66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
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import pytest
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MODELS = ["ai21labs/Jamba-tiny-random"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [20])
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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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# To pass the small model tests, we need full precision.
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assert dtype == "float"
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with hf_runner(model, dtype=dtype) as hf_model:
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hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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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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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_state_cleanup(
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vllm_runner,
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model: str,
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dtype: str,
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example_prompts,
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) -> None:
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# This test is for verifying that the Jamba state is cleaned up between
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# steps, If its not cleaned, an error would be expected.
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try:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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for _ in range(10):
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vllm_model.generate_greedy([example_prompts[0]] * 100, 1)
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except ValueError:
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pytest.fail("Jamba inner state wasn't cleaned up between states, "
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"could be related to finished_requests_ids")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_model_print(
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vllm_runner,
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model: str,
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dtype: str,
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) -> None:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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# This test is for verifying whether the model's extra_repr
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# can be printed correctly.
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print(vllm_model.model.llm_engine.model_executor.driver_worker.
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model_runner.model)
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