[Core] Refactor model loading code #4097
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See also partially related PR/discussion about properly supporting local HF cache in offline-only mode or when HF hub can't be reached: #3125 |
Yard1
changed the title
| VLLM_USE_MODELSCOPE = os.environ.get("VLLM_USE_MODELSCOPE", | ||
| "False").lower() == "true" |
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Is this more standard?
| VLLM_USE_MODELSCOPE = os.environ.get("VLLM_USE_MODELSCOPE", | |
| "False").lower() == "true" | |
| VLLM_USE_MODELSCOPE = os.environ.get("VLLM_USE_MODELSCOPE") is not None |
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I would say the first approach is more common (you want to have a falsy value as well)
| if isinstance(load_config.load_format, type): | ||
| return load_config.load_format(load_config) |
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Are these two lines related to this optimization?
| """Gets a tokenizer for the given model name via Huggingface/modelscope.""" | ||
| if VLLM_USE_MODELSCOPE: | ||
| # download model from ModelScope hub, | ||
| # lazy import so that modelscope is not required for normal use. | ||
| # pylint: disable=C. | ||
| from modelscope.hub.snapshot_download import snapshot_download | ||
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| # Only set the tokenizer here, model will be downloaded on the workers. | ||
| if not os.path.exists(tokenizer_name): | ||
| tokenizer_path = snapshot_download( | ||
| model_id=tokenizer_name, | ||
| cache_dir=download_dir, | ||
| revision=tokenizer_revision, | ||
| # Ignore weights - we only need the tokenizer. | ||
| ignore_file_pattern=["*.pt", "*.safetensors", "*.bin"]) | ||
| tokenizer_name = tokenizer_path | ||
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Why don't we need these codes before but need them now?
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the entire model was downloaded on init of ModelConfig. Now the model will be downloaded by the workers instead, but we still need the tokenizer here
| return model.eval() | ||
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| class DummyModelLoader(BaseModelLoader): |
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Is it for testing? Can you spsecify if that's the case?
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this is already existing behavior in vllm
This PR refactors the weight loading code.
ModelLoaderinterface is introduced, with implementations for all currently supported ways of loading weights (from disk, dummy, using tensorizer). It can be easily extended to support new loading methods.LoadConfigobject is introduced to hold generic model loader configurationload_weightsmethods on model classes have been modified to simply take in an iterator over (name, weight tensor)This PR contains many changes, but most of them are just code moved around or modified slightly (eg. all of the model files have had the exact same modification). The main additions are in
vllm/model_executor/model_loader/loader.py. There are no logic changes, aside from some very slight differences to how ModelScope is interacted with.PR Checklist (Click to Expand)
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