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Collect imatrix statistics with one fused float32 reduction - #3044
imatrix collects each input's per-column sum of squares in its forward hook. It did so by copying 1,024-row slices of the bfloat16 inputs to float32, squaring them and summing. On a GPU, torch.linalg.vector_norm(x, 2, dim=0, dtype=torch.float32) squares and accumulates in float32 inside the reduction kernel, without a float32 copy of the inputs. Squaring its result gives the same statistic.
The hook runs once per weight per forward batch, and for an HF fused-MoE layer once per expert: about 97K calls in a GLM-5.3-Flash statistics forward and 246K in Qwen3.8-Flash-Next's. At an expert's share of the tokens, a call's cost is its kernel launches, so the hook uses as few as its memory allows:
Up to 1,536 rows: one reduction. From 1,021 rows, a CUDA reduction this long spreads each column over several thread blocks and stages their partial sums in a global-memory buffer of 8 bytes per input element. The float32 slices peak at 12 bytes per element of a 1,024-row slice, so one reduction stays within them up to 1,536 rows, where the two are equal.
Longer inputs: 512-row groups. The hook reduces a (groups, 512, columns) view in one call, then adds up the group sums and the rows left over. Inputs over 262,144 rows (512 groups) go through in slices of that many rows. No reduction spans more than 512 rows or 512 group sums, so the temporaries stay at most 8 MiB for 4,096 columns at any input length, against 48 MiB for the slices. One reduction over all rows would stage up to 512 MiB.
The fused reduction runs only for bf16, fp16 and fp32 inputs on a GPU. Everything else keeps the float32 slices:
On a CPU, the same call copies the whole input to float32 first. The profiler shows 512 MiB for a 32,768 x 4,096 bf16 input, against 16 MiB for the slices.
The call rejects float64 inputs ("convertible without narrowing").
On a GB300, per call. Up to 8,192 rows the times are wall time per call; above that, GPU time and peak temporary memory:
bf16 input (rows x columns)
Float32 slices (before)
One reduction over all rows
This PR
16-700 x 4,096
0.055 ms
0.024 ms
0.024 ms (2.3x)
1,040 x 2,560 (Qwen3.8's average expert share)
0.101 ms
0.027 ms
0.027 ms (3.7x)
1,500 x 2,560
0.103 ms
0.027 ms
0.028 ms (3.7x)
3,700 x 4,096 (GLM-5.3's average expert share)
0.188 ms
0.027 ms
0.079 ms (2.4x)
8,192 x 4,096
0.364 ms
0.027 ms
0.043 ms (8.4x)
32,768 x 4,096
1.39 ms, 48 MiB
0.067 ms, 152 MiB
0.066 ms (21x), 1 MiB
131,072 x 4,096
5.26 ms, 48 MiB
0.190 ms, 256 MiB
0.187 ms (28x), 4 MiB
524,288 x 4,096
21.2 ms, 48 MiB
0.730 ms, 512 MiB
0.718 ms (29x), 8 MiB
1,048,576 x 4,096
42.0 ms, 48 MiB
1.45 ms, 512 MiB
1.44 ms (29x), 8 MiB
Between 1,537 and about 8,192 rows, the groups and the leftover rows take six launches, so this PR is slower there than one reduction over all rows, whose buffer would exceed the slices' (about 115 MiB at 3,700 x 4,096). The relative error against float64 stays at or below 3e-7 on an RTX PRO 6000 (2.1e-7 at 524,988 x 4,096); the slices reach 1e-6 at 512K rows.
I also tried torch.compile and a Triton kernel. Neither is faster at expert sizes: a compiled call took 0.16-0.31 ms on an RTX PRO 6000, slower than the slices, and the Triton kernel took 0.042 ms per call on GB300, against 0.027 ms for one reduction.
Usage
No API change.
Testing
Real models on 4x GB300 (oci-jhb): single-pass IQ2_XXS imatrix, 1,024 x 512 calibration tokens, flash-linear-attention installed, this PR at d5b4249ea4. Each model ran in one job that timed the statistics forward with main's hook and this PR's in turn (old, new, old, new) after a warm-up forward, and discarded those passes' statistics:
Statistics forward, old hook
This PR
KL to BF16 (paired delta, 95%)
GLM-5.3-Flash (all experts)
131.5 s, 131.6 s
116.4 s, 116.4 s (-11.6%)
0.721839 -> 0.721824 (-0.0000 ± 0.0018)
Qwen3.8-Flash-Next (gate_up experts)
227.7 s, 228.3 s
211.8 s, 209.1 s (-7.7%)
0.287096 -> 0.286681 (-0.0004 ± 0.0016)
Timing both hooks in one process matters. A run's first forward carries one-time setup of 10-37 s that varies between runs (GLM's warm-up forward took 136 s here), which hid GLM's gain when each hook ran in its own job.
The KL deltas are float32 rounding: the statistic differs from the slices' in the last bits, which changes the encoder's pick where two codes nearly tie. The old-hook reference is the mean of two runs for GLM, whose KL varies between runs (0.7210-0.7227), and one run for Qwen, whose old-hook KL is identical token by token across runs.
The whole job barely changes, since loading, encoding and KL scoring take most of it.
New GPU test test_imatrix_statistic_takes_no_more_gpu_memory_than_float32_slices, at 1,536, 2,048 and 33,068 rows x 4,096 and at 1,048,876 x 512. It measures the peak temporary memory of the fused reduction and of the float32 slices on the same input, and requires the fused one to be no higher. Peak memory is what decides whether a run fits, so this is the same check as running both under equal headroom. At 1,536 rows the two peaks are equal to the byte. One reduction over all rows fails the three longer cases, a single-reduction threshold of 2,048 rows fails the 2,048-row case, and summing all 512-row groups at once fails the last.
New GPU test test_imatrix_second_moments_of_bf16_inputs_on_gpu, for 1,500, 2,048, 2,500 and 262,844 rows: one reduction, whole groups only, groups and leftover rows, and two slices of 512 groups. It runs bf16 inputs whose columns differ widely in scale, and compares the importance with the float64 statistic to a relative 1e-5. Breaking the single reduction, dropping the groups or the leftover rows, or keeping only the first slice fails the matching cases.
test_imatrix_weights_the_search_by_the_root_of_the_inputs_second_moments now also runs float64 inputs. It fails on the first commit of this PR with the narrowing error.
tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py: 10 passed on 2x RTX PRO 6000. tests/unit/torch/quantization/{test_ggml_imatrix,test_ggml_gptq}.py and plugins/test_fused_experts.py: 177 passed. These include the existing CPU statistic tests, dense and per expert of fused MoE experts, which run the slices.
The 1,021-row switch and the equal peaks at 1,536 rows hold on both the RTX PRO 6000 (torch 2.12) and GB300 (torch 2.13).
Before your PR is "Ready for review"
Is this change backward compatible?: ✅ (same statistic to float32 rounding)
If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
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Walkthrough
Imatrix calibration now uses a dedicated helper to calculate per-column sums of squares. CUDA inputs with supported dtypes use grouped float32 vector norms. Other inputs use chunked float32 square-and-sum operations. Tests cover GPU importance calculations, peak GPU memory allocation, and float32 and float64 inputs.
Changes: Imatrix calibration
Layer / File(s)
Summary
Input-column reduction and validation modelopt/torch/quantization/model_calib.py, tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py, tests/unit/torch/quantization/test_ggml_imatrix.py
The capture path uses _imatrix_sum_of_squares. Supported CUDA inputs use groups of 512 rows, including a separate reduction for a trailing partial group. CPU inputs and GPU inputs with unsupported dtypes use 1024-row float32 chunks. GPU tests compare BF16 importance values with a double-precision reference and check peak allocation. The unit test covers float32 and float64 inputs.
Docstring coverage is 66.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…
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PASS. The pull-request diff changes one modelopt Python file and two test files. The added modelopt code only performs tensor reductions and adds no torch.load(..., weights_only=False), numpy.load(...…
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PASS. The pull-request diff changes one modelopt Python file and two test files. The added modelopt code only performs tensor reductions and adds no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), trust_remote_code=True, eval(), exec(), or # nosec usage. It also adds no dependency changes in pyproject.toml or requirements.txt.
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Inline comments:
Review comments at @modelopt/torch/quantization/model_calib.py:
- Line 2681: Restore bounded CPU reduction in imatrix_calibrate: for CPU inputs
not already float32, accumulate squared row chunks of at most 1,024 rows in
float32 instead of converting the full input at once. Keep the existing
vector_norm reduction for CUDA, XPU, and CPU float32 inputs.
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modelopt/torch/quantization/model_calib.py
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Comment: the new reduction rejects previously accepted float64 calibration inputs (fresh-20261028T202642Z/PR-3044).
Needs action:
Fix float64 handling in model_calib.py:2679, retaining bounded float32 conversion for that dtype; extend the existing dense imatrix statistic test to catch this regression. See inline comment.
Provide SHA-addressable base/head snapshots to complete the pinned review; the available file API exposes only PR-branch contents without commit metadata.
Remove the benchmark claim at model_calib.py:2678; CONTRIBUTING.md requires benchmark numbers to stay in the PR description.
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Bot comment.
[P2] Preserve float64 calibration inputs. torch.linalg.vector_norm requires its requested dtype to accommodate the input without narrowing, so passing float64 x with dtype=torch.float32 raises a RuntimeError. The deleted implementation explicitly used rows.float() and handled this input. This is reachable through mtq.quantize(nn.Linear(512, 8, bias=False, dtype=torch.float64), <the existing test's _config(IMATRIX)>, lambda m: m(double_inputs)): _register_weight_input_hooks passes raw layer inputs, while GGMLImatrixEncoder.validate delegates to validate_weight, which accepts float64 weights. Calibration now stops before encoding. Please keep the fused path for the usual dtypes and retain a bounded float32-conversion fallback for float64, then parametrize the existing dense statistic test to check the float32 second moments for double inputs. This finding is from static analysis; I did not execute a reproduction.
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Confirmed and fixed in 7251213: vector_norm raised on float64 inputs, on both CPU and GPU. float64 now takes the chunked float32 path, as it did before this PR. test_imatrix_weights_the_search_by_the_root_of_the_inputs_second_moments now also runs float64 inputs. It fails on the previous commit with the narrowing error. A new GPU test, test_imatrix_second_moments_of_bf16_inputs_on_gpu, checks the fused bf16 statistic against float64.
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Bot comment.
Please remove the 3-6x faster benchmark line and retain the first line explaining the memory rationale. CONTRIBUTING.md explicitly says benchmark numbers belong in the PR description, not source comments; the description already contains the measurements.
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Done in 7251213: the timing is gone from the comment. The timings stay in the PR description. The comments now say why each path is used: on a GPU the fused reduction makes no float32 copy, and on a CPU it would.
imatrix summed each input's per-column squares by copying 1,024-row
slices to float32, squaring and summing them. torch.linalg.vector_norm
with dtype=float32 squares and accumulates in float32 inside one kernel,
with no float32 copy: 3-10x faster on an RTX PRO 6000 (1.19 -> 0.18 ms
for 32K x 4,096 bf16, 0.050 -> 0.016 ms for a small per-expert batch)
and as precise (both within 3e-7 of float64).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Review:
- On a CPU, vector_norm(dtype=float32) copies the whole input to
float32 first (512 MiB for 32K x 4,096 bf16, against 16 MiB for the
1,024-row slices), and it rejects float64 inputs outright. The fused
reduction now runs only for bf16, fp16 and fp32 inputs on a GPU;
everything else squares float32 slices as before.
- The dense statistic test also runs float64 inputs, and a GPU test
checks the fused statistic of bf16 inputs against float64.
- Drop the timing from the comment; it is in the PR description.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Inline comments:
Review comments at @modelopt/torch/quantization/model_calib.py:
- Line 2736: Update the sum_sq reduction to avoid unbounded CUDA scratch-memory
use for large BF16, FP16, and FP32 inputs: measure memory use against the
previous chunked float32 path at representative sizes and retain that chunked
path as a bounded fallback for memory-constrained runs.
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Nudge: the CPU and float64 regressions are fixed, but the CUDA memory tradeoff still needs validation before merging.
Needs action:
Confirm low-headroom CUDA behavior in imatrix_calibrate: the PR reports ~140 MiB scratch versus 30–48 MiB before. Provide a reproducible constrained-memory check, retaining a chunked fallback if previously fitting calibration runs fail.
No action needed:
✔️ Resolved since the last review: bounded CPU conversion, float64 acceptance with regression coverage, and source benchmark-comment removal. The existing test edit expands coverage without weakening assertions.
Review: one CUDA reduction over all of an input's rows stages partial
sums in a global buffer once it covers about 1,000 rows, and that
buffer grows with the input: 142 MiB for 32K x 4,096 bf16 rows and
512 MiB for 512K, against 48 MiB for the 1,024-row float32 slices.
Reducing a (groups, 512, columns) view in one kernel, then adding the
group sums and the rows left over, keeps every reduction below that
size. Up to 512K rows the temporaries take no more memory than the
slices did (1 MiB at 32K x 4,096), and about 1% of the input beyond,
at the speed of the single reduction.
A GPU test bounds the reduction's peak memory, and the bf16 statistic
test covers inputs with only groups, only leftover rows, and both.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Re the CUDA memory point in the latest bot review: confirmed, and fixed in bbe3b08. The ~140 MiB was not fixed. A single CUDA reduction over about 1,000 rows or more stages partial sums in a global buffer that grows with the input: 142 MiB at 32K x 4,096 bf16 and 512 MiB at 512K, against 48 MiB for the old 1,024-row float32 slices.
The hook now reduces 512-row groups in one call, then adds the group sums and the leftover rows. No reduction reaches the size where the buffer appears. Peak temporaries are now 1 MiB at 32K x 4,096 and 4 MiB at 131K. That is no more than the slices up to 512K rows, and about 1% of the input beyond. Speed is unchanged (6.3-6.9x the slices at 32K+ rows). With temporaries no larger than before, a calibration run that fit before still fits, so there is no separate fallback.
The reproducible check is test_imatrix_statistic_takes_no_input_sized_gpu_buffer in tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py. It measures the reduction's peak with torch.cuda.max_memory_allocated and requires it to stay below a float32 copy of 1,024 rows. The previous commit's single reduction fails it at 142 MiB. The PR description has the time and memory table from 16 rows to 1M rows.
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Comment: the CPU and float64 fixes are verified, but the grouped CUDA reduction still regresses peak memory on larger inputs.
Needs action:
💬 Author added 512-row grouping — still needs bounded group batches in model_calib.py:2683; the final reduction and its buffer remain unbounded. See inline.
Extend test_imatrix_statistic_takes_no_input_sized_gpu_buffer beyond 512K rows; compare the sliced and grouped paths under equal CUDA headroom.
Move the new memory benchmark numbers out of model_calib.py:2666, retaining a short rationale, as required by CONTRIBUTING.md.
No action needed:
✔️ Resolved since the last review: bounded CPU conversion and float64 acceptance with regression coverage. The unit-test edit expands coverage without weakening assertions.
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Bot comment.
[P2] Bound the number of groups too. The new grouping fixes the reported 32K-row peak, but group_sums contains [rows // 512, columns] float32 values and this reduction sums all groups at once. At 524,288 input rows, the final reduction already spans 1,024 groups; the 512-row limit does not apply to it. Both the group-results allocation and second-stage scratch can therefore grow beyond the old bounded path. The PR's own table reports 96 MiB at 1,048,576 × 4,096 versus 48 MiB before, contradicting the reply that previously fitting calibration runs still fit.
imatrix_calibrate.capture imposes no row limit, so a dense mtq.quantize(..., forward_loop=lambda m: m(inputs)) or a sufficiently large expert input still takes this path unconditionally and can OOM with headroom that was sufficient before. Could we process bounded outer batches of 512-row groups, or retain the sliced fallback for oversized inputs? Extend the GPU memory test beyond 512K rows and compare both paths with equal headroom; its current 33,068-row input has only 64 groups and cannot catch this remaining regression.
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Confirmed, and fixed in ee4941f. My earlier comment was wrong above 512K rows: summing every 512-row group at once let the group sums, and the reduction over them, grow with the input. That reached 96 MiB at 1M x 4,096 and 192 MiB at 2M, against 48 MiB for the slices.
Inputs over 262,144 rows (512 groups) now go through the grouped reduction in slices of that many rows. No reduction spans more than 512 rows or 512 group sums. On an RTX PRO 6000 the peak is 8 MiB at 4,096 columns for 512K, 1M and 2M rows alike, at the same speed (6.3-6.6x the slices). Inputs up to 262,144 rows take no extra step.
test_imatrix_statistic_takes_no_more_gpu_memory_than_float32_slices now runs at 33,068 x 4,096 and at 1,048,876 x 512. It measures the peak of the fused reduction and of the old 1,024-row float32 slices on the same input, and requires the fused one to be no higher. Peak memory decides whether a run fits, so that is the equal-headroom comparison, without depending on how a memory cap rounds allocations. bbe3b08 fails the 1M case, and one reduction over all rows fails both. test_imatrix_second_moments_of_bf16_inputs_on_gpu gained a 262,844-row case that crosses a slice boundary; returning only the first slice fails it.
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Bot comment.
[P3] The original timing comment was removed, but this introduces another device-specific benchmark (512 MiB for 512K × 4,096) into source. CONTRIBUTING.md requires benchmark numbers to stay in the PR description and comments to stay within one or two lines. Please retain only the rationale, for example: # Reduce short row groups to avoid large CUDA staging buffers. The description already contains the measurements.
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Done in ee4941f: the comment now keeps only the reason, "longer CUDA reductions stage partial sums in a global buffer that grows with the input". The measurements are in the PR description.
Review: summing every 512-row group at once still let the group sums,
and the reduction over them, grow with the input past 512K rows, to
96 MiB at 1M x 4,096 bf16 rows against 48 MiB for the float32 slices.
Inputs over 512 x 512 rows now go through the grouped reduction in
slices of that many rows, so no reduction spans more than 512 rows or
group sums: the temporaries stay at 8 MiB for 4,096 columns at any
length, at the same speed. Smaller inputs take no extra step.
The memory test compares the fused reduction's peak with the float32
slices' on the same input, at 32K rows and past 1M, and the bf16
statistic test covers an input split into slices. Drop the measured
sizes from the code comment.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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LGTM: the prior regressions are resolved; human sign-off remains for the justified existing-test edit.
Needs action:
Sign off on tests/unit/torch/quantization/test_ggml_imatrix.py: float64 parametrization expands coverage, and the float32 reference preserves the existing statistic contract.
No action needed:
✔️ 4 prior concerns resolved, including float64 compatibility and bounded CUDA memory with regression coverage beyond 512K rows.
At that size the hook's cost is its kernel launches. The grouped path's
six launches made inputs of 513 to 1,023 rows slower than the float32
slices (0.061 vs 0.046 ms at 700 x 4,096), and cost Qwen3.8's typical
1,040-row expert input 0.079 ms on GB300, against 0.027 ms for one
reduction.
One reduction stages partial sums only from 1,021 rows, at 8 bytes per
input element. The float32 slices peak at 12 bytes per element of a
1,024-row slice, so one reduction stays within them up to 1,536 rows.
The bf16 statistic test runs a 1,500-row input through that call. The
memory test adds 1,536 rows, where both peaks are equal, and 2,048,
which a higher threshold would exceed.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
…eir wrapper in its model folder (#3067)
> [!NOTE]
> **Split out of #2745 on 2026-10-27**, so that the Nemotron-H part is
reviewed on its own. #2745, #3122 and #3074 (the calibration this PR
builds on) have all merged.
>
> **Base:** `main`.
>
> **In the tree:** #2741's helper base merges this PR instead of #2745
and #3074. The expert-parallel chain quantizes Nemotron-H's experts on
the `torch_a2a` forward, which refuses an eager-only wrapper.
<details><summary>Series layout</summary>
- `main`, with #3088 (FSDP2 weight-access window, `_fsdp2_owned_params`)
and #2745 (fused experts under grouped_mm), both merged 2026-10-28
- #3122 expert-parallel experts calibrated and folded on each rank's own
experts (merged)
- #3074 calibration on grouped_mm (merged)
- **#3067 Nemotron-H's experts under grouped_mm (this PR)**
- #2739 meta-DCP checkpoint load (merged)
- #2736 distribution plan, run group, collectives (merged)
- #2740 calibration on per-rank data
- #2750 keep-fused fold
- #2751 distributed-writer pieces
- #2752 no-gather export (on #2751, #2750, #2779 and #2740)
- #2753 expert-parallel export
- #2737 plan decision
- #2779 load routing
- #2741 expert-parallel loader (on #2779, #3067 and #2740)
- #2742 dense sharding
- #2743 block-FP8 dequantization
- #2780 expert-parallel load routing
- #2746 expert parallelism in the model specs
- #2747 on-the-fly decompression
- #2748 Kimi-K2.5 and Kimi-VL adapters
- #2749 Kimi-K3 adapter
- #2754 expert-parallel PTQ from hf_ptq.py (on #2746 and #2753)
</details>
### What does this PR do?
Type of change: New feature
#2745 quantizes fused experts under `grouped_mm`, the experts backend
transformers selects by default, in the gated layout (`gate_up_proj`).
Nemotron-H's experts are non-gated, `down_proj(act_fn(up_proj(x)))`,
with a single 3-D `up_proj`. On #2745 and #3074 they keep main's generic
`_QuantNonGatedFusedExperts` and calibrate on the eager forward, which
main forces and #3074 keeps for them.
- **Their wrapper moves to Nemotron-H's model folder.**
`_QuantNemotronHExperts` lives in
`modelopt/torch/models/nemotron_h/modeling_ptq.py`. It registers on
transformers' `NemotronHExperts`, which every supported release (5.5 on)
ships, as `modelopt/torch/models/README.md` asks of wrappers only one
model needs. The generic plugin's `_QuantNonGatedFusedExperts` and its
`up_proj`-only structural detection are removed: no other transformers
experts class has that layout.
- **They calibrate on `grouped_mm`.** The wrapper is not eager-only:
#2745's grouped interception reaches the non-gated layout through
`_first_proj_attr`, and both forwards give each expert's quantizers the
same rows. Under expert parallelism (#2741 on), they run the `torch_a2a`
forward, which refuses an eager-only wrapper.
- **The backend normalization still finds them.**
`_is_fused_experts_module` also counts a class registered with a
`_QuantFusedExperts` subclass. So a backend the quantizers cannot see
(`batched_mm`) is still redirected for Nemotron-H. Transformers'
non-gated FP8 experts (an FP8 Nemotron-H's) are still forced onto eager.
**Scope.** This applies to transformers' built-in Nemotron-H
(`NemotronHExperts`), whose default experts backend is `grouped_mm`.
Loading a checkpoint's own modeling code instead (the Auto classes with
`trust_remote_code=True`) builds per-expert `nn.Linear` MLPs, whose
per-expert quantization this PR leaves unchanged.
`examples/hf_ptq/hf_ptq.py` builds the built-in model even with
`--trust_remote_code` whenever the built-in config represents the
checkpoint, as it does NVIDIA-Nemotron-3-Nano-30B-A3B's.
**Behavior change.** Nemotron-H's MoE calibration runs on `grouped_mm`
where transformers selects it, so some of its activation scales can
differ from main's, as #3074's changelog entry says for fused experts.
Loading with `experts_implementation="eager"` calibrates as before.
### Usage
No API change. To calibrate exactly as earlier releases did:
```python
model = AutoModelForCausalLM.from_pretrained(path, experts_implementation="eager")
```
### Testing
- `tests/unit/torch/models/test_nemotron_h_modeling_ptq.py` (new) runs
transformers' real `NemotronHExperts`. It covers:
- the registered wrapper and its `up_proj` quantizers;
- a passthrough forward on eager and on `grouped_mm`;
- `grouped_mm` and eager calibration and GPTQ Hessians agree for every
expert, up to rounding;
- the per-expert export of `up_proj` and `down_proj`;
- the redirect of an unsupported backend.
main's tests of the non-gated layout ran a hand-written stand-in, which
the registration does not reach, so they move here.
- `test_fused_experts.py`'s FP8 force-eager test also runs non-gated FP8
experts. It and the backend-redirect test fail with the registry lookup
in `_is_fused_experts_module` reverted.
- `tests/gpu/torch/models/test_modeling_ptq_registration.py` checks that
`NemotronHExperts` is registered whichever module is imported first.
- Locally at this head: pre-commit, mypy included;
`tests/unit/torch/{models,export}`, `test_fused_experts.py` and
`test_huggingface.py` on transformers 5.18 and 5.5.4 and on torch 2.8,
with the same failures as main in each environment (diffusers). The
series' PRs merged together add no unit-test failure over main.
- GPU, NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 (a BF16 checkpoint, no
`quantization_config`), one GPU, transformers 5.18.0,
`general/ptq/nvfp4_default-kv_fp8`, 64 calibration samples of 512
tokens, exported tensors compared one by one:
| Runs compared | Weight scales (12,008) | Expert input scales (5,934) |
Other input scales (70) |
|---|---|---|---|
| `grouped_mm`, twice (run-to-run floor) | all identical | all identical
| all identical |
| `grouped_mm` vs `experts_implementation="eager"` | all identical |
1,932 differ, max relative 4.39% | 32 differ, max relative 1.66% |
The weights and weight scales do not depend on the backend; activation
scales move with it, in the MoE layers and in the layers after them, as
#3074's changelog entry says for fused experts.
`tests/gpu/torch/models/test_modeling_ptq_registration.py` passes on 1
GPU.
- Re-cut onto #2745's last review fixes (2026-10-27): the plugin-order
test of the non-gated stand-in is dropped with the stand-in. Pre-commit
and this PR's unit tests pass on transformers 5.18 and 5.5.4. A
remote-code Nemotron-H keeps its experts as an `nn.ModuleList` of
`nn.Linear` MLPs, which the fused detection never claimed, and the
remote-code VL model builds transformers' `NemotronHForCausalLM`, whose
experts this PR registers.
- Review suggestion, 2026-10-27, last round: the wrapper's docstring
says the explicit registration skips the transposed-layout check and
relies on transformers' `(num_experts, out_dim, in_dim)` layout, which
every supported release uses.
- Rebased with #2745 onto main f03651e (2026-10-27, night); this
PR's own diff is unchanged.
- Review suggestions, 2026-10-27, last round: the test pins that
`grouped_mm` went through `_grouped_linear`, two calls rather than one
`F.linear` call per expert and projection, and that the export releases
the fused `up_proj` and `down_proj`. Re-cut onto #3074, the second half
of #2745's split; this PR's own diff is otherwise unchanged.
- Review suggestions, 2026-10-28: an already-converted Nemotron-H
experts module still counts as fused experts for a second `quantize()`,
pinned by the registration test, which fails without the change;
`up_proj`'s input amax is compared exactly, and only `down_proj`'s keeps
a tolerance.
- Review suggestion, 2026-10-28, second round: the converted-experts
check reuses `_is_quant_fused_experts_module`.
- Review suggestion, 2026-10-28, third round: `_is_fused_experts_module`
no longer counts already-converted experts, undoing the first round's
change: its one caller runs inside `replace_quant_module`, which refuses
a quantized model, so none reaches it.
- Declined review suggestion, 2026-10-28, third round, raised before: a
warning for unregistered non-gated fused experts. transformers'
`NemotronHExperts`, which is registered, is the only class with the
layout, and Nemotron-H's remote code builds per-expert MLPs.
- Declined review suggestions, 2026-10-28, fourth round: skip guards on
the grouped tests, since `NemotronHExperts` and `_grouped_linear` are in
every release the pin allows; and a shape assert in `_setup`, which
cannot tell a consistently transposed pair from the stored one, while
the export test built on transformers' own `NemotronHExperts` pins the
per-expert shapes.
- Declined review suggestion, 2026-10-28, fifth round (CodeRabbit):
resolving a converted experts module's original class in
`_is_fused_experts_module`; its one caller runs before any module is
converted.
- Rebased on 2026-10-28, late night, onto #3074 on main `1ca8bd1fc4d`
(after #3088 and #2745 merged), so the diff shows this PR's own slice
only (+292/−362 in 6 files). Conflicts were with #3074's later changes:
`_is_fused_experts_module`'s docstring keeps #3074's GPT-OSS sentence,
the FP8 force-eager test runs both #3074's backend cases and this PR's
gated and non-gated experts, and the FP8 wrapper's comment no longer
compares it with the removed non-gated wrapper.
- Review suggestion (cjluo-nv's bot), same night: the non-gated cases
that went with the stand-in run on the real `NemotronHExperts` again:
the eager forward recovers each `up_proj` slice's expert, and the
quantizer-only iterator yields the weight iterator's quantizers in its
order without indexing the fused weight (it fails if the iterator names
the gated projection). pre-commit passes against main;
`test_fused_experts.py` and this PR's tests pass on transformers 5.18
and 5.5.4.
- Rebased on 2026-10-28, late night, onto #3074 on main `bdc45368d10`
(#2736 merged), with its own diff unchanged. Tests and pre-commit pass
as for #3074.
- Restacked on 2026-10-29 onto #3074 after its expert-parallel fix moved
to #3122 underneath it, with its own diff unchanged (one import-list
conflict with #3074's new `assert_fused_experts_intercepted` import).
Its tests, `test_nemotron_h_modeling_ptq.py` and `test_moe_a2a.py` pass
(185), and pre-commit passes against main.
- Rebased on 2026-10-30 onto #3074 on main `99c3834f62c`, with its own
diff unchanged and no conflicts. pre-commit passes against main.
- Restacked on 2026-10-30 onto #3074 on main `55898ee4e3b` (#3122
merged), with its own diff unchanged and no conflicts. pre-commit passes
against main.
- Restacked on 2026-10-30 onto #3074 on main `059588b89a5`, with its own
diff unchanged and no conflicts. pre-commit passes against main.
- Rebased on 2026-10-30 onto main `d464d30b120` after #3074 merged,
dropping #3074's commits; its own diff is unchanged, with no conflicts.
pre-commit passes against main; now main-based, the full CI runs, GPU
lanes included.
- Review suggestion (Fridah-nv), 2026-10-30: the explicitly registered
wrapper refuses experts transformers marks `is_transposed`, at setup,
since nothing in the shapes tells a transposed layout apart. A test
marks a real `NemotronHExperts` transposed and checks the conversion
raises; it fails without the change. The Nemotron-H tests pass on
transformers 5.18 and 5.5.4; pre-commit passes against main.
- Rebased on 2026-10-31 onto main `e352caca3c3` (after #3002, #3044 and
#3156 merged), with its own diff unchanged and no conflicts. pre-commit
passes against main.
- Review suggestion (Claude), 2026-10-31: the `is_transposed` refusal
moved from the Nemotron-H wrapper into `_QuantFusedExperts._setup`, so
the native FP8 wrapper, the other one registered explicitly, refuses
transposed experts too. A test marks native FP8 experts transposed and
checks their conversion raises; it and the Nemotron-H test fail without
the check. `test_fused_experts.py`, `tests/unit/torch/models`,
`test_moe_a2a.py` and `test_calib.py` pass on transformers 5.18 (487)
and 5.5.4 (468, 8 skipped); pre-commit passes against main.
- Wording, 2026-10-31: `grouped_mm` and eager compute the same expert
products in different orders, and neither is the reference for the
other. Two tests and their docstrings now say the backends agree:
`test_grouped_mm_and_eager_calibrate_each_expert_alike` and
`test_gptq_hessians_agree_across_backends`. No behaviour change. The
Nemotron-H tests pass on transformers 5.18 and 5.5.4; pre-commit passes
against main.
- Rebased on 2026-11-01 onto main `cf38ec30ca4` (after #2784 and #3163
merged), with its own diff unchanged and no conflicts. pre-commit passes
against main; `test_fused_experts.py`, `tests/unit/torch/models`,
`test_moe_a2a.py` and `test_calib.py` pass on transformers 5.18 (487)
and 5.5.4 (468, 8 skipped).
- Rebased on 2026-11-01, evening, onto main `51645db66dc` (#3140
merged), with its own diff unchanged and no conflicts. pre-commit passes
against main; `test_fused_experts.py`, `tests/unit/torch/models`,
`test_moe_a2a.py` and `test_calib.py` pass on transformers 5.18 (487)
and 5.5.4 (468, 8 skipped).
- Rebased on 2026-11-02 onto main `2d4e8d019e7` (after #2779 and #3135
merged), with its own diff unchanged and no conflicts. pre-commit passes
against main; `test_fused_experts.py`, `tests/unit/torch/models`,
`test_moe_a2a.py` and `test_calib.py` pass on transformers 5.18 (516)
and 5.5.4 (482, 23 skipped).
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ❌ For transformers' built-in
Nemotron-H, MoE calibration now runs on `grouped_mm`, so some activation
scales differ from earlier releases; `experts_implementation="eager"`
restores the earlier calibration. A load of a checkpoint's own modeling
code (the Auto classes with `trust_remote_code=True`) calibrates as
before.
- 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: #3074's entry covers fused experts moving to `grouped_mm`.
- Did you get Claude approval on this PR?: ❌ Not yet.
### Additional Information
Part of expert-parallel PTQ for Hugging Face MoE models; merges after
#2745.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **New Features**
* Added quantization support for Nemotron-H expert layers, including
calibration and export with eager and grouped matrix multiplication
implementations.
* **Behavior Changes**
* Non-gated expert layouts are no longer detected automatically.
Quantization support is available for expert variants with a registered
wrapper, including Nemotron-H.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
> [!NOTE]
> **The expert-parallel PTQ series is now a tree instead of one stack**
(re-cut 2026-10-27, from main 1521053), so PRs that do not depend on
each other can be reviewed and merged in parallel. This PR was [8/21].
Rebased onto main 7c3c868 later the same day, after #2739 merged,
and onto main f03651e that night.
>
> **Re-cut:** this PR's diff is unchanged; only its base moved. Review
fixes since are listed at the end of Testing.
<details><summary>Series layout</summary>
- `main`
- #2745 fused experts under grouped_mm (merged)
- #3122 [1/2] expert-parallel fused experts on each rank's own experts
(merged)
- #3074 [2/2] calibration on grouped_mm (merged)
- #3067 Nemotron-H's experts under grouped_mm
- **#2740 calibration on per-rank data (this PR)**
- #2739 meta-DCP checkpoint load (merged)
- #2736 distribution plan, run group, collectives (merged)
- #2750 keep-fused fold (merged)
- #2751 distributed-writer pieces
- #2752 no-gather export (on #2751, #2750, #2779 and #2740)
- #2753 expert-parallel export
- #2737 plan decision (merged)
- #2779 load routing
- #2741 expert-parallel loader (on #2779, #3067 and #2740)
- #2742 dense sharding
- #2743 block-FP8 dequantization
- #2780 expert-parallel load routing
- #2746 expert parallelism in the model specs
- #2747 on-the-fly decompression
- #2748 Kimi-K2.5 and Kimi-VL adapters
- #2749 Kimi-K3 adapter
- #2754 expert-parallel PTQ from hf_ptq.py (on #2746 and #2753)
</details>
### What does this PR do?
Type of change: New feature
The pieces a calibration loop needs once the model is spread across
ranks.
- `shard_dataloader` gives each rank its own shard of the data.
- Map-style datasets use a `DistributedSampler` with `drop_last` off, so
every rank runs the same number of steps; a rank that exits early hangs
the others on the per-step collectives.
- An `IterableDataset` gets a strided copy of the stream, which must
yield the same samples in the same order on every rank (a stream that
shuffles needs a fixed seed of its own). A streaming loader with worker
processes is refused: a dataset that splits itself across workers, as a
Hugging Face streaming dataset does, would lose samples silently. It is
not main's `vlm_dataset_utils._ShardedIterable`, which pads each rank to
a count its loader knows: this wraps any loader's stream, which may have
no length.
- `distributed_forward_loop` builds the `forward_loop` that
`mtq.quantize` calls. It moves each batch to the `device` it is given
and runs the full distributed forward, so the expert-parallel
collectives fire inside it, with KV caching off as on the single-process
path. Each batch is a mapping of the forward's keyword arguments;
anything else is refused with a `TypeError` at the first batch.
- A model whose distribution plan (#2736) is expert-parallel passes the
`ep_mesh` its experts are sharded over, and the loop re-plants the
`torch_a2a` forward wrappers that quantizer insertion clobbers on
transformers >= 5.16. A module whose forward is still the wrapper
planted there is skipped, so passes do not stack wrappers; one whose
forward was rebound since (another conversion, an export) is planted
again. The planted wrappers stay on the model, so its later forwards (a
preview's, an export's) work once the loop has run. `mtq.auto_quantize`
calls its `forward_step` directly and would plant nothing, so it refuses
a model whose recorded plan shards its experts, before converting
anything. Any other model passes none. A mesh missing for an
expert-parallel model, or given for another, raises.
- It returns the allocator's reserved pool before the first forward and
after the last. NCCL's buffers live outside the torch allocator, so a
full reserved pool starves them. A process that never started CUDA skips
this, and the garbage collection it costs.
- **Every rank runs the same batch size.** Equal step counts need equal
batch sizes, and hf_ptq's default `--batch_size 0` sizes calibration
with `get_max_batch_size` on each rank: it measured every visible GPU,
and on an out-of-memory error halved the batch and probed again, a
different number of collective-bearing forwards per rank. For a model
whose recorded plan spreads it over several ranks it is now collective:
each rank measures the GPU it computes on
(`torch.cuda.current_device()`) from one forward of the single-sample
input, without probing the size it arrives at (a rank running out of
memory partway through a probe's forward would leave its peers waiting
in that forward's collectives), and every rank takes the smallest size
any rank allows, reduced with `all_reduce(..., min)` (#2736). A single
process, and a model with no plan recorded (a Megatron model, say), are
unchanged.
- **Calibration that keeps its statistics on the rank is refused.**
Every rank calibrates on its own data, so an algorithm must share what
it gathers across the ranks or the ranks calibrate their copies, or
their shards, of a weight differently. `max` all-reduces its amax, GPTQ
merges its Hessians across data-parallel replicas, and MSE and
SmoothQuant work from max's synced amax. AWQ-Lite does not: it averages
its activation scales over data parallelism but picks each layer's scale
from its own rank's search, and a 2-rank probe showed the replicas'
weights and `pre_quant_scale` diverging. `awq_clip` now averages each
candidate clip's loss over data parallelism before the ranks pick one,
in both branches: each rank accumulates its own losses, and after the
forward loop every module's losses and token count are all-reduced once,
in module order, so the collective count does not depend on how many
forwards a rank ran a module (an MoE expert's is each rank's own). Its
static-block branch averaged per forward before, and its per-tensor
branch, which NVFP4 takes, not at all; this also makes Megatron
data-parallel replicas agree on the per-tensor clip. `local_hessian` and
`imatrix` share nothing either, and neither do max and MSE with
`distributed_sync` off. `mtq.quantize`, `mtq.calibrate` and
`mtq.auto_quantize` refuse these (`awq_lite`, `awq_full`, `svdquant`,
which runs `awq_lite`, `local_hessian`, `imatrix`, and any config that
turns `distributed_sync` off, with `False` or `None`, also as another
algorithm's `weight_scale_algorithm` or `scale_algorithm`) on a model
whose recorded plan spans several ranks, before converting anything
(`auto_quantize` in its format check). A model with no plan recorded,
such as a Megatron model, is unchanged. On an expert-parallel plan, any
calibration is refused unless every module holding routed experts
carries, as its expert-parallel group, the rank's row of the `(edp, ep)`
mesh the expert-parallel load lays the ranks out in, ep innermost (in
row order, which picks each rank's expert shard), and, as its
data-parallel group, its column, the plan's `edp` ranks holding the same
experts: each rank's per-expert quantizers hold its own experts, and
max's and GPTQ's data-parallel sync would otherwise merge different
experts' scales. The modules holding routed experts are those carrying
an expert-parallel group and, once quantization has converted them, the
quantized modules under a container named `experts`, as transformers'
expert-parallel plans name it; unseeded experts beside a seeded router
are refused once converted, before they calibrate. #2741's
expert-parallel loader gives the routed experts those groups. A module
with no routed experts and no expert-parallel group, such as a VLM's
vision tower that hf_ptq quantizes on its own with every quantizer
disabled, passes.
- **hf_ptq's `--use_fsdp2` path is not changed here.** Its load
(`parallel_load_and_prepare_fsdp2`) records no plan, so the batch sizing
and the refusal above do not apply to it yet. Recording the plan there
lands with #2752, the first PR whose export reads it on that path,
together with stopping a planned run from retrying a batch after a
rank-local out-of-memory error; #2754 then moves the load onto
`load_pretrained`, which records the plan itself. `nvfp4_awq_clip`'s
data-parallel clip (below) needs no plan, so it applies to `--use_fsdp2`
runs now.
`shard_dataloader` refuses a loader that picks its own samples (a custom
`batch_sampler`, a sampler other than sequential, random or distributed,
or one of those over another source than the loader's dataset, also
inside a plain `BatchSampler` or with auto-batching off) instead of
rebuilding it, and keeps a plain `BatchSampler`'s batch size and
`drop_last` in the rank's shard; a loader with auto-batching off
(`batch_size=None`) shards one item per step, as it loads. With one
rank, `shard_dataloader` returns the loader itself, its sampler and
shuffling included. `distributed_forward_loop` runs its forwards without
gradients. `get_max_batch_size` reads the recorded plan, as the
calibration guard does. `distributed_forward_loop` refuses an `ep_mesh`
whose size is not the plan's `ep_size`.
### Usage
```python
from modelopt.torch.utils.distributed import distributed_forward_loop, rank, shard_dataloader, size
loader = shard_dataloader(calib_loader, rank(), size())
# ep_mesh: the mesh an expert-parallel model's experts are sharded over; None for any other model
forward_loop = distributed_forward_loop(model, loader, device=device, ep_mesh=ep_mesh)
model = mtq.quantize(model, quant_cfg, forward_loop=forward_loop)
```
### Testing
- New `tests/unit/torch/utils/test_distributed_calibration.py` (42
cases), on CPU, in one process except where noted. It covers:
- strided shards that cover the data exactly once, with equal batch
counts on uneven splits, for map-style and streaming datasets; a
streaming loader with workers refused; map-style and streaming loaders
with auto-batching off sharded one item per step;
- every loader setting carried over, a plain `BatchSampler`'s batch size
and `drop_last` included;
- the wrapper re-planting, on the planned modules only, without
stacking, and again after another conversion drops it;
- the loop running this rank's shard through the top-level model, with
`use_cache` off and the allocator cache freed around it in a process
that started CUDA, and not in one that did not; a batch that is not a
mapping refused;
- the `ep_mesh` refused when missing for an expert-parallel plan, given
for another, or over the whole world for an `ep_size=2` plan;
- rank-local calibration refused through `quantize`, `calibrate` and
`auto_quantize` (before conversion), also when another algorithm runs it
for its weight scales or max is told not to sync, with the model left as
it was;
- on two gloo ranks, max calibration on an expert-parallel plan run when
the experts carry their groups, and refused with no expert-parallel
group anywhere or with the experts' data-parallel group over every rank
(before conversion), and with the group on the router only (once the
experts are converted, before they calibrate);
- a module without routed experts calibrating under an expert-parallel
plan, with every quantizer disabled (hf_ptq's vision-tower call) and
quantized.
- `distributed_forward_loop` runs on real GPU ranks in #2742's
expert-parallel calibration test, which checks each rank's per-expert
scales against the whole model; this PR's own tests run it in one
process.
- `tests/unit/torch/utils/test_dataset_utils.py`, on two real gloo ranks
(`cpu_dist_workers`) with only the CUDA memory queries faked per rank:
each rank measures only its own GPU, probes once, and both take the
smaller batch size. A rank keeping its own size, measuring every GPU, or
skipping the reduction fails it, the last after the group's 10 s timeout
rather than by hanging; main's single-process retry test still passes.
The two-rank test also runs a model with no plan recorded, which each
rank measures on every visible GPU, as a single process does, without a
refusal.
- Mutation-checked: deciding the reduction or the mesh other than by the
plan fails a test (5 mutants), and so does dropping either refusal call,
the nested-algorithm check, the `distributed_sync` check, hf_ptq's plan
recording, the expert-parallel group refusal, or the `ep_mesh` size
check. Refusing every expert-parallel calibration, or reading the plan
strictly in `get_max_batch_size`, fails a test too. Dropping the check
on converted experts or the data-parallel size check, keeping a replant
flag once set, or planting on every pass each fail a test. Rebuilding a
lone rank's loader, refusing a module without routed experts, or passing
any module with no expert-parallel group each fail a test too.
- The shard tests are one table over dataset kind and world size, the
two replant tests are one, and cases a remaining test covers are
dropped. Each drop is checked by mutation.
- A subset sampler is refused, also under a plain `BatchSampler` and
with auto-batching off, and so is a custom batch sampler; a lone rank
keeps each such loader as it is. The sampler, batching,
non-mapping-batch and `auto_quantize` cases each fail without their fix
(mutation-checked).
- Locally on this revision, the stack rebased on main c6fa613:
pre-commit over the whole PR against main, at every PR of the stack; and
at the stack's tip the full `tests/unit` on transformers 5.18 and 5.5
and on torch 2.8, and the hf_ptq example tests, each with the same
failures as main in that environment. CI runs the full unit suite.
- GPU tests were not re-run on this revision.
- Re-cut on 2026-10-27 (see the note at the top): pre-commit, mypy
included, passes at this head over the whole change against main, and
the series' PRs merged together give a tree byte-identical to the former
stack's tip. This PR's own unit tests pass at this head on CPU (torch
2.14, transformers 5.18); CI runs the full unit suite.
- Review fixes, 2026-10-27: the a2a forward wrappers are re-planted only
where quantizer insertion rebound the forward, so a wrapper still in
place is never wrapped twice (found by review: the double wrap ran a
module's input transforms twice, which no real run has shown to fail; on
4 GPUs at transformers 5.16.1 the expert-parallel load tests pass with
either behaviour, and the CPU calibration-loop test is what tells them
apart); the install probe checks each style it calls. Each behaviour fix
has a test that fails without it; pre-commit, mypy included, and this
PR's own unit tests pass at this head.
- Review fixes, 2026-10-27, round 2: the batch size is measured on the
GPU the rank computes on (`torch.cuda.current_device()`); a streaming
loader with workers is refused; the docstring and the hf_ptq README say
why the measurement does not probe. Each behaviour fix has a test that
fails without it; pre-commit, mypy included, and this PR's own unit
tests pass at this head.
- Rebased on 2026-10-27 onto main 7c3c868, after #2739 merged:
pre-commit, mypy included, and this PR's own unit tests pass at this
head, and the series' PRs merged together add no unit-test failure over
main (tests/unit/torch utils, export, quantization and models, on
transformers 5.18 and 5.5.4 and on torch 2.8). The hf_ptq README's FSDP2
AutoQuantize note follows main's KV-cache AutoQuantize support (#2664):
weight and KV-cache AutoQuantize with `--use_fsdp2` are experimental,
and refused when a candidate format is one of those this PR refuses.
- Review fix, 2026-10-27 night: `shard_dataloader` refuses a
`RandomSampler` that does not draw the whole dataset once (other
`num_samples`, or with replacement), as it refuses other samplers.
- Review suggestion, 2026-10-27, last round: the comment over the sync
guard names the algorithms' defaults it relies on.
- Rebased on 2026-10-28, late night, onto #2736 at `cf4597dcbeb`, on
main `1ca8bd1fc4d` (after #3088 and #2745 merged), without conflicts;
this PR's own diff is unchanged. Its tests with #2736's and the `hf_ptq`
argument tests pass on transformers 5.18 (220), and pre-commit passes
against main.
- Rebased on 2026-10-28, late night, onto main `bdc45368d10` after #2736
merged; #2736's squash has the tree of its last head, so this PR's tree
is unchanged.
- Review fixes, 2026-10-29: the two-rank batch-size test makes its
bounded group with `bounded_collectives`, whose barrier keeps a rank
from destroying the group while its peer is still connecting (one CI run
had failed so); the batch sizing's distributed helpers are imported at
module top; the rank-local refusal reads each algorithm's options
through its mode's schema, so a `distributed_sync` of `"false"` is
refused; the experts' groups are checked by the ranks they span, an
expert-parallel group of `ep_size` ranks and a data-parallel group of
`edp` ranks meeting it only at the rank itself (a test answers four
ranks' group queries, which the two-rank pool cannot build). Each
behaviour fix fails its test without it (mutation-checked). Pre-commit
passes against main; this PR's tests, `test_distributed.py`,
`test_quantize_cpu.py` and the hf_ptq argument tests pass on CPU with
transformers 5.18 and 5.5.4.
- Review fix, 2026-10-29, second round: `awq_clip`'s per-tensor branch
averages each candidate clip's loss over data parallelism, through the
helper its static-block branch now uses too (same group, same op, every
rank in the same order), and `awq_clip` is allowed on a distributed
model; this also makes Megatron data-parallel replicas agree on the
per-tensor clip. A two-rank CPU test of the static-block branch fails
without the averaging (mutation-checked); a two-GPU NVFP4 test of the
per-tensor branch is in `tests/gpu` (NVFP4 fake quantization needs CUDA;
not run here). The hf_ptq README and the changelog say `nvfp4_awq_clip`
works with `--use_fsdp2` on several GPUs. Pre-commit passes against
main; `test_calib.py` and this PR's tests pass on CPU.
- GPU check, 2026-10-29, of the NVFP4 per-tensor branch on GB200 (torch
2.11.0a0, transformers 5.9.0), with the second round's per-forward
averaging; without the data-parallel mean the even and odd ranks pick
different per-tensor clips:
| Run | 1 GPU | 2 GPUs | 4 GPUs |
|---|---|---|---|
|
`test_calib_cuda.py::test_nvfp4_awq_clip_picks_the_same_clip_on_every_rank`
| skipped (needs 2+) | passed, 23.2 s | passed, 24.1 s |
| The same test with the averaging reverted | – | failed: clips 95 vs
100 | – |
- GPU check, 2026-10-29, at `cdb4e952468`, with the fourth round's
once-per-module averaging after the forward loop, on GB200 (torch
2.11.0a0, transformers 5.9.0); with that averaging disabled, the even
and odd ranks pick different per-tensor clips again:
| Run at `cdb4e952468` | 1 GPU | 2 GPUs | 4 GPUs |
|---|---|---|---|
|
`test_calib_cuda.py::test_nvfp4_awq_clip_picks_the_same_clip_on_every_rank`
| skipped (needs 2+) | passed, 20.7 s | passed, 24.5 s |
| The same test with `sync_across_dp` disabled (`return` as its first
statement) | – | failed: clips 95 vs 100 | – |
- GPU check, 2026-10-29, at `01a1df717e3`, with the fifth round's
empty-input guard, on GB200 (torch 2.11.0a0, transformers 5.9.0). The
test now also runs a second linear on no tokens on the odd ranks;
without the guard, that linear's clip stays 0 on every replica:
| Run at `01a1df717e3` | 1 GPU | 2 GPUs | 4 GPUs |
|---|---|---|---|
|
`test_calib_cuda.py::test_nvfp4_awq_clip_picks_the_same_clip_on_every_rank`
| skipped (needs 2+) | passed, 21.1 s | passed, 25.0 s |
| The same test without the empty-input guard in `_clip_search` | – |
failed on `amax[1] > 0`: amax = [100, 0] | – |
- Review fixes, 2026-10-29, third round: a `distributed_sync` of `None`,
which skips the sync as `False` does, is refused too, nested scale
algorithms included; the experts' groups are checked against the `(edp,
ep)` mesh, the expert-parallel group as the rank's row in row order and
the data-parallel group as its column, so diagonal data-parallel groups
and a reordered expert-parallel group are refused (the four-rank test
adds both). Each fails its test without the change (mutation-checked).
Pre-commit passes against main; this PR's tests pass on CPU.
- Review fixes, 2026-10-29, fourth round: `awq_clip` averages its clip
search over the data-parallel ranks once per module after the forward
loop, with the token count, in both branches, instead of once per
forward, which hung when ranks ran an MoE expert a different number of
times (the two-rank test now does; it fails without the change, on its
bounded group's timeout) (the same round accepted the experts' groups of
a mesh with either axis innermost; the eighth round reverts that).
Pre-commit passes against main; `test_calib.py` and this PR's tests pass
on CPU, and the quantization unit tests pass with the change.
- Review fix, 2026-10-29, fifth round: `awq_clip` skips its clip search
for an input with no tokens, which an MoE expert a rank routes none to
can still get. Before, the per-tensor branch added NaN losses, which the
post-loop sum spread to the replicas that had tokens (a clip of 0 for
them), and the static-block branch failed to reshape the empty input.
The two distributed tests now run an expert on no tokens on one rank,
and check that its clips are positive and those a single process picks
from the other ranks' tokens; each fails without the change
(mutation-checked; the GPU one in the table above). Pre-commit passes
against main; `test_calib.py` and this PR's tests pass on CPU.
- Review fix, 2026-10-29, sixth round: hf_ptq's `--use_fsdp2` plan
recording, its README notes, changelog entry and argument test leave
this PR for #2752 (see above); the new GPU test
`tests/gpu/torch/quantization/test_fsdp2.py::test_planned_batch_size_then_max_calibration`
(2+ GPUs) sizes an FSDP2 model's batch by its recorded plan with the
real `get_max_batch_size`, rank 0's own estimate far above the others',
checks every rank takes the smallest, then calibrates each rank's shard
with max through `distributed_forward_loop` and checks the steps and
scales agree. Its logic ran on two CPU ranks (FSDP2 on gloo, the CUDA
memory queries faked): it passes, and without the batch size's reduction
it fails at once on the sizes ([40, 4]); GPU results in the table below.
Rebased onto main `2b900c8fa41`; this PR's own diff is otherwise
unchanged. Pre-commit passes against main.
- GPU check, 2026-10-29, at `10b3c1f0e35`, of the new FSDP2 test on
GB200 (torch 2.11.0a0, CUDA 13.1, NCCL 2.29.2, transformers 5.9.0);
without the cross-rank minimum, rank 0 keeps its own estimate while the
others take theirs:
| Run at `10b3c1f0e35` | 1 GPU | 2 GPUs | 4 GPUs |
|---|---|---|---|
| `test_fsdp2.py::test_planned_batch_size_then_max_calibration` |
skipped (needs 2+) | passed, 22.3 s | passed, 25.0 s |
| The same test without the minimum's `all_reduce` in the planned branch
| – | failed: sizes [36, 4] | – |
- Review fix, 2026-10-29, seventh round: stacked on #3074 (rebased from
main `2b900c8fa41` onto #3074's `ec9e09c3fc0`; this PR's own diff is
unchanged). New GPU test
`tests/gpu/torch/utils/test_moe_a2a.py::test_expert_parallel_calibration_matches_the_whole_model`
(2+ GPUs, transformers ≥ 5.16): a tiny Qwen3-MoE loaded by transformers'
own expert-parallel load on the `torch_a2a` styles and experts forward,
quantized with FP8 through `distributed_forward_loop`, each rank on its
own batch; every expert's weight scales and every activation scale match
the whole model calibrated on every rank's batch, with bounded
collectives. Its logic ran on two CPU ranks with transformers 5.18: it
passes, re-planting the forward wrappers quantizer insertion drops, and
fails without the re-plant, or with the conversion forcing eager as
before #3074. GPU results in the table below. Pre-commit passes against
#3074 and main.
- GPU check, 2026-10-29, at `d90a479b6b5`, of the new expert-parallel
test on GB200 (torch 2.11.0a0, CUDA 13.1, NCCL 2.29.2, transformers
5.18.0): the expert-parallel calibration matches the whole model's, and
needs both the forward re-plant and #3074:
| Run at `d90a479b6b5` | 1 GPU | 2 GPUs | 4 GPUs |
|---|---|---|---|
|
`test_moe_a2a.py::test_expert_parallel_calibration_matches_the_whole_model`
| skipped | passed, 21.6 s | passed, 26.7 s |
| The same test with the re-plant disabled (`_reinstall_a2a_forwards`
returns 0) | – | failed: "torch_a2a experts forward requires the EP
`process_group`" | – |
| The same test without #3074 (`normalize_experts_impl_on_the_fly`
forcing eager, main's behaviour) | – | failed:
"Qwen3MoeExperts.forward() takes 4 positional arguments but 5 were
given" | – |
- Restacked on 2026-10-29 onto #3074 at `f68837967d3`, now [2/2] on
#3122 [1/2] after #3074's split; no conflicts, and this PR's own diff is
unchanged. The new expert-parallel test's logic still passes on two CPU
ranks on this base. Pre-commit passes against #3074's head.
- GPU check, 2026-10-29, at `54f0c1fd8e9` (on #3074 `f68837967d3`, on
#3122 `33736db61fa`), on GB200 (torch 2.11.0a0, transformers 5.18.0):
the expert-parallel calibration still matches the whole model with
#3122's per-rank expert calibration underneath:
| Run at `54f0c1fd8e9` | 2 GPUs | 4 GPUs |
|---|---|---|
|
`test_moe_a2a.py::test_expert_parallel_calibration_matches_the_whole_model`
| passed, 23.5 s | passed, 27.0 s |
- Review fixes, 2026-10-29, eighth round (Claude's review of
`54f0c1fd8e9`): the experts' groups are checked against the ep-innermost
`(edp, ep)` mesh only, the layout the expert-parallel load builds:
accepting either axis innermost let transposed groups pass when `edp ==
ep_size` (a new four-rank case, `groups-transposed`, fails with that
check). `awq_clip` reduces each module's clip search in one all-reduce,
the token count riding in the losses' buffer (the ninth round reduces
the losses in place instead), and every module's reduction runs before
any weight gather. Pre-commit passes against #3074; `test_calib.py` and
this PR's tests pass on CPU, and dropping the token count's sync fails
the two-rank test.
- On GPUs, with the rest of the expert-parallel series on top
(transformers 5.13), before this round's change to `awq_clip`:
- Qwen3-30B-A3B at EP 4 and EP 2 on 4 GPUs, deterministic, batch size 1:
every exported scale is bit-identical between the two. In the EP 2 run
(edp 2), the same row/column check, reading the groups from the field
that stack's loader seeds, accepted the loader's real `(edp, ep)`
groups.
| Exported scale | Differing, EP 4 vs EP 2 |
|---|---|
| `weight_scale` | 0 of 18,624 |
| `input_scale` | 0 of 18,624 |
| `k_scale` | 0 of 48 |
| `v_scale` | 0 of 48 |
- Qwen3-0.6B with `nvfp4_awq_clip` and `--use_fsdp2` on 2 GPUs:
`awq_clip` ran and the sharded checkpoint was exported, end to end. That
run had the per-forward averaging this round replaces.
- Review fixes, 2026-10-29, ninth round (Claude's review of
`2dfeb9c23f0`): `awq_clip`'s candidate losses are views of one buffer,
which the sync all-reduces and averages in place, with the token count
in its own int64 all-reduce: no copy of the losses, where the packed
buffer held up to three, and every module's reductions still run before
any weight gather. The refusal names `distributed_sync` only for max and
MSE, the calibrations that have it. Pre-commit passes against #3074;
`test_calib.py` and this PR's tests pass on CPU, and the two-rank test
fails without either all-reduce, or with the candidates' losses copied
out of the buffer.
- Review follow-up, 2026-10-29, tenth round (Claude's review of
`b517c7581b6`): a docstring fix (`07affa0fe76`). The new expert-parallel
test's job, run on two CPU ranks with transformers 5.18, shows that its
dense linears are `QuantLinear` with the seeded data-parallel group and
no tensor-parallel group (Qwen3-MoE's expert plan shards no dense
module). Seeding them without that group fails the test: `o_proj`'s
input amax is off by 0.293 (relative).
- Review follow-up, 2026-10-29, eleventh round (Claude's review of
`07affa0fe76`): a docstring note (`e546bcca0b1`) that every rank still
reads and decodes every sample of a sharded stream. The experts' check
that `quantize()` runs before conversion reads the groups a loader
seeds. #2741's load is the first to seed them; until it lands, the unit
tests and the expert-parallel GPU test, which seed them by hand,
exercise that check.
- Rebased on 2026-10-30 onto main `99c3834f62c` through #3122
`1e74c31b49a` and #3074 `3f3544435d8` (from #3074's `f5d36d4f794`), as
`fd036d44f28`; no conflicts, and this PR's own diff is unchanged.
Pre-commit passes against main.
- GPU check, 2026-10-30, at `fd036d44f28`, on GB200 (torch 2.11.0a0,
transformers 5.9.0): `awq_clip`'s in-place data-parallel sync (the ninth
round's change) still gives every rank the same clip:
| Run at `fd036d44f28` | 2 GPUs | 4 GPUs |
|---|---|---|
|
`test_calib_cuda.py::test_nvfp4_awq_clip_picks_the_same_clip_on_every_rank`
| passed, 18 s | passed, 24 s |
- Restacked on 2026-10-30 after #3122 merged: onto #3074 `39697ad985b`,
which is on main `55898ee4e3b` (from `3f3544435d8`), as `3f56acfbb76`;
no conflicts, and this PR's own diff is unchanged. Pre-commit passes
against main. This PR's unit tests (`test_distributed_calibration.py`,
`test_calib.py`, `test_dataset_utils.py`, `test_distributed.py`) pass on
CPU with transformers 5.18 and 5.5.4, and the expert-parallel GPU test's
job passes on two CPU ranks with transformers 5.18.
- Rebased on 2026-10-30 onto #3074 `a6758d77eb3`, on main `059588b89a5`,
which has #3128 (from `3f56acfbb76`), as `186efae02e5`. #3128's AWQ
guard (`_refuse_awq_on_a_sharded_fsdp2_model`) and this PR's
(`_refuse_unsupported_distributed_calibration`) met at the same three
call sites in `model_quant.py`: `calibrate()`, `quantize()` and
`auto_quantize`'s candidate check. Both stay, #3128's first. The
changelog and the README keep both sides, and `186efae02e5` makes this
PR's `nvfp4_awq_clip` README note its own paragraph, saying why it is
not refused. This PR's own diff is otherwise unchanged. Pre-commit
passes against main. On CPU with transformers 5.18 and 5.5.4,
`test_quantize_cpu.py` (with #3128's guard test) and this PR's unit
tests pass, and the expert-parallel GPU test's job passes on two CPU
ranks with transformers 5.18.
- Rebased on 2026-10-30 onto main `d464d30b120` after #3074 merged
(#3074's squash; from `186efae02e5`), as `9a87d153c89`: #3074's own
commits drop out; no conflicts, and this PR's own diff is unchanged,
with #3128's guard still first at the three call sites. This PR now
targets main, so the full CI runs, GPU included. Pre-commit passes
against main. On CPU with transformers 5.18 and 5.5.4,
`test_quantize_cpu.py` and this PR's unit tests pass, and the
expert-parallel GPU test's job passes on two CPU ranks with transformers
5.18.
- Review fixes, 2026-10-30, twelfth round (cjluo-nv's review of
`9a87d153c89`): `mtq.auto_quantize` refuses a model whose recorded plan
shards its experts, before converting it (`fbbe2275cf8`); a two-rank
test with seeded experts and an INT8 candidate checks the module types
and state are unchanged, and fails without the refusal.
`shard_dataloader` compares a sampler's source with the loader's dataset
(`e1976572627`, typed in `9ac1486cdab`); a SequentialSampler,
RandomSampler or DistributedSampler over a shorter source is refused,
and the three new cases fail with the old check. Pre-commit passes
against main. On CPU with transformers 5.18 and 5.5.4,
`test_quantize_cpu.py` and this PR's unit tests pass, and the
expert-parallel GPU test's job passes on two CPU ranks with transformers
5.18.
- Review fix, 2026-10-31, thirteenth round (Claude's review of
`9ac1486cdab`): the forward re-plant now plants only over a bound
method, the forward quantizer insertion leaves, as its docstring says
(`aba16882660`); a planned module whose forward the load never wrapped
is left alone, which a new test checks and the old guard fails.
Pre-commit passes against main. On CPU with transformers 5.18 and 5.5.4,
`test_quantize_cpu.py` and this PR's unit tests pass, and the
expert-parallel GPU test's job still re-plants the two experts' wrappers
and passes on two CPU ranks with transformers 5.18.
- Rebased on 2026-10-31 onto main `e352caca3c3` (from `aba16882660`,
adding #3002, #3044 and #3156), as `dea9b793553`; no conflicts (#3044
and #3002 touch `model_calib.py`, which merges cleanly), and this PR's
own diff is unchanged. Pre-commit passes against main. On CPU with
transformers 5.18 and 5.5.4, `test_quantize_cpu.py`, the ggml GPTQ and
imatrix unit tests and this PR's unit tests pass, and the
expert-parallel GPU test's job passes on two CPU ranks with transformers
5.18.
- Review follow-up, 2026-10-31, fourteenth round (Claude's review of
`dea9b793553`): a unit test checks that every calibration
`_RANK_LOCAL_CALIBRATION` refuses names a registered calibrate mode, so
a renamed or misspelled one fails instead of silently losing its refusal
(`d10be569b04`; it fails with one name misspelled); `7791f6d0f3b` drops
a redundant test import. The sampler check keeps comparing lengths (see
its thread). CI's GPU job passed at `dea9b793553`; the three multi-GPU
tests skip on its single-GPU runner, and their run at `7791f6d0f3b` on 2
and 4 GPUs is below. Pre-commit passes against main; the changed test
files pass on CPU with transformers 5.18 and 5.5.4.
- GPU check, 2026-10-31, at `7791f6d0f3b`, on 2 and 4 GPUs (torch 2.15,
transformers 5.18.0): all three multi-GPU tests pass, none skipped. Call
times; the first test also pays the shared worker pool's startup, so
times do not compare across tests:
| Run at `7791f6d0f3b` | 2 GPUs | 4 GPUs |
|---|---|---|
|
`test_calib_cuda.py::test_nvfp4_awq_clip_picks_the_same_clip_on_every_rank`
| passed, 12.0 s | passed, 13.7 s |
| `test_fsdp2.py::test_planned_batch_size_then_max_calibration` |
passed, 1.3 s | passed, 3.1 s |
|
`test_moe_a2a.py::test_expert_parallel_calibration_matches_the_whole_model`
| passed, 3.9 s | passed, 10.1 s |
- Trimmed on 2026-10-31 (`95148c3ff8b`), after a review counted the PR
over the ~500-line budget: four docstrings and a comment that restated
the code are shorter, with no code change. 492 added source lines (`git
diff --shortstat e352cac...95148c3 -- . ':!tests' ':!docs'`).
Pre-commit passes against main, and the unit tests pass on CPU with
transformers 5.18 and 5.5.4.
- Rebased on 2026-11-01 onto main `cf38ec30ca4` (from `95148c3ff8b`), as
`ff372fbc30c`: no conflicts, range-diff 63 of 63 commits unchanged;
pre-commit with all hooks, mypy included, passes against main; on CPU,
272 unit tests pass with transformers 5.18 (the expert-parallel job on
two CPU ranks included) and 271 with 5.5.4.
- Rebased on 2026-11-01 onto main `51645db66dc` after #3140 merged (from
`ff372fbc30c`), as `6a06a5421f7`: no conflicts, range-diff 60 of 63
commits unchanged and 3 differing only in a context line #3140 changed;
pre-commit with all hooks, mypy included, passes against main; on CPU,
269 unit tests pass with transformers 5.18 (the expert-parallel job on
two CPU ranks included) and 268 with 5.5.4, three fewer than before
because #3140 moved the `fsdp2_wrap` tests to their own file.
- Rebased on 2026-11-02 onto main `2d4e8d019e7` after #2779 and #3135
merged (from `6a06a5421f7`), as `5829a854612`: no conflicts, range-diff
63 of 63 commits unchanged, 492 added source lines; pre-commit with all
hooks, mypy included, passes against main; on CPU, 269 unit tests pass
with transformers 5.18 (the expert-parallel job on two CPU ranks
included) and 268 with 5.5.4.
- Review fixes, 2026-11-02 (meenchen's reviews of `5829a854612`): each
calibrate mode descriptor declares `_distributed_calibration` (True for
none, max, mse, smoothquant, awq_clip, gptq, nvfp4_act_headroom and lsq;
False for awq_lite, awq_full, svdquant, local_hessian and imatrix) and
checks its own options and nested algorithms; a mode that declares
neither is refused under a multi-rank plan, and
`_RANK_LOCAL_CALIBRATION` is gone (`c6585dce94d`, `49911504cf4`). The 13
built-in modes refuse what they did before; a test checks every mode
modelopt registers declares (it fails with one declaration removed),
another that an undeclared mode is refused (it fails when undeclared
modes pass). `e8d80fed068` keeps the `get_max_batch_size` and
`shard_dataloader` docstrings to their contract, with the reasons moved
to comments. Pre-commit with all hooks passes against main; on CPU, 273
unit tests pass with transformers 5.18 (the expert-parallel job on two
CPU ranks included) and 272 with 5.5.4. Claude's review of `49911504cf4`
approved.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ Additive: `get_max_batch_size`
and the calibration refusals change only for a model whose recorded plan
spans several ranks. `awq_clip`'s per-tensor (NVFP4) clip changes under
data parallelism, where its replicas now agree (changelog, Bug Fixes).
- 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)?:
✅ A Bug Fixes entry for AWQ-Clip's data-parallel clip; the feature's
entry lands with #2754.
- Did you get Claude approval on this PR?: ❌ Not yet.
### Additional Information
Part 8 of 21 of expert-parallel PTQ for Hugging Face MoE models; merges
after #2739, which has merged.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **New Features**
* Distributed calibration now shards map-style and streaming datasets
across ranks, keeping batch counts aligned and preserving data loader
settings.
* Calibration supports expert- and tensor-parallel models with improved
device handling, cache management, and compatibility with parallel model
wrappers.
* Distributed batch-size detection probes each rank’s local GPU and
selects the smallest supported batch size across ranks.
* **Bug Fixes**
* Calibration and quantization now reject selected rank-local
calibration methods on distributed models before modifying model state.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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left a comment
What does this PR do?
Type of change: new feature (performance)
imatrix collects each input's per-column sum of squares in its forward hook. It did so by copying 1,024-row slices of the bfloat16 inputs to float32, squaring them and summing. On a GPU,
torch.linalg.vector_norm(x, 2, dim=0, dtype=torch.float32)squares and accumulates in float32 inside the reduction kernel, without a float32 copy of the inputs. Squaring its result gives the same statistic.The hook runs once per weight per forward batch, and for an HF fused-MoE layer once per expert: about 97K calls in a GLM-5.3-Flash statistics forward and 246K in Qwen3.8-Flash-Next's. At an expert's share of the tokens, a call's cost is its kernel launches, so the hook uses as few as its memory allows:
(groups, 512, columns)view in one call, then adds up the group sums and the rows left over. Inputs over 262,144 rows (512 groups) go through in slices of that many rows. No reduction spans more than 512 rows or 512 group sums, so the temporaries stay at most 8 MiB for 4,096 columns at any input length, against 48 MiB for the slices. One reduction over all rows would stage up to 512 MiB.The fused reduction runs only for bf16, fp16 and fp32 inputs on a GPU. Everything else keeps the float32 slices:
On a GB300, per call. Up to 8,192 rows the times are wall time per call; above that, GPU time and peak temporary memory:
Between 1,537 and about 8,192 rows, the groups and the leftover rows take six launches, so this PR is slower there than one reduction over all rows, whose buffer would exceed the slices' (about 115 MiB at 3,700 x 4,096). The relative error against float64 stays at or below 3e-7 on an RTX PRO 6000 (2.1e-7 at 524,988 x 4,096); the slices reach 1e-6 at 512K rows.
I also tried
torch.compileand a Triton kernel. Neither is faster at expert sizes: a compiled call took 0.16-0.31 ms on an RTX PRO 6000, slower than the slices, and the Triton kernel took 0.042 ms per call on GB300, against 0.027 ms for one reduction.Usage
No API change.
Testing
Real models on 4x GB300 (oci-jhb): single-pass IQ2_XXS imatrix, 1,024 x 512 calibration tokens, flash-linear-attention installed, this PR at
d5b4249ea4. Each model ran in one job that timed the statistics forward with main's hook and this PR's in turn (old, new, old, new) after a warm-up forward, and discarded those passes' statistics:New GPU test
test_imatrix_statistic_takes_no_more_gpu_memory_than_float32_slices, at 1,536, 2,048 and 33,068 rows x 4,096 and at 1,048,876 x 512. It measures the peak temporary memory of the fused reduction and of the float32 slices on the same input, and requires the fused one to be no higher. Peak memory is what decides whether a run fits, so this is the same check as running both under equal headroom. At 1,536 rows the two peaks are equal to the byte. One reduction over all rows fails the three longer cases, a single-reduction threshold of 2,048 rows fails the 2,048-row case, and summing all 512-row groups at once fails the last.New GPU test
test_imatrix_second_moments_of_bf16_inputs_on_gpu, for 1,500, 2,048, 2,500 and 262,844 rows: one reduction, whole groups only, groups and leftover rows, and two slices of 512 groups. It runs bf16 inputs whose columns differ widely in scale, and compares the importance with the float64 statistic to a relative 1e-5. Breaking the single reduction, dropping the groups or the leftover rows, or keeping only the first slice fails the matching cases.test_imatrix_weights_the_search_by_the_root_of_the_inputs_second_momentsnow also runs float64 inputs. It fails on the first commit of this PR with the narrowing error.tests/gpu/torch/quantization/test_ggml_imatrix_gpu.py: 10 passed on 2x RTX PRO 6000.tests/unit/torch/quantization/{test_ggml_imatrix,test_ggml_gptq}.pyandplugins/test_fused_experts.py: 177 passed. These include the existing CPU statistic tests, dense and per expert of fused MoE experts, which run the slices.The 1,021-row switch and the equal peaks at 1,536 rows hold on both the RTX PRO 6000 (torch 2.12) and GB300 (torch 2.13).
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