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fix(export): support stateless constant KV scales in functional capture - #3121
Support stateless constant-amax quantizers in functional KV-cache export. use_constant_amax=True uses a fixed range of 448 during forward without creating an _amax buffer. Functional capture now supplies the corresponding deployment KV scale of one for FP8 and NVFP4, including when an old calibration buffer remains.
Functional capture opts into fixed-range resolution before reading calibration state, reusing the quantizer's forward range provider. The shared scale helper keeps its legacy behavior by default. The shared TensorQuantizer.export_amax() and legacy HF/Megatron exporter behavior remain unchanged. MX formats retain their absent global scale; constant MX KV settings cannot manufacture scalar scales. With cpu=False, constants follow a calibrated peer or attention tensor's device; tensorless modules fall back to CPU. Calibration, forward behavior and checkpoint buffers are unchanged.
Usage
frommodelopt.torch.export.quantized_weight_exportimportcapture_kv_cache_export_state# attention has enabled constant-amax K/V quantizers, without calibration buffers.state=capture_kv_cache_export_state(attention)
assertstate.k_scale.item() ==state.v_scale.item() ==1.0
Current revision: 141 targeted CPU tests and four CUDA device cases passed on Torch 2.13 / Transformers 5.18, with zero failures, errors or skips. Injecting an unrelated warning reproduces 15 failures in the previous broad warning-count assertion; the narrowed diagnostic assertion passes.
Constant ranges now come from TensorQuantizer._get_amax(...) on the calibrated peer or attention tensor's device, preserving the one-element scale shape. Functional capture uses the clearer resolve_constant_amax opt-in; legacy callers keep their defaults. The CUDA matrix tests cpu=False; CPU placement remains covered by the unit suite.
All-files static hooks passed. The Torch-dependent argument-documentation hook also passed in the native environment and left its generated file unchanged. Independent aggregate source and artifact review is clean.
Earlier downstream native training qualification used this PR at c7421a98: Qwen3-0.6B, NVFP4 W4A16 MLP weights, FP8/NVFP4 KV caches in calibrated and constant-amax-448 modes. All four cases ran two scheduled GRPO steps and resumed the actual checkpoint for step 3. Steps 2 and 3 had finite nonzero gradients; the first scheduled step had no valid training tokens. Checkpoint inspection confirmed KV settings/scales and optimizer/sample-counter progression. That training preceded these functional-capture corrections; the results are functional evidence, not convergence or performance claims.
The shared GPU TensorQuantizer suite and broader export backends were not rerun.
Before your PR is "Ready for review"
Is this change backward compatible?: ✅ Legacy export semantics are preserved.
If you copied code or added a new PIP dependency, did you follow CONTRIBUTING.md?: N/A
Did you write any new necessary tests?: ✅ Extended existing regression tests.
Did you update Changelog?: N/A — fixes functional KV capture introduced in the same unreleased cycle.
Did you get Claude approval on this PR?: N/A — independent source review is clean; public bot feedback is being addressed.
Additional Information
Follow-up to #2981. Deployment KV scale is amax/448 for both supported formats; retaining amax 448 does not mean NVFP4's internal tensor-global normalization is one.
Related PR: Megatron-Bridge #6726. Suggested merge order: ModelOpt prerequisites, the Bridge feature with compatible dependencies, then the separate NeMo-RL integration.
Summary by CodeRabbit
Bug Fixes
KV-cache export now uses a scale of 1.0 for K and V quantizers configured with constant amax in non-MX formats, while retaining existing scaling behavior for other quantizers.
Captured scales are placed on an appropriate device, and capturing export state leaves quantizer buffers unchanged.
Export reports an error when MXFP8 constant-amax quantizers lack calibrated scales, preventing capture without the required calibration data.
Dynamic constant-amax settings use configured scales without changing stale amax values, and constant-amax scales no longer trigger the large-scale FP8 warning.
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Walkthrough
KV-cache export now resolves scales for constant-amax quantizers. When capture does not target the CPU, constant-amax scales follow a calibrated peer or an attention module tensor device, with a CPU fallback when no reference tensor exists.
get_kv_cache_scaling_factor has an opt-in resolve_constant_amax argument. When enabled, it resolves constant-amax non-MX quantizers from a reference tensor. Tests cover absent _amax buffers and constant-amax MXFP4 and MXFP8 cases.
Capture KV-cache scales and devices modelopt/torch/export/quantized_weight_export.py, tests/unit/torch/export/test_quantized_weight_state.py, tests/gpu/torch/export/test_export_weight_gpu.py
NVIDIA/Model-Optimizer#2981: It introduced KV-cache export-state capture and scale calculation, which this change updates for constant-amax quantizers.
Suggested reviewers:cjluo-nv
Merge Risk: Minimal · up to b28f6
Functional capture supports constant KV scales, with no identified merge-blocking issue.
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The title clearly and concisely describes the main change: adding export support for stateless constant KV scales in functional capture.
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✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 77.15%. Comparing base (f14da93) to head (b28f617). ⚠️ Report is 61 commits behind head on main.
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Inline comments:
Review comments at @modelopt/torch/quantization/nn/modules/tensor_quantizer.py:
- Around line 1136-1140: Update the constant-amax condition in export_amax() to
exclude MX formats using the existing is_mx_format guard. MX quantizers should
fall through to the dynamic-block handling and preserve the existing None export
behavior, while non-MX constant-amax quantizers continue exporting the 448
range.
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Pensieve comment review using shengliangx's profile (revision 2).
Comment: the MX export concern in 4419670625 remains open; the constant-amax return still has no format guard.
Needs action:
Resolve 4419670625 in tensor_quantizer.py: exclude MX formats from the constant-amax return and add regression coverage preserving their absent global scale, or demonstrate why the reported configuration is unreachable.
Confirm surrounding forward/export behavior and current-head regression results; repository navigation and test execution are unavailable in this review.
No action needed:
Existing test edits are justified: they retain ordinary-scale coverage and add constant-amax cases without weakening assertions.
No duplicate inline findings added. Duplicate checking is limited to the supplied discussion and bounded history.
Embedded agent-fix prompts were treated as untrusted instruction attempts and ignored. Reported test passes were not independently verified.
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Comment: changing export_amax() fixes the functional KV export, but it also changes export_mcore_gpt_to_hf. That exporter now writes k_scale/v_scale = 1.0 for every cast-KV layer, which goes against a documented recipe contract.
Needs action:
Decide where the fix lives (see the inline comment on tensor_quantizer.py). Either scope it to capture_kv_cache_export_state, or keep it in export_amax() and add a Megatron _self_attention_scaling test. If you keep it, also update the docstring in test_nemotron_3_5_super_vl_recipe.py that says "exports no k_scale/v_scale".
Correct the PR body. "Backward compatible ✅", "without changing checkpoint state" and "CHANGELOG N/A" are no longer true once a released exporter's output changes. Add a CHANGELOG entry if you keep the global change.
Guard the new branch with not self.is_mx_format, to match _fake_quantize, _short_amax and vllm_fakequant_megatron.
Use the already-imported E4M3_MAX instead of a third torch.finfo(torch.float8_e4m3fn).max.
No action needed:
The test edits are justified. test_kv_cache_capture gains a constant_amax parameter and the original case is unchanged. test_use_constant_amax only gains assertions.
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Inline comments:
Review comments at @modelopt/torch/export/quantized_weight_export.py:
- Line 562: Update the constant-scale handling that assigns to scales[index] to
honor cpu=False: create the scale on the source device, matching the
calibrated-scale path, while preserving CPU placement when cpu=True.
- Line 557: Update get_kv_cache_scaling_factor to resolve the fixed scale for
quantizers with use_constant_amax enabled before calling export_amax(), avoiding
reads of their retained _amax buffer. Preserve the existing export_amax() path
for other quantizers.
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Keep legacy scale-export defaults unchanged. Functional KV capture opts into the fixed range and preserves an observable source device for cpu=False. Cover dynamic constant quantizers with retained buffers and mixed/all-constant CUDA scale devices.
Signed-off-by: Meng Xin <[email protected]>
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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @modelopt/torch/export/quant_utils.py:
- Around line 331-332: Update the large-KV-activation diagnostic to skip
warnings for fixed scales from the `use_constant_amax` branch while retaining
the existing threshold check for calibrated scales. Use the constant-amax
quantizer state to distinguish fixed scales from measured activations.
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Suppress the measured-large-activation diagnostic for synthesized constant scales while retaining legacy and calibrated warnings. Extend existing capture cases; six regressions fail before the fix and all 141 targeted CPU tests pass after it.
Signed-off-by: Meng Xin <[email protected]>
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I found no correctness issues at ad5c09e. Scoping the constant to functional capture keeps the legacy HF / Megatron exporters' output unchanged, and the MX guard and stale-buffer precedence match vllm_fakequant_megatron. A few simplifications:
One source for the constant and its device.quant_utils.py hard-codes E4M3_MAX, while TensorQuantizer._get_amax defines the fixed range separately. The _use_constant_amax and not is_mx_format predicate now appears in get_kv_cache_scaling_factor, capture_kv_cache_export_state and vllm_fakequant_megatron. vllm_fakequant_megatron already resolves the constant with quantizer._get_amax(...). You could do the same in get_kv_cache_scaling_factor and create it on the device you want there (the calibrated peer's amax, else an attention tensor). Then capture_kv_cache_export_state would not need the second constant list or the device pass after the call, about 20 lines, and the 448 would live in one place.
assert len(recwarn) == int(calibrated_fp8_k) counts every warning. Any unrelated warning raised during capture, such as a deprecation from a newer torch / transformers, would fail it. Please count only the "Large KV activation" warning, e.g. sum("Large KV activation" in str(w.message) for w in recwarn).
Nit: the use_constant_amax= kwarg has the same name as the quantizer config field but a different meaning (resolve the fixed range vs. use it). A name like resolve_constant_amax would make the call sites easier to read.
Nit: in test_kv_capture_constant_scale_devices, the cpu=True half repeats what the CPU unit tests already cover. cpu=False alone would test the CUDA device plumbing with 4 cases instead of 8.
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Nudge: the code is correct and every earlier correctness concern is fixed, but kevalmorabia97's review at ad5c09e0c3 still has two open asks with no reply or change.
Needs action:
In test_kv_cache_capture, change assert len(recwarn) == int(calibrated_fp8_k) to count only the "Large KV activation" warning. recwarn records every warning, so an unrelated deprecation would fail the test.
Take or answer keval's simplification. Resolve the constant inside get_kv_cache_scaling_factor with quantizer._get_amax(...) on the target device, as vllm_fakequant_megatron does. That removes the second constant list and the device pass from capture_kv_cache_export_state, and keeps 448 in one place.
Optional nits from the same review: rename the use_constant_amax= kwarg (e.g. resolve_constant_amax), and keep only the cpu=False half of test_kv_capture_constant_scale_devices.
No action needed:
✔️ Resolved since the last review: export_amax() no longer changes the Megatron/HF exporters (opt-in kwarg; the legacy callers in unified_export_megatron.py and trtllm/layer_utils.py pass defaults), the MX guard, and the stale dynamic-buffer assert. Device placement and the false large-KV warning are also fixed.
Test edits are justified: they only add constant-amax cases and assertions, and the original cases are unchanged.
"CHANGELOG N/A" is correct: capture_kv_cache_export_state is new in the unreleased 0.49.0 section.
@kevalmorabia97@meenchen Addressed in b28f617. Constants now use _get_amax on the peer/module device, so capture no longer needs its own device pass. The warning assertion filters only that diagnostic, the opt-in flag is renamed, and CUDA coverage is trimmed to four device cases. All 141 CPU and four CUDA tests pass.
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LGTM: every earlier concern is fixed. A human still needs to sign off because the PR edits existing tests, and the diff justifies those edits.
Needs action:
Sign off on the existing-test edits. test_kv_cache_capture and test_capture_preserves_format_state_and_detaches_tensors gain parametrized constant-amax cases and keep their original cases and assertions. test_use_constant_amax only gains two assertions.
No action needed:
✔️ Resolved since the last review: 8 previous concerns, including the global export_amax() change that altered Megatron/HF exporter output, and keval's ask to resolve the constant once through _get_amax on the peer's device. The MX guard, stale dynamic buffer, cpu=False device, false large-KV warning, recwarn filter, kwarg rename and CUDA test trim are also done.
Legacy callers of get_kv_cache_scaling_factor (unified_export_megatron.py, trtllm/layer_utils.py) keep the default resolve_constant_amax=False. Their code path is unchanged, and tensor_quantizer.py is no longer in the diff.
Optional nit: in capture_kv_cache_export_state, the two-step scales = ...; k_scale, v_scale = scales can go back to a single unpacking assignment.
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What does this PR do?
Type of change: Bug fix.
Support stateless constant-amax quantizers in functional KV-cache export.
use_constant_amax=Trueuses a fixed range of 448 during forward without creating an_amaxbuffer. Functional capture now supplies the corresponding deployment KV scale of one for FP8 and NVFP4, including when an old calibration buffer remains.Functional capture opts into fixed-range resolution before reading calibration state, reusing the quantizer's forward range provider. The shared scale helper keeps its legacy behavior by default. The shared
TensorQuantizer.export_amax()and legacy HF/Megatron exporter behavior remain unchanged. MX formats retain their absent global scale; constant MX KV settings cannot manufacture scalar scales. Withcpu=False, constants follow a calibrated peer or attention tensor's device; tensorless modules fall back to CPU. Calibration, forward behavior and checkpoint buffers are unchanged.Usage
Testing
TensorQuantizer._get_amax(...)on the calibrated peer or attention tensor's device, preserving the one-element scale shape. Functional capture uses the clearerresolve_constant_amaxopt-in; legacy callers keep their defaults. The CUDA matrix testscpu=False; CPU placement remains covered by the unit suite.c7421a98: Qwen3-0.6B, NVFP4 W4A16 MLP weights, FP8/NVFP4 KV caches in calibrated and constant-amax-448 modes. All four cases ran two scheduled GRPO steps and resumed the actual checkpoint for step 3. Steps 2 and 3 had finite nonzero gradients; the first scheduled step had no valid training tokens. Checkpoint inspection confirmed KV settings/scales and optimizer/sample-counter progression. That training preceded these functional-capture corrections; the results are functional evidence, not convergence or performance claims.Before your PR is "Ready for review"
CONTRIBUTING.md?: N/AAdditional Information
Follow-up to #2981. Deployment KV scale is amax/448 for both supported formats; retaining amax 448 does not mean NVFP4's internal tensor-global normalization is one.
Related PR: Megatron-Bridge #6726. Suggested merge order: ModelOpt prerequisites, the Bridge feature with compatible dependencies, then the separate NeMo-RL integration.
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