65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
"""Compare the outputs of HF and distributed vLLM when using greedy sampling.
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vLLM will allocate all the available memory, so we need to run the tests one
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by one. The solution is to pass arguments (model name) by environment
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variables.
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Run:
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```sh
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cd $VLLM_PATH/tests
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TEST_DIST_MODEL=facebook/opt-125m pytest \
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distributed/test_basic_distributed_correctness.py
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TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf \
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distributed/test_basic_distributed_correctness.py
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```
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"""
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import os
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import pytest
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import torch
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MODELS = [
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os.environ["TEST_DIST_MODEL"],
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]
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DISTRIBUTED_EXECUTOR_BACKEND = "DISTRIBUTED_EXECUTOR_BACKEND"
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VLLM_ATTENTION_BACKEND = "VLLM_ATTENTION_BACKEND"
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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reason="Need at least 2 GPUs to run the test.")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [5])
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def test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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) -> None:
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distributed_executor_backend = os.getenv(DISTRIBUTED_EXECUTOR_BACKEND)
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backend_by_env_var = os.getenv(VLLM_ATTENTION_BACKEND)
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enforce_eager = backend_by_env_var == "FLASHINFER"
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hf_model = hf_runner(model, dtype=dtype)
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hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
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del hf_model
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vllm_model = vllm_runner(
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model,
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dtype=dtype,
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tensor_parallel_size=2,
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enforce_eager=enforce_eager,
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distributed_executor_backend=distributed_executor_backend)
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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for i in range(len(example_prompts)):
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hf_output_ids, hf_output_str = hf_outputs[i]
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vllm_output_ids, vllm_output_str = vllm_outputs[i]
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assert hf_output_str == vllm_output_str, (
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f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
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assert hf_output_ids == vllm_output_ids, (
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f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
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