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Chenjie LuoandClaude Opus 5.5
e5b63320ab
### What does this PR do? Type of change: new feature (not yet user-reachable) **First of two PRs adding IQ1_M** at 1.75 bits per weight, just above IQ1_S. This one lands the **PyTorch codec**: encoder and decoder. It is deliberately **not registered**, so no quantizer dispatches to it and the `ggml` package does not export it. #2595 adds the CUDA encoder, registers the format and adds its recipe. With both, ModelOpt supports all five GGML IQ formats at one and two bits. On the mixed-precision checkpoint #2511 measured (`unsloth/Qwen3.8-27B-GGUF`), IQ1_M covers **25 tensors and 1.2 B parameters**. With all five formats we can read 89.0% of that file; the rest is k-quants and F32. ### What's distinctive about it **IQ1_M is the most irregular layout of the five.** There is no leading block scale field at all. The FP16 super-block scale is reassembled from the top nibble of each of four scale words: ```c scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); ``` It is also finer grained than IQ1_S: a local scale per **two** groups rather than four, and a delta shift chosen **per group** rather than per sub-block. That is where its extra 0.1875 bits go. ### Shared with IQ1_S rather than copied IQ1_M searches exactly as IQ1_S does: the same 2048-entry grid, the same ±1/8 delta, every (shift, local scale) choice for every 8-value vector. It differs only in how it selects among those choices afterwards. So the search moves out of IQ1_S's encoder into `_search_shifted_grid`, which both call, and `iq1_m.py` keeps only its selection and packing. **IQ1_S's encoded bytes are unchanged**, checked by hashing its output before and after on a fixed input. ### A scale-anchor correction IQ1_M anchors its scale differently from IQ1_S: the ratio **rises with a block's peak-to-RMS** rather than being flat, and clamps higher. It uses `clamp(0.58 + 0.035 * peak_to_rms, 0.65, 0.95)` against IQ1_S's flat `0.61`. Measured over 15 Qwen3.8-27B MLP weights: | | flat 0.61 | correct anchor | | |---|---|---|---| | relative reconstruction MSE | 0.17372 | **0.17291** | **−0.47%** | It is consistent on every tensor, with no outliers. The anchor changes quality without touching layout, so neither round-trip nor conformance tests would catch it drifting. `test_scale_anchor_follows_peak_to_rms` now pins it, for all five formats; see Testing. ### Family parity Two surface asymmetries close here, so the five are uniform. `IQ1_S` now exposes `_predict_iq1_s_scales` like the other four, instead of computing its anchor inline. `IQ1_M` exposes `iq1_m_grid`, aliasing the IQ1_S table it shares. ### Testing **The decoder is validated against llama.cpp's own output, not just round-tripped:** ``` IQ1_M: 25 tensors, 4,730,880 blocks → 0 mismatched, max|diff| 0.0 ``` This mattered: **my first IQ1_M decoder had a real bug.** A `repeat_interleave` on the wrong axis produced `[h0,h1,h0,h1]` where llama.cpp needs `[h0,h0,h1,h1]`. A round-trip against our own encoder still passed, because the encoder made the matching mistake. Only comparison against bytes we did not produce caught it. Blocks from that checkpoint ship as conformance vectors, and mutation testing confirms they catch a mis-set scale nibble. The decoder unpacks every field in one vectorized pass, since fake quant decodes on every forward: 5.2 ms for a 5632×2048 weight (IQ1_S: 3.3). - `tests/unit/torch/quantization/test_ggml_backend.py`, `test_iq_formats.py`, `tests/unit/torch/export/test_convert_hf_config.py`, `tests/unit/recipe/test_presets.py`: **153 passed**, 15 of them IQ1_M codec cases, including the llama.cpp conformance check - `test_scale_anchor_follows_peak_to_rms` pins every format's scale anchor. It predicts scales for blocks whose peak-to-RMS is exactly 1, 4, 8 and 16, reaching both clamps and two points on each slope, and compares them against anchors written out in the test. Mutations each fail exactly the mutated format: reverting IQ1_M to IQ1_S's flat 0.61, moving either IQ1_M clamp, changing its taper by 0.001, moving an IQ2_S or IQ2_XS clamp, and changing IQ1_S's anchor to 0.62. - `tests/gpu/torch/quantization/test_iq_formats_cuda.py`, `test_iq1_s_cuda.py`, `test_iq2_xs_cuda.py`: **42 passed**. IQ1_S's CUDA-vs-PyTorch parity still holds after its encoder refactor. - IQ1_S and IQ1_M PyTorch encoder output and IQ1_M decoder output hash identically to the pre-split version of this PR. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ IQ1_M adds no codebook; it reuses the IQ1_S table already carried in `codebooks.py`. The new conformance vectors come from `unsloth/Qwen3.8-27B-GGUF`, which is Apache-2.0 like its base model `Qwen/Qwen3.8-27B`; the vectors' docstring now records that. No new dependencies. - Did you write any new necessary tests?: ✅ - Did you update Changelog?: N/A. Nothing is user-reachable yet; #2595 carries the entry. - Did you get Claude approval on this PR?: ❌ Not yet run. ### Additional Information Merge order: #2511 (IQ2_XXS) → #2525 (format registry) → #2512 (IQ2_S codec) → #2565 (IQ2_S CUDA encoder and registration), all merged → **this** → #2595 (IQ1_M CUDA encoder and registration). 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added IQ1_M quantization and dequantization for compact, GGML-compatible blocks of 256 values. * Added access to the IQ1_M grid and configurable chunk sizes for processing data. * **Bug Fixes** * Improved IQ1_S scale prediction and grid-search organization while preserving its encoding behavior. * **Tests** * Added IQ1_M conformance data and included the format in shared IQ-format test coverage. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>