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[OMNIML-6029] [4/5] llm_sparsity: load the model from local disk, downloading it first if needed - #2611
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 9 remain after this review. 📝 WalkthroughWalkthroughBoth scripts resolve checkpoints to local paths before loading models and tokenizers. The attention sparsity script removes a separate tokenizer save call. The weight sparsity script documents saving the tokenizer after model export when a pad token was added. ChangesCheckpoint loading and export
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~10 minutes Change: Bug fix Merge Risk: ⚪ Minimal · up to Both scripts load from resolved local checkpoints, and no concrete failure in the documented export workflows is established. External tokenizer runtime behavior remains unverified, but it does not demonstrate a merge-blocking risk. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
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… copy_non_model_files (#2607) ### What does this PR do? Type of change: new feature ### Summary of the series Every pipeline that quantizes, prunes, sparsifies or distills a Hugging Face model now starts the same way: **make the checkpoint local, then load from it**. `ensure_local_checkpoint` returns a local directory as-is and downloads a Hub model ID in full if it is not cached yet -- once, on rank 0 -- and the model, tokenizer and processor are then loaded from that local directory. Before, a Hub ID went straight to `from_pretrained`, which fetched only the files loading needed, so every later step that needed the source as files found its own way back to it: `hf_ptq` through a hand-rolled cache lookup (`_resolve_model_path`, whose `TRANSFORMERS_CACHE` glob picked the lexicographically largest commit hash rather than `refs/main`) and a hardcoded download allowlist, regenerating tokenizer files along the way; the Megatron export by fetching only `*.py` for a Hub ID; other callers of `export_hf_checkpoint` not at all. With the source on local disk, exports follow one rule: an export writes the **model files** -- weights in any format and the metadata describing them -- and carries every other source file (**non-model files**: tokenizer, processor, remote code, chat templates, README, ...) over verbatim, from **local disk only**. Merge in order: 1. #2607 — Add `ensure_local_checkpoint` to load models from local disk, and `copy_non_model_files` ← **this PR** 2. #2608 — HF exporters write off-index safetensors and non-model files 3. #2609 — `hf_ptq`: load the model from local disk, downloading it first if needed 4. #2610 — `megatron_bridge`: load the HF model from local disk; Megatron export copies its non-model files 5. #2611 — `llm_sparsity`: load the model from local disk, downloading it first if needed ### This PR [1/5] Adds the two building blocks to `modelopt/torch/utils/plugins/hf_checkpoint_utils.py`, re-exported from `modelopt.torch.export`. Nothing calls them yet; #2609–#2611 make `ensure_local_checkpoint` the first step of each pipeline. - **`ensure_local_checkpoint(model_name_or_path, group=None) -> (hub_model_id, local_checkpoint_path)`** makes the whole checkpoint local before anything loads it: a local directory as-is, a Hub ID downloaded in full if not cached yet (guessing which files are redundant would risk dropping ones the checkpoint needs). Load the model, tokenizer and processor from `local_checkpoint_path`; `hub_model_id` is the Hub ID, or `None` for a local path, for records that must stay valid on other hosts. - Under `torch.distributed`, rank 0 of `group` resolves it and broadcasts the result -- or its error, so a failed download raises everywhere rather than stranding the waiting ranks. Rank 0 decides for local paths too, since ranks checking for themselves could disagree about a node-local directory. Without a `group`, every rank waits on a temporary gloo group whose 24 h timeout outlasts a large download; with one, the caller's group carries the wait. A rank that cannot see the resolved path raises, asking for shared storage. - `snapshot_download` silently returns a possibly incomplete cached snapshot when the Hub is unreachable. With `HF_HUB_OFFLINE` the cache is used quietly (the user's choice); with the Hub unexpectedly unreachable it is used with a warning, or the Hub's error is raised if nothing is cached. An input that is neither a local directory nor a Hub model ID the Hub can access -- a mistyped local path, say -- raises a `ValueError` saying so, chained to the Hub's error; a gated repo still raises `GatedRepoError`. - **`copy_non_model_files(source, export_dir)`** copies every file of a local checkpoint, recursively, except weight files in any format, top-level export metadata (`config.json`, `hf_quant_config.json`, stale quant configs, `.experiment.json`), files the export already wrote, hidden directories (`.git`, `.cache`), and links that resolve outside the checkpoint or its Hub `blobs/` (checked with the existing `resolve_checkpoint_file`). It never writes through a link already in the export, removes a partial copy when one fails, and skips `export_dir` when it lies inside the source (e.g. an earlier export). It returns `/`-separated paths on every platform. **What happens to the existing copy helpers.** `copy_non_model_files` replaces both copy rules the series' callers use today. #2609 moves `hf_ptq` off `copy_non_safetensor_files_from_ckpt`. #2610 moves the Megatron export onto `copy_non_model_files` (dropping its Hub-fetching `copy_hf_ckpt_remote_code` branch), moves `prune_minitron` and `export_distilled_megatron_to_hf` off `copy_hf_ckpt_remote_code`, and deprecates `copy_non_safetensor_files_from_ckpt`. `copy_hf_ckpt_remote_code` stays for now: puzzletron's `init_child_from_parent` still uses it, and migrating puzzletron is out of scope for this series. Also: module constants move to the top next to a note defining model versus non-model files; `off_index_safetensors_files` / `copy_off_index_safetensors` accept a `None` source and report what they copy; comments stop calling off-index weight files "sidecars". ### Usage ```python from modelopt.torch.export import copy_non_model_files, ensure_local_checkpoint hub_model_id, local_checkpoint_path = ensure_local_checkpoint("Qwen/Qwen3-8B") # every rank calls it model = AutoModelForCausalLM.from_pretrained(local_checkpoint_path) ... copy_non_model_files(local_checkpoint_path, export_dir) # after the export wrote its own files ``` ### Testing `pytest tests/unit/torch/utils tests/unit/torch/export tests/examples/hf_ptq` (GPU end-to-end and `test_dataset_utils` excluded): 684 passed. New tests cover the copy rule (weights, owned metadata, subdirectories, hidden dirs, links inside/outside/dangling, Hub `blobs/`), online/offline/unreachable/missing-repo resolution, and -- over two real gloo processes -- rank-0-only download, error propagation, an explicit group, a subgroup without global rank 0, and a local path hidden from one rank. The resolution paths were also checked live against the Hub. CPU only (torch 2.12, transformers 5.9, huggingface_hub 1.19); `transformer_engine` cannot be imported in this environment, so GPU and Megatron tests are left to CI. ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ (additive) - 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)?: N/A (entries land with the behaviour changes in #2608–#2610) - Did you get Claude approval on this PR?: ❌ ### Additional Information Stacked series; this is the base. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Checkpoint exports can resolve local and Hub-hosted checkpoints, using cached snapshots when offline or Hub access is unavailable. Missing offline cache entries are reported as errors. * Exports can copy eligible non-model checkpoint files without overwriting existing export files. Hidden directories and unsafe or dangling links are excluded. * Distributed exports coordinate checkpoint resolution across processes and report failures, including when other processes cannot access the resolved path. * Safetensors files not listed in an index are treated as standalone weight files and can be copied into exports when eligible. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Review comments at @examples/llm_sparsity/attention_sparsity/hf_sa.py:
- Around line 239-244: After export_hf_checkpoint completes, call
save_pretrained on the runtime tokenizer with export_dir so the configured pad
token is persisted in the exported tokenizer. Remove the comment stating that
tokenizer files remain unchanged.
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…2608) ### What does this PR do? Type of change: bug fix, new feature ### Summary of the series Every pipeline that quantizes, prunes, sparsifies or distills a Hugging Face model now starts the same way: **make the checkpoint local, then load from it**. `ensure_local_checkpoint` returns a local directory as-is and downloads a Hub model ID in full if it is not cached yet -- once, on rank 0 -- and the model, tokenizer and processor are then loaded from that local directory. Before, a Hub ID went straight to `from_pretrained`, which fetched only the files loading needed, so every later step that needed the source as files found its own way back to it: `hf_ptq` through a hand-rolled cache lookup (`_resolve_model_path`, whose `TRANSFORMERS_CACHE` glob picked the lexicographically largest commit hash rather than `refs/main`) and a hardcoded download allowlist, regenerating tokenizer files along the way; the Megatron export by fetching only `*.py` for a Hub ID; other callers of `export_hf_checkpoint` not at all. With the source on local disk, exports follow one rule: an export writes the **model files** -- weights in any format and the metadata describing them -- and carries every other source file (**non-model files**: tokenizer, processor, remote code, chat templates, README, ...) over verbatim, from **local disk only**. Merge in order: 1. #2607 — Add `ensure_local_checkpoint` to load models from local disk, and `copy_non_model_files` 2. #2608 — HF exporters write off-index safetensors and non-model files ← **this PR** 3. #2609 — `hf_ptq`: load the model from local disk, downloading it first if needed 4. #2610 — `megatron_bridge`: load the HF model from local disk; Megatron export copies its non-model files 5. #2611 — `llm_sparsity`: load the model from local disk, downloading it first if needed ### This PR [2/5] `export_hf_checkpoint` put the tensors of off-index safetensors (GLM-4.7's `mtp.safetensors`) into `exclude_modules` but left copying the files to the `hf_ptq` example, so any other caller exported exclusions for weights the checkpoint lacked. It likewise left the source's non-model files to the caller, while its streaming paths copied only top-level `*.py`. Every HF export path now does both itself, from the model's source checkpoint on local disk: the regular, streaming and FSDP2 paths; the layerwise path, into the exporter's own directory (a recipe's `layerwise.export_dir` can set it apart from `export_dir`); and the vLLM fake-quant export. `save_non_weight_artifacts` copies the non-model files in place of its `*.py` glob. A Hub-ID source gets a warning instead of a fetch, since its cache holds only what loading needed. Until #2609, `hf_ptq` still runs its own copy afterwards -- harmless, as files already present are skipped -- and prints that warning for a Hub-ID `--pyt_ckpt_path`. ### Usage No API change; `export_hf_checkpoint(model, export_dir=...)` on a model loaded from a local directory now also writes the source's non-model and off-index files. ### Testing Same suite as #2607: 688 passed. New tests: the regular path copies non-model files (tiny Llama loaded from a local directory), a Hub-ID source warns without fetching, and the layerwise path writes `mtp.safetensors` into the exporter's directory even when `export_dir` differs. A CPU end-to-end FP8 export of `hf-internal-testing/tiny-random-LlamaForCausalLM` carried the tokenizer and README. CPU only (torch 2.12, transformers 5.9, huggingface_hub 1.19); `transformer_engine` cannot be imported in this environment, so GPU and Megatron tests are left to CI. ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ (exports gain files; nothing is removed) - 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)?: ✅ - Did you get Claude approval on this PR?: ❌ ### Additional Information Stacked on #2607. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Bug Fixes** * Hugging Face and vLLM exports now preserve off-index safetensors and other non-model files from local source checkpoints, including files not listed in the checkpoint index. * Layerwise exports place copied safetensors in the correct output directory. * Exports from Hub sources warn when source files cannot be copied locally, without downloading them. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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… needed (#2609) ### What does this PR do? Type of change: bug fix ### Summary of the series Every pipeline that quantizes, prunes, sparsifies or distills a Hugging Face model now starts the same way: **make the checkpoint local, then load from it**. `ensure_local_checkpoint` returns a local directory as-is and downloads a Hub model ID in full if it is not cached yet -- once, on rank 0 -- and the model, tokenizer and processor are then loaded from that local directory. Before, a Hub ID went straight to `from_pretrained`, which fetched only the files loading needed, so every later step that needed the source as files found its own way back to it: `hf_ptq` through a hand-rolled cache lookup (`_resolve_model_path`, whose `TRANSFORMERS_CACHE` glob picked the lexicographically largest commit hash rather than `refs/main`) and a hardcoded download allowlist, regenerating tokenizer files along the way; the Megatron export by fetching only `*.py` for a Hub ID; other callers of `export_hf_checkpoint` not at all. With the source on local disk, exports follow one rule: an export writes the **model files** -- weights in any format and the metadata describing them -- and carries every other source file (**non-model files**: tokenizer, processor, remote code, chat templates, README, ...) over verbatim, from **local disk only**. Merge in order: 1. #2607 — Add `ensure_local_checkpoint` to load models from local disk, and `copy_non_model_files` 2. #2608 — HF exporters write off-index safetensors and non-model files 3. #2609 — `hf_ptq`: load the model from local disk, downloading it first if needed ← **this PR** 4. #2610 — `megatron_bridge`: load the HF model from local disk; Megatron export copies its non-model files 5. #2611 — `llm_sparsity`: load the model from local disk, downloading it first if needed ### This PR [3/5] **Loading.** `main()` now makes the model local before anything else: `ensure_local_checkpoint(--pyt_ckpt_path)` returns a local directory as-is or downloads a Hub ID once, and every later step -- loading the model, tokenizer and processor, the MXFP4 cast, and the export's copy of the source's files -- reads that local copy. `--pyt_ckpt_path` stays as given; the local directory is `args.local_checkpoint_path`, and `args.hub_model_id` (the Hub ID, or `None`) still names the layerwise resume directory. This replaces the per-step ways back to the source -- `_resolve_model_path`, a hardcoded sidecar allowlist, `_resolved_local_dir`, `hf_hub_download` fallbacks. **Exporting.** The unified exporters copy the source's non-model files themselves (#2608), so the example no longer does -- except after the deprecated TensorRT-LLM export, which does not. It also stops regenerating tokenizer and processor files: the source's are carried over verbatim. So a `pad_token` that `get_tokenizer` sets for calibration (the EOS token, for a model without one) no longer leaks into the exported tokenizer; #2597 already stopped it overwriting an existing pad token. It also removes the default padding side and pad token threaded through six functions only to undo that before saving. The three notebooks do the same: make the model local first, load from it, and leave the tokenizer files to the export. Mostly deletions (+84 / −466). ### Usage No flag change. `--pyt_ckpt_path org/model` downloads the whole repo once before loading; to skip files, download it yourself (e.g. `hf download org/model --exclude 'original/*'`) and pass the local path. ### Testing Same suite as #2607: 685 passed (the `hf_ptq` tests moved to the library in #2607 or were dropped with the code they covered). Notebooks validated as JSON. CPU only (torch 2.12, transformers 5.9, huggingface_hub 1.19); `transformer_engine` cannot be imported in this environment, so GPU and Megatron tests are left to CI. ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ (`--pyt_ckpt_path` unchanged; exports carry the source's files as-is) - 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?: N/A (removes code; the new helpers are tested in #2607/#2608) - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A (the `pad_token` fix is #2597's entry; the verbatim copy is #2608's) - Did you get Claude approval on this PR?: ❌ ### Additional Information Stacked on #2608. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Updates** * Models and tokenizers are loaded from a locally resolved checkpoint, which is reused across quantization and export steps. * TensorRT-LLM exports include non-model files from the local checkpoint, and multimodal exports save the source configuration. * Quantized exports no longer separately save tokenizer files or restore tokenizer padding settings. Local checkpoint paths are also used when restoring compressed weights and resolving source configurations. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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The export now carries the source's non-model files only from local disk, so hf_sa.py and export_hf_ckpt.py make the model local first with ensure_local_checkpoint, keeping their argument as given and reading from hf_model_path. hf_sa.py no longer saves its tokenizer into the export: it sets pad_token to eos only for generation, so the source's files are the right ones. export_hf_ckpt.py keeps its save, after the export, because it can add a pad token and resize the embeddings. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
…s hub_model_id As in hf_ptq: name the local checkpoint directory and the Hub identifier for what they are. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Approving: this is a small, correct examples-only change. Both scripts now make the checkpoint local and load from that copy, so export_hf_checkpoint can copy the source's non-model files.
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ensure_local_checkpointreturn order(hub_model_id, local_checkpoint_path)and the export flow._source_checkpointfalls back toconfig._name_or_path, which is now the local snapshot, so_copy_non_model_files_from_sourceruns and doesn't take the warning branch. hf_sa.pyno longer callstokenizer.save_pretrained, so the export ships the source's tokenizer files unchanged. The EOS pad token it sets is only used for generation in the script, so that's the right result.export_hf_ckpt.pystill saves the tokenizer after the export, so its added[PAD]token and resized embeddings stay consistent. The order matters becausecopy_non_model_filesskips files that already exist in the export.args.hub_model_idis set in both scripts but nothing reads it yet. It's harmless and matches thehf_ptqpattern.- The PR adds no new tests. The existing GPU example tests (
tests/examples/llm_sparsity/attention_sparsity/test_attention_sparsity.py) run these code paths in CI.
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What does this PR do?
Type of change: bug fix
Summary of the series
Every pipeline that quantizes, prunes, sparsifies or distills a Hugging Face model now starts the same way: make the checkpoint local, then load from it.
ensure_local_checkpointreturns a local directory as-is and downloads a Hub model ID in full if it is not cached yet -- once, on rank 0 -- and the model, tokenizer and processor are then loaded from that local directory.Before, a Hub ID went straight to
from_pretrained, which fetched only the files loading needed, so every later step that needed the source as files found its own way back to it:hf_ptqthrough a hand-rolled cache lookup (_resolve_model_path, whoseTRANSFORMERS_CACHEglob picked the lexicographically largest commit hash rather thanrefs/main) and a hardcoded download allowlist, regenerating tokenizer files along the way; the Megatron export by fetching only*.pyfor a Hub ID; other callers ofexport_hf_checkpointnot at all.With the source on local disk, exports follow one rule: an export writes the model files -- weights in any format and the metadata describing them -- and carries every other source file (non-model files: tokenizer, processor, remote code, chat templates, README, ...) over verbatim, from local disk only.
Merge in order:
ensure_local_checkpointto load models from local disk, andcopy_non_model_fileshf_ptq: load the model from local disk, downloading it first if neededllm_sparsity: load the model from local disk, downloading it first if needed ← this PRmegatron_bridge: load the HF model from local disk; Megatron export copies its non-model filesThis PR [4/5]
Loading.
hf_sa.pyandexport_hf_ckpt.pynow make the model local first withensure_local_checkpoint-- a local directory as-is, a Hub ID downloaded once -- and load the model and tokenizer from that copy (args.local_checkpoint_path), keeping their argument as given. That is also what letsexport_hf_checkpoint, which carries the source's non-model files only from local disk (#2608), include them.Exporting.
hf_sa.pyno longer saves its tokenizer into the export: it setspad_tokento EOS only for generation, so the source's files are the right ones.export_hf_ckpt.pykeeps its save, after the export, because it can add a pad token and resize the embeddings.Usage
No flag change.
Testing
Both scripts byte-compile; their end-to-end tests need a GPU and are left to CI. No library code changes in this PR.
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.).CONTRIBUTING.md: N/AAdditional Information
Based on
main(#2607–#2609 are merged); #2610 is stacked on it.Summary by CodeRabbit