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
This leaves no real code outside torch-mlir.
This also renames the "npcomp backend contract" to "linalg on tensors
backend contract" as the name of the abstraction layer that RefBackend
(IREE too) accepts.
Also contains the following changes:
- Remove derefineOp canonicalizer because it's not safe.
- Support for optional tensor and list tensors in reduceOpVariant. This
only works for some special detected and easy to handle cases. For list,
it covers the case list is got from a `ListConstruct`. For optional, it
covers the case optional is constructed from a `DerefineOp`.
- Remove the `inferReturnTypes` for `FromBuiltinTensorOp` because it's
not safe to deduce types from the input. For example, a built-in tensor
of i8 could be converted to si8 or ui8. It's better to let the user
specify the return type explicitly.
A few remain in examples/docs that will be naturally be updated in due
time.
This regresses the list support and the general direction of more widely
supported control flow, lists/dicts/globals that we were going for with
the TorchScript path. The idea is that we are deferring that work to
make torch-mlir a very clean standalone thing. We will reboot it,
probably using some of the tools of iree_pydm to make it simpler, and in
a more natural place (such as an iree-torch repo that depends on IREE and
torch-mlir to build a working PyTorch frontend solution for IREE -- it
was really weird that npcomp depended on IREE).
`tools/torchscript_e2e_test.sh` is all green.
This needs a few passes I put into torch-mlir/lib/RefBackend (not to be
confused with `npcomp/lib/RefBackend`, which will soon be deleted).
For the sake of review, since this brings together a lot of things, I
split this into its own commit. I temporarily commented out some "list"
stuff that we are going to remove as part of the torch-mlir refocus.
Implements a python package for taking a `torch.fx.GraphModule`
and turning it into MLIR in the `torch` dialect that can then
be further compiled by `npcomp`. This is a WIP, so the coverage
of PyTorch operations is very small.
Fixes https://github.com/llvm/mlir-npcomp/issues/311
The key change is that TorchPlugin is folded into
`torch_mlir.dialects.torch.importer.jit_ir` (it imports the PyTorch
JIT's IR, so that's a good, scoped name for it).
The CMake option `-DTORCH_MLIR_ENABLE_JIT_IR_IMPORTER=OFF` disables it,
which allows building without a PyTorch native dependency.
After this change, there are now just two subdirectories in the
`python_packages` directory in our combined build:
- `npcomp_core` with all the npcomp stuff
- `torch_mlir` with all the `torch-mlir` stuff.
The combined `torch_mlir` build will be packaged for use by `pip`.
There isn't anything super useful for wider use in `npcomp_core` so for
now we aren't going to package that one.
Sean Silva
Yi Zhang
powderluv
Ramiro Leal-Cavazos
Anush Elangovan
George Petterson
Stella Laurenzo