fix(quantization): disable the KV cache for layerwise calibration by Fridah-nv · Pull Request #2248 · NVIDIA/Model-Optimizer

Fridah-nv · 2026-08-25T18:45:04Z

What does this PR do?

Type of change: bug fix

Layerwise calibration replays each decoder layer on its captured inputs, and those kwargs carried the model's past_key_values — so a layer attended over the keys and values its own earlier replay wrote. Everything downstream of attention in the layer was then calibrated against a zeroed attention output on all but the last layer.

The code tried to prevent exactly this, with Cache.reset(). But reset() does not clear a cache:

def reset(self) -> None:
    """Resets the cache values while preserving the objects"""
    if self.is_initialized:
        self.keys.zero_()      # zeroed, but still at full length
        self.values.zero_()

So the replay attended over a same-length, all-zero cache instead of no cache. Models passing an explicit sliding-window mask raised a shape mismatch instead, since the next update then doubled kv_len.

The fix does not clear the cache — it stops one being built. Calibration never reads a KV cache, so the layerwise loop is wrapped in the existing _disable_use_cache (modelopt/torch/utils/dataset_utils.py, already used by the export path). That helper handles nested multimodal configs and configs that never assign the attribute, and its docstring already names this failure class for hybrid Mamba/attention models. Non-HF models no-op. It also lowers peak calibration memory, since the cache was allocated and never read.

Who was affected. create_forward_loop already wraps its body in _disable_use_cache, so examples/hf_ptq and the shipped recipes never built a cache in the first place. Measured on Qwen/Qwen2.5-1.5B-Instruct with general/ptq/nvfp4_default-kv_none-gptq, fix simulated away:

shipped _forward_loop    caches reaching a layer=0     written to disk=0
bare user loop           caches reaching a layer=222   written to disk=54

So the exposure is a user-supplied forward_loop via mtq.quantize, not the shipped path — and every measurement below uses a bare model(batch) loop to exercise the bug, not what hf_ptq runs. A caller can also re-enable caching under the wrap (use_cache resolves to the caller's value when passed, and past_key_values can be handed in directly — examples/alpamayo/quantize.py does both), so capture additionally refuses a cache outright rather than miscalibrating quietly.

Not a transformers regression. reset() has these semantics across ModelOpt's whole supported range — verified on 4.57.6 (tf_min) and 5.12.1:

4.57.6   after_update=((1,2,8,4),64)  after_reset=((1,2,8,4),0)  after_2nd_update=(1,2,16,4)
5.12.1   after_update=((1,2,8,4),64)  after_reset=((1,2,8,4),0)  after_2nd_update=(1,2,16,4)

What is affected

Not the numeric format — two conditions, both required: the quantizer carries a calibrated per-tensor amax, and it sits downstream of attention inside the layer. GPU sweep (RTX 6000 Ada, tiny-llama, 4 layers):

activation format	calibrated amax?	cache-dependent wrong	cache-independent wrong	collapsed to 0.0
FP8 per-tensor static	yes	12/16	0/12	3
NVFP4 dynamic block	yes	12/16	0/12	3
NVFP4 static block	yes	12/16	0/12	3
MXFP8 dynamic block	no (E8M0, no per-tensor amax)	immune
FP8 `constant_amax`	pinned	0/16	0/12	0

FP8-static and NVFP4-dynamic behave identically — "dynamic" refers to the block scales; the per-tensor amax behind the FP8 scale-of-scales is still calibrated from data. Cache-independent quantizers (q/k/v_proj, which consume the layer input) are never wrong. *_bmm_quantizer KV scales are never wrong either — they quantize the K/V being written. The fix restores exact parity with non-layerwise in every row.

Attention does not have to be quantized to be affected, because the corruption is in the activations:

h = x + attn(norm1(x))     # attn output zeroed
y = h + moe(norm2(h))      # so the router and the experts see the wrong h

On one Mixtral layer, pre-fix vs post-fix: attn_out amax 0.000000 vs 0.026733, router input differs by 1.46, and 2 of 16 tokens route to a different expert — so expert amaxes are wrong both in magnitude and in which tokens they saw.

End-to-end on a shipped recipe

general/ptq/nvfp4_experts_only-kv_fp8_layerwise, real MoE model, GPU:

level	before fix	after fix
activation amaxes wrong vs non-layerwise	6 / 8	0 / 8
KV-cache (`*_bmm_quantizer`) amaxes wrong	0 / 8	0 / 8
exported checkpoint tensors differing	36 / 231	0 / 231
fake-quantized logits vs non-layerwise	max\|Δ\|=8.4e-03, rel 2.0e-02	exactly 0

The 36 tensors are all input_scale on expert w1/w2/w3, e.g. model.layers.0.block_sparse_moe.experts.0.w1.input_scale: 0.0012555803 -> 0.0012032646. So the corruption reached the published artifact; after the fix layerwise reproduces the whole-model checkpoint and its logits bit for bit.

This degraded silently rather than failing loudly because the 0.0 collapse lands on o_proj, which an experts-only recipe does not quantize. The expert MLP inputs are downstream of attention too, but land on wrong-but-nonzero values.

Multi-pass calibrators: weights change too

gptq and awq_lite replay the layer forward loop twice (max_calibrate then the Hessian pass; the AWQ cache pass then the search pass), so the poisoned activations fed the weight updates. Measured on W4A16 — weight-only, zero activation quantizers — comparing exported checkpoints before vs after the fix:

algorithm	tensors changed	of which `.weight`
`awq_lite`	42 / 99	15
`gptq`	50 / 95	25

So the blast radius is not limited to activation scales, and nvfp4_default-kv_none-gptq is affected in its exported weights. (Found in review — the changelog originally said "weight-only recipes are unchanged", which was wrong.)

Both orderings were corrupted, in complementary patterns

Non-layerwise is not a valid oracle for qdq=True — each layer is meant to see QDQ error from calibrated predecessors. Instead compare against layerwise run with caching structurally impossible (use_cache=False, so past_key_values is never created): a cache-free reference valid for any ordering. Reproducing the original code exactly (calib_func path = reset(), run-mode replay = verbatim):

ordering	original code	after fix	corrupted layers
`qdq=False` (max)	16/28 correct	28/28	0, 1, 2
`qdq=True` (GPTQ)	14/28 correct	28/28	1, 2, 3

The layer sets are complementary, because the contamination arrives by a different route in each ordering:

qdq=False — the pre-capture pass runs the layer before calib_func, so every batch is contaminated and the layer's own attention output is zeroed while it is being calibrated. o_proj collapses to exactly 0.0. The last layer skips that pass and is clean.
qdq=True — calib_func runs first on an untouched cache slot, so layer 0 is correct. The run-mode replay then attends over the leftovers calib_func just wrote, corrupting the inputs captured for layer 1, and it propagates from there.

Nothing collapses under qdq=True; the deltas are ~0.5%, in both directions:

L1.self_attn.q_proj   cache-free=3.171875   original=3.187500
L2.mlp.gate_proj      cache-free=2.937500   original=2.953125
L3.mlp.down_proj      cache-free=0.038574   original=0.038330

That run is single-pass max with qdq=True forced, so its 14/28 is not multi-pass self-poisoning — it is the run-mode replay inheriting calib_func's cache writes. A second contamination route, and the reason the run branch now clears the cache itself rather than relying on calib_func not having written to it.

So activation scales were wrong under both orderings and re-calibration is warranted either way; but for a GPTQ user the activation error is sub-1%, and the material damage is to the weights via the Hessian pass.

Post-fix both orderings are 28/28 against the cache-free reference — layerwise with a cache is bit-identical to layerwise where a cache cannot exist. That proves cache-independence, not general qdq=True correctness.

Why existing tests missed it

Two safety nets, with intersecting blind spots — the bug sits exactly where a real KV cache and calibrated activation scales coincide, which nothing tested together:

	fixture	configuration	why it missed
`test_layerwise_no_qdq_matches_sequential_amax`	toy `_DecoderBlock` (`attn = nn.Linear`) — no KV cache	activations quantized ✅	right oracle, but no cache exists to go stale
#1571's end-to-end (Qwen3-8B)	real model with a real KV cache ✅	NVFP4 W4A16	W4A16 quantizes no activations

Measured: across the 80 pre-existing tests that reach layerwise calibration, 84 layer replays, 0 with a live KV cache — the buggy branch was never executed. And W4A16 produces 0 calibrated activation quantizers and 0 input_scale tensors, so #1571's "905 tensors bit-identical" was true over a set that structurally excludes every tensor this bug touches. Its other comparison (new config form vs legacy flat form) was layerwise-vs-layerwise, which cannot detect this either.

Testing

Unit — one test in tests/unit/torch/quantization/test_layerwise_calibrate.py, each verified to fail when the code it guards is removed:

test_layerwise_calibration_and_kv_caching[llama|nemotron_h_hybrid|gpt_oss_sliding_window] — no cache reaches a decoder layer during layerwise calibration, config.use_cache is restored afterwards, and a forward_loop that re-enables caching is refused. Swept over attention styles because correctness rests on the model honouring the flag, and because the guard is duck-typed against each cache type.

(deepseek_v3 is absent from the sweep: its tiny fixture cannot run a forward at all — shape '[-1, 8, 0]' is invalid, a degenerate MLA head dim — which is pre-existing and unrelated.)

tests/unit/torch/quantization/ — 921 passed, 7 skipped. pre-commit clean.

End-to-end, bare forward loop — this matrix exercises the bug; a bare model(batch) loop is what leaves caching on — Qwen/Qwen2.5-1.5B-Instruct (28 layers, pretrained weights), comparing every calibrated amax and every weight. Oracle differs per setting. Non-layerwise is only valid when qdq_from_prev=False; gptq forces it True, and now that the fix is disabling the cache, a "cache-free reference" for gptq would be the identical run — so for that row the informative number is the sensitivity one: what the fix changes.

#	setting	oracle	compared	result
1	`mse` W4A4	non-layerwise	730	MATCH
2	`local_hessian` W4A16	non-layerwise	534	MATCH
3	`awq_lite` W4A16	non-layerwise	534	MATCH
4	`gptq` W4A4	see caveat above	730	MATCH
5	`gptq` + resume	uninterrupted run	730	MATCH

W4A16 on rows 2–3 is deliberate: with zero activation quantizers, any movement is provably a weight effect, which is where the multi-pass calibrators are exposed.

Measured against the previous (cache-clearing) implementation of this fix, removing it moved 108 weight tensors under awq_lite and 192 under gptq on this model — the clearest evidence that the blast radius reaches exported weights and not just activation scales.

A tiny random-init model detects less than the real checkpoint does, which is why the matrix runs on pretrained weights.

End-to-end, shipped forward loop — the same five settings through create_forward_loop, i.e. what examples/hf_ptq actually runs:

arm	result
with fix	5/5 MATCH
fix removed	5/5 MATCH

Both arms identical, so the fix is a no-op on the shipped path and this matrix has no detection power for the bug — it is evidence of scope, not of correctness. The bare-loop matrix above is what demonstrates the fix. Together they say: the bug is real and the fix resolves it, and no hf_ptq user was affected.

Additional Information

Interaction with #2136. That PR documents this symptom in its "found on the way, not fixed here" section, and its _fusion_probe carries the same hasattr(cache, "reset") → cache.reset() block, commenting that "the cache would give the probe kv_len twice the mask width" — the same doubling, which reset() does not actually prevent. Once this lands, #2136 can drop that block — with caching disabled for the layerwise loop its probe has no cache to reset. Not touched here.

Scope. This fixes activation calibration under layerwise. It does not address quantizers on modules outside the decoder stack (lm_head, embeddings), which layerwise never calibrates at all — a separate gap.

Before your PR is "Ready for review"

Is this change backward compatible?: ✅ — no API change. Activation amaxes from layerwise recipes will differ (they were wrong); re-run calibration for any layerwise recipe that quantizes activations.
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?: ✅
Did you get Claude approval on this PR?: ❌ — draft.

🤖 Generated with Claude Code

…cache Layerwise calibration replays each decoder layer on its captured inputs. The captured kwargs carry the model's ``past_key_values``, which the preceding capture pass has already written to, so the replay had to start from an empty cache. It called ``Cache.reset()`` for that -- but ``reset()`` "resets the cache values while preserving the objects": it zeroes the key/value tensors and leaves them at full length. The replay therefore attended over a same-length, all-zero cache instead of no cache at all. The result was silently wrong activation scales. ``self_attn.o_proj``'s input amax collapsed to exactly ``0.0`` on every layer but the last -- the one with no preceding capture pass, so its cache was never initialized and ``reset()`` was a no-op -- while ``down_proj`` picked up a plausible but wrong value from the residual alone. Models that pass an explicit sliding-window mask raised a shape mismatch instead, since the replay's concatenated cache is twice the mask width. Drop the cache instead; the layer recomputes keys and values from the captured inputs. Activation amaxes now match the non-layerwise path exactly on every architecture checked (llama, mixtral, nemotron, nemotron_h, and gpt_oss, which previously raised). Not a transformers regression: ``reset()`` has these semantics across the whole supported range, verified on 4.57.6 (tf_min) and 5.12.1. Weight-only recipes are unaffected -- weight amaxes never depended on the cache. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

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Walkthrough

Layerwise calibration now removes KV-cache objects from captured and resumed inputs, then replays sanitized arguments without clearing them again. Tests cover supported cache forms and FP8 calibration values. The changelog updates checkpoint and algorithm guidance.

Changes

Layerwise calibration

Layer / File(s)	Summary
Capture and replay handling `modelopt/torch/quantization/utils/layerwise_calib.py`, `modelopt/torch/quantization/model_calib.py`	Cache removal now supports positional, modern keyword, and legacy keyword inputs. Captured and resumed checkpoint inputs are sanitized before replay. Layer replay forwards keyword arguments unchanged.
Calibration regression and guidance `tests/unit/torch/quantization/test_layerwise_calibrate.py`, `CHANGELOG.rst`	Tests validate cache-free inputs, cache removal, and FP8 amax parity with nonzero activation values. The changelog requires a fresh checkpoint directory and lists algorithms that can change exported weights.

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Merge Risk: ⚪ Minimal · up to 3310a

The change corrects layerwise replay cache handling and adds regression coverage; no actionable merge-blocking risk remains after normal checks and review.

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Trims the helper docstring to the one non-obvious point a reader needs -- that Cache.reset() zeroes in place rather than clearing -- and cuts the changelog entry to what an external user must act on. Also names the shipped recipes affected: the experts-only layerwise recipes enable NVFP4 input quantizers on the MoE experts, which sit after attention and so inherit the zeroed attention output, so "weight-only recipes are unaffected" understated the blast radius. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Fridah-nv · 2026-08-25T19:24:22Z

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Inline comments:
In `@CHANGELOG.rst`:
- Line 46: Revise the changelog entry to remove the stale KV-cache and
zeroed-attention-output implementation details, while retaining the user-visible
activation-amax calibration fix and instructing users to rerun calibration for
activation-quantizing layerwise recipes, including the shipped
nvfp4_experts_only-kv_fp8_layerwise recipes; keep weight-only recipes unchanged.

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Scope: trigger comment was a bare /claude review, so this is a full review. 3 changed files (+60/-16), all reviewed: modelopt/torch/quantization/model_calib.py, tests/unit/torch/quantization/test_layerwise_calibrate.py, CHANGELOG.rst. Also read utils/layerwise_calib.py, config.py, and the shipped modelopt_recipes/general/ptq/ layerwise and gptq recipes for the composition and blast-radius questions the diff alone cannot answer.

Findings: CRITICAL: 0 | IMPORTANT: 1 | SUGGESTION: 3

The fix itself is correct. I traced the cache lifetime through both orderings in layerwise_calibrate:

Capture-mode kwargs always carry an empty cache (the parent forward early-stops at the capture layer, and the preceding run-mode layer writes into its own older cache object, not the current forward's).
qdq_from_prev=False: cache_outputs_for_next_layer_calib replays the layer for real before calib_func, populating the captured cache — that is the bug, and _with_empty_kv_cache removes it.
qdq_from_prev=True (GPTQ): calib_func runs first, and multi-pass calibrators self-poison across passes — also fixed, since the helper applies to every _layer_forward_loop invocation.

Empty-cache vs None is equivalent for a prefill replay (DynamicCache.update on an empty cache returns the same K/V; hybrid Mamba mixers take the full-scan path either way), so dropping the cache is the right call rather than allocating a fresh one. The reset() semantics claim in the docstring matches what Cache.reset does — zeroes in place, keeps the length, so the next update appends and doubles kv_len. No other cache.reset() call sites remain in modelopt/. Root-cause fix, not a symptom patch.

Most impactful finding — [IMPORTANT Compatibility]: the CHANGELOG entry understates the impact. weight-only recipes are unchanged does not hold for the multi-pass calibrators, because the first replay is what poisons the cache for the second:

gptq: the Hessian pass (model_calib.py:2252) is the second replay, so the Hessians — and therefore the GPTQ weight updates — were computed from a zeroed attention output. modelopt_recipes/general/ptq/nvfp4_default-kv_none-gptq.yaml ships method: gptq plus layerwise.enable: true at W4A4 and is affected, but is not named in the entry.
awq_lite: cache pass (:1577) and search pass (:1634) are both unconditional and it defaults to qdq_from_prev=False, so a layerwise AWQ-lite config is corrupted with zero activation quantizers — wrong best_scale, hence wrong pre_quant_scale and weights.

The weight-only claim is true only for the top-level max path, which skips the forward via skip_forward_without_activation_calib. Suggested rewrite is in the inline comment; the code needs no change for this.

Suggestions (non-blocking):

layerwise_calib.py's run branch replays the same cached tuples verbatim, so the captured cache now has two consumers and only one drops it. Safe today only because this fix stops calib_func mutating it — worth applying the helper there too, or naming the invariant in a comment.
Function-level get_tiny_llama import in the new test (CONTRIBUTING asks for module-top imports in test files too).
calib_mutates_weights: False in the new test is inert without checkpoint_dir and diverges from the shipped recipe's default.

Risk: low for the code, medium for the release note. The change is 12 lines with no API, config-schema, modelopt_state, or export-path surface, and the new test is the only one in the suite that reaches the cache branch. The residual risk is communication: users on layerwise GPTQ/AWQ-lite could read the current entry and skip re-calibration for checkpoints whose weights were derived from a zeroed attention output. Worth also confirming nvfp4_default-kv_none-gptq end-to-end, since it is the shipped recipe whose weights — not just scales — move.

… changelog Addresses review on #2248. The run-mode replay in LayerActivationCollector consumes the same captured kwargs as calib_func, so both consumers of that shared cache object now clear it. It was correct before only because calib_func no longer wrote to the cache -- an implicit ordering dependency that reordering the two blocks would silently break. Verified a no-op today: the recipe end-to-end and the cache-independence oracle return identical results either way. The helper moves to layerwise_calib.py, which owns both replay paths (model_calib.py already imports from it, so the reverse would be circular). The changelog said "weight-only recipes are unchanged", which is wrong for the multi-pass calibrators: gptq replays for its Hessian pass and awq_lite for its search pass, so the corrupted activations fed the weight updates. Measured on a W4A16 weight-only config with zero activation quantizers, the fix changes 15 exported .weight tensors under awq_lite and 25 under gptq. Entry now names the affected shipped recipes and drops the root-cause detail that belongs in the PR. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

… test Addresses review on #2248. With no checkpoint_dir the flag never reaches the weights.pt / quantizer_buffers.pt branch it controls, and writeback=False is a no-op for a resident CPU model -- so it only made the test diverge from the default that the shipped nvfp4_experts_only-kv_fp8_layerwise recipe runs. Still fails without the fix on o_proj's input amax. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Fridah-nv · 2026-08-25T20:54:07Z

/claude review

claude

Claude review (re-review at 0f12053; prior review was at 20df3d3)

Scope: bare /claude review, so a full review. 4 changed files (+63/-17), all reviewed: modelopt/torch/quantization/model_calib.py, modelopt/torch/quantization/utils/layerwise_calib.py, tests/unit/torch/quantization/test_layerwise_calibrate.py, CHANGELOG.rst. Also read the checkpoint/resume half of layerwise_calib.py, config.py's LayerwiseConfig, mode.py's layerwise dispatch, the mse/local_hessian calib paths, and every shipped modelopt_recipes/** YAML that mentions layerwise — the composition and blast-radius questions the diff alone cannot answer. (Note: a two-dot diff against the origin/main tip also surfaces reverse-hunks for speculative-decoding commits that are on main but not on this branch; those are not part of this PR and I ignored them.)

Findings: CRITICAL: 0 | IMPORTANT: 1 | SUGGESTION: 2

All four findings from the previous round are resolved. The helper moved to layerwise_calib.py and is now applied to the run branch too (closing the "two consumers, one drops it" gap), the CHANGELOG names the GPTQ/AWQ-lite weight impact and nvfp4_default-kv_none-gptq, get_tiny_llama is a module-level import, and the inert calib_mutates_weights override is gone.

The fix is correct, and the run-branch addition makes it complete. layer_inputs / cached_inputs have exactly two consumers (model_calib.py:2127 and layerwise_calib.py:254) and both now go through _with_empty_kv_cache. Dropping the cache — rather than allocating a fresh one — is right: DynamicCache is indexed per layer_idx, so in a prefill-only pass layer N's slot is empty in the non-layerwise reference too, which makes past_key_values=None exactly equivalent rather than merely close. Mask width stays consistent because skip-mode layers never write to the parent's cache, so it is empty when the parent builds the mask. The Cache.reset() characterization in the docstring is accurate, and no cache.reset() call sites remain in modelopt/. I also checked the cache_params-style hybrid path: HF Mamba mixers take the prefill branch and overwrite conv/ssm state when cache_position[0] == 0 rather than reading it, which is why nemotron_h was already clean and why that class of staleness does not need the same treatment.

Spot-checked the minimax_m3_vl/mxfp8_nvfp4_experts immunity claim, which the PR body could not measure: that recipe's activation quantizers are MXFP8 (*input_quantizer) and constant_amax: 2688.0 (expert inputs), and it is method: mse with fp8_scale_sweep: false, whose weight search is activation-independent. Immune, as claimed.

Most impactful finding — [IMPORTANT Compatibility]: resuming a partial pre-fix checkpoint silently mixes wrong and right amaxes. manifest.json carries no format/version key, and from_folder's drift check skips any key missing from the manifest, so a directory written by a pre-fix ModelOpt is indistinguishable from a post-fix one. A run interrupted at layer K pre-fix and resumed after upgrading loads layers 0..K-1 verbatim from disk and calibrates K..N correctly, exporting a half-miscalibrated model with no warning — and the shipped nvfp4_default-kv_none-gptq.yaml pins checkpoint_dir: output/layerwise_ckpts/, so the stale directory is the default location. "Re-run calibration" does not cover this, because re-running resumes. A completed pre-fix directory is safe (detect_resume_point returns None once last + 1 >= total). The inline comment has a format_version fix that reuses the existing drift machinery.

Suggestions (non-blocking): (1) the "under gptq and awq_lite the exported weights change too" enumeration omits local_hessian (its second forward_loop pass builds the Hessian from the poisoned activations, so refined weight amaxes move), awq_clip, and smoothquant; generalizing is also shorter. (2) Sanitizing at capture as well would stop layer_inputs pinning a live Cache for the whole loop and stop next_inputs.pt pickling a transformers Cache that _move_to_device cannot move to CPU.

Risk: low for the runtime change, medium for the resume path. Twelve lines, no API, config-schema, modelopt_state, or export-path surface; the new test is the only one in the suite that reaches the cache branch, and it fails without the fix. The residual risk is in the upgrade story rather than in the algorithm.

…gelog Addresses the re-review on #2248. Sanitizing at capture means the cache is never stored in collected_inputs, so it is not pinned for the whole layer loop and not pickled into next_inputs.pt -- _move_to_device recurses into tensors, dicts and lists only, so a transformers Cache was written out as-is and unpickled with weights_only=False on resume. The two replay-site calls stay: they are free on the None fast path and still cover inputs restored from an older checkpoint. Verified a no-op on the current results. The changelog enumerated gptq and awq_lite, which was non-exhaustive in the same direction as the earlier "weight-only recipes are unchanged": local_hessian also runs a second forward_loop to accumulate its Hessian, and awq_clip and smoothquant are activation-driven too. Generalized to the property rather than a list, and points at a fresh checkpoint_dir, since re-running a recipe on a partial directory resumes rather than recalibrates. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Sanitizing at capture makes the two replay-site clears unreachable: measured over a calibration run, capture sees a live cache on 12 of 12 invocations and both replay sites on 0 of 21. The only input that could still carry one is a next_inputs.pt written before this fix, which layerwise -- an experimental feature -- does not promise to resume. So the fix is now one call at the boundary where model-produced kwargs enter stored state, and everything downstream trusts the invariant. model_calib.py no longer needs the helper at all; its diff is just the removal of the broken block. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Fridah-nv · 2026-08-25T21:20:21Z

/claude review

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In `@modelopt/torch/quantization/model_calib.py`:
- Line 2128: The checkpoint replay path around _CheckpointState.setup_resume
must preserve the empty-cache invariant: normalize resumed next_inputs so
past_key_values is None before the replay closure invokes m, or reject/version
checkpoints that lack this guarantee. Keep normal non-resume input handling
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modelopt/torch/quantization/model_calib.py
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claude

Claude review (third round, at e03c53b; previous rounds were at 20df3d3 and 0f12053)

Scope: bare /claude review, so a full review. 4 changed files (+62/-16), all reviewed: modelopt/torch/quantization/utils/layerwise_calib.py, modelopt/torch/quantization/model_calib.py, tests/unit/torch/quantization/test_layerwise_calibrate.py, CHANGELOG.rst. Also re-read the checkpoint/resume half of layerwise_calib.py (setup_resume, from_folder, _move_to_device, save) because moving the clear to capture-time changes which consumers the invariant covers. (Note: a two-dot diff against the origin/main tip also surfaces reverse hunks for speculative-decoding commits that are on main but not on this branch; not part of this PR, ignored.)

Findings: CRITICAL: 0 | IMPORTANT: 0 | SUGGESTION: 2

The refactor to a single capture-time clear is correct, and I verified the consumer set closes. Every replay of stored kwargs now flows from the capture branch: _layer_forward_loop (model_calib.py:2127) and the run branch (layerwise_calib.py:254) both read lists that originate in info.collected_inputs, and next_inputs.pt is written from the same tuples — so the two replay-site clears really were unreachable, matching the 0-of-21 measurement in the commit message. Clearing at the boundary where model-produced kwargs enter stored state is the better place for it: it also stops layer_inputs pinning a live Cache for the whole loop and stops _move_to_device silently passing an unmovable Cache through into the checkpoint. model_calib.py's diff reducing to a pure deletion is the right outcome. No cache.reset() call sites remain anywhere in modelopt/torch/quantization/.

Dropping the cache rather than allocating a fresh one remains exactly (not approximately) equivalent for a prefill replay, since DynamicCache is indexed per layer_idx and layer N's slot is empty in the non-layerwise reference too. Mask width is unaffected because HF builds the mask at model level before the layer loop, from a cache that is fresh per forward_loop call.

All prior-round findings are resolved. The CHANGELOG now names the fresh-checkpoint_dir requirement, the shipped affected recipes, and the full activation-dependent algorithm set (gptq, awq_lite, awq_clip, local_hessian, smoothquant); get_tiny_llama is a module-level import; the inert calib_mutates_weights override is gone; and the prior round's suggestion to sanitize at capture is what this commit implements.

Suggestions (non-blocking, both inline):

Resume is the one entry point not covered by the capture invariant. get_first_layer_inputs seeds collected_inputs straight from setup_resume's torch.load, and _move_to_device passes a Cache through untouched, so a pre-fix next_inputs.pt resumes without error. Layer K's own slot is empty in that cache, so single-pass max is fine — but a multi-pass calibrator fills it on pass 1 and attends over it on pass 2, silently reproducing this bug, and nvfp4_default-kv_none-gptq.yaml pins checkpoint_dir: output/layerwise_ckpts/ as the default location. I accept the commit message's position that experimental layerwise does not promise cross-version resume — my point is only that breaking it is silent, and manifest.json has no format key for from_folder's existing drift check to trip on. A format_version that reuses that machinery is the enforced version of the CHANGELOG sentence; sanitizing resumed_inputs is the cheaper partial one.
The sanitizer keys on the exact kwarg name past_key_values, so it no-ops for a cache passed positionally or as past_key_value — the pre-4.54 name that ModelOpt itself still has to translate for Kimi-style remote code (speculative/utils.py:546). Either duck-type over args+kwargs, or state the assumption in the docstring, which currently reads as unconditional.

On the test: test_layerwise_replay_does_not_attend_over_its_own_kv_cache is well-targeted — a real KV cache plus calibrated activation scales is precisely the intersection the two pre-existing safety nets missed, the max+layerwise config genuinely resolves qdq_from_prev=False so non-layerwise is a valid oracle, and pinning the zero-collapse separately from the equality is the right belt-and-braces. Note that I could not execute pytest in this environment (sandbox denied it), so the 919-passed run and the confirmed-fails-without-the-fix claim are yours as reported, not re-verified by me.

Risk: low. Twelve lines of runtime change, no API, config-schema, modelopt_state, or export-path surface; the deletion in model_calib.py is provably dead code; the new test is the only one in the suite reaching the cache branch. The residual risk is confined to the pre-fix-checkpoint upgrade path, which is documented rather than enforced.

Approving — no blocking issues.

…esume path Addresses review on #2248. Name-matching only past_key_values missed two shapes that occur in practice. Remote-code models written against older transformers pass the cache as past_key_value -- ModelOpt itself patches that for Kimi-K2 (speculative/utils.py:546), which is exactly the class of model layerwise calibration exists for -- and a custom parent may pass it positionally. In both cases the clear silently no-opped and layerwise produced wrong-but-plausible amaxes. Duck-typed detection over args and kwargs covers all three, without importing transformers into a core util. Capture is also not the only entry point into stored inputs: get_first_layer_inputs seeds them straight from next_inputs.pt on resume, and _move_to_device passes a Cache through untouched, so a checkpoint written before this fix still carried a live one. A file is a boundary like the model forward is, so it gets the same clear. Adds a test pinning the clear at its call site. The equivalence test cannot cover it: an unsanitized cache only accumulates across replays, which max-calibration's max reduction absorbs on a small model, so amaxes still matched while the invariant was broken -- that test passed with capture unsanitized. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Cuts the helper docstring from fourteen lines of prose to two facts a reader cannot infer from the code: why a replay must not keep the cache, and why Cache.reset() is not the way to drop it. The name-vs-shape rationale moves onto _is_kv_cache, which is what does the matching. Same for the three test docstrings and the resume-site comment. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Drops the layerwise-vs-non-layerwise equivalence test added earlier. It duplicated test_layerwise_no_qdq_matches_sequential_amax's property, and it could not fail for the reason it was written: without the clear the cache merely accumulates across replays, which max calibration's max absorbs, so the amaxes still matched. The structural test covers a reset()-style regression too, since a zeroed cache is still a cache in the captured inputs. What remains is three tests, one per call site, each verified to fail when that site's clear is removed: capture stores no cache, resume strips one left in a checkpoint, and the helper matches a cache by shape rather than by keyword. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

capture and resume assert the same invariant -- stored layer inputs hold no cache -- at the two entry points into stored state, so they are one test over an entry_point parameter. Both parameters still fail independently when their own call site's clear is removed. Also asserts against transformers' Cache type rather than _is_kv_cache. Using the predicate under test made the assertions self-referential: breaking _is_kv_cache to return False left the shape-matching test passing, because the check it makes went false too. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

realAsma · 2026-08-25T22:40:57Z

RB: Can you summarize why this PR is needed? Is this related to the effect of KV Caching during PTQ? We dont need KV caching during PTQ. Should we just disable KV Caching for huggingface models during PTQ? Will that solve the issue?

realAsma · 2026-08-25T22:43:58Z

🤖 Bot comment.

🐝 Looking into this now.

realAsma · 2026-08-25T22:44:14Z

🤖 Bot comment.

@realAsma — yes, this is a layerwise PTQ interaction with HF KV caching, not a need for KV caching during PTQ.

Layerwise calibration first captures each decoder layer's inputs, then replays that layer. A normal HF forward can create/write past_key_values as a side effect; the captured kwargs then retain that cache. On replay, Cache.reset() only zeroes the buffers—it leaves their sequence length—so attention sees a same-length zero cache instead of no cache. That corrupts calibration for modules downstream of attention; multi-pass methods such as GPTQ/AWQ can also derive incorrect weight updates.

Disabling use_cache for the whole HF PTQ path would likely avoid this specific path when it is honored, but it is broader and not a complete invariant: cache objects can be positional or use nonstandard keyword names, and resumed layerwise checkpoints may already contain one. This PR instead strips any cache from the stored layer-replay inputs (capture and resume), so replay is cache-free regardless of how the model supplied it. Its reported post-fix comparison matches a use_cache=False reference, which is the relevant proof here.

Layerwise replays each layer's captured inputs several times, and those inputs carried the model's past_key_values, so a layer attended over the keys and values its own earlier replay wrote. Everything downstream of attention in the layer was then calibrated against a zeroed attention output on all but the last layer. The code tried to prevent exactly this with Cache.reset(), but that "resets the cache values while preserving the objects" -- it zeroes the key/value tensors and leaves them at full length, so the replay attended over a same-length all-zero cache instead of no cache. Sliding-window models raised a shape mismatch instead, since the next update then doubled kv_len. Calibration never reads a cache, so rather than clearing it per input, don't build one: wrap the layerwise loop in the existing _disable_use_cache. That helper already handles nested multimodal configs and configs that never assign the attribute, and its docstring already names this failure class for hybrid Mamba/attention models. It also drops peak calibration memory, since the cache was allocated and never read. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Fridah-nv · 2026-08-25T23:29:20Z

/claude review

claude

Claude review (fourth round, at c3300d8; prior rounds were at 20df3d3, 0f12053, e03c53b)

Scope: bare /claude review, so a full review. 3 changed files (+106/-65), all reviewed: modelopt/torch/quantization/model_calib.py, tests/unit/torch/quantization/test_layerwise_calibrate.py, CHANGELOG.rst. Also read utils/dataset_utils.py (_disable_use_cache, _iter_use_cache_configs, _forward_loop), the checkpoint/resume half of utils/layerwise_calib.py (setup_resume, get_first_layer_inputs), and the create_forward_loop call sites — because the approach changed since the last round and that moves which consumers the invariant covers. (Note: a two-dot diff against the origin/main tip surfaces reverse hunks for speculative-decoding commits that are on main but not on this branch; not part of this PR, ignored — I used gh pr diff.)

Findings: CRITICAL: 0 | IMPORTANT: 0 | SUGGESTION: 2

This is a different fix from the one approved at e03c53b, so I re-verified from scratch rather than carrying that approval forward. The capture-time sanitizer in layerwise_calib.py is gone; instead the whole layerwise loop is wrapped in the existing _disable_use_cache, and model_calib.py's diff reduces to a deletion plus one indent level. Attacking cache creation rather than cache cleanup is the better fix — it removes the class of bug instead of patching each consumer, drops peak calibration memory, and reuses a helper that is already a proper @contextmanager with restore-on-exception (including the delattr path for configs that never assigned the attribute) and eight existing unit tests.

I verified the wrap covers every forward. All three forward_loop entry points are inside it — get_first_layer_inputs (bootstrap), cache_outputs_for_next_layer_calib (both the qdq_from_prev=False pre-capture and the True post-capture branch), and calib_func's _layer_forward_loop replays. Nothing before the with runs a forward (from_folder is pure I/O, LayerActivationCollector.__init__ does not forward), and ckpt.full_restore after it only loads state. Ordering inside is right: unpatch/pbar-close in finally run before the config restore. Nesting with _forward_loop's own _disable_use_cache is idempotent — inner restores to False, outer to the original.

The Cache.reset() characterization is accurate (zeroes in place, preserves length, so the next update appends and doubles kv_len), and no cache.reset() call sites remain in modelopt/. Deleting the block rather than fixing it is correct given the wrap.

On the new test. test_layerwise_calibration_builds_no_kv_cache is the right shape for this approach: asserting the invariant structurally beats asserting amaxes, for exactly the reason the docstring gives — a retained cache only accumulates, and max calibration's max absorbs that on a tiny model, so amaxes can match while the invariant is broken. Sweeping llama / nemotron_h / gpt_oss is well-motivated now that correctness rests on the model honouring the flag, and the config.use_cache restore assertion guards the helper's own contract. list.extend returning None keeps the pre-hook contract valid, and the fixtures are already top-imported unguarded elsewhere in this directory (plugins/test_huggingface.py), so the new module-level transformers import adds no collection risk. I could not execute pytest in this environment, so the 921-passed run and the fails-without-the-wrap claim for all three parameters are yours as reported, not re-verified by me.

Suggestions (non-blocking, both inline):

The fix now depends entirely on the model deriving use_cache from config, and the defensive drop is gone — so the residual routes fail silently rather than loudly: a forward_loop passing use_cache=True explicitly (an explicit True beats the config; this repo has such loops at examples/alpamayo/quantize.py:181,236), a caller-supplied past_key_values (which now compounds on every replay rather than doubling), and .generate()-based enc-dec calibration (governed by generation_config.use_cache, which _iter_use_cache_configs does not walk). A capture-time assertion that no Cache reached the layer would enforce in-product the same invariant the new test checks externally, and closes suggestion 2 as well.
Resume is the one entry point the invariant structurally cannot reach. setup_resume loads next_inputs.pt with weights_only=False, so a pre-fix pickled Cache unpickles silently and is replayed as-is — config.use_cache gates cache creation, not update() on a cache it is handed. Bounded to layer K, but silent, and nvfp4_default-kv_none-gptq.yaml pins a default checkpoint_dir, so "re-run calibration" without deleting that directory resumes rather than recalibrating. A format_version in manifest.json would let from_folder's existing drift check enforce what the CHANGELOG currently only advises.

One thing to sanity-check on your side, not a finding: _forward_loop (dataset_utils.py:1169) already wraps its body in _disable_use_cache, and examples/hf_ptq/hf_ptq.py builds its loop via create_forward_loop. By that path no cache should have been captured pre-fix — yet you measured 36 changed export tensors on nvfp4_experts_only-kv_fp8_layerwise. Your measurements beat my inference, so I assume the recipe runner supplies its own loop; worth confirming nothing there re-enables caching in a way this wrap also misses, since that would bear on the CHANGELOG's list of affected shipped recipes. The new test's own lambda m: m(...) loop independently demonstrates the bug is reachable through public API either way.

Minor: the PR body says "two tests ... each verified to fail when the code it guards is removed", but the diff adds one (parametrized three ways). Worth correcting before merge so the claim matches the diff.

Risk: low. No API, config-schema, modelopt_state, or export-path surface; the runtime change is one context-manager wrap plus a deletion of provably-dead code, reusing a helper already exercised on the export path. The behavioural change is confined to activation amaxes (and, via the multi-pass calibrators, weights), which the CHANGELOG now documents with the fresh-checkpoint_dir requirement and the full activation-dependent algorithm set. Residual risk is the pre-fix-checkpoint upgrade path, documented rather than enforced.

Approving — no blocking issues.

Disabling use_cache stops the model building a cache, but a caller can still hand one in: use_cache resolves to the caller's value when passed, and past_key_values can be passed directly -- examples/alpamayo/quantize.py does both. Layerwise replays captured inputs, so either route silently reproduces the bug. Capture now refuses a cache instead. The changelog also overstated the blast radius. create_forward_loop already wraps its body in _disable_use_cache, so the shipped hf_ptq path never built a cache to begin with: measured on Qwen2.5-1.5B with nvfp4_default-kv_none-gptq and the fix simulated away, the shipped loop puts 0 caches on a layer and 0 in the checkpoint, while a bare user loop puts 222 and 54. Telling users to re-run the shipped recipes was wrong; the exposure is custom forward loops via mtq.quantize. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Fridah-nv marked this pull request as ready for review August 25, 2026 19:24

Fridah-nv requested review from a team as code owners August 25, 2026 19:24

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