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138 lines
4.6 KiB
138 lines
4.6 KiB
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Contains commands to print information about the environment and version.
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Usage:
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transformers env
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transformers version
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"""
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import contextlib
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import io
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import os
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import platform
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from typing import Annotated
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import huggingface_hub
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import typer
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from .. import __version__
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from ..integrations.deepspeed import is_deepspeed_available
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from ..utils import (
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is_accelerate_available,
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is_torch_available,
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is_torch_hpu_available,
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is_torch_npu_available,
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is_torch_xpu_available,
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)
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def env(
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accelerate_config_file: Annotated[
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str | None,
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typer.Argument(help="The accelerate config file to use for the default values in the launching script."),
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] = None,
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) -> None:
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"""Print information about the environment."""
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import safetensors
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safetensors_version = safetensors.__version__
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accelerate_version = "not installed"
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accelerate_config = accelerate_config_str = "not found"
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if is_accelerate_available():
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import accelerate
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from accelerate.commands.config import default_config_file, load_config_from_file
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accelerate_version = accelerate.__version__
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# Get the default from the config file.
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if accelerate_config_file is not None or os.path.isfile(default_config_file):
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accelerate_config = load_config_from_file(accelerate_config_file).to_dict()
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accelerate_config_str = (
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"\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()])
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if isinstance(accelerate_config, dict)
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else f"\t{accelerate_config}"
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)
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pt_version = "not installed"
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pt_cuda_available = "NA"
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pt_accelerator = "NA"
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if is_torch_available():
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import torch
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pt_version = torch.__version__
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pt_cuda_available = torch.cuda.is_available()
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pt_xpu_available = is_torch_xpu_available()
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pt_npu_available = is_torch_npu_available()
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pt_hpu_available = is_torch_hpu_available()
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if pt_cuda_available:
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pt_accelerator = "CUDA"
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elif pt_xpu_available:
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pt_accelerator = "XPU"
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elif pt_npu_available:
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pt_accelerator = "NPU"
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elif pt_hpu_available:
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pt_accelerator = "HPU"
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deepspeed_version = "not installed"
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if is_deepspeed_available():
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# Redirect command line output to silence deepspeed import output.
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with contextlib.redirect_stdout(io.StringIO()):
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import deepspeed
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deepspeed_version = deepspeed.__version__
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info = {
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"`transformers` version": __version__,
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"Platform": platform.platform(),
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"Python version": platform.python_version(),
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"Huggingface_hub version": huggingface_hub.__version__,
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"Safetensors version": f"{safetensors_version}",
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"Accelerate version": f"{accelerate_version}",
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"Accelerate config": f"{accelerate_config_str}",
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"DeepSpeed version": f"{deepspeed_version}",
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"PyTorch version (accelerator?)": f"{pt_version} ({pt_accelerator})",
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"Using distributed or parallel set-up in script?": "<fill in>",
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}
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if is_torch_available():
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if pt_cuda_available:
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info["Using GPU in script?"] = "<fill in>"
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info["GPU type"] = torch.cuda.get_device_name()
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elif pt_xpu_available:
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info["Using XPU in script?"] = "<fill in>"
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info["XPU type"] = torch.xpu.get_device_name()
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elif pt_hpu_available:
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info["Using HPU in script?"] = "<fill in>"
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info["HPU type"] = torch.hpu.get_device_name()
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elif pt_npu_available:
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info["Using NPU in script?"] = "<fill in>"
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info["NPU type"] = torch.npu.get_device_name()
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info["CANN version"] = torch.version.cann
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print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
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print(_format_dict(info))
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return info
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def version() -> None:
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"""Print CLI version."""
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print(__version__)
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def _format_dict(d: dict) -> str:
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return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"
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