3 Commits

Author	SHA1	Message	Date
Kazuaki Ishizaki	1cd6ebe095	Fix typos in messages under torch (#89049) ... This PR fixes typos of messages in `.py` files under torch directory. Only in `torch/onnx/symbolic_opset16.py`, fix a typo in comment to make the operator name correct. Pull Request resolved: https://github.com/pytorch/pytorch/pull/89049 Approved by: https://github.com/lezcano	2022-11-17 04:18:14 +00:00
Ralf Gommers	48ddc9762b	Upgrade mypy to version 0.812 (#55712) ... Summary: Fixes https://github.com/pytorch/pytorch/issues/54211 This was a little more annoying than expected, because the `exclude = ` key in `mypy.ini` is weird. I'll file an upstream issue about that. I ignored one file, `torch/distributed/elastic/agent/server/api.py` that had ~8 errors that were hard to figure out. This can be done in a follow-up. Pull Request resolved: https://github.com/pytorch/pytorch/pull/55712 Reviewed By: walterddr Differential Revision: D27694976 Pulled By: malfet fbshipit-source-id: 228d8be6af040343ce46595dabaca212e69ccc68	2021-04-12 18:08:28 -07:00
Alexander	002d978428	Sparse benchmarking utils (#48397) ... Summary: This is a benchmarking tooling to work with sparse tensors. To implement this, we extended PR `benchmarking util` [https://github.com/pytorch/pytorch/issues/38338](https://github.com/pytorch/pytorch/pull/38338) for sparse tensors. In order to extend the proposed utility library the **FuzzedTensor** class was extended by creating the new **FuzzedSparseTensor** class. In addition two new operator classes were added, the `UnaryOpSparseFuzzer` and `BinaryOpSparseFuzzer`. The class `FuzzedSparseTensor` adds new input parameters to the constructor: 1. `sparse_dim`: The number of sparse dimensions in a sparse tensor. 2. `nnz`: Number of non-zero elements in the sparse tensor. 3. `density`: The density of the sparse tensor. 4. `coalesced`: As we know the sparse tensor format permits coalesced/uncoalesced sparse tensors. and removes `probability_contiguous`, `max_allocation_bytes`, `roll_parameter`, `tensor_constructor` as they are dense-tensors related parameters. In addition, I've extended the `torch.utils.benchmark.examples` to work with the new classes `FuzzedSparseTensor`, `UnaryOpSparseFuzzer` and `BinaryOpSparseFuzzer`. Hopefully, this tooling and these examples will help to make other benchmarks in other PRs. Looking forward to your thoughts and feedback. cc robieta, mruberry, ngimel Pull Request resolved: https://github.com/pytorch/pytorch/pull/48397 Reviewed By: ejguan Differential Revision: D26008137 Pulled By: mruberry fbshipit-source-id: 2f37811c7c3eaa3494a0f2500e519267f2186dfb	2021-01-22 09:40:59 -08:00