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[1/3] Run GPTQ on HF fused MoE experts - #2840
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly identifies the main change: adding GPTQ calibration for Hugging Face fused MoE experts. The [1/3] prefix is minor noise but does not reduce clarity. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Security Anti-Patterns PASS. The PR changes four modelopt Python files and no examples. Added-code searches found no unsafe torch.load, numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, eval/exec, or # n… Full details: Security Anti-Patterns
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KL divergence vs BF16: GPTQ on the fused experts beats imatrix on both MoEs. Setup:
imatrix over max is -0.0108 +- 0.0023 (Qwen) and -0.0192 +- 0.0035 (GLM), so on these MoEs GPTQ's gain is 3-5x imatrix's. Cost:
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Nudge: the per-expert GPTQ design looks right and has a strong bit-exact test, but it changes the default IQ1/IQ2 recipes, and GPU, layerwise and real-MoE validation are still pending.
Needs action:
- Post the promised KL, peak-memory and time results for Qwen3.8-Flash-Next and GLM-5.3-Flash before merge. This PR switches the built-in IQ1/IQ2 recipes from imatrix to GPTQ on the experts.
- Add a unit test for fused experts with a non-GGML weight format on the base
GPTQHelper, e.g. INT4 or NVFP4. The new test only coversGGMLGPTQHelper, but theweight_quantizer/weightplumbing and thecopy_write-back incalib_utils.pyapply to every format. - Add a layerwise-calibration test that uses
ToyMoE. The docs say large MoEs depend on layerwise mode, andgptq()now builds hooks and handles for each layer'sfused_experts. - Confirm the memory cost of allocating Hessians at construction in
model_calib.py. Allin_features²Hessians for every expert and projection exist before the forward pass, and they only go to CPU once GPU usage is above 65%. Without layerwise mode this can OOM, so consider a warning.
No action needed:
- Design review gate: the 22 files are mostly YAML and docs. The core change, about 155 lines, extends the existing
GPTQHelperand reuses the_fused_expert_projectionsrefactor from the imatrix path. It doesn't add a new system. weight[idx]fromiter_weights_for_calibrationis a view, socopy_writes into the 3-D parameter._current_expert_idxis set before both input quantizers run.- The test edits are justified: the imatrix MoE fixture moved to
_test_utilsandconftest.pywith coverage unchanged. The newconftest.pyhas the standard NVIDIA header.
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Review comments at @tests/unit/torch/quantization/test_ggml_gptq.py:
- Around line 210-231: Add a `pytest.importorskip("transformers")` guard at the
start of `test_gptq_quantizes_each_fused_expert_as_a_linear_fed_only_its_tokens`
so the test is skipped when the fused-expert wrapper’s dependency is
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CHANGELOG.rstmodelopt/torch/quantization/config.pymodelopt/torch/quantization/ggml/gptq.pymodelopt/torch/quantization/model_calib.pymodelopt/torch/quantization/utils/calib_utils.pymodelopt_recipes/configs/ptq/presets/model/iq1_m.yamlmodelopt_recipes/configs/ptq/presets/model/iq1_m_experts_only.yamlmodelopt_recipes/configs/ptq/presets/model/iq1_s.yamlmodelopt_recipes/configs/ptq/presets/model/iq1_s_experts_only.yamlmodelopt_recipes/configs/ptq/presets/model/iq2_s.yamlmodelopt_recipes/configs/ptq/presets/model/iq2_s_experts_only.yamlmodelopt_recipes/configs/ptq/presets/model/iq2_xs.yamlmodelopt_recipes/configs/ptq/presets/model/iq2_xs_experts_only.yamlmodelopt_recipes/configs/ptq/presets/model/iq2_xxs.yamlmodelopt_recipes/configs/ptq/presets/model/iq2_xxs_experts_only.yamlmodelopt_recipes/general/ptq/iq1_m.yamlmodelopt_recipes/general/ptq/iq1_s.yamlmodelopt_recipes/general/ptq/iq2_s.yamlmodelopt_recipes/general/ptq/iq2_xs.yamlmodelopt_recipes/general/ptq/iq2_xxs.yamlmodelopt_recipes/ptq.mdtests/_test_utils/torch/quantization/models.pytests/unit/torch/quantization/conftest.pytests/unit/torch/quantization/test_ggml_gptq.pytests/unit/torch/quantization/test_ggml_imatrix.py
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GPTQ only collected Hessians for modules with a 2-D weight and one weight quantizer, so HF fused MoE experts (one 3-D parameter, a quantizer per expert) were skipped and left to round-to-nearest or imatrix. gptq() now builds one helper per expert and projection. Each Hessian comes from a forward hook on the projection's shared input quantizer, keyed by the expert currently running, so it holds exactly the (input-quantized) tokens routed to that expert. GPTQHelper takes an optional weight_quantizer and weight for that, and writes its result back in place, so an expert's slice is updated inside the 3-D parameter. An expert no calibration token reaches gets the plain search, as a dense layer calibration never reaches does. The IQ recipes, docs and changelog no longer say GPTQ skips the experts. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
- Skip the fused-experts tests when transformers is missing: the shared moe fixture now calls pytest.importorskip, as the HF plugin wraps them. - Run the fused-expert GPTQ test with INT4 too, so the base GPTQHelper's weight plumbing and in-place write-back are covered. At the toy width a GGML weight is a single 256-column group whose GPTQ encode ignores the Hessian, so INT4 is also the case that checks per-expert routing. - Add a layerwise test on a two-layer toy MoE stack: every layer's experts are written, layer 0 matches a single pass, and layer 1 is calibrated on layer 0's quantized output. - Report the fused-expert Hessian size and warn before allocating when it exceeds the free memory of the GPUs holding the experts, pointing to layerwise calibration. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Review follow-up in d60c4ca. The branch is rebased on Addressed:
Results on real MoEs (4x GB300, 1,024 calibration samples, #2864 and #2877 on top). KL to BF16 is in the comment above. Calibration time per checkpoint, in minutes:
* Qwen's imatrix runs predate a model-specific attention fast path that both GPTQ runs used. Accuracy of experts-only IQ2 checkpoints calibrated with
Where imatrix is already within about 2 points of BF16, GPTQ is within noise. Where imatrix breaks down (GLM IQ2_XS scored 72.47 GPQA and 49.31 AA-LCR, against BF16's 92.33 and 76.69), GPTQ recovers 12.4 and 10.1 points. It does so mostly by cutting endless repetition: samples without an answer fall from 24% to 8.5% on GPQA. The GPTQ evals ran with vLLM repetition detection, which can only lower a score. 🤖 Generated with Claude Code |
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LGTM: all prior concerns are resolved, and the per-expert GPTQ path reuses the existing GPTQHelper and _fused_expert_projections with about 172 added core lines. A human needs to sign off on the moved test fixture.
Needs action:
- Confirm the
test_ggml_imatrix.pyedit as the human sign-off. The edit is justified: the toy MoE and themoefixture moved verbatim to_test_utilsandconftest.py, and the imatrix assertions are unchanged. - Confirm GPU CI passes. The author ran only the CPU unit tests, and this PR changes the default IQ1/IQ2 recipes to run GPTQ on the experts.
No action needed:
- ✔️ Resolved since the last review: real-MoE KL, accuracy, time and memory results are now posted. Also added: an INT4 base-
GPTQHelperrouting test, a layerwise two-layerToyMoEtest, a Hessian-memory warning ingptq(), and thetransformersimportorskip in the shared fixture. - Checked:
update_hessianalready skips zero-token batches, and an unreached expert falls back to the identityh_inv._print_mse_errorreads the original weight beforecopy_writes into the 3-D slice. The input-quantizer hooks are removed infinally. - Design gate: no new subsystem. It extends the existing helper and reuses the projection iterator shared with imatrix.
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Review comments at @modelopt/torch/quantization/model_calib.py:
- Around line 2438-2448: Update the gptq_fused_block_scalar kernel-launch path
to select w_block.device with the existing CUDA device context before invoking
_gptq_scalar_kernel. Preserve the current arguments and launch configuration
while ensuring fused experts on non-current CUDA devices launch on the tensor’s
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CHANGELOG.rstmodelopt/torch/quantization/model_calib.pytests/unit/torch/quantization/conftest.pytests/unit/torch/quantization/test_ggml_gptq.py
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| for weight, quantizer in module.iter_weights_for_calibration(): | ||
| handle = expert_handles.pop(id(quantizer), None) | ||
| if handle is not None: | ||
| handle.update_weights(block_size, perc_damp, weight) |
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[P1] Persist GPTQ updates for native FP8 experts
Could we ensure the expert weights passed to update_weights() are persistent, writable model weights? Native _QuantFP8Experts also enters this path, but its inherited iter_weights_for_calibration() calls get_expert_weight(), which returns a newly allocated, scaled/dequantized tensor for FP8-backed parameters. update_weights() copies the GPTQ result into that temporary; neither the stored FP8 parameter nor its scales are updated. The next forward/export dequantizes the original weights again, silently losing the GPTQ result.
I reproduced this on CPU with Transformers 5.18 FP8Experts (two experts, hidden/intermediate size 256, native FP8 blocks 128x128) and INT4 GPTQ (128-column groups). Starting from max calibration, then running mtq.calibrate(..., algorithm={"method": "gptq", "block_size": 128, "perc_damp": 0.01}, forward_loop=...), all four projections report successful GPTQ updates with 96 routed tokens each, but their dequantized weights and model predictions remain exactly unchanged. Calling unpack_weight() before the same GPTQ calibration makes the updates persist and changes predictions.
Please materialize persistent floating-point weights under the appropriate weight-access/writeback context, or otherwise explicitly persist the optimized result in the representation used by subsequent inference/export. A regression test using native FP8Experts and comparing against its unpacked equivalent would catch this; the current floating-point toy experts always return views and cannot exercise the issue.
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Thanks, reproduced. With Transformers 5.18 FP8Experts, the stored weights stay unchanged after GPTQ, and after imatrix too, since imatrix makes the same in-place write through iter_weights_for_calibration(). After unpack_weight(), both updates persist.
cedb19e makes GPTQ and imatrix refuse fused experts that store a projection they would update in 8 bits. They raise a ValueError asking for unpack_weight() before any calibration state changes, the same contract fold_weight already enforces. test_native_fp8_experts_reject_packed_weight_updates checks both methods: the refusal leaves the state untouched, and the updates persist once the experts are unpacked.
I chose refusing over unpacking inside calibration because unpack_weight() swaps the parameters for float copies. I'd rather not do that implicitly where layerwise and offload contexts manage those parameters.
Native Transformers FP8 experts store their weights in 8 bits and hand calibration a dequantized copy per expert, so the in-place update of GPTQ and imatrix landed in that copy and was silently lost. Both now refuse such experts before any calibration state changes and ask for unpack_weight(), as folding already does; after unpacking, the updates persist. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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### What does this PR do? Type of change: Bug fix Standalone on `main`, split out of the GPTQ-for-MoE stack (#2840 merged ← #2864 ← #2945). It touches only the fused kernel, so it can merge in any order with that stack. GPTQ with `fused: true` runs the NVFP4 column update in a Triton kernel (`gptq_fused_block_scalar`, in modelopt since 0.45). Triton launches on the **current** CUDA device, and the wrapper launched without selecting the device of the tensors it was given. A model that `device_map="auto"` spreads over several GPUs keeps later decoder layers off the current GPU. Once layerwise GPTQ reached such a layer, the kernel ran on GPU 0 with pointers into GPU 1: - **NVLink systems (GB300):** `CUDA error: an illegal memory access was encountered`, reported later at an unrelated op. - **PCIe systems:** `ValueError: Pointer argument cannot be accessed from Triton`. The fix launches the kernel under `torch.cuda.device(w_block.device)`, as the other quantization Triton wrappers (`fp4_kernel.py`, `nvfp4_fp8_sweep.py`, `bmm2_qdq.py`) already do. Found while running layerwise GPTQ on Qwen3.8-Flash-Next with the experts' `down_proj` in static NVFP4 (`fused: true`). The model sits on 4 x GB300 with layers 0-22 on GPU 0. Three runs on three different nodes all died right after layer 22. ### Usage No API change. ### Testing - **New GPU test** `test_fused_nvfp4_gptq_runs_on_the_weights_gpu` (2 GPUs): the fused update with every tensor on `cuda:1` and `cuda:0` current matches the same update run on `cuda:0` bit for bit. It fails without the fix with the Triton pointer error on the PCIe GPUs I ran it on. - **Existing tests:** on `main`, `tests/gpu/torch/quantization/test_gptq.py` passes on 2x RTX PRO 6000 (11 tests, including the existing fused-vs-unfused NVFP4 ones). - **End to end:** the Qwen3.8-Flash-Next layerwise GPTQ that died after layer 22 completes with this change on oci-jhb; details in the comment below. ### 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`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ (bug present since 0.45) - Did you get Claude approval on this PR?: ❌ 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> <!-- coderabbit-ai-generated:v1 --> ## Summary by CodeRabbit * **Bug Fixes** * Fixed GPTQ fused quantization failing with CUDA or Triton errors when calibration runs across multiple GPUs. * Added a check that quantization results remain consistent when processing data on a different GPU than the current device. <!-- 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>
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### What does this PR do? Type of change: new feature (performance) **Stack (merge in order):** [1/3] #2840 GPTQ on HF fused MoE experts (merged) ← [2/3] #2864 speed-up ← [3/3] #2945 Hessian placement by measured headroom. The fused NVFP4 GPTQ kernel device fix, #2877, is standalone on `main` and can merge in any order. Layerwise GPTQ on a large HF fused-MoE model took about 25 minutes per decoder layer: 20 hours for Qwen3.8-Flash-Next's gate_up experts (512 experts, 48 layers) and over a day for GLM-5.3-Flash. Profiling one Qwen-shaped layer showed the GPTQ weight update was only 9 s of that. The time went to forward passes, for three reasons this PR removes: 1. **A forward nobody used.** `gptq()` ran `max_calibrate` with the calibration forward before its own Hessian forward. Weight amax comes from the weights (`weight_only_quantize`), so the forward only feeds activation quantizers. GPTQ now passes `skip_forward_without_activation_calib=True`, as `max` and `imatrix` already do, and weight-only recipes run one forward instead of two. 2. **Hessians on the CPU while GPUs sat idle.** `GPTQHelper` moves a Hessian to the CPU when its weight's GPU is over 65% used. With the model sharded across 4 GB300s, the GPU holding the current layer was 79% used and two others were empty, so every expert Hessian went to the CPU. Each hook call then copied the whole matrix (26 MB for Qwen) to the host and rescaled it there. A single process now puts such a Hessian on the emptiest GPU it already holds memory on that is under the limit and has the Hessian's bytes free, and uses the CPU only otherwise. Only GPUs the process already uses are candidates, so no other job's GPU is touched; distributed runs keep the old rule, since another GPU may belong to another rank. `update_hessian` also folds its incremental average into one `addmm_` instead of a separate scale pass and add pass over the matrix. 3. **Batch 1.** `hf_ptq.py` picked `--batch_size 1` for layerwise runs because its auto batch-size probe runs a full-model forward. A layerwise batch only holds one layer's activations, so the default is now a token budget: 8,192 tokens, 16 samples at `--calib_seq 512`. HF fused experts run a Python loop over experts on every forward, so per-sample cost is almost flat in batch size: 250 ms per sample at batch 1. 4. **A logged error per expert.** Each expert's GPTQ helper logged its Hessian-weighted error, which costs two weight-by-Hessian matmuls and a host sync: 576 times per GLM-5.3-Flash layer. Expert helpers now skip it, and `gptq()` logs one line per experts module with how many experts it updated and how many calibration never reached. Dense layers keep their line. ### Usage No API change. `hf_ptq.py` users who want the old batching pass `--batch_size 1`: ```bash python hf_ptq.py --pyt_ckpt_path <model> --recipe <layerwise GPTQ recipe> --batch_size 1 ``` ### Testing **Phase timing on one Qwen3.8-Flash-Next-shaped layer**, measured locally on an RTX PRO 6000. The layer has 512 experts, hidden 2560, intermediate 640 and top-10 routing; gate_up is IQ2_XXS GPTQ with 32 samples of 512 tokens: | | GPTQ total | forwards | expert updates | |---|---|---|---| | before, batch 1 | 29.6 s | 2 x 9.8 s | 9.2 s | | after, batch 1 | 19.0 s | 1 x 8.9 s | 9.3 s | | after, batch 16 | 11.2 s | 1 x 1.0 s | 9.3 s | A GLM-5.3-Flash-shaped layer at batch 16 (288 experts, hidden 4096, intermediate 2048, gate_up and down quantized): 21.4 s, of which 19.8 s is the expert updates. **End to end on oci-jhb** (GB300 x4, Qwen3.8-Flash-Next, gate_up IQ2_XXS, layerwise GPTQ, 1,024 x 512 calibration tokens): | Qwen3.8-Flash-Next unless noted | GPTQ time per layer | layerwise calibration (48 layers; GLM 45) | whole job (load, calibrate, KL scoring) | |---|---|---|---| | before (batch 1) | 1,206-1,663 s on linear-attention layers, up to 1,862 s on full-attention ones | ~20 h | ~21 h | | this PR (batch 16) | 29-35 s (first layer 65 s, incl. warm-up) | **44 min** | 70 min | | GLM-5.3-Flash before (batch 1, gate_up + down, 288 experts) | 739-2,131 s | ~25 h (projected) | | | GLM-5.3-Flash, this PR (batch 16) | 37-44 s; 126-136 s on the last 8 MoE layers | **55 min** | 79 min | With only the first and third changes, a layer whose Hessians went to the CPU took 474 s against about 80 s for one whose Hessians stayed on a GPU, which is what the second change fixes. Qwen3.8-Flash-Next's 12 full-attention layers also need a fix outside ModelOpt. transformers' reference sparse-attention indexer (`Qwen4ExpTextQSAIndexer`) loops in Python over every (sample, query) pair, which adds about 340 s per pass over 1,024 x 512 tokens at any batch size. At 512-token calibration, every visible token fits its 2,048-token budget, so its output equals the attention mask it receives. The run above patched the indexer to return that mask directly. The patch is bit-identical to the loop, checked on padded and float masks, and is not part of this PR. Without it, those layers took 374 s each instead of 33 s. KL divergence against BF16 for the same experiment (wikitext-2, 100 x 512 tokens, second half scored, 25,500 tokens; differences paired per chunk): | gate_up IQ2_XXS | mean KL | vs max | |---|---|---| | max | 0.2971 | | | imatrix | 0.2863 | -0.0108 +- 0.0023 | | GPTQ, this PR (batch 16) | **0.2492** | -0.0479 +- 0.0033 (GPTQ - imatrix: -0.0371 +- 0.0034) | On GLM-5.3-Flash (all experts), GPTQ in this PR reaches KL 0.6298 against 0.7211 for imatrix and 0.7402 for max; both models' results are on #2840. On GLM, the last 8 MoE layers took about 3x longer. By then every GPU this process uses was over the 65% limit, so their Hessians went to the CPU. A limit based on absolute headroom would keep them on a GPU: 65% of a 284 GB GB300 leaves about 100 GB unused. That is a possible follow-up. There is no batch-1 KL to compare against: the batch-1 GPTQ runs kept being preempted and were cancelled. Batch size changes results only through floating-point rounding: `hf_ptq.py` pads every calibration row to one length before batching, so batch 1 and batch 16 see the same pad tokens. On phi-2 (NVFP4 W4A4, layerwise, 128 x 512 cnn_dailymail calibration; KL to BF16 on wikitext-2, 40 x 512 tokens, paired per chunk), batch 16 minus batch 1 is -0.0058 +- 0.0032 for GPTQ and +0.0058 +- 0.0052 for local-Hessian, neither significant. **Unit tests:** - The related unit tests (`tests/unit/torch/quantization/{test_gptq,test_ggml_gptq,test_ggml_imatrix,test_layerwise_calibrate,test_calib}.py`, `plugins/test_fused_experts.py`, `tests/unit/recipe`) and `tests/examples/hf_ptq/test_hf_ptq_args.py`: all pass on CPU with transformers 5.18. - New `test_gptq_runs_a_max_calibration_forward_only_for_activation_amax` counts the calibration forwards: 1 for weight-only, 2 with an input quantizer. It fails without the change. - New `test_layerwise_calibration_batch_reaches_the_dataloader` drives `quantize_main` with the layerwise `general/ptq/iq2_xxs` recipe up to the calibration dataloader, without a checkpoint, and checks the batch it receives: 16 at 8,192 tokens, capped at the sample count, 1 for sequences over 8,192 tokens, and an explicit `--batch_size 1` kept as is. It fails with the old batch-1 default. `--calib_seq` must now be positive (`test_calib_seq_must_be_positive`). - New GPU tests, which need 2 GPUs. CI's GPU runner has one and skips them, so I ran them on 2x RTX PRO 6000: - `test_hessian_of_a_full_gpu_goes_to_an_idle_gpu`: a Hessian whose GPU reports over 65% used is allocated on another GPU and accumulates the same values. It fails without the change. - `test_hessian_of_a_full_gpu_falls_back_to_the_cpu`: the Hessian goes to the CPU when the idle GPU is full, when it lacks the Hessian's bytes, and on any rank of a multi-rank run. The too-small case fails without the capacity check. - All 14 tests in `tests/gpu/torch/quantization/test_gptq.py` pass, and all 15 with #2877 on top. - #2840's bit-exact fused-vs-dense GPTQ test still passes. The fused `addmm_` changes the Hessian's float rounding for every GPTQ user, but both paths share it. - Skipping the per-expert error log, on GLM-5.3-Flash (gate_up and down IQ2_XXS, layerwise, 1,024 x 512 tokens, 4x GB300): GPTQ time per MoE layer 41.4 s against 43.5 s before. KL to BF16 does not change: a Qwen3.8-Flash-Next run with this commit gives 0.249200, as before. **Not run:** multi-rank GPTQ end to end (it keeps the old CPU rule), and `hf_ptq.py` layerwise on a dense model at the new default batch. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ for the API. `hf_ptq.py`'s layerwise default batch changes from 1 to 16 at `--calib_seq 512`. Batches pad each sequence to the longest in the batch, and pad tokens feed Hessians and activation amax, so results move slightly, as they already do in non-layerwise runs. `--batch_size 1` restores the old behavior. - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ - Did you get Claude approval on this PR?: ❌ ### Additional Information The expert updates are now the largest remaining phase on GLM, about 20 s per layer. Encoding one 256-column group for all experts in a single call is bit-exact with per-expert calls and 18x faster per group. Batched Cholesky and batched GEMMs are not bit-exact, so a follow-up would batch only the encoder. 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> <!-- coderabbit-ai-generated:v1 --> ## Summary by CodeRabbit * **New Features** * Layerwise calibration now defaults to batches totaling up to 8,192 tokens, limited by available samples. Use `--batch_size 1` for the previous batch size. * GPTQ skips an unnecessary calibration pass when activation quantization does not require one. * With CPU offloading enabled, GPTQ can use a less-utilized GPU for Hessian calculations when sufficient memory is available; otherwise, it uses the CPU. * GPTQ now reports how many expert weights were updated and how many received no calibration tokens. * **Bug Fixes** * Calibration sequence lengths of zero or less are now rejected. * **Documentation** * Updated calibration guidance to explain the default batch size, performance tradeoffs, and batch size used in the documented run. <!-- 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>
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Stack (merge in order): [1/3] #2840 GPTQ on HF fused MoE experts ← [2/3] #2864 speed-up ← [3/3] #2877 fused NVFP4 GPTQ kernel device fix.
What does this PR do?
Type of change: new feature
GPTQ only collected Hessians for modules with a 2-D
weightand oneweight_quantizer(is_quantized_linear), so HF fused MoE experts (one 3-Dgate_up_proj/down_projparameter with aModuleListof per-expert weight quantizers) were skipped. They fell back to round-to-nearest, or to imatrix in the IQ recipes, which gets only part of GPTQ's gain: on a dense model, imatrix alone closes 53–63% of the gap from the plain encode to GPTQ. In an MoE those experts are most of the weights.gptq()now builds oneGPTQHelperper expert and projection. Its Hessian comes from a forward hook on the projection's shared input quantizer, keyed by_current_expert_idx, so it holds exactly the tokens routed to that expert, after input quantization, as the dense path's Hessian does.GPTQHelpertakes an optionalweight_quantizerandweight, gainsaccumulate(), andupdate_weights()writes the result back in place, so an expert's slice is updated inside the 3-D parameter.GGMLGPTQHelperpins each expert's codes to its own quantizer.unpack_weight(), as folding already does.in_features²float32: 22.5 GiB per MoE layer for GLM-5.3-Flash's experts and 13.3 GiB for Qwen3.8-Flash-Next's, 945 and 638 GiB for the whole model. Large MoEs therefore need layerwise calibration, which the IQ recipes already enable;gptq()reports the size and warns before allocating when it exceeds the free memory of the GPUs holding the experts.[gptq (layerwise), imatrix]) now run GPTQ on the fused experts too, and imatrix then only encodes weights GPTQ leaves. The<format>_experts_onlyrecipes stay imatrix-only.ptq.md, config docstrings and the changelog no longer say GPTQ skips the experts. The toy fused-MoE test model moved to_test_utilsso the GPTQ and imatrix tests share it.Usage
Testing
test_gptq_quantizes_each_fused_expert_as_a_linear_fed_only_its_tokens, run with IQ2_XXS (GGMLGPTQHelper) and INT4 (the baseGPTQHelper): every expert's result, both projections, is bit-identical to dense GPTQ on annn.Linearholding that expert's weight and fed only that expert's tokens; for IQ2_XXS the pinned GGML payload matches too. At the toy width a GGML weight is a single 256-column group, whose GPTQ encode does not use the Hessian, so the INT4 case is the one that checks routing: it fails if every token goes to expert 0's Hessian. Both fail onmain.test_layerwise_gptq_calibrates_fused_experts_on_the_earlier_layers_output: on a two-layer toy MoE, layerwise GPTQ writes every layer's experts, layer 0 matches a single pass, and layer 1 is calibrated on layer 0's quantized output. 3.8 s on CPU.test_native_fp8_experts_reject_packed_weight_updates(GPTQ and imatrix): packed FP8 experts are refused with the state untouched, and the updates persist afterunpack_weight(). Fails without the guard.tests/unit/torch/quantization/{test_ggml_gptq,test_ggml_imatrix,test_gptq,test_layerwise_calibrate,test_calib}.py,plugins/test_fused_experts.pyandtests/unit/recipeon CPU with transformers 5.18: 761 passed at the top of the stack.down_projexperts through the fused Triton kernel. KL to BF16 and downstream accuracy against imatrix are in the comments.Before your PR is "Ready for review"
copy_instead of replacing.data)CONTRIBUTING.md: N/AAdditional Information
Follows #2735 (imatrix calibration for the GGML formats).
🤖 Generated with Claude Code
Summary by CodeRabbit