import argparse import torch from hivemind.utils.logging import get_logger, use_hivemind_log_handler from tqdm.auto import trange from src.bloom.block import BloomBlock from src.bloom.model import DistributedBloomConfig from src.bloom.ops import build_alibi_tensor use_hivemind_log_handler("in_root_logger") logger = get_logger(__file__) logger.warning("inference_one_block will soon be deprecated in favour of tests!") def print_device_info(device=None): """Prints device stats. Code from https://stackoverflow.com/a/53374933/12891528""" device = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu")) logger.info(f"Using device: {device}") # Additional Info when using cuda if device.type == "cuda": logger.info(torch.cuda.get_device_name(0)) logger.info(f"Memory Usage:") logger.info(f"Allocated: {round(torch.cuda.memory_allocated(0) / 1024 ** 3, 1)} GB") logger.info(f"Cached: {round(torch.cuda.memory_cached(0) / 1024 ** 3, 1)} GB") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run a single bloom block locally on dummy data") parser.add_argument("--config", required=True, type=str, help="Path to a config json file") parser.add_argument("--state_dict", default=None, type=str, help="Optional path to saved block state dict") parser.add_argument("--layer_index", default=0, type=int, help="Optional path to saved block state dict") parser.add_argument("--num_steps", default=500, type=int, help="How many inference steps to run") parser.add_argument("--device", default=None, type=str, help="Run inference on this device") args = parser.parse_args() if args.device is None: args.device = "cuda" if torch.cuda.is_available() else "cpu" config = DistributedBloomConfig.from_json_file(args.config) block = BloomBlock(config, args.layer_index).to(args.device) cache = None for i in trange(args.num_steps): dummy_input = torch.randn(1, 1, config.hidden_size, device=args.device) alibi = build_alibi_tensor(i + 1, config.num_attention_heads).to(args.device) with torch.no_grad(): outputs, cache = block.forward(dummy_input, alibi=alibi, use_cache=True, layer_past=cache) print_device_info(args.device)