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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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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>
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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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