pytorch

d0d6b1f2 - [torchgen] Generate out variant for functional operator (#81437)

[torchgen] Generate out variant for functional operator (#81437) Summary: Previously we don't generate out variant (both schema and kernel) for an operator with functional variant only. This adds support for that and adds test. ## Changes on `native_function_generation.py` We are generating out variant for all functional variants if possible. This PR introduces a lot of newly generated out variants and `native_functions.yaml` needs to incorporate the changes by adding `autogen` keywords. The logic for determining what operators we should generate an out variant for is the following: 1. No existing out variant for this `NativeFunction` 2. Contains an existing in place, mutable or functional variant 3. Contains at least 1 tensor like return(s) For operators matching the first two conditions but failing the third, I listed them in `FUNCTIONAL_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT`. ## Special handling The following operators satisfy all 3 criteria above but we chose to not autogen them, with some reasons. * `mkldnn_adaptive_avg_pool2d`, the generated out variant `mkldnn_adaptive_avg_pool2d.out` is colliding with the `mkldnn_adaptive_avg_pool2d_out` kernel in `adaptive_avg_pool2d.out` operator. I manually created `mkldnn_adaptive_avg_pool2d.out` and renamed `mkldnn_adaptive_avg_pool2d_out` to `mkldnn_adaptive_avg_pool2d_out_stub`. * `min`, `max` and `mean`. There already exist `min.out`, `max.out` and `mean.out` but they are having different semantics with the functional ones. I manually created `min.unary_out`, `max.unary_out` and `mean.dtype_out` to disambiguate. ## Autograd Changes We introduced a logic to not match derivatives info in `derivatives.yaml` to out variant, since we are generating `NOT_IMPLEMENTED` kernels for those out variants anyway. The issue we are seeing with the original logic is that it doesn't handle `TensorOption` arguments really well. For example we have these two operators: * `_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor` * `_to_copy.out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!)` If we uses `_to_copy` derivative info, there will be compilation error since `dtype` is missing from `_to_copy.out` signature. Test Plan: Rely on unit test Differential Revision: D37832342 Pull Request resolved: https://github.com/pytorch/pytorch/pull/81437 Approved by: https://github.com/iseeyuan, https://github.com/bdhirsh