Various improvements on sparsity metadata:
(1) define single data structure for all sparsity related metadata
(2) handle batched dense dimensions, as well as dense subtensor
dimensions
(3) refine sparsity propagation for deeper networks
This PR introduces a sparse_jit wrapper that can run simple models with
sparse tensor inputs end-to-end. The implementation shows all required
components on modifying sparse tensor types with a 1:N relation on the
call sites. Two tests shows that the JIT runs end-to-end while computing
the correct results.
More details to follow (generalizing to COO and different ranks, as well
as support for *output* sparse tensors), but the general concepts are
all here now.
**_Update: Thanks to Rob, bump to proper LLVM/MLIR hash is done!_**
_**NOTE that all parameter passing changes are nicely done "downstream"
in MLIR, so very little changes are required in torch-mlir code
proper**_
---------
Co-authored-by: Franz Haniel <77495327+frafranz@users.noreply.github.com>
Co-authored-by: Franz Haniel <franz.haniel@amd.com>
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>
Adds an escape hatch from creating a DenseResourceElementsAttr for
single value tensors into DenseElementsAttr.
For 0d or 1element, splats are better as DenseElementsAttr. Don't use
DenseResourceElementsAttr for it
Note that we are waiting for actual FX traced graph support for sparse
tensors. For details see
https://github.com/pytorch/pytorch/issues/117188
Until then, however, we provide this clever importer that builds the FX
traced graph for for the dense case and then puts a sparse annotation
back on the parameters.
With import test.
Changes made during upstreaming:
* Removed comments attributing some copied code back to torch-mlir
(since it is now repatriated).
* Re-organized imports.
* Inlined RefMapping/RefTracker and TypeSubclassMap from an external
utility module.
* Added FxImporter class comments.
* Updated stack trace extraction to be fail safe.
* Added an entry-point for `import_frozen_exported_program` which uses
the shiny new upstream `torch.export.export()` API (versus the
lower-level/older API that Turbine is presently using). This
necessitated a small FX rewrite to line external state management up
with current conventions.
* Adapted one of Turbine's importer tests to go with this initial
submission. Turbine unfortunately has a lot of more-integration-ey
tests, and I would like to extract those as more of unit tests of the
importer features and upstream them that way vs trying to copy directly.
For now, one overall test with the initial submission gets us moving.
I acknowledge that there are some code quality things that could be
improved in this submission: this was authored over the course of many
months (and often via some trial and error). I would like to keep it
relatively converged with the downstream for the next few steps while
getting the test suite upstreamed. And then it will be easier to take a
hygienic pass through the code.
Including co-authors for contributors in the git log of the original
repository.
Co-authored-by: Ean Garvey <87458719+monorimet@users.noreply.github.com>
Co-authored-by: Avinash Sharma <aviator1994@gmail.com>
Co-authored-by: Arham Khan <arhammkhan@gmail.com>
Co-authored-by: brucekimrokcmu <kwangkyk@alumni.cmu.edu>
Co-authored-by: saienduri <77521230+saienduri@users.noreply.github.com>
Aart Bik
saienduri
Daniel Garvey
Stella Laurenzo