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Improve layerwise GPTQ memory and speed: Hessian headroom, batch probe and offload fixes - #2945
Follow-up to #2864 (merged). Four commits, all about the GPU memory and speed of layerwise GPTQ calibration:
Place GPTQ Hessians by measured headroom instead of a 65% usage cap.
Size the default layerwise calibration batch by probing each kind of decoder layer. Fridah-nv raised this on [2/3] Speed up layerwise GPTQ on MoE models #2864: its fixed 8,192-token default could run out of memory on a model or GPU whose decoder-layer forward is larger than the ones it was tuned on.
Raise the layerwise token cap from 8,192 to 32,768 tokens, now that the probe keeps the batch within memory. A model whose layers fit the larger batch runs its MoE layers' per-expert loops a quarter as often.
Keep offloaded layers' quantizer state off the GPU, and stop layerwise OOM retries. Two bugs in calibrating a CPU- or disk-offloaded model, found running the first three commits on a single L40S.
The first two fit together. The batch probe leaves half of a GPU's available memory for quantizers and Hessians, and the Hessian placement keeps room for the measured calibration forward.
What does this PR do?
Type of change: Bug fix (performance), new feature
1. Hessian placement by measured headroom
GPTQHelper kept a Hessian on a GPU only while the GPU was under 65% used (_HESSIAN_GPU_MEM_LIMIT, inherited from GPTQ Lite). The rule exists so the calibration forward that follows still has room. But a fraction of the card is a poor proxy for that:
on a 284 GB GB300 it reserves about 100 GB, far more than a layer's forward needs;
the reading counts PyTorch's cached-but-unused blocks as used;
each Hessian is decided separately, so one layer's Hessians can end up split between a GPU and the CPU.
On GLM-5.3-Flash over 4x GB300 (layerwise GPTQ on all experts, IQ2_XXS), every GPU the process used passed 65% halfway through the model. From then on each MoE layer's Hessians went to the CPU and the layer took 142 s instead of 43 s: 21 of 42 MoE layers, 34.5 extra minutes of an 83-minute calibration. Each GPU still had 90-100 GB free.
A GPU now takes a Hessian if, after allocating it, the memory available to an allocation still covers a reserve. Available means the driver's free memory plus this process's cached blocks, minus the free pieces of partly used blocks, which a large allocation cannot use. The reserve is 1.5x the most a GPTQ calibration forward has added on that GPU, and at least 10% of it.
The forward's need is measured around gptq()'s Hessian forward as peak minus allocated-before. PyTorch's peak counters are reset first. Otherwise an earlier peak, such as the previous layer's Hessians and GPTQ updates, would count as forward memory and send Hessians to the CPU for nothing.
Until a forward has been measured on a GPU (the first layer, and every non-layerwise run), the reserve is 35% of the GPU, which keeps today's behavior.
Each calibration run measures afresh, so a run on another model never inherits a smaller reserve.
The Computing Hessians ... log line now says where the Hessians went.
2. Layerwise batch probe
get_max_layerwise_batch_size (in modelopt/torch/quantization/utils/layerwise_calib.py) probes the batch the way get_max_batch_size does for full-model calibration, but per decoder layer:
It runs the first decoder layer of each kind on one and two dummy samples. Layers are grouped by module classes and parameter shapes: dense or MoE, full or linear attention, and per-layer widths such as Nemotron-NAS's. The growth of its peak memory on each GPU is that GPU's cost per sample. Every other GPU that runs a layer of that kind is charged the same cost, so with a device_map over several GPUs, the GPU with the least headroom sets the batch. A warm-up forward first keeps one-off buffers such as cuBLAS's workspace out of the measurement.
The dummy rows carry one left-pad token, so the model builds the explicit attention mask that padded calibration batches get.
A batch fits when the layer's forward stays within half of the GPU's available memory, after the inputs layerwise calibration caches for every sample (this layer's and the next).
A layer that fits one sample but runs out of memory on two gives a batch of one, rather than stopping the run.
All ranks return the smallest batch over ranks, so FSDP2 ranks keep identical dataloaders.
hf_ptq.py calls it only when --batch_size is left at its default; an explicit value passes straight through.
The second commit also corrects two statements about padding. hf_ptq.py pads every calibration sample to one length before batching, so batch size doesn't change which tokens are padding. #2864's changelog entry and the single-GPU PTQ blog note said otherwise.
4. Offloaded calibration
Quantizer state of offloaded layers piled up on the GPU. Accelerate's offload hooks only move the tensors a module had when it was dispatched. The state calibration adds afterwards stayed on the execution device for every offloaded layer: NVFP4's per-block _amax (about 0.25 bytes per weight), its calibrator's copy, and GGML payload pins. The first time a module's weights are materialized for weight access, its offload hook now also carries its quantizers' state. A forward gets GPU copies; afterwards the CPU tensors are the master copy again. A quantizer's own tensors are always copied back into that master first, because its setters (amax, pre_quant_scale, bias_value, global_amax) write through .data and leave _version unchanged. For a Qwen3-30B-A3B MoE layer's per-block _amax that copy takes 9-11 ms, against about 60 ms to onload the layer. The calibrators' tensors (the amax calibrator's and, under static bias, the bias calibrator's) are copied back only when the forward replaced or modified them. Tensors the hook already manages, such as buffers under a hook that places submodules, are left alone. GGML's packed-weight cache is keyed to the weight that was just released, so it is dropped with it.
A GPU OOM in a layerwise capture forward made it replay the wrong activations. The calibration forward loop catches an OOM, splits the data batch and retries. In a layerwise capture forward, the only real work is the previous layer, in 'run' mode, replaying the batches it cached, one per call and at their cached size. The retry cannot shrink that work, and each retried call replays the next cached batch. The captured activations then no longer match the data batch, or the cache runs out first (AssertionError: Layer model.layers.43 is in 'run' mode but has no cached inputs to replay). An OOM there now stops with an error asking for a smaller calibration batch size.
Usage
No API change and no new flag. With a layerwise recipe and no --batch_size, hf_ptq.py now prints the probed batch:
Layerwise calibration enabled. Using batch_size=55, the most that fits a decoder layer's forward, up to 64 (32768 tokens).
Testing
Hessian placement on 4x GB300. Layerwise GPTQ on the routed experts at IQ2_XXS, batch 16, 1,024 x 512 calibration tokens, KL to BF16 over wikitext-2 (100 x 512 tokens, second half scored). "Before" is #2864 at 34a1cd8. "After" is the first commit, which also includes #2864's later per-expert-log commit, worth about 2 s per MoE layer.
Before (65% cap)
After
GLM-5.3-Flash layerwise calibration
83.0 min
42.2 min (-49%)
GLM-5.3-Flash GPTQ per MoE layer
43 s, but 142 s for the last 21 of 42
34-44 s for all 42
Qwen3.8-Flash-Next layerwise calibration
47.8 min
45.7 min (-4%)
Qwen3.8-Flash-Next GPTQ per layer
30-37 s
29-33 s
KL to BF16, GLM / Qwen
0.6232 / 0.2492
0.6192 / 0.2539
GLM placement: in the GLM run, every layer's Hessians stayed on GPUs and none went to the CPU. For 31 of 42 layers they all sat on one GPU. For the other 11, part went to an idle GPU. No layer ran out of memory.
KL: the changes are not significant, paired per chunk: GLM -0.0040 +- 0.0050 (p 0.43), Qwen +0.0047 +- 0.0031 (p 0.14). They come from summing Hessians on a GPU rather than the CPU, which rounds differently.
Batch probe on the same models and GPUs (second commit, cap 16), run up to the probe and stopped before calibration, also without the cap:
Model
Fit per layer kind
Probed batch (cap 16)
Uncapped
Probe time
GLM-5.3-Flash
linear attn + MLP: 16; full attn + MoE: 245; linear attn + MoE: 16
GLM-5.3-Flash previously ran out of memory at batch 64 in its linear attention (chunk_kimi_delta_attention) and ran fine at 16. Uncapped, the probe picks 16. Both models get batch 16, the batch the timings above used.
Token cap 32,768 (third commit) on the same models and GPUs, full layerwise GPTQ, KL paired per chunk against the runs above:
Probed batch
Layerwise calibration
KL to BF16
GLM-5.3-Flash
16 (memory)
42.2 -> 41.2 min
0.6192 -> 0.6197 (+0.0005 +- 0.0033, p 0.87)
Qwen3.8-Flash-Next
64 (cap)
45.7 -> 21.1 min (-54%)
0.2539 -> 0.2493 (-0.0046 +- 0.0026, p 0.08)
Smaller GPUs (computelab), single GPU, all three commits:hf_ptq.py with layerwise NVFP4 GPTQ (general/ptq/nvfp4_default-kv_none-gptq), 512 x 512 cnn_dailymail calibration tokens, default --batch_size. GPU memory sampled every 2 s with nvidia-smi.
GPU
Model
Placement
Probed batch
Result
Peak GPU memory
H100 NVL 94 GB
Qwen3.6-35B-A3B
whole model on the GPU
55
completed, 40 layers
89.5 GB
H100 NVL 94 GB
Qwen3.6-35B-A3B
--max_gpu_memory_gb 40, rest on CPU
64
completed
63.4 GB
L40S 46 GB
Qwen3-30B-A3B
--max_gpu_memory_gb 30, per-layer export
64
completed, 48 layers in 1 h 40 min
35.4 GB
L40S 46 GB
Qwen3.6-35B-A3B
--max_gpu_memory_gb 30, per-layer export
64
completed, 40 layers in 2 h 31 min
38.8 GB
Offloaded without per-layer export (fourth commit). Without layerwise.export_dir, every finished offloaded layer's quantizer state stayed on the GPU. On the L40S with a 30 GB weight budget that grew by about 8 GB over the model and ran out of memory near the end, even after splitting the batch down to one sample. With the fourth commit, same L40S, no per-layer export, default batch size:
Model
Before
With the fourth commit (batch 16)
Qwen3-30B-A3B (48 layers)
OOM at layer 44 (batch 64), peak 45.1 GB
completed in 1 h 56 min, peak 42.4 GB
Qwen3.6-35B-A3B (40 layers)
OOM at layer 34 (batch 29, then split down to one sample), peak 45.5 GB
completed in 2 h 56 min, peak 43.9 GB
GPU memory still rises while the layers that device_map keeps on the GPU are calibrated, since their quantizer state belongs there. It stays flat over the offloaded layers, then rises during export. On phi-2 with CPU offload (--max_gpu_memory_gb 2), memory allocated during calibration grew about 30 MiB per offloaded layer before and is now flat, ending at 2,075 MiB instead of 2,723. hf_ptq.py's export there is bit-identical to main's, every tensor, and so are its generations before and after PTQ, with layerwise calibration time unchanged (9 min 44 s vs 10 min 33 s on a shared GPU).
Linearity (phi-2, RTX PRO 6000): a decoder layer's forward peak is 45 MiB per sample at 512 tokens and 180 MiB at 2,048. Measured at batches 1, 2, 8 and 32, it matches the probe's prediction exactly.
Calibration overhead: during layerwise GPTQ and layerwise local-Hessian on phi-2, the decoder layer's forward peaks at 1.18x the plain forward the probe measures (106 vs 90 MiB at batch 2), well inside the half the probe leaves free.
End to end:hf_ptq.py with layerwise GPTQ on phi-2, single GPU and --use_fsdp2 on 2 GPUs: the probe picks 16, calibration runs all 32 layers, and the export completes.
Ranks agree: two ranks with different memory (local fits 56 and 14) both return 14.
Tests, on this branch, 2x RTX PRO 6000 (CI's GPU runner has one GPU and skips the 2-GPU ones): tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.py 29 passed. The offload and export tests (tests/gpu/torch/quantization/plugins/test_accelerate_gpu.py, tests/gpu/torch/export/{test_offload_export,test_vllm_fakequant_hf_export}.py, tests/gpu/torch/utils/test_model_load_utils.py) also pass: 29 of 30 in one run, where test_transformers_mse_calibrate_fp32_amax_save_restore[dtype0] hit the 120 s timeout as the first test on a shared GPU, as it does on main; alone it passes in 3 s. CPU: tests/unit/torch/quantization/{test_gptq,test_ggml_gptq,test_layerwise_calibrate,test_calib}.py, plugins/{test_accelerate,test_huggingface}.py, tests/unit/torch/export/test_offload_export.py and tests/examples/hf_ptq/test_hf_ptq_args.py 214 passed. A wider CPU run on the first commit alone (adding test_ggml_imatrix, test_quantize_cpu, plugins/test_fused_experts.py and tests/unit/recipe) passed 882.
New test_a_measured_calibration_forward_sets_the_hessian_reserve: at 30% available, a GPU keeps a Hessian only once a small forward has been measured. It falls back with no measurement, or with a large one.
New test_gptq_records_its_calibration_forward_memory: a forward that allocates 64 MiB raises the recorded need at least that much.
New CPU test test_a_calibration_run_forgets_earlier_forward_measurements: a stale measurement does not survive a new mtq.quantize. It fails without the reset.
New GPU test test_layerwise_batch_probe_fits_the_heaviest_kind_of_layer: a model with a second, heavier kind of decoder layer gets a smaller batch, and that layer's forward at the probed batch stays within half the budget. It fails when only the first layer is probed.
test_layerwise_calibration_batch_reaches_the_dataloader gains a case where memory, not the token budget, sets the batch, and follows the new cap.
test_layerwise_calibrate_cpu_offloaded now also checks that the offloaded layer's quantizer and calibrator state is on the CPU between uses. It fails without the fourth commit; its existing comparison with a GPU-only reference still passes.
New CPU test test_out_of_memory_in_a_replayed_layer_is_not_split: an OOM while a layer replays its cached batches, under the real calibration forward loop, raises instead of retrying. It fails without the fourth commit with the AssertionError above.
Review fixes (fifth commit), each with a test that fails without it:
test_offloaded_quantizer_keeps_what_its_setter_wrote (GPU): an offloaded weight quantizer's CPU _amax, set through its setter while the layer is materialized, keeps the new value.
test_layerwise_batch_probe_falls_back_to_one_sample (GPU): a layer that runs out of memory on two samples, in its forward or while its inputs are captured, gives a batch of one.
test_gptq_records_its_calibration_forward_memory now also allocates a 1 GiB peak before GPTQ and checks that it is not counted.
test_layerwise_batch_probe_fits_the_heaviest_kind_of_layer gains a case where the heavier layer has the same class but a wider FFN.
On 2x RTX PRO 6000: tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.py and plugins/test_accelerate_gpu.py 50 passed. tests/unit/torch/utils 385 passed. In tests/unit/torch/quantization, one test fails, test_utils.py::test_persistent_materialization_only_skips_unshard_for_its_layer[True], and it fails on the previous head too. Two other modules there need optional packages this environment lacks.
Second review round (sixth commit), each with a test that fails without it:
test_layerwise_batch_probe_fits_every_gpu_a_kind_of_layer_runs_on (2 GPUs): two identical layers on cuda:0 and cuda:1, with less memory on cuda:1. The batch is what cuda:1 fits (28, against 113 when only cuda:0 counts).
test_available_gpu_memory_is_what_one_allocation_can_get (GPU): after fragmenting the cache, a single allocation of everything reported beyond the driver's free memory comes from the cache without reserving more.
test_offloaded_layer_keeps_bias_calibration_off_the_gpu (GPU): a static-bias input quantizer's bias calibrator is on the CPU after layerwise calibration of an offloaded layer.
On 2x RTX PRO 6000: tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.py and plugins/test_accelerate_gpu.py 53 passed. tests/unit/torch/quantization/{test_layerwise_calibrate,test_gptq,test_ggml_gptq}.py, plugins/test_accelerate.py and tests/unit/torch/utils 470 passed.
Not run: VLMs, and FSDP2 CPU offload, which does not use accelerate's hooks and is unchanged.
Before your PR is "Ready for review"
Is this change backward compatible?: ✅ (no API change; unmeasured runs keep the 35% Hessian reserve; an explicit --batch_size is unchanged and the default can only shrink from [2/3] Speed up layerwise GPTQ on MoE models #2864's)
If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
The constants (1.5x margin, 10% floor and 35% before measuring for Hessians; half of available memory for the probed batch) are conservative choices, not tuned values.
Layerwise calibration automatically selects a batch size based on available GPU memory, up to a 32,768-token limit and the number of available samples. Explicitly configured batch sizes remain unchanged.
GPTQ accounts for calibration memory when placing Hessians and falls back to CPU when GPU memory is insufficient.
Bug Fixes
Improved quantizer state handling for CPU- and disk-offloaded layers during calibration.
Calibration replay out-of-memory errors now explain how to retry with a smaller batch size.
Documentation
Clarified that calibration samples use fixed-length padding and that activation input scales may vary slightly with batch size.
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Layerwise HF PTQ now probes decoder-layer GPU memory to select a calibration batch size within token and sample limits. GPTQ tracks calibration-forward memory when placing Hessians. Accelerate offload hooks carry quantizer state across offloaded forwards, and layerwise replay converts CUDA out-of-memory errors into runtime errors.
The probe captures representative decoder-layer inputs, measures forward memory, and estimates a fitting batch size. It returns the minimum candidate across ranks and removes calibration hooks after probing. GPU tests cover layer sizes, out-of-memory cases, and multi-GPU limits.
Apply and document the batch limit examples/hf_ptq/hf_ptq.py, tests/examples/hf_ptq/test_hf_ptq_args.py, docs/source/announcements/20260928-single-gpu-ptq.rst, CHANGELOG.rst, modelopt/torch/quantization/utils/layerwise_calib.py, tests/unit/torch/quantization/test_layerwise_calibrate.py
HF PTQ applies the probed size under token and sample limits. The documentation describes memory-based batch selection and uniform padding. Replay out-of-memory errors become runtime errors that advise using a smaller batch.
Calibration forwards record per-GPU peak allocations. Calibration resets recorded measurements when a function is provided. GPTQ wraps Hessian collection in memory tracking. Tests cover memory recording and reset behavior.
Place Hessians using memory reserves modelopt/torch/quantization/utils/calib_utils.py, modelopt/torch/quantization/model_calib.py, modelopt/torch/utils/perf.py, tests/gpu/torch/quantization/test_gptq.py
GPTQ uses available-memory checks and reserve thresholds to select Hessian placement. Its status message reports Hessian handle counts by device. Tests cover GPU selection and CPU fallback.
Quantizer state for offloaded forwards
Layer / File(s)
Summary
Carry quantizer state across offloaded forwards modelopt/torch/quantization/plugins/accelerate.py, tests/gpu/torch/quantization/plugins/test_accelerate_gpu.py
Accelerate hooks move CPU quantizer state to the execution device before an offloaded forward, then restore or copy state to CPU and clear quantizer caches. Tests check that quantizer state tensors remain on CPU between uses.
sequenceDiagram
participant HFPTQ as hf_ptq.py
participant BatchProbe as get_max_layerwise_batch_size
participant DecoderLayer as decoder layer
participant CUDA as CUDA devices
participant Ranks as distributed ranks
HFPTQ->>BatchProbe: request maximum batch size
BatchProbe->>DecoderLayer: capture inputs for representative layer
BatchProbe->>CUDA: measure forward memory
CUDA-->>BatchProbe: per-device peak allocation
BatchProbe->>Ranks: gather candidate batch sizes
Ranks-->>BatchProbe: minimum batch size
BatchProbe-->>HFPTQ: selected batch size
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Possibly related PRs
NVIDIA/Model-Optimizer#2864: Introduced the earlier layerwise batch default and fixed-fraction GPTQ Hessian placement that this PR changes.
NVIDIA/Model-Optimizer#2995: Uses the automatic layerwise batch selection contract introduced here for deterministic runs.
NVIDIA/Model-Optimizer#3019: Changes the GPTQ update path whose memory is measured for Hessian-placement headroom.
Suggested reviewers:fridah-nv, meenchen
Merge Risk: Moderate · previous review 03fb8
Initial calibration input capture can abort instead of retrying with a smaller batch. Confirm that bias-calibrator state is also restored off the GPU before merging.
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changed the title
[4/4] Place GPTQ Hessians by measured headroom instead of a 65% usage cap
[3/3] Place GPTQ Hessians by measured headroom instead of a 65% usage cap
Oct 22, 2026
❌ Patch coverage is 96.35417% with 7 lines in your changes missing coverage. Please review.
✅ Project coverage is 79.27%. Comparing base (b4f3003) to head (8c7a668). ⚠️ Report is 1 commits behind head on main.
### 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>
### 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>
cjluo-nv
changed the title
[3/3] Place GPTQ Hessians by measured headroom instead of a 65% usage cap
Fit layerwise calibration to GPU memory: place Hessians by measured headroom and probe the batch size
Oct 23, 2026
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @modelopt/torch/quantization/utils/calib_utils.py:
- Around line 98-100: Reset peak CUDA memory statistics for each process-held
GPU immediately before the layerwise GPTQ caller enters the tracking context and
invokes forward_loop(model). Keep track_calib_forward_memory() unchanged so
callers sharing CUDA peak counters retain its documented no-reset behavior.
Review comments at @modelopt/torch/quantization/utils/layerwise_calib.py:
- Around line 651-652: Update the batch-2 probe in _layer_batch_fit to catch
torch.cuda.OutOfMemoryError, release cached CUDA memory, and return 1 so all
ranks can continue; re-raise the exception for either batch-1 probe. Keep the
existing probe and peak-measurement behavior unchanged otherwise.
- Line 697: Update the representative-layer grouping at kinds.setdefault to
include memory-relevant per-layer dimensions, such as the MLP intermediate size,
so layers with different memory requirements are not grouped together;
alternatively, probe every decoder layer.
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cjluo-nv
changed the title
Fit layerwise calibration to GPU memory: place Hessians by measured headroom and probe the batch size
Improve layerwise GPTQ memory and speed: Hessian headroom, batch probe and offload fixes
Oct 24, 2026
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @modelopt/torch/quantization/plugins/accelerate.py:
- Around line 141-144: Update the onloaded tensor restoration branch to preserve
updates made through `.data.copy_()`, which do not change `_version`. When the
saved master tensor matches the current tensor’s identity, shape, and dtype,
copy the current data into the master before restoring it; otherwise retain the
CPU fallback.
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PR #2945 needs fixes for quantizer writeback and probe OOM handling before merging.
Needs action:
Fix offloaded quantizer writeback in plugins/accelerate.py so updates through existing .data.copy_ setters survive; add a regression test.
Handle a two-sample probe OOM in utils/layerwise_calib.py by falling back to batch one, with cleanup and regression coverage.
Reuse shared GPU enumeration and available-memory helpers instead of duplicating them in calib_utils.py and layerwise_calib.py.
Move the new local imports to module scope, or document concrete circular-import or optional-dependency reasons.
No action needed:
The architectural rationale addresses full-model probing and Accelerate's dispatch-time state limitations; the implementation extends existing layerwise machinery.
Existing test edits are justified by the new memory-placement rule and batch cap, with previous assertions retained or adapted.
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Bot comment.
The _version check does not detect mutations through .data. Existing quantizer setters use exactly this pattern: _amax_setter_helper, pre_quant_scale, bias_value, and StaticBlockScaleQuantizer.global_amax call tensor.data.copy_(...). These updates leave the original tensor's version unchanged, so this branch restores the old CPU master and silently discards the update. Please make writeback reliable for these existing setters (for example, always copy back unless unchanged state can be established reliably), and test a pre-existing CPU buffer updated through its setter while materialized.
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Fixed in 24529bd. A quantizer's own parameters and buffers are now always copied back into their CPU master after an offloaded forward, so updates made through the .data.copy_ setters survive. The copy goes into the existing master rather than a new CPU tensor: for a Qwen3-30B-A3B MoE layer's per-block _amax that is 9-11 ms against a 76 ms .cpu(). Calibrator tensors, which are never written through .data, keep the version check. New GPU test test_offloaded_quantizer_keeps_what_its_setter_wrote sets a CPU-resident _amax through its setter while the layer is materialized; it fails without the fix.
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Bot comment.
The probe unconditionally runs batch two, but that can OOM even when batch one fits and layerwise calibration could complete with it. Neither _layer_batch_fit nor its caller catches this; OOM during input capture is additionally converted into a generic replay RuntimeError by the patched root, although no cached batch is being replayed here. Please handle probe OOM separately from calibration replay OOM, release temporary/captured inputs, and return a conservative batch-one fit when the one-sample forward succeeded. Add a test whose layer fits one sample but raises for two.
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Fixed in 24529bd. _layer_batch_fit now returns 1 when one sample fits but two run out of memory, either in the layer's forward or while capturing its inputs (where the patched root re-raises the OOM as a RuntimeError from it). It drops the probe's inputs and empties the cache first. Every rank then still reaches the allgather. An OOM at one sample still raises. The root's OOM message no longer says a cached batch was being replayed. New GPU test test_layerwise_batch_probe_falls_back_to_one_sample covers both places; it fails without the fix.
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Bot comment.
_used_gpus and _available_gpu_bytes duplicate the new _process_gpus and _available_bytes in calib_utils.py verbatim. Please put these GPU-memory primitives in a shared utility (such as modelopt/torch/utils/perf.py) and use it from both placement and probing, so the definition of available memory cannot drift.
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Fixed in 24529bd. Both copies are now get_process_gpus() and get_available_gpu_memory() in modelopt/torch/utils/perf.py, next to get_used_gpu_mem_fraction. Hessian placement and the batch probe both use them.
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Bot comment.
Please move _disable_use_cache to the top-level imports. 'Not needed by the rest of this module' is not a concrete reason to keep an import local. If a circular import actually requires this placement, document that cycle instead. The new TensorQuantizer import in plugins/accelerate.py::_own_quantizers likewise needs a module-level import or a concrete explanation.
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Fixed in 24529bd. _disable_use_cache and TensorQuantizer are now imported at module scope. Neither import had a cycle or an optional dependency behind it.
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Changes requested: prior fixes are resolved, but representative-layer grouping still misses GPU-specific memory limits in multi-GPU device maps.
Needs action:
Fix representative selection in utils/layerwise_calib.py to account for execution-device placement; add a two-GPU regression with identical layers and unequal available memory (see inline comment).
Confirm human review of the embedded agent instructions in the CodeRabbit comments; I treated them as untrusted data, not review directives.
No action needed:
✔️ Resolved since the last review: quantizer writeback, probe OOM fallback, stale peak counters, per-layer widths, duplicated memory helpers, and local imports.
The design rationale justifies extending existing layerwise machinery instead of full-model probing or dispatch-time-only offload hooks.
Existing test edits are justified by the new placement rule and batch cap; coverage is retained or expanded.
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Bot comment.
The representative key includes classes and shapes but not execution placement. With a single-process device_map putting identical decoder layers on cuda:0 and cuda:1, only the first layer is probed. Its forward grows memory only on cuda:0; _layer_batch_fit skips cuda:1 because its per-sample growth is zero. Consequently cuda:1's smaller available-memory budget never constrains the batch, and calibration can OOM when it reaches that GPU. The rank allgather does not help within one process. Include execution-device layout in representative selection (including Accelerate hook execution devices for offloaded tensors), or apply measured costs conservatively across all matching placements. Add a two-GPU regression with identical layer shapes and unequal headroom.
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Fixed in 03fb875. The probe still runs only the first layer of each kind, but now applies that layer's measured per-sample cost, fixed cost and cached-input bytes to every GPU that runs a layer of that kind. It finds those GPUs with get_module_device, so accelerate's execution devices count. Each of those GPUs' available memory then limits the batch, with no extra forwards. New 2-GPU test test_layerwise_batch_probe_fits_every_gpu_a_kind_of_layer_runs_on puts two identical layers on cuda:0 and cuda:1, with less memory on cuda:1. The batch is what cuda:1 fits: 28, against 113 without the fix.
When static bias calibration runs, TensorQuantizer.collect updates _bias_calibrator. Its _calib_bias, _calib_max, and _calib_min tensors are not in sources. They therefore remain on GPU after an offloaded layer finishes, and retained memory grows across such layers. Add _bias_calibrator to the state enumeration. (raw.githubusercontent.com)
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Review comment at @modelopt/torch/quantization/plugins/accelerate.py around
lines 97 - 101:
Update the offload state enumeration in the function containing `sources` to
include `quantizer._bias_calibrator` alongside `_parameters`, `_buffers`, and
`_calibrator`, so its calibration tensors are included in the offload state.
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @modelopt/torch/utils/perf.py:
- Line 104: Update the capacity estimate used by _gpu_has_room so cached
reserved-but-unallocated bytes are not treated as available for a large Hessian
unless the allocation path can recover from failure and use the CPU fallback;
otherwise use a conservative estimate based on allocatable free memory.
---
Outside diff comments:
Review comments at @modelopt/torch/quantization/plugins/accelerate.py:
- Around line 97-101: Update the offload state enumeration in the function
containing `sources` to include `quantizer._bias_calibrator` alongside
`_parameters`, `_buffers`, and `_calibrator`, so its calibration tensors are
included in the offload state.
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CodeRabbit's outside-diff comment on plugins/accelerate.py:97-101 is fixed in 03fb875. The offload hook now also carries a static-bias quantizer's _bias_calibrator tensors, treated like the amax calibrator's: copied back only when the forward replaced or modified them. New GPU test test_offloaded_layer_keeps_bias_calibration_off_the_gpu checks they are on the CPU after layerwise calibration of an offloaded layer. It fails without the fix.
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Nudge: prior correctness concerns are resolved, but embedded agent instructions in the CodeRabbit comments still require human review.
Needs action:
Confirm human review of the embedded agent instructions in the CodeRabbit comments; they were treated as untrusted data, not review directives.
No action needed:
✔️ 9 previous code concerns addressed, including GPU-specific batch limits and quantizer writeback; fragmentation accounting and bias-calibrator offload now have regression coverage.
The design rationale justifies extending existing layerwise probing and Accelerate hooks rather than using full-model probing or dispatch-time-only state management.
Existing test edits are justified by the changed Hessian placement rule and batch cap; coverage is retained or expanded.
GPU test results are author-reported; I inspected the code and tests but did not execute them.
LayerActivationCollector._early_stop_forward catches OOM around every root forward. Initial capture also invokes this path before replay inputs exist. A ForwardLoop callback that retries only torch.cuda.OutOfMemoryError can therefore abort when the handler converts the capture OOM to RuntimeError.
Preserve the original exception during capture. Convert only OOMs attributable to replay.
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Review comment at @modelopt/torch/quantization/utils/layerwise_calib.py around
lines 370 - 378:
Update the OOM handler in LayerActivationCollector._early_stop_forward to
preserve and re-raise the original torch.cuda.OutOfMemoryError during initial
input capture; convert it to RuntimeError only when the OOM occurs during replay
of cached inputs.
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Outside diff comments:
Review comments at @modelopt/torch/quantization/utils/layerwise_calib.py:
- Around line 370-378: Update the OOM handler in
LayerActivationCollector._early_stop_forward to preserve and re-raise the
original torch.cuda.OutOfMemoryError during initial input capture; convert it to
RuntimeError only when the OOM occurs during replay of cached inputs.
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keep it a constant in #2945, and don't add a flag yet.
Why a flag isn't needed now
You can already set the batch directly. --batch_size N skips the probe and the cap entirely. A flag for the cap would only help someone who wants the memory probe but with a different ceiling.
The cap adjusts itself. It counts tokens, not samples, so it shrinks with longer sequences: 64 samples at --calib_seq 512, 8 at 4096. Memory is the probe's job, not the cap's.
32K is the range we validated. Going from 8K to 32K took Qwen's layerwise calibration from 47.8 to 21.1 min. Nothing above 32K has been measured, and a flag would invite people to go past what we've tested. The probe's linear memory estimate is also least checked at large batches: GLM ran out of memory at batch 64 in its linear attention before fla was installed.
The library side is already tunable. get_max_layerwise_batch_size(..., max_batch_size=...) takes the ceiling as an argument. Only the hf_ptq.py example fixes it.
A Hessian stayed on a GPU only while the GPU was under 65% used, so on
large GPUs the Hessians of a big MoE went to the CPU with ~100 GB free:
on GLM-5.3-Flash over 4x GB300, half the MoE layers took 142 s instead
of 43 s. A GPU now takes a Hessian if, after it, the memory available to
an allocation (driver-free plus this process's cached blocks) still
covers 1.5x the most a GPTQ calibration forward has added on that GPU,
and at least 10% of it. Until a forward has been measured, the old 35%
reserve applies; each calibration run measures afresh. The Hessian log
line names where they went.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
…layer
hf_ptq.py gave layerwise calibration a fixed 8,192-token batch, which can
run out of memory on a model or GPU whose decoder-layer forward is larger
than the ones it was tuned on. get_max_layerwise_batch_size runs the first
decoder layer of each kind (by module classes) on one and two dummy samples,
takes the growth of its peak memory per GPU, and keeps the largest batch
whose forward stays within half of the GPU's available memory after the
inputs layerwise calibration caches. The token budget stays the cap. All
ranks agree on the smallest batch.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
The batch probe now keeps a layerwise batch within GPU memory, so the
token budget no longer has to be the guard against running out of it.
At 32,768 tokens (64 samples at --calib_seq 512) a model whose decoder
layers fit the larger batch runs its MoE layers' per-expert loops a
quarter as often; one that does not, such as GLM-5.3-Flash with its
linear attention, keeps the batch the probe picks.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
… OOM retries
Accelerate's offload hooks only move the tensors a module had when it was
dispatched, so the quantizer state calibration adds (NVFP4's per-block
_amax and its calibrator's copy, GGML payload pins) stayed on the execution
device for every CPU- or disk-offloaded layer. On a single L40S that grew
by about 8 GB over Qwen3.6-35B-A3B and ran out of memory near the last
layers at any batch size. The first time a module's weights are
materialized for weight access, its offload hook now also carries its
quantizers' state: GPU copies for a forward, the CPU master copy after,
with anything the forward changed copied back.
During a layerwise capture forward, an out-of-memory error made the
calibration forward loop split the data batch and retry. The layer in
'run' mode replays its cached batches one per call at their cached size,
so the retry could not shrink the work and replayed the next batch's
inputs instead, misaligning the captured activations or failing on an
empty cache. It now stops with an error that asks for a smaller batch.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
…lpers
- The offload hook now always copies a quantizer's own tensors back into
their CPU master after a forward. Quantizer setters (amax,
pre_quant_scale, bias_value, global_amax) write through .data, which
leaves _version unchanged, so the version check restored the stale
master and dropped the update. Copying into the existing master costs
9-11 ms per offloaded forward for a Qwen3-30B-A3B MoE layer's NVFP4
per-block amax (onload about 60 ms); a new CPU tensor cost 76 ms.
Calibrator tensors, which are never written through .data, keep the
version check.
- The batch probe returns 1 when one sample fits but two run out of
memory, in the layer or while capturing its inputs, instead of
stopping the run (and leaving other ranks waiting in the allgather).
- track_calib_forward_memory resets the peak counters first. Nothing
reset them between layers, so from the second layer on the measured
forward included the previous layer's Hessians and GPTQ updates and
the reserve sent Hessians to the CPU for nothing.
- The probe groups decoder layers by parameter shapes as well as module
classes, so layers of one class with different widths, like
Nemotron-NAS's, are each probed.
- The GPU enumeration and available-memory helpers live once, in
modelopt.torch.utils.perf; the dataset_utils and TensorQuantizer
imports move to module scope.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
- The batch probe runs only the first layer of each kind, so with a
device_map spreading identical layers over GPUs, only that layer's GPU
limited the batch. Each kind's measured cost now applies to every GPU
that runs a layer of that kind, without extra forwards.
- get_available_gpu_memory leaves out inactive split blocks, the free
pieces of partly used cached segments: a large allocation such as a
Hessian cannot use them, so counting them could place a Hessian on a
GPU where allocating it runs out of memory instead of falling back to
the CPU. One memory_stats call replaces memory_reserved plus
memory_allocated, which is cheaper too.
- The offload hook also carries a static-bias quantizer's bias
calibrator, which otherwise stayed on the GPU for offloaded layers.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
…3019)
### What does this PR do?
Type of change: new feature (performance)
Adds `hessian_input_dtype` to the GPTQ and `local_hessian` algorithm
configs: `"float32"` (the default, today's behavior) or `"bfloat16"`.
`update_hessian` copies every batch of activations to float32 and
accumulates `XᵀX` with a float32 GEMM, which runs on CUDA cores. With
`"bfloat16"`, a tensor-core GEMM reads the activations in bfloat16 and
writes float32 (`out_dtype=torch.float32`). For bfloat16 activations,
the products are exact in float32, so it differs from the float32 GEMM
only in how it rounds the sums. It also needs no float32 copy of each
batch, which is twice the size of the bfloat16 batch.
Against a float64 reference, the bfloat16 option's GPTQ Hessian is off
by up to 2.8e-5 of the mean diagonal at 32K tokens per batch (1.3e-5 at
4K), versus 4.7e-6 for the float32 GEMM. A bfloat16 output would be off
by 9.2e-3. GPTQ then adds 1% of the mean diagonal as damping, so the
difference does not reach the result, which matches the KL below. The
Hessian is stored in float32 either way, so the option saves only the
transient float32 copy, not Hessian memory.
- When the Hessian lives on another device, as #2945 places it by free
memory, the float32 product is copied over.
- Where CUDA GEMMs don't take `out_dtype` (CPU inputs, or an older
torch), `"bfloat16"` falls back to float32 with a one-time warning
instead of being ignored. That check is cached per device.
- It applies to every GPTQ Hessian: fused MoE experts and linear layers
alike.
- `local_hessian` builds 16x16 per-block Hessians the same way, so it
gets the same option: a batched GEMM (`bmm`) that reads bfloat16 and
writes float32. On an RTX PRO 6000 at 32K tokens it is 6.3x faster for
4,096 features (2.47 -> 0.40 ms per update) and 7.7x for 2,048, and it
no longer makes the 512 / 256 MiB float32 copy. Its error is up to
1.4e-4 of the block diagonal, versus 1.9e-5 for float32. That Hessian
only weights the error of each candidate scale in the search, and it is
never inverted.
- The field lives in a small mixin both configs share. One helper,
`bfloat16_hessian_gemm`, decides the GEMM for both and warns when it is
unavailable.
- imatrix keeps its float32 statistic: it is a per-column sum of
squares, not a GEMM, so tensor cores do not apply.
### Usage
In a recipe or config:
```yaml
algorithm:
method: gptq
hessian_input_dtype: bfloat16 # default: float32
```
### Testing
**GEMM alone on GB300.** Accumulating about 360 batches of 1 to 2,000
tokens each into one Hessian, steady state:
| Hessian width | float32 GEMM | bfloat16 in, float32 out |
|---|---|---|
| 2,560 (Qwen3.8-Flash-Next experts) | 96 ms | **11 ms (8.7x)** |
| 4,096 (GLM-5.3-Flash experts) | 244 ms | **15 ms (16x)** |
**Real models on 4x GB300 (oci-jhb), float32 vs bfloat16.** Layerwise
GPTQ on the routed experts at IQ2_XXS, 1,024 x 512 calibration tokens,
KL to BF16 over wikitext-2 (100 x 512 tokens, second half scored).
Measured on top of #2945 and #3002, with flash-linear-attention
installed, before this PR made the option opt-in:
| | GPTQ, all layers | Layerwise calibration | Whole job | KL to BF16
(paired delta, 95%) |
|---|---|---|---|---|
| GLM-5.3-Flash | 730 s -> **553 s (-24%)** | 16:30 -> **13:09 (-20%)**
| 40:41 -> **36:04 (-11%)** | 0.6306 -> 0.6293 (-0.0013 ± 0.0080, p
0.75) |
| Qwen3.8-Flash-Next | 624 s -> **581 s (-7%)** | 15:26 -> **14:33
(-6%)** | 37:27 -> **35:46 (-5%)** | 0.2480 -> 0.2494 (+0.0015 ± 0.0052,
p 0.58) |
The Qwen run with the bfloat16 GEMM rebuilt the GGML CUDA extension for
its new source tree, which took 142 s. The table subtracts that one-time
build, which is unrelated to this change. KL moves within run-to-run
noise on both models.
- GPU test `test_bf16_hessian_input_gives_the_float32_hessian`:
`input_dtype=torch.bfloat16` gives the float64 reference Hessian to
within 1e-4 (the diagonal is about 2), with the Hessian on the inputs'
GPU and on the CPU. Where the fast GEMM exists, an update allocates less
than a float32 copy of its batch. Without the bfloat16 path the update
allocates 16 MiB, the float32 copy, and the test fails.
- GPU test `test_gptq_hessian_input_dtype_reaches_the_hessian`: through
`mtq.quantize` with a GPTQ config, `hessian_input_dtype: bfloat16`
lowers the measured Hessian-forward peak by the float32 copy of the
batch. It fails if the option does not reach `update_hessian`.
- New GPU tests in
`tests/gpu/torch/quantization/test_local_hessian_gpu.py`.
`test_bf16_local_hessian_input_gives_the_float32_hessian` checks the
per-block Hessian against float64, and that no float32 copy is made.
`test_local_hessian_input_dtype_reaches_the_hessian` runs `mtq.quantize`
with `local_hessian`, `hessian_input_dtype: bfloat16` and `debug: true`,
and gets a Hessian that differs from the float32 one only by rounding.
Each fails without its piece.
-
`tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.py`:
36 passed on 2x RTX PRO 6000. `test_gptq.py`,
`test_local_hessian_gpu.py` and `test_nvfp4_fp8_sweep_kernel.py`: 69
passed. The first version, with the bfloat16 GEMM always on, also passed
`test_gptq.py` on 4x GB300 (torch 2.13, transformers 5.16).
-
`tests/unit/torch/quantization/{test_gptq,test_ggml_gptq,test_local_hessian,test_config_validation}.py`
and `tests/unit/recipe`: 666 passed.
- New CPU tests
`test_bf16_hessian_input_on_cpu_gives_the_float32_result_and_warns_once`,
one each in `test_gptq.py` and `test_local_hessian.py`. On CPU, where
GEMMs take no `out_dtype`, `hessian_input_dtype: bfloat16` gives the
float32 result exactly and warns once over three batches. Without the
once-per-device cache they see 3 and 9 warnings and fail.
- **torch 2.8.0+cu128**, the oldest torch this package supports, on 2x
RTX PRO 6000 with transformers 5.18. `mm`, `addmm` and `bmm` all have
`out_dtype` overloads there, so every Hessian GEMM takes the fast path.
`tests/gpu/torch/quantization/{test_gptq,test_local_hessian_gpu}.py`: 24
passed, including the four `hessian_input_dtype` tests, which skip
without the fast path.
`tests/unit/torch/quantization/{test_gptq,test_local_hessian}.py`: 19
passed, on torch 2.8 and 2.12 alike. torch 2.6 has no such overloads, so
there `"bfloat16"` falls back to float32 with the warning.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ (new option; the default keeps
the float32 GEMM)
- 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?: ❌
🤖 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**
* GPTQ and local-Hessian calibration now support an optional `bfloat16`
input mode for Hessian calculations, while retaining float32
accumulation. This can reduce calibration memory use on supported CUDA
hardware.
* The default remains `float32`. If the hardware does not support the
required operations, calibration falls back to float32 and displays a
warning.
<!-- 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>
#3003)
### What does this PR do?
Type of change: documentation
Transformers runs the linear-attention layers of Qwen3-Next, Qwen3.5,
Qwen3.8, GLM-5.x Flash, Kimi Linear and OLMo Hybrid through readable
PyTorch reference kernels unless the
[flash-linear-attention](https://github.com/fla-org/flash-linear-attention)
kernels are importable. The reference path is slow and needs far more
memory per sample, which caps the layerwise calibration batch. This adds
a note to the `hf_ptq` README and one line to the ptq skill on
installing the kernels: `pip install fla-core`.
- **`fla-core` alone is enough.** The kernels Transformers looks for
(`fla.ops.kda.chunk_kda`,
`fla.ops.gated_delta_rule.chunk_gated_delta_rule`) live in `fla-core`,
which depends only on `einops`. The `flash-linear-attention` package
holds layers and models Transformers does not use, and pins
`transformers>=5.6.2,<6`.
- **GPU only.** Once the kernels are importable, Transformers uses them
for every device, and a CPU forward of these models then fails
(`RuntimeError: 0 active drivers`). That is also why the package is
documented rather than added to `examples/hf_ptq/requirements.txt`,
which the example CI installs.
### Usage
```bash
pip install fla-core
```
### Testing
Layerwise GPTQ on the routed experts at IQ2_XXS on 4x GB300 (oci-jhb),
1,024 x 512 calibration tokens, with #2945 and #3002,
`flash-linear-attention` and `fla-core` 0.6.0:
| | Without the kernels | With them |
|---|---|---|
| GLM-5.3-Flash: batch the probe picks | 16 | 64 (linear-attention
layers fit 181-295 samples instead of 16) |
| GLM-5.3-Flash: layerwise calibration | 35:41 | **16:30** (31:15 at
batch 16) |
| Qwen3.8-Flash-Next: layerwise calibration (batch 64) | 18:23 |
**15:26** |
- With `flash-linear-attention` installed but not `fla-core`, nothing
changed: Transformers could not import `fla.ops.kda.chunk_kda` and kept
the reference path. With `fla-core` alone, it binds
`fla.ops.kda.chunk.chunk_kda` for GLM-5.x and
`fla.ops.gated_delta_rule.chunk.chunk_gated_delta_rule` for Qwen, and a
tiny Qwen3-Next runs on GPU through them; on CPU it fails as described
above.
- The kernels compute slightly differently from the reference: the
unquantized GLM-5.3-Flash under them is 0.018 KL from the reference
logits, which were computed without them. KL to that reference after
GPTQ: Qwen 0.2493 -> 0.2480 (not significant); GLM 0.6189 -> 0.6230 at
batch 16 (+0.0041 +- 0.0045, p 0.36) and 0.6306 at batch 64 (+0.0118 +-
0.0050, p 0.02), paired per chunk over 100 x 512 wikitext-2 tokens.
### 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 (documents
an optional third-party package; nothing added to requirements)
- Did you write any new necessary tests?: N/A
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A
- 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
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<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **Documentation**
* Added guidance to install `fla-core>=0.5.2` before GPU calibration for
supported models with linear-attention layers, where optimized kernels
can reduce calibration time.
* Clarified that these kernels also run on CPU tensors but can cause CPU
runs to fail, so installation is recommended only in GPU environments.
* Included an example reporting over 2× faster layerwise GPTQ
calibration for GLM-5.3-Flash on 4× GB300.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Signed-off-by: Chenjie Luo <108829653+cjluo-nv@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
### What does this PR do?
Type of change: new feature (performance)
In a layerwise GPTQ run of a model that `device_map` spreads over
several GPUs, only the GPU holding the current layer did that layer's
work. The fused-MoE expert updates, a large share of each MoE layer's
time, ran one after another on that GPU while the others sat idle.
The updates of one layer's experts are independent. For helpers whose
update reads only the weight, its Hessian and the format's codec, the
expert updates now run in one worker thread per GPU this process already
uses that has room for an update. Each worker copies an expert's weight
and Hessian to its GPU, runs the update there and writes the result back
into the expert's weight. That covers the GGML formats when their CUDA
encoders are loaded (`GGMLGPTQHelper.updates_on_any_device()`); helpers
that read quantizer state from the weight's device, such as NVFP4's, and
GGML updates on the PyTorch fallback encoder, whose scratch grows with
`block_chunk_size`, keep running in turn.
- **Single process only.** In a multi-rank run, the GPUs this process
can see may belong to other ranks, so updates stay on the weight's
device. Hessian offloading from #2945 now takes its candidate GPUs from
the same helper.
- **Room check.** A GPU qualifies only if it has room for the largest
expert update: 8 Hessians (the Hessian, its inverse and their
temporaries), 2 float32 copies of the weight (the working copy and a
dtype-conversion temporary), up to 5 GPTQ blocks of columns (the block's
errors and up to four group-sized temporaries around each triangular
solve, each as large as the weight when the input is no wider than a
block), and 64 MiB for the CUDA codec's tables and scratch. Room is
measured with #2945's `get_available_gpu_memory`, which also counts
PyTorch's reusable cached blocks. With fewer than two qualifying GPUs,
nothing changes.
- **Lazy setup.** The first update runs in the caller's thread, so setup
that is not thread-safe, such as loading a format's CUDA extension,
happens once before the workers start.
- **Autograd.** Grad and inference mode are per thread, so worker
threads take the caller's, as the imatrix encode workers do.
- **Offload.** `gptq()` drops its expert weight slices before leaving
each module's weight window, so under offload they don't keep that
module's onloaded weights on the GPU while the next module's are
onloaded.
The results are bit-identical to the sequential updates.
`hessian_input_dtype` (#3019) applies while the Hessians accumulate,
before any update runs, so it is unaffected. #3043 runs imatrix encodes
one thread per GPU, each weight on its own GPU; this PR moves GPTQ
expert updates to other GPUs. Both now share one runner,
`run_on_each_gpu`, which runs the first item in the caller's thread and
gives each worker the caller's grad and inference mode.
### Usage
No API change.
### Testing
**Real models on 4x GB300 (oci-jhb).** Layerwise GPTQ on the routed
experts at IQ2_XXS, 1,024 x 512 calibration tokens, KL to BF16 over
wikitext-2 (100 x 512 tokens, second half scored). Measured on top of
#2945, which sets the batch size and Hessian placement these runs used:
| | Without this PR | With this PR | KL |
|---|---|---|---|
| GLM-5.3-Flash (batch 16), layerwise calibration | 41:12 | **35:41
(-13%)** | 0.6197 -> 0.6189 (-0.0009 +- 0.0037, p 0.82) |
| Qwen3.8-Flash-Next (batch 64), layerwise calibration | 21:08 | **18:23
(-13%)** | 0.249348, identical per token |
Qwen's KL is identical token by token. GLM's moves within run-to-run
noise: its Hessian placement varies between runs with free memory (for
example 222 vs 217 experts' Hessians on one GPU), which changes how they
are summed.
**Update step alone, 2x RTX PRO 6000.** One fused-MoE layer of 32
experts, IQ2_XXS, 64 expert updates:
| Expert shape (hidden x intermediate) | One GPU | Two GPUs | Peak per
worker GPU | Room check (block size 256) |
|---|---|---|---|---|
| 2,048 x 768 | 0.49 s | 0.32 s | 108 MiB | 224 MiB |
| 4,096 x 1,536 | 1.39 s | 0.89 s | 432 MiB | 687 MiB |
- New GPU test `test_expert_updates_on_another_gpu_match_one_gpu` (2
GPUs, with and without inference mode): expert updates spread over two
GPUs give bit-identical weights and codes to running them in turn on
one, with the codes pinned on the weight's GPU. Each expert input spans
two GGML groups, so the Hessian shapes what is compared. It fails when
the GGML helper's updates stay on their own GPU, when the codes are left
on the worker's GPU, and when a worker uses only the Hessian's diagonal.
- New unit test
`test_gptq_drops_each_fused_experts_weights_before_the_next_window`: no
updated expert weight slice outlives its module's weight window. It
fails without the fix.
- Room check, measured on 2x RTX PRO 6000 (peak allocated on the worker
GPU for one update, CUDA encoder): across 17 combinations of format
(IQ1_S, IQ1_M, IQ2_XXS, IQ2_XS, IQ2_S, Q8_0), expert shape and block
size (256 to 2048), the peak is 29-63% of the room. For example, IQ2_XXS
65536x256 at block 256 peaks at 325 MiB (room 514), 16384x2048 at block
2048 at 361 MiB (1088), and 28672x4096 at block 1024 at 879 MiB (2032).
A Hessian-only bound was exceeded by every expert weight wider than its
input, and a bound without the block term by any input no wider than a
block.
- New GPU test `test_an_update_that_may_move_fits_its_room`: an update a
helper lets move peaks on the worker GPU within its room check (IQ2_XXS
16384x2048 and 65536x256, and IQ1_S 1024x512, on the CUDA encoder; IQ1_S
on the PyTorch fallback). It fails when fallback updates may move (IQ1_S
1024x512 peaks at 96 MiB against 76 MiB), with a Hessian-only bound, and
without the block term (IQ2_XXS 65536x256 peaks at 325 MiB against 194
MiB).
- A local script (not a committed test) ran this with
`hessian_input_dtype: bfloat16`. Every expert Hessian accumulation took
the bfloat16 GEMM, and the two-GPU updates matched one GPU bit for bit.
- After rebasing onto #3122 and addressing review:
`tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate,test_local_hessian_gpu,test_ggml_imatrix_gpu,test_iq_formats_cuda}.py`
117 passed on 2x RTX PRO 6000 (CI's GPU runner has one GPU and skips the
2-GPU tests).
`tests/unit/torch/quantization/{test_gptq,test_ggml_gptq,test_ggml_imatrix,test_local_hessian,test_layerwise_calibrate}.py`
and `plugins/test_fused_experts.py` 250 passed on CPU (transformers
5.18).
- Full suites: locally at df3f78f, on H200 at 958a02d (958a02d
only adds the extension guards to two `test_gptq.py` tests, re-run
locally):
- `tests/gpu/torch/quantization` on 2x RTX PRO 6000 (torch 2.12,
transformers 5.18): 1050 passed, 35 skipped, 8 failed. 4
`test_quant_conv_gpu` wan cases fail on the local `diffusers`, which
lacks `autoencoder_kl_wan`. 4 `test_q8_0_cuda` cases fail the same way
at the base (55898ee) on these GPUs.
- `tests/gpu/torch/quantization` on 4x H200 NVL (computelab, TRT-LLM
1.4.0rc3 image: torch 2.14, transformers 5.5.4), split over two GPU
pairs: 989 passed, 41 skipped, 1 failed.
`test_fsdp2_gradient_qwen_moe_autoquant` fails the same way at the base
in this image (FSDP2 `set_model_state_dict` gets an empty state dict).
Not collected: `test_deepspeed` (no deepspeed) and two ONNX-export
modules. All of `test_gptq.py`, including the 2-GPU tests, passed.
- `tests/unit/torch/quantization` on CPU: 1595 passed, 1 skipped. Not
collected: three diffusers and ONNX-export modules, from stale local
packages.
- Not run: multi-rank runs, where the change does nothing; real models
after rebasing onto #3019, #3043 and #3122, which do not touch the
update step.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ (no API change; bit-identical
results)
- 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)?:
N/A (speeds up GPTQ on fused MoE experts, which is unreleased)
- 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
* **New Features**
* GPTQ calibration can distribute eligible expert-weight updates across
multiple GPUs and run updates on a selected device.
* Calibration can choose Hessian storage and available GPUs based on
memory availability.
* **Bug Fixes**
* Expert-weight updates now keep packed quantized data aligned with the
expert’s device.
* Calibration tracks updated and unreached experts when processing
expert weights.
<!-- 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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meenchen
left a comment
Follow-up to #2864 (merged). Four commits, all about the GPU memory and speed of layerwise GPTQ calibration:
The first two fit together. The batch probe leaves half of a GPU's available memory for quantizers and Hessians, and the Hessian placement keeps room for the measured calibration forward.
What does this PR do?
Type of change: Bug fix (performance), new feature
1. Hessian placement by measured headroom
GPTQHelperkept a Hessian on a GPU only while the GPU was under 65% used (_HESSIAN_GPU_MEM_LIMIT, inherited from GPTQ Lite). The rule exists so the calibration forward that follows still has room. But a fraction of the card is a poor proxy for that:On GLM-5.3-Flash over 4x GB300 (layerwise GPTQ on all experts, IQ2_XXS), every GPU the process used passed 65% halfway through the model. From then on each MoE layer's Hessians went to the CPU and the layer took 142 s instead of 43 s: 21 of 42 MoE layers, 34.5 extra minutes of an 83-minute calibration. Each GPU still had 90-100 GB free.
A GPU now takes a Hessian if, after allocating it, the memory available to an allocation still covers a reserve. Available means the driver's free memory plus this process's cached blocks, minus the free pieces of partly used blocks, which a large allocation cannot use. The reserve is 1.5x the most a GPTQ calibration forward has added on that GPU, and at least 10% of it.
gptq()'s Hessian forward as peak minus allocated-before. PyTorch's peak counters are reset first. Otherwise an earlier peak, such as the previous layer's Hessians and GPTQ updates, would count as forward memory and send Hessians to the CPU for nothing.Computing Hessians ...log line now says where the Hessians went.2. Layerwise batch probe
get_max_layerwise_batch_size(inmodelopt/torch/quantization/utils/layerwise_calib.py) probes the batch the wayget_max_batch_sizedoes for full-model calibration, but per decoder layer:device_mapover several GPUs, the GPU with the least headroom sets the batch. A warm-up forward first keeps one-off buffers such as cuBLAS's workspace out of the measurement.--calib_seq 512) after the third commit, up from [2/3] Speed up layerwise GPTQ on MoE models #2864's 8,192.hf_ptq.pycalls it only when--batch_sizeis left at its default; an explicit value passes straight through.The second commit also corrects two statements about padding.
hf_ptq.pypads every calibration sample to one length before batching, so batch size doesn't change which tokens are padding. #2864's changelog entry and the single-GPU PTQ blog note said otherwise.4. Offloaded calibration
Quantizer state of offloaded layers piled up on the GPU. Accelerate's offload hooks only move the tensors a module had when it was dispatched. The state calibration adds afterwards stayed on the execution device for every offloaded layer: NVFP4's per-block
_amax(about 0.25 bytes per weight), its calibrator's copy, and GGML payload pins. The first time a module's weights are materialized for weight access, its offload hook now also carries its quantizers' state. A forward gets GPU copies; afterwards the CPU tensors are the master copy again. A quantizer's own tensors are always copied back into that master first, because its setters (amax,pre_quant_scale,bias_value,global_amax) write through.dataand leave_versionunchanged. For a Qwen3-30B-A3B MoE layer's per-block_amaxthat copy takes 9-11 ms, against about 60 ms to onload the layer. The calibrators' tensors (the amax calibrator's and, under static bias, the bias calibrator's) are copied back only when the forward replaced or modified them. Tensors the hook already manages, such as buffers under a hook that places submodules, are left alone. GGML's packed-weight cache is keyed to the weight that was just released, so it is dropped with it.A GPU OOM in a layerwise capture forward made it replay the wrong activations. The calibration forward loop catches an OOM, splits the data batch and retries. In a layerwise capture forward, the only real work is the previous layer, in 'run' mode, replaying the batches it cached, one per call and at their cached size. The retry cannot shrink that work, and each retried call replays the next cached batch. The captured activations then no longer match the data batch, or the cache runs out first (
AssertionError: Layer model.layers.43 is in 'run' mode but has no cached inputs to replay). An OOM there now stops with an error asking for a smaller calibration batch size.Usage
No API change and no new flag. With a layerwise recipe and no
--batch_size,hf_ptq.pynow prints the probed batch:Testing
Hessian placement on 4x GB300. Layerwise GPTQ on the routed experts at IQ2_XXS, batch 16, 1,024 x 512 calibration tokens, KL to BF16 over wikitext-2 (100 x 512 tokens, second half scored). "Before" is #2864 at 34a1cd8. "After" is the first commit, which also includes #2864's later per-expert-log commit, worth about 2 s per MoE layer.
Batch probe on the same models and GPUs (second commit, cap 16), run up to the probe and stopped before calibration, also without the cap:
GLM-5.3-Flash previously ran out of memory at batch 64 in its linear attention (
chunk_kimi_delta_attention) and ran fine at 16. Uncapped, the probe picks 16. Both models get batch 16, the batch the timings above used.Token cap 32,768 (third commit) on the same models and GPUs, full layerwise GPTQ, KL paired per chunk against the runs above:
Smaller GPUs (computelab), single GPU, all three commits:
hf_ptq.pywith layerwise NVFP4 GPTQ (general/ptq/nvfp4_default-kv_none-gptq), 512 x 512 cnn_dailymail calibration tokens, default--batch_size. GPU memory sampled every 2 s withnvidia-smi.--max_gpu_memory_gb 40, rest on CPU--max_gpu_memory_gb 30, per-layer export--max_gpu_memory_gb 30, per-layer exportOffloaded without per-layer export (fourth commit). Without
layerwise.export_dir, every finished offloaded layer's quantizer state stayed on the GPU. On the L40S with a 30 GB weight budget that grew by about 8 GB over the model and ran out of memory near the end, even after splitting the batch down to one sample. With the fourth commit, same L40S, no per-layer export, default batch size:GPU memory still rises while the layers that
device_mapkeeps on the GPU are calibrated, since their quantizer state belongs there. It stays flat over the offloaded layers, then rises during export. On phi-2 with CPU offload (--max_gpu_memory_gb 2), memory allocated during calibration grew about 30 MiB per offloaded layer before and is now flat, ending at 2,075 MiB instead of 2,723.hf_ptq.py's export there is bit-identical tomain's, every tensor, and so are its generations before and after PTQ, with layerwise calibration time unchanged (9 min 44 s vs 10 min 33 s on a shared GPU).hf_ptq.pywith layerwise GPTQ on phi-2, single GPU and--use_fsdp2on 2 GPUs: the probe picks 16, calibration runs all 32 layers, and the export completes.Tests, on this branch, 2x RTX PRO 6000 (CI's GPU runner has one GPU and skips the 2-GPU ones):
tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.py29 passed. The offload and export tests (tests/gpu/torch/quantization/plugins/test_accelerate_gpu.py,tests/gpu/torch/export/{test_offload_export,test_vllm_fakequant_hf_export}.py,tests/gpu/torch/utils/test_model_load_utils.py) also pass: 29 of 30 in one run, wheretest_transformers_mse_calibrate_fp32_amax_save_restore[dtype0]hit the 120 s timeout as the first test on a shared GPU, as it does onmain; alone it passes in 3 s. CPU:tests/unit/torch/quantization/{test_gptq,test_ggml_gptq,test_layerwise_calibrate,test_calib}.py,plugins/{test_accelerate,test_huggingface}.py,tests/unit/torch/export/test_offload_export.pyandtests/examples/hf_ptq/test_hf_ptq_args.py214 passed. A wider CPU run on the first commit alone (addingtest_ggml_imatrix,test_quantize_cpu,plugins/test_fused_experts.pyandtests/unit/recipe) passed 882.test_a_measured_calibration_forward_sets_the_hessian_reserve: at 30% available, a GPU keeps a Hessian only once a small forward has been measured. It falls back with no measurement, or with a large one.test_gptq_records_its_calibration_forward_memory: a forward that allocates 64 MiB raises the recorded need at least that much.test_a_calibration_run_forgets_earlier_forward_measurements: a stale measurement does not survive a newmtq.quantize. It fails without the reset.test_layerwise_batch_probe_fits_the_heaviest_kind_of_layer: a model with a second, heavier kind of decoder layer gets a smaller batch, and that layer's forward at the probed batch stays within half the budget. It fails when only the first layer is probed.test_layerwise_calibration_batch_reaches_the_dataloadergains a case where memory, not the token budget, sets the batch, and follows the new cap.test_layerwise_calibrate_cpu_offloadednow also checks that the offloaded layer's quantizer and calibrator state is on the CPU between uses. It fails without the fourth commit; its existing comparison with a GPU-only reference still passes.test_out_of_memory_in_a_replayed_layer_is_not_split: an OOM while a layer replays its cached batches, under the real calibration forward loop, raises instead of retrying. It fails without the fourth commit with theAssertionErrorabove.test_offloaded_quantizer_keeps_what_its_setter_wrote(GPU): an offloaded weight quantizer's CPU_amax, set through its setter while the layer is materialized, keeps the new value.test_layerwise_batch_probe_falls_back_to_one_sample(GPU): a layer that runs out of memory on two samples, in its forward or while its inputs are captured, gives a batch of one.test_gptq_records_its_calibration_forward_memorynow also allocates a 1 GiB peak before GPTQ and checks that it is not counted.test_layerwise_batch_probe_fits_the_heaviest_kind_of_layergains a case where the heavier layer has the same class but a wider FFN.tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.pyandplugins/test_accelerate_gpu.py50 passed.tests/unit/torch/utils385 passed. Intests/unit/torch/quantization, one test fails,test_utils.py::test_persistent_materialization_only_skips_unshard_for_its_layer[True], and it fails on the previous head too. Two other modules there need optional packages this environment lacks.test_layerwise_batch_probe_fits_every_gpu_a_kind_of_layer_runs_on(2 GPUs): two identical layers on cuda:0 and cuda:1, with less memory on cuda:1. The batch is what cuda:1 fits (28, against 113 when only cuda:0 counts).test_available_gpu_memory_is_what_one_allocation_can_get(GPU): after fragmenting the cache, a single allocation of everything reported beyond the driver's free memory comes from the cache without reserving more.test_offloaded_layer_keeps_bias_calibration_off_the_gpu(GPU): a static-bias input quantizer's bias calibrator is on the CPU after layerwise calibration of an offloaded layer.tests/gpu/torch/quantization/{test_gptq,test_layerwise_calibrate}.pyandplugins/test_accelerate_gpu.py53 passed.tests/unit/torch/quantization/{test_layerwise_calibrate,test_gptq,test_ggml_gptq}.py,plugins/test_accelerate.pyandtests/unit/torch/utils470 passed.Before your PR is "Ready for review"
--batch_sizeis unchanged and the default can only shrink from [2/3] Speed up layerwise GPTQ on MoE models #2864's)CONTRIBUTING.md: N/AAdditional Information
The constants (1.5x margin, 10% floor and 35% before measuring for Hessians; half of available memory for the probed batch) are conservative choices, not tuned values.
Review thread on #2864: #2864 (comment)
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