Simplifies and optimizes dict construction using the `fromkeys` classmethod ctor. This also makes it really obvious when all the keys will have the same static value, which could be a bug if unintentional. It is also significantly faster than using a dict comprehension. The rule is in preview, but I am adding a forward fix for when it becomes stable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118637
Approved by: https://github.com/albanD
Enable pytest for a few unique files. pytest runs tests in a different order than unittest (but still a consistent ordering with respect to itself) and some tests change global state, causing other tests to fail.
`test_transpose_non_contiguous` in `test_torchinductor.py` gets impacted from some other test but I'm not sure which one, so my solution is to reset the metrics before the rest of the test is run.
`test_register_patterns` in `test_quantize_fx.py` adds extra keys to global variables, so remove them when the test is done via unittest's `addCleanUp` which also works on pytest.
pytest doesn't really have an equivalent for `load_tests` so change it to be like `test_jit` that imports all the classes. I also attempted to dynamically import them, but I failed.
`test_public_api_surface` in `test_fx.py` checks for a backwards compatibility classification. There is a different test in test_fx that results in `fuser_utils` being imported. pytest runs this test before `test_public_api_surface` while unittest runs it after, so pytest sees `fuser_utils` when crawling through the modules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96397
Approved by: https://github.com/huydhn
Fixes https://github.com/pytorch/torchdynamo/issues/1708
Our FX subgraph partitioner works by taking all of the original output nodes from a subgraph, and replacing it with a new `call_module` node in the graph.
If the original subgraph outputs had fake tensors and other metadata stored in their `.meta` attribute though, then this information was getting lost when we spliced in the subgraph.
Losing metadata on an FX graph also seems like an easy trap to fall into, so I'm wondering if there are any better guardrails that we can add. I ended up fixing in this PR by adding an optional kwarg to propagate meta info directly in the `fx.Node.replace_all_uses_with`, just because propagating metadata seems like a pretty core thing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87255
Approved by: https://github.com/wconstab, https://github.com/SherlockNoMad
This PR introduces two components.
CapabilityBasedPartitioner for FX graph: given a list of supported operators, this partitioner tries to forms the largest subgraphs that only contain the supported ops.
Fuser utility: given a list of nodes in FX graph, it lifts them as a sub-GraphModule in the original graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79439
Approved by: https://github.com/jjsjann123, https://github.com/davidberard98
