Commit Graph

6 Commits (90e3d69c251ea17604416968f538d1092f51f287)

Author SHA1 Message Date
Sambhav Jain 0b2f9c89a2
Bring back `dynamic_shapes` constraints in fx importer API (#3026)
https://github.com/llvm/torch-mlir/pull/2992 dropped `constraints` from
the fx importer API,
[breaking](https://github.com/cruise-automation/mlir-tcp/actions/runs/8284385380/job/22669774071)
downstream AOT compile tests in `mlir-tcp` that use it. This knob has
been soft-deprecated for a while now, replaced by `dynamic_shapes` - a
more ergonomic interface. This PR brings back dynamic_shapes constraints
in the new supported form. Also added a python lit test with dynamic
shaped annotations.
2024-03-14 10:26:34 -07:00
Vivek Khandelwal 6e84752c39
build: manually update PyTorch version (#2992)
Set PyTorch and TorchVision version to nightly release 2024-03-07.
This commit also removes the deprecated constraints API:
342e7929b8

Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2024-03-07 21:42:38 +05:30
Sambhav Jain 3cbe6c98ec
Expose `func_name` to the main fx import API (#2949)
As titled.
2024-02-26 10:08:14 -08:00
Stella Laurenzo 5253282c55
[fx] Support mutation in ExportedProgram. (#2916)
As of https://github.com/pytorch/pytorch/pull/118969, `ExportedProgram`
has the long awaited fixes to correctly categorize various things
relating to parameters, buffers, mutated inputs and constants.

With this additional modeling, we are finally able to implement
(safely/soundly) the mutable semantics that were attempted on the
TorchScript path. The difference is that on that path, we had to
conservatively treat everything as mutable and run some dodgy heuristics
(which have been the cause of many bugs relating to
"MaximizeValueSemantics") to try to get back to an immutable state.

The new model supports mutability at the graph edges, allowing both user
inputs and buffers to be mutated (there is some more support than that,
but that is all I fully tracked through to implementation).

Therefore, when we receive programs like this, we now can selectively
enable mutation at the edges. This happens to be the mutability model
that IREE supports, which I expect to be a primary beneficiary. However,
there is nothing stopping anyone else from handling the `!torch.tensor`
types and the existing copy/overwrite ops that will be selectively
added.

Since this relies on API changes that will not release until 2.3, I'm
being a bit cautious about not refactoring existing facilities.
2024-02-16 09:46:30 -08:00
saienduri 8e2e5eeae9
add support for decomposition (#2879)
This commit adds decomposition support into the core aten operators
before importing the module from torch.

Also, this commit deals with the lifted tensor constants in
torch.export.export(). We don't want to add unnecessary placeholder
nodes in the graph (extra args in the block module), and should treat
them like the constants that they are. The unnecessary clone is also
removed for max efficiency.
2024-02-14 21:00:52 -08:00
saienduri bfcf93ea21
Rename torch_mlir.compile APIs and introduce FX based analogs (#2842)
Link to related RFC:
https://discourse.llvm.org/t/rfc-rename-torch-mlir-compile-apis-and-introduce-fx-based-analogs/76646
This commit updates the documentation, tests, CMake files, and API for
the proposed changes in the RFC. There is a new torch_mlir/fx.py for
user level APIs related to importing modules and a corresponding test
for this path can be found at test/python/fx_importer/basic_test.py.

---------

Co-authored-by: MaheshRavishankar <mravisha@amd.com>
2024-02-06 19:07:59 -08:00