Qualcomm AI Engine Direct - add pass for extra padding then maxpool2d #16534
+185
−11
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Qualcomm AI Engine Direct - add pass for extra padding then maxpool2d
Summary:
The padding value used in max_pool2d operations differs between PyTorch and QNN implementations. PyTorch uses negative infinity, while QNN uses zero. To ensure consistent max_pool2d output across both frameworks, we handle this by padding tensor with constant in advance then doing max_pool2d without constant padding. Note that for the quantization flow, we set quant_min as the padding value. If, at runtime, there is a value smaller than quant_min, it could result in an accuracy drop.
Test plans:
python backends/qualcomm/tests/test_qnn_delegate.py TestQNNQuantizedOperator.test_qnn_backend_max_pool2d -b build-android -H ${HOST} -s ${SN} -m ${CHIPID}
python backends/qualcomm/tests/test_qnn_delegate.py TestQNNFloatingPointOperator.test_qnn_backend_max_pool2d -b build-android -H ${HOST} -s ${SN} -m ${CHIPID}