pytorch

c7e9abb6 - Making ops c10-full: list of optional tensors (#49138)

Making ops c10-full: list of optional tensors (#49138) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49138 See for details: https://fb.quip.com/QRtJAin66lPN We need to model optional types explicitly, mostly for schema inference. So we cannot pass a `Tensor?[]` as `ArrayRef<Tensor>`, instead we need to pass it as an optional type. This PR changes it to `torch::List<c10::optional<Tensor>>`. It also makes the ops c10-full that were blocked by this. ## Backwards Compatibility - This should not break the Python API because the representation in Python is the same and python_arg_parser just transforms the python list into a `List<optional<Tensor>>` instead of into a `List<Tensor>`. - This should not break serialized models because there's some logic that allows loading a serialized `List<Tensor>` as `List<optional<Tensor>>`, see https://github.com/pytorch/pytorch/pull/49138/files#diff-9315f5dd045f47114c677174dcaa2f982721233eee1aa19068a42ff3ef775315R57 - This will break backwards compatibility for the C++ API. There is no implicit conversion from `ArrayRef<Tensor>` (which was the old argument type) to `List<optional<Tensor>>`. One common call pattern is `tensor.index({indices_tensor})`, where indices_tensor is another `Tensor`, and that will continue working because the `{}` initializer_list constructor for `List<optional<Tensor>>` can take `Tensor` elements that are implicitly converted to `optional<Tensor>`, but another common call pattern was `tensor.index(indices_tensor)`, where previously, the `Tensor` got implicitly converted to an `ArrayRef<Tensor>`, and to implicitly convert `Tensor -> optional<Tensor> -> List<optional<Tensor>>` would be two implicit conversions. C++ doesn't allow chaining. two implicit conversions. So those call sites have to be rewritten to `tensor.index({indices_tensor})`. ghstack-source-id: 119269131 Test Plan: ## Benchmarks (C++ instruction counts): ### Forward #### Script ```py from torch.utils.benchmark import Timer counts = Timer( stmt=""" auto t = {{op call to measure}}; """, setup=""" using namespace torch::indexing; auto x = torch::ones({4, 4, 4}); """, language="cpp", ).collect_callgrind(number=1_000) print(counts) ``` #### Results | Op call |before |after |delta | | |------------------------------------------------------------------------|---------|--------|-------|------| |x[0] = 1 |11566015 |11566015|0 |0.00% | |x.index({0}) |6807019 |6801019 |-6000 |-0.09%| |x.index({0, 0}) |13529019 |13557019|28000 |0.21% | |x.index({0, 0, 0}) |10677004 |10692004|15000 |0.14% | |x.index({"..."}) |5512015 |5506015 |-6000 |-0.11%| |x.index({Slice(None, None, None)}) |6866016 |6936016 |70000 |1.02% | |x.index({None}) |8554015 |8548015 |-6000 |-0.07%| |x.index({false}) |22400000 |22744000|344000 |1.54% | |x.index({true}) |27624088 |27264393|-359695|-1.30%| |x.index({"...", 0, true, Slice(1, None, 2), torch::tensor({1, 2})})|123472000|123463306|-8694|-0.01%| ### Autograd #### Script ```py from torch.utils.benchmark import Timer counts = Timer( stmt=""" auto t = {{op call to measure}}; """, setup=""" using namespace torch::indexing; auto x = torch::ones({4, 4, 4}, torch::requires_grad()); """, language="cpp", ).collect_callgrind(number=1_000) print(counts) ``` Note: the script measures the **forward** path of an op call with autograd enabled (i.e. calls into VariableType). It does not measure the backward path. #### Results | Op call |before |after |delta | | |------------------------------------------------------------------------|---------|--------|-------|------| |x.index({0}) |14839019|14833019|-6000| 0.00% | |x.index({0, 0}) |28342019|28370019|28000| 0.00% | |x.index({0, 0, 0}) |24434004|24449004|15000| 0.00% | |x.index({"..."}) |12773015|12767015|-6000| 0.00% | |x.index({Slice(None, None, None)}) |14837016|14907016|70000| 0.47% | |x.index({None}) |15926015|15920015|-6000| 0.00% | |x.index({false}) |36958000|37477000|519000| 1.40% | |x.index({true}) |41971408|42426094|454686| 1.08% | |x.index({"...", 0, true, Slice(1, None, 2), torch::tensor({1, 2})}) |168184392|164545682|-3638710| -2.16% | Reviewed By: bhosmer Differential Revision: D25454632 fbshipit-source-id: 28ab0cffbbdbdff1c40b4130ca62ee72f981b76d