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[OMNIML-6081] Fix the FSDP2 export of W4A8 (SequentialQuantizer) models, and refuse compressing them - #3128
Two fixes for W4A8 AWQ models (W4A8_AWQ_BETA_CFG), whose weight quantizer is a SequentialQuantizer (INT4 blocks, then FP8), and a guard for the multi-rank AWQ case the first fix would otherwise let through.
1. The FSDP2 export.export_hf_checkpoint on an FSDP2 model quantized with W4A8 AWQ (--qformat w4a8_awq_beta in examples/hf_ptq with --use_fsdp2) fails on every rank:
AttributeError: 'SequentialQuantizer' object has no attribute '_fake_quant'
The export's AWQ resmooth step opens an fsdp2_aware_weight_update window on each sharded layer to fold the pre-quant scales: export_hf_checkpoint -> _export_fsdp2_checkpoint_streaming -> _prepare_model_for_export -> requantize_resmooth_fused_llm_layers -> _fuse_shared_input_modules -> _fusion_update_context (the AWQ layernorm fusion opens one too). Around its unshard, re-registration and reshard, the window calls enable_fake_quant, which read and wrote weight_quantizer._fake_quant. A SequentialQuantizer is a container that has no _fake_quant of its own, so the read raises.
enable_fake_quant now saves, sets and restores _fake_quant on each TensorQuantizer stage of a weight quantizer. A plain TensorQuantizer is its own only stage, so other formats are unchanged. It reads all the original values before setting any, so a quantizer reached twice is restored correctly. It now restores them in a finally, so a window that raises (an FSDP2 unshard running out of memory, say) no longer leaves them changed.
2. mtq.compress now refuses W4A8 weights. On main, a W4A8 model compressed with mtq.compress can neither run nor be reloaded:
Its forward dequantizes the INT4 stage and then real-quantizes the FP8 stage, so F.linear receives an FP8QTensor (TypeError). There is no W4A8 real-quant GEMM, so GPU takes the same path.
compress_restore calls set_from_modelopt_state on the weight quantizer, which SequentialQuantizer does not have.
Any later weight assignment (an FSDP2 unshard, a loader re-assigning the packed weight) reads the container's missing _fake_quant too.
compress_convert now raises NotImplementedError naming the module before it changes the model. It does so for any quantized linear with a SequentialQuantizer in its weight quantizer (a plain W4A8 linear, or Transformer Engine's grouped linear, whose per-expert quantizers become sequential), also when the compress config excludes the layer: it would still become a RealQuantLinear that can neither be restored nor take a new weight. main's Megatron GPU tests already skip W4A8 for compress ("not supported for compress"). No path in the repo compresses W4A8 successfully today, so the refusal breaks none:
examples/hf_ptq --low_memory_mode --qformat w4a8_awq_beta failed right after compress ('SequentialQuantizer' object has no attribute 'num_bits' in the device-map step); it now stops at compress.
The QAT trainer's compress option, examples/llm_qat --compress and examples/llm_eval with compress run a forward right after compress, which failed.
examples/megatron_bridge/quantize.py --compress saves a Megatron checkpoint and skips generation; nothing in the repo can use a compressed W4A8 one (from reading the code; not run).
examples/alpamayo and examples/diffusers never use W4A8, and the Hugging Face export does not call mtq.compress.
Reach.W4A8_AWQ_BETA_CFG is the only built-in preset with a sequential weight quantizer. Both problems predate #3099: enable_fake_quant came with the FSDP2 export (#432), and every release since 0.39.0 has it. main's FSDP2 GPU export tests left W4A8 out for this reason (#TODO: Fix unit test for this case); this PR turns those cases back on.
3. AWQ calibration of an FSDP2 model sharded over several ranks is refused. Under hf_ptq --use_fsdp2, each rank calibrates AWQ-lite on its own slice of the data, and its alpha-search loss is not combined across ranks. So each rank folds its own pre_quant_scale into its weight shard: on CPU with 2 gloo ranks and a tiny Llama, 10 of 14 linears differ, and INT4 AWQ behaves the same. With fix 1, the export would write those mismatched shards instead of crashing. mtq.quantize and mtq.calibrate now raise NotImplementedError for awq_lite / awq_full on an FSDP2 model sharded over more than one rank, before the model changes. The decision reads only the run's size, the model's FSDP2 sharding and the algorithm, so every rank raises alike, before any collective. One rank, as hf_ptq --use_fsdp2 runs on one GPU, is still allowed. examples/hf_ptq refuses the same combination at argument validation, before the distributed load, for --qformat, PTQ recipes and AutoQuantize recipes alike, and its README's --use_fsdp2 notes say so. mtq.auto_quantize checks every candidate at entry, so a search with an AWQ candidate, such as its default NVFP4_AWQ_LITE_CFG, is refused before it changes the model; the KV-cache search already accepts only max. #2740's refusal, based on the recorded distribution plan, replaces this guard when it lands.
Usage
No API change. On one rank (hf_ptq --use_fsdp2 on one GPU, say), this now exports, where it used to raise; on several ranks the mtq.quantize call now refuses AWQ (fix 3):
mtq.compress on a W4A8 model now raises NotImplementedError up front.
Testing
tests/unit/torch/export/test_fsdp2_parallel_export.py::test_streaming_export_of_a_sharded_w4a8_model (new, CPU, one gloo rank): a tiny Llama (2 layers, hidden 128) is quantized with W4A8_AWQ_BETA_CFG and sharded with fully_shard on each decoder layer and the root. Its FSDP2 streaming export must match the unsharded export tensor for tensor.
tests/unit/torch/quantization/test_utils.py::test_enable_fake_quant_restores_each_stage_when_the_window_raises (new, CPU): a window that raises over a two-stage weight quantizer, whose stages start with different flags, must give each stage its own flag back.
tests/unit/torch/quantization/test_compress.py::test_compress_refuses_w4a8_weights_before_changing_the_model (new file, CPU, parametrized over a plain W4A8 linear and a GroupedQuantizer holding the sequential quantizer): compressing raises, and the model is left unconverted. main had no CPU test file for compress.py; its compress tests are CUDA ones in tests/gpu/torch/quantization/test_real_quantize_cuda.py.
tests/gpu/torch/export/test_fsdp2_export.py: W4A8 is re-enabled in test_fsdp2_weight_update_context_for_fuse_layers and test_fsdp2_weight_update_context_for_export_quantized_weight (4 cases). CI's single-GPU lane ran them at f06c347: 4 passed. On 2 GPUs at 182e0de: 4 passed, and 4 failed with main's enable_fake_quant (table below). A CPU dry run of those 4 cases (cuda swapped for cpu, gloo) passes on 1, 2 and 4 ranks with the fix. Without it, all 4 fail on 2 ranks, with the AttributeError on every rank.
Each new test fails without its fix (checked by reverting the fix only).
The CPU unit tests for the export, enable_fake_quant's callers and mtq.compress's consumers (test_fsdp2_parallel_export.py, test_sharded_weight_packing.py, test_offload_export.py, test_unified_export_hf.py, test_export_weight.py, test_utils.py, test_mse_calibrator.py, test_quantize_cpu.py, test_int8_tensor.py, test_nvfp4_four_over_six.py, test_hf_patching.py, the Hugging Face plugin tests and others) pass: 331 passed.
Real model, examples/hf_ptq with --use_fsdp2 and W4A8 AWQ on Qwen3-8B, including the export, on 2 GPUs, before and after the fix: before, the export crashes on both ranks; after, it exports (table below).
2026-10-29, round 2 (7354499): enable_fake_quant restores its flags in a finally and picks the stages with isinstance(TensorQuantizer) (review); mtq.compress refuses W4A8 weights. Ran the two new tests with and without their fix, the CPU suites above, and pre-commit. CI at f06c347 passed (47 checks), its single-GPU lane including the 4 re-enabled W4A8 cases.
2026-10-29, round 3 (3453fdc): the compress refusal also catches a sequential quantizer inside a GroupedQuantizer, and its message names the mechanism and covers excluded layers (review). Ran the parametrized test against three versions of compress.py (without the walk, the grouped case fails with the old AttributeError), the related CPU suites (196 passed), and pre-commit.
2026-10-30 (182e0de): CHANGELOG only. The entry now says the FSDP2 export of a W4A8 model no longer crashes and that AWQ calibration of an FSDP2 model across more than one rank is not supported yet (review). Ran pre-commit. Still pending on a GPU cluster: the 2-GPU run of the four re-enabled W4A8 cases, and the real-model examples/hf_ptq --use_fsdp2 W4A8 run before and after the fix.
2026-10-30, 2 GPUs (182e0de): the four re-enabled W4A8 cases pass, and fail without the fix with the expected AttributeError (table below). The real-model examples/hf_ptq --use_fsdp2 W4A8 run on Qwen3-8B, before and after the fix, is done too (table below).
2026-10-30 (78aa164): the multi-rank AWQ guard (fix 3, review). tests/unit/torch/quantization/test_quantize_cpu.py::test_awq_on_a_sharded_fsdp2_model_is_refused checks it on two gloo ranks, through both entry points, and that the model is left unquantized; it fails without the guard (DID NOT RAISE on both ranks). The W4A8 export test now shards before calibrating, as hf_ptq --use_fsdp2 does on one GPU; a guard that refused one rank fails it. The two GPU tests that calibrated AWQ after sharding on every GPU are adapted: test_fsdp2_export.py's helpers calibrate before sharding, and test_fsdp2.py::test_nested_fsdp2_backward skips INT4 AWQ on several GPUs. CPU dry run of test_fsdp2_export.py on 2 gloo ranks: before the adaptation, its 12 AWQ cases hit the guard; after it, all cases pass but the 4 NVFP4 AWQ-lite ones, which need CUDA on main too. Ran pre-commit and the related CPU suites (421 passed). GPU results at this head are below.
2026-10-30 (d538a02): mtq.auto_quantize checks every candidate at entry (review), so on a model FSDP2-sharded over several ranks an AWQ candidate is refused before the search changes the model, instead of partway through it; the refusal's message reports the FSDP2 shard mesh's size. test_awq_on_a_sharded_fsdp2_model_is_refused now also runs mtq.auto_quantize with {FP8, W4A8 AWQ} on two gloo ranks; without the entry check it fails, the refusal then coming from calibrate() mid-search. Ran pre-commit and the related CPU suites, the AutoQuantize and KV-cache AutoQuantize tests included (302 passed). The GPU results at 78aa164 below carry over.
2026-10-30 (060836a): hf_ptq --use_fsdp2 refuses AWQ formats and recipes, AutoQuantize candidates included, on more than one rank at argument validation, and the README says so (review). tests/examples/hf_ptq/test_example_utils.py::test_fsdp2_refuses_awq_on_several_ranks_before_loading covers it; without the check its two refused cases fail. The two adapted GPU tests now change only what the guard refuses: test_fsdp2_export.py shards before calibrating again except for AWQ on more than one rank, and test_nested_fsdp2_backward gates only its shard-then-quantize model there, so INT4 AWQ's quantize-then-shard comparison runs on several GPUs (review). CPU dry run with gloo, on 1 and 2 ranks: the nested-backward helper passes for all three configs, and test_fsdp2_export.py's two helpers pass for every config except NVFP4 AWQ-lite, which needs CUDA on main too. Ran pre-commit and the hf_ptq example tests (153 passed).
2026-10-30 (75db98e): text only (review). The CHANGELOG bullet is now three Bug Fixes entries, and the hf_ptq README note and error message offer dropping --use_fsdp2 first, as the single-process device_map still uses all the GPUs. Ran pre-commit and the hf_ptq example tests (62 passed).
GPU test's 4 W4A8 cases, CPU dry run, 1 and 4 gloo ranks
not run
4 pass on each
enable_fake_quant enter and exit on 2,000 quantized linears (CPU):
Weight quantizer
Before
After
FP8 (TensorQuantizer)
46.6 ms
23.9 ms
W4A8 (SequentialQuantizer)
raises
38.5 ms
tests/gpu/torch/export/test_fsdp2_export.py on 2 GPUs: 2x NVIDIA GB200 (184.3 GiB each), torch 2.11.0a0 (nv26.02), transformers 5.9.0, CUDA 13.1, pytest -p no:rerunfailures. Before = main's enable_fake_quant (99c3834) on the same tree.
Calibration completes, then the export fails on both ranks with AttributeError: 'SequentialQuantizer' object has no attribute '_fake_quant' in enable_fake_quant
The exported hf_quant_config.json has quant_algoW4A8_AWQ, an FP8 KV cache, group size 128 and pre_quant_scale, with lm_head and embed_tokens excluded.
Tensor
dtype
Count
weight (packed INT4)
U8
252
weight_scale
F32
252
weight_scale_2
F32
252
input_scale
F32
252
pre_quant_scale (o_proj, down_proj: not folded into a layernorm)
BF16
72
weight of the unquantized modules
BF16
147
This run, at 182e0de, shows that the export no longer crashes. With 2 ranks, each rank's own pre_quant_scale (fix 3) applies to this checkpoint, so it is not an accuracy result. Since 78aa164 the same 2-GPU command stops at calibration with the guard's NotImplementedError, so the crash fix is shown on one GPU below.
At 78aa164: Qwen3-8B, examples/hf_ptq with --use_fsdp2 --qformat w4a8_awq_beta --calib_size 128 --batch_size 4, NVIDIA GB200, torch 2.11.0a0 (nv26.02), transformers 5.13.0, CUDA 13.1.
Exports W4A8_AWQ with an FP8 KV cache, group size 128 and pre_quant_scale, lm_head and embed_tokens excluded; same tensor layout as the 2-GPU export above
Both ranks raise NotImplementedError: Calibrating with awq_lite is not supported yet on an FSDP2 model sharded over 2 ranks: ... at calibration; no hang
4 m 12 s
tests/gpu/torch/export/test_fsdp2_export.py and tests/gpu/torch/quantization/test_fsdp2.py at 78aa164, same environment:
GPUs
Passed
Skipped
Failed
1
45
8, the cases that need 2 or more GPUs
0
2
52
1, test_nested_fsdp2_backward with INT4 AWQ, by design
0
d538a02 changes only mtq.auto_quantize's entry check and the refusal's message, so these results carry over: none of these runs calls auto_quantize. 060836a narrows the two GPU tests' adaptations (Testing above). Re-run of both files at 060836a: NVIDIA GB200, torch 2.11.0a0 (nv26.02), transformers 5.9.0, pytest -p no:rerunfailures.
GPUs
Passed
Skipped
Failed
1
45
8, the cases that need 2 or more GPUs
0
2
53
0
0
On 2 GPUs test_nested_fsdp2_backward with INT4 AWQ now runs, with only its shard-then-quantize model gated. Both export helpers pass for all 8 configs, with and without bias: the AWQ ones quantize before sharding, the others shard first.
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Is this change backward compatible?: ✅ (mtq.compress on a W4A8 model now raises up front; such a compressed model could not run or be reloaded before)
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Additional Information
Follow-up to #3099 (review thread on W4A8 weight-update windows).
Summary by CodeRabbit
Bug Fixes
Fixed fake quantization for models with sequential weight quantizers. All stages are enabled during use, and their previous settings are restored even if an error occurs.
Compatibility
Compression now reports W4A8 AWQ models with an explicit not-supported error; compressed W4A8 models cannot run or reload.
Fixed W4A8 AWQ export with FSDP2. Streaming export produces consistent results, but AWQ calibration across multiple FSDP2 ranks is not yet supported.
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The change updates fake-quant state handling for nested weight quantizers, rejects compression for quantized linear modules with sequential weight quantizers, and adds W4A8 AWQ coverage for FSDP2 export.
Changes: W4A8 quantization and FSDP2 export
Layer / File(s)
Summary
Nested fake-quant state handling modelopt/torch/quantization/utils/core_utils.py, tests/unit/torch/quantization/test_utils.py
enable_fake_quant enables each nested TensorQuantizer and restores its original flag after the context exits, including when it raises an exception. A unit test checks both sequential quantizer stages.
compress_convert raises NotImplementedError when a quantized linear module uses a SequentialQuantizer for its weight quantizer. A unit test checks the exception and confirms that the model is not real-quantized. The changelog records the W4A8 restriction.
The export test parameter sets include W4A8 AWQ. A new test compares resident and streaming export keys and tensor values for a one-rank sharded model.
NVIDIA/Model-Optimizer issue 1699 covers distributed model loading and exporting with FSDP2. This change adds W4A8 export coverage in that area.
Possibly related PRs
NVIDIA/Model-Optimizer#2889: Both changes address export behavior involving SequentialQuantizer weight quantizers.
Suggested reviewers:kinjalpatel27, sugunav14
Merge Risk: Minimal · previous review 182e0
The changelog distinguishes the export crash fix from unsupported multi-rank calibration. No issue in this change is established as blocking merge.
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The title names two changes that appear in the changeset: the FSDP2 export fix for W4A8 models built on SequentialQuantizer (via the enable_fake_quant update in core_utils.py and its tests) and the refusal to compress them (via the NotImplementedError added in compress.py and its test). The title is specific and matches the main change, so it passes.
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[IMPORTANT Correctness] The restore is still not guarded, so an exception inside the window leaks _fake_quant = True permanently.
What:yield is bare — if the body raises, the loop at 1022 never runs and every weight quantizer reached by this window keeps _fake_quant = True. Each of the other three context managers in this file guards its restore (no_requires_grad 996-999, enable_quant 1077-1080, disable_calib 1092-1095), and the caller fsdp2_aware_weight_update wraps its own body in try/finally precisely so that "an early error (e.g. an OOM from the unshard below) propagates and FSDP state stays intact" (core_utils.py:1080-1082). This helper is the one piece of that sequence that does not hold to it.
Why it matters: all three FSDP2 call sites wrap operations that realistically raise — root_module.unshard() (OOM, NCCL), the FSDPParam/QFSDPParam construction, and root_module.reshard(). A leaked True is read by pack_real_quantize_weight at qtensor/base_qtensor.py:230 (and not module.weight_quantizer._fake_quant), so compression is then silently skipped for those layers — the failure mode is an uncompressed export rather than an error, which is exactly the kind of silent-skip CONTRIBUTING's correctness guidance asks you to avoid. tests/gpu/torch/export/test_fsdp2_export.py::test_fsdp2_weight_update_context_error_propagates shows errors do flow through this window, so the path is live, not hypothetical.
This predates the PR, but the PR rewrites exactly these lines and the fix is two lines, so it is cheaper to land here than to leave behind.
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Fixed in ffa435a: the restore now runs in a finally, so a window that raises gives every quantizer its own flag back. tests/unit/torch/quantization/test_utils.py::test_enable_fake_quant_restores_each_stage_when_the_window_raises raises inside the window over a two-stage W4A8 weight quantizer whose stages start with different flags. Without the finally it fails with the flags left at [True, True].
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[SUGGESTION] This rewrite is the right shape for the W4A8 bug — compress.py:91 broadcasts {"fake_quant": False} through SequentialQuantizer.set_from_attribute_config, so the flag genuinely lives per stage, and _QuantizerContainerBase._delegated_properties only exposes the read-only fake_quant property (tensor_quantizer.py:533-535), so the container cannot carry a writable copy. Two notes on the comprehension while you are here:
The one non-FSDP caller still matches nothing.model_calib._mse_quant_func calls enable_fake_quant(quantizer) with a bare TensorQuantizer (model_calib.py:775), alongside enable_quant(quantizer) and disable_calib(quantizer) which both act on it directly. A TensorQuantizer has no weight_quantizer attribute, so the outer filter at 1014 rejects it and the window is a no-op — the MSE sweep's intent that quantizer(x) fake-quantizes is not actually enforced. It is benign today (fake_quant defaults to True and mtq.compress flips it only after calibration), and it predates this PR, but the new hasattr(q, "_fake_quant") filter removes the only way it would ever announce itself. Accepting a quantizer directly would cover both callers, e.g. iterate module.modules() and take m.weight_quantizer.modules() when present, else [m].
hasattr(q, "_fake_quant") is broader than the intent. The attribute you want to exclude is the container itself; isinstance(q, TensorQuantizer) says that directly and keeps a future quantizer type that legitimately lacks the flag from being silently dropped from the window instead of raising.
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Taken in ffa435a: the stages are picked with isinstance(q, TensorQuantizer), as core_utils does elsewhere.
Not changed. _mse_quant_func's enable_fake_quant(quantizer) is pre-existing and, as you say, benign: MSE calibration runs before mtq.compress, while _fake_quant still holds its default True. Letting enable_fake_quant take a bare quantizer would change the MSE calibration path, which this bug fix does not touch, so it is better as a change of its own.
On the flag's consumers: besides pack_real_quantize_weight, RealQuantParameterDict.__setitem__ in quant_linear.py reads it too, on the SequentialQuantizer it captured at conversion. A compressed W4A8 model also cannot run (its FP8 stage real-quantizes the dequantized weight) or be restored (SequentialQuantizer has no set_from_modelopt_state). So 7354499 makes mtq.compress refuse W4A8 weights up front.
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Claude review — 1 IMPORTANT, 1 SUGGESTION
Full review (4 files changed, all reviewed: modelopt/torch/quantization/utils/core_utils.py, both test files, CHANGELOG.rst). The trigger comment carried no scoping instructions.
Findings
CRITICAL: 0 · IMPORTANT: 1 · SUGGESTION: 1
Severity
Location
Issue
IMPORTANT Correctness
core_utils.py:1018-1023
Bare yield — an exception in the window leaks _fake_quant = True, which silently disables weight compression afterwards
SUGGESTION
core_utils.py:1011-1017
enable_fake_quant(quantizer) from model_calib.py:775 still matches nothing; hasattr(q, "_fake_quant") is broader than isinstance(q, TensorQuantizer)
Assessment
The diagnosis and the fix are correct. I traced the flag through the paths that read it and the change holds up:
compress.py:91 sets {"fake_quant": False} via set_quantizer_attributes_partial, which reaches _QuantizerContainerBase.set_from_attribute_config and broadcasts to each stage — so for a SequentialQuantizer the flag really does live per stage, never on the container. Walking weight_quantizer.modules() is the right place to save and set it.
_QuantizerContainerBase._delegated_properties lists fake_quant, but TensorQuantizer.fake_quant (tensor_quantizer.py:533-535) is a read-only property with no setter, so the existing delegation mechanism could not have carried the write. Adding the per-stage walk rather than extending delegation is the smaller change here.
The one consumer, pack_real_quantize_weight (qtensor/base_qtensor.py:230), runs under SequentialQuantizer.convert_to_single_quantizer, so it reads stage 0 as module.weight_quantizer. Setting stage 0 inside the window is therefore sufficient to suppress compression during unshard() — no gap there.
Reading all originals before writing any is the right call for a quantizer reached twice through shared references, and the zip restore is index-safe where the old pop(0) was order-coupled to a second traversal. Dropping that second module.modules() walk is also what buys the ~2x speedup reported in the description.
Backward compatible: no signature, config, modelopt_state or public-API change. A plain TensorQuantizer is its own only stage, so every non-W4A8 format behaves exactly as before.
The one blocking item is the unguarded yield. It is pre-existing, not a regression, but this PR rewrites those exact lines, the three sibling context managers in the same file all guard their restores, and the enclosing fsdp2_aware_weight_update deliberately uses try/finally so a failed unshard leaves FSDP state intact — this helper is the gap in that sequence, and its leak degrades into a silently uncompressed export rather than an error.
Tests
Test changes are proportionate and follow the repo's be-stingy guidance: one CPU unit test on the session gloo_world_of_one fixture (~1.2 s, no subprocesses) plus re-enabling 4 previously-skipped GPU cases, rather than a new file. Widening _tiny_quantized_llama with hidden_size=64 / intermediate_size=2 * hidden_size keeps the existing callers byte-identical (128 either way), so no existing coverage shifts. The author verified the test fails without the fix by reverting, as the evidence rule asks.
One gap the description already states plainly: the 4 re-enabled GPU cases have not been run on a GPU yet — only a CPU/gloo dry run with cuda swapped for cpu. Worth having that GPU run (and the pending 2-GPU examples/hf_ptq --use_fsdp2 W4A8 run) land before merge, since re-enabling GPU-only cases is the part CI on this PR will be first to exercise.
The CHANGELOG.rst entry is correctly scoped — one sentence, user-facing, filed under fixes for a bug present since 0.39.0 rather than one introduced this cycle.
Risk: low. A 14-line change to one helper, with narrow blast radius and the only behavior change confined to formats whose weight quantizer is sequential.
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Review comments at @modelopt/torch/quantization/utils/core_utils.py:
- Around line 1022-1023: Wrap the yield in enable_fake_quant with a try/finally
and restore each quantizer’s saved _fake_quant value in the finally block, so
restoration occurs even if unsharding or the context body raises. Keep the
existing restoration loop and saved values.
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shengliangxu
changed the title
[OMNIML-6081] Fix the FSDP2 export of W4A8 (SequentialQuantizer) models
[OMNIML-6081] Fix the FSDP2 export of W4A8 (SequentialQuantizer) models, and refuse compressing them
Oct 29, 2026
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[SUGGESTION] The refusal misses a GroupedQuantizer whose members are SequentialQuantizers.
isinstance(module.weight_quantizer, SequentialQuantizer) only catches the plain-linear layout. For TEGroupedLinear, _setup() installs self.weight_quantizer = GroupedQuantizer(*(TensorQuantizer() ...)) (plugins/transformer_engine.py:151), and the fused-experts name normalizer surfaces each member as *weight_quantizer, so a list-valued cfg upgrades the members to SequentialQuantizer (conversion.py:455-460) while module.weight_quantizer stays a GroupedQuantizer. That layout hits exactly the three breakages you documented — set_from_modelopt_state on the container in compress_restore, and no W4A8 real-quant GEMM — but slips past the guard and fails later with the old opaque error instead.
A recursive check covers both layouts in the same line, and keeps working if another container type is added:
Suggested change
# A compressed SequentialQuantizer weight can neither run forward nor be restored.
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Taken in 3453fdc: the check walks module.weight_quantizer.modules(), so a GroupedQuantizer with sequential members (Transformer Engine's grouped linear under W4A8) is refused too. test_compress_refuses_w4a8_weights_before_changing_the_model is now parametrized over both layouts. Without the walk, the grouped case fails later with AttributeError: 'GroupedQuantizer' object has no attribute '_fake_quant'.
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[SUGGESTION] The refusal ignores config.compress, so it also rejects a model that opts those layers out of compression.
The scan runs before compress_cfg is consulted, so mtq.compress(model, {"*w4a8_layer*": False, "default": True}) on a mixed model — the documented way to skip layers, e.g. after auto_quantize with W4A8_AWQ_BETA_CFG among the candidates — now raises even though those layers would have kept fake_quant=True and never been real-quantized. That case does work on main for forward (the layer becomes a RealQuantLinear that pack_real_quantize_weight skips); only save/restore was broken, because update_compress_metadata collects get_modelopt_state() for everyRealQuantLinear regardless of fake_quant.
Two options, either is fine:
Keep the unconditional refusal (restore is broken for these layers either way) but say so in the message, so a user hitting it on an excluded layer knows it is deliberate rather than a bug — e.g. "... is not supported, even when excluded from the compress config: its state cannot be saved or restored."
Or move the scan after compress_cfg is resolved and only refuse layers whose resolved fake_quant is False.
Separately, the message hardcodes W4A8 while the predicate is the general SequentialQuantizer mechanism; a hand-written two-stage config (INT8→FP8, say) would be mislabeled. f"{name}: compressing a sequential weight quantizer (as in W4A8) is not supported." covers both.
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Kept the unconditional refusal, and the message now says so (3453fdc): <name>: compressing a sequential weight quantizer (as in W4A8) is not supported, even when the compress config excludes the layer. That also covers a hand-written two-stage config.
Refusing only the layers that get compressed would leave the excluded ones broken. On main, mtq.compress(model, {"compress": {"default": False}}) still turns a W4A8 linear into a RealQuantLinear. Its forward runs, but beyond the restore failure, any later weight assignment fails: RealQuantParameterDict.__setitem__ reads weight_quantizer._fake_quant, which a SequentialQuantizer does not have. Checked on CPU: lin.weight = nn.Parameter(...) raises that AttributeError.
Scope: the trigger comment carried no scoping instructions, so this is a full review. 7 files changed, all reviewed: modelopt/torch/quantization/utils/core_utils.py, modelopt/torch/quantization/compress.py, the three test files, the GPU test re-enable, and CHANGELOG.rst.
Both round-1 findings are resolved
IMPORTANT (bare yield) — fixed. enable_fake_quant now restores in a finally, matching the no_requires_grad / enable_quant neighbours in the same file, so an OOM inside an unshard() no longer leaks _fake_quant = True and silently disables weight compression afterwards. test_enable_fake_quant_restores_each_stage_when_the_window_raises pins it, with the two stages starting at different flags so a "restore everything to True" regression would fail it.
I re-traced the three breakages the description claims, and they hold:
_QuantizerContainerBase._delegated_properties is ["fake_quant", "is_enabled", "amax"] — the private _fake_quant is not delegated, so __getattr__ falls through to nn.Module.__getattr__ and raises. That is the reported AttributeError, and it applies to GroupedQuantizer too, not just SequentialQuantizer.
pack_real_quantize_weight runs under SequentialQuantizer.convert_to_single_quantizer(module) (qtensor/base_qtensor.py:243), so _compress_and_update_module_weight reads stage 0 and packs the INT4 stage into a QTensorWrapper while stage 1 stays FP8 — the TypeError in F.linear described in the PR.
compress_restore calls module.weight_quantizer.set_from_modelopt_state(...) unconditionally for every RealQuantLinear in metadata["real_quantizer_state"], and SequentialQuantizer has only get_modelopt_state. So restore was broken independently of whether the weight was actually packed.
Placement is right:apply_mode calls manager.add_mode(m, config, metadata)afterconvert returns (opt/conversion.py:419-420), so raising inside compress_convert leaves no real_quantize entry in modelopt_state. The model is unconverted in both senses, not just free of RealQuantLinear as the new test asserts. The scan also precedes the RealQuantModuleRegistry.register(...) loop, so the global registry is not mutated either — worth keeping that ordering in mind if the block ever moves.
Reach claim verified:w4a8_awq_beta.yaml is the only preset whose *weight_quantizer cfg is a list; w4a8_mxfp4_fp8.yaml and w4a8_nvfp4_fp8.yaml use a single cfg, so they are plain TensorQuantizers and unaffected. The only other list-valued recipes are the three model_type/*/ptq/w4a8_awq-kv_fp8_cast.yaml files, which are the same format.
enable_fake_quant rewrite
Correct, and a small improvement beyond the stated fix: because the walk is now weight_quantizer.modules() filtered to TensorQuantizer, it also fixes GroupedQuantizer (TEGroupedLinear), which raised the same AttributeError on main for the same reason. Reading every original before writing any is right for a quantizer reached twice through a shared reference (nn.Module.modules() dedups within one walk but not across the two parents), and zip is index-safe where pop(0) was coupled to a second traversal. No hot-path concern: both call sites pass a single FSDP unit or a single leaf module, not the whole model, so the walk stays O(layer).
No modelopt_state schema, signature, config-field or public-API change; a plain TensorQuantizer is its own only stage, so every non-W4A8 format behaves exactly as before.
Findings (all non-blocking)
Severity
Location
Issue
SUGGESTION
compress.py:73-78
Refusal misses a GroupedQuantizer of SequentialQuantizers (TEGroupedLinear / Megatron MoE + W4A8), which hits the same three breakages but falls through to the old opaque error. A recursive any(... for q in module.weight_quantizer.modules()) covers both layouts in one line.
SUGGESTION
compress.py:74-75
Refusal ignores config.compress, so {"*w4a8*": False, "default": True} on a mixed model is rejected even though those layers would never have been packed. Either say so in the message or scope the scan to layers whose resolved fake_quant is False. Also, the message hardcodes W4A8 while the predicate is the general SequentialQuantizer mechanism.
SUGGESTION
core_utils.py:1012-1018 (unchanged from round 1)
enable_fake_quant(quantizer) at model_calib.py:775 and in test_mse_calibrator.py passes a TensorQuantizer, which has no weight_quantizer, so the window is still a silent no-op there. Latent only — MSE calibration runs during mtq.quantize, before anything sets fake_quant=False — but a one-line fallback (treat a TensorQuantizer argument as its own stage) would make the two call sites mean what they read as. Author's call; it is pre-existing.
Tests
Proportionate, and each one fails without its fix. The new hidden_size parameter on _tiny_quantized_llama keeps the existing callers byte-identical (2 * 64 == 128), and the new W4A8 case needs the 128-wide default for INT4 blocks to tile. The GPU re-enable (4 cases across the two parametrizations) is the right regression net for the AttributeError; the CPU dry run at 1/2/4 gloo ranks is good evidence, and the pending real GPU run is correctly flagged as outstanding in the description. The AssertionError inside the pytest.raises(RuntimeError) block in the restore test would surface as a failure rather than be swallowed, so that in-window assert is safe.
Risk
Low. Two small, well-scoped changes to a crash path and a path that could not work. The only behavioural change for existing users is a loud NotImplementedError where the previous outcome was a crash or an unloadable checkpoint, and it is in the changelog.
🪄 Fix comments: Fix CodeRabbit comments on this PR
Prompt to fix review comments
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @modelopt/torch/quantization/compress.py:
- Line 75: Update the SequentialQuantizer guard in pack_real_quantize_weight to
catch modules with a non-null weight and a SequentialQuantizer weight_quantizer,
including embeddings; do not rely on is_quantized_linear, which excludes
embeddings. Safely handle modules without a weight_quantizer.
- Line 75: Update the SequentialQuantizer preflight in compress_convert to
resolve the ordered pattern and default selection first, and raise only for
quantized linear layers selected for compression. Preserve the existing
module-conversion behavior for excluded layers.
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Reviewing files that changed from the base of the PR and between f06c347 and 7354499.
Files selected for processing: 5
CHANGELOG.rst
modelopt/torch/quantization/compress.py
modelopt/torch/quantization/utils/core_utils.py
tests/unit/torch/quantization/test_compress.py
tests/unit/torch/quantization/test_utils.py
Files skipped from review as they are similar to previous changes: 1
CHANGELOG.rst
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Nudge: the fix is correct and well tested, but multi-rank FSDP2 W4A8 export used to crash and now succeeds, while still exposed to the AWQ-lite divergence across ranks. The PR documents that divergence as a known, older problem it doesn't fix.
Needs action:
Confirm the merge order against #2740/#2752, or qualify the CHANGELOG entry. Right now it advertises hf_ptq --use_fsdp2 --qformat w4a8_awq_beta as fixed. On 2 or more ranks, each rank folds its own pre_quant_scale into its weight shard, so the export can hold mismatched shards (see inline comment).
Record the pending 2-GPU run of the four re-enabled W4A8 cases in tests/gpu/torch/export/test_fsdp2_export.py, and the real-model hf_ptq --use_fsdp2 run, in the PR before merging.
No action needed:
Verified: the SequentialQuantizer container doesn't delegate _fake_quant. RealQuantParameterDict.__setitem__ and compress_restore's set_from_modelopt_state both fail on it. The Megatron tests already skip W4A8 compress, and the HF export mentions mtq.compress only in a comment. So the refusal blocks no flow that worked before.
apply_mode records the mode only after convert, so the refusal really does leave the model unchanged.
The GPU test edits switch W4A8 coverage back on, which is justified. The new test file's header matches LICENSE_HEADER. The local import has a stated reason (a circular import), the same one other functions in that file give.
- Fix the column-major ``DequantizeLinear`` surgery leaving a negative or default ``axis`` attribute unchanged while transposing the weight it refers to, which changed the model's output. Re-run the surgery on affected models.
- Fix QAT of Hugging Face fused MoE experts with non-reentrant activation checkpointing, as in the ``examples/llm_qat`` FSDP2 config: the experts' ``F.linear`` interception stayed installed after the recompute, and training crashed with ``aten.mm.default got mixed torch.Tensor and DTensor``.
- Fix quantization-aware training of Diffusers models failing in the backward pass with ``NotImplementedError`` once their attention modules are quantized, with or without MHA quantization.
- Fix ``export_hf_checkpoint`` of an FSDP2 model quantized with W4A8 AWQ (``W4A8_AWQ_BETA_CFG``, e.g. ``examples/hf_ptq`` with ``--use_fsdp2 --qformat w4a8_awq_beta``), which failed with ``AttributeError: 'SequentialQuantizer' object has no attribute '_fake_quant'``. ``mtq.compress`` now refuses W4A8 weights with ``NotImplementedError``, since a compressed W4A8 model could neither run nor be reloaded.
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Bot comment.
This entry presents examples/hf_ptq --use_fsdp2 --qformat w4a8_awq_beta as fixed. With 2 or more ranks, though, AWQ-lite still picks a pre_quant_scale per rank (the PR's own "Not fixed here" section). Here's how that reaches the checkpoint:
requantize_resmooth_fused_llm_layers runs on every rank.
_fusion_update_context → fsdp2_aware_weight_update unshards the weight, and preprocess_linear_fusion rescales the full weight with that rank's own scale.
The finally rebuilds each FSDPParam from the rank's own full tensor, so each rank keeps its chunk of its own version.
The owner then gathers the mixed chunks and writes out its own pre_quant_scale.
The export used to crash; now it can quietly write a wrong checkpoint until #2740/#2752 refuse that combination. Could you either land after them, or reword this to say the export no longer crashes and that multi-rank awq_lite isn't supported yet?
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Reworded in 182e0de. The entry now says the export no longer fails with the AttributeError, and that AWQ calibration of an FSDP2 model across more than one rank is not supported yet. It no longer presents the hf_ptq --use_fsdp2 W4A8 flow as fixed. The divergence itself stays with #2740 and #2752, which refuse that combination.
GPU evidence: CI's single-GPU lane ran the four re-enabled W4A8 cases in tests/gpu/torch/export/test_fsdp2_export.py at f06c347, and they passed. The 2-GPU run of those cases, and the real-model examples/hf_ptq --use_fsdp2 W4A8 run before and after the fix, are pending on a GPU cluster. Their results will be recorded in the PR's Testing section before merge.
On item 2 of the review above: the 2-GPU run is done. At 182e0de, the four re-enabled W4A8 cases in tests/gpu/torch/export/test_fsdp2_export.py pass, and the whole file passes (43). With main's enable_fake_quant the four cases fail with the expected AttributeError. The table and environment are in the description's Testing section. The real-model examples/hf_ptq --use_fsdp2 W4A8 run, before and after the fix, follows.
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[IMPORTANT Compatibility] This guard also fires from inside AutoQuantize, where it refuses a path the repo declares supported and refuses it after the model has already changed.
AutoQuantizeSearcher.before_search calibrates one recipe at a time through this very calibrate() (algorithms.py:1330: calibrate(self.model, algorithm=recipe.config.algorithm, ...)), and AWQ recipes are a first-class part of that search space — algorithms.py:1319 even special-cases "awq" not in str(recipe.config.algorithm) to shorten AWQ calibration. So on any multi-rank FSDP2 model:
the shipped recipe modelopt_recipes/general/auto_quantize/w4a8_awq_beta_fp8_at_6p0bits.yaml (search over {W4A8 AWQ-beta, FP8}) now raises;
examples/hf_ptq --use_fsdp2 + --auto_quantize_bits with any AWQ qformat now raises (examples/hf_ptq/autoquant_utils.py:136 warns FSDP2 AutoQuantize is experimental but supports it, and score_batch_size at line 194 multiplies by world_size(), so multi-rank sharded is the intended shape);
KV-cache AutoQuantize raises too, even though model_quant.py:556 states "Distributed KV-cache AutoQuantize supports DP/FSDP2, not TP."
Two problems with that:
It is not "before the model changes." The docstring and the CHANGELOG promise the refusal lands before any mutation. That holds for a direct mtq.quantize / mtq.calibrate, but inside the search loop the FP8 candidate may already have been calibrated, its quantizer states saved into self.quantizer_states, and ModeloptStateManager(...).state_dict().pop() already applied. The user gets a NotImplementedError partway through a long search on a model that is no longer in its entry state.
It is all-or-nothing. A search whose candidate list is {W4A8 AWQ-beta, FP8} is now unrunnable on >1 FSDP2 rank, where dropping the AWQ candidate (or refusing up front) would leave a useful run.
Suggested fix — hoist the decision to where the whole format list is known, so it refuses before before_search calibrates anything. In _auto_quantize / the KV-cache validator (next to the existing dist.is_dtensor_sharded check at line 556), iterate quantization_formats and call _refuse_awq_on_a_sharded_fsdp2_model(model, fmt.get("algorithm")) for each, so the run fails at entry with the same message. If you would rather keep AWQ searches usable, warn and filter those recipes out of the candidate list instead. Either way the per-call guard here can stay as the backstop.
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Fixed in d538a02. auto_quantize now checks every candidate in the loop that already validates their algorithms: the global formats, the fixed config and each module_search_spaces entry. On a model FSDP2-sharded over several ranks, an AWQ candidate is refused there with NotImplementedError: AutoQuantize candidate 'W4A8_AWQ_BETA_CFG' calibrates with awq_lite, .... That happens before apply_mode converts the model and before before_search calibrates anything. So w4a8_awq_beta_fp8_at_6p0bits and hf_ptq --use_fsdp2 --auto_quantize_bits with an AWQ qformat now fail at entry on several ranks, with the model unchanged, instead of partway through the search.
The check in calibrate() stays as the backstop.
Candidates are not filtered out: refusing keeps a user from silently getting a narrower search than the one they asked for. The default formats include NVFP4_AWQ_LITE_CFG, and the CHANGELOG now says so.
KV-cache AutoQuantize is not affected. _validate_search_inputs runs _validate_kv_only_config on every KV candidate before apply_mode, and that refuses any algorithm other than None and max, so no AWQ candidate reaches its calibrate().
test_quantize_cpu.py::test_awq_on_a_sharded_fsdp2_model_is_refused now also runs mtq.auto_quantize with {FP8, W4A8 AWQ} on two gloo ranks. It checks that the refusal names the candidate and that the model is left unconverted. Without the entry check it fails, because the refusal then comes from calibrate() partway through the search.
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[SUGGESTION] The decision and the message both use dist.size(), the global world size, but is_fsdp2_model only tells you some submodule is an FSDPModule — not how wide its mesh is. Under 2D parallelism or HSDP the FSDP2 shard mesh can be narrower than the world (e.g. fully_shard over a 4-rank shard dim inside an 8-rank job), so the message "sharded over 8 ranks" misstates the layout the user actually has, which is the one thing they need in order to act on the refusal.
Refusing is still the right call in those cases (each rank calibrates on its own data either way), so this is only about the diagnostic. Taking the shard-mesh size from the first FSDPModule's mesh — and falling back to dist.size() when it is unavailable — would make the message match the run. Worth doing while the guard is fresh, since #2740's distribution-plan-based refusal will want the same number.
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Taken in d538a02. The message now reports the size of the FSDP2 shard mesh: the last dimension of the first FSDPModule's mesh, which is the shard dimension under HSDP. It falls back to dist.size() when that mesh is unavailable. The decision is unchanged.
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Claude review — 1 IMPORTANT, 1 SUGGESTION
Scope. Full review (trigger comment carried no scoping instructions). The PR touches 10 files / +164 −25; I reviewed all four source files (compress.py, model_quant.py, utils/core_utils.py, CHANGELOG.rst) and all six test files. The git diff origin/main HEAD in this checkout also shows unrelated churn (distributed_writer.py, plugins/megatron.py, moe.py, the apply_without_qdq removal in core_utils.py) because the local base tip has moved past the PR's base; I scoped the diff to the 10 files GitHub reports as changed and reviewed only those.
Findings
#
Severity
Where
Issue
1
IMPORTANT Compatibility
model_quant.py:73-89
The AWQ guard also fires inside AutoQuantize's per-recipe calibration, so multi-rank FSDP2 AutoQuantize with any AWQ candidate now hard-fails — and fails mid-search, after the model has already changed
2
SUGGESTION
model_quant.py:79-86
The refusal reports dist.size() (global world size) as the shard width; under 2D/HSDP the FSDP2 mesh can be narrower, so the message misstates the user's layout
Most impactful: finding 1. AutoQuantizeSearcher.before_search calibrates each candidate recipe through this same calibrate() (algorithms.py:1330), and AWQ recipes are a first-class part of that search space (algorithms.py:1319 special-cases them to shorten calibration). The shipped recipe modelopt_recipes/general/auto_quantize/w4a8_awq_beta_fp8_at_6p0bits.yaml — a per-layer search over {W4A8 AWQ-beta, FP8} — and examples/hf_ptq --use_fsdp2 --auto_quantize_bits with an AWQ qformat therefore both stop working on more than one rank. KV-cache AutoQuantize is hit too, even though model_quant.py:556 declares "Distributed KV-cache AutoQuantize supports DP/FSDP2, not TP." Worse, inside the search loop the refusal no longer has the "before the model changes" property the docstring and CHANGELOG promise: an earlier candidate may already have been calibrated and had its mode popped off the state manager. Hoisting the check over the whole quantization_formats list at auto_quantize entry (next to the existing is_dtensor_sharded check) restores that property; filtering AWQ candidates with a warning would instead keep those searches runnable.
What I checked and found correct
enable_fake_quant (fix 1). Reads every original flag before writing any, so a quantizer reached twice via two linears sharing one weight_quantizer restores correctly; isinstance(q, TensorQuantizer) picks only leaf stages, since SequentialQuantizer and GroupedQuantizer both derive from _QuantizerContainerBase, not TensorQuantizer (tensor_quantizer.py:1969,2037) — so the container no longer gets a shadowing _fake_quant written onto it, which is a strict improvement for the grouped case too. The finally is right, and the ..nn import is correctly kept local.
No consumer is left reading the container. The two sites that gate compression on _fake_quant are safe for a sequential quantizer: base_qtensor.py:230 runs inside SequentialQuantizer.convert_to_single_quantizer(module), and quant_linear.py:258 lives in RealQuantParameterDict, installed only by RealQuantLinear._setup() — which fix 2 now makes unreachable for W4A8. The three fixes are consistent with each other.
Compress refusal (fix 2) is narrowly scoped. Walking module.weight_quantizer.modules() catches both the plain and the GroupedQuantizer-wrapped case, and it validates before the conversion loop mutates anything. Of the W4A8 presets only w4a8_awq_beta.yaml has a list-valued (i.e. sequential) *weight_quantizer cfg; w4a8_mxfp4_fp8.yaml and w4a8_nvfp4_fp8.yaml each use a single weight quantizer, so the PR's "only built-in preset with a sequential weight quantizer" claim holds and no working compress path regresses.
Guard uniformity.dist.size() and is_fsdp2_model are both local reads, and the mode-name derivation runs before any collective, so every rank raises identically with no risk of a rank-dependent collective. get_modelike_from_algo_cfg(None) resolves to the registered _no_calibrate, so a config without an "algorithm" key does not trip the assert. config.get("algorithm") matches what the existing tail of quantize() already does.
Tests. Each new test targets its own fix, and the test_fsdp2.py skip condition is sound: dist_workers' world size is torch.cuda.device_count() (tests/gpu/conftest.py:38), so device_count() > 1 is exactly the pool size the guard would reject. The new process-spawning CPU test uses the session-scoped cpu_dist_workers pool, which CLAUDE.md permits in tests/unit.
Risk: low-to-moderate. Fixes 1 and 2 are well-targeted and well-evidenced. The risk is concentrated in fix 3, whose blast radius reaches further than the mtq.quantize / mtq.calibrate entry points the PR description discusses.
AutoQuantize calibrates its candidates one by one through calibrate(),
so on an FSDP2 model sharded over several ranks the AWQ guard fired
partway through the search, after earlier candidates had changed the
model. auto_quantize now checks every candidate before the search
starts and names the AWQ one in the error; calibrate() keeps the check
as a backstop. The KV-cache search already rejects any algorithm but
max. The message reports the FSDP2 shard mesh's size, which can be
narrower than the run's.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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[IMPORTANT Compatibility] The guard itself is the right call — but the user-facing surface that steers people into exactly this combination is not updated, and the refusal lands late in an expensive job.
What. On more than one rank this now rejects every AWQ format on an FSDP2 model. examples/hf_ptq --use_fsdp2 is the documented multi-node PTQ path, and AWQ formats are first-class there: int4_awq, nvfp4_awq_lite and w4a8_awq_beta are all in _AUTO_QUANTIZE_QFORMATS (examples/hf_ptq/autoquant_utils.py:84-102), and the shipped recipes modelopt_recipes/general/auto_quantize/w4a8_awq_beta_fp8_at_6p0bits.yaml plus the modelopt_recipes/model_type/*/ptq/w4a8_awq-kv_fp8_cast.yaml set are AWQ. The README's FSDP2 section (examples/hf_ptq/README.md:609-650) tells users AutoQuantize recipes "use the same torchrun launch" and carries a note block listing the --use_fsdp2 restrictions (Nemotron-H MTP, --calib_with_images) — this new one is missing from it.
Why it matters. A user following that README with one of those shipped recipes on 2+ ranks now gets NotImplementedError where they previously got a checkpoint. Refusing is better than the silently mismatched shards, so the behaviour change is fine — the problem is that nothing in the examples or recipe docs says so, and the refusal fires after the distributed model load and calibration-data setup, i.e. minutes into a multi-node job on a large model rather than at argument parsing.
Suggested fix (docs + fail-fast, no change to this function):
Add the restriction to the --use_fsdp2 note block in examples/hf_ptq/README.md, next to the existing MTP / --calib_with_images notes.
Wire it into the existing fail-fast list in examples/hf_ptq/example_utils.py:95-124, which already raises --use_fsdp2 does not support: ... before distributed loading:
ifworld_size() >1andany("awq"infmtforfmtin_requested_qformats(args)):
issues.append("AWQ qformats on more than one rank (each rank folds its own smoothing scale)")
Alternatively, for auto_quantize specifically, dropping AWQ candidates from the search with a warning (rather than refusing the whole call) would keep the shipped AQ recipe runnable on multi-rank FSDP2 — your call, but then w4a8_awq_beta_fp8_at_6p0bits.yaml should say what it degrades to.
examples/hf_ptq/example_utils.py::validate_fsdp2_supported now lists "AWQ calibration (awq_lite) on more than one rank" when the process group has more than one rank and the run would calibrate with AWQ. That includes --qformat presets, a PTQ recipe's quantize, and an AutoQuantize recipe's candidate_formats and module_search_spaces. It mirrors the library's criterion (awq_lite / awq_full) and runs before the distributed load, so a multi-node job fails at startup. The library guard stays.
examples/hf_ptq/README.md: a note next to the MTP note in the --use_fsdp2 section says AWQ formats and recipes, AutoQuantize candidates included, are rejected on more than one rank before loading, and what to use instead.
AutoQuantize candidates are still refused rather than dropped, so w4a8_awq_beta_fp8_at_6p0bits fails loudly on several ranks instead of running a narrower search than the one asked for.
tests/examples/hf_ptq/test_example_utils.py::test_fsdp2_refuses_awq_on_several_ranks_before_loading covers a W4A8 qformat and the W4A8 AutoQuantize recipe on 2 ranks (refused), FP8 on 2 ranks and W4A8 on 1 rank (allowed). Both refused cases fail without the check.
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[SUGGESTION] This skip is wider than the guard it works around.
_test_nested_fsdp2_backward (lines 71-113) builds three models: model (quantized while unsharded), fsdp_model (a deepcopy of the already quantized model, then fully_sharded), and fsdp_model_quant_after (sharded first, then quantized). Only the third calibrates AWQ on a sharded model, so only it hits the new refusal — the fsdp_model path is exactly the quantize-then-shard order the PR now recommends and stays supported.
Skipping the whole parametrization therefore drops all multi-GPU AWQ + FSDP2 forward/backward/optimizer-step coverage, which is what this test exists for, for a case the PR does not make unsupported. Consider gating only the quant-after branch instead — e.g. compute quant_after = not (quant_cfg is mtq.INT4_AWQ_CFG and size > 1) inside _test_nested_fsdp2_backward and skip the fsdp_model_quant_after construction plus its two allclose asserts — so the out_ref vs out_test comparison keeps running on >1 GPU.
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Taken in 060836a. The whole-case skip is gone. _test_nested_fsdp2_backward now gates only the shard-then-quantize model: its construction, forward/backward and optimizer step, and its two allclose asserts, when the run has more than one rank and the config calibrates with AWQ. So the quantize-then-shard out_ref vs out_test comparison, step included, now runs for INT4 AWQ on several GPUs too. The gate reads the config's algorithm rather than quant_cfg is mtq.INT4_AWQ_CFG, since the config reaches the worker pickled. CPU dry run with gloo: all three configs pass on 1 and 2 ranks.
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[SUGGESTION] The reorder is necessary for the two AWQ entries in the parametrization (NVFP4_AWQ_LITE_CFG, the newly re-enabled W4A8_AWQ_BETA_CFG), but it is applied unconditionally to all five. FP8_DEFAULT_CFG, NVFP4_DEFAULT_CFG and W4A8_MXFP4_FP8_CFG calibrate fine on a sharded model and remain supported, and these two helpers were the main multi-GPU coverage of "calibrate after fully_shard" for them — what remains in tests/gpu/torch/quantization/test_fsdp2.py only covers INT8/INT4-blockwise.
Consider keeping the original shard-then-quantize order for the non-AWQ configs and only quantizing first when the config's algorithm is AWQ, e.g.:
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Taken in 060836a, in both _fuse_layers and _export_quantized_weight_test. They shard before calibrating again, except where the guard refuses: an AWQ config on more than one rank (size > 1 and "awq" in str(quant_config.get("algorithm"))). That one quantizes first and shards after. So FP8, NVFP4, W4A8-MXFP4 and the other non-AWQ configs keep calibrating on the sharded model on any GPU count, and the AWQ ones do too on one GPU.
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Claude review (round 4) — 1 IMPORTANT, 2 SUGGESTION
Scope. The trigger comment carried no scoping instructions, so this is a full review. 10 files / +184 −25; I reviewed all four source files (compress.py, model_quant.py, utils/core_utils.py, CHANGELOG.rst) and all six test files. I pulled the diff with gh pr diff rather than a local two-dot diff, since this checkout's base tip has moved past the PR's base.
Round-3 findings are resolved
IMPORTANT (AWQ guard fired mid-search inside AutoQuantize) — fixed. The check is now hoisted over all_processed_formats at auto_quantize entry (model_quant.py:1019-1029), which runs after the _AUTO_QUANTIZE_SUPPORTED_ALGORITHMS validation but before the searcher is constructed or apply_mode touches the model, so the "refuses before the model changes" property the docstring and CHANGELOG promise now holds for AutoQuantize too. module_search_spaces and fixed_quantization_config candidates are in that list, so no candidate slips past.
SUGGESTION (message reported global world size as the shard width) — applied, via _get_fsdp2_mesh(...).size(-1) with a dist.size() fallback. Keeping global dist.size() in the condition while reporting the mesh width in the message is the right asymmetry: a 1-wide shard mesh under HSDP still has replicas whose folded scales would diverge, so refusing on world size is the conservative side.
What I verified this round
The premise holds.awq_lite averages only act_scale across the DP group (model_calib.py:1812-1816); the per-alpha loss is accumulated rank-locally (update_loss, 1713-1719) and best_alpha/best_scale are picked from that local loss (1724-1731) with no reduction. The divergence the PR measured is therefore not just a missing ParallelState on FSDP2 models — it would persist even with a DP group wired up. Refusing rather than papering over it is correct, and awq_clip's per-block clipping is rightly left out of the refused set.
Every rank refuses alike.dist.size(), is_fsdp2_model and the mode-name derivation are all local reads, and _get_fsdp2_mesh only inspects FSDP state — no collective on either side of the branch, so no rank-dependent-collective hazard. is_fsdp2_model is itself any(isinstance(m, FSDPModule) ...), so the next(...) in the message path cannot raise StopIteration. get_modelike_from_algo_cfg(None) resolves to _no_calibrate, and the top-level from torch.distributed.fsdp import FSDPModule matches what utils/distributed.py, core_utils.py and the export path already do. config.get("algorithm") mirrors the existing tail of quantize().
The KV-cache AutoQuantize lifecycle is unaffected and does not need the guard._auto_quantize_kv_cache returns early at model_quant.py:911-917, bypassing the new entry loop — but kv_cache_auto_quant.py:204-209 already rejects any candidate whose algorithm is not None or "max", so an AWQ candidate can never reach the mid-search calibrate() there. No gap.
enable_fake_quant reads every original flag before writing any (correct for a quantizer reached twice through two parents, which nn.Module.modules() dedups within one walk but not across two), isinstance(q, TensorQuantizer) selects only leaf stages so no shadowing _fake_quant is written onto a container, the finally matches the sibling context managers in the same file, and the ..nn import is correctly kept local. Both call sites pass a single FSDP unit or leaf module, so the walk stays O(layer).
The compress refusal validates before the conversion loop and before RealQuantModuleRegistry.register, so a refusal leaves both the model and the global registry untouched; apply_mode adds the mode only after convert returns, so no real_quantize entry lands in modelopt_state. Walking module.weight_quantizer.modules() covers the plain and GroupedQuantizer-wrapped layouts in one predicate.
No modelopt_state schema, signature, config-field or public-API change. A plain TensorQuantizer is its own only stage, so non-sequential formats behave exactly as before.
Findings
#
Severity
Where
Issue
1
IMPORTANT Compatibility
model_quant.py:92
The refusal is right, but examples/hf_ptq's README and its existing --use_fsdp2 fail-fast list still steer users into multi-rank FSDP2 + AWQ — including the shipped general/auto_quantize/w4a8_awq_beta_fp8_at_6p0bits.yaml recipe — and the error now arrives minutes into a multi-node job rather than at argument parsing
2
SUGGESTION
tests/gpu/.../test_fsdp2.py:140-141
The skip is wider than the guard: of the three models _test_nested_fsdp2_backward builds, only fsdp_model_quant_after calibrates after sharding, so all multi-GPU AWQ + FSDP2 backward coverage is dropped for a case that stays supported
3
SUGGESTION
tests/gpu/.../test_fsdp2_export.py:180,217
The quantize-then-shard reorder is applied to all five configs, removing the main multi-GPU "calibrate after fully_shard" coverage for the three non-AWQ ones
Most impactful: finding 1 — and it is documentation/plumbing rather than logic; the guard itself stays as written. int4_awq, nvfp4_awq_lite and w4a8_awq_beta are all in examples/hf_ptq/autoquant_utils.py's _AUTO_QUANTIZE_QFORMATS; the w4a8_awq_beta_fp8_at_6p0bits AutoQuantize recipe and the model_type/*/ptq/w4a8_awq-kv_fp8_cast.yaml set ship in-tree; and the README's "Multi-Node Post-Training Quantization with FSDP2" section points users at AutoQuantize recipes over the same torchrun launch while listing only the MTP and --calib_with_images restrictions. examples/hf_ptq/example_utils.py:95-124 already has the mechanism to reject an unsupported --use_fsdp2 combination before the distributed load; adding this one there, plus a README note, closes it.
Tests
Proportionate, and each targets its own fix. test_awq_on_a_sharded_fsdp2_model_is_refused covers all three entry points on two gloo ranks, asserts the model is left unquantized, and runs under bounded_collectives() so a rank-divergent collective fails fast instead of hanging CI — exactly the right shape for a guard that must be rank-uniform. The W4A8 export test now shards before calibrating, matching what hf_ptq --use_fsdp2 does on one GPU, and the new hidden_size default keeps the existing callers byte-identical. The two GPU-test adaptations are what findings 2 and 3 are about: both are coverage trades, not defects.
Risk
Low for the three code changes.enable_fake_quant and the compress refusal are tightly scoped and well evidenced (GPU runs, CPU dry runs at 1/2/4 ranks, a real Qwen3-8B run, and each test checked by reverting its fix). The residual risk is in fix 3's blast radius on the example and recipe surface, which finding 1 covers.
hf_ptq --use_fsdp2 on more than one rank with an AWQ format or recipe,
AutoQuantize candidates included, now fails at argument validation,
before the distributed load, instead of at calibration minutes later.
The check mirrors the library's: awq_lite or awq_full. The README's
--use_fsdp2 notes say so.
The two GPU tests adapted for the refusal now change only what it
refuses, AWQ on more than one rank: test_fsdp2_export.py shards before
calibrating again for every other case, and test_nested_fsdp2_backward
runs its quantize-then-shard comparison on several GPUs, gating only
the shard-then-quantize model.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
> *Nemotron-H checkpoints containing MTP weights are not supported with `--use_fsdp2` and are rejected before distributed model loading. Remove `--use_fsdp2` to load and calibrate the MTP modules; the distributed loader does not construct them.*
> *AWQ calibration (`awq_lite`, `awq_full`), as the AWQ formats and recipes use, AutoQuantize candidates included, is not supported with `--use_fsdp2` on more than one rank and is rejected before distributed model loading: each rank would fold its own smoothing scale into its weight shard. Run it on one GPU, or use a non-AWQ format.*
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[SUGGESTION] The remedy this note (and the matching error message in example_utils.py:143) offers omits the one that keeps the user's GPUs: dropping --use_fsdp2 and letting get_model spread the model over the same GPUs with a single-process device_map. AWQ calibration works fine there — the refusal is specific to FSDP2 sharding over several ranks, not to multi-GPU — but "Run it on one GPU, or use a non-AWQ format" reads as a hardware restriction and will push users to halve their capacity or change format when neither is necessary.
Same wording in the error path: "(use one GPU, or a non-AWQ format)". The enclosing message does end with "Remove --use_fsdp2 ... ", so a careful reader can piece it together, but the parenthetical actively points the other way.
Suggested note text:
> *AWQ calibration (`awq_lite`, `awq_full`) — as the AWQ formats and recipes use, AutoQuantize candidates included — is not supported with `--use_fsdp2` on more than one rank, and is rejected before distributed model loading: each rank would fold its own smoothing scale into its weight shard. Drop `--use_fsdp2` (single-process `device_map` still spans all the GPUs), run on one rank, or use a non-AWQ format.*
and, for the issue line, (drop --use_fsdp2, run on one rank, or use a non-AWQ format).
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Taken in 75db98e. The README note now ends "Drop --use_fsdp2 (the single-process device_map still spreads the model over all the GPUs), run on one rank, or use a non-AWQ format." The example_utils.py issue line now ends "(drop --use_fsdp2, run on one rank, or use a non-AWQ format)".
- Fix QAT of Hugging Face fused MoE experts with non-reentrant activation checkpointing, as in the ``examples/llm_qat`` FSDP2 config: the experts' ``F.linear`` interception stayed installed after the recompute, and training crashed with ``aten.mm.default got mixed torch.Tensor and DTensor``.
- Fix quantization-aware training of Diffusers models failing in the backward pass with ``NotImplementedError`` once their attention modules are quantized, with or without MHA quantization.
- Fix weight calibration, GPTQ and weight folding of Hugging Face fused MoE experts sharded by the expert parallelism of Transformers 5.16 and later, which used another rank's expert weights on every rank but the first.
- ``export_hf_checkpoint`` of an FSDP2 model quantized with W4A8 AWQ (``W4A8_AWQ_BETA_CFG``) no longer fails with ``AttributeError: 'SequentialQuantizer' object has no attribute '_fake_quant'``; AWQ calibration of an FSDP2 model sharded over more than one rank, whose ranks would fold different smoothing scales into their weight shards, is rejected with ``NotImplementedError`` until it is supported, as is ``mtq.auto_quantize`` with an AWQ candidate, such as its default ``NVFP4_AWQ_LITE_CFG``, before the search starts. ``mtq.compress`` now refuses W4A8 weights with ``NotImplementedError``, since a compressed W4A8 model could neither run nor be reloaded.
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[SUGGESTION] This one bullet carries three independent user-facing changes in three long sentences, and includes the raw internal traceback string. CLAUDE.md asks for one or two sentences per entry, written for external users, with root cause and implementation detail left to the PR description. Splitting it also makes each change findable in the release notes — a user hitting the compress refusal will not search for export_hf_checkpoint.
- Fix the Hugging Face export of an FSDP2 model quantized with W4A8 AWQ (``W4A8_AWQ_BETA_CFG``), which failed on every rank.
- AWQ calibration of an FSDP2 model sharded over more than one rank is now rejected with ``NotImplementedError``, in ``mtq.quantize``, ``mtq.calibrate`` and ``mtq.auto_quantize`` (which checks every candidate before the search starts): each rank would fold a different smoothing scale into its weight shard. Calibrate in a single process, or use a non-AWQ algorithm.
- ``mtq.compress`` now rejects W4A8 weights with ``NotImplementedError``; a compressed W4A8 model could neither run nor be reloaded.
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Taken in 75db98e. The bullet is now three Bug Fixes entries:
the FSDP2 W4A8 export fix, which names the exception type without the raw message;
the multi-rank FSDP2 AWQ refusal, in mtq.quantize, mtq.calibrate and mtq.auto_quantize (before its search starts, default formats included) and in examples/hf_ptq --use_fsdp2 before loading, with what to do instead;
Scope. The trigger comment carried no scoping instructions, so this is a full review. 13 files / +263 −35; I reviewed all four source files (compress.py, model_quant.py, utils/core_utils.py, examples/hf_ptq/example_utils.py), the README and CHANGELOG, and all seven test files. I pulled the diff with gh pr diff — this checkout local base tip has moved well past the PR base, so a two-dot git diff origin/main HEAD shows unrelated reverse churn (distributed_writer.py, plugins/megatron.py, plugins/transformer_engine.py, moe.py, the apply_without_qdq removal, the gc.freeze removal in opt/plugins/transformers.py) that is not part of this PR. None of that is reviewed here.
Round-4 findings are resolved
IMPORTANT (the example surface still steered users into multi-rank FSDP2 + AWQ) — fixed, and fixed in the right place: validate_fsdp2_supported already rejects unsupported --use_fsdp2 combinations before parallel_load_and_prepare_fsdp2 runs, so the refusal now lands at argument validation rather than minutes into a multi-node job. _awq_calibrations derives the algorithm the run will actually use rather than pattern-matching the format name, which is what makes it track the library guard: it resolves through the same get_modelike_from_algo_cfg and intersects the same {awq_lite, awq_full} set, so awq_clip (INT4_AWQ_CLIP_CFG) is correctly not refused on either side, and the two guards cannot drift on which algorithms count.
SUGGESTION 2 (test_fsdp2.py skip wider than the guard) — fixed. _test_nested_fsdp2_backward no longer skips; it drops only the fsdp_model_quant_after leg, so the quantize-then-shard and reference legs keep their multi-GPU AWQ backward coverage.
SUGGESTION 3 (test_fsdp2_export.py reorder applied to all five configs) — fixed. The reorder is now gated on awq_on_ranks = size > 1 and "awq" in str(quant_config.get("algorithm")), so the three non-AWQ configs still calibrate afterfully_shard on several ranks, which was the coverage at risk.
What I verified this round
The recipe walk covers every candidate the library guard would see.ModelOptAutoQuantizeRecipe exposes exactly three places an AWQ algorithm can hide — quantize (the fixed PTQ baseline, which auto_quantize processes as fixed_quantization_config), auto_quantize.candidate_formats, and auto_quantize.module_search_spaces[].candidate_formats — and _awq_calibrations reads all three. That is the same set as all_processed_formats in model_quant.py:1010-1018, so nothing the entry check would reject slips past the example check. kv_auto_quantize is correctly left out: kv_cache_auto_quant.py already rejects any candidate whose algorithm is not None or "max", so an AWQ candidate can never reach it.
The --qformat branch is sufficient, because AutoQuantize in examples/hf_ptq is recipe-driven only — there is no --auto_quantize_bits flag left in the example, so the stale concern from round 3 about an AWQ qformat list does not apply. QUANT_CFG_CHOICES[qformat] is the same dict hf_ptq.py:1101 hands to mtq.quantize, and preset.get("algorithm") reads it at the level quantize() itself reads with config.get("algorithm").
No crash paths in the new helper. Every args access is a getattr with a default, so the pre-existing test_fsdp2_qwen_vlm_requires_text_only_calibration namespace (no qformat, no recipe) still works; QUANT_CFG_CHOICES.get(... or "") makes an unset or unknown qformat a no-op rather than a KeyError (and hf_ptq.py:1098 still catches the typo later); every calibrate algorithm reachable from a preset or recipe is registered in mode.py at import, not by a plugin, so get_modelike_from_algo_cfg(..., check=True) cannot assert on a valid config; and algorithm as None, a string, a dict, a list or a QuantizeAlgorithmConfig are all handled by that helper. load_recipe(args.recipe) with no overrides matches what hf_ptq.py:942 and autoquant_utils.py:392 already do.
The guard does not refuse anything that works today.validate_fsdp2_supported is only reached under --use_fsdp2, where the model is always FSDP2-sharded, so dist_utils.size() > 1 is the same condition the library guard uses — the example check is strictly an earlier copy, never a wider one.
The refused set is principled, not arbitrary. I checked the obvious sibling: smoothquant folds a shared per-input-channel pre_quant_scale into the weight too, but its act_amax is DP-synced by max_calibrate and its weight_scale = module.weight.abs().amax(dim=0) reduces across the sharded dim through DTensor, so it does not have the AWQ divergence. That divergence does not come from weight statistics — awq_lite averages only act_scale across DP (model_calib.py:1812-1816) while update_loss accumulates the per-alpha loss rank-locally and best_alpha/best_scale are picked from that local loss with no reduction. Refusing exactly awq_lite/awq_full is the right cut.
enable_fake_quant reads every original flag before writing any (correct for a quantizer reached twice through two parents, which nn.Module.modules() dedups within one walk but not across two), isinstance(q, TensorQuantizer) selects only leaf stages so no shadowing _fake_quant is written onto a SequentialQuantizer or GroupedQuantizer container, the finally matches the sibling context managers in the same file, and the ..nn import is correctly kept local. Both call sites pass a single FSDP unit or leaf module, so the walk stays O(layer).
The compress refusal validates before the conversion loop and before RealQuantModuleRegistry.register, so a refusal leaves both the model and the global registry untouched, and apply_mode adds the mode only after convert returns, so no real_quantize entry lands in modelopt_state. is_quantized_linear requires hasattr(module, "weight_quantizer"), so the .modules() walk cannot AttributeError on the modules it reaches.
Rank uniformity.dist.size(), dist.is_fsdp2_model, the mode-name derivation and _get_fsdp2_mesh are all local reads with no collective on either side of the branch, so every rank raises identically — and the new CPU test runs under bounded_collectives(), which fails fast rather than hanging CI if that ever regresses. is_fsdp2_model is itself any(isinstance(m, FSDPModule) ...), so the next(...) in the message path cannot raise StopIteration. The top-level from torch.distributed.fsdp import FSDPModule matches seven other modules in modelopt/torch/.
No modelopt_state schema, signature, config-field or public-API change. A plain TensorQuantizer is its own only stage, so non-sequential formats behave exactly as before.
Findings (both non-blocking)
#
Severity
Where
Issue
1
SUGGESTION
README.md:617, example_utils.py:143
The remedy text — "Run it on one GPU, or use a non-AWQ format" — omits the one that keeps the user GPUs: dropping --use_fsdp2 and letting a single-process device_map span the same devices, where AWQ calibration still works
2
SUGGESTION
CHANGELOG.rst:101
One bullet carries three independent user-facing changes in three long sentences, including the raw internal traceback string; CLAUDE.md asks for one or two sentences without implementation detail
Neither touches logic. The guard, the refusal and the enable_fake_quant rewrite all stand as written.
Tests
Proportionate, and each targets its own fix. The new test_fsdp2_refuses_awq_on_several_ranks_before_loading is the right shape for the new helper: it parametrizes both input routes (preset and the in-tree general/auto_quantize/w4a8_awq_beta_fp8_at_6p0bits recipe) and pins both negatives — a non-AWQ format on two ranks, and AWQ on one rank — so a guard that over-refused would fail it. nullcontext is already imported in that file, and the refused=False cases fall through to checkpoint_has_mtp exactly as the sibling test above them already does. test_awq_on_a_sharded_fsdp2_model_is_refused covers all three library entry points on the fixed two-rank cpu_dist_workers pool (so the "sharded over {size} ranks" match is stable), asserts the model is left unquantized, and leaves no global state behind as that fixture requires.
The evidence trail is unusually complete for a change of this size: GPU runs of the four re-enabled W4A8 cases with and without the fix, CPU dry runs at 1/2/4 gloo ranks, a real Qwen3-8B hf_ptq --use_fsdp2 run on 1 and 2 GPUs before and after, and each new test checked by reverting only its fix.
Risk: low. Four tightly-scoped changes: a crash fix, a refusal on a path that could not work, a refusal on a path that silently produced mismatched shards, and an earlier copy of that last refusal in the example. The only behavioural change for existing users is a loud NotImplementedError where the previous outcome was a crash, an unloadable checkpoint, or a quietly wrong one.
The one Bug Fixes bullet carried three independent changes; it is now
three entries: the FSDP2 W4A8 export fix, the multi-rank FSDP2 AWQ
refusal, and the compress refusal. The hf_ptq README note and error
message now offer dropping --use_fsdp2 first, as the single-process
device_map still spreads the model over all the GPUs, then one rank or
a non-AWQ format.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
…2a instead of forcing eager (#3074)
> [!NOTE]
> **[2/2]**: #3122 ([1/2]) is this PR's expert-parallel correctness fix,
split out on 2026-10-29 at a reviewer's request so that it lands on its
own, with a Bug Fixes changelog entry. This PR keeps the two
backward-breaking changes, calibration on `grouped_mm` by default and
the new `calibrate()` error, and sat on #3122 until #3122 merged on
2026-10-30. Before that, it was [3/3] of the OMNIML-6073 split, after
#3088 (FSDP2's weight-access window) and #2745 (`grouped_mm`
quantization), both merged. #3067 (Nemotron-H) sits on this PR.
>
> **Base:** `main`. #3122 merged on 2026-10-30, so this PR sits on main,
its own diff unchanged.
>
> **In the tree:** #2741's helper base merges #3067, which carries this
PR and #2745.
<details><summary>Series layout</summary>
- `main`
- #3088 FSDP2 weight-access window (merged)
- #2745 fused experts under grouped_mm (merged)
- #3122 expert-parallel experts calibrated and folded on each rank's own
experts (merged)
- **#3074 calibration on grouped_mm (this PR)**
- #3067 Nemotron-H's experts under grouped_mm
- #2739 meta-DCP checkpoint load (merged)
- #2736 distribution plan, run group, collectives (merged)
- #2740 calibration on per-rank data
- #2750 keep-fused fold
- #2751 distributed-writer pieces
- #2752 no-gather export (on #2751, #2750, #2779 and #2740)
- #2753 expert-parallel export
- #2737 plan decision
- #2779 load routing
- #2741 expert-parallel loader (on #2779, #3067 and #2740)
- #2742 dense sharding
- #2743 block-FP8 dequantization
- #2780 expert-parallel load routing
- #2746 expert parallelism in the model specs
- #2747 on-the-fly decompression
- #2748 Kimi-K2.5 and Kimi-VL adapters
- #2749 Kimi-K3 adapter
- #2754 expert-parallel PTQ from hf_ptq.py (on #2746 and #2753)
</details>
### What does this PR do?
Type of change: New feature, with a backward-incompatible change to MoE
calibration (see the changelog entry)
#2745 quantizes the `grouped_mm` experts forward, transformers' default,
while calibration still runs every model on eager. This PR moves
calibration onto `grouped_mm` and the `torch_a2a` expert-parallel
forward, and makes everything that reads the experts' inputs or weights
during calibration work there. Each gap below otherwise failed silently,
by collecting no amax or the wrong one rather than by raising.
- **Backend normalization.** `normalize_experts_impl_on_the_fly`
replaces `force_eager_experts_impl_on_the_fly`. `eager`, `grouped_mm`
and `torch_a2a` are kept; a backend nothing intercepts (`batched_mm`,
the opaque `sonicmoe` kernel, hub kernels) is moved to `grouped_mm`
before calibration, or to `eager` on a transformers without
`_grouped_linear`. A model whose experts quantize only on the eager
forward keeps main's forced eager: native FP8 experts, a class with its
own wrapper (GPT-OSS's quantizes `down_proj`'s input by intercepting the
eager forward's matmuls), and Nemotron-H's non-gated experts until
#3067. Such a model is refused on `torch_a2a`, before any config
changes, instead of calibrating on raw FP8 bytes or not at all.
- **Per-expert captures.** `grouped_mm` and `torch_a2a` feed a
projection's input quantizer one expert-sorted batch. The weight-input
captures (local Hessian, imatrix, GPTQ) split it back per expert by the
cumulative token offsets the grouped interception records, following
whichever path ran. The input quantizers calibrate on the rows the
grouped GEMM computes only: under transformers' own expert parallelism,
the rows routed to other ranks' experts are sorted past the offsets, and
before transformers 5.17 the down projection's input holds them
uninitialized.
- **Expert parallelism.** #3122 makes everything that reads the expert
weights outside the forward index each rank's own experts: max
calibration's weight pass, `fold_weight`, the weight-input captures and
GPTQ. Here the grouped captures split the expert-sorted batch per local
expert. The grouped weight quantization needs no indexing: transformers
swaps expert-parallel DTensors for their local shards around the experts
forward.
- **Fail loudly.** `calibrate()` raises, naming the modules, when a
fused-experts forward that had to reach a quantizer ran but neither hook
fired: a hand-written `bmm` forward on a class without
`@use_experts_implementation`, which no config change redirects. Each
`calibrate()` judges its own forwards and raises with the model back in
its training mode. Under the expert parallelism of transformers 5.16 and
later a module's verdict is reduced over its experts' expert-parallel
group, so every rank of the group raises, not just the ranks whose
experts saw rows. Not reported: experts no calibration token reached
(Ernie-4.5-VL's vision experts under text-only calibration, which fall
back to weight-derived scales as on main), an eager forward whose rows
were all routed to other ranks' experts, and weight-only experts under
max or MSE, whose weight quantizers calibrate on the weights alone.
**Behavior change.** main forced MoE calibration onto eager;
transformers resolves an unrequested backend to `grouped_mm`, so
fused-experts models now calibrate there. A single-GPU Qwen3-30B-A3B
export, NVFP4 or FP8, matches main in every weight and weight scale, but
4,647 activation scales move, by up to 10.2%. main's own repeat is
byte-identical, and loading with `experts_implementation="eager"`
reproduces main's checkpoint byte for byte (all 74,835 tensors for NVFP4
and 56,211 for FP8), so the backend alone moves them. The changelog says
so.
| Moved activation scales, Qwen3-30B-A3B NVFP4, 1 GPU | Tensors |
|---|---|
| Experts' `down_proj` input (33 layers, 128 experts each) | 4,224 |
| Experts' shared `gate_proj` and `up_proj` input (1 layer) | 256 |
| Attention input and KV-cache | 167 |
| Total, in 45 of 48 layers (none in layers 0, 1 and 5) | 4,647 |
The largest moves are 10.2% on the experts (layer 25's `down_proj`
input) and 7.8% on attention.
*Why they move.* Both forwards feed an input quantizer the same rows: a
single GPU has no padding rows (transformers' sentinel rows exist only
under its own expert parallelism, where calibration skips them;
transformers zeroes them only from 5.17), the routing weights apply
after `down_proj` on both, and neither casts. They round the block's
output differently. Eager adds a token's 8 expert outputs into a bf16
buffer with `index_add_`, rounding after each add, while
`grouped_mm_experts_forward` sums them in fp32 and rounds once, and its
GEMMs run other kernels. From the first MoE block's output on, the
residual stream differs by rounding, and where a router's 8th and 9th
scores nearly tie, the routing flips. A flipped token feeds `down_proj`
another expert's activations, and `down_proj`'s input scale is a max
over routed (token, expert) rows, so it moves by more than rounding
would. The moved scales fit that: the experts' shared
`gate_proj`/`up_proj` input, whose rows are every token however it is
routed, moved in one layer only, against 33 for `down_proj`.
*Why `grouped_mm` off expert parallelism too,* rather than eager there
and `grouped_mm` on `torch_a2a` only: it is the forward transformers
runs the model on when nothing else is asked for, its fp32 sum is the
more precise of the two combines, and expert-parallel calibration runs
the grouped path anyway (`torch_a2a` runs the local experts through
`_grouped_linear`), so a model calibrates on one forward whether or not
it is sharded. `experts_implementation="eager"` keeps earlier releases'
checkpoints.
### Usage
```python
# To calibrate exactly as earlier releases did:
model = AutoModelForCausalLM.from_pretrained(path, experts_implementation="eager")
```
```bash
# The same from hf_ptq.py:
python hf_ptq.py --pyt_ckpt_path <model> --qformat nvfp4 --experts_implementation eager # or grouped_mm
```
### Testing
- `tests/unit/torch/quantization/plugins/test_fused_experts.py`, against
synthetic and real transformers experts modules:
- backend normalization, including the fallback to eager on a
transformers without `_grouped_linear`;
- GPTQ's Hessians and local Hessian's captures under `grouped_mm` and
`torch_a2a` match eager's per expert, and a capture follows whichever
path ran;
- the interception check raises when the forward ran past both hooks,
also after an earlier calibration reached them, and not for unreached
experts, an eager forward routed only to other ranks' experts, or
weight-only experts beside a calibrated dense input quantizer under max
(each expert's weight amax equals its weight's per-channel abs max).
Under local Hessian and GPTQ, which read the experts' inputs, those
weight-only experts still raise;
- GPT-OSS, a real tiny model, calibrating `down_proj`'s input on the
eager forward.
- Expert parallelism, on CPU under gloo:
- the tests that experts sharded the way transformers shards them
calibrate, fold, capture and build GPTQ helpers on each rank's own
experts moved to #3122 with that fix;
- every rank of the experts' expert-parallel group raises when only one
rank's experts ran past both hooks;
- with fake quantizers inserted, experts split over two ranks train
through the `torch_a2a` forward like the whole quantized module: the
fake-quantized case of `test_moe_a2a.py`'s whole-module test;
- the transport's hop slicing under a graph: `test_moe_a2a.py`'s
chunked-hops test also runs forward and backward through hops sliced
below the limit, and checks the output and input gradients bit for bit
against the unsliced run.
- The transport's (#2738, merged) docs say again that ModelOpt quantizes
its local experts, now that the `_grouped_linear` override does. Its
hop-slicing comments say what the slicing guarantees, every hop within
`all_to_all_single`'s int32 element offsets, rather than that it
prevents a hang: with the slicing disabled, hops past 2**31 elements
completed exactly on NCCL 2.29, 2.30 and 2.31, including at 32 ranks
over the network on 2.29 and 2.30.
- Against main, on tiny real models: GPT-OSS calibrates on eager with
`down_proj`'s input amax equal to main's; Ernie-4.5-VL-MoE quantizes in
FP8 and NVFP4 under text-only calibration, as on main.
- Byte-for-byte checkpoint comparison against main on one GPU, measured
on earlier revisions of #2745: Qwen3-30B-A3B, eager and `grouped_mm`,
NVFP4 and FP8; Qwen2.5-0.5B (dense) is byte-identical. On GPU,
Nemotron-3-Nano-30B-A3B's NVFP4 calibration with its experts pinned to
eager and #3067's with `experts_implementation="eager"` give
byte-identical exports, all 18,012 tensors.
- The GPU tests of QAT under expert parallelism are in #2742, the first
PR with an FSDP2-sharded dense part they also run.
- Review fixes, 2026-10-27, carried from #2745: the FSDP2 window's
ownership test (unnamed meshes, every shard state); an empty experts
forward is no work the interception check expects; the non-gated experts
are pinned to eager whichever plugin runs first.
- Review fixes, 2026-10-27, last round, each with a test that fails
without it:
- `grouped_mm` is kept, and redirected to, only where `_grouped_linear`
can be intercepted; one capability constant gates the interception, the
supported set and the redirect target;
- an eager experts forward counts as work for the interception check
only when it routes a row to this rank's experts, so a rank whose
experts calibration routed nothing to no longer raises alone;
- an enabled expert weight quantizer needs the interception only when
the calibration captures the experts' inputs to calibrate their weights
(GPTQ, local Hessian, imatrix), so weight-only experts under max or MSE
no longer raise; the message names what falls back.
- Split from #2745 on 2026-10-27, night: #2745's and this PR's commits
together give #2745's previous tree exactly, plus this round's fixes.
Pre-commit, mypy included, and this PR's unit tests pass at this head on
transformers 5.18 and 5.5.4 and on torch 2.8, and the series' PRs merged
together add no unit-test failure over main f03651e
(tests/unit/torch utils, export, quantization and models). This
revision's GPU tests run in CI only.
- Review suggestions, 2026-10-28: the weight-input captures (local
Hessian, imatrix, GPTQ) read the path that ran, not the one the config
named when they were registered, so they follow whichever forward runs
and the refusal is gone, pinned by a test that fails on the code before;
the interception check's forward count, and the host sync its routing
test costs, stop after the first hook call of each calibration; the
check's docstring says it is rank-local. Rebased over #2745's fixes,
taking back the `torch_a2a` test fixtures #2745 no longer carries.
- Rebased over #2745's second-round fixes: the grouped interceptor
records the offsets and clears the eager expert index, and #2745's test
that captures skip the grouped batch gives way to this PR's per-expert
capture tests.
- Review follow-up, 2026-10-28, third round: drops the note #2745 now
adds, that the grouped captures skip their batch, since here they split
it per expert. A new test pins that a calibration forward stops
counting, and skips its host-syncing routing test, once a hook has
fired; it fails with that gate removed.
- Review suggestions, 2026-10-28, fourth round: the experts forward
clears `_grouped_offs` when it returns, so a capture reached outside a
forward skips instead of splitting its input by a finished forward's
offsets; the interception check's docstring says that a rank whose
experts saw no rows waits in its next collective until the launcher
stops it or the collective times out, so the error is in another rank's
log. Rebased over #2745's cleanup.
- Rebased on 2026-10-28, evening, onto #2745's head `e7c449fcf87`, after
#2745 moved onto main `7092db6ac5b` and took its last review rounds.
Conflicts only in the changelog (both sides' entries kept),
`_QuantFusedExperts`'s docstring (it keeps #2745's note that the
`grouped_mm` fallback does not call `F.linear`) and the fused-experts
tests (#2745's transposed-container tests kept). The FP8 experts
wrapper's comment from #2745 now names
`normalize_experts_impl_on_the_fly`, this PR's name for the function
that keeps it on eager. pre-commit, mypy included, passes at this head
against main.
- Review fix, 2026-10-28, evening: `calibrate()`'s interception check
reads the HF plugin from `sys.modules` instead of importing it, so a
plugin that failed to load (which `import_plugin` only warns about) no
longer fails every calibration with its error.
`test_a_plugin_that_failed_to_load_leaves_calibration_alone` breaks the
plugin's import and fails without the change. pre-commit, mypy included,
passes at this head.
- Split, 2026-10-28, evening, at the bot review's request: the FSDP2
window's ownership filter and its ownership test moved to #3088, with a
test that the window leaves an ignored expert-parallel DTensor sharded.
This PR sits on a helper base merging #2745 and #3088, and its source
diff is 334 lines. At this head `test_fused_experts.py`,
`test_moe_a2a.py` and `test_utils.py` pass on CPU (200 passed, 6
skipped), and pre-commit, mypy included, passes against main.
- Review suggestions, 2026-10-28, evening: `_local_experts` refuses a
DTensor whose local shard does not hold the module's experts (one
replicated over the ranks, say) instead of indexing the global experts,
and the native FP8 wrapper's `unpack_weight` refuses expert weights
sharded over the experts instead of filling a buffer for every expert.
One two-rank test covers both and fails without either. The suggested
all-reduce in the interception check is declined (a collective in every
distributed `calibrate()`). Focused tests pass (201 passed, 6 skipped),
and pre-commit passes against main.
- Review suggestions, 2026-10-28, late evening: the per-expert split of
a grouped batch requires exactly one exclusive end per expert, within
the rows, as transformers' `grouped_mm` and `a2a_moe` build the offsets,
and raises on any other form (a leading zero would credit each expert
with the previous one's rows). A test feeds three such forms and fails
without the check. The `_grouped_offs` comment names all three captures
and the attribute's role as the which-forward-ran signal. Rebased onto
the helper base re-merged with #3088's update; focused tests pass (204
passed, 6 skipped), and pre-commit passes against main.
- Review suggestion, 2026-10-28, late evening: `_local_experts` checks
for a DTensor through the plugin's guarded `torch.distributed.tensor`
import, so a torch without distributed does not fail every fused-MoE
call. Rebased onto the helper base re-merged with #3088's update;
focused tests pass (204 passed, 6 skipped), and pre-commit passes
against main.
- 2026-10-28, night: `hf_ptq.py` gains `--experts_implementation`,
passed on every load path: `get_model` (the compressed-tensors branch
included), the low-memory load, and the FSDP2 load, through the config
its meta model is built from. The changelog's way back to earlier
releases' scales is now reachable from the script. A test loads a tiny
Qwen3-MoE with `eager` and fails without the plumbing. The
`tests/examples/hf_ptq` unit tests pass (110), and pre-commit passes
against main.
- Review fixes, 2026-10-28, night: on the FSDP2 path the flag is set on
the pre-fetched config's `_experts_implementation`, as
`build_meta_causal_lm` sets `attn_implementation`, and a test captures
the config the FSDP2 loader receives (it fails without the assignment);
`get_model` passes the flag only when given, on every branch.
`calibrate()`'s new error for a fused-experts forward that reaches
neither hook has its own changelog entry. Rebased onto #2745, itself
rebased onto main `bc0b6e03237` after #3088 merged; focused and
`tests/examples/hf_ptq` tests pass (316 passed, 6 skipped), and
pre-commit passes against main.
- Review suggestions and a rebase, 2026-10-28, late night: the backend
normalization warns once for each change it makes to a caller's choice,
but not for a `None` backend pinned to eager, which already dispatches
to the eager forward. Rebased onto #2745 on main `e9f432affbb` (#3091):
`DTensor` now comes from the plugin's plain `torch.distributed.tensor`
import, since the guarded import is gone with the torch 2.10 floor.
Focused and `tests/examples/hf_ptq` tests pass (325 passed, 6 skipped),
and pre-commit passes against main.
- Review suggestion and a rebase, 2026-10-28, late night: the
backend-redirect notice goes through `warn_rank_0`, so an
expert-parallel run prints one copy. Rebased onto #2745's comment and
warning-text update; `_is_fused_experts_module`'s new docstring names
`normalize_experts_impl_on_the_fly`, which keeps GPT-OSS on eager here.
`test_fused_experts.py` and `test_utils.py` pass (197), and pre-commit
passes against main.
- Bot review of `2e253a509d0`, 2026-10-28, late night: the FSDP2 test
runs a Qwen3.5-MoE config too and checks the text config its experts
dispatch on, which the one assignment reaches through transformers'
recursive `_experts_implementation` setter (5.5.4 and 5.18; a tiny
Qwen3.5-MoE built on meta dispatches its experts on `eager` with the
flag and on `grouped_mm` without it). The test fails when the value
reaches the root config only, and the comment at the assignment says the
setter carries it. Declined: resolving the routing indices' parameter
name from the forward's signature, since every transformers MoE block
passes them positionally. The `tests/examples/hf_ptq` unit tests pass on
5.18 and 5.5.4 (120), and pre-commit passes against main.
- Bot review of `042e4bf1e55` (approved), 2026-10-28, late night: the
interception error names the ways forward: a class no backend change
redirects needs its own quantized wrapper, and meanwhile the rest of the
model quantizes with these modules' quantizers disabled, which the check
honours. Declined: caching the grouped offsets' host copy across the two
projections' captures (one small sync per experts module per batch under
GPTQ, local Hessian and imatrix, whose per-expert matmuls dominate).
pre-commit passes against main.
- Rebased on 2026-10-28, late night, onto main `1ca8bd1fc4d` after #2745
merged (squashed with the tree of its last head, so this PR's tree is
unchanged); the base is now `main`, so the GPU tests run. pre-commit
passes against main.
- CI fix, 2026-10-28, late night: the first run on main failed two cases
of `tests/examples/hf_ptq/test_hf_ptq_vision_quantization.py` in the
trtllm `hf_ptq` lane (2 failed, 267 passed): its stand-in for hf_ptq's
parsed arguments lacked the new `experts_implementation`, which
`load_model` now reads. The stand-in gains it; the file passes on
transformers 5.18 and 5.5.4.
- Rebased on 2026-10-28, late night, onto main `bdc45368d10` (#2736
merged), with its own diff unchanged. Its tests with #3067's, #2736's
`test_distributed.py` and the `hf_ptq` unit tests pass (376), and
pre-commit passes against main.
- CI, 2026-10-29: the first full run on main at `df2e3fe4a76`, GPU lanes
included, passes.
- Split, 2026-10-29, at a reviewer's request: the expert-parallel
indexing fix, its tests and a Bug Fixes changelog entry moved to #3122
([1/2], on main), and this PR now sits on it, its own diff otherwise
unchanged. The review's three smaller items are taken: the interception
check reduces its verdict over the experts' expert-parallel group (a
two-rank test fails with the rank-local check); a test checks that every
experts class transformers decorates with `use_experts_implementation`
takes the routing indices second (transformers 5.5.4 and 5.18); and
`--experts_implementation` offers `eager` and `grouped_mm` as argparse
choices. pre-commit, mypy included, passes against main; this PR's
tests, `test_moe_a2a.py` and the `hf_ptq` unit tests pass (315).
- Review suggestion (Claude), 2026-10-29: the interception check reads
the expert-parallel group from the experts' weight, a DTensor sharded
over the experts from transformers 5.16 on; from 5.6 to 5.15 the shards
are plain tensors that name no group, so the check stays rank-local
there, as before. The docstrings say so. Restacked onto #3122's two
follow-ups; pre-commit passes against main.
- Comment, 2026-10-29: at `calibrate()`'s gate for the interception
check, a sentence says that both conditions, the forward loop having run
and the HF plugin being loaded, must come out the same on every rank,
since under expert parallelism the check all-reduces over the experts'
group. pre-commit passes against main.
- Test fix, 2026-10-29:
`tests/gpu/torch/quantization/test_fsdp2_autoquant.py`'s 2-GPU KV
AutoQuantize test builds hf_ptq's arguments by hand and lacked the new
`experts_implementation`, which `load_model`'s FSDP2 branch reads. It
now passes `None`. The 1-GPU PR runners skip that test, and main's
nightly runs it. No other test or example builds hf_ptq's arguments by
hand on a path that reads the flag. pre-commit passes against main.
- Rebased on 2026-10-30 onto #3122 on main `99c3834f62c`, with its own
diff unchanged and no conflicts. pre-commit passes against main.
- Review (sychen52), 2026-10-30: with every weight quantizer collecting
statistics without quantizing (calibration), the grouped forward returns
the stored weight itself instead of building a weight-sized copy slice
by slice on each forward. A CPU test checks the identity and the
collected per-expert amax, and fails on the previous code. On CPU, one
call on a Qwen3-30B-A3B-sized `gate_up_proj` (128 × 1536 × 2048, bf16,
768 MiB) raised peak RSS by 755 MiB before and 0 MiB after; a GPU
`max_memory_allocated` comparison is queued. The changelog entry now
says the experts stay on `grouped_mm` for every forward after
`mtq.quantize` or `mto.restore` (QAT/QAD, AutoQuantize scoring,
fake-quant evaluation), with `experts_implementation="eager"` as the way
back. `test_fused_experts.py` and `test_moe_a2a.py` pass on transformers
5.18 (220) and 5.5.4 (214, 6 skipped); pre-commit passes against main.
- Review (kevalmorabia97), 2026-10-30: the three changes no test covered
now have one each, or are gone. The `_local_experts` call in
`_quantize_grouped_weight` is dropped as unreachable: the weight arrives
as this rank's local shard inside the experts forward. New tests pin
that a grouped forward reaching neither hook is counted whatever its
routing indices hold, and that a capture after a grouped forward does
not split by its offsets. The calibration pass-through calls only
quantizers that calibrate (Claude's suggestion), with a constant-amax
test case. Each test fails with its change reverted.
`test_fused_experts.py`, `test_moe_a2a.py` and `test_hf_ptq_args.py`
pass on transformers 5.18 (283) and 5.5.4 (277, 6 skipped); pre-commit
passes against main.
- GPU calibration peak memory, 2026-10-30 (sychen52's request):
`torch.cuda.max_memory_allocated` around the `mtq.quantize(...)` call
only, after `reset_peak_memory_stats`. Qwen3-30B-A3B on the default
`grouped_mm` backend, recipe `general/ptq/fp8_default-kv_fp8`,
`calib_size` 32, `batch_size` 1, one NVIDIA GB200 for both revisions
(torch 2.11, transformers 5.13.0, CUDA 13.1).
| Revision | Calibration peak (bytes) | MiB |
|---|---|---|
| Before the pass-through change (`3f3544435d8`) | 864,901,632 | 824.8 |
| After (`27c5f77feac`) | 174,759,936 | 166.7 |
| Drop | 690,141,696 | 658.2 |
- Rebased on 2026-10-30 onto main `55898ee4e3b` after #3122 merged,
dropping #3122's pre-squash commits; its own diff is unchanged, with no
conflicts. pre-commit passes against main.
- CI fix, 2026-10-30: the routing-order test read transformers' modeling
sources with the platform's default encoding, cp1252 on Windows, which
16 of them (`modeling_altclip.py`, say) cannot be decoded with; it now
reads them as UTF-8, and the windows unit job's only failure goes with
it. The `linux` job's one failure on the previous head, a NAS sorting
test this PR does not touch, passed on rerun at the same commit.
pre-commit passes against main.
- `eager` against main, byte for byte, 2026-10-30 (re-verifying at this
PR's head the claim that `experts_implementation="eager"` reproduces
main): PTQ of Qwen/Qwen3-30B-A3B with recipe
`general/ptq/nvfp4_default-kv_fp8`, `calib_size` 512, calibration
sequence length 2048, `batch_size` 1, cnn_dailymail, without expert
parallelism (one process), on one NVIDIA GB200 (torch 2.11, transformers
5.13.0, CUDA 13.1). This PR at `27c5f77feac` with
`--experts_implementation eager`, against main at `99c3834f62c`, both
compared at zero tolerance. Later revisions of this PR leave its own
diff unchanged (a rebase) or change tests only.
| Scale kind | Tensors | Differing |
|---|---|---|
| `input_scale` | 18,624 | 0 |
| `weight_scale` | 18,624 | 0 |
| `weight_scale_2` | 18,624 | 0 |
| `k_scale` | 48 | 0 |
| `v_scale` | 48 | 0 |
- Review (juhi10071998), 2026-10-30: under transformers' own expert
parallelism the grouped forward sorts the rows routed to other ranks'
experts past `offs[-1]`, which the grouped GEMM neither reads nor
writes; before transformers 5.17 nothing zeroes them in the down
projection's input, so its input quantizer calibrated on uninitialized
rows. While an input quantizer collects statistics it now sees only the
rows before `offs[-1]` (a host sync, during calibration only). A test
hands the grouped projections a NaN or a large finite tail, as pre-5.17
expert parallelism leaves it, and checks the input amaxes match a run
without one; both cases fail without the change.
`test_fused_experts.py`, `test_moe_a2a.py`, `test_calib.py` and
`test_hf_ptq_args.py` pass on transformers 5.18 (307) and 5.5.4 (301, 6
skipped); pre-commit passes against main.
- Rebased on 2026-10-30 onto main `059588b89a5` (after #3131, #3124,
#2751, #3104, #3128, #3134 and #3149 merged), with its own diff
unchanged and no conflicts. pre-commit passes against main.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ❌ Fused-experts MoE calibration
now runs on `grouped_mm`, so some activation scales differ from earlier
releases; `experts_implementation="eager"` restores the earlier
checkpoint exactly.
- 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](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ Backward Breaking Changes.
- Did you get Claude approval on this PR?: ✅ At `042e4bf1e55`; the
error-message change after it is from that review.
### Additional Information
Split out of #2745 (part 3 of 21 of expert-parallel PTQ for Hugging Face
MoE models), which merged on 2026-10-28.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
<!-- coderabbit-ai-generated:v1 -->
## Summary by CodeRabbit
* **Behavior Changes**
* Hugging Face MoE calibration now uses Transformers’ default
`grouped_mm` backend instead of forcing eager execution. Calibrated
activation scales may differ; set `experts_implementation="eager"` to
use the earlier calibration behavior.
* Eager-only expert wrappers continue to use eager execution. Using them
with `torch_a2a` raises an error.
* **Bug Fixes**
* Improved calibration support for grouped and expert-parallel
execution, including local expert weights and experts receiving no
routed inputs.
* Calibration now reports when fused experts run without interception or
required inputs are not captured.
* Improved FSDP2 parameter handling during weight access and writeback.
* **New Features**
* Added calibration support for newer Transformers fused-expert
implementations, including Qwen3-MoE and native FP8 experts.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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What does this PR do?
Type of change: Bug fix
Two fixes for W4A8 AWQ models (
W4A8_AWQ_BETA_CFG), whose weight quantizer is aSequentialQuantizer(INT4 blocks, then FP8), and a guard for the multi-rank AWQ case the first fix would otherwise let through.1. The FSDP2 export.
export_hf_checkpointon an FSDP2 model quantized with W4A8 AWQ (--qformat w4a8_awq_betainexamples/hf_ptqwith--use_fsdp2) fails on every rank:The export's AWQ resmooth step opens an
fsdp2_aware_weight_updatewindow on each sharded layer to fold the pre-quant scales:export_hf_checkpoint->_export_fsdp2_checkpoint_streaming->_prepare_model_for_export->requantize_resmooth_fused_llm_layers->_fuse_shared_input_modules->_fusion_update_context(the AWQ layernorm fusion opens one too). Around its unshard, re-registration and reshard, the window callsenable_fake_quant, which read and wroteweight_quantizer._fake_quant. ASequentialQuantizeris a container that has no_fake_quantof its own, so the read raises.enable_fake_quantnow saves, sets and restores_fake_quanton eachTensorQuantizerstage of a weight quantizer. A plainTensorQuantizeris its own only stage, so other formats are unchanged. It reads all the original values before setting any, so a quantizer reached twice is restored correctly. It now restores them in afinally, so a window that raises (an FSDP2 unshard running out of memory, say) no longer leaves them changed.2.
mtq.compressnow refuses W4A8 weights. On main, a W4A8 model compressed withmtq.compresscan neither run nor be reloaded:F.linearreceives anFP8QTensor(TypeError). There is no W4A8 real-quant GEMM, so GPU takes the same path.compress_restorecallsset_from_modelopt_stateon the weight quantizer, whichSequentialQuantizerdoes not have._fake_quanttoo.compress_convertnow raisesNotImplementedErrornaming the module before it changes the model. It does so for any quantized linear with aSequentialQuantizerin its weight quantizer (a plain W4A8 linear, or Transformer Engine's grouped linear, whose per-expert quantizers become sequential), also when the compress config excludes the layer: it would still become aRealQuantLinearthat can neither be restored nor take a new weight. main's Megatron GPU tests already skip W4A8 for compress ("not supported for compress"). No path in the repo compresses W4A8 successfully today, so the refusal breaks none:examples/hf_ptq --low_memory_mode --qformat w4a8_awq_betafailed right after compress ('SequentialQuantizer' object has no attribute 'num_bits'in the device-map step); it now stops at compress.compressoption,examples/llm_qat --compressandexamples/llm_evalwith compress run a forward right after compress, which failed.examples/megatron_bridge/quantize.py --compresssaves a Megatron checkpoint and skips generation; nothing in the repo can use a compressed W4A8 one (from reading the code; not run).examples/alpamayoandexamples/diffusersnever use W4A8, and the Hugging Face export does not callmtq.compress.Reach.
W4A8_AWQ_BETA_CFGis the only built-in preset with a sequential weight quantizer. Both problems predate #3099:enable_fake_quantcame with the FSDP2 export (#432), and every release since 0.39.0 has it. main's FSDP2 GPU export tests left W4A8 out for this reason (#TODO: Fix unit test for this case); this PR turns those cases back on.Found by meenchen in the review of #3099.
3. AWQ calibration of an FSDP2 model sharded over several ranks is refused. Under
hf_ptq --use_fsdp2, each rank calibrates AWQ-lite on its own slice of the data, and its alpha-search loss is not combined across ranks. So each rank folds its ownpre_quant_scaleinto its weight shard: on CPU with 2 gloo ranks and a tiny Llama, 10 of 14 linears differ, and INT4 AWQ behaves the same. With fix 1, the export would write those mismatched shards instead of crashing.mtq.quantizeandmtq.calibratenow raiseNotImplementedErrorforawq_lite/awq_fullon an FSDP2 model sharded over more than one rank, before the model changes. The decision reads only the run's size, the model's FSDP2 sharding and the algorithm, so every rank raises alike, before any collective. One rank, ashf_ptq --use_fsdp2runs on one GPU, is still allowed.examples/hf_ptqrefuses the same combination at argument validation, before the distributed load, for--qformat, PTQ recipes and AutoQuantize recipes alike, and its README's--use_fsdp2notes say so.mtq.auto_quantizechecks every candidate at entry, so a search with an AWQ candidate, such as its defaultNVFP4_AWQ_LITE_CFG, is refused before it changes the model; the KV-cache search already accepts onlymax. #2740's refusal, based on the recorded distribution plan, replaces this guard when it lands.Usage
No API change. On one rank (
hf_ptq --use_fsdp2on one GPU, say), this now exports, where it used to raise; on several ranks themtq.quantizecall now refuses AWQ (fix 3):mtq.compresson a W4A8 model now raisesNotImplementedErrorup front.Testing
tests/unit/torch/export/test_fsdp2_parallel_export.py::test_streaming_export_of_a_sharded_w4a8_model(new, CPU, one gloo rank): a tiny Llama (2 layers, hidden 128) is quantized withW4A8_AWQ_BETA_CFGand sharded withfully_shardon each decoder layer and the root. Its FSDP2 streaming export must match the unsharded export tensor for tensor.tests/unit/torch/quantization/test_utils.py::test_enable_fake_quant_restores_each_stage_when_the_window_raises(new, CPU): a window that raises over a two-stage weight quantizer, whose stages start with different flags, must give each stage its own flag back.tests/unit/torch/quantization/test_compress.py::test_compress_refuses_w4a8_weights_before_changing_the_model(new file, CPU, parametrized over a plain W4A8 linear and aGroupedQuantizerholding the sequential quantizer): compressing raises, and the model is left unconverted. main had no CPU test file forcompress.py; its compress tests are CUDA ones intests/gpu/torch/quantization/test_real_quantize_cuda.py.tests/gpu/torch/export/test_fsdp2_export.py: W4A8 is re-enabled intest_fsdp2_weight_update_context_for_fuse_layersandtest_fsdp2_weight_update_context_for_export_quantized_weight(4 cases). CI's single-GPU lane ran them at f06c347: 4 passed. On 2 GPUs at 182e0de: 4 passed, and 4 failed with main'senable_fake_quant(table below). A CPU dry run of those 4 cases (cudaswapped forcpu, gloo) passes on 1, 2 and 4 ranks with the fix. Without it, all 4 fail on 2 ranks, with theAttributeErroron every rank.enable_fake_quant's callers andmtq.compress's consumers (test_fsdp2_parallel_export.py,test_sharded_weight_packing.py,test_offload_export.py,test_unified_export_hf.py,test_export_weight.py,test_utils.py,test_mse_calibrator.py,test_quantize_cpu.py,test_int8_tensor.py,test_nvfp4_four_over_six.py,test_hf_patching.py, the Hugging Face plugin tests and others) pass: 331 passed.examples/hf_ptqwith--use_fsdp2and W4A8 AWQ on Qwen3-8B, including the export, on 2 GPUs, before and after the fix: before, the export crashes on both ranks; after, it exports (table below).enable_fake_quantrestores its flags in afinallyand picks the stages withisinstance(TensorQuantizer)(review);mtq.compressrefuses W4A8 weights. Ran the two new tests with and without their fix, the CPU suites above, and pre-commit. CI at f06c347 passed (47 checks), its single-GPU lane including the 4 re-enabled W4A8 cases.GroupedQuantizer, and its message names the mechanism and covers excluded layers (review). Ran the parametrized test against three versions ofcompress.py(without the walk, the grouped case fails with the oldAttributeError), the related CPU suites (196 passed), and pre-commit.test_fused_experts_keep_fused.py, whose fold windows also go throughenable_fake_quant(179 passed).examples/hf_ptq --use_fsdp2W4A8 run before and after the fix.AttributeError(table below). The real-modelexamples/hf_ptq --use_fsdp2W4A8 run on Qwen3-8B, before and after the fix, is done too (table below).CHANGELOG.rst: [OMNIML-6073] [1/2] Calibrate and fold expert-parallel fused experts on each rank's own experts #3122 appended its Bug Fixes entry at the same place, so both entries are kept. The code and test diff is unchanged (range-diff). Ran pre-commit and the CPU suites above, plus the fused-experts tests [OMNIML-6073] [1/2] Calibrate and fold expert-parallel fused experts on each rank's own experts #3122 touched (257 passed).tests/unit/torch/quantization/test_quantize_cpu.py::test_awq_on_a_sharded_fsdp2_model_is_refusedchecks it on two gloo ranks, through both entry points, and that the model is left unquantized; it fails without the guard (DID NOT RAISEon both ranks). The W4A8 export test now shards before calibrating, ashf_ptq --use_fsdp2does on one GPU; a guard that refused one rank fails it. The two GPU tests that calibrated AWQ after sharding on every GPU are adapted:test_fsdp2_export.py's helpers calibrate before sharding, andtest_fsdp2.py::test_nested_fsdp2_backwardskips INT4 AWQ on several GPUs. CPU dry run oftest_fsdp2_export.pyon 2 gloo ranks: before the adaptation, its 12 AWQ cases hit the guard; after it, all cases pass but the 4 NVFP4 AWQ-lite ones, which need CUDA on main too. Ran pre-commit and the related CPU suites (421 passed). GPU results at this head are below.mtq.auto_quantizechecks every candidate at entry (review), so on a model FSDP2-sharded over several ranks an AWQ candidate is refused before the search changes the model, instead of partway through it; the refusal's message reports the FSDP2 shard mesh's size.test_awq_on_a_sharded_fsdp2_model_is_refusednow also runsmtq.auto_quantizewith {FP8, W4A8 AWQ} on two gloo ranks; without the entry check it fails, the refusal then coming fromcalibrate()mid-search. Ran pre-commit and the related CPU suites, the AutoQuantize and KV-cache AutoQuantize tests included (302 passed). The GPU results at 78aa164 below carry over.hf_ptq --use_fsdp2refuses AWQ formats and recipes, AutoQuantize candidates included, on more than one rank at argument validation, and the README says so (review).tests/examples/hf_ptq/test_example_utils.py::test_fsdp2_refuses_awq_on_several_ranks_before_loadingcovers it; without the check its two refused cases fail. The two adapted GPU tests now change only what the guard refuses:test_fsdp2_export.pyshards before calibrating again except for AWQ on more than one rank, andtest_nested_fsdp2_backwardgates only its shard-then-quantize model there, so INT4 AWQ's quantize-then-shard comparison runs on several GPUs (review). CPU dry run with gloo, on 1 and 2 ranks: the nested-backward helper passes for all three configs, andtest_fsdp2_export.py's two helpers pass for every config except NVFP4 AWQ-lite, which needs CUDA on main too. Ran pre-commit and the hf_ptq example tests (153 passed).--use_fsdp2first, as the single-processdevice_mapstill uses all the GPUs. Ran pre-commit and the hf_ptq example tests (62 passed).AttributeErrorenable_fake_quantrestore test[True, True]DID NOT RAISEAttributeErroron the containerenable_fake_quantenter and exit on 2,000 quantized linears (CPU):TensorQuantizer)SequentialQuantizer)tests/gpu/torch/export/test_fsdp2_export.pyon 2 GPUs: 2x NVIDIA GB200 (184.3 GiB each), torch 2.11.0a0 (nv26.02), transformers 5.9.0, CUDA 13.1, pytest-p no:rerunfailures. Before = main'senable_fake_quant(99c3834) on the same tree.quant_config5)enable_fake_quantAttributeError: 'SequentialQuantizer' object has no attribute '_fake_quant'inenable_fake_quantReal model on 2 GPUs:
examples/hf_ptq/hf_ptq.py --pyt_ckpt_path Qwen/Qwen3-8B --qformat w4a8_awq_beta --calib_size 128 --batch_size 4 --use_fsdp2(torchrun --nproc_per_node=2), 2x NVIDIA GB200, torch 2.11.0a0 (nv26.02), transformers 5.13.0, CUDA 13.1.AttributeError: 'SequentialQuantizer' object has no attribute '_fake_quant'inenable_fake_quantThe exported
hf_quant_config.jsonhasquant_algoW4A8_AWQ, an FP8 KV cache, group size 128 andpre_quant_scale, withlm_headandembed_tokensexcluded.weight(packed INT4)weight_scaleweight_scale_2input_scalepre_quant_scale(o_proj,down_proj: not folded into a layernorm)weightof the unquantized modulesThis run, at 182e0de, shows that the export no longer crashes. With 2 ranks, each rank's own
pre_quant_scale(fix 3) applies to this checkpoint, so it is not an accuracy result. Since 78aa164 the same 2-GPU command stops at calibration with the guard'sNotImplementedError, so the crash fix is shown on one GPU below.At 78aa164: Qwen3-8B,
examples/hf_ptqwith--use_fsdp2 --qformat w4a8_awq_beta --calib_size 128 --batch_size 4, NVIDIA GB200, torch 2.11.0a0 (nv26.02), transformers 5.13.0, CUDA 13.1.AttributeError: 'SequentialQuantizer' object has no attribute '_fake_quant'W4A8_AWQwith an FP8 KV cache, group size 128 andpre_quant_scale,lm_headandembed_tokensexcluded; same tensor layout as the 2-GPU export aboveNotImplementedError: Calibrating with awq_lite is not supported yet on an FSDP2 model sharded over 2 ranks: ...at calibration; no hangtests/gpu/torch/export/test_fsdp2_export.pyandtests/gpu/torch/quantization/test_fsdp2.pyat 78aa164, same environment:test_nested_fsdp2_backwardwith INT4 AWQ, by designd538a02 changes only
mtq.auto_quantize's entry check and the refusal's message, so these results carry over: none of these runs callsauto_quantize. 060836a narrows the two GPU tests' adaptations (Testing above). Re-run of both files at 060836a: NVIDIA GB200, torch 2.11.0a0 (nv26.02), transformers 5.9.0, pytest-p no:rerunfailures.On 2 GPUs
test_nested_fsdp2_backwardwith INT4 AWQ now runs, with only its shard-then-quantize model gated. Both export helpers pass for all 8 configs, with and without bias: the AWQ ones quantize before sharding, the others shard first.Before your PR is "Ready for review"
Make sure you read and follow Contributor guidelines and your commits are signed (
git commit -s -S).Make sure you read and follow the Security Best Practices (e.g. avoiding hardcoded
trust_remote_code=True,torch.load(..., weights_only=False),pickle, etc.).mtq.compresson a W4A8 model now raises up front; such a compressed model could not run or be reloaded before)CONTRIBUTING.md: N/AAdditional Information
Follow-up to #3099 (review thread on W4A8 weight-update windows).
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