torch-mlir/python
Ramiro Leal-Cavazos a8cbfff95b
Reduce memory usage of e2e tests by reducing input sizes (#1653)
There are a few e2e tests that take several very large tensors as
input, which leads to the e2e test suite leaking too much
memory. Running things locally resulted in a total memory usage of
12.5 GB when running the suite sequentially on the refbackend.

Many of the tests that take large tensors don't actually need
such large tensors to pass, and some that take several large tensors
as input are just doing the same thing multiple times. This commit
reduces the size of some of the tensors and removes repetitive parts
of tests to reduce the memory usage to a total of 3 GB.
2022-11-29 10:03:36 -08:00
..
test [torch_mlir.compile] Handle the case of already-scripted models better 2022-11-16 10:47:13 -08:00
torch_mlir Revert "build: update llvm tag to 147fe9de" 2022-11-25 12:41:56 +05:30
torch_mlir_e2e_test Reduce memory usage of e2e tests by reducing input sizes (#1653) 2022-11-29 10:03:36 -08:00
CMakeLists.txt [torchdynamo] Initial TorchDynamo support 2022-11-24 04:10:25 -08:00
TorchMLIRModule.cpp Miscellaneous fixes for Windows builds (#1376) 2022-09-29 12:07:43 -05:00