Equivalence summary

Every comparison below is a byte compare of the outputs: every predicted value as raw doubles (or at 17 significant digits in printed output). "same" means byte-identical between the official LightGBM 4.7.0 wheel and the TAKT library: the free package's library and the full version's library, both as delivered.

Equivalence matrix (tools/eqmatrix.py): 4,497 prediction cases, 1,060,559,499 values

• Models: 23, trained on public data: LightGBM's own examples (regression, binary, 5-class multiclass and one-vs-all, lambdarank and rank_xendcg), scikit-learn's bundled sets (DART on breast cancer, random forest on digits, linear trees on diabetes), California housing (1,024 leaves; poisson, tweedie, gamma, huber, quantile, cross-entropy and cross-entropy-lambda objectives; 30% NaN with and without use_missing; 30% zeros with zero_as_missing), OpenML adult and credit-g (categorical) and kick (categorical- and missing-heavy).
• Inputs: each model's test split plus adversarial rows: NaN, ±inf, −0.0, subnormals, values at ±1e-35 and their float neighbours, values exactly on and next to every split threshold, out-of-range, negative and non-integer categories.
• Input forms: numpy float64 and float32 in C and Fortran order, CSR float64 and float32, CSC float64, pandas, pyarrow; the row-pointer C API (LGBM_BoosterPredictForMats) separately.
• Prediction types: normal, raw score, leaf index, SHAP contributions.
• Iteration ranges: all trees, the first k, a window [s, s+k).
• Threads: 1 and 4.
• Also: prediction early stopping; repeated calls in the pattern of permutation importance and partial dependence; models changed in memory between calls (set_leaf_output, rollback_one_iter, update, refit, model_from_string).

LightGBM's own test suites

• C++ test suite (LightGBM 4.7.0, testlightgbm): 34 of 34 pass with the unmodified source, the full version and the free package.
• Python test suite (tests/python_package_test): 1,689 passed, 19 skipped with the official wheel and with both TAKT wheels; the same 4 graphviz plotting tests fail with all three on a machine without the dot binary.

Concurrency and long runs

• tools/concurrency.py: 5 shared boosters, 246 prediction tasks (the original data, permuted features, what-if scenarios, float32; normal, raw, leaf), 8–16 Python threads, shuffled order in every round, compared with the official wheel's sequential results; also with TAKT_LGBM_CACHE_MB=16. Full version and free package: 0 mismatches.
• Soak runs with TAKT_LGBM_VERIFY=1 (every accelerated call recomputed with LightGBM's original code and compared): a mix of analyses, batch prediction, iteration ranges, mutable boosters and new shapes on several threads. Full version: four runs, 10.5 hours in total on both machines, 2,427,751 calls, 0 differences. Free package: 30 minutes, 125,380 calls, 0 differences, 0 errors.

ABI

The TAKT libraries export exactly the same 6,469 dynamic symbols as the official lib_lightgbm.so, need the same shared libraries (libstdc++, libgomp, libm, libc and the others the official one needs) and no symbol versions newer than the official one (glibc 2.27 at most). Built with the official compiler (gcc 12.2.1, gcc-toolset-12) in the manylinux_2_28 image with the official build configuration.

Note on bit-identity across systems

Some of LightGBM's outputs (probabilities, for example) pass through the system maths library, and numpy's own results can depend on the CPU it dispatches to. The TAKT library reproduces the official library's output on the same system; compare the two on the same machine.