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[OMNIML-6284] Make awq_lite independent of how the calibration tokens are batched - #2996
awq_lite estimated each linear layer's activation scale as the mean of per-batch means (each mean computed in the activation dtype, so rounded to bf16) and accumulated the alpha-search loss as a sum of per-batch means. Every batch therefore weighed the same whatever its token count: a smaller last batch, or an MoE expert that receives few tokens in some batches, changed the scales, and the alpha that wins the search could flip with the batch size. The reference AWQ takes one mean over the whole calibration set.
Now |x| is summed in fp32 and divided by the token count (awq_lite.num_tokens, which was already tracked but unused), and the squared error is summed instead of averaged, so the result no longer depends on how the same tokens are split into batches. With equal-sized batches the result is the old one up to the bf16 rounding of the batch means.
Scope: the statistic is token-weighted within a rank. Data-parallel ranks are still averaged with equal weight (sync_act_scale_across_dp, unchanged by this PR), which is the token-weighted mean when the ranks see equal token counts, as dense layers do. For MoE experts with uneven routing across ranks, summing the activation sums and token counts across the group before dividing would be needed; that changes collectives and needs a multi-rank test, so it is left for a follow-up.
Testing
Qwen3-8B, --qformat int4_awq, 100 calibration samples of length 512 (so batch size 8 ends with a partial batch), GB200, torch 2.11.0a0, transformers 5.13.0. hf_ptq.py was run to the end of awq_lite calibration and each layer's best_alpha and quantizer scales were dumped. Counts are of the 252 quantized linears; tensors differing by more than 1%.
Pair
best_alpha differs
weight_quantizer._amax differs
input_quantizer._pre_quant_scale differs
main, batch size 8 vs 1
7
177
100 (max rel 0.60)
this PR, batch size 8 vs 1
1
2
2 (max rel 0.17)
main vs this PR, batch size 8 (same batches, same kernels)
6
178
98
main vs this PR, batch size 1 (no partial batch)
2
2
2
So the batch-size dependence of AWQ scales on this model drops from 177 to 2 tensors, and the estimator alone (same batches, same kernels) accounts for it. A partial last batch is common with the default --calib_size 1024 and an auto-probed batch size.
Accuracy: the scales above change, so the same model was evaluated with each estimator. Qwen3-8B (bf16), INT4_AWQ_CFG, calibration with --batch_size 8 --calib_size 100 (the last batch is partial, which is where the estimators differ), MMLU 5-shot through examples/llm_eval/mmlu.py with --limit 1000 (the cap only binds on professional_law, so 13,508 of the 14,042 test questions are used), GB200, transformers 5.13.0. One quantization and evaluation per arm, with the same flags and data; the arms differ only in model_calib.py (main at 0638a27 vs this PR).
Overall
STEM
Humanities
Social sciences
Other (business, health, misc.)
Questions
13508
3018
4171
3077
3242
main
0.739
0.738
0.658
0.816
0.772
this PR
0.740
0.742
0.660
0.817
0.770
Correct, this PR - main
+13
+13
+6
+2
-8
No measurable accuracy change: 13 more correct answers out of 13,508 (the binomial standard error of one accuracy at this size is 0.38 pp, about 51 questions), 30 of 57 subjects higher, 18 lower and 9 tied (two-sided sign test p = 0.11). The largest sub-category swings are small samples: engineering is the single subject electrical_engineering (-3 of 145 questions, -2.1 pp) and math is +8 of 1,064. With one run per arm this supports "no regression", not "improvement".
All 17 MMLU sub-categories
Sub-category
Questions
main
this PR
Correct, this PR - main
math
1064
0.668
0.676
+8
health
1640
0.761
0.760
-2
physics
640
0.777
0.778
+1
business
437
0.870
0.870
0
biology
454
0.863
0.872
+4
chemistry
303
0.683
0.693
+3
computer science
412
0.735
0.735
0
economics
742
0.807
0.807
0
engineering
145
0.800
0.779
-3
philosophy
2012
0.646
0.647
+3
other
1165
0.752
0.747
-6
history
930
0.844
0.846
+2
geography
198
0.859
0.864
+1
politics
648
0.810
0.809
-1
psychology
1157
0.812
0.813
+2
culture
332
0.840
0.840
0
law
1229
0.539
0.539
+1
Unit test: test_awq_multi_batch now also splits the same tokens as (1, 3, 4) instead of only equal batches; that case fails without this change, and all of test_calib.py (20 tests) passes. On a small float32 model the same 128 tokens split 32+96 vs 64+64 moved a layer's alpha (0.1 vs 0.2) and its scales by up to 29% before, and by about 1e-7 after.
Not measured: accuracy on other models or tasks than Qwen3-8B on MMLU, MoE models (checked on a tiny Qwen3-MoE: awq_lite only visits the attention linears, HF fused experts get none, so this affects dense models, uneven batches and non-fused MoE experts), awq_clip (it keeps its own per-batch accumulation).
Before your PR is "Ready for review"
Is this change backward compatible?: ❌ AWQ scales calibrated with uneven batches change (equal batches: unchanged up to bf16 rounding of the batch means); noted in the CHANGELOG.
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?: ✅ (extended an existing test)
Did you update Changelog?: ✅
Did you get Claude approval on this PR?: ❌ (to be requested with /claude review)
Additional Information
Found while measuring PTQ reproducibility (see the scale_compare / --deterministic PR, linked in a comment): on main repeated runs are bit-identical, and the large batch-size differences in AWQ scales came from this estimator.
awq_lite averaged |x| per batch in the activation dtype, then averaged those
batch means, and summed the per-batch mean squared error of the alpha
search. Every batch therefore weighed the same whatever its token count, so
a smaller last batch, or an MoE expert that gets few tokens in some
batches, changed the scales, and each batch mean was rounded to bf16. It now
sums |x| in fp32 and divides by the token count, and sums the squared
error, as the reference AWQ does over the whole calibration set.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
AWQ-Lite now accumulates activation magnitudes and squared-error losses as sums. It normalizes activation scales by total tokens. The multi-batch test checks scales across two batch partitions, and the changelog records the behavior change.
AWQ-Lite accumulates activation magnitudes and squared-error losses as sums, then normalizes activation scales by total tokens. The test compares scales across two batch partitions. The changelog notes the change.
Calibration can produce incorrect scales when data-parallel ranks process unequal numbers of tokens. Correct the synchronization before merging unless that behavior is explicitly accepted.
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The title clearly and concisely describes the main change: making AWQ-Lite calibration independent of token batching.
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Security Anti-Patterns
No explicit security anti-pattern is introduced. The PR changes only AWQ arithmetic, a changelog entry, and tests; the added production lines contain no unsafe torch.load or numpy.load options, hardco…
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Explanation
No explicit security anti-pattern is introduced. The PR changes only AWQ arithmetic, a changelog entry, and tests; the added production lines contain no unsafe torch.load or numpy.load options, hardcoded trust_remote_code=True, external-input eval/exec, or # nosec. No dependency manifest changes are present. The reported data-parallel normalization issue is outside this security check.
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Related: the scale_compare tool and hf_ptq --deterministic that were used to measure this are in #2995. The two PRs are independent and can merge in either order.
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Review comments at @modelopt/torch/quantization/model_calib.py:
- Line 1661: Update the activation-scale normalization flow around
`module.awq_lite.act_scale` so `sync_act_scale_across_dp` synchronizes
activation sums and token counts across the data-parallel group before dividing.
Avoid dividing by each rank’s local `num_tokens` before synchronization, so the
resulting scale is token-weighted when ranks have different token counts.
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CHANGELOG.rst
modelopt/torch/quantization/model_calib.py
tests/unit/torch/quantization/test_calib.py
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Major · Functional Correctness · Quick win
Normalize activation sums after data-parallel synchronization.
When data-parallel ranks see different token counts, this division creates a local mean on each rank. sync_act_scale_across_dp then averages those means equally. For ranks with one token at activation 10 and three tokens at activation 0, the result is 5 instead of the token-weighted mean of 2.5. Sum activation statistics and token counts across the data-parallel group before dividing, so uneven expert routing does not change the calibrated scale.
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Review comment at @modelopt/torch/quantization/model_calib.py at line 1661:
Update the activation-scale normalization flow around
`module.awq_lite.act_scale` so `sync_act_scale_across_dp` synchronizes
activation sums and token counts across the data-parallel group before dividing.
Avoid dividing by each rank’s local `num_tokens` before synchronization, so the
resulting scale is token-weighted when ranks have different token counts.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
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Agreed that this is the right next step, and I would take it as a follow-up. This PR makes the statistic token-weighted within a rank; the data-parallel average across ranks is unchanged (equal weight per rank, as before), which equals the token-weighted mean whenever the ranks see equal token counts, as dense layers do. Weighting by token count across ranks for MoE experts with uneven routing means summing the activation sums and token counts across the group before dividing: a change to the collectives that needs its own multi-rank test. The description now states this scope.
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@shengliangxu Understood. This PR makes activation statistics token-weighted within each rank. It leaves the existing equal-weight data-parallel average unchanged.
A separate follow-up for token weighting across ranks is reasonable, with a multi-rank test for uneven expert token counts. The reported description update makes that scope explicit.
Would you like me to open a GitHub issue for that follow-up?
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✅ Project coverage is 78.77%. Comparing base (0638a27) to head (0826672). ⚠️ Report is 2 commits behind head on main.
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[IMPORTANT Correctness] The divisor changed to num_tokens, but the enclosing guard (line 1657) still tests num_cache_steps > 0. These are no longer the same predicate, so this can divide by zero.
num_cache_steps and num_tokens are incremented together (lines 1590-1591), but not by the same amount:
The early return reads numel() on the DTensor (global shape metadata), while the accumulation reads it after to_local(). A rank whose local shard is empty — e.g. Shard(0) on a token/batch dim smaller than the shard world size, which Shard._split_tensor resolves to zero-row shards — therefore passes line 1581 with local shape (0, D): get_act_scale_sum returns zeros, num_cache_steps becomes 1, and num_tokens stays 0 // D == 0.
Why it matters:act_scale / 0 on a zeros tensor yields NaN. The NaN check on line 1663 catches it, but has_nan is then all-reduced over the data-parallel group with any (line 1666), so one rank with an empty shard disables awq_lite for that layer across the entire DP group — the whole group falls back to max calibration with a warning. That is a regression: before this PR the same rank computed 0 / 1 == 0, get_scale clamped to a neutral all-ones scale, and ReduceOp.AVG merely diluted that rank's contribution while every rank kept real AWQ scales.
Suggested fix — clamp the divisor rather than moving the loop guard. Note that changing line 1657 to num_tokens > 0 would be worse: get_dist_syncd_obj on line 1666 is a DP-group collective, so which ranks enter this loop body must stay uniform across the group, and a rank-dependent guard here would hang instead. Since act_scale is all zeros exactly when num_tokens == 0, dividing by 1 reproduces the previous neutral-scale behaviour without touching the collectives:
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Thanks for tracing this. I measured the premise and it does not hold, so I am leaving the code as it is. Before this PR a rank with an empty local shard did not compute 0 / 1: the old get_act_scale took x.abs().view(-1, C).mean(0), and the mean over zero rows is NaN (torch.zeros(0, 8, dtype=torch.bfloat16) gives [nan, ...]), so act_scale was already NaN, the same NaN check fired, and has_nan disabled awq_lite for that layer across the DP group. With this PR the same input gives zeros / num_tokens (0) = NaN, so the outcome is the same and this is not a regression. Clamping the divisor would turn that case into a neutral scale, which is a behavior change in a path (DTensor activations with an empty local shard) that the unit tests cannot reach, so I would rather not fold it into this PR; happy to take it as a separate change if a maintainer wants it.
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Claude review — 1 blocking issue
Scope: full review (the trigger comment carried no extra scoping). All 3 changed files reviewed: modelopt/torch/quantization/model_calib.py, tests/unit/torch/quantization/test_calib.py, CHANGELOG.rst. Note that git diff origin/main HEAD here also surfaces reverse-diff noise (the branch is behind main under fetch-depth: 1); I confirmed against the PR's file list that only those three files belong to this PR.
model_calib.py:1661 — [IMPORTANT Correctness] the divisor moved to num_tokens but the enclosing guard still tests num_cache_steps > 0, and those are no longer the same predicate. The input.numel() == 0 early return on line 1581 reads the DTensor's global numel, while num_tokens on line 1591 is accumulated from the local tensor after to_local(). A rank with an empty local shard therefore reaches num_cache_steps == 1, num_tokens == 0 and divides by zero. The NaN is caught, but has_nan is all-reduced with any over the DP group, so a single empty shard disables awq_lite for that layer across the whole group — where before the PR that rank computed 0 / 1 and every rank kept real AWQ scales. The suggested fix clamps the divisor; moving the loop guard instead would make a DP-group collective rank-dependent and hang. Details in the inline comment.
What checks out
The estimator fix is correct.get_act_scale_sum summing |x| in fp32 and dividing once by the total token count is the reference AWQ statistic; it also removes the old bf16-rounded per-batch mean(0). reshape in place of contiguous().view is a strict improvement — no view failure on a non-contiguous abs() output, and no forced copy.
The loss change is argmin-safe. Every alpha sees the identical forward set, so sum-vs-mean is a uniform constant within a module; the only behavioural delta is exactly the intended one — equal weight per token instead of per batch. loss[alpha] is an explicit fp32 scalar (line 1485) and (out - out_actual).float() keeps it there, so the larger magnitudes carry no precision or overflow risk. loss is only ever consumed by min() in update_best_params, never as an absolute metric.
Accumulator/divisor pairing is consistent elsewhere: act_scale and num_tokens both derive from the post-to_local() input; num_tokens counts cache-pass (routed) tokens, matching the cache-pass act_scale since _if_calib is only set afterwards; and named_modules() dedupes, so a tied module is normalized once.
No API or compat surface.AWQLiteHelper is a closure-local class and get_act_scale a local closure, so the rename reaches nothing. The only external readers of these fields are tests/unit/torch/models/test_nemotron_h_mtp.py (num_cache_steps, num_search_steps) and awq_clip.num_tokens, none affected by num_tokens becoming an int. With debug=True users still observe act_scale as a mean. num_cache_steps is not dead (lines 1657, 1685, 1723).
Aligns torch with ONNX.modelopt/onnx/quantization/int4.py:736 already takes one mean(0) over the whole calibration activation and assigns (not accumulates) the loss, so it never had this bug — this change converges the two paths rather than diverging them.
DP weighting is unchanged, not regressed.sync_act_scale_across_dp's ReduceOp.AVG was equal-weight-per-rank both before and after. The PR description scopes the token-weighted cross-rank reduction to a follow-up with sound reasoning (new collectives, needs a multi-rank test), so I am not duplicating CodeRabbit's comment on it.
CHANGELOG entry is two sentences, in the right Bug Fixes section, and states the user-visible consequence.
Risk
Low-to-moderate. The core math is right and the single-rank path is well covered by the parametrized test_awq_multi_batch. The residual risk is concentrated in the DTensor/empty-shard interaction above, which the test suite does not reach. I could not execute pytest in this environment, so the test results in the PR description are unverified by me — the analysis above is static.
Accuracy check added to the description (Testing): Qwen3-8B INT4_AWQ, MMLU 5-shot, calibration batch size 8 with 100 samples (so the last batch is partial), GB200, transformers 5.13.0, one run per arm.
No measurable accuracy change from the estimator on this model: 13 more correct answers out of 13,508, with 30 of 57 subjects higher, 18 lower and 9 tied. The category and sub-category tables are in the description.
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What does this PR do?
Type of change: Bug fix
awq_liteestimated each linear layer's activation scale as the mean of per-batch means (each mean computed in the activation dtype, so rounded to bf16) and accumulated the alpha-search loss as a sum of per-batch means. Every batch therefore weighed the same whatever its token count: a smaller last batch, or an MoE expert that receives few tokens in some batches, changed the scales, and the alpha that wins the search could flip with the batch size. The reference AWQ takes one mean over the whole calibration set.Now
|x|is summed in fp32 and divided by the token count (awq_lite.num_tokens, which was already tracked but unused), and the squared error is summed instead of averaged, so the result no longer depends on how the same tokens are split into batches. With equal-sized batches the result is the old one up to the bf16 rounding of the batch means.Scope: the statistic is token-weighted within a rank. Data-parallel ranks are still averaged with equal weight (
sync_act_scale_across_dp, unchanged by this PR), which is the token-weighted mean when the ranks see equal token counts, as dense layers do. For MoE experts with uneven routing across ranks, summing the activation sums and token counts across the group before dividing would be needed; that changes collectives and needs a multi-rank test, so it is left for a follow-up.Testing
Qwen3-8B,
--qformat int4_awq, 100 calibration samples of length 512 (so batch size 8 ends with a partial batch), GB200, torch 2.11.0a0, transformers 5.13.0.hf_ptq.pywas run to the end ofawq_litecalibration and each layer'sbest_alphaand quantizer scales were dumped. Counts are of the 252 quantized linears; tensors differing by more than 1%.best_alphadiffersweight_quantizer._amaxdiffersinput_quantizer._pre_quant_scalediffersSo the batch-size dependence of AWQ scales on this model drops from 177 to 2 tensors, and the estimator alone (same batches, same kernels) accounts for it. A partial last batch is common with the default
--calib_size 1024and an auto-probed batch size.Accuracy: the scales above change, so the same model was evaluated with each estimator. Qwen3-8B (bf16),
INT4_AWQ_CFG, calibration with--batch_size 8 --calib_size 100(the last batch is partial, which is where the estimators differ), MMLU 5-shot throughexamples/llm_eval/mmlu.pywith--limit 1000(the cap only binds on professional_law, so 13,508 of the 14,042 test questions are used), GB200, transformers 5.13.0. One quantization and evaluation per arm, with the same flags and data; the arms differ only inmodel_calib.py(main at 0638a27 vs this PR).No measurable accuracy change: 13 more correct answers out of 13,508 (the binomial standard error of one accuracy at this size is 0.38 pp, about 51 questions), 30 of 57 subjects higher, 18 lower and 9 tied (two-sided sign test p = 0.11). The largest sub-category swings are small samples: engineering is the single subject electrical_engineering (-3 of 145 questions, -2.1 pp) and math is +8 of 1,064. With one run per arm this supports "no regression", not "improvement".
All 17 MMLU sub-categories
Unit test:
test_awq_multi_batchnow also splits the same tokens as (1, 3, 4) instead of only equal batches; that case fails without this change, and all oftest_calib.py(20 tests) passes. On a small float32 model the same 128 tokens split 32+96 vs 64+64 moved a layer's alpha (0.1 vs 0.2) and its scales by up to 29% before, and by about 1e-7 after.Not measured: accuracy on other models or tasks than Qwen3-8B on MMLU, MoE models (checked on a tiny Qwen3-MoE:
awq_liteonly visits the attention linears, HF fused experts get none, so this affects dense models, uneven batches and non-fused MoE experts),awq_clip(it keeps its own per-batch accumulation).Before your PR is "Ready for review"
CONTRIBUTING.md: N/A/claude review)Additional Information
Found while measuring PTQ reproducibility (see the
scale_compare/--deterministicPR, linked in a comment): on main repeated runs are bit-identical, and the large batch-size differences in AWQ scales came from this estimator.