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