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test: I2_S kernel tests without a model + mlx_lm correctness gate (#3) - #6
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…el needed)
utils/test_gemm_kernel.sh is a timing benchmark on random data and never
checks a result (it also needs Linux-only -fopenmp/clock_gettime/libggml.so).
Add utils/i2s_kernel_test/: a standalone test that compares
L0 dequantize_row_i2_s / quantize_i2_s against the documented 128-weight /
32-byte I2_S layout,
L1 ggml_gemv_i2_i8_s / ggml_gemm_i2_i8_s against sum(code*y),
L2 ggml_mul_mat(I2_S, F32) end to end (int8 activation quantization,
act-sum correction, weight scale) at 1 and 4 threads,
for K/M/N including the BitNet b1.58 2B4T shapes (K=2560, 6912).
run.sh builds it against a static ggml build tree; --mad additionally
compiles src/ggml-bitnet-mad.cpp (not part of the CMake build) so the
kernels patched by microsoft#586/microsoft#633 can be tested.
Refs #3
The first draft used rel-L2 < 0.03 against float W@x. With an activation outlier (amax/rms ~ 20) the int8 absmax quantization alone gives ~4-8 %, so the fixed bound flagged a bit-exact kernel (L2 emulation error ~1e-7). Use 4 * (amax/rms) / (127*sqrt(12)), derived from the quantization step, and keep the primary check (exact emulation, 1e-4) unchanged. Refs #3
…el-level run tolerances.json fixes, with rationale, how close the engine must be to the mlx_lm bitnet reference: teacher-forced top-1 agreement / KL / cosine, free-running greedy prefix, and a perplexity ratio on a fixed text. compare.py implements them; test_compare.py checks on synthetic data that documented int8-activation noise passes and that scrambled weights, constant output, NaN/Inf/1e27 garbage, flattened distributions and perplexity blow-ups fail. No GGUF or reference weights existed on the host when this was written. Refs #3
…estrator, synthetic model, README run_gate.py exits 3 (BLOCKED, with the list of missing files) when the official GGUF or the reference weights are not present locally; it never downloads. engine_logits dumps per-position logits through libllama on the CPU and records llama_print_system_info(). make_synthetic_model.py writes a tiny random BitNet in mlx and GGUF(I2_S) form from the same ternary weights so the plumbing can be tested without the real checkpoint. Refs #3
…capped Documented post-hoc change. v1 used a fixed 0.25-logit near-tie margin. The first synthetic end-to-end run (llama.cpp#6 kernel + relu2 vs mlx_lm) passed every teacher-forced and perplexity check (top-1 0.972, mean KL 0.0028, min cosine 0.9993, |ln ppl ratio| 0.0197 vs bound 0.02) but failed one of five free-running greedy checks: a flip at reference margin 0.446 while the measured margin-error sigma is ~0.15. v2 uses max(0.25, 4*sigma) with sigma measured in the same run, and fails outright if sigma > 10 % of the reference logit std, so an engine that is wrong by O(1) can never excuse itself (unit-tested). When the cap is exceeded the band falls back to the 0.25 floor so top-1 agreement stays meaningful. Everything else (KL, cosine, perplexity bounds) is unchanged. v1 results are kept in the evidence directory. Refs #3
The official ggml-model-i2_s.gguf has no tokenizer.ggml.pre key, so llama.cpp falls back to the 'default' pre-tokenizer and splits e.g. "(n):\n" differently from the Llama-3 tokenizer the model was trained with (engine ids 7,77,1680,198 vs reference 1471,997 for 'def fibonacci(n):\n'). With tokenizer.ggml.pre=llama-bpe the engine reproduces the reference ids exactly (also checked against a Llama-3.2 GGUF that carries the key). The tokenizer-parity check therefore fails on the unmodified official GGUF; the gate can pass --override-pre llama-bpe to the engine to judge the numerics separately. Refs #3
…tize tests synthetic-f32.gguf carries the same ternary*scale values as float so that 'llama-quantize --token-embedding-type f16 f32.gguf out.gguf I2_S 1' (what setup_env.py does) can be compared byte-for-byte with the canonical I2_S packing in synthetic-i2s.gguf.
eejd
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November 1, 2026 20:19
This was referenced Nov 1, 2026
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Draft. Part of #3 (correctness gate "committed before any fix"). Contains no fix; it needs a static ggml build tree, e.g. the one from the
fix/macos-arm64-buildPR.Kernel tests that need no model –
utils/i2s_kernel_test/utils/test_gemm_kernel.shis a timing benchmark on random data that never checks a result, and it cannot run on macOS (needslibggml.soin./build,-fopenmp; verbatim:Error: Cannot find libggml.so at .../build/3rdparty/llama.cpp/ggml/src/libggml.so). The new test compares against an independent scalar reference of the documented layout (128 weights ↔ 32 bytes, bytegpholds weightsgp,32+gp,64+gp,96+gpat bits 6,4,2,0; code 0/1/2 = −1/0/+1; kernel contractΣ code·y): L0dequantize_row_i2_s/quantize_i2_s, L1ggml_gemv_i2_i8_s/ggml_gemm_i2_i8_s, L2ggml_mul_mat(I2_S,F32)at 1 and 4 threads, for K up to 6912 (2B4T shapes 2560/6912).Results on Apple M6 (58 tests linked, 29 per
src/ggml-bitnet-mad.cppvariant):main(+ build fixes)ggml-bitnet-mad.cpp:445:129: error: function definition is not allowed here,1089:2: error: expected '}'); with its broken scalar hunk dropped: 0 / 29quantize_i2_semitting a different layout thandequantize_row_i2_sreads (llama-quantize … I2_Soutput is ~2 % byte-identical to the canonical packing even with llama.cpp#2).Gate –
utils/i2s_gate/run_gate.pycompares the engine (libllama CPU,-ngl 0, per-position logits viaengine_logits) with mlx_lm'sbitnetmodel (float32) on fixed prompts: teacher-forced top-1/KL/cosine, free-running greedy prefix, perplexity on a fixed text, tokenizer parity. Exit 3 +BLOCKEDwhen a model file is missing; it never downloads.tolerances.jsonwas committed before any model-level run; v2 (documented in the file and the commit message) changed one rule after the first synthetic run: the near-tie band ismax(0.25, 4σ)with σ measured in the run and capped at 10 % of the logit std.test_compare.py(24 unit tests, synthetic data) shows scrambled/constant/NaN/garbage/flattened logits fail and ~0.1-logit noise passes.Run on the official
ggml-model-i2_s.ggufvsmlx-community/bitnet-b1.58-2B-4T(both were present in/hive-cache/codeagent/models/hub): see thefix/i2s-arm-kernel-relu2PR. Summary: unpatched →FAIL(NaN logits); kernel fix only →FAIL(ppl 43.99 vs 7.15, SiLU); kernel fix + relu² →PASS(ppl 7.1785 vs 7.1532, mean KL 0.0026). Finding: the official GGUF has notokenizer.ggml.pre, so tokenizer parity fails on prompts with(n):\nunless--override-pre llama-bpeis used.