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67ef2683d9
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
Resolves #126888
- #126888
This PR is split from PR #126898.
- #126898
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127689
Approved by: https://github.com/Skylion007
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
import operator
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from functools import reduce
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from typing_extensions import deprecated
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import torch
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import torch._utils
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from ..function import Function
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class Type(Function):
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@staticmethod
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@deprecated(
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"`torch.autograd._functions.Type` is deprecated as of PyTorch 2.1, "
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"please use `torch.tensor.to(dtype=dtype)` instead.",
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category=FutureWarning,
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)
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def forward(ctx, i, dest_type):
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ctx.input_type = type(i)
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ctx.input_device = -1 if not i.is_cuda else i.get_device()
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return i.type(dest_type)
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@staticmethod
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def backward(ctx, grad_output):
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if ctx.input_device == -1:
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return grad_output.type(ctx.input_type), None
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else:
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with torch.cuda.device(ctx.input_device):
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return grad_output.type(ctx.input_type), None
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# TODO: deprecate this
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class Resize(Function):
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@staticmethod
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def forward(ctx, tensor, sizes):
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ctx.sizes = sizes
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ctx.numel = reduce(operator.mul, sizes, 1)
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if tensor.numel() != ctx.numel:
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raise RuntimeError(
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(
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"requested resize to {} ({} elements in total), "
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"but the given tensor has a size of {} ({} elements). "
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"autograd's resize can only change the shape of a given "
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"tensor, while preserving the number of elements. "
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).format(
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"x".join(map(str, sizes)),
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ctx.numel,
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"x".join(map(str, tensor.size())),
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tensor.numel(),
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)
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)
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ctx.input_sizes = tensor.size()
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if tensor.is_quantized:
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tensor.copy_(tensor)
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return tensor.contiguous().view(*sizes)
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if tensor.is_contiguous():
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result = tensor.new(tensor).contiguous().view(*sizes)
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return result
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else:
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return tensor.contiguous().view(*sizes)
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@staticmethod
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def backward(ctx, grad_output):
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assert grad_output.numel() == ctx.numel
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return grad_output.contiguous().view(ctx.input_sizes), None
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