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Source code for torch.nn.utils.convert_parameters

import torch
from typing import Iterable, Optional


[docs]def parameters_to_vector(parameters: Iterable[torch.Tensor]) -> torch.Tensor: r"""Convert parameters to one vector Args: parameters (Iterable[Tensor]): an iterator of Tensors that are the parameters of a model. Returns: The parameters represented by a single vector """ # Flag for the device where the parameter is located param_device = None vec = [] for param in parameters: # Ensure the parameters are located in the same device param_device = _check_param_device(param, param_device) vec.append(param.view(-1)) return torch.cat(vec)
[docs]def vector_to_parameters(vec: torch.Tensor, parameters: Iterable[torch.Tensor]) -> None: r"""Convert one vector to the parameters Args: vec (Tensor): a single vector represents the parameters of a model. parameters (Iterable[Tensor]): an iterator of Tensors that are the parameters of a model. """ # Ensure vec of type Tensor if not isinstance(vec, torch.Tensor): raise TypeError(f'expected torch.Tensor, but got: {torch.typename(vec)}') # Flag for the device where the parameter is located param_device = None # Pointer for slicing the vector for each parameter pointer = 0 for param in parameters: # Ensure the parameters are located in the same device param_device = _check_param_device(param, param_device) # The length of the parameter num_param = param.numel() # Slice the vector, reshape it, and replace the old data of the parameter param.data = vec[pointer:pointer + num_param].view_as(param).data # Increment the pointer pointer += num_param
def _check_param_device(param: torch.Tensor, old_param_device: Optional[int]) -> int: r"""This helper function is to check if the parameters are located in the same device. Currently, the conversion between model parameters and single vector form is not supported for multiple allocations, e.g. parameters in different GPUs/PrivateUse1s, or mixture of CPU/GPU/PrivateUse1. Args: param ([Tensor]): a Tensor of a parameter of a model old_param_device (int): the device where the first parameter of a model is allocated. Returns: old_param_device (int): report device for the first time """ # Meet the first parameter support_device_types = ["cuda", torch._C._get_privateuse1_backend_name()] if old_param_device is None: old_param_device = param.get_device() if param.device.type in support_device_types else -1 else: warn = False if param.device.type in support_device_types: # Check if in same GPU/PrivateUse1 warn = (param.get_device() != old_param_device) else: # Check if in CPU warn = (old_param_device != -1) if warn: raise TypeError('Found two parameters on different devices, ' 'this is currently not supported.') return old_param_device

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