Commit Graph

5 Commits (63fb1e5aad7b0c346527d1814234ecf5d98a505e)

Author SHA1 Message Date
Prateek Gupta 7256c9e395 [TORCH][MLIR] Fix the return types of `aten.native_layer_norm`.
This commit fixes the 2nd and 3rd return types of the `aten.native_layer_norm`.
Previously the mean and rSTD were returned with reduction dims removed.
This commit fixes this and keeps the reduction dims of the results.

Signed-Off-By: Prateek Gupta <prateek@nord-labs.com>
2022-03-17 12:08:32 +05:30
Sean Silva a5fe0cf063 Introduce new shape library design.
See the documentation in `docs/shape_lib.md` and
`docs/adding_a_shape_function.md` for an overview of the system.

This completely overhauls how we represent shape functions. In
particular, RefineTypes does not infer shapes anymore (only dtypes).
Shape functions are now written in (TorchScript'able) Python.

Recommended review order:

1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`.
1. Code and tests for ReifyShapeCalculations, DropShapeCalculations.
1. Code and tests for SimplifyShapeCalculations.
1. shape_lib_gen.py
1. Code and tests for new RefineTypes pass.
1. Random folders/canonicalizers in TorchOps.cpp and associated test in
   `canonicalize.mlir`.
1. New ReadOnly trait inferred from the registry.
1. Any miscellaneous remaining stuff.

Example `-print-ir-after-all` for ElementwiseUnaryModule:
[IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212).

Example `-print-ir-after-all` for ElementwiseBinaryModule:
[IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-15 12:41:58 -07:00
Gaurav Shukla 056cd2078d Revert "[LINALG] Decompose `aten.batch_norm` into `aten.native_batch_norm`"
This reverts commit 442ff4605c.
2022-02-25 15:46:55 +05:30
Gaurav Shukla 442ff4605c [LINALG] Decompose `aten.batch_norm` into `aten.native_batch_norm`
- This commit decomposes the `aten.batch_norm` op into the
  `aten.native_batch_norm` op, instead of lowering it to the
  `linalg.generic` op.
- It also adds run-time asserts in the `aten.native_batch_norm` lowering
  to make sure that the shape of the weight, bias, running_mean, and
  running_var must match the num of features.
- Since the `aten.native_batch_norm` op is not supported at TOSA backend,
  all the modules that are dependent on the `aten.native_batch_norm` op
  will fail and therefore they should be removed from the TOSA `passing`
  set.
- It also moves `checkNotNone` to utility.

Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-16 23:41:38 +05:30
Gaurav Shukla 2fefe68ffd [TORCH][MLIR] Add E2E support for `aten.native_batch_norm` op
- This commit adds support for `aten.native_batch_norm` operation.
- The current implementation only supports inference mode of
  `aten.native_batch_norm` op.

Signed-Off-By: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-08 02:54:03 +05:30