Enable transpose_a support for LoRA Correction by Shehrozkashif · Pull Request #3864 · openvinotoolkit/nncf

Shehrozkashif · 2026-01-29T13:19:34Z

Summary of Changes

Updated process_stats to handle transpose_a for LoRA Correction.
LoRA algorithm now reads transpose_a from the weight node and processes activations accordingly.
Added tests:
- test_process_stats_with_transpose_a_changes_layout to verify activation processing.
- test_lora_transpose_a_fix to ensure LoRA compression works correctly with transpose_a=False.
Ensures LoRA Correction works correctly without errors when transpose_a is False.

Details of Changes

process_stats now supports a transpose_a flag that adjusts activation layouts when processing statistics.
LoraCorrectionAlgorithm.calculate_adapters reads the transpose_a attribute from the weight node and passes it to calculate_low_rank_matrices.
Low-rank adapter calculation (calculate_low_rank_matrices) now transposes residuals when transpose_a=True.
Tests added in tests/openvino/native/quantization/test_weights_compression.py to verify correctness.

Reason for Changes

Previously, LoRA Correction did not correctly handle layers with transpose_a=True.
These changes ensure that activations are processed with the correct layout and low-rank adapters are computed correctly, preventing errors during weight compression.

Related Tickets

#3230 — Support transposed input for data-aware weight compression methods
Draft PR reference: Support transposed input for data-aware Weights Compression #3296 (LoRA Correction updates for transpose_a)

Tests

test_process_stats_with_transpose_a_changes_layout confirms that activation layout changes when transpose_a=True.
test_lora_transpose_a_fix ensures LoRA Correction executes without errors for supported transpose configurations.
All pre-commit and linter checks passed.

- Updated to handle for LoRA Correction. - LoRA algorithm now reads from weight node and processes activations accordingly. - Added tests: - for activation processing. - ensures LoRA compression works with transpose_a=False. - Ensures LoRA Correction works correctly without errors when transpose_a is False.

Shehrozkashif · 2026-01-29T13:21:43Z

@daniil-lyakhov, I hope I'm in the right direction?

daniil-lyakhov

Hello @Shehrozkashif,
thank you for the PR! In general the direction is correct, please adress a couple comments from me

src/nncf/quantization/algorithms/weight_compression/activation_stats.py

src/nncf/quantization/algorithms/weight_compression/lora_correction.py

daniil-lyakhov · 2026-01-29T14:16:01Z

tests/openvino/native/quantization/test_weights_compression.py

Can we extend existing tests instead? https://github.com/openvinotoolkit/nncf/blob/develop/tests/openvino/native/quantization/test_weights_compression.py#L1613-L1617

Yes, that makes sense. I can update the existing tests to cover the act_ch_axis/transpose handling instead of adding separate ones, so the verification of LoRA Correction with transposed inputs is integrated with the current test suite.

Please don't forget to update the tests

Shehrozkashif · 2026-01-30T10:09:35Z

@daniil-lyakhov Passed act_ch_axis from statistics to process_stats in LoRA Correction and added a conditional transpose of X so that residual multiplication works correctly for transposed inputs.

Shehrozkashif · 2026-02-04T18:38:01Z

@daniil-lyakhov Hi, I’ve updated the test decorator to skip the configurations where transpose_a=True or transpose_b=True, since LoRA correction does not support transposed activations yet. This change keeps the test function intact and avoids runtime failures while preserving all other test cases.

Shehrozkashif · 2026-02-12T10:40:14Z

Hi @daniil-lyakhov, quick reminder on this PR. I’ve updated the tests and addressed previous feedback. Please let me know if anything else is needed. Thanks!

daniil-lyakhov · 2026-02-12T11:16:05Z

tests/openvino/native/quantization/test_weights_compression.py

No new tests were enabled, could you please enable them and check everyting is working?

@daniil-lyakhov Thank you for the feedback. I have now fully enabled the tests and refactored the implementation to match the pattern used in PR #3794.

Updates:

Enabled Tests: I have unskipped the transpose_a=True test cases in test_lora_adapters_in_the_graph.

Refactored Implementation:

I reverted the changes to statistics.py (no act_ch_axis stored in WCTensorStatistic).

act_ch_axis is now calculated on-the-fly in openvino_backend.py using get_activation_channel_axis and passed directly to lora_correction_algo.calculate_adapters.

lora_correction.py was updated to accept and use this argument.

Test Overrides: I overrode test_compression_skipped_with_transposed_activations for this specific test class to exclude LoRA Correction from the expected failures, as it now supports transposed activations (while keeping the check for GPTQ/Scale Estimation).

I have verified precisely that tests/openvino/native/quantization/test_weights_compression.py::test_lora_adapters_in_the_graph passes for transpose_a=True. Please review.

…ed cases

Copilot

Pull request overview

This PR enables support for transpose_a=True in the LoRA Correction algorithm for weight compression. The LoRA Correction algorithm previously did not handle MatMul operations with transposed activation inputs correctly. This PR updates the algorithm to read the transpose_a attribute from weight nodes and process activations accordingly.

Changes:

Removed the check that blocked LoRA Correction for nodes with transpose_a=True
Updated process_stats function to accept an act_ch_axis parameter for proper handling of different activation layouts
Modified LoRA adapter calculation to account for activation channel axis and conditionally transpose activations
Updated adapter insertion to use the correct transpose_a value when creating adapter MatMul operations
Added test coverage for transpose_a=True scenarios
Added test to verify other algorithms (scale_estimation, GPTQ) still correctly reject transpose_a=True

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
tests/openvino/native/quantization/test_weights_compression.py	Added test parameters for transpose_a=True cases and new test for unsupported algorithms with transposed activations
src/nncf/quantization/algorithms/weight_compression/openvino_backend.py	Updated insert_adapters to read and use transpose_a flag, and calculate activation channel axis for LoRA
src/nncf/quantization/algorithms/weight_compression/lora_correction.py	Modified calculate_adapters and calculate_low_rank_matrices signatures to accept act_ch_axis and added conditional transpose logic
src/nncf/quantization/algorithms/weight_compression/algorithm.py	Removed transpose_a check for LoRA and updated variable naming for clarity
src/nncf/common/tensor_statistics/statistics.py	Minor refactoring to inline variable usage

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Copilot · 2026-02-16T09:57:57Z

src/nncf/quantization/algorithms/weight_compression/lora_correction.py

+        # Conditionally transpose X so samples are rows and channels are columns
+        if act_ch_axis != 0:  # if channel is not already the first axis
+            X = fns.transpose(X, axes=(1, 0))  # [SS, H]


The conditional transpose logic appears incorrect. The process_stats function always returns X with shape [HiddenDim, SampleSize] (as documented in its docstring line 29), regardless of the act_ch_axis value. The act_ch_axis parameter is only used within process_stats for sampling logic, not for determining the output layout. Therefore, this conditional check if act_ch_axis != 0 doesn't achieve the intended purpose, and the transpose should either always be applied or never be applied. The expected shape after this line should be [SS, H] based on the comment, which means the transpose should always happen since process_stats returns [H, SS].

Suggested change

# Conditionally transpose X so samples are rows and channels are columns

if act_ch_axis != 0: # if channel is not already the first axis

X = fns.transpose(X, axes=(1, 0)) # [SS, H]

# Transpose X so samples are rows and channels are columns.

# process_stats returns X with shape [H, SS], so we convert to [SS, H].

X = fns.transpose(X, axes=(1, 0)) # [SS, H]

Copilot · 2026-02-16T09:57:57Z

tests/openvino/native/quantization/test_weights_compression.py

+    ),
 )
-def test_lora_adapters_in_the_graph(params, transpose_b):
+def test_lora_adapters_in_the_graph(params, transpose_a, transpose_b):


The PR description mentions two new tests (test_process_stats_with_transpose_a_changes_layout and test_lora_transpose_a_fix) that are not present in the diff. These tests are important to verify that the transpose_a support is working correctly. Either the tests were not included in this PR, or the PR description needs to be updated to reflect the actual tests that were added.

Copilot · 2026-02-16T09:57:58Z

src/nncf/common/tensor_statistics/statistics.py

    def _get_serialized_data(self) -> dict[str, Tensor]:
-        backend = self.mean_values[0].backend
-        device = self.mean_values[0].device
        return {
            self.MEAN_STAT: fns.stack(self.mean_values),
            self.SHAPE_STAT: fns.tensor(
                self.shape_values,
-                backend=backend,
+                backend=self.mean_values[0].backend,
                dtype=TensorDataType.int32,
-                device=device,
+                device=self.mean_values[0].device,
            ),


These refactoring changes to inline variable usage are unrelated to the PR's stated goal of enabling transpose_a support for LoRA Correction. While the refactoring is a reasonable style improvement, it should ideally be in a separate commit or PR to keep changes focused and easier to review. Including unrelated refactoring makes it harder to understand the core changes and could complicate any future bisecting or reverting.

Shehrozkashif requested a review from a team as a code owner January 29, 2026 13:19

github-actions bot added the NNCF OpenVINO Pull requests that updates NNCF OpenVINO label Jan 29, 2026

daniil-lyakhov reviewed Jan 29, 2026

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Merge branch 'openvinotoolkit:develop' into support-transpose

86ee4d8

Shehrozkashif force-pushed the support-transpose branch from 44ea48b to 86ee4d8 Compare January 30, 2026 10:08

github-actions bot removed the NNCF OpenVINO Pull requests that updates NNCF OpenVINO label Jan 30, 2026

Merge branch 'openvinotoolkit:develop' into support-transpose

c6632b4

github-actions bot added the NNCF OpenVINO Pull requests that updates NNCF OpenVINO label Feb 4, 2026

daniil-lyakhov reviewed Feb 12, 2026

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Extend LoRA adapter tests with transpose scenarios and skip unsupport…

f6aa62b

…ed cases

Shehrozkashif force-pushed the support-transpose branch from a92ea05 to f6aa62b Compare February 14, 2026 18:34

Shehrozkashif requested a review from daniil-lyakhov February 14, 2026 18:40

daniil-lyakhov requested a review from Copilot February 16, 2026 09:50

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Shehrozkashif commented Jan 29, 2026

Summary of Changes

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Reason for Changes

Related Tickets

Tests

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