Add EKFAC tests and fix a couple of bugs#125
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LouisYRYJ merged 3 commits intoEleutherAI:ekfac-finalfrom Jan 16, 2026
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Add EKFAC tests and fix a couple of bugs#125LouisYRYJ merged 3 commits intoEleutherAI:ekfac-finalfrom
LouisYRYJ merged 3 commits intoEleutherAI:ekfac-finalfrom
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https://github.com/EleutherAI/bergson/blob/main/.github/workflows/build.yml#L7-L9 means the tests aren't run for this PR since it's targeting the ekfac-final branch currently. |
The backward_hook was using g.reshape(-1, O) which includes padding positions in the covariance computation. This causes incorrect results when batches have different sequence lengths. Before this commit, the added test failed with: > FAILED tests/ekfac_tests/test_batch_size_invariance.py::test_trace_batch_invariant[seq_lengths1-20] - AssertionError: Scalars are not close! > > Expected 1.231401894309304 but got 0.8983965093439276. > Absolute difference: 0.33300538496537635 (up to 1e-4 allowed) > Relative difference: 0.27042786478102654 (up to 0.01 allowed)
The condition `if not hessian_cfg.use_dataset_labels:` was inverted, causing the empirical Fisher (with dataset labels) to use sampled labels and vice versa. Add test_fim_accuracy.py which verifies that KFAC approximates the Fisher Information Matrix within tolerance for both empirical FIM (dataset labels) and true FIM (sampled labels).
This is still missing FSDP support and test_apply_ekfac.py from EleutherAI#68 Co-Authored-By: LouisYRYJ <louis.yousif@yahoo.de>
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* ekfac implementation done (untested) * remove unnecessary squeeze * add tkfac * fix claude issues * shampoo * minor fix * Add EKFAC tests and fix a couple of bugs (#125) * Fix mask bug and add batch size invariance test wih toy model The backward_hook was using g.reshape(-1, O) which includes padding positions in the covariance computation. This causes incorrect results when batches have different sequence lengths. Before this commit, the added test failed with: > FAILED tests/ekfac_tests/test_batch_size_invariance.py::test_trace_batch_invariant[seq_lengths1-20] - AssertionError: Scalars are not close! > > Expected 1.231401894309304 but got 0.8983965093439276. > Absolute difference: 0.33300538496537635 (up to 1e-4 allowed) > Relative difference: 0.27042786478102654 (up to 0.01 allowed) * Fix use_dataset_labels condition and add FIM accuracy test The condition `if not hessian_cfg.use_dataset_labels:` was inverted, causing the empirical Fisher (with dataset labels) to use sampled labels and vice versa. Add test_fim_accuracy.py which verifies that KFAC approximates the Fisher Information Matrix within tolerance for both empirical FIM (dataset labels) and true FIM (sampled labels). * Add ground truth ekfac tests This is still missing FSDP support and test_apply_ekfac.py from #68 Co-Authored-By: LouisYRYJ <louis.yousif@yahoo.de> * ekfac_tests/test_batch_size_invariance.py: Fix error thresholds when running on CPU * Cleanup EKFAC tests - Replace set_all_seeds by existing setup_reproducibility - Reuse approximate_hessians instead of doing something equivalent manually. * Add --token_batch_size option to EKFAC tests * Add --n_samples option to EKFAC tests Allow configuring the number of samples from pile-10k dataset via pytest command line option instead of hardcoding 100. The dataset directory is now named dynamically (e.g., pile_100_examples). * hessians: Fix distributed support and test it Restore the calls to dist.barrier that existed in #13, without them the process would hang when running with world_size > 1. For testing, we add _allocate_batches_world to compute the batches for the ground truth. The tests don't pass due to numerical errors, this is handled in the next commit by changing our comparison logic. * ekfac_tests: Use appropriate metrics for each comparison - Eigenvectors: Check |cosine_similarity| ≈ 1 per column, which naturally handles sign ambiguity (eigenvectors are only defined up to sign) - Covariances: Check relative Frobenius norm since values should match exactly - Eigenvalue corrections: Align signs based on eigenvector orientation, then check relative error (λ[i,j] transforms as sign_G[i] * sign_A[j]) - Also reenable CPU tests which pass after this change. * ekfac_tests: Relax thresholds for distributed runs With world_size > 1, floating-point reduction order differs between ground truth (single process) and distributed run, causing larger numerical differences in some layers. For eigenvectors, use average |cos_sim| instead of minimum - this tolerates occasional outlier eigenvectors while maintaining a stricter threshold (1e-3 vs 0.1 that would be needed for min). For eigenvalue corrections, use atol=0.2 when world_size > 1. * adjust test + normalize shampoo and tkfac * minor fixes, correct tensor handling in shampoo and tkfac, introduce apply_hessian (WIP) --------- Co-authored-by: Guillaume Martres <smarter@ubuntu.com>
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This includes the tests that were originally in #13 and the tests added in #68
This is still missing FSDP support and test_apply_ekfac.py from #68
Co-Authored-By: LouisYRYJ louis.yousif@yahoo.de