torch-mlir/examples
Sean Silva 7731211d02 Remove eager_mode
This was an experimental attempt at rolling out own op-by-op executor
with `__torch_dispatch__`, but it proved difficult to make it robust.
Op-by-op execution is very easy to implement robustly now with the
PyTorch 2.0 stack, so we don't need eager_mode.

Downstream users were using eager_mode to implement lockstep numerical
accuracy debuggers. We implemented the same functionality with
TorchDynamo in https://github.com/llvm/torch-mlir/pull/1681 so now there
is not much reason to continue maintaining it.
2022-12-09 03:50:00 -08:00
..
ltc_backend_bert.py Fix LTC lib_torch_mlir_ltc.so import error (#1283) 2022-08-25 18:25:01 -04:00
ltc_backend_mnist.py Fix LTC lib_torch_mlir_ltc.so import error (#1283) 2022-08-25 18:25:01 -04:00
torchdynamo_resnet18.py [torchdynamo] Add ResNet18 example with TorchDynamo 2022-12-07 09:25:27 -08:00
torchscript_mhlo_backend_resnet.py [MHLO] bert-tiny and resnet18 example from torchscript to mhlo (#1266) 2022-08-23 16:44:36 -07:00
torchscript_mhlo_backend_tinybert.py [MHLO] bert-tiny and resnet18 example from torchscript to mhlo (#1266) 2022-08-23 16:44:36 -07:00
torchscript_resnet18.py torch_mlir.compile: Allow OutputType as a string. 2022-07-08 17:37:27 -07:00
torchscript_resnet18_all_output_types.py torch_mlir.compile: Allow OutputType as a string. 2022-07-08 17:37:27 -07:00
torchscript_resnet_inference.ipynb torch_mlir.compile: Allow OutputType as a string. 2022-07-08 17:37:27 -07:00