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b4f5efa7b2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/48116 If you port kernels to be structured, you get Meta kernels automatically generated for you. This is one payoff of structured kernels. Code generation was mercifully really simple, although at risk of "swiss cheese" syndrome: there's two new conditionals in the codegen to tweak behavior when generating for meta keys. It's not too bad right now but there's a risk of things getting out of hand. One way to rationalize the logic here would be to transmit "TensorMeta-ness" inside the TensorOptions (so tensor_from_meta can deal with it); then the "Meta" kernel magic would literally just be generating empty out_impls to call after all the scaffolding is done. But I didn't do this because it seemed like it would be more annoying short term. Also had to teach resize_ to work on meta tensors, since we use them to implement the out kernels. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Test Plan: Imported from OSS Reviewed By: bhosmer, ailzhang Differential Revision: D25056640 Pulled By: ezyang fbshipit-source-id: f8fcfa0dbb58a94d9b4196748f56e155f83b1521 |
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| api | ||
| selective_build | ||
| __init__.py | ||
| code_template.py | ||
| gen.py | ||
| local.py | ||
| model.py | ||
| utils.py | ||