[OMNIML-5899] Export Q8_0 checkpoints and add recipes - #2517
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 8 remain after this review. 📝 WalkthroughWalkthroughThis change adds Q8_0 weight-only quantization with 32-value GGML blocks. Unified HF and Megatron export support GGML packing. The change also adds a PTQ recipe and updates related documentation and tests. ChangesQ8_0 quantization and export
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant QuantizedWeight
participant export_quantized_weight
participant GGML_FORMAT_REGISTRY
QuantizedWeight->>export_quantized_weight: provide formatted weight
export_quantized_weight->>GGML_FORMAT_REGISTRY: select registered quantizer
GGML_FORMAT_REGISTRY-->>export_quantized_weight: provide quantizer
export_quantized_weight->>QuantizedWeight: pack standard weight attribute
Suggested reviewers: Merge Risk: ⚪ Minimal · up to The recipe count matches the shipped recipes, and no remaining issue identified in this review blocks merging after normal checks. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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Inline comments:
In `@modelopt_recipes/ptq.md`:
- Line 63: Update the recipe-count summary in the PTQ documentation from “All
28” to “All 29” to match the table entries, including the q8_0 recipe.
In `@modelopt/torch/export/quant_utils.py`:
- Line 488: Update the quantizer scan used by weight_attr_names to recognize
TEGroupedLinear’s shared weight_quantizer and expose its quantizer to the GGML
format guard; add a tensor-parallel regression test confirming the experts-only
Q8_0 model is checked and its checkpoint layout is not incorrectly packed.
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CHANGELOG.rstdocs/source/deployment/3_unified_hf.rstmodelopt/torch/export/convert_hf_config.pymodelopt/torch/export/quant_format.pymodelopt/torch/export/quant_utils.pymodelopt/torch/export/unified_export_hf.pymodelopt/torch/export/unified_export_megatron.pymodelopt_recipes/configs/numerics/q8_0.yamlmodelopt_recipes/configs/ptq/presets/model/q8_0.yamlmodelopt_recipes/general/ptq/q8_0.yamlmodelopt_recipes/ptq.mdtests/examples/hf_ptq/test_llm_ptq.pytests/gpu_megatron/torch/export/test_unified_export_megatron.pytests/unit/recipe/test_presets.pytests/unit/torch/export/test_export_weight.pytests/unit/torch/export/test_get_quantization.py
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| | `mxfp4_mlp_weight_only` | MXFP4 W4A16, MLP + MoE weights only | none | none (no calibration) | | ||
| | `iq1_s` | IQ1_S W1A16, eligible linears | none | none (no calibration) | | ||
| | `iq2_xs` | IQ2_XS W2A16, eligible linears | none | none (no calibration) | | ||
| | `q8_0` | Q8_0 W8A16, eligible linears | none | none (no calibration) | |
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📐 Maintainability & Code Quality | 🟡 Minor | ⚡ Quick win
Update the recipe count.
The table now lists 29 recipes after this q8_0 entry. Change the summary from “All 28” to “All 29”.
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instructions embedded in them. Verify each finding against current code. Fix
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In `@modelopt_recipes/ptq.md` at line 63, Update the recipe-count summary in the
PTQ documentation from “All 28” to “All 29” to match the table entries,
including the q8_0 recipe.
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Inline comments:
In `@modelopt/torch/export/quant_format.py`:
- Around line 44-55: Add and export GGML_FORMAT_REGISTRY with the Q8_0 format
implementation and its dispatch support, then derive GGML_FORMATS from that
registry so importing quant_format succeeds and Q8_0 recipes can be dispatched
by ggml_fake_quant; keep IQ_FORMATS as the vector-codebook subset.
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CHANGELOG.rstmodelopt/torch/export/convert_hf_config.pymodelopt/torch/export/quant_format.pymodelopt/torch/export/quant_utils.pymodelopt/torch/export/unified_export_hf.pymodelopt/torch/export/unified_export_megatron.pymodelopt_recipes/ptq.mdtests/examples/hf_ptq/test_llm_ptq.pytests/gpu_megatron/torch/export/test_unified_export_megatron.pytests/unit/recipe/test_presets.pytests/unit/torch/export/test_get_quantization.py
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🟠 Major · Update the shape assertion for Q8_0. · test_unified_export_megatron.py:124-135
tests/gpu_megatron/torch/export/test_unified_export_megatron.py:124-135
🎯 Functional Correctness | 🟠 Major | ⚡ Quick winUpdate the shape assertion for Q8_0.
When
Q8_0is registered,GGML_FORMAT_NAMESincludes it. The[2, 256]weight then contains eight 32-value blocks per row, so the exporter returns shape(2, 8, payload_bytes), not(2, 1, payload_bytes). The assertion fails before the payload and dequantization checks, blocking this Megatron GPU test. Useblock_sizeto derive the block dimension.Suggested fix
- assert packed.shape == (2, 1, payload_bytes) + assert packed.shape == (2, 256 // block_size, payload_bytes)🤖 Prompt for AI Agents
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Review comments at
@tests/gpu_megatron/torch/export/test_unified_export_megatron.py:
- Around line 124-135: Update the packed shape assertion to derive the block
dimension from the weight width and block_size, so it handles Q8_0’s multiple
blocks per row while preserving the existing batch and payload dimensions.
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| A TEGroupedLinear stores its per-expert quantizers in one ``GroupedQuantizer`` rather than | ||
| beside its ``weight0..N`` parameters, so inspect that container directly. |
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[IMPORTANT Export] The docstring now reads as if the TEGroupedLinear gap is closed, but only half of it is — and the half that still leaks is the one that shapes the checkpoint.
The text replaced here was explicit:
Known gap, shared with
get_quantization_format:weight_attr_namesyields nothing for a TEGroupedLinear […] Closing it belongs inweight_attr_names, where it affects every format, rather than in this helper.
This PR closes it in this helper, and drops the note. weight_attr_names is unchanged, so get_quantization_format still can't see a TEGroupedLinear:
getattr(module, "weight", None)isNone(the exporter'shas_weight/delattrdance atunified_export_megatron.py:1403/1577confirms the attribute only exists transiently), so the first branch is skipped.- The per-parameter loop yields
weight0..N, andrepresentative_weight_quantizer(module, "weight0")looks forweight0_weight_quantizer/weight0_weight_quantizers, neither of which exists. - Recursing into children reaches the
GroupedQuantizerand itsTensorQuantizers, none of which has a weight parameter.
Net effect for an experts-only Q8_0 Megatron model:
| call | before | after |
|---|---|---|
_any_rank_uses_iq_quantization() |
False — no TP/PP guard |
True — guard fires ✅ |
_get_quantization_format(self.model) (save_pretrained:320) |
NONE |
NONE ❌ |
So at TP=PP=1 the export now proceeds, packs every expert into uint8 GGML blocks and records q8_0 per layer via _record_layer_quant_config, while the elif quantization_format in GGML_FORMATS arm at unified_export_megatron.py:351 never fires and the top-level quantization stays None. A consumer that keys off the top-level quant_algo sees an unquantized checkpoint whose weights are packed blocks.
Two ways out, either is fine:
- Move the fix to
weight_attr_namesas the old comment recommended, soget_quantization_formatandget_weight_block_sizesee the module too — that fixes it for every format at once, not just GGML. - Keep the local fix, but restore a one-line note that
get_quantization_formatstill reportsNONEfor such a module, so the next reader doesn't assume the model-level format is now correct.
Worth a regression test either way: an experts-only GGML model asserted against get_quantization_format, not just uses_iq_quantization.
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Fixed in 296bc86: weight_attr_names now exposes a TEGroupedLinear GroupedQuantizer through its logical weight name, so both uses_iq_quantization and get_quantization_format detect Q8_0. The regression now asserts both results.
| # packer are read from GGML_FORMAT_REGISTRY directly. IQ_FORMATS remains the vector-codebook | ||
| # subset for callers that specifically need it. |
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[SUGGESTION] IQ_FORMATS no longer has any caller in modelopt/ after this PR — every production use (convert_hf_config, quant_utils, unified_export_hf, unified_export_megatron) moved to GGML_FORMATS, and the only remaining consumer is tests/gpu_megatron/torch/export/test_unified_export_megatron.py, which uses it to keep the 256-block-only tests off Q8_0.
Keeping the constant is reasonable, but the justification is now inaccurate, which is the kind of comment that makes a future reader hunt for callers that aren't there. Suggest naming the actual reason:
| # packer are read from GGML_FORMAT_REGISTRY directly. IQ_FORMATS remains the vector-codebook | |
| # subset for callers that specifically need it. | |
| # subset, currently used only by tests that assume a 256-value block. |
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Claude review — Q8_0 export + recipes
Full-scope review (16 changed files, 248/-121). Reviewed all of modelopt/torch/export/ (5 files), all three new recipe YAMLs, the docs/changelog prose, and all five touched test files. Nothing deliberately skipped.
Findings: CRITICAL: 0 · IMPORTANT: 1 · SUGGESTION: 1 (plus one prior CodeRabbit finding re-verified as still live — see below).
Most impactful
1. [IMPORTANT] The TEGroupedLinear gap is half-closed, and the docstring says it is closed — quant_utils.py:461
uses_iq_quantization now inspects the GroupedQuantizer container directly, which is a genuine improvement: _any_rank_uses_iq_quantization() returns True for an experts-only GGML model, so the TP=PP=1 guard finally fires instead of silently packing TP shards as whole weights.
But the replaced comment explicitly said the gap is shared with get_quantization_format and that the fix belongs in weight_attr_names. weight_attr_names is unchanged, so get_quantization_format still returns NONE for a TEGroupedLinear (no weight attribute; weight0..N have no matching weight0_weight_quantizer). Consequence at TP=PP=1: experts get packed into uint8 GGML blocks and layer_config_dict records q8_0, while _get_quantization_format(self.model) at unified_export_megatron.py:320 returns NONE, the elif quantization_format in GGML_FORMATS arm at line 351 never fires, and the top-level quantization stays None. A loader keying off the top-level quant_algo sees an unquantized checkpoint whose weights are packed blocks.
Not a regression from this PR — the same checkpoint came out before it — but the deleted note was the only record that it is still open. Either move the fix into weight_attr_names (fixes every format at once) or restore a one-line note.
2. [Re-verified, blocking] test_unified_export_megatron.py:124 will fail on Q8_0. CodeRabbit raised this as an outside-diff comment on 9676be5 and it is still live: the test was reparametrized to GGML_FORMAT_NAMES but line 124 still asserts packed.shape == (2, 1, payload_bytes). A [2, 256] weight at block_size=32 packs to (2, 8, 34), so the assertion fails before the payload and dequantization checks ever run. Not re-posted inline to avoid duplication; packed.shape == (2, 256 // block_size, payload_bytes) matches the fix already applied in test_export_weight.py:126. Worth noting the PR description says Megatron GPU coverage was not run locally, which is exactly why this slipped.
3. [SUGGESTION] quant_format.py:48 comment — IQ_FORMATS has no modelopt/ caller left after this PR; the surviving consumer is the Megatron test file's 256-block-only cases.
Verified correct
- Block arithmetic is consistent everywhere it appears: 34 bytes = 2 (FP16 scale) + 32 int8;
34 * 8 / 32 = 8.5bpw matcheseffective_bits: 8.5inconfigs/numerics/q8_0.yaml, the docs paragraph,ptq.md, and thetest_presets.pycontract test againstQ8_0_EFFECTIVE_BITS. GGML_FORMATSsubstitution is complete — all 14IQ_FORMATScall sites across the four export modules were converted; no stragglers remain inmodelopt/.FUSION_FREE_FORMATS |= GGML_FORMATSis correct for Q8_0 — per-32-block FP16 scale, noinput_amaxorpre_quant_scaleto unify across q/k/v or gate/up groups.- Block-size mismatch is guarded on both paths —
_quant_algo_to_group_configrejectsgroup_size not in (None, 32)andprocess_layer_quant_configrejectsblock_size_value != 32, so a recipe pairingnum_bits: q8_0withblock_sizes: {-1: 256}fails at export rather than shipping a mislabeled checkpoint. GroupedQuantizerimport and iteration are sound — it is intensor_quantizer.__all__and reachable from..quantization.nn; it subclassesnn.ModuleListsofor quantizer in grouped_quantizerworks, andgetattr(quantizer, "num_bits", None)correctly tolerates aSequentialQuantizermember.- The three new recipe YAMLs are byte-for-byte structural clones of the
iq2_xsset, differing only in name, block size, effective bits, and prose.ptq.md's "All 30" matchesls modelopt_recipes/general/ptq/*.yaml. - Megatron split-then-pack paths are unaffected by the smaller block — gated MLP, QKV, and MoE splits all cut along the output dim while packing runs along the last dim, so leaving those tests on
IQ_FORMAT_NAMESis the right call. - Weight-only enforcement carries over — the enabled-input-quantizer and
pre_quant_scalerejections inget_quantization_formatand theweight_name != "weight"guard in_export_quantized_weightnow cover Q8_0 by construction.
Merge prerequisite
GGML_FORMAT_REGISTRY and Q8_0_* do not exist on main yet, so import modelopt.torch.export.quant_format fails on this branch standalone. That is expected for slice 3/3 of the declared #2515 → #2516 → #2517 stack (the dependency points backwards, so the series stays acyclic), but CI here cannot go green until #2516 lands. Worth confirming the [x/N] title prefix convention from CLAUDE.md before this goes up for final review.
Risk
Low-to-moderate. The production diff is a disciplined mechanical widening from IQ-specific to GGML-wide naming with one real behavior change (the GroupedQuantizer scan), and that change strictly tightens a guard. The Q8_0 arithmetic checks out everywhere it is written down. The live test failure is a one-line fix; the IMPORTANT finding is a documentation regression over a pre-existing export gap rather than a new defect, so it can be resolved with a comment if closing the gap is out of scope for this slice.
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Changes requested: the Megatron tests contain a wrong Q8_0 shape assertion and stale exception matches that will fail with the updated exporter.
Needs action:
- Fix the packed-shape assertion and both fused-MoE exception matches in
tests/gpu_megatron/torch/export/test_unified_export_megatron.py; see inline comments. Run this suite against the integrated #2515 → #2516 → #2517 stack. - Explain in the PR body why Q8_0 extends the existing GGML packed-export path rather than the W8A16 integer-weight representation; retain the existing registry and recipe-loader reuse.
No action needed:
- Existing test parameterization changes are justified by Q8_0’s different block geometry; no coverage was removed.
- New recipe headers match
LICENSE_HEADER. - The branch currently lacks
GGML_FORMAT_REGISTRY; the documented dependency on #2516 accounts for this, so merge order remains essential.
| @@ -119,15 +121,15 @@ def test_megatron_name_remapping_exports_iq_payload(qformat): | |||
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| packed_key = "model.layers.0.mlp.down_proj.weight" | |||
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Bot comment.
The newly added Q8_0 parameter produces eight 32-value blocks per 256-element row, so its packed shape is (2, 8, 34), not (2, 1, 34). Change this assertion to (2, linear.weight.shape[-1] // block_size, payload_bytes) so the new case can pass while preserving the IQ shape checks.
| raise NotImplementedError( | ||
| "Fused-MoE IQ export requires a deployment loader that supports " | ||
| "[num_experts, out_features, in_features // 256, payload_bytes]" | ||
| "Fused-MoE GGML export requires a deployment loader that supports " |
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Bot comment.
This changes the exception text to Fused-MoE GGML export requires, but both test_megatron_packed_experts_reject_iq_without_deployment_loader and test_megatron_gpt_oss_packed_experts_reject_iq_without_deployment_loader still match Fused-MoE IQ export requires. Update those matches alongside this change; all existing IQ parameters otherwise fail despite correctly rejecting unsupported export.
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Fixed in a6b37ac: both fused-MoE rejection tests now match "Fused-MoE GGML export requires", consistent with the generalized exporter. CUDA/Megatron execution still requires CI; it was not run on the local Mac.
|
Addressed the latest review findings in 296bc86: the Megatron packed-shape assertion now derives the number of blocks from the format block size, and grouped expert quantizers are visible to model-level format discovery. The combined Q8_0 stack passed 108 focused tests; changed files also passed Ruff and formatting checks. |
### What does this PR do? Type of change: new feature Adds the Q8_0 CUDA packing layer for the three-PR Q8_0 series: - packs 32-value blocks into the 34-byte GGML-compatible payload; - exposes `q8_0_pack` through the shared GGML extension; - validates device, dtype, row alignment, and CUDA launch bounds; - tests byte layout, accepted dtypes, non-finite handling, float64 narrowing, FP16 scale boundaries, reconstruction error, and invalid inputs. This PR contains only the kernel and extension boundary. The codec/backend and export/recipe layers remain in the later PRs. ### Usage ```python from modelopt.torch.quantization.extensions import get_cuda_ext_ggml extension = get_cuda_ext_ggml(raise_if_failed=True) packed = extension.q8_0_pack(weight) ``` ### Testing - Combined #2515 -> #2516 -> #2517 stack: 108 focused CPU codec, backend, export, and recipe tests passed. - Ruff, formatting, and whitespace checks passed for the changed Python test. - The shared-extension Q8_0 and existing IQ tests require CUDA CI; the latest run is pending on the current PR head. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: yes - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: yes; no dependency was added and no implementation code was copied - Did you write any new necessary tests?: yes - Did you update `CHANGELOG.rst`?: N/A; the user-facing entry is in #2517 - Did you get Claude approval on this PR?: pending ### Provenance The CUDA encoder was independently written for ModelOpt. It implements the packed-format contract and scalar quantization formula documented by the pinned llama.cpp definitions: - [Q8_0 packed structure](https://github.com/ggml-org/llama.cpp/blob/9b05354ec6fb58b4e665e9a39ebc40285c015638/ggml/src/ggml-common.h) - [Q8_0 scalar reference formula](https://github.com/ggml-org/llama.cpp/blob/9b05354ec6fb58b4e665e9a39ebc40285c015638/ggml/src/ggml-quants.c) No llama.cpp implementation code is incorporated into this CUDA source. Human code-owner confirmation of this provenance and attribution is requested before merge. ### Related PRs Merge order: 1. **Kernel - this PR** 2. [#2516 - Q8_0 quantization codec and backend](#2516) 3. [#2517 - Q8_0 checkpoint export and recipes](#2517) All three PRs target `main`. --------- Signed-off-by: Hung-Yueh Chiang <hungyuehc@nvidia.com>
### What does this PR do? Type of change: new feature Adds Q8_0 encoding, decoding, and the GGML fake-quant backend. Each 32-weight block stores one FP16 scale and 32 signed int8 values in 34 bytes (8.5 bits per weight). - Generalizes dispatch to `GGML_FORMAT_REGISTRY` / `GGMLFormat` while preserving `IQFormat` as a type alias. `IQ_FORMAT_REGISTRY` is an IQ-only compatibility dictionary sharing the same format records, not a mutation-propagating view. - Retains IQ1_S, IQ1_M, IQ2_XXS, IQ2_XS, and IQ2_S registrations alongside Q8_0. - Uses the merged `q8_0_pack` CUDA extension when available and the PyTorch encoder otherwise. - Matches canonical reciprocal-then-multiply rounding, using the unrounded FP32 scale to choose int8 values and FP16 only for serialized scale storage. - Adds exact-byte rounding regressions and checks that the compatibility registry shares the same format records. Removes a redundant zero-block buffer copy. Checkpoint export, recipes, documentation, and the user-facing Q8_0 changelog remain in #2517. ### Base and dependencies The target remains `main`. Current head `71e3183a7b7eac7e5e3888ee45a206c572028af9` includes main at `67a68f8fd4902a7b67c78a2f00f40562b081fe72`, including #2595 (IQ1_M registration), #2615 (shared IQ CUDA encoders), and #2604 (packed-weight cache reuse and CUDA IQ decoding). All five IQ formats, Q8_0, and the compatibility aliases are retained. The comparison against `main` contains only the intended 12 Q8_0 files, with 207 added source lines excluding tests and docs. The inherited IQ refactor and export changes are not part of this PR's diff. #2515 has already merged and supplies the Q8_0 kernel. This PR retains the small reciprocal-rounding correction to that kernel needed for exact reference parity. ### Usage ```python import torch from modelopt.torch.quantization.ggml import quantize_q8_0, dequantize_q8_0 weight = torch.randn(2, 64, dtype=torch.bfloat16) packed, shape = quantize_q8_0(weight) restored = dequantize_q8_0(packed, shape) ``` The final weight dimension must be divisible by 32. Backend dispatch also accepts `num_bits="q8_0"`, `backend="ggml"`, and `block_sizes={-1: 32}`. ### Testing - Current head `71e3183a7`: **183 focused CPU tests passed**, covering Q8_0, all six registered backends, IQ formats, export metadata, and recipe presets. - Current-head applicable pre-commit checks passed: Ruff, formatting, mypy, CUDA formatting, license headers, security checks, merge markers, line endings, and file size. - Previous head `2beb70ffa`: **all six Q8_0 CUDA cases passed**, including exact-byte reciprocal rounding and unrounded-scale regressions; [GPU job log](https://github.com/NVIDIA/Model-Optimizer/actions/runs/36890153545/job/110468900810). That GPU lane completed with 1,663 passed and 67 skipped. The overall workflow was cancelled after a different lane was cancelled; it is not an all-green workflow result. - Current head `71e3183a7`: the GPU CI mirror now points to the exact PR head. [GPU CI](https://github.com/NVIDIA/Model-Optimizer/actions/runs/37061248628), [example CI](https://github.com/NVIDIA/Model-Optimizer/actions/runs/37061248555), and [regression CI](https://github.com/NVIDIA/Model-Optimizer/actions/runs/37061248637) are running. Previous-head results do not validate this new head. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: yes; existing IQ names and registrations are retained. - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`?: yes; no new dependency. - Did you write any new necessary tests?: yes. - Did you update `CHANGELOG.rst`?: N/A here; #2517 carries the single Q8_0 feature entry. - Did you get Claude approval on this PR?: pending current-head review. ### Related PRs 1. [#2515 — Q8_0 CUDA packing kernel](#2515) — merged. 2. [#2595 — IQ1_M registration](#2595) — merged. 3. **#2516 — Q8_0 quantization codec and backend** — this PR. 4. [#2517 — Q8_0 checkpoint export and recipes](#2517) — follows this PR. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added Q8_0 quantization support, including weight packing, unpacking, and fake quantization. * Added Q8_0 to the available GGML formats, alongside existing IQ formats, through a shared quantization interface. * Added support for formats with different block sizes when validating weights. * Q8_0 uses CUDA acceleration when available and falls back to PyTorch when needed. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Hung-Yueh Chiang <hungyuehc@nvidia.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Co-authored-by: Chenjie Luo <chenjiel@nvidia.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Hung-Yueh Chiang <hungyuehc@nvidia.com>
Signed-off-by: Hung-Yueh Chiang <hungyuehc@nvidia.com>
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
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+ Coverage 69.18% 78.39% +9.21%
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Lines 69615 69617 +2
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+ Hits 48160 54578 +6418
+ Misses 21455 15039 -6416
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Nudge: the prior code and design findings are resolved, but the requested integrated Megatron/CUDA validation remains unconfirmed.
Needs action:
- 💬 Author replied: CUDA/Megatron execution requires CI — attach passing current-head results for
test_unified_export_megatron.pyand the Q8_0 HF example before merging.
No action needed:
- ✔️ 5 prior concerns resolved, including grouped-expert format discovery, Megatron block-shape and exception assertions, registry availability, and recipe count.
- The PR body justifies GGML packing over generic W8A16; the existing registry, export machinery, and recipe loader are reused.
- Existing test edits retain IQ coverage and legitimately generalize block geometry and error messages. New recipe headers match
LICENSE_HEADER. - Tests were inspected, not executed in this review environment.
| # Registering a format therefore declares it exportable, and that is intended rather than a side | ||
| # effect: fake quant is dequantize(quantize(w)), so a format cannot be dispatched without the | ||
| # packer and block geometry that are all export reads. | ||
| IQ_FORMATS = frozenset(IQ_FORMAT_REGISTRY) |
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do we still need IQ_FORMATS?
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No, GGMLFormat is more general. We have a backward support at this line
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/claude review |
Signed-off-by: Hung-Yueh Chiang <hungyuehc@nvidia.com>
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/claude review |
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/ok to test 2b39510 |
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What does this PR do?
Type of change: New feature, tests, documentation.
Add packed Q8_0 export for Hugging Face and Megatron and a built-in
general/ptq/q8_0recipe. This is the export-and-recipes slice of the Q8_0 series; the kernel and codec already landed in #2515 and #2516.GGML_FORMAT_REGISTRY, preserving IQ1_S, IQ1_M, IQ2_XXS, IQ2_XS, and IQ2_S.<module>.weightbecomes a shapeduint8payload; no separate shape tensor is stored.main: reuse an unchanged weight's existing bytes, and repack after an in-place weight update. The cache tests now cover all six GGML formats.Why a GGML path rather than generic W8A16? Q8_0 is a particular packed byte contract, not just an eight-bit weight setting. Its FP16 scales are embedded inside each block, so routing it through a generic integer export path with standalone scale tensors would produce a different checkpoint. Metadata marks these payloads as
quant_method: modelopt,packing: ggml; this PR does not add a GGUF file writer or a deployment loader.Limits: each Q8_0 weight's final dimension must be divisible by 32 (IQ formats retain their 256-value constraint). These are weight-only formats: enabled activation quantizers and AWQ pre-scales are rejected. Megatron GGML export requires TP=1 and PP=1; fused-MoE GGML export remains unsupported.
Usage
For an already loaded HF model:
The recipe needs no calibration data.
effective_bits: 8.5describes packed storage cost; it does not change the encoder's arithmetic.Testing
After integrating
mainthrougha57c5ebdd(including merged #2516):test_export_weight.py,test_get_quantization.py,test_convert_hf_config.py,test_presets.py,test_q8_0.py,test_ggml_backend.py, andtest_iq_formats.pyon local CPU. Coverage includes all six formats' export payloads, cache reuse and invalidation, Q8_0 metadata, and grouped-quantizer detection.general/ptq/q8_0.yaml; the Q8_0 HF example test collects successfully.git diff --checkpassed.(out_features, in_features // block_size, payload_bytes)and both fused-MoE rejection tests match the generalized GGML error.Before your PR is "Ready for review"
Contributor and security guidelines followed; commits are signed and include sign-off.
Additional Information
Q8_0 series, all targeting
main:main.This diff contains export plumbing, recipes, documentation, and matching tests only. It adds no codec or CUDA implementation. Conflict cleanup preserves the upstream shared
weight_attr_namessupport for grouped quantizers rather than duplicating it here.Summary by CodeRabbit