Files
pytorch/torch/autograd/_functions/tensor.py
mansiag05 850ba8c96d [Code Clean] Clean asserts in torch/autograd. (#165627)
Replaces 78 assert statements across 10 files in torch.autograd with explicit if-checks raising AssertionError to prevent assertions from being disabled with Python -O flag. This ensures error checking remains active in optimized builds.

fix partially #164878

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165627
Approved by: https://github.com/albanD
2025-10-20 23:03:47 +00:00

73 lines
2.4 KiB
Python

# mypy: allow-untyped-defs
import operator
from functools import reduce
from typing_extensions import deprecated
import torch
import torch._utils
from torch.autograd.function import Function
class Type(Function):
@staticmethod
@deprecated(
"`torch.autograd._functions.Type` is deprecated as of PyTorch 2.1, "
"please use `torch.tensor.to(dtype=dtype)` instead.",
category=FutureWarning,
)
# pyrefly: ignore # bad-override
def forward(ctx, i, dest_type):
ctx.input_type = type(i)
ctx.input_device = -1 if not i.is_cuda else i.get_device()
return i.type(dest_type)
@staticmethod
# pyrefly: ignore # bad-override
def backward(ctx, grad_output):
if ctx.input_device == -1:
return grad_output.type(ctx.input_type), None
else:
with torch.accelerator.device_index(ctx.input_device):
return grad_output.type(ctx.input_type), None
# TODO: deprecate this
class Resize(Function):
@staticmethod
# pyrefly: ignore # bad-override
def forward(ctx, tensor, sizes):
ctx.sizes = sizes
ctx.numel = reduce(operator.mul, sizes, 1)
if tensor.numel() != ctx.numel:
raise RuntimeError(
(
"requested resize to {} ({} elements in total), "
"but the given tensor has a size of {} ({} elements). "
"autograd's resize can only change the shape of a given "
"tensor, while preserving the number of elements. "
).format(
"x".join(map(str, sizes)),
ctx.numel,
"x".join(map(str, tensor.size())),
tensor.numel(),
)
)
ctx.input_sizes = tensor.size()
if tensor.is_quantized:
tensor.copy_(tensor)
return tensor.contiguous().view(*sizes)
if tensor.is_contiguous():
result = tensor.new(tensor).contiguous().view(*sizes)
return result
else:
return tensor.contiguous().view(*sizes)
@staticmethod
# pyrefly: ignore # bad-override
def backward(ctx, grad_output):
if grad_output.numel() != ctx.numel:
raise AssertionError(
f"Expected grad_output to have {ctx.numel} elements, but got {grad_output.numel()}"
)
return grad_output.contiguous().view(ctx.input_sizes), None