pytorch

813b01e4 - Use at::AutoNonVariableTypeMode before calling ATen tensor factory functions (#22364)

Use at::AutoNonVariableTypeMode before calling ATen tensor factory functions (#22364) Summary: As part of the Variable/Tensor merge, one invariant for tensor libraries such as ATen / Caffe2 / XLA is that they should only deal with Tensors, not Variables. However, currently in `variable_factories.h` we are potentially passing Variables into those tensor libraries without the `at::AutoNonVariableTypeMode` guard, which will cause those libraries to treat those Variables as Variables (i.e. their `is_variable()` is true), not Tensors. Consider the following example for `full_like`: ```cpp inline at::Tensor full_like(const at::Tensor & self, at::Scalar fill_value) { ... // Both ATen and XLA rely on `at::full_like` to dispatch to library specific implementations. // // When `self` is a Variable, since we are not using `at::AutoNonVariableTypeMode`, // `at::full_like` will also use `self` as a Variable (and it will see that `self.is_variable()` is true), // which breaks the invariant that ATen / XLA should never deal with Variables. at::Tensor tensor = at::full_like(self, fill_value, self.options().is_variable(false)); at::Tensor result = autograd::make_variable_consuming(std::move(tensor), /*requires_grad=*/false); ... return result; } ``` Instead, the invariant-preserving implementation would be: ```cpp inline at::Tensor full_like(const at::Tensor & self, at::Scalar fill_value) { ... at::Tensor tensor = ([&]() { at::AutoNonVariableTypeMode non_var_type_mode(true); // Both ATen and XLA rely on `at::full_like` to dispatch to library specific implementations. // // When `self` is a Variable, since we have `at::AutoNonVariableTypeMode` in the scope, // `at::full_like` will use `self` as a Tensor (and it will see that `self.is_variable()` is false), // which preserves the invariant that ATen / XLA should only deal with Tensors. return at::full_like(self, fill_value, self.options().is_variable(false)); })(); at::Tensor result = autograd::make_variable_consuming(std::move(tensor), /*requires_grad=*/false); ... return result; } ``` This PR makes the suggested change for all variable factory functions. cc. ailzhang This should allow us to remove all `tensor_data()` calls in the XLA codebase. Pull Request resolved: https://github.com/pytorch/pytorch/pull/22364 Differential Revision: D16074862 Pulled By: yf225 fbshipit-source-id: 3deba94b90bec92a757041ec05d604401a30c353