pytorch

cce5982c - Add unary ops: exp and sqrt (#42537)

Add unary ops: exp and sqrt (#42537) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/42537 [First PR: Add private API to support tensor lists: _foreach_add(TensorList tensors, Scalar scalar)](https://github.com/pytorch/pytorch/pull/41554). **Motivation** [GitHub issue](https://github.com/pytorch/pytorch/issues/38655) Current PyTorch optimizer implementations are not efficient in cases when we work with a lot of small feature tensors. Starting a lot of kernels slows down the whole process. We need to reduce the number of kernels that we start. As an example, we should be looking at [NVIDIAs Apex](https://github.com/NVIDIA/apex). In order to track progress, we will pick PyTorchs DCGAN model with Adam optimizer and once the optimizer is reimplemented with tensor lists, benchmark the model performance against original model version, Apexs version with original Adam optimizer and it’s FusedAdam optimizer. **Current API restrictions** - List can't be empty (will fixed in upcoming PRs). - All tensors in the list must have the same dtype, device and size. **Broadcasting** At this point we don't support broadcasting. **What is 'Fast' and 'Slow' route** In particular cases, we cant process an op with a fast list CUDA kernel. Still, we can do with a regular for-loop where the op will be applied to each tensor individually through the dispatch mechanisms. There are a few checks that decide whether the op will be performed via a 'fast' or 'slow' path. To go the fast route, - All tensors must have strided layout - All tensors must be dense and not have overlapping memory - The resulting tensor type must be the same. ---------------- **In this PR** Adding APIs: ``` torch._foreach_exp(TensorList tl1) torch._foreach_exp_(TensorList tl1) torch._foreach_sqrt(TensorList tl1) torch._foreach_sqrt_(TensorList tl1) ``` **Tests** Tested via unit tests **TODO** 1. Properly handle empty lists 2. Properly handle bool tensors **Plan for the next PRs** 1. APIs - Pointwise Ops 2. Complete tasks from TODO 3. Rewrite PyTorch optimizers to use for-each operators for performance gains. Test Plan: Imported from OSS Reviewed By: cpuhrsch Differential Revision: D23331889 Pulled By: izdeby fbshipit-source-id: 8b04673b8412957472ed56361954ca3884eb9376