1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253 |
- 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 BloomConfig
- 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 = BloomConfig.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)
|