Instructions to use jonl521/modeling_simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonl521/modeling_simple with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jonl521/modeling_simple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .venv/lib/python3.14/site-packages/torch/distributed/remote_device.py from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/torch/distributed/remote_device.py
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/torch/distributed/remote_device.py
-
curl -L -o remote_device.py https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/torch/distributed/remote_device.py
4.6 kB
| # mypy: allow-untyped-defs | |
| from typing import Optional, Union | |
| import torch | |
| class _remote_device: | |
| """ | |
| Represents a device on a remote worker. | |
| Args: | |
| remote_device (str or torch.device): Represents a device on a remote worker. | |
| The string format should be one of the following: | |
| 1. "<workername>/<device>", where the device field can be parsed as torch.device type. | |
| E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". | |
| In addition, the device field can be optional and the default value is "cpu". | |
| 2. "rank:<rank>/<device>", where <rank> is the rank of the | |
| process and device can be parsed as torch.device type. | |
| E.g., "rank:0/cpu", "rank:0", "rank:0/cuda:0" | |
| 3. <workername> and <rank> are optional and formats like "cpu" | |
| and "cuda:1", just represent local devices. | |
| """ | |
| def __init__(self, remote_device: Union[str, torch.device]): | |
| PARSE_ERROR = ( | |
| f"Could not parse remote_device: {remote_device}. The valid format is " | |
| "'<workername>/<device>' or 'rank:<rank>/<device>' or '<device>'" | |
| ) | |
| self._worker_name = None | |
| self._rank = None | |
| self._device: Optional[Union[str, int, torch.device]] = None | |
| if isinstance(remote_device, torch.device): | |
| self._device = remote_device | |
| elif isinstance(remote_device, str): | |
| fields = remote_device.split("/") | |
| if len(fields) == 2: | |
| self._worker_name, self._device = fields | |
| elif len(fields) == 1: | |
| # Check if this is a valid device. | |
| if _remote_device._is_valid_local_device(fields[0]): | |
| self._device = fields[0] | |
| else: | |
| self._worker_name = fields[0] | |
| self._device = "cpu" | |
| else: | |
| raise ValueError(PARSE_ERROR) | |
| else: | |
| raise TypeError(f"Invalid type for remote_device: {type(remote_device)}") | |
| # Do some basic sanity check (no empty string) | |
| if self._worker_name is not None and not self._worker_name: | |
| raise ValueError(PARSE_ERROR) | |
| # Validate the device. | |
| self._device = torch.device(self._device) | |
| # Check for rank based format. | |
| if self._worker_name is not None: | |
| fields = self._worker_name.split(":") | |
| if len(fields) == 2: | |
| # rank:<rank>/device format, extract rank | |
| if fields[0] == "rank" and fields[1].isdigit(): | |
| self._rank = int(fields[1]) # type: ignore[assignment] | |
| self._worker_name = None | |
| else: | |
| raise ValueError(PARSE_ERROR) | |
| elif len(fields) > 2: | |
| raise ValueError(PARSE_ERROR) | |
| def _is_valid_local_device(device): | |
| # Check for torch.device | |
| try: | |
| torch.device(device) | |
| return True | |
| except Exception: | |
| return False | |
| def worker_name(self) -> Optional[str]: | |
| """Return the name of remote worker representing the remote device and ``None`` if no worker name is available.""" | |
| return self._worker_name | |
| def rank(self) -> Optional[int]: | |
| """ | |
| Returns the rank of remote worker representing the remote device. | |
| Returns ``None`` if no rank is available. | |
| """ | |
| return self._rank | |
| def device(self) -> torch.device: | |
| """Return the local device on the remote worker.""" | |
| return self._device # type: ignore[return-value] | |
| def __repr__(self): | |
| if self._device is not None: | |
| if self._worker_name is not None: | |
| return f"{self._worker_name}/{self._device}" | |
| elif self._rank is not None: | |
| return f"rank:{self._rank}/{self._device}" | |
| else: | |
| return str(self._device) | |
| else: | |
| if self._worker_name is not None: | |
| return f"{self._worker_name}" | |
| elif self._rank is not None: | |
| return f"{self._rank}" | |
| else: | |
| raise RuntimeError("Invalid state!") | |
| def __eq__(self, other): | |
| return isinstance(other, _remote_device) and ( | |
| self._worker_name == other._worker_name | |
| and self._device == other._device | |
| and self._rank == other._rank | |
| ) | |
| def __hash__(self): | |
| return hash(self._worker_name) ^ hash(self._device) ^ hash(self._rank) | |