Arm backend: Fix torch.matmul() failures for 2D tensor inputs #14624
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- ConvertMmToBmmPass converts an MM node to BMM nodes, turns input
and output tensors from rank-2 to rank-3 via unsqueeze/squeeze,
and inserts q-dq before and after BMM node when necessary.
- After ConvertMmToBmmPass:
x -> q -> dq -> unsqueeze -> q_2 -> dq_2 ->
\
bmm -> q_4 -> dq_4
/
y -> q_1 -> dq_1 -> unsqueeze -> q_3 -> dq_3 ->
- Therefore, if the original matmul was 2D, the bmm already has
DQ nodes on its inputs and Q node on its output.
If AnnotateDecomposedMatmulPass (pytorch#10654) is still applied in
this case, it produces illegal sequences such as:
x -> q -> unsqueeze -> q_2 (invalid)
- Fix by checking whether the BMM is already surrounded by DQ nodes
on its inputs and Q nodes on its output.
Change-Id: I9949d59b0b4a96fa34a88b0734014567ea6f24cc
Signed-off-by: Yufeng Shi <yufeng.shi@arm.com>
Co-authored-by: Oscar Andersson <oscar.andersson@arm.com>
YufengShi-dudu
added
partner: arm
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14624
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 2 Unrelated FailuresAs of commit bb2fbb9 with merge base dcc3978 ( NEW FAILURE - The following job has failed:
BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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If possible, I suggest we get this fix into the 1.0 release branch |
digantdesai
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Oct 6, 2025
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Thanks @YufengShi-dudu for the bug fix. Please mark the PR to be cherry-picked in 1.0. Thanks again.
|
@pytorchbot cherry-pick --onto release/1.0 -c regression |
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- ConvertMmToBmmPass converts an MM node to BMM nodes, turns input and
output tensors from rank-2 to rank-3 via unsqueeze/squeeze, and inserts
q-dq before and after BMM node when necessary.
- After ConvertMmToBmmPass:
```
x -> q -> dq -> unsqueeze -> q_2 -> dq_2 ->
\
bmm -> q_4 -> dq_4
/
y -> q_1 -> dq_1 -> unsqueeze -> q_3 -> dq_3 ->
```
- Therefore, if the original matmul was 2D, the bmm already has DQ nodes
on its inputs and Q node on its output. If AnnotateDecomposedMatmulPass
(#10654) is still applied in this case, it produces illegal sequences
such as: x -> q -> unsqueeze -> q_2 (invalid)
- Fix by checking whether the BMM is already surrounded by DQ nodes on
its inputs and Q nodes on its output.
Change-Id: I9949d59b0b4a96fa34a88b0734014567ea6f24cc
cc @digantdesai @freddan80 @per @zingo @oscarandersson8218
Signed-off-by: Yufeng Shi <yufeng.shi@arm.com>
Co-authored-by: Oscar Andersson <oscar.andersson@arm.com>
(cherry picked from commit 9a7fb42)
Cherry picking #14624The cherry pick PR is at #14845 and it is recommended to link a regression cherry pick PR with an issue. The following tracker issues are updated: Details for Dev Infra teamRaised by workflow job |
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ConvertMmToBmmPass converts an MM node to BMM nodes, turns input and output tensors from rank-2 to rank-3 via unsqueeze/squeeze, and inserts q-dq before and after BMM node when necessary.
After ConvertMmToBmmPass:
Therefore, if the original matmul was 2D, the bmm already has DQ nodes on its inputs and Q node on its output. If AnnotateDecomposedMatmulPass (Arm backend: Add support for single input matmul #10654) is still applied in this case, it produces illegal sequences such as: x -> q -> unsqueeze -> q_2 (invalid)
Fix by checking whether the BMM is already surrounded by DQ nodes on its inputs and Q nodes on its output.
Change-Id: I9949d59b0b4a96fa34a88b0734014567ea6f24cc
cc @digantdesai @freddan80 @per @zingo @oscarandersson8218