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41 lines
1.5 KiB
41 lines
1.5 KiB
from typing import List
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from torch import Tensor
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def has_inf_or_nan(tensor):
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try:
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# if tensor is half, the .float() incurs an additional deep copy, but it's necessary if
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# Pytorch's .sum() creates a one-element tensor of the same type as tensor
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# (which is true for some recent version of pytorch).
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tensor_sum = float(tensor.float().sum())
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# More efficient version that can be used if .sum() returns a Python scalar
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# tensor_sum = float(tensor.sum())
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except RuntimeError as instance:
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# We want to check if inst is actually an overflow exception.
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# RuntimeError could come from a different error.
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# If so, we still want the exception to propagate.
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if "value cannot be converted" not in instance.args[0]:
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raise
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return True
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else:
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if tensor_sum == float('inf') or tensor_sum == -float('inf') or tensor_sum != tensor_sum:
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return True
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return False
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def zero_gard_by_list(tensor_list: List[Tensor], set_to_none: bool = True) -> None:
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"""
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Clear the gradient of a list of tensors,
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Note: copied from torch.optim.optimizer.
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"""
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for param in tensor_list:
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if param.grad is not None:
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if set_to_none:
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param.grad = None
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else:
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if param.grad.grad_fn is not None:
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param.grad.detach_()
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else:
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param.grad.requires_grad_(False)
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param.grad.zero_()
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