Set PyTorch and TorchVision version to nightly release 2023-02-27.
This commit also adds the lowering for aten.add and aten.Float.Scalar op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
- Update llvm tag to 5e111eb275eee3bec1123b4b85606328017e5ee5
- mhlo now points to a99159c45ee5c497f8dce01eff807a6d57629b61
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This patch replaces all MHLO operations with their StableHLO
counterparts and adds a validation pass to ensure that no MHLO operations
remain before translating all Stablehlo operations to the MHLO dialect
for further lowering to the Linalg dialect.
This patch also updates all lit tests so that they refer to the
`convert-torch-to-stablehlo` pass and so that they check for StableHLO
operations.
This reverts commit eaab9be207, since it
is causing the post-merge CI tests to fail, causing subsequent PRs to be
blocked. Specifically, the tests
`ElementwiseAtenLogicalAndOpPromoteBroadcastModule_basic` and
`ElementwiseAtenLogicalXorOpPromoteBroadcastModule_basic` fail because
the oracle does not match the computed result. This patch reverts the
commit to make the post-merge builds green again.
-- The dtype of the result of `aten.embedding` should match that of
the `weight` operand's (operand[0]) instead of hardcoding to f32.
-- This commit aims to provide a fix for the same.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
pytorch/pytorch@140a3139 reverted a change from yesterday, causing the
RollPyTorch action to break. This patch reverts the corresponding
change in the torch-mlir LTC code.
This patch also re-enables tests that were previously marked as XFAIL.
As [@ezyang suggested](https://github.com/pytorch/pytorch/issues/90276#issuecomment-1339791275),
use `torch._dynamo.optimizations.training.aot_autograd` instead of raw
`make_fx`. This is more future proof and gives us the backward pass and
functionalization. We don't currently get functionalization because of
https://github.com/pytorch/pytorch/issues/90759
This also incidentally fixes the source location handling, which makes
`lockstep_basic.py` give an accurate source location!
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.
Yuanqiang Liu
Vivek Khandelwal
Gaurav Shukla
Ziheng Jiang
Chi_Liu
Tanyo Kwok
Jiahao Li
Ashay Rane
Gleb Kazantaev
Srirammaswamy
powderluv
Shivam Gupta
Chi_Liu
pranavmulticore
Abhishek Varma
Prashant Kumar
Sean Silva