Fix missing quantizedBiasType setting for 16x8 requantization#3597
Merged
Conversation
Collaborator
Author
|
cc: @rameshkunasi |
suleshahid
approved these changes
Jun 16, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
When a model is compiled without explicit bias tensors,
requantize_flatbuffer_utilspreviously skipped the bias upgrade logic entirely. As a result, thequantizedBiasTypehint in the operator's options was not updated. This caused TFLM to fall back to a 32-bit accumulator for these layers, which overflows.This change explicitly injects
op.builtinOptions.quantizedBiasType= int64 directly into the operator options for supported operators, fixing precision loss for bias-less layers running 16x8 kernels.BUG=n/a