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description = "Create a default config file for Accelerate with only a few flags set." |
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def write_basic_config(mixed_precision="no", save_location: str = default_json_config_file, use_xpu: bool = False): |
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""" |
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Creates and saves a basic cluster config to be used on a local machine with potentially multiple GPUs. Will also |
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set CPU if it is a CPU-only machine. |
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Args: |
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mixed_precision (`str`, *optional*, defaults to "no"): |
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Mixed Precision to use. Should be one of "no", "fp16", or "bf16" |
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save_location (`str`, *optional*, defaults to `default_json_config_file`): |
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Optional custom save location. Should be passed to `--config_file` when using `accelerate launch`. Default |
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location is inside the huggingface cache folder (`~/.cache/huggingface`) but can be overriden by setting |
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the `HF_HOME` environmental variable, followed by `accelerate/default_config.yaml`. |
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use_xpu (`bool`, *optional*, defaults to `False`): |
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Whether to use XPU if available. |
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""" |
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path = Path(save_location) |
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path.parent.mkdir(parents=True, exist_ok=True) |
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if path.exists(): |
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print( |
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f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`." |
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) |
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return False |
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mixed_precision = mixed_precision.lower() |
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if mixed_precision not in ["no", "fp16", "bf16", "fp8"]: |
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raise ValueError( |
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f"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}" |
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) |
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config = { |
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"compute_environment": "LOCAL_MACHINE", |
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"mixed_precision": mixed_precision, |
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} |
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if torch.cuda.is_available(): |
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num_gpus = torch.cuda.device_count() |
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config["num_processes"] = num_gpus |
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config["use_cpu"] = False |
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if num_gpus > 1: |
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config["distributed_type"] = "MULTI_GPU" |
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else: |
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config["distributed_type"] = "NO" |
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elif is_xpu_available() and use_xpu: |
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num_xpus = torch.xpu.device_count() |
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config["num_processes"] = num_xpus |
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config["use_cpu"] = False |
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if num_xpus > 1: |
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config["distributed_type"] = "MULTI_XPU" |
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else: |
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config["distributed_type"] = "NO" |
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elif is_npu_available(): |
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num_npus = torch.npu.device_count() |
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config["num_processes"] = num_npus |
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config["use_cpu"] = False |
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if num_npus > 1: |
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config["distributed_type"] = "MULTI_NPU" |
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else: |
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config["distributed_type"] = "NO" |
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else: |
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num_xpus = 0 |
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config["use_cpu"] = True |
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config["num_processes"] = 1 |
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config["distributed_type"] = "NO" |
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config["debug"] = False |
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config = ClusterConfig(**config) |
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config.to_json_file(path) |
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return path |
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def default_command_parser(parser, parents): |
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parser = parser.add_parser("default", parents=parents, help=description, formatter_class=SubcommandHelpFormatter) |
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parser.add_argument( |
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"--config_file", |
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default=default_json_config_file, |
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help=( |
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"The path to use to store the config file. Will default to a file named default_config.yaml in the cache " |
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"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have " |
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"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed " |
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"with 'huggingface'." |
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), |
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dest="save_location", |
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) |
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parser.add_argument( |
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"--mixed_precision", |
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choices=["no", "fp16", "bf16"], |
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type=str, |
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help="Whether or not to use mixed precision training. " |
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"Choose between FP16 and BF16 (bfloat16) training. " |
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"BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", |
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default="no", |
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) |
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parser.set_defaults(func=default_config_command) |
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return parser |
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def default_config_command(args): |
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config_file = write_basic_config(args.mixed_precision, args.save_location) |
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if config_file: |
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print(f"accelerate configuration saved at {config_file}") |
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