[Core] Implement sharded state loader #4690
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We should add a test to ensure it's working correctly (loaded weights and outputs are the same as with the default loader) |
examples/save_sharded_state.py
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| def main(args): | ||
| engine_args = EngineArgs.from_cli_args(args) | ||
| model_path = engine_args.model | ||
| if not Path(model_path).is_dir(): | ||
| raise ValueError("model path must be a local directory") | ||
| # Create LLM instance from arguments | ||
| llm = LLM(**dataclasses.asdict(engine_args)) | ||
| # Prepare output directory | ||
| Path(args.output).mkdir(exist_ok=True) | ||
| # Dump worker states to output directory | ||
| model_executor = llm.llm_engine.model_executor | ||
| model_executor.save_sharded_state(path=args.output, | ||
| pattern=args.pattern, | ||
| max_size=5 * 1024**3) | ||
| # Copy metadata files to output directory | ||
| for file in os.listdir(model_path): | ||
| if not any( | ||
| file.endswith(ext) for ext in (".bin", ".pt", ".safetensors")): | ||
| shutil.copy(f"{model_path}/{file}", args.output) |
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shouldn't this set the config.json or quant_config.json next to the model weights to inform vLLM loading the model what type of quantization the model checkpoint is in?
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Currently, it's just copying the config.json and quant_config.json from the input checkpoint that's being converted, which works for the use cases we've tested. Actually, I am not sure if it's correct to override these configs (or add a quant_config.json where it wasn't there previously) because then the config may mismatch the final loaded states?
Sure, that makes sense. What's the spec of the test runner machine we should target? OK to assume cuda device is present? |
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@Yard1 added test, please take a look |
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One question I have is that can this be implemented using safetensor's partial read? safetensors have all the metadata in headers so you can access the tensors partially |
Conceptually, I think so. Though for larger models like Arctic we prefer this implementation for a few reasons:
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Ah, I hadn't seen that PR, thanks for bringing it up! From what I understand, both PRs address the problem of model loading speed, particularly for larger models, but the approaches seem pretty different. #3729 modifies the existing model loading path so each worker loads a different set of tensors, then broadcasts the shards to each other. This PR loads the model once using the default path and dumps each worker state to disk, then subsequent model loads can direct read these states, skipping the default model loading path altogether. Some first thoughts on the pros/cons:
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LGTM! The code looks clean and mergable. Thanks for addressing the comments from @Yard1.
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
This PR implements a new model loader that directly loads the sharded states of each worker when using
DistributedGPUExecutor. When using tensor parallelism, this avoids each worker reading the full checkpoint just to load a small shard of it. Our tests using Arctic (#4652) showed a 10x improvement in model loading speed from NVMe when using 8x tensor parallelism.For quantization methods like DeepSpeed's FP_Quantize (also used in #4652) that quantize after loading, this PR allows easy creation of a quantized checkpoint that is directly loaded into each worker, further speeding up model loading.
This PR is separated out from #4652.
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