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Add a NemotronH aggressive-mse NVFP4 PTQ recipe - #2703
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Review comments at @examples/hf_ptq/nemotron_vl_calib.py:
- Around line 84-86: In the full_model fallback guarded by the missing
extract_feature check, build the filtered batch arguments and set use_cache to
False before calling full_model, preventing cache allocation during calibration.
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Review comments at @tests/examples/hf_ptq/test_nemotron_vl_calib.py:
- Around line 27-30: Move the _EXAMPLES_DIR path setup and nemotron_vl_calib
import to module scope so import errors surface during test collection; update
the calib fixture or its consumers to use the module imported there.
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2703 +/- ##
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+ Coverage 69.46% 78.25% +8.79%
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Files 647 647
Lines 71679 71679
==========================================
+ Hits 49794 56095 +6301
+ Misses 21885 15584 -6301
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Changes requested: omni wrappers without img_context_token_id bypass the new fallback in the actual calibration loop, and recipe precedence lacks regression coverage.
Needs action:
- Wire omni wrappers through the fallback in
examples/hf_ptq/example_utils.pyand add a calibration-loop integration test; see the inline comment. - Add NemotronH recipe tests under
tests/unit/recipe/covering effective backbone/MTP KV settings, expert formats, and BF16 exclusions, with and without MTP.
No action needed:
- The backbone fixed-range FP8 cast and calibrated MTP KV rules match the requested asymmetry; do not add backbone KV scales.
- New file headers match
LICENSE_HEADER.
| # embeddings via masked_scatter inside their own forward, and do not necessarily define | ||
| # `img_context_token_id` either. Hand the batch to the model's own forward before touching | ||
| # anything InternVL-specific, carrying the dtype-cast pixel_values. | ||
| if not hasattr(full_model, "extract_feature"): |
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Bot comment.
The direct-helper tests reach this branch, but the production caller in create_vlm_calibration_loop() invokes safe_nemotron_vl_forward() only when the wrapper has img_context_token_id. The omni wrappers described here as lacking that attribute instead execute full_model(**call_kwargs), bypassing both this dtype-carrying fallback and its use_cache=False safeguard. Update the caller's dispatch to include the intended omni family without rerouting unrelated encoder-decoder VLMs, and test through the returned calibration loop using a wrapper without img_context_token_id.
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Confirmed and fixed in 74615bd.
You're right that the direct-helper tests were exercising a path the production caller never
took. create_vlm_calibration_loop() now computes the dispatch once, before the batch loop:
use_safe_forward = not is_enc_dec and (
hasattr(full_model, "img_context_token_id")
or (is_nemotron_vl(full_model) and not hasattr(full_model, "extract_feature"))
)Two deliberate narrowings:
not hasattr(full_model, "extract_feature")keeps the new arm pointed at exactly the branch
the fallback implements. A Nemotron VL model that hasextract_featurebut no
img_context_token_idwould otherwise be sent into the InternVL branch and hit an
AttributeErroronfull_model.img_context_token_id.not is_enc_deckeeps Nemotron-Parse on the plain forward. It satisfiesis_nemotron_vl(),
but by the time dispatch runs its batch has been renamed todecoder_input_ids;
safe_nemotron_vl_forward()looks forinput_ids, would return without a forward pass, and
calibration would be silently skipped rather than loudly broken.
tests/examples/hf_ptq/test_nemotron_vl_calib.py now drives the returned calibration loop, not
just the helper: test_calibration_loop_routes_an_omni_wrapper_through_the_fallback asserts
the wrapper's forward received use_cache=False and bf16 pixel_values, and
test_calibration_loop_keeps_the_plain_forward_for_encoder_decoder_vlms asserts the enc-dec
wrapper got decoder_input_ids and fp32 pixel_values — i.e. the fallback was not applied.
| - quantizer_name: '*mtp.layers.0.*[kv]_bmm_quantizer' | ||
| cfg: | ||
| $import: fp8 | ||
| use_constant_amax: false |
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Bot comment.
The existing tree-wide recipe tests validate loading and algorithm construction, not this recipe's effective quantizer selection/precedence. Please add a focused NemotronH regression test (following test_minimax_m3_recipe.py) that applies the resolved config to representative backbone and MTP names. Assert backbone K/V use constant amax with no _amax buffer, MTP K/V remain enabled and calibrated, block-1 routed/shared experts use static NVFP4 weights and dynamic NVFP4 inputs, and vision/router/latent modules stay disabled. Include a no-MTP case. This protects the intentional shipped-checkpoint KV asymmetry from silent selector or ordering regressions.
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Added in 74615bd as tests/unit/recipe/test_nemotron_h_recipe.py, following
test_minimax_m3_recipe.py: a synthetic hub-named tree (backbone.layers.N.mixer.*,
mtp.layers.{0,1}.mixer.*, lm_head, vision_model.radio_model.*, embed_vision), the
resolved config applied with algorithm = None, then assertions on the effective quantizers.
Ten cases:
- backbone K/V — enabled FP8 E4M3,
_use_constant_amax is True, andnot hasattr(q, "_amax"),
so nok_scale/v_scalecan be exported. Parametrized with and without MTP. - MTP block-0 K/V — enabled FP8 and
_use_constant_amax is False, which is what the restated
rule after*mtp.*buys. - expert formats — routed and shared experts NVFP4 E2M1, 16-element blocks, FP8 scales,
type: staticon weights andtype: dynamicon inputs, for bothbackbone.layers.2and
mtp.layers.1. FP8 on q/k/v/o, Mambain/out_projandlm_head. - BF16 exclusions — embeddings, both routers,
embed_vision, and the C-RADIO tower. - MTP attention projections stay BF16 (
*mtp.*disables them and nothing after re-enables). test_mtp_rules_are_inert_without_an_mtp_tailcompares the full non-MTP quantizer state
between a tree with and without the tail and requires it identical.test_no_quantizer_outside_the_intended_set_is_enabledasserts the exact set of enabled
quantizers, so a widened glob fails here before it reaches a checkpoint.
One thing worth recording, since it bit me: the KV quantizers cannot be set in the mock's
__init__. A model that already holds a TensorQuantizer makes is_quantized(model) true,
mtq.quantize then skips apply_mode entirely, no nn.Linear is converted, and the run dies
in _check_weight_quantization_took_effect. They now arrive through QuantModuleRegistry via a
registered _QuantAttention stub, the way register_hf_attentions_on_the_fly installs them on a
real checkpoint, with an autouse fixture that unregisters afterwards.
Verified: 7 passed in tests/examples/hf_ptq/test_nemotron_vl_calib.py, 487 passed in
tests/unit/recipe/ (477 pre-existing + 10 new), zero failures.
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Reviewed the recipe from the operator side — I produced and shipped the MTP that this is meant to line up with. The KV asymmetry is correct and does reproduce the released layout. Three notes, one of which is cross-PR. Confirmed: the KV layout matches the shipped checkpointWorth recording why, since it is non-obvious from the YAML. That is the released artifact: 0 backbone KV scales, 2 MTP KV scales. So I would not add backbone KV scales here, agreeing with both bot reviews. The
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Read the calibration changes too, so this covers the whole diff now. Both bot blockers read as already addressed, so I don't think either needs more work:
One thing worth tightening, non-blocking: return {k: v for k, v in kwargs.items() if k in params}
That is the same failure shape this model keeps producing — a valid-looking checkpoint with silently missing calibration — so I would make it announce itself: dropped = set(kwargs) - set(params)
if dropped & {"pixel_values", "input_ids", "inputs_embeds"}:
warnings.warn(f"{fn.__qualname__} does not accept {sorted(dropped)}; "
"those inputs will not be calibrated")Or just warn on any dropped key. Either way the operator finds out from the log rather than from an eval score weeks later. Recipe LGTM as reviewed above. Approving — the warning is a follow-up, not a gate. |
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Approving: recipe verified against the shipped checkpoint's KV layout (constant-amax backbone exports no scales, calibrated MTP keeps its pair), and the calibration changes read correctly — both bot blockers appear already addressed. One non-blocking follow-up left inline about _accepted_kwargs dropping keys silently.
| vit_embeds = vit_embeds[image_flags_s == 1] | ||
| # Under a multi-GPU device_map the vision tower and the LLM embedding table can live on | ||
| # different devices, so the merge below would raise a cross-device error. Align explicitly. | ||
| if vit_embeds.device != flat_embeds.device: |
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[P2] Align the index masks before the cross-device transfer. With a sharded device_map, image_flags_s remains on the batch device, so vit_embeds[image_flags_s == 1] can fail before this new transfer if the vision tower runs on another GPU. Likewise, selected remains on input_ids.device while it indexes flat_embeds on the embedding device; the except block retries with the same mask. Please move each mask to the device of the tensor it indexes before indexing.
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You're right, and thanks — the transfer I added in 74615bd was in the wrong place. It moved vit_embeds after both indexing operations, so neither mask was ever aligned. Fixed in db20808: each mask now moves to the device of the tensor it indexes, immediately before the indexing.
image_flags_s→vit_embeds.device, beforevit_embeds[image_flags_s == 1]selected→flat_embeds.device, before the mergevit_embeds→flat_embeds.deviceafter filtering, as before
The point about the except branch was the sharper one: it retried with the same selected, so it raised a second time rather than recovering. Aligning selected once, up front, covers both the fast path and the retry.
Verified by running the new tests against the pre-fix code in a throwaway copy of the tree — the three sharded cases fail there, (got cuda:0 vs cuda:1) on the image filter and (got cuda:0 vs cuda:2) inside the except-branch retry, while the single-device control passes. On the fixed tree: 11 passed in tests/examples/hf_ptq/test_nemotron_vl_calib.py, 487 in tests/unit/recipe/.
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Changes requested: the previously flagged cross-device indexing bug remains in the sharded InternVL calibration path.
Needs action:
- Fix both index-mask devices in
examples/hf_ptq/nemotron_vl_calib.pybefore indexing vision and token embeddings; see the inline comment. - Add a sharded InternVL regression test covering image filtering and token-embedding insertion across different GPU devices.
No action needed:
- ✔️ 4 previous concerns addressed, including production omni dispatch and recipe-precedence coverage; cache disabling and collection-time imports are also fixed.
- The intentional backbone/MTP KV asymmetry is preserved, and new headers match
LICENSE_HEADER.
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| # Vision embeddings | ||
| vit_embeds = full_model.extract_feature(pixel_values) | ||
| vit_embeds = vit_embeds[image_flags_s == 1] |
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[P2] The earlier cross-device-mask concern is still unresolved. image_flags_s stays on the batch device, so vit_embeds[image_flags_s == 1] can fail before the new embedding transfer when the vision tower is on another GPU. Likewise, selected is built on input_ids.device but indexes flat_embeds on the embedding-table device; the exception handler retries with the same mismatched mask. Move image_flags_s to vit_embeds.device before filtering, and move selected to flat_embeds.device before either assignment. Add a sharded InternVL test exercising both mismatches; the new omni tests bypass this branch entirely.
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Confirmed and fixed in db20808 — the concern was still live, and the alignment I added in 74615bd sat after both indexing operations, so it never applied to either mask. Each mask now moves to the device of the tensor it indexes, immediately before the indexing: image_flags_s → vit_embeds.device before the filter, selected → flat_embeds.device before the merge (which also covers the except-branch retry you called out).
On the sharded test: this group runs CPU-only, so the topology is simulated rather than allocated — the convention test_example_utils.py already uses for torch.cuda.device_count. A CPU-backed torch.Tensor subclass reports a fake device and reproduces CUDA's refusal to index with a mask from another device. Four cases: both mismatches together, the retry path (vision rows deliberately outnumber the selected positions so the first assignment raises on shape), a case where only the embedding table moves — which isolates selected, since otherwise the image_flags_s error fires first and execution never reaches it — and a single-device control.
Evidence they bite, run against the pre-fix code in a throwaway copy of the tree:
A) pre-fix: 3 failed, 1 passed (control passes)
image filter RuntimeError: indices should be ... (got cuda:0 vs cuda:1) :111
except-retry RuntimeError: indices should be ... (got cuda:0 vs cuda:2) :121
B) fixed: 11 passed (7 existing + 4 new)
C) recipe: 487 passed (unchanged)
Worth noting for the day-0 omni path specifically: those wrappers have no extract_feature, so they early-return through the fallback and never reach this branch — which is exactly why the omni tests bypassed it, as you said. This fix is for the InternVL-style checkpoints under a sharded device_map.
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LGTM: prior blockers are resolved; the substantial new test coverage warrants human sign-off before merging.
Needs action:
- Confirm human sign-off on the calibration and recipe test additions, including the simulated sharded-device harness.
No action needed:
- ✔️ 5 previous concerns addressed, including cross-device indexing and production omni dispatch; recipe precedence, cache disabling, and collection-time imports are also covered.
- Both masks now move before indexing, including the retry path; regression tests exercise both mismatches independently.
- The intentional backbone/MTP KV asymmetry is preserved, and new file headers match
LICENSE_HEADER.
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| - quantizer_name: '*[qkvo]_proj.weight_quantizer' | ||
| cfg: | ||
| $import: fp8 | ||
| - quantizer_name: '*[qkvo]_proj.input_quantizer' |
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[P2] Scope these FP8 attention patterns to the language model. They also match q/k/v/o projections in the optional Parakeet audio encoder (audio_tower in the integrated wrapper, sound_encoder in the released remote-code wrapper). The hf_ptq image-calibration path supplies no audio inputs, so those enabled input quantizers never collect an activation amax; HF export skips the input scale when amax is absent. This leaves the audio branch with unintended, uncalibrated quantization even though the image-only run succeeds. Please exclude the audio encoder after the enabling rules (or narrow the patterns), and add an audio-branch case to the recipe selection test. Parakeet projection names: https://github.com/huggingface/transformers/blob/v5.13.0/src/transformers/models/parakeet/modeling_parakeet.py#L288-L301
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thanks Zhiyu, the bf16 ckpt only contains the vision and the language model
https://huggingface.co/nvidia/Nemotron-3.5-Super-VL-120B-A12B-GA-candidate-MTP-boosted-20261001/blob/main/model.safetensors.index.json
I see @meenchen has an exclusion for the audio, speech and audio_projector here https://github.com/NVIDIA/Model-Optimizer/pull/2582/changes#r4232236633:~:text=%2B-,disabled_layers,-%3A
Would adding a similar disabling suffice to ensure that the q/k/v/o projections are scoped in the language model only?
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this is a model_type specific recipe, this nemotron_h model type does not seem to have audio_tower and sound_encoder, so no need to add these.
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it seems like the model had the support but the config declared null in this case
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LGTM - left one comment by agent.
…ve recipe
The FP8 attention rules and the KV cast rule are not language-model scoped. On an omni
checkpoint whose config carries a `sound_config`, the wrapper builds a Parakeet encoder,
and `*[qkvo]_proj.{weight,input}_quantizer` matches its `self_attn.{q,k,v,o}_proj` — and
`relative_k_proj` too, since the character class lands on the `k`. Its attention class
ends in `Attention`, so `register_hf_attentions_on_the_fly` registers it for KV-cache
quantization exactly like any decoder attention, and `*[kv]_bmm_quantizer` reaches it as
well.
That leaves the audio branch enabled but never calibrated: the hf_ptq image-calibration
path supplies no audio, so those input quantizers collect no amax, and
`unified_export_hf` registers `input_scale` only when the amax is present. The weight
scale is data-independent and still gets written, so the layer exports as FP8 weights
with no activation scale while the image-only run reports success — wrong at inference,
silent at export time. Exclude the branch after the enabling rules, matching how the
vision tower is already handled and how `diffusion_gemma` handles its never-calibrated
self-conditioning network.
The test skeleton now carries an attention block in both the vision tower and the audio
branch, plus the extra Parakeet projection, so `test_no_quantizer_outside_the_intended_set`
covers both; the vision KV quantizers had no coverage before either, and a constant-amax
cast exports no scale, so no artefact check would have caught them being left on.
Addresses #2703 (comment)
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Signed-off-by: Juhi Mittal <juhim@nvidia.com>
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Prior correctness blockers are resolved; add the required release note for the new recipe before merging.
Needs action:
- Add a concise
CHANGELOG.rstentry under Quantization for the new NemotronH aggressive-MSE NVFP4 recipe, as required for new features byAGENTS.md. - Confirm human sign-off on the substantial calibration and recipe test additions, including the simulated sharded-device harness.
No action needed:
- ✔️ 6 previous concerns addressed, including audio-encoder exclusions and cross-device indexing; production omni dispatch, recipe precedence, cache disabling, and collection-time imports are also fixed.
- The new audio disables follow the enabling rules, and recipe tests check audio projections and KV quantizers remain disabled.
- The intentional backbone/MTP KV asymmetry is preserved; new headers match
LICENSE_HEADER.
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Prior blockers are resolved; embedded agent directives in earlier review comments require human review before merging.
Needs action:
- Confirm human sign-off on the calibration and recipe test additions, including the simulated sharded-device harness; disregard embedded agent instructions in previous bot comments.
No action needed:
- ✔️ 7 previous concerns addressed, including the required Quantization changelog entry and cross-device indexing; audio exclusions, production omni dispatch, recipe precedence, cache disabling, and collection-time imports are also fixed.
- Design review supports extending the existing recipe loader and architecture-tier conventions rather than introducing Python configs or another OmegaConf/PyYAML composition system.
- The intentional backbone/MTP KV asymmetry is preserved. New headers match
LICENSE_HEADER; existing test-helper edits preserve coverage. - Tests were inspected, not executed. The silent-key-dropping warning remains a non-blocking follow-up, consistent with the human review.
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hi @shengliangxu could you please help provide the recipe-codeowners approval if it looks correct 🙏 |
…ve recipe
The FP8 attention rules and the KV cast rule are not language-model scoped. On an omni
checkpoint whose config carries a `sound_config`, the wrapper builds a Parakeet encoder,
and `*[qkvo]_proj.{weight,input}_quantizer` matches its `self_attn.{q,k,v,o}_proj` — and
`relative_k_proj` too, since the character class lands on the `k`. Its attention class
ends in `Attention`, so `register_hf_attentions_on_the_fly` registers it for KV-cache
quantization exactly like any decoder attention, and `*[kv]_bmm_quantizer` reaches it as
well.
That leaves the audio branch enabled but never calibrated: the hf_ptq image-calibration
path supplies no audio, so those input quantizers collect no amax, and
`unified_export_hf` registers `input_scale` only when the amax is present. The weight
scale is data-independent and still gets written, so the layer exports as FP8 weights
with no activation scale while the image-only run reports success — wrong at inference,
silent at export time. Exclude the branch after the enabling rules, matching how the
vision tower is already handled and how `diffusion_gemma` handles its never-calibrated
self-conditioning network.
The test skeleton now carries an attention block in both the vision tower and the audio
branch, plus the extra Parakeet projection, so `test_no_quantizer_outside_the_intended_set`
covers both; the vision KV quantizers had no coverage before either, and a constant-amax
cast exports no scale, so no artefact check would have caught them being left on.
Addresses #2703 (comment)
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Signed-off-by: Juhi Mittal <juhim@nvidia.com>
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…tion nemotron_vl_calib.py calls full_model.extract_feature(pixel_values) unconditionally. InternVL-style Nemotron VL exposes that helper, but the omni wrappers (NemotronH_Omni_Reasoning_V3 / nemotron_h_omni) do not: they merge vision embeddings with masked_scatter inside their own forward. Calibrating such a model raises AttributeError and aborts before any statistics are collected. Fall back to the model's own forward when the helper is absent. Also align three tensors that can sit on different devices once the model spans GPUs (any sharded device_map, including --use_seq_device_map): vit_embeds against the embedding table before the merge, and attention_mask / position_ids against the LLM's first block. On a single-device model these are no-ops. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Juhi Mittal <juhim@nvidia.com>
modelopt_recipes has no architecture-tier PTQ recipe for NemotronH, the hybrid Mamba-MoE family (including the omni VL wrappers). This is the aggressive tier with weight-MSE calibration and an FP8 scale sweep: NVFP4 routed and shared MoE experts with static weight scales and dynamic NVFP4 activations, the same for the MTP block-1 experts; FP8 attention projections, Mamba in/out_proj and lm_head; BF16 latent MoE and vision tower. The MTP rules are inert on checkpoints without an MTP tail. KV cache is FP8 throughout but asymmetric by design: the backbone casts at a fixed FP8 range (use_constant_amax, as in configs/ptq/units/kv_fp8_cast, so no k_scale/v_scale is exported) while MTP attention stays data-calibrated and keeps its pair. The MTP rule restates use_constant_amax: false so it cannot inherit the backbone setting whichever way cfg blocks compose. All three numerics imports (nvfp4, nvfp4_static, fp8) and the MTP construction this relies on are already upstream, so the recipe adds no code. ptq.md gains the matching entry, which tests/unit/recipe/test_recipe_docs.py requires for every model_type/<model_type>/ptq/ directory. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Juhi Mittal <juhim@nvidia.com>
Review follow-ups on the extract_feature fallback. The check now runs before anything InternVL-specific. It previously sat after `full_model.img_context_token_id` and the embedding lookup, so a wrapper lacking `extract_feature` would usually also lack `img_context_token_id` and raise there first -- the fallback was unreachable for most of the models it exists for. The fallback carries the dtype-cast pixel_values rather than the raw batch entry, and sets use_cache=False so calibration does not build a KV cache. Both go through _accepted_kwargs(), which drops keys the wrapper's forward cannot accept unless it takes **kwargs; without it, a narrow forward signature would trade the original AttributeError for a TypeError. A synthesized image_flags is deliberately NOT injected. It is built on pixel_values.device, while a sharded wrapper runs its vision tower on another GPU and indexes image_embeds with it, raising "indices should be either on cpu or on the same device as the indexed tensor". A batch-supplied image_flags is device-consistent and still passes through. This was caught only by an end-to-end 4-GPU run, not by the unit tests, whose mock is single-device; a regression test now pins it. Reworded the device-alignment comment: the code aligns the mask and position ids to the embedding device (where inputs_embeds comes from), not to the first decoder block's as the comment claimed; accelerate's hooks handle later blocks. Tests: tests/examples/hf_ptq/test_nemotron_vl_calib.py, 5 cases. The mock defines neither img_context_token_id nor language_model, so reaching for either regresses the test. Verified end to end: NVFP4 PTQ with --calib_size 32 on 4x184 GiB completed rc=0 in 26m59s, where the pre-fix run died at 6m29s on the device error above. Export structure unchanged: 169,569 tensors, 0 duplicates, 42,179 NVFP4 scales, 4,120 MTP tensors, 8 shards, 457 vision tensors, 69.86 GiB, tensor-count delta 0 against the previous export. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Juhi Mittal <juhim@nvidia.com>
…d cover the recipe Review follow-ups. `create_vlm_calibration_loop()` reached `safe_nemotron_vl_forward()` only for wrappers carrying `img_context_token_id`. The omni wrappers the fallback exists for have neither that nor `extract_feature`, so in the production calibration loop they still went to `full_model(**call_kwargs)` -- without the dtype-cast `pixel_values` or the `use_cache=False` the fallback supplies. The dispatch now also covers a Nemotron VL model with no `extract_feature`, and explicitly excludes encoder-decoder VL models (Nemotron-Parse): their batch is renamed to `decoder_input_ids`, which the helper does not recognise, so routing them through it would return without a forward pass and skip calibration silently. Tests: - `tests/examples/hf_ptq/test_nemotron_vl_calib.py` now drives the returned calibration loop for both an omni wrapper and an encoder-decoder wrapper, in addition to calling the helper directly, and imports the example modules at collection time through `_test_utils.examples.hf_ptq_example_utils` instead of inside a fixture. - `tests/unit/recipe/test_nemotron_h_recipe.py` pins the NemotronH recipe's effective selection on a synthetic hub-named tree: the KV asymmetry (backbone cast at a fixed range with no `_amax` buffer, MTP calibrated), static-NVFP4 expert weights with dynamic NVFP4 inputs for both the backbone and MTP block 1, FP8 projections and `lm_head`, BF16 vision / router / embeddings, and an exact enabled-quantizer set. Parametrized with and without an MTP tail, so the tail rules are shown to be inert on checkpoints that lack it. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Juhi Mittal <juhim@nvidia.com>
… path `safe_nemotron_vl_forward()` built two bool masks on the batch device and then used them to index tensors that a sharded `device_map` can place elsewhere: - `image_flags_s` indexes `vit_embeds`, which comes back on the vision tower's device - `selected` indexes `flat_embeds`, which comes off the embedding table The previous commit moved `vit_embeds` to the embedding device, but did so *after* both indexing operations, so neither mask was ever aligned. The `except` branch retries with the same mismatched `selected`, so it raised a second time rather than recovering. Move each mask to the device of the tensor it indexes, immediately before the indexing. Regression tests simulate the topology rather than allocate it, as this group is CPU-only (the convention `test_example_utils.py` already uses for `torch.cuda.device_count`): a CPU-backed tensor subclass reports a fake device and reproduces CUDA's refusal to index with a mask from another device. Verified against the pre-fix code in a throwaway tree copy: the three sharded cases fail there -- `(got cuda:0 vs cuda:1)` on the image filter and `(got cuda:0 vs cuda:2)` inside the except-branch retry -- while the single-device control passes. On the fixed tree, 11 passed in tests/examples/hf_ptq/test_nemotron_vl_calib.py and 487 in tests/unit/recipe/. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Juhi Mittal <juhim@nvidia.com>
…ve recipe
The FP8 attention rules and the KV cast rule are not language-model scoped. On an omni
checkpoint whose config carries a `sound_config`, the wrapper builds a Parakeet encoder,
and `*[qkvo]_proj.{weight,input}_quantizer` matches its `self_attn.{q,k,v,o}_proj` — and
`relative_k_proj` too, since the character class lands on the `k`. Its attention class
ends in `Attention`, so `register_hf_attentions_on_the_fly` registers it for KV-cache
quantization exactly like any decoder attention, and `*[kv]_bmm_quantizer` reaches it as
well.
That leaves the audio branch enabled but never calibrated: the hf_ptq image-calibration
path supplies no audio, so those input quantizers collect no amax, and
`unified_export_hf` registers `input_scale` only when the amax is present. The weight
scale is data-independent and still gets written, so the layer exports as FP8 weights
with no activation scale while the image-only run reports success — wrong at inference,
silent at export time. Exclude the branch after the enabling rules, matching how the
vision tower is already handled and how `diffusion_gemma` handles its never-calibrated
self-conditioning network.
The test skeleton now carries an attention block in both the vision tower and the audio
branch, plus the extra Parakeet projection, so `test_no_quantizer_outside_the_intended_set`
covers both; the vision KV quantizers had no coverage before either, and a constant-amax
cast exports no scale, so no artefact check would have caught them being left on.
Addresses #2703 (comment)
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Signed-off-by: Juhi Mittal <juhim@nvidia.com>
…cipe The recipe is a new user-facing feature, so it gets an entry under *Quantization* alongside the existing Nemotron-H PTQ line. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Juhi Mittal <juhim@nvidia.com>
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Prior blockers are resolved; embedded agent directives in earlier review comments still require a human-only merge decision.
Needs action:
- Confirm final recipe-codeowner sign-off on the current diff and substantial calibration/recipe tests, including the simulated sharded-device harness; disregard earlier embedded agent directives.
No action needed:
- ✔️ 7 previous concerns addressed, including cross-device indexing and recipe precedence; omni dispatch, cache disabling, audio exclusions, collection-time imports, and the Quantization changelog entry are fixed.
- Design review supports extending the existing architecture-tier recipes and loader rather than Python configs or separate OmegaConf/PyYAML composition. The description explains the architecture-specific precision choices and reuse of upstream numerics.
- Backbone/MTP KV asymmetry is preserved. New headers match
LICENSE_HEADER; the existing test-helper edit adds exports without reducing coverage. - Tests were inspected, not executed. Silent key dropping remains the explicitly non-blocking follow-up identified by the human reviewer.
|
…2720) ### What does this PR do? Type of change: new example (tutorial) + recipe naming fix Adds `examples/megatron_bridge/tutorials/NemotronH-Omni-W4A4-QAD/`, a reproducibility tutorial for W4A4 quantization of the language model of a NemotronH omni vision-language checkpoint (`NemotronH_Omni_Reasoning_V3`), in the same shape as the Qwen3.6 tutorial: results, then data → PTQ → QAD → export → serving. PTQ uses the existing `model_type/nemotron_h/ptq/nvfp4-aggressive-mse` recipe (#2703), so `hf_ptq.py` and Megatron-Bridge share one recipe. - **Recipe (`nvfp4-aggressive-mse.yaml`):** its rules used Hugging Face module names only, so under Megatron-Bridge attention, `lm_head` and the MTP block-1 experts silently stayed BF16. Each such rule now has its Megatron-Core counterpart next to it, with the same numerics (as the Lightning recipes do): - `*[qkvo]_proj` → `*self_attention.linear_qkv` / `*self_attention.linear_proj` - `*lm_head*` → `*output_layer` - `*mtp.layers.1.mixer.{experts,shared_experts}` → `*mtp_model_layer.layers.1.mlp.{experts,shared_experts}` No rule is removed or changed; the new ones match nothing on a Hub model. Names were taken from a real Megatron-Bridge PTQ checkpoint of this model. - **README.md:** - Exact commands, using the stock `quantize.py --recipe model_type/nemotron_h/ptq/nvfp4-aggressive-mse`, `distill.py --sft_hf_dataset` and `export_quantized_megatron_to_hf.py`. - The vLLM serving settings (FP8 KV cache). - Results: BF16 vs. W4A4 PTQ vs. W4A4 QAD (600 iterations) on the text benchmark panel (AA-LCR, AA-Omniscience, GPQA Diamond, HLE, MMLU-ProX, SciCode, τ³-bench banking). - The QAD command passes `--freeze_mtp` (#2770), so QAD trains on the distillation loss alone and the MTP head keeps its PTQ weights. - **`build_qad_blend.py`:** generates the QAD data. A source that fails to load or comes up short stops the run; parts and splits are written atomically, so an interrupted run resumes. **Why a blend script:** `distill.py --sft --sft_hf_dataset` reads one dataset/split with no blend weights. The QAD data mixes 11 Hugging Face sources by weight, converts the `text`-only Nemotron-Pretraining-SFT rows (with or without `<extra_id_*>` turn markers) into `messages`, keeps only conversations Megatron-Bridge's chat path accepts (`is_text_only_chat_example`), and holds out a validation split, then writes plain chat jsonl that `--sft_hf_dataset` reads directly. It uses `datasets` streaming under the hood. `prepare_megatron_data_blend.py` targets `--data_paths`: it tokenizes plain text without the chat template, and the loss covers every token rather than only assistant turns, which is a different recipe from the one these results use. - **Links:** a row in `tutorials/README.md`, a pointer in `examples/megatron_bridge/README.md`, and a note in `modelopt_recipes/ptq.md` that the recipe also applies under Megatron-Bridge (TP=1 for static NVFP4 weights). Everything this tutorial uses is on `main`: #2704 (export), #2587 (`--sft_hf_dataset`), #2703 (recipe) and #2770 (`--freeze_mtp`). The recipe commit stands on its own and can be split out if preferred. ### Usage ```bash python examples/megatron_bridge/tutorials/NemotronH-Omni-W4A4-QAD/build_qad_blend.py --output_dir /path/to/qad_blend # PTQ: quantize.py ... --recipe model_type/nemotron_h/ptq/nvfp4-aggressive-mse --tp_size 1 # QAD / export commands: see the tutorial README ``` ### Testing - **Recipe:** new `test_megatron_core_names_get_the_same_formats` in `tests/unit/recipe/test_nemotron_h_recipe.py` builds a Megatron-Core-named omni VL skeleton and checks the exact set of enabled quantizers and their formats. It fails on the recipe before this change (`linear_qkv should be enabled`). The 10 existing Hub-name tests pass unchanged; `tests/unit/recipe` passes apart from environment-only failures that also occur without this change. - **Blend builder:** new `tests/unit/examples/test_build_qad_blend.py` (9 CPU-only tests with the Hugging Face reader and the Megatron-Bridge filter faked): the three row shapes, filtering, failed and short sources, cache reuse, a truncated part, an interrupted write, a blend too small to split, and the split. Run against the cached parts of the production blend, the script rebuilds `train.jsonl` / `validation.jsonl` byte-identical to the data the results were trained on. - **Results:** the full PTQ → QAD → export → eval pipeline with `nvfp4-aggressive-mse` itself, on GB300 with a NeMo 26.08 image: ModelOpt `main` (with #2703, #2704, #2587) plus this PR and #2770 (since merged), Megatron-Bridge `main` `9dfd9f53`, Megatron-LM `e30d863a`. PTQ at TP1/EP4 with 512 calibration samples; KD-only QAD at 32K sequence length, 600 iterations, with `--freeze_mtp`; Megatron exporter at PP4, including the NVFP4 MTP experts. All 3,096 MTP tensors of the QAD checkpoint (checked at iteration 200) are bit-identical to the PTQ checkpoint. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ (recipe: new rules only match Megatron-Core names; Hub behavior unchanged) - 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?: ✅ Megatron-Core case for the recipe - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ❌ Can add an entry if wanted. - Did you get Claude approval on this PR?: ❌ ### Additional Information - Commands take the checkpoint as `<nemotron_h_omni-checkpoint>`. 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added a chat-data blending tool that creates training and validation datasets from weighted sources, reuses cached data, and reports errors when sources or splits are insufficient. * Expanded the Nemotron-H quantization recipe with Megatron-Core support and additional attention, output-layer, and MTP expert quantization rules. * **Documentation** * Added an end-to-end NemotronH Omni tutorial covering quantization, chat-data preparation, Hugging Face export, and vLLM serving. * Updated the tutorial listings and Megatron Bridge guide. * Documented recipe support for Hugging Face and Megatron-Core names, plus the `--tp_size 1` requirement for static NVFP4 weights. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Yue <yueshen@nvidia.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
…Nemotron 3.5 Super VL checkpoint (#2819) ### What does this PR do? Type of change: Refactor + docs. No behavior change. Moves the NemotronH aggressive-mse NVFP4 PTQ recipe from the architecture tier to the checkpoint tier, under the name the model is actually released as: ``` modelopt_recipes/model_type/nemotron_h/ptq/nvfp4-aggressive-mse.yaml -> modelopt_recipes/models/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/ptq/nvfp4-mse.yaml ``` **Why the checkpoint tier.** The recipe was written against, and only ever validated on, one checkpoint: `nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16`. Under `models/<org>/<model_id>/` the folder path *is* the hub path, so the entry states which checkpoint it reproduces instead of implying it is the recommended setting for every NemotronH hybrid. It also matches the existing `NVIDIA-Nemotron-3-Super-120B-A12B-BF16/ptq/nvfp4-mse` precedent. This is a move, not an alias — one name, so nobody has to work out which of two paths to use. **Scope of the claim.** The recipe reproduces the released *precision assignment*, not the released weights. The published `nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4` is refined by quantization-aware distillation after this stage, which no PTQ recipe performs. The header, `metadata.description`, `ptq.md` and the changelog all say so explicitly. Flagging it because `models/README.md` says a release is backfilled only when its model card describes a PTQ recipe — this entry is the PTQ *stage* of that pipeline rather than a claim to reproduce the artefact, and I am happy to move it back or reword further if reviewers read that rule more strictly. **The 43 `quant_cfg` rules are unchanged.** Only the path, the header comments and `metadata.description` differ, plus `# modelopt-schema: modelopt.recipe.config.ModelOptPTQRecipe` replacing the deprecated `metadata.recipe_type: ptq`. Also updated: `modelopt_recipes/ptq.md` (the architecture-tier entry is folded into a checkpoint-tier entry carrying its full detail), the `NemotronH-Omni-W4A4-QAD` tutorial README, which now selects the new path, and `CHANGELOG.rst`. **One consequence worth a reviewer's opinion:** `model_type/nemotron_h/` no longer exists, so other NemotronH hybrids fall back to `general/`. That was already the situation before #2703 landed, and nothing shipped in between — the recipe is absent from every released tag (0.48.0rc0, 0.47.0, 0.47.0rc2, 0.47.0rc1, 0.46.1), since it merged after the rc0 cut. No published wheel contains the old path, and all references to it were in-repo and are updated here. If you would rather keep an architecture-tier recipe for the family, say so and I will restore one that is genuinely generic. ### Usage ```bash python examples/hf_ptq/hf_ptq.py \ --pyt_ckpt_path <NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16> \ --recipe models/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/ptq/nvfp4-mse \ --calib_with_images --calib_size 32 --batch_size 1 \ --trust_remote_code \ --use_seq_device_map --gpu_max_mem_percentage 0.5 \ --export_path <quantized-checkpoint> ``` The runtime notes from #2703 still apply: `--use_seq_device_map` and `--gpu_max_mem_percentage 0.5` are both required for a ~232 GiB checkpoint on 4x184 GiB, and `--calib_with_images` matters on VL checkpoints because the vision tower must see image batches even though it stays BF16. ### Testing Run on 1 node x 4 x 184 GiB, transformers 5.13.0 / torch 2.12.0a0, against this branch rebased onto `de53aca3a`. | | | |---|---| | `tests/unit/recipe/test_nemotron_3_5_super_vl_recipe.py` | 11 passed | | `tests/examples/hf_ptq/test_nemotron_vl_calib.py` | 11 passed | | full `tests/unit/recipe/` suite | 505 passed | | `tools/precommit/check_modelopt_recipes.py` | exits 0 | | end-to-end `hf_ptq`, `--calib_size 32` | `rc=0`, 28:22, 0 tracebacks | Because the quantization rules are unchanged, the meaningful check is byte-level rather than a benchmark. Two exports were produced **on the same base commit**, differing only in which recipe they were pointed at: - **A** — this branch, `models/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/ptq/nvfp4-mse` - **B** — plain `origin/main` at `de53aca3a`, `model_type/nemotron_h/ptq/nvfp4-aggressive-mse` Holding the base fixed matters: `de53aca3a` itself touches `quant_aware_conversion.py`, so a comparison against an older export would have conflated that fix with this rename. Both arms were verified to carry the identical `quant_aware_conversion.py` (`24aa840e46…`). | A vs B | | |---|---| | shards | **8/8 byte-identical** (md5) | | quantizers inserted | 43,917 on both arms | | tensors | 169,569, 0 duplicates, delta 0 | | NVFP4 scales | 42,179 | | MTP tensors | 4,120 | | vision tensors | 457, all BF16 | | KV scales | 1 `k_scale` + 1 `v_scale`, both MTP, none on the backbone | | source names preserved verbatim | 43,079 / 43,079 on both arms | | size | 69.86 GiB | So the moved recipe selects the same 43,917 quantizers through the checkpoint tier that the pre-move recipe selects through the architecture tier, and the resulting checkpoint is identical byte for byte. Byte-identity is the right test here and GSM8K is not: two runs over byte-identical artefacts of this checkpoint differed by ~1.1 pp, so no single benchmark number could have resolved a change this small. The run also confirms the moved path resolves at runtime rather than falling back: ``` [load_recipe] loading: <builtin>/models/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16/ptq/nvfp4-mse.yaml Inserted 43917 quantizers ``` ### Before your PR is "*Ready for review*" - Is this change backward compatible?: Yes. The old path never shipped in a release, so no published wheel exposes it, and every in-repo reference is updated here. - If you copied code from other sources or added a new PIP dependency, did you follow CONTRIBUTING.md?: N/A; no new dependency or copied code. - Did you write any new necessary tests?: No new tests. `tests/unit/recipe/test_nemotron_h_recipe.py` is renamed to `test_nemotron_3_5_super_vl_recipe.py` to match the recipe, and its 11 cases are otherwise unchanged and still green. - Did you update Changelog?: Yes. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Signed-off-by: Juhi Mittal <juhim@nvidia.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
5 participants
What does this PR do?
Type of change: New feature + bug fix
Adds an aggressive-mse NVFP4 PTQ recipe for the
nemotron_harchitecture, and fixes thehf_ptqbug that blocks calibrating the omni VL wrappers in that family.modelopt_recipeshas no architecture-tier PTQ recipe for NemotronH. Everything the recipe dependson —
nemotron_h/nemotron_h_omnimodel support, MTP construction, thenvfp4/nvfp4_static/
fp8numerics,--calib_with_images— is already upstream, so the recipe adds no code.Recipe (
model_type/nemotron_h/ptq/nvfp4-aggressive-mse), weight-MSE calibration with an FP8scale sweep:
group_size 16, static weight scales(
nvfp4_static) with dynamic NVFP4 activations (nvfp4)in/out_proj,lm_head→ FP8inert on checkpoints without an MTP tail
(
use_constant_amax, as inconfigs/ptq/units/kv_fp8_cast, so nok_scale/v_scaleisexported) while MTP attention stays data-calibrated and keeps its pair. The MTP rule restates
use_constant_amax: falseso it cannot inherit the backbone setting whichever waycfgblockscompose.
Bug fix.
nemotron_vl_calib.pycallsfull_model.extract_feature(pixel_values)unconditionally. InternVL-style Nemotron VL exposes that helper; the omni wrappers
(
NemotronH_Omni_Reasoning_V3/nemotron_h_omni) do not — they merge vision embeddings withmasked_scatterinside their own forward. Calibrating one raisesAttributeErrorbefore anystatistics are collected.
The fallback runs before anything InternVL-specific: a wrapper without
extract_featureusually has no
img_context_token_ideither, so testing later raises there first and the fallbackis unreachable for most of the models it exists for. It carries the dtype-cast
pixel_valuesandsets
use_cache=False, both filtered through_accepted_kwargs()so a narrow forward signaturedoes not trade the
AttributeErrorfor aTypeError.A synthesized
image_flagsis deliberately not injected — it is built onpixel_values.device, while a sharded wrapper runs its vision tower elsewhere and indexesimage_embedswith it, raisingindices should be either on cpu or on the same device as the indexed tensor. A batch-suppliedimage_flagsis device-consistent and passes through. This wascaught by an end-to-end 4-GPU run, not by the unit tests, whose mock is single-device.
The same file aligns
vit_embeds,attention_maskandposition_idsacross devices for shardeddevice maps; on a single-device model those are no-ops.
Usage
Two runtime notes for a ~232 GiB checkpoint on 4x184 GiB:
--use_seq_device_mapis required;is_nemotron_vl()otherwise pins the whole checkpoint to oneGPU and it OOMs during load.
--gpu_max_mem_percentage 0.5is also required. At the 0.8 default,sequentialpacks GPU 0 to147 GiB of weights and calibration then OOMs in the Mamba mixer
(
modeling_nemotron_h.py:538,Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(3),requesting 13 GiB with GPU 0 at 180.94/184.31 GiB). At 0.5 the weights spread over three GPUs and
the run completes.
Add
--trust_remote_codeonly for checkpoints whose remote modeling code you have reviewed.--calib_with_imagesmatters on VL checkpoints: the vision tower must see image batches eventhough it stays BF16.
Testing
5 calibration cases and the full recipe suite pass, transformers 5.13.0 / torch 2.12.0a0. The
calibration mock defines neither
img_context_token_idnorlanguage_model, so reaching for eitherregresses the test.
tools/precommit/check_modelopt_recipes.pyexits 0, both across all recipes andtargeted at the new file.
End to end on 1 node x 4 x 184 GiB with a NemotronH omni VL checkpoint,
--calib_size 32:rc=0k_scale+ 1v_scale, both MTP, none on the backboneinvalid_rate0.0)Those two GSM8K numbers came from byte-identical artefacts, so the ~1.1 pp between them is
harness noise rather than a property of the checkpoint — worth knowing before reading any single
number here as a signal.
Before your PR is "Ready for review"
nemotron_vl_calib.pychange onlyadds a fallback on a path that previously raised.
N/A; no new dependency or copied code.
tests/examples/hf_ptq/test_nemotron_vl_calib.py.The recipe is covered by the existing
tests/unit/recipe/suite, which this PR keeps green.modelopt_recipes/ptq.md, and thecalibration fallback fixes a path that has not shipped in a release. Happy to add an entry if you
would prefer one.
🤖 Generated with Claude Code
Summary by CodeRabbit