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Speed up FSDP2 MoE calibration by dropping redundant expert-weight gathers - #2359
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughFused-MoE calibration now iterates over weight quantizers without materializing distributed weight slices. Static-block promotion uses this path and warns once for DTensor ChangesFSDP2 fused-MoE quantization
Priority: ➖ Normal Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: 🔵 Low · up to The calibration optimization avoids redundant fused-expert weight gathers, but unexpected DTensor amax values may produce a warning from every participating rank and create noisy distributed-job logs. This is bounded and mergeable with owner awareness. Suggested reviewers: Sequence Diagram(s)sequenceDiagram
participant QuantModule
participant QuantFusedExperts
participant StaticBlockPromotion
participant CalibrationTests
QuantModule->>QuantFusedExperts: Iterate weight quantizers
QuantFusedExperts-->>QuantModule: Return per-expert quantizers
QuantModule->>StaticBlockPromotion: Provide quantizer-only calibration path
StaticBlockPromotion-->>StaticBlockPromotion: Warn once for DTensor amax
CalibrationTests->>QuantFusedExperts: Verify order and no weight indexing
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✅ Passed checks (5 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 61.54% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 13 functions across 4 files. (1 skipped: 1 unsupported.)
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In `@modelopt/torch/quantization/utils/core_utils.py`:
- Line 1134: Update the warning path in the function containing
warned_dtensor_amax so warnings.warn executes only on distributed rank zero when
_amax is a DTensor, while preserving the dtype-promotion logic on every rank and
the existing local warning suppression behavior.
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CHANGELOG.rstmodelopt/torch/quantization/nn/modules/quant_module.pymodelopt/torch/quantization/plugins/huggingface.pymodelopt/torch/quantization/utils/core_utils.pytests/unit/torch/quantization/plugins/test_fused_experts.py
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| # local. If that ever stops holding, the reduction becomes a per-quantizer | ||
| # collective and this loop needs a batched reduction instead. | ||
| warned_dtensor_amax = True | ||
| warnings.warn( |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
Emit this warning from one rank.
When _amax is a DTensor, participating FSDP2 ranks execute this path. warned_dtensor_amax is local to each rank. Line 1134 therefore emits one warning per rank instead of one warning for the distributed job. Gate warnings.warn to rank zero while keeping promotion on all ranks.
As per coding guidelines, use distributed-safe warning/logging patterns where applicable.
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In `@modelopt/torch/quantization/utils/core_utils.py` at line 1134, Update the
warning path in the function containing warned_dtensor_amax so warnings.warn
executes only on distributed rank zero when _amax is a DTensor, while preserving
the dtype-promotion logic on every rank and the existing local warning
suppression behavior.
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| - Update HuggingFace checkpoint export to use name-based tied-weight deduplication instead of the previous address-based approach. The address-based deduplication could incorrectly drop an untied weight that happened to share memory with a tied one, producing an incomplete checkpoint (observed as a false positive on MiniMax-M2.7). | ||
| - Fix EAGLE-3 training with context parallelism (``--cp_size > 1`` in ``examples/speculative_decoding``), which failed to start on ``accelerate >= 1.13`` and then raised ``got mixed torch.Tensor and DTensor``. | ||
| - Polygraphy minimum dependency upgraded to ``0.53.4`` to solve ONNX AutoCast failures when marking optional graph outputs. | ||
| - Speed up ``mtq.quantize`` on FSDP2-sharded fused-MoE models. Promoting static-block weight quantizers gathered each expert's slice of the fused weight across ranks even though only quantizer state is read, adding tens of thousands of collectives to calibration on large MoE models. |
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[SUGGESTION] This entry is in the 0.47 Bug Fixes section, but main has already opened a new dev section above it — 0.48.0 (2026-10-xx), which this branch predates. Because the entry is appended at the tail of 0.47's list and main's change is a whole new section above, the merge resolves cleanly and the entry silently stays under 0.47 — a release that's already cut (0.47 (2026-09-xx), and today is 2026-09-08).
Why it matters: the fix will actually ship in 0.48, so users scanning 0.47's notes get a fix they don't have, and 0.48's notes omit one they do. This is easy to miss precisely because there's no merge conflict to prompt a second look.
On the next rebase, move it to the 0.48.0 → Bug Fixes section. The *Quantization* sub-section convention in CLAUDE.md applies to **New Features**, so a flat entry under 0.48.0's **Bug Fixes** matches how the surrounding releases are structured.
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Claude review — approved
Findings: CRITICAL: 0 · IMPORTANT: 0 · SUGGESTION: 1
Scope: full review (the trigger comment carried no scoping instructions). All 5 changed files reviewed — 3 source, 1 test, 1 changelog. Note the branch is ~19 commits behind main, so a two-dot git diff origin/main HEAD mixes in unrelated main-branch drift (e.g. a sync_moe_expert_amax docstring change and the whole 0.48.0 changelog section); I reviewed against gh pr diff to isolate the PR's actual 100 additions.
Verification performed
I checked the four things that could have made this change wrong, and all held up:
-
The two iterators agree.
_QuantFusedExperts.iter_weight_quantizers_for_calibrationwalks the same(_first_proj_attr, _first_proj_weight_quantizers_attr)/("down_proj", "down_proj_weight_quantizers")pairs with the same skip condition asiter_weights_for_calibration, andyield from quantizersyields exactly the setenumerate(quantizers)did, in order._QuantNonGatedFusedExpertsonly overrides_first_proj_attr/_is_gated, so it inherits both consistently. -
No MRO divergence. This was my main concern with a base-class delegate: a class inheriting both
_TransposedExpertsCalibMixin(which overridesiter_weights_for_calibration, huggingface.py:616) and_QuantFusedExperts(which now overrides both) would resolve the two methods to different implementations and silently yield different quantizers. There is no such class —_QuantGptOssExpertsand_QuantLlama4TextExpertspair the mixin with_QuantFunctionalMixin/QuantModule, never with_QuantFusedExperts. -
The override is needed only where it was applied. The other three
iter_weights_for_calibrationoverrides do not index a fused tensor and so cost nothing through the base delegate: vLLM_QuantFusedMoEBaseyields wholew13_weight/w2_weight, TE grouped yields separateweight{i}parameters, and_TransposedExpertsCalibMixindoes a.transpose(-1,-2)that stays local metadata for a dim-0-sharded DTensor. For GPT-OSS specifically I confirmed the dynamic-attribute getter is inert here —_enable_weight_quantizationdefaultsFalseand promote runs outsideenable_weight_quantization(), so_get_quantized_weightreturns the raw weight rather than fake-quantizing a fused weight the loop discards. -
The stated root cause is real, not just plausible.
_finalize_with_shared_statecallspromote_static_block_weight_quantizers(model)at model_calib.py:154 with no weight-access window, while the siblingweight_only_quantizeat model_calib.py:194 wraps its identical iteration inenable_weight_access_and_writeback. That asymmetry is exactly what the PR describes, and it explains why this one call site paid for the per-expert slice.
I also read the full promote_static_block_weight_quantizers body to confirm the loop genuinely never touches the weight — it reads is_enabled, is_static_block_quant, amax, is_nvfp4_static, _num_bits and _name only. And I confirmed the remaining weight-consuming call sites all really need the weight (weight.to_local() in temporarily_fold_weights, weight.dtype for enable_lsq, the weight itself for the MSE calibrator), so none of them should switch.
Notes
The DTensor-_amax warn-once check is a sound defensive addition: fully_shard shards parameters and leaves buffers replicated, so _amax should stay local and reduce_amax non-collective — and if that invariant ever breaks, the loop degrades into a per-quantizer collective, which is worth surfacing rather than absorbing. DTensor is already imported at core_utils.py:30, so no new import was needed.
The test design is good: test_does_not_index_the_fused_weight asserts the negative via a __getitem__-raising tensor subclass, then asserts the weight iterator still raises — so the guard cannot silently stop testing anything if the fused layout changes. Both tests are parametrized over gated and non-gated.
I could not execute the test suite in this environment (command approval), so the pass/fail status of the new tests is unverified here — CI covers that.
The one SUGGESTION is a changelog placement issue, posted inline: the entry lands in the already-cut 0.47 section rather than 0.48.0, and it will merge without conflict, so nothing prompts a second look.
Risk
Low. Purely additive and behavior-preserving: a new method whose base implementation delegates to the existing iterator, one override that provably yields the same quantizers, and one call site switched in a loop that never reads the weight. No public API, modelopt_state, mode-registration, config-schema or export-path surface is touched, so there is no restore or backward-compatibility exposure. The reported 79.3 → 19.0 min pre-export win is consistent with the mechanism, and the author is right not to reach for enable_weight_access_and_writeback here — that helper needs the root model, whereas this function is documented and tested to accept a submodule.
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Approving: a small, well-scoped perf change that adds a quantizer-only iterator, switches only the promote loop to it, and pins the new iterator's ordering and no-indexing behavior with tests for both gated and non-gated fused experts.
I verified the other four iter_weights_for_calibration callers (weight_only_quantize, _mse_calibrate_weights, lsq, TE grouped fold) all consume the weight and are untouched, the base delegation keeps every other subclass correct, and no existing test was modified.
No action needed.
Testing: No test plan or testing section found in the PR description.
Suggested test plan:
- Run the new
TestIterWeightQuantizersForCalibrationtests (gated and non-gated) plus the existing fused-experts suite to confirm ordering parity withiter_weights_for_calibrationand that noweight[idx]indexing occurs. - Verify the override's skip condition matches the weight iterator in edge cases: missing/None
down_projor_first_proj_attrweight, absent or empty quantizerModuleList, and gated vs non-gated first-proj attribute naming. - Check
promote_static_block_weight_quantizersstill returns the sameconvertedcount and produces identical quantizer state (amax, block config, NVFP4 static promotion) before/after the change on a small MoE and a non-MoE model — compare quantized outputs or state dicts. - Confirm the base
QuantModule.iter_weight_quantizers_for_calibrationworks for all otherQuantModulesubclasses (Linear, conv, SequentialQuantizer cases) so no subclass regresses via the default delegation. - Exercise a multi-GPU FSDP2 run (fused MoE, NVFP4) end-to-end through
mtq.quantize, confirming the calibration/promote phase speedup and that the resulting checkpoint/export matches the non-FSDP or pre-change result. - Force
_amaxto be a DTensor (or simulate it) to verify the warn-once path fires exactly once and doesn't break the reduction path.
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…izers promote_static_block_weight_quantizers reads only quantizer state, but reached it via iter_weights_for_calibration, whose fused-MoE override yields weight[idx] per expert. Under FSDP2 that weight is a DTensor, so every expert slice dispatched a redistribute collective to produce a value the loop then discards -- tens of thousands of them on a large MoE. Add iter_weight_quantizers_for_calibration. The base delegates to the weight iterator and drops the weight, so every subclass gets a correct implementation for free; only the fused-MoE class, where fetching the weight view is itself expensive, overrides it to walk the per-expert quantizer ModuleList directly. Also warn once if _amax is ever a DTensor, which would make the per-quantizer global-amax reduction a collective as well. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Suguna Velury <178320438+sugunav14@users.noreply.github.com>
State the mechanism only; measurements belong in the PR description. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Suguna Velury <178320438+sugunav14@users.noreply.github.com>
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Suguna Velury <178320438+sugunav14@users.noreply.github.com>
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No conflicts. Main's #2359 speeds up FSDP2 MoE calibration by adding iter_weight_quantizers_for_calibration to the fused-experts wrappers, which reads _first_proj_attr and _first_proj_weight_quantizers_attr rather than renaming them -- so the structural fused-expert check in get_expert_linear_names, which keys on those same attribute names, is unaffected. Checked for the failure the last two merges hit, where main added a caller of a migrated helper without threading model_type: its commit adds none, and the threading in layerwise export is intact. Both CHANGELOG entries survive the merge. tests/unit/torch/{models,export,quantization}: 1209 passed, 7 skipped. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
### What does this PR do? **Type of change:** Performance enhancement FSDP2 checkpoint export gathered the whole model onto rank 0 and wrote it from a single process. On large models that made export the slowest phase of a PTQ run and could exhaust host memory. The model is now split into export units — one per decoder layer, plus one for everything else that owns state — and the units are dealt round-robin across ranks. Every rank enters the unshard window for every unit, because the gather is a collective; only the owner keeps the result, packs it, and streams it to its own shard files. Rank 0 names the shards and writes the index once every rank has finished. Two properties follow. Peak host memory per rank is ~model/world instead of the whole checkpoint on rank 0. And packing runs on a gathered, full weight, so it is byte-for-byte the single-process code path — no amax narrowing, no block-quantized fallback, no scale-buffer heuristics. Measured on B300 (bia), NVFP4, `hf_ptq.py --use_fsdp2`: | model | world | export | artifact | |---|---|---|---| | Qwen3-235B-A22B | 8 (1 node) | **168.6s** | 146,361 keys / 16 shards / 0.122 TiB | | oakhaven-max 4.5 TB | 64 (8 nodes) | **1359.7s** | 568,398 keys / 158 shards / 1.292 TiB | Checkpoints were validated, not just timed: every index entry present, no leftover `__shard_part*`, all ranks exit 0. A repeat of the oakhaven configuration landed at 1411.7s, so treat ~1.36–1.41 ks as its range. No speedup ratio is quoted: the old rank-0 path was last measured on different hardware on a different day, and this cluster drifts up to 14% run-to-run on an identical tree, so a cross-day ratio would not reproduce. A same-day baseline of `main` is the outstanding item. Two alternatives were considered and rejected. `torch.distributed.checkpoint`'s `get_model_state_dict` full-gathers to rank 0 only with no per-module owner knob, so it reproduces the exact host-memory bound this removes, and per layer it was not faster than `full_tensor()`. Packing each rank's shard in place was ~9% faster at Qwen3-235B w8 on the trees measured at the time, but rebuilt `FSDPParam` objects from torch's private FSDP internals and carried ~400 lines of format-specific shard handling; gathering to the owner deletes all of it. Configurations that would silently corrupt a checkpoint now raise instead: FSDP2 composed with another DTensor parallelism (FSDP2 + TP on a 2-D mesh; HSDP is fine), models with no discoverable decoder layers, a decoder layer object reused across layers, and a container that holds the decoder layers while owning direct state of its own. ### Usage No API change — `export_hf_checkpoint` selects the path automatically. ```python from modelopt.torch.export import export_hf_checkpoint # model is FSDP2-wrapped; every rank must call this (it unshards collectively) export_hf_checkpoint(model, export_dir="./exported") ``` Testing - `test_fsdp2_streaming_export_matches_reference` — world = GPU count under real FSDP2; merged checkpoint compared tensor-by-tensor against a single-process export of the same seeded model, index asserted to span more than one shard file. Covers the ownership split and the cross-rank index merge. - `test_fsdp2_gathered_pack_matches_reference` — gathered packing vs whole-weight packing over nvfp4 / fp8 / int8 / fp8-per-channel-per-token / int4-blockwise - World=1 CPU tests for unit enumeration, shard sub-splitting by `max_shard_size`, tied-alias dropping, and `extra_state_dict` - `tests/unit/torch/utils/test_perf.py` — sampling and threshold logic of `maybe_clear_cuda_cache`, which replaces an unconditional `torch.cuda.empty_cache()` after every packed weight 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.). - Is this change backward compatible?: ✅ <!-- No API change; non-FSDP2 and offloaded export paths are unchanged. --> - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A - Did you write any new necessary tests?: ✅ - Did you update Changelog?: ✅ - Did you get Claude approval on this PR?: ✅ ### Additional Information Pre-export time (load, quantizer insertion, calibration) is now the dominant phase of an FSDP2 PTQ run. It is unaffected by this PR and addressed separately in #2359. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Bug Fixes** - Improved FSDP2 Hugging Face checkpoint export by streaming rank-owned layers to shard files, reducing export time and host-memory usage for large models. - Improved export handling for multimodal models with missing architecture metadata. - Standardized weight packing across supported parallelism configurations. - **Performance** - Reduced repeated module lookups and unnecessary CUDA cache cleanup. - Improved shard generation, merging, and index completeness. - **Compatibility** - Expanded support for FSDP2 quantized export workflows and shard-local packing. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Suguna Velury <178320438+sugunav14@users.noreply.github.com> Signed-off-by: sugunav14 <178320438+sugunav14@users.noreply.github.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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What does this PR do?
Type of change: Perf enhancement
promote_static_block_weight_quantizersruns at the end ofmax_calibrate. All it does is readquantizer state -- it takes the weight the iterator hands it and throws it away. For this it goes through
iter_weights_for_calibration, and on a fused-MoE module that iterator yieldsweight[idx], once per expert.Under FSDP2 the fused weight is a DTensor spread across ranks, so
weight[idx]isn't a cheap view.Slicing it makes PyTorch pull the whole fused expert weight back from every rank, just to hand over
one slice that the loop then drops. That happens once per expert, per projection, per layer -- tens
of thousands of round-trips on a large MoE.
The fix adds
iter_weight_quantizers_for_calibration:QuantModuleimplementation delegates toiter_weights_for_calibrationand dropsthe weight, so every subclass gets a correct implementation for free and the two iterators
cannot drift apart.
overrides it, walking the per-expert quantizer
ModuleListdirectly. It keeps the same skipcondition as the weight iterator, since fetching an attribute is free and only indexing
collectives.
promote_static_block_weight_quantizersthen iterates quantizers instead of(weight, quantizer)pairs. No other caller changes: the other four call sites genuinely use the weight.
Why not just wrap the promote loop in
enable_weight_access_and_writeback, the wayweight_only_quantizedoes? It would still gather per module for weights theloop never reads.
weight_only_quantizeneeds the window because it actually computes amax from theweight; promote only needs the quantizer.
Also included: a warn-once check if
_amaxis ever aDTensor. It should not be —_amaxis aregistered buffer, and
fully_shardshards parameters, not buffers — which is why thereduce_amaxin this loop stays local. If that assumption ever breaks, the reduction becomes aper-quantizer collective too, and the warning says so rather than letting it degrade silently.
Results
Measured on Qwen-3.8 2.4T at world 64 (8 nodes, B300), NVFP4:
60.3 min saved, 4.2x. The block is gone rather than shortened -- the run logs 13 silent minutes
in total, all of it model load. Calibration itself is unchanged at 2:23, so the saving comes from
the promote loop rather than from work moving elsewhere. End-to-end projects to 41.7 min.
Usage
No API change.
mtq.quantizepicks this up automatically.Before your PR is "Ready for review"
iter_weights_for_calibrationand all itscallers that use the weight are untouched.
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Performance
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