits']
# # for loss
loss = 0
for pos in range(target_length - 1):
predicted_word_token_logits = model_output_logits[:, -1, :].unsqueeze(1)
pos_loss = cross_entropy(predicted_word_token_logits.view(-1,predicted_word_token_logits.size(-1)), answers[:, pos].contiguous().view(-1), ignore_index=self.EOS_TOKEN_ID,label_smoothing=0.1)
loss += pos_loss
loss = loss / target_length
# Delete variables to free up memory
del combined_embeds
del model_output_logits
torch.cuda.empty_cache()
return loss
def validate_model_phase2(model, val_dataloader, tokenizer, config):
model.eval()
total_loss = 0
with torch.no_grad():
# try:
for images, ques, ans in tqdm(val_dataloader):
loss = model(images, ques, ans)
total_loss+=loss.item()
print(f"Validation Loss: {total_loss/len(val_dataloader)}")
# except Exception as e:
# pass
model.train()
def train_model_phase2(model, train_loader, val_dataloader, tokenizer, config):
phi2_optim = torch.optim.Adam(filter(lambda p: p.requires_grad, model.phi2_model.parameters()), lr=1e-5)
proj_optim = torch.optim.Adam(filter(lambda p: p.requires_grad, model.projection_layer.parameters()), lr=1e-5)
model.phi2_model.train()
model.projection_layer.train()
pbar = tqdm(train_loader)
for epoch in range(1, config.get("epochs")):
print(f"Epoch: {epoch}")
torch.cuda.empty_cache()
step = 1
try:
for idx, (images, ques, ans) in enumerate(pbar):
try:
phi2_optim.zero_grad()
proj_optim.zero_grad()
loss = model(images, ques, ans)
loss.backward()
phi2_optim.step()
proj_optim.step()
pbar.set_description(f"Epoch: {epoch}: Training Loss = {loss.item()}")
torch.cuda.empty_cache()
step+=1
if (step%1000==0):
torch.save(model.projection_layer.state_dict(), './ckpts/model_phase2.pth')
model.phi2_model.save_pretrained('./ckpts/Qlora_adaptor/', save_adapter=True, save_config=True)
except Exception as e:
print("in frp",e)
continue
validate_model_phase2(model, val_dataloader, tokenizer, config)
torch.save(model.projection_layer.state_dict(), './ckpts/model_phase2.pth')
model.phi2_model.save_pretrained('./ckpts/Qlora_adaptor/', save_adapter=True, save_config=True)
except Exception as e:
print(e)
continue |