66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# unit test for `examples/offline_inference/torchrun_example.py`
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import random
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import torch.distributed as dist
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from vllm import LLM, SamplingParams
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from vllm.distributed.parallel_state import get_world_group
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# Create prompts
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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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]
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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# set different `gpu_memory_utilization` and `swap_space` for different ranks,
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# to test if all ranks agree on the same kv cache configuration.
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llm = LLM(model="facebook/opt-125m",
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tensor_parallel_size=2,
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distributed_executor_backend="external_launcher",
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gpu_memory_utilization=random.uniform(0.7, 0.9),
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swap_space=random.randint(1, 4),
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seed=0)
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outputs = llm.generate(prompts, sampling_params)
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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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def test_consistent_across_ranks(obj):
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if torch_rank == 0:
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dist.broadcast_object_list([obj], src=0, group=cpu_group)
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else:
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container = [None]
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dist.broadcast_object_list(container, src=0, group=cpu_group)
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assert container[0] == obj
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test_consistent_across_ranks(
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llm.llm_engine.vllm_config.cache_config.num_cpu_blocks)
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test_consistent_across_ranks(
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llm.llm_engine.vllm_config.cache_config.num_gpu_blocks)
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# make sure we can access the model parameters from the calling process
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# of the `LLM` instance.
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params = list(llm.llm_engine.model_executor.driver_worker.worker.model_runner.
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model.parameters())
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test_consistent_across_ranks(len(params))
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# all ranks should have the same outputs
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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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test_consistent_across_ranks(prompt)
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test_consistent_across_ranks(generated_text)
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print(f"Rank {torch_rank}, Prompt: {prompt!r}, "
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f"Generated text: {generated_text!r}")
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