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https://github.com/vllm-project/vllm.git
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922979bfcc
Signed-off-by: Lu Fang <fanglu@fb.com> Signed-off-by: Zhuohan Li <zhuohan123@gmail.com> Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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experimental support for data-parallel inference with torchrun
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Note the data load balancing and distribution is done out of the vllm engine,
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no internal lb supported in external_launcher mode.
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"""
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from vllm import LLM, SamplingParams
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# Create prompts, the same across all ranks
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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] * 50
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# Create sampling parameters, the same across all ranks
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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# Use `distributed_executor_backend="external_launcher"` so that
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# this llm engine/instance only creates one worker.
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# it is important to set an explicit seed to make sure that
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# all ranks have the same random seed, so that sampling can be
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# deterministic across ranks.
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llm = LLM(
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model="microsoft/Phi-mini-MoE-instruct",
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tensor_parallel_size=1,
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data_parallel_size=2,
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pipeline_parallel_size=1,
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enable_expert_parallel=False,
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distributed_executor_backend="external_launcher",
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max_model_len=4096,
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gpu_memory_utilization=0.6,
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seed=1,
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)
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dp_rank = llm.llm_engine.vllm_config.parallel_config.data_parallel_rank
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dp_size = llm.llm_engine.vllm_config.parallel_config.data_parallel_size
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prompts = [
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f"{idx}.{prompt}" for idx, prompt in enumerate(prompts) if idx % dp_size == dp_rank
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]
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outputs = llm.generate(prompts, sampling_params)
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# all ranks will have the same outputs
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print("-" * 50)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}\n")
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print("-" * 50)
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"""
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Further tips:
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1. to communicate control messages across all ranks, use the cpu group,
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a PyTorch ProcessGroup with GLOO backend.
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```python
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from vllm.distributed.parallel_state import get_world_group
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cpu_group = get_world_group().cpu_group
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torch_rank = dist.get_rank(group=cpu_group)
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if torch_rank == 0:
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# do something for rank 0, e.g. saving the results to disk.
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```
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2. to communicate data across all ranks, use the model's device group,
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a PyTorch ProcessGroup with NCCL backend.
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```python
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from vllm.distributed.parallel_state import get_world_group
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device_group = get_world_group().device_group
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```
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3. to access the model directly in every rank, use the following code:
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```python
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llm.llm_engine.model_executor.driver_worker.worker.model_runner.model
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```
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"""
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