"""Train RemoteCLIP with multi-positive symmetric contrastive loss and torchrun.""" import argparse import importlib.util import json import os from contextlib import nullcontext from functools import partial from pathlib import Path import numpy as np import torch import yaml from torch import distributed as dist from torch.distributed.nn import functional as dist_nn from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, Dataset, DistributedSampler ROOT = Path(__file__).resolve().parents[1] def load_model_module(): spec = importlib.util.spec_from_file_location("remoteclip", ROOT / "model/remoteclip.py") module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) return module class PairDataset(Dataset): def __init__(self, path, config): archive = np.load(path) required = {"images", "tokens", "pair_ids"} if not required.issubset(archive.files): raise ValueError(f"NPZ requires {sorted(required)}") self.images, self.tokens, self.pair_ids = (archive[key] for key in ("images", "tokens", "pair_ids")) self.data_source = str(archive["data_source"]) if "data_source" in archive else "provided" self.protocol = str(archive["protocol"]) if "protocol" in archive else "provided_npz" if self.protocol != config["data"]["protocol"]: raise ValueError("NPZ protocol does not match configuration") if self.images.ndim != 4 or self.images.shape[1:] != (3, 224, 224) or self.images.dtype != np.float32: raise ValueError("images must be float32 [N,3,224,224]") if self.tokens.ndim != 2 or self.tokens.shape[1:] != (77,) or self.tokens.dtype != np.int64: raise ValueError("tokens must be int64 [N,77]") if self.pair_ids.ndim != 1 or self.pair_ids.dtype != np.int64: raise ValueError("pair_ids must be int64 [N]") if not (len(self.images) == len(self.tokens) == len(self.pair_ids)) or len(self.images) == 0: raise ValueError("images, tokens, and pair_ids must have the same non-zero sample count") vocabulary_size = config["data"]["vocabulary_size"] if self.tokens.min() < 0 or self.tokens.max() >= vocabulary_size: raise ValueError(f"token ids must be in [0,{vocabulary_size})") sot, eot, pad = (config["data"][key] for key in ("sot_token_id", "eot_token_id", "pad_token_id")) if not np.all(self.tokens[:, 0] == sot): raise ValueError("standard CLIP sequences must start with SOT") eot_mask = self.tokens == eot if not np.all(eot_mask.sum(axis=1) == 1): raise ValueError("each token sequence must contain exactly one EOT") eot_positions = eot_mask.argmax(axis=1) for row, position in zip(self.tokens, eot_positions): if np.any(row[1:position] == pad) or np.any(row[position + 1:] != pad): raise ValueError("tokens before EOT must be non-padding and all tokens after EOT must be padding") if not (0 <= pad < sot < eot < vocabulary_size): raise ValueError("CLIP token configuration must satisfy pad < SOT < EOT < vocabulary_size") def __len__(self): return len(self.images) def __getitem__(self, index): return tuple(torch.as_tensor(array[index]) for array in (self.images, self.tokens, self.pair_ids)) def gather_with_local_grad(features): if not dist.is_initialized(): return features # Autograd all_gather uses reduce-scatter in backward. Combined with DDP's # parameter-gradient averaging, this is the exact gradient of one global loss. return torch.cat(dist_nn.all_gather(features), dim=0) def gather_ids(pair_ids): if not dist.is_initialized(): return pair_ids gathered = [torch.zeros_like(pair_ids) for _ in range(dist.get_world_size())] dist.all_gather(gathered, pair_ids) return torch.cat(gathered) def model_kwargs(config): return {"vocabulary_size": config["data"]["vocabulary_size"], "context_length": config["data"]["context_length"], "eot_token_id": config["data"]["eot_token_id"], **config["model"]} def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--config", type=Path, default=ROOT / "conf/config.yaml") parser.add_argument("--data", type=Path) parser.add_argument("--checkpoint", type=Path) parser.add_argument("--device", choices=("auto", "cpu", "cuda")) args = parser.parse_args() config = yaml.safe_load(args.config.read_text()) world_size, rank = int(os.environ.get("WORLD_SIZE", 1)), int(os.environ.get("RANK", 0)) local_rank = int(os.environ.get("LOCAL_RANK", 0)) requested = args.device or config["runtime"]["device"] use_cuda = torch.cuda.is_available() and requested != "cpu" if requested == "cuda" and not use_cuda: raise RuntimeError("CUDA requested but unavailable") if world_size > 1: dist.init_process_group("nccl" if use_cuda else "gloo") device = torch.device(f"cuda:{local_rank}" if use_cuda else "cpu") if use_cuda: torch.cuda.set_device(local_rank) torch.manual_seed(config["seed"] + rank) data_path = args.data or ROOT / config["data"]["root"] / "train.npz" dataset = PairDataset(data_path, config) sampler = DistributedSampler(dataset, shuffle=True, drop_last=True) if world_size > 1 else None loader = DataLoader(dataset, batch_size=config["training"]["batch_size"], sampler=sampler, shuffle=sampler is None, num_workers=config["training"]["num_workers"], drop_last=world_size > 1) module = load_model_module() model = module.RemoteCLIP(**model_kwargs(config)).to(device) raw_model = model if world_size > 1: model = DistributedDataParallel(model, device_ids=[local_rank] if use_cuda else None) raw_model = model.module optimizer = torch.optim.AdamW(model.parameters(), lr=config["training"]["learning_rate"], weight_decay=config["training"]["weight_decay"]) amp = bool(config["runtime"]["amp"] and use_cuda) scaler = torch.amp.GradScaler("cuda", enabled=amp) history = [] for epoch in range(config["training"]["epochs"]): if sampler is not None: sampler.set_epoch(epoch) model.train(); total = 0.0 for images, tokens, pair_ids in loader: images, tokens, pair_ids = images.to(device), tokens.to(device), pair_ids.to(device) autocast = partial(torch.amp.autocast, "cuda") if amp else nullcontext with autocast(): image_features, text_features, scale = model(images, tokens) loss = module.multi_positive_clip_loss(gather_with_local_grad(image_features), gather_with_local_grad(text_features), gather_ids(pair_ids), scale) optimizer.zero_grad(set_to_none=True); scaler.scale(loss).backward() scaler.step(optimizer); scaler.update(); total += loss.detach().item() statistics = torch.tensor([total, len(loader)], dtype=torch.float64, device=device) if world_size > 1: dist.all_reduce(statistics, op=dist.ReduceOp.SUM) record = {"epoch": epoch + 1, "contrastive_loss": statistics[0].item() / max(statistics[1].item(), 1)} history.append(record) if rank == 0: print(f"epoch={record['epoch']} contrastive_loss={record['contrastive_loss']:.6f}") if rank == 0: checkpoint_path = args.checkpoint or ROOT / config["paths"]["checkpoint"] checkpoint_path.parent.mkdir(parents=True, exist_ok=True) torch.save({"model": raw_model.state_dict(), "optimizer": optimizer.state_dict(), "scaler": scaler.state_dict() if amp else None, "config": config, "epoch": config["training"]["epochs"], "history": history}, checkpoint_path) metrics = ROOT / config["paths"]["training_metrics"]; metrics.parent.mkdir(parents=True, exist_ok=True) metrics.write_text(json.dumps({"history": history, "protocol": dataset.protocol, "data_source": dataset.data_source, "world_size": world_size, "amp": amp}, indent=2) + "\n") print(f"checkpoint={checkpoint_path}") if world_size > 1: dist.destroy_process_group() if __name__ == "__main__": main()