Fix #2984: prevent DDP race on shared HF cache during model loading - #3030
Fix #2984: prevent DDP race on shared HF cache during model loading#3030sonalibiswas242 wants to merge 3 commits into
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📝 WalkthroughWalkthroughThe change adds rank-0 Hugging Face model prefetching for distributed execution. The utility skips local paths, synchronizes ranks after downloading, and is used by the W4A16 and W8A8 DDP examples before model loading. ChangesDistributed model prefetching
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🟠 High · up to If the rank-0 model download fails, the other distributed workers can hang indefinitely at synchronization instead of receiving the failure, leaving the training job stuck. Merge should wait until prefetch failures are propagated consistently across ranks. Sequence Diagram(s)sequenceDiagram
participant DDP
participant Rank0
participant HuggingFaceHub
participant ModelLoader
DDP->>Rank0: initialize distributed execution
Rank0->>HuggingFaceHub: snapshot_download(model_id)
Rank0->>DDP: synchronize at barrier
DDP->>ModelLoader: load model after synchronization
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This pull request introduces a prefetch_model_on_rank0 utility to download Hugging Face models to the local cache on rank 0 before other ranks concurrently load the model, preventing cache corruption in DDP environments. The utility is integrated into the Llama 3 and SmoothQuant DDP examples. Feedback suggests modifying the prefetch logic to check LOCAL_RANK instead of global rank 0 to correctly support multi-node DDP setups where each node has its own local cache.
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Inline comments:
In `@src/llmcompressor/utils/dist.py`:
- Around line 48-51: Update the rank-0 prefetch flow around snapshot_download so
failures are captured, a success/failure status is broadcast to all ranks, and
every rank raises when prefetch fails. Invoke dist.barrier() only after the
broadcast confirms successful completion, using the existing distributed
utilities and preserving the current rank-0-only download behavior.
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📒 Files selected for processing (3)
examples/quantization_w4a16/llama3_ddp_example.pyexamples/quantization_w8a8_int8/smoothquant_ddp_example.pysrc/llmcompressor/utils/dist.py
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vllm-project/compressed-tensors(manual)
| if dist.get_rank() == 0: | ||
| snapshot_download(model_id_or_path, **snapshot_download_kwargs) | ||
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| dist.barrier() |
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🩺 Stability & Availability | 🟠 Major | ⚡ Quick win
Propagate rank-0 prefetch failures before the barrier.
If snapshot_download raises on rank 0, rank 0 exits before dist.barrier(). All other ranks then block indefinitely at Line 51.
Catch the rank-0 exception. Broadcast a failure status to every rank. Raise on every rank when prefetch fails. Call dist.barrier() only after a successful prefetch.
Proposed fix
+ error = None
if dist.get_rank() == 0:
- snapshot_download(model_id_or_path, **snapshot_download_kwargs)
+ try:
+ snapshot_download(model_id_or_path, **snapshot_download_kwargs)
+ except Exception as exc:
+ error = f"{type(exc).__name__}: {exc}"
+ errors = [error]
+ dist.broadcast_object_list(errors, src=0)
+ if errors[0] is not None:
+ raise RuntimeError(f"Rank 0 model prefetch failed: {errors[0]}")
dist.barrier()📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if dist.get_rank() == 0: | |
| snapshot_download(model_id_or_path, **snapshot_download_kwargs) | |
| dist.barrier() | |
| error = None | |
| if dist.get_rank() == 0: | |
| try: | |
| snapshot_download(model_id_or_path, **snapshot_download_kwargs) | |
| except Exception as exc: | |
| error = f"{type(exc).__name__}: {exc}" | |
| errors = [error] | |
| dist.broadcast_object_list(errors, src=0) | |
| if errors[0] is not None: | |
| raise RuntimeError(f"Rank 0 model prefetch failed: {errors[0]}") | |
| dist.barrier() |
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In `@src/llmcompressor/utils/dist.py` around lines 48 - 51, Update the rank-0
prefetch flow around snapshot_download so failures are captured, a
success/failure status is broadcast to all ranks, and every rank raises when
prefetch fails. Invoke dist.barrier() only after the broadcast confirms
successful completion, using the existing distributed utilities and preserving
the current rank-0-only download behavior.
Source: Path instructions
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…el loading Adds prefetch_model_on_rank0() which has rank 0 fully populate the HF cache via snapshot_download before all ranks call from_pretrained, avoiding concurrent writes to the same cache dir. Wired into two DDP examples as a reference pattern; same fix applies to the other ~9 DDP examples in a follow-up if this approach is accepted. Ran unit and collection-only tests (31 passed, 0 failed). GPU/multi-GPU integration tests could not be run locally due to lack of CUDA hardware; these should be verified in CI. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01JXryyLXjfU9noDriTh91Wy Signed-off-by: Sonali Biswas <sonalibiswas242@gmail.com>
… rank 0 only lives on one node; in multi-node setups each node has its own local HF cache, so other nodes still raced on from_pretrained. Use LOCAL_RANK (set by torchrun) to prefetch on each node's local rank 0 instead. Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Signed-off-by: Sonali Biswas <91608355+sonalibiswas242@users.noreply.github.com> Signed-off-by: Sonali Biswas <sonalibiswas242@gmail.com>
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01JXryyLXjfU9noDriTh91Wy Signed-off-by: Sonali Biswas <sonalibiswas242@gmail.com>
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Problem
Every DDP example calls
from_pretrained(MODEL_ID, ...)on all ranksimmediately after
init_dist(). Thedist.barrier()insideinit_dist()only synchronizes the process group — it says nothing about the filesystem
or HF cache state. Right after it returns, every rank races into
from_pretrainedindependently, andtransformers/huggingface_hubinternally performs cache checks and downloads for each shard. With N
ranks hitting the same shared
HF_HOME/TRANSFORMERS_CACHEat once(the typical single-node multi-GPU
torchrunsetup), this can producepartially-written blobs, reads of a config/index file mid-write, or
corrupted/incomplete cache entries.
I checked the repo for an existing rank-0-downloads-first guard and
didn't find one — no
accelerator.is_main_process,PartialState, ormanual
if rank == 0: download(); barrier()pattern exists insrc/or
examples/.Fix
Adds
prefetch_model_on_rank0()insrc/llmcompressor/utils/dist.py.Rank 0 calls
snapshot_downloadto fully populate the shared cache,then all ranks hit a
dist.barrier(), so every rank's subsequentfrom_pretrainedcall reads from an already-complete local cacheinstead of racing to write it. No-op for local paths or non-distributed
runs.
Wired into two examples as a reference pattern:
examples/quantization_w8a8_int8/smoothquant_ddp_example.pyexamples/quantization_w4a16/llama3_ddp_example.pyThe same unguarded pattern exists in ~9 other DDP examples in the repo
(AWQ, AutoRound, imatrix, sequential offloading, MoE examples). Happy
to open a follow-up PR to wire the helper into those as well once this
approach is confirmed — wanted to keep this first PR small and reviewable.
I also checked
copy_python_files_from_model_cache(which callshf_hub_downloadforconfig.json), since it looked similarlyunguarded — but it's already wrapped in
if is_source_process():incompressed_tensors_utils.py, so no fix needed there.Testing
tests/llmcompressor/observers/test_fusion_handler.py(exercisesllmcompressor.utils.dist): 28 passedtests/llmcompressor/transformers/smoothquant/test_smoothquant_distributed.py -m unit: 3 passedtests:
test_smoothquant_distributed.py,test_compression_ddp.py,test_quantization_ddp.py,test_dist_disk_offload.py,test_distributed.py,test_example_scripts.pyI don't have multi-GPU hardware available locally, so I wasn't able to
run the actual
@requires_gpu(2)/@pytest.mark.multi_gpuintegrationtests. Would appreciate a maintainer running those in CI to confirm.
Fixes #2984