File size: 4,675 Bytes
7002f4e | 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 | """Train compact RemoteCLIP with bidirectional InfoNCE."""
import importlib.util
import json
import os
import random
from pathlib import Path
import numpy as np
import torch
import yaml
from torch import distributed as dist
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset, DistributedSampler
ROOT = Path(__file__).resolve().parents[1]
def load_model_class():
spec = importlib.util.spec_from_file_location("remoteclip_model", ROOT / "model" / "remoteclip.py")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module.RemoteCLIP
class PairDataset(Dataset):
def __init__(self, path):
archive = np.load(path)
self.images = archive["train_images"]
self.tokens = archive["train_tokens"]
self.data_source = str(archive["data_source"]) if "data_source" in archive.files else "unknown"
self.protocol = str(archive["protocol"]) if "protocol" in archive.files else "unknown"
def __len__(self):
return len(self.images)
def __getitem__(self, index):
return torch.from_numpy(self.images[index]), torch.from_numpy(self.tokens[index])
def main():
with (ROOT / "conf" / "config.yaml").open(encoding="utf-8") as handle:
config = yaml.safe_load(handle)
data_path = ROOT / config["data"]["path"]
if not data_path.exists():
raise FileNotFoundError(
f"Missing training data: {data_path.relative_to(ROOT)}. "
"Run `python scripts/fake_data.py` first."
)
world_size = int(os.environ.get("WORLD_SIZE", "1"))
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
if world_size > 1:
dist.init_process_group("nccl" if torch.cuda.is_available() else "gloo")
device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu")
if device.type == "cuda":
torch.cuda.set_device(local_rank)
seed = config["seed"] + local_rank
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
dataset = PairDataset(data_path)
sampler = DistributedSampler(dataset, shuffle=True) if world_size > 1 else None
loader = DataLoader(
dataset,
batch_size=config["train"]["batch_size"],
shuffle=sampler is None,
sampler=sampler,
num_workers=config["train"]["num_workers"],
)
RemoteCLIP = load_model_class()
model = RemoteCLIP(
vocabulary_size=config["data"]["vocabulary_size"],
context_length=config["data"]["context_length"],
**config["model"],
).to(device)
if world_size > 1:
model = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None)
optimizer = torch.optim.AdamW(
model.parameters(),
lr=config["train"]["learning_rate"],
weight_decay=config["train"]["weight_decay"],
)
history = []
for epoch in range(config["train"]["epochs"]):
if sampler is not None:
sampler.set_epoch(epoch)
model.train()
total = 0.0
for images, tokens in loader:
output = model(images.to(device), tokens.to(device))
optimizer.zero_grad(set_to_none=True)
output["loss"].backward()
optimizer.step()
total += output["loss"].item()
loss = total / len(loader)
history.append({"epoch": epoch + 1, "contrastive_loss": loss})
if local_rank == 0:
print(f"epoch={epoch + 1} contrastive_loss={loss:.6f}")
if local_rank == 0:
checkpoint = ROOT / config["paths"]["checkpoint"]
metrics = ROOT / config["paths"]["training_metrics"]
checkpoint.parent.mkdir(parents=True, exist_ok=True)
metrics.parent.mkdir(parents=True, exist_ok=True)
base_model = model.module if hasattr(model, "module") else model
torch.save(
{
"model": base_model.state_dict(),
"config": config,
"data_source": dataset.data_source,
"protocol": dataset.protocol,
},
checkpoint,
)
metrics.write_text(
json.dumps(
{"history": history, "data_source": dataset.data_source, "protocol": dataset.protocol},
indent=2,
)
+ "\n",
encoding="utf-8",
)
print(
f"checkpoint={checkpoint.relative_to(ROOT)} data_source={dataset.data_source} "
f"protocol={dataset.protocol}"
)
if world_size > 1:
dist.destroy_process_group()
if __name__ == "__main__":
main()
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