Change our representation of sizes and strides to contain SymInts
instead of int64_t.
Right now it's not actually possible to create a Tensor with symbolic
shape, so this change is intended to be a no-op.
But the intended behavior is:
- If you create a Tensor with symbolic shape, a `CustomSizes` policy
will be set, and the `has_symbolic_sizes_strides_` bit will be set. (not
currently implemented)
- Calling any TensorImpl function that naively interacts with sizes and
strides will throw. For hot-path functions (`sizes()`, `strides()`), we
make use of the existing policy check to throw. For others, we just have
a regular `TORCH_CHECK(!has_symbolic_sizes_strides_)`.
This also undoes the explicit constructor I made in
https://github.com/pytorch/pytorch/pull/77666; it ended up being more
annoying than useful when making these changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78272
Approved by: https://github.com/Krovatkin, https://github.com/Chillee
Change our representation of sizes and strides to contain SymInts
instead of int64_t.
Right now it's not actually possible to create a Tensor with symbolic
shape, so this change is intended to be a no-op.
But the intended behavior is:
- If you create a Tensor with symbolic shape, a `CustomSizes` policy
will be set, and the `has_symbolic_sizes_strides_` bit will be set. (not
currently implemented)
- Calling any TensorImpl function that naively interacts with sizes and
strides will throw. For hot-path functions (`sizes()`, `strides()`), we
make use of the existing policy check to throw. For others, we just have
a regular `TORCH_CHECK(!has_symbolic_sizes_strides_)`.
This also undoes the explicit constructor I made in
https://github.com/pytorch/pytorch/pull/77666; it ended up being more
annoying than useful when making these changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77994
Approved by: https://github.com/Krovatkin
