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