Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[AutoBump] Merge with f08bfc4f (Oct 04) (66) #431

Merged
merged 2 commits into from
Dec 18, 2024

Conversation

mgehre-amd
Copy link
Collaborator

No description provided.

zjgarvey and others added 2 commits October 3, 2024 20:11
Addresses ~200 onnx model compile failures in
<https://github.com/nod-ai/SHARK-TestSuite> related to
<iree-org/iree#18631>.

This change simplifies the result of the generated broadcast op
substantially, but reduces the case coverage slightly.

The case which will become unsupported: 
- trying to actually broadcast a dynamic dim that is secretly 1. 

When does this case appear in practical scenarios?
- for a model where onnx shape inference cannot figure out that a dim
should be 1.

Why do I think we should not support this case for now?
1. For all models with dynamic dim expand ops, the previous path
uniformly generates uglier linalg IR (making it harder for IREE to fuse
properly with other ops).
2. For models failing shape inference castastrophically enough to fail
to see a dim is statically 1, we can try to apply constant folding in
the onnx model before importing.

Leaving this as a draft PR, since it may be more appropriate to fix the
compilation failure in IREE rather than torch-mlir.

### Example of broadcast required in previous path:

```mlir
    %300 = linalg.generic {indexing_maps = [#map11], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} outs(%299 : tensor<?x12x?x?xi1>) {
    ^bb0(%out: i1):
      %306 = linalg.index 0 : index
      %307 = linalg.index 3 : index
      %308 = arith.index_cast %285 : i64 to index
      %309 = arith.cmpi eq, %308, %c1 : index
      %310 = arith.select %309, %c0, %306 : index
      %311 = arith.index_cast %286 : i64 to index
      %312 = arith.cmpi eq, %311, %c1 : index
      %313 = arith.select %312, %c0, %307 : index
      %extracted_79 = tensor.extract %reshape_78[%310, %c0, %c0, %313] : tensor<?x1x1x?xi1>
      linalg.yield %extracted_79 : i1
    } -> tensor<?x12x?x?xi1>
```

### Example of broadcast with simplified shape list:

```mlir
    %409 = linalg.generic {indexing_maps = [#map15, #map11], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%reshape_135 : tensor<?x1x1x?xi1>) outs(%408 : tensor<?x12x?x?xi1>) {
    ^bb0(%in: i1, %out: i1):
      linalg.yield %in : i1
    } -> tensor<?x12x?x?xi1>
```
@mgehre-amd mgehre-amd requested a review from jorickert December 18, 2024 13:30
Base automatically changed from bump_to_9ab0db57 to feature/backport_ea1_ops December 18, 2024 13:32
@mgehre-amd mgehre-amd merged commit 026b82f into feature/backport_ea1_ops Dec 18, 2024
4 checks passed
@mgehre-amd mgehre-amd deleted the bump_to_f08bfc4f branch December 18, 2024 20:42
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

3 participants