- get_model/get_tokenizer imported from example_utils (not hf_ptq)
- KV_QUANT_CFG_CHOICES imported from hf_ptq (not mtq)
- Removed dead _FORCE_AMAX_CPU global and global reference in run_export_only
- Fixed stale comments
- All 16 imports and references verified against the actual B200 runtime
- Zero divergences from modelopt example path except get_model()
Raw from_pretrained OOMs during weight conversion — torch.cat on expert
gate_up_proj tries to allocate 31.5GB on a GPU with only 25.9GB free.
modelopt's get_model() handles max_memory/device_map properly for models
that need sequential device mapping.
Key changes:
- snapshot_amax_to_cpu(): copies all quantizer _amax to CPU and saves
to disk (~50MB) right after mtq.quantize() returns, before any other
GPU operation can corrupt them
- force_all_amax_to_cpu(): nuclear option, moves _pre_quant_scale and
_global_amax to CPU too
- _FORCE_AMAX_CPU flag + patched amax setter: after calibration, any
future amax writes go to CPU instead of GPU
- --validate-only mode to check saved state without running anything
- restore_amax_from_snapshot() for --export-only recovery
- torch.cuda.empty_cache() + gc.collect() between steps
- Patches: export_amax CPU fallback, get_activation_scaling_factor
clamp instead of assert
- New scripts/quantize_nvfp4.py: runs full ModelOpt pipeline in-process
- Saves calibrated state after calibration (insurance against export crashes)
- Patches modelopt for V4: ModuleList quantizers, stale GPU tensor safety
- --export-only flag to retry export from saved calibration state
- Removed old model_opt_nvfp4_full.py (shell wrapper)
- Updated README with new pipeline docs and bug #5/#6
