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Megatron-Bridge: GGML PTQ, QAD and fake-quant export for large MoE and VLM models - #2873
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Docstring coverage is 47.62% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 42 functions across 15 files. (1 skipped:… | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly summarizes the main change: adding Megatron-Bridge GGML PTQ, QAD, and fake-quant export support for large MoE and VLM models. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Security Anti-Patterns The authoritative PR diff changes only existing Python modules and examples; it adds no torch.load(..., weights_only=False),numpy.load(..., allow_pickle=True), hardcodedtrust_remote_code=True,…Full details: Security Anti-Patterns
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torch.load(..., weights_only=False),numpy.load(..., allow_pickle=True), hardcodedtrust_remote_code=True,eval()/exec()on external input, or# nosec. It also adds no PIP dependency inpyproject.tomlorrequirements.txt. The changed code therefore introduces none of the explicitly listed security anti-patterns.
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Docstring coverage is 47.62% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 42 functions across 15 files. (1 skipped: 1 unsupported.)
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Requesting changes for one bug: the new EP>1 SequentialMLP export picks expert writers with the dense TP rank, so with TP>1 some ranks' experts never get written. Everything else looks correct and well tested.
Needs action:
- Fix
writes_own_expertsinunified_export_megatron.py: gate onget_expert_tensor_parallel_rank() == 0instead oftp_rank == 0. With TP>1 and ETP=1, EP ranks fall on TP ranks other than 0, so their experts are never written (see inline). - Add a regression test for that layout if one fits, e.g.
tensor_model_parallel_size=2, expert_model_parallel_size=2on 2 ranks intest_unified_export_megatron.py. - Optionally, skip layers whose filtered expert dict is empty, so EP>0 ranks don't write empty
-epNNNshards for dense layers (see inline).
No action needed:
- Design review: this isn't a new subsystem. It extends the existing exporter, the sharded state dict and the layerwise calibration patching, so there's no parallel mechanism to justify.
- Logic is about 180 added lines, under budget, and there are no licensing changes.
- The test edits are justified. The SequentialMLP EP refusal test was renamed and narrowed to packed local experts because that behaviour changed, and the FP8
quantization_configassertion was added to an existing test. --dsa_kernel_backendhas no tests, which is acceptable for flag plumbing.
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #2873 +/- ##
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+ Coverage 76.99% 78.94% +1.94%
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Files 658 658
Lines 73986 74023 +37
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+ Hits 56965 58435 +1470
+ Misses 17021 15588 -1433
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Actionable comments posted: 4
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Inline comments:
Review comments at @modelopt/torch/export/plugins/mcore_custom.py:
- Around line 398-401: Update the `ep_shards` collection and metadata
aggregation loop so they include only EP shard files produced by the current
export, preventing stale files in a reused directory from entering the index or
overriding current weight-map entries.
Review comments at @modelopt/torch/export/plugins/vllm_fakequant_megatron.py:
- Around line 328-329: Update the fake-quant export flow around the
_hf_config.quantization_config cleanup to remove any copied hf_quant_config.json
from the output directory after the base exporter copies source files and before
fresh quantizer files are saved.
Review comments at @modelopt/torch/quantization/plugins/megatron.py:
- Line 568: Update _QuantMegatronTEGroupedLinear.sharded_state_dict to retain
pinned GGML GPTQ payloads from self.module.weight_quantizer in the grouped
checkpoint output. Ensure its key-selection logic recognizes keys containing
PINNED_PAYLOAD_PREFIX alongside grouped quantizer state so the payload survives
save and reload.
Review comments at @modelopt/torch/utils/plugins/megatron_generate.py:
- Around line 264-266: Update the early return in megatron_prefill so a rank
cannot exit independently during pipeline-parallel execution. Restrict the
layerwise calibration early-stop path to pipeline parallelism of one, or
coordinate the stop across all PP ranks before returning; preserve the existing
single-stage behavior.
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📒 Files selected for processing: 16
CHANGELOG.rstexamples/megatron_bridge/distill.pyexamples/megatron_bridge/export_quantized_megatron_to_hf.pyexamples/megatron_bridge/quantize.pymodelopt/torch/export/plugins/mcore_custom.pymodelopt/torch/export/plugins/vllm_fakequant_megatron.pymodelopt/torch/export/unified_export_megatron.pymodelopt/torch/quantization/ggml/common.pymodelopt/torch/quantization/plugins/custom.pymodelopt/torch/quantization/plugins/megatron.pymodelopt/torch/quantization/utils/layerwise_calib.pymodelopt/torch/utils/plugins/megatron_generate.pytests/examples/megatron_bridge/test_distill.pytests/gpu_megatron/torch/export/test_unified_export_megatron.pytests/gpu_megatron/torch/export/test_vllm_fakequant_megatron_export.pytests/gpu_megatron/torch/quantization/plugins/test_megatron.py
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Both of my earlier points are fixed, but CodeRabbit's stale-shard finding in mcore_custom.py still stands. It can silently point the index at old expert weights.
Needs action:
- Fix the
-ep*.jsonglob insave_safetensors_by_layer_index. Either only aggregate EP ranks below the current EP world size, or delete stale-epNNNshards before writing (see inline). - Reply to CodeRabbit's grouped-linear payload comment (
megatron.py)._QuantMegatronTEGroupedLinear.sharded_state_dictstill drops_ggml_pinned_*keys with a warning. Confirm GGML GPTQ only runs on SequentialMLP experts, or addPINNED_PAYLOAD_PREFIXto its key filter too. - Remove a copied
hf_quant_config.jsonin the fake-quantsave_pretrained, or explain why it can't be there. Right now onlyconfig.json'squantization_configis dropped. - Reply to CodeRabbit's PP>1 early-stop comment on
megatron_prefill. If PP>1 layerwise calibration was already unsupported, a short guard or note settles it. - Have a human inspect the embedded CodeRabbit "Prompt for AI Agents" / "Prompt to fix review comments" blocks before merge. I read them only as untrusted data.
No action needed:
- ✔️ Resolved since the last review:
writes_own_expertsnow gates on expert-TP/expert-DP rank 0, and empty-epNNNshards are skipped. Skipping a TP=2 test is fine given the existing TP>1 export TODO. - No design concern: this extends the existing exporter, sharded-state-dict and layerwise patching. It's under the size budget, has no licensing changes, and the existing test edits are justified by the behaviour change.
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Nudge: every earlier code concern is fixed or settled, and the only remaining blocker is a person checking the CodeRabbit bot text before merge.
Needs action:
- Have a person read the "Prompt for AI Agents" and "Prompt to fix review comments" blocks that CodeRabbit put in its comments on this PR before merging. They are instructions written for AI agents; I treated them only as data.
No action needed:
- ✔️ Resolved since the last review: stale
-epNNNshards are now deleted between the barriers before writing (and the test checks this), the copiedhf_quant_config.jsonis removed from fake-quant exports (with a test), and themegatron_prefillearly return only applies when pipeline parallelism is 1. The author also explained that GGML GPTQ can't run on TE grouped experts, so theirsharded_state_dicthas no pinned payload to drop. - No design concern: the PR extends the existing exporter, sharded state dict and layerwise-calibration patching. It is under the size budget (about 180 logic lines) and makes no licensing changes.
- Both test edits are justified. The EP refusal test was narrowed to packed local experts because that behaviour changed, and the FP8 config and
hf_quant_config.jsonchecks were added to an existing test.
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Inline comments:
Review comments at
@tests/gpu_megatron/torch/export/test_vllm_fakequant_megatron_export.py:
- Line 252: Remove the redundant `hf_quant_config.json` existence assertion from
the test body; `_assert_exported_quantizers`, already called by the test, should
remain the single place that checks this condition.
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📒 Files selected for processing: 5
modelopt/torch/export/plugins/vllm_fakequant_megatron.pymodelopt/torch/export/unified_export_megatron.pymodelopt/torch/utils/plugins/megatron_generate.pytests/gpu_megatron/torch/export/test_unified_export_megatron.pytests/gpu_megatron/torch/export/test_vllm_fakequant_megatron_export.py
🚧 Files skipped from review as they are similar to previous changes: 1
- modelopt/torch/export/plugins/vllm_fakequant_megatron.py
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| def _check_unsupported_states(quantizer: TensorQuantizer): | ||
| for k in quantizer.state_dict(): | ||
| if k not in ["_amax", "_pre_quant_scale", "_global_amax"]: | ||
| # GGML payloads pinned by GPTQ are loaded with the weights from the sharded checkpoint. |
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Do you have an example which unsupported states we have here?
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The states this restore hook handles are _amax, _pre_quant_scale and _global_amax; anything else gets the "Restore of … is not supported" warning. The one this PR adds an exception for is GGML GPTQ's _ggml_pinned_<format> buffer, the packed codes GPTQ chose. With this PR the sharded checkpoint saves and reloads it with the weights, so warning that its restore "might be incorrect" was wrong. Other quantizer buffers outside that list still warn as before.
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| try: | ||
| return module_self._original_forward(*args, **kwargs) | ||
| except _EarlyStopForwardError: | ||
| if depth[0] > 1: # the outermost patched forward stops the pass |
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qq what do we want to achieve here?
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For a VLM wrapper both the wrapper and its language_model get the early-stop patch, and the wrapper's forward calls the language model's. Without the counter, the inner (language model) call would catch the stop and return None, and the wrapper would keep running its own post-processing on None and crash. The counter lets only the outermost patched call turn the stop into None; inner calls re-raise. I reworded the code comment to say this in 7b7c482.
sugunav14
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| depth = [0] | ||
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| def _early_stop_forward(module_self, *args, **kwargs): | ||
| depth[0] += 1 |
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Did you test if this works for image-text calibration case? My understanding is that it would translate to depth=2 case. Is that right?
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Yes: when image-text calibration calls the wrapper, the wrapper's patched forward is depth 1 and the language model's is depth 2. The stop is raised by a decoder layer inside the language model; the depth-2 call re-raises it and the wrapper, the outermost, returns None. I hadn't tested that path, since our GLM PTQ calibrates on text. Added in e703054: test_layerwise_calibrate_vlm_text_forward_loop[True] drives the wrapper, whose forward post-processes the language model's output as a real VLM does. It fails with 'NoneType' object has no attribute 'float' without the depth counter and passes with it. The toy wrapper has no vision tower, but the call stack is the same as a real image-text forward.
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…d VLM models - Keep GPTQ's pinned GGML payloads in the Megatron sharded state dict, so a reloaded PTQ checkpoint exports the calibrated codes instead of re-encoding. - Layerwise calibration through VLM wrappers whose text forward drives language_model. - Export SequentialMLP experts at EP>1: global expert ids, per-EP-rank expert shards. - export_quantized_megatron_to_hf.py --vllm_fakequant_export; the fake-quant export drops a pre-quantized source's quantization_config. - distill.py --sft_in_batch_packing, --moe_aux_loss_coeff, --dsa_kernel_backend (also quantize.py). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
…EP shards With TP>1 and ETP=1, an EP rank can sit on a dense TP rank other than 0, so gating on the dense TP rank left its experts unwritten. Layers without local experts no longer write empty -epNNN shards. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
…P>1 prefill path - The export writer deletes model-*-epNNN shards left by an earlier export at a larger EP, so the index cannot point at old expert weights. - The fake-quant export removes an hf_quant_config.json copied from a pre-quantized source. - megatron_prefill returns early for layerwise calibration only at PP=1. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
… assertion Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
…-text path) Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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…iners, trim slow GPU tests (#2836) ### What does this PR do? Type of change: CI / tests (follow-up to #2832) Related: #2870 (independent, targets `main`) runs GPU CI only for PRs targeting `main`. - **gpu_megatron timeouts, root cause:** Megatron's `destroy_model_parallel()` only drops its references to the NCCL groups, so each test's communicators stayed alive in the persistent workers: about 20 groups and 0.4 GiB of device memory per test (1221 groups and 24 GiB after 61 tests of `test_megatron.py`). Later tests got slower until they hit the 120 s timeout (the nightly cancellations), and smaller GPUs hit NCCL CUDA errors. `megatron_worker_teardown` now destroys every non-default group; `test_megatron.py` stays at 1 group and ~1-3 GiB and passes 131/131 in 4:54 on 2 GPUs (was 3 failures and slower). - **Worker pool:** a failing rank is now reported before its teardown runs. In `gpu_megatron`, teardown starts with `dist.barrier()`, which blocked against a still-running peer, so the parent never heard of the failure and the 30 s grace period could not start (it waited for the pytest timeout). New test fails without the fix. Also the remaining #2832 review nits (fixture-scope note, comments) and the sub-millisecond `get_live_pool` test moved to `tests/unit`. - **Wheel cache for the `gpu` job:** `mamba_ssm`, `causal-conv1d` and `fast-hadamard-transform` were compiled from sdist on every run (~10 min of the job, e.g. 4 + 6 min on 10-16). They are now pinned in `noxfile.py` and built into a wheelhouse that `actions/cache` keeps per container image (key includes the `noxfile.py` hash); a wheel is only built when the wheelhouse lacks it. A plain wheelhouse rather than pip's or uv's cache: pip does not cache wheels it builds from a local source tree (the C++20-patched sdist) or reliably from a git URL, its cache lives under `HOME` (`/github/home` in container jobs), and the GPU sessions use the container's own pip (`venv_backend="none"`), not uv. A directory of wheels keyed on the image and `noxfile.py` is explicit and is saved only once complete. - **CUDA-extension cache never worked for the nightly:** across 25 nightlies the `gpu` job never found its `torch_extensions` cache (205-350 s of extension compile each night; `gpu_megatron` hit about half the nights). `actions/cache` saves only when the job succeeds, and the nightly was failing or timing out every night, so main's cache scope was never populated (PR runs save into their own `pull-request/N` scope, which the nightly cannot read). The extension cache and the wheelhouse are now restored with `actions/cache/restore` and saved with `actions/cache/save` under `always()` (new `.github/actions/save-extensions`). - **Container bumps:** pytorch 26.05/26.07 -> 26.09. **TensorRT-LLM stays at rc20**: no later release runs the multimodal quickstart that `test_qwen_vl` uses (rc21-rc23 import `MultimodalConfig`, which `tensorrt_llm.llmapi` does not export yet; rc24+ import `fla`, which the image lacks). NeMo 26.08 is already the newest public image. **vLLM stays at v0.30.0**: v0.31.0 breaks the vLLM plugin (`test_kimi_gemm_rs_ar_bypasses_enabled_fake_quantizers`: `gemm_rs_ar` has no `.apply`; `test_flashinfer_metadata_builder_patch_stashes_common_metadata`: `FlashInferMetadataBuilder.cache_config`; `test_tiny_llama_quantize` times out) and needs plugin changes by its owners. The `gpu_vllm` job on `v0.20.0` (legacy FusedMoE registration, vLLM < 0.24) is dropped; v0.30.0 is the only vLLM job, and its timeout goes from 15 to 30 min: on PRs (1 GPU) its tests take ~10 min plus ~5 min to pull the image, so it timed out twice with all tests passing; the 2-GPU nightly also runs the TP2 tests and needs ~22 min. - **Extension cache key:** keyed only on the C++/CUDA sources and their build scripts, so editing a Triton kernel no longer forces a rebuild (that rebuild pushed `gpu_vllm` past its 15 min limit). The key now also includes the job name: example and GPU jobs on the same image shared one key, and an example job that builds no extensions finished first and saved an empty cache. `gpu` and `gpu_megatron` then restored it as an exact hit, recompiled on every run (`test_cuda_ext` took 35-46 s in every `gpu` run of this PR), and could never save their own (`Unable to reserve cache ... another job may be creating this cache`). This also affects `main`. The save step now skips when no `.so` was built, so a run cancelled before building anything cannot claim the key with an empty cache. The `tensorrt:26.05` images used by the ONNX/diffusers examples stay: `torch-tensorrt` (<2.13) needs `libnvinfer.so.10`, which newer images drop. - **mamba_ssm / causal-conv1d on pytorch:26.09:** their `setup.py` hardcodes `-std=c++17` but the 26.09 torch headers need C++20 (`ATen requires C++20`), so the wheels are built with the standard patched to C++20 (checked to compile and import against torch 2.13 headers). - **NVFP4 scale conversion without TensorRT-LLM's `auto_deploy`** (removed in rc29, so the next bump would otherwise break): ModelOpt's NVFP4 dequantize/export paths (and `gpu_trtllm` collection) failed with `ModuleNotFoundError`. The cutlass-swizzled block-scale conversion is a tensor permutation, now implemented in ModelOpt as the inverse of `swizzle_nvfp4_scales` (same math as TensorRT-LLM's helper; bit-identical to rc20's on 6 shapes) with a CPU round-trip test. - **conv3d implicit-GEMM JIT build:** `implicit_gemm_cuda.py` passed `-std=c++17`, which fails against the 26.09 torch headers (165 GPU test failures); torch's `load()` now picks the standard. - **FSDP2 + layerwise calibration bug:** `persistent_materialization` patched `FSDPParamGroup.unshard/reshard` on the class, so while one layer was materialized *every* FSDP module skipped unshard; a full forward then ran on sharded DTensor params (`test_layerwise_calibrate_fsdp2` failed on torch 2.14 with `'FSDPParam' object has no attribute '_unsharded_param'`). Now patched only on the param groups inside the layer, and only when the layer itself is FSDP-wrapped (otherwise nothing was gathered for its FSDP submodules, so they must still unshard). CPU/gloo regression test fails without the fix; a second test covers a shared `fully_shard([a, b])` group, nested contexts, a pre-existing instance override and a non-FSDP layer (fails without the group dedup or the FSDP-wrapped check). - **EAGLE training crash (found by the new container):** `hf_eagle.py` compiled `_eagle_forward` with `mode="max-autotune"`, which enables inductor CUDA graphs; recording them intermittently fails (`cudaStreamCaptureStatusInvalidated` / "graph recording observed an input tensor deallocate"). It failed `test_llama_eagle3` on 5 of the last 10 nightlies and on the `pytorch:26.09` dispatch run. Now `max-autotune-no-cudagraphs` (same autotuning, no graphs). Training-speed impact not measured. - **NVFP4 FP8-sweep kernel compile time:** the kernel was autotuned over three tile configs, so first use compiled all three (~70 s per dtype on the `pytorch:26.09` runner), pushing the MSE-sweep export and calibration tests past the 120 s timeout. It now uses the config that was fastest on B300 (BLOCKS_PER_PROGRAM=64, 8 warps; on Ada all three were within ~8% from 256² to 16384x8192). Cold first call drops from 13.6 s to 5.4 s locally, which also helps users' first MSE calibration in each process. - **Stale xfails:** removed the five `xfail(raises=RuntimeError)` markers in `test_megatron_preprocess_data.py`; they xpassed in all 151 PR runs and every nightly that reached them over the last week (one of them never touches HF). - **Dedupe:** removed `tests/gpu_megatron/_extensions/test_torch_extensions.py`, an exact copy of `tests/gpu/_extensions/` (the megatron conftest already prebuilds the extensions). - **Puzzletron e2e trimmed:** Puzzletron is not actively maintained, so CI runs only Nemotron-3-Nano (hybrid MoE), gpt-oss and Qwen3-VL; the other six models are `manual` (~40 s each, ~4 min of the `gpu` job). Run them with `--run-manual`. - **New 2-GPU nightly failures (from tests merged Oct 19):** `gpu_vllm`'s new TP2 tests (#2842) failed because vLLM needs 160 MiB of `/dev/shm` and the container had Docker's default 64 MiB; the GPU test container now gets `--shm-size=2gb`, like the example jobs. `test_distill_llm_sft_hf_chat[True]` (#2873, packed SFT on a tiny MoE at TP2) failed on the sm_120 runner with "No dot product attention backend is available": the tiny model's head_dim of 2 leaves only TE's unfused attention, which the 2-GPU sm_120 runner rejects for packed input (it passes on Ada); the packed case now uses head_dim 8 so the flash and cuDNN backends also qualify. Both only run on the 2-GPU nightly, not in single-GPU PR CI. - **`gpu_megatron` timeout 60 -> 75 min:** PR runs (1 GPU) take ~42 min, but the nightly runs every test on 2 GPUs: 46.5 min on Oct 19, then cancelled at 60 min with 99% done on Oct 20, and at 89% in this branch's dispatch run. - **Unit CI timeout on `main` (Oct 20):** `test_hf_dflash2.py::test_overfits_a_single_batch` took 51-61 s on most unit runners (2-7 s on others) and hit its 60 s timeout, also in main's nightly; both accuracies pass 0.9 by step ~21, so it now runs 30 steps instead of 60 (8 s on one CPU thread, was 16 s). - **Blackwell illegal-memory-access flake:** `test_fp8_real_quantize` (both 2-GPU dispatch runs) and `test_layerwise_calibrate_vlm_text_forward_loop[True]` (PR CI) hit the intermittent CUDA illegal memory access that `skip_flaky_on_blackwell` already skips for ~50 tests in `test_megatron.py`; they get the same marker. Not caused by this PR: `test_megatron.py` alone passed on the same 2-GPU runners in all 4 variants (this branch, and with main's conftest and/or worker pool), and the whole file passes on 2x Ada. - **`test_mamba_search_space` timeout 120 -> 150 s:** across ~135 PR `gpu_megatron` jobs on Oct 16-17 it usually took 64-76 s, ~15% took 85-103 s on slow runners, and it reached 112, 119 and 123 s (twice; one timed out here). Nightlies were ~72 s. ### Testing - Pool: the new test (rank 0 fails, rank 1 hangs, teardown barrier) fails without the fix (`queue.Empty` after the 30 s job timeout) and passes with it; CPU unit tests that use the shared pool pass. - Cache helper: stub-session check, cold cache runs `pip wheel` per missing package, warm cache runs none and installs the cached wheels. - Dispatched on this branch: Regression tests on `pytorch:26.09` passed; Unit tests passed. PR CI on 9d9ab38: everything green except `gpu` (2 FP8-sweep timeouts, fixed above); `gpu_megatron` passed in 32.5 min (60 min limit); the torch-extension cache was restored. - FP8 sweep: `tests/gpu/torch/quantization/test_nvfp4_fp8_sweep_kernel.py` and the accelerate MSE tests pass locally (Ada, torch 2.14); the `hf_ptq.py` export case was not run locally. - PR CI on f222d46: all checks green except `gpu_vllm` (all 190 tests passed, then hit the old 15 min limit) and `gpu_megatron` (`test_mamba_search_space` at 123 s, timeout raised above). The `gpu` job restored the wheelhouse and installed all three wheels without compiling. - FSDP: `tests/unit/torch/quantization/test_utils.py` passes on CPU; GPU FSDP2/layerwise/export tests pass in `nemo:26.08` (56 passed, 1 skipped). - Puzzletron/mamba: in `nemo:26.08` the six puzzletron models skip as manual and `test_mamba_search_space` passes with the new marker. - PR CI on f1311ea (per-job cache keys): all checks green. Each GPU job started cold on its new key and saved a populated extension cache (`gpu` 7 MB, `gpu_megatron` 5 MB, `gpu_trtllm` 5 MB, `gpu_vllm` 2 MB; before, `gpu` and `gpu_megatron` only ever had a 0 MB one). Re-running `gpu_trtllm` restored its cache: `test_cuda_ext` 33.2 s -> 3.1 s and the pytest step 202 s -> 79 s. - Eagle mode: `torch._inductor.list_mode_options` confirms `max-autotune` sets `triton.cudagraphs` and the new mode does not; `tests/unit/torch/speculative` (339) and `tests/gpu/torch/speculative` (14) pass. - 2-GPU nightly fixes (8f58d40): not reproduced locally (the packed test passes on Ada). Dispatched on this branch (2-GPU runners): [Example tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/39373464121) all 13 lanes passed, including `test_distill_llm_sft_hf_chat[True]`; in [GPU tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/39373461370) `gpu` and `gpu_trtllm` passed and both `gpu_vllm` TP2 tests passed, but `gpu_vllm` hit its 20 min limit at 51% of tests (raised to 30 min below) and `gpu_megatron` hit its 60 min limit at 89% (raised to 75 min below). [Re-dispatched GPU tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/39388346112) on f25ff98: `gpu`, `gpu_trtllm` and `gpu_vllm` passed (`gpu_vllm` 211 passed in 15 min); `gpu_megatron` 402 passed and failed only `test_fp8_real_quantize` (the Blackwell flake above). Looked at but left alone: the 11 unified-export cases (each hits a different fuse-flag branch). Shared worker pools across megatron modules and a preloaded forkserver (importing modelopt initializes CUDA) did not work out. - Is this change backward compatible?: ✅ - 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?: N/A - Did you get Claude approval on this PR?: ❌ 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Sonnet 5.5 <noreply@anthropic.com>
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What does this PR do?
Type of change: new feature, bug fix
Gaps found while running GGML IQ2 PTQ → QAD → vLLM evaluation of GLM-5.3-Flash (321B MoE + vision tower) with
examples/megatron_bridgeon GB300. None of it is GLM-specific; the GLM recipe itself is in #2539.Quantization
_ggml_pinned_*buffers). The Megatron sharded state dict dropped them (with a warning), so a PTQ checkpoint reloaded for QAD or export re-encoded every weight from scratch. They are now saved and loaded with the weights, sharded along the weight's tensor-parallel axis, and the restore no longer warns that they are unsupported.language_modeldirectly, so the early-stop patch on the wrapper'sforwardnever ran and calibration failed.get_mcore_layerwise_calibration_layersnow finds the decoder underlanguage_model, the early stop also patches the language model (only the outermost patched forward swallows the stop), andmegatron_prefill(skip_return_logits=True)returns when the forward stopped early.Export
--no_moe_grouped_gemm(SequentialMLP) checkpoints export at--ep_size> 1. GGML export and GPTQ need per-expert linears, so large MoEs could only export at EP=1, which ran out of GPU then host memory for a 600 GB checkpoint. Experts are now written under their global ids, and each EP rank other than 0 writes its own experts tomodel-XXXXX-of-YYYYY-epNNN.safetensorsshards that the index picks up. Packed local-expert rules (Llama 4, GPT-OSS) still refuse EP > 1.export_quantized_megatron_to_hf.py --vllm_fakequant_exportexports withexport_mcore_gpt_to_hf_vllm_fq, folding weight quantizers into BF16 weights so formats vLLM has no kernels for (e.g. GGML IQ) can be evaluated in a stock vLLM.quantization_config. From a pre-quantized source (e.g. FP8 blockwise),config.jsonkept the FP8 config and vLLM read the BF16 weights as FP8.examples/megatron_bridgeflagsdistill.py --sft_in_batch_packing: pack each micro-batch's--sft_hf_datasetconversations into one THD sequence (--mbsconversations per pack) instead of padding each to--seq_length; Megatron-Bridge forces padding whenever EP > 1. Uses the per-micro-batch dataloader and pads each conversation to a multiple of 2 x CP.distill.py --moe_aux_loss_coeff: override the MoE load-balancing loss weight. The aux loss runs over every token, padding included; on GLM's KDA/DSA layers it drove the pad-token embedding gradient to Inf at 32K.distill.py/quantize.py --dsa_kernel_backend {none,tilelang,cudnn}: fused DeepSeek sparse-attention kernels. The unfused fallback builds dense[seq, seq]scores per head and does not fit at long sequence lengths.Usage
Testing
New or changed tests (each GPU test run in
nvcr.io/nvidian/nemo:26.10.rc3, 2x RTX 6000 Ada):test_unified_export_megatron.py::test_sequential_experts_export_at_ep(new, 2 ranks): a tiny Qwen3-MoE with SequentialMLP experts exported at EP=2; every rank's experts are read back from the index under their global ids and match bit for bit, and the checkpoint's names match the HF reference.test_megatron_export_rejects_packed_local_experts_at_epreplaces the old SequentialMLP refusal test.test_vllm_fakequant_megatron_export.py::test_mcore_vllm_export: the source now carries an FP8quantization_config, and the export'sconfig.jsonmust not.test_megatron.py::test_ggml_gptq_payload_sharded_state_dict(new, 2 ranks, TP=2): GPTQ with thegeneral/ptq/iq2_xsrecipe, save and reload, pinned payloads identical.test_megatron.py::test_layerwise_calibrate_vlm_text_forward_loop(new): layerwise calibration of a wrapper withoutdecoder, driven through itslanguage_modelwithmegatron_prefill.tests/examples/megatron_bridge/test_distill.py::test_distill_llm_sft_hf_chat[packed](new case): tiny Qwen3-MoE student,--sft_in_batch_packing --mbs 2 --moe_aux_loss_coeff 0.quantization_configcheck, the payload test with only the sharding hunks reverted, the VLM test with only the layerwise/prefill hunks reverted); the packed case fails on the unknown flags.test_unified_export_megatron.py103 passed,test_vllm_fakequant_megatron_export.py7 passed,test_megatron.py131 passed / 35 skipped / 3 failed. The 3 (test_te_grouped_vs_sequential_default_loss[*],test_auto_quantize_moe_ep) fail the same way onmainin a full-file run on this machine and pass when run alone, with or without this PR.tests/unit/torch/quantization/test_ggml_*,test_layerwise_calibrate.py,tests/unit/recipe: 661 passed.--dsa_kernel_backendhas no test (flag plumbing; needs a DSA model, used in the GLM runs below).At scale (oci-jhb GB300, every path above in use):
--dsa_kernel_backend cudnn); fake-quant export at EP16 in ~6 min (all 354 tensors of the 4-layer mini model identical to EP1 before the full run); QAD from that checkpoint at EP32 on 32 nodes for 200 iterations with--sft_in_batch_packing --moe_aux_loss_coeff 0; vLLM evaluation of the exports across 6 benchmarks.--sft_in_batch_packing --moe_aux_loss_coeff 0(oci-hsg, 32 nodes).Before your PR is "Ready for review"
CONTRIBUTING.md: N/AAdditional Information
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