File size: 7,373 Bytes
7a564e3 | 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 | """Stage 2: MoE Decoder Supervised Finetuning on GPU.
Loads Stage 1 checkpoint, freezes X-Encoder, trains Predictor + MoE Decoder on VQA data.
Uses dummy VQA data if real VQAv2 not available.
"""
import yaml
import torch
import os
import time
import json
from model.vlm import VLJEPAModel
from model.tokenizer import BPETokenizer
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from PIL import Image
class SimpleVQADataset(Dataset):
"""VQA dataset from available images + generated Q&A pairs."""
def __init__(self, image_dir, tokenizer, img_size=384, max_q=64, max_a=32):
self.tokenizer = tokenizer
self.max_q = max_q
self.max_a = max_a
self.transform = transforms.Compose([
transforms.Resize((img_size, img_size)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
self.images = sorted([
os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(".jpg")
])
# Generate diverse VQA pairs for training
self.qa_templates = [
("What do you see in this image?", "objects and scene"),
("Describe this image.", "a visual scene"),
("What is happening here?", "activity in scene"),
("How many objects are there?", "several"),
("What colors are visible?", "various colors"),
("Is there a person in this image?", "possibly"),
("What is the main subject?", "the central object"),
("What is in the background?", "background elements"),
("Is this indoor or outdoor?", "a scene"),
("What time of day is it?", "daytime"),
]
self.samples = []
for i, img_path in enumerate(self.images):
q, a = self.qa_templates[i % len(self.qa_templates)]
self.samples.append((img_path, q, a))
def __len__(self):
return len(self.samples)
def _pad(self, ids, max_len):
ids = ids[:max_len]
mask = [True] * len(ids) + [False] * (max_len - len(ids))
ids = ids + [self.tokenizer.pad_id] * (max_len - len(ids))
return ids, mask
def __getitem__(self, idx):
img_path, question, answer = self.samples[idx]
image = Image.open(img_path).convert("RGB")
image = self.transform(image)
q_ids, q_mask = self._pad(self.tokenizer.encode(question), self.max_q)
a_ids, a_mask = self._pad(self.tokenizer.encode(answer), self.max_a)
return {
"image": image,
"question_ids": torch.tensor(q_ids, dtype=torch.long),
"question_mask": torch.tensor(q_mask, dtype=torch.bool),
"answer_ids": torch.tensor(a_ids, dtype=torch.long),
"answer_mask": torch.tensor(a_mask, dtype=torch.bool),
}
def main():
with open("configs/default.yaml") as f:
config = yaml.safe_load(f)
config["train_stage2"]["batch_size"] = 4 # RTX 3090
config["train_stage2"]["max_epochs"] = 15
device = torch.device("cuda")
print(f"Device: {device}")
print(f"GPU: {torch.cuda.get_device_name()}")
tokenizer = BPETokenizer(vocab_size=config["decoder"]["vocab_size"])
tokenizer.load("checkpoints/tokenizer.json")
print(f"Tokenizer: {len(tokenizer)} tokens")
# Load model and Stage 1 checkpoint
model = VLJEPAModel(config).to(device)
stage1_ckpt = "checkpoints/stage1_final.pt"
if os.path.exists(stage1_ckpt):
ckpt = torch.load(stage1_ckpt, map_location=device)
model.load_state_dict(ckpt["model_state_dict"])
print(f"Loaded Stage 1 checkpoint (epoch {ckpt['epoch']}, loss {ckpt['loss']:.4f})")
else:
print("WARNING: No Stage 1 checkpoint found. Training from scratch.")
# Freeze X-Encoder for Stage 2
model.freeze_x_encoder()
params = model.count_parameters()
print(f"Total params: {params['total']:,}")
print(f"Trainable params: {params['trainable']:,}")
# Dataset
image_dir = "data/flickr8k/Images"
dataset = SimpleVQADataset(image_dir, tokenizer, img_size=config["vision"]["img_size"])
loader = DataLoader(dataset, batch_size=config["train_stage2"]["batch_size"],
shuffle=True, num_workers=4, pin_memory=True)
print(f"Dataset: {len(dataset)} VQA samples, {len(loader)} batches")
# Optimizer (only trainable params)
trainable_params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.AdamW(trainable_params, lr=config["train_stage2"]["learning_rate"], weight_decay=0.01)
total_steps = config["train_stage2"]["max_epochs"] * len(loader)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps)
model.train()
max_epochs = config["train_stage2"]["max_epochs"]
lb_weight = config["train_stage2"]["load_balance_weight"]
start = time.time()
for epoch in range(max_epochs):
total_loss = 0
total_decode = 0
total_lb = 0
n = 0
for batch in loader:
output = model.forward_stage2(
images=batch["image"].to(device),
query_ids=batch["question_ids"].to(device),
query_padding_mask=batch["question_mask"].to(device),
answer_ids=batch["answer_ids"].to(device),
load_balance_weight=lb_weight,
)
loss = output["loss"]
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), config["train_stage2"]["gradient_clip"])
optimizer.step()
scheduler.step()
total_loss += loss.item()
total_decode += output["decode_loss"].item()
total_lb += output["load_balance_loss"].item()
n += 1
avg_loss = total_loss / n
avg_decode = total_decode / n
avg_lb = total_lb / n
elapsed = time.time() - start
gpu_mem = torch.cuda.max_memory_allocated() / 1e9
print(f"Epoch {epoch+1}/{max_epochs}: loss={avg_loss:.4f} decode={avg_decode:.4f} lb={avg_lb:.4f} | {elapsed:.0f}s | GPU: {gpu_mem:.1f}GB", flush=True)
if (epoch + 1) % 5 == 0:
ckpt_path = f"checkpoints/stage2_epoch{epoch+1}.pt"
torch.save({"epoch": epoch+1, "model_state_dict": model.state_dict(), "loss": avg_loss}, ckpt_path)
print(f" Saved {ckpt_path}", flush=True)
# Final save
torch.save({"epoch": max_epochs, "model_state_dict": model.state_dict(), "loss": avg_loss}, "checkpoints/stage2_final.pt")
# Test generation
model.eval()
with torch.no_grad():
sample = dataset[0]
img = sample["image"].unsqueeze(0).to(device)
q = sample["question_ids"].unsqueeze(0).to(device)
qm = sample["question_mask"].unsqueeze(0).to(device)
tokens = model.generate(img, q, qm, max_new_tokens=20)
text = tokenizer.decode(tokens[0].tolist())
print(f"\nTest generation:")
print(f" Q: {dataset.samples[0][1]}")
print(f" A: '{text}'")
total_time = time.time() - start
print(f"\nStage 2 complete. Final loss: {avg_loss:.4f}")
print(f"Total time: {total_time:.0f}s ({total_time/60:.1f} min)")
if __name__ == "__main__":
main()
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