Overly specific docs can get stale easily. It looks like
https://llvm.github.io/torch-mlir/package-index/ has included Windows
packages since around https://github.com/llvm/torch-mlir/pull/1521.
Here's an example release:
https://github.com/llvm/torch-mlir/releases/tag/snapshot-20240118.1087
```
torch-2.3.0.dev20240109+cpu-cp311-cp311-linux_x86_64.whl
torch-2.3.0.dev20240109+cpu-cp311-cp311-win_amd64.whl
torch-2.3.0.dev20240109+cpu-cp38-cp38-linux_x86_64.whl
torch-2.3.0.dev20240109-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
torch-2.3.0.dev20240109-cp311-none-macosx_10_9_x86_64.whl
torch_mlir-20240118.1087-cp311-cp311-linux_aarch64.whl
torch_mlir-20240118.1087-cp311-cp311-linux_x86_64.whl
torch_mlir-20240118.1087-cp311-cp311-macosx_11_0_universal2.whl
torch_mlir-20240118.1087-cp311-cp311-win_amd64.whl
torch_mlir-20240118.1087-cp38-cp38-linux_x86_64.whl
```
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.
Addresses leftover comment from earlier PRs: #1254 , #1265 to remove `torch_dispatch` frontend. In addition, moves the main arch diagram into `docs/` directory for consistency.
Prior to this patch, the top-level README did not include the line for
running the Python regression tests in `//python/test`. This patch
fixes the problem by adding a line to run the `check-torch-mlir-python`
target.
- update diagram to use the name "Eager Mode" instead of
`torch.dispatch`, which wasn't a very accurate name
- rename `resnet_inference.ipynb` to
`torchscript_resnet_inference.ipynb` - this is in preparation to LTC
and Eager Mode versions
- remove mention of TorchFX - turns out that all TorchFX modules are
actually scriptable modules, so there is literally "zero code" vs
using the TorchScript path
- remove LazyTensorCore example, and instead point at the current
in-development `torch_mlir_ltc_backend` branch.
Note: there were actually some pretty useful utilities built out in the
examples directory, but they now live inside the Eager Mode
`python/torch_mlir/eager_mode/ir_building.py` (and need to be rolled
into a proper home with the upcoming rewrite of our top-level
`torch_mlir.compile` API).
This is intended to explore support for non-structured ops that can't
be modeled by Linalg dialect. `tm_tensor.scan` and `tm_tensor.scatter`
are added as the first such ops. The dialect should aim to be
upstreamed in the future.
* Also adds a requirements.txt and updates docs to reference it versus stringy pip install.
* Adds doc with instructions on creating a wheel.
Fixes#370
* Picks up Python configure changes (was pinned to a bad intermediate commit).
* Uses the new mlir_configure_python_dev_packages() to ensure CMake python is found consistently.
* Fixes the JIT importer to build as a MODULE vs SHARED (needed for linking to Python as a module, per config changes).
* Adds some notes to the README to help folks build a smaller set focused just on this project.
Implement the `lazytensor` python package for converting
lazy computations captured by the Lazy Tensor Core into MLIR.
This PR also fixes a few things with `torchfx` and its example
miheer vaidya
Dmitry Babokin
Scott Todd
lzw
Ramiro Leal-Cavazos
Boian Petkantchin
wkwkes
nvda
Yi Wang
Sean Silva
Daniel Ellis
Sambhav Jain
Henry Tu
powderluv
Ashay Rane
Clément Fournier
Maksim Levental
Ahmed S. Taei
Prashant Kumar
Yi Zhang
Stella Laurenzo
Phoenix Meadowlark