65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
import os
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import pytest
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.model_utils import check_outputs_equal
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MODELS = [
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"Qwen/Qwen3-VL-2B-Instruct",
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]
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@pytest.mark.parametrize("model", MODELS)
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def test_qwen3_vl_sp_tp2(model: str) -> None:
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prompts = [
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"Hello, my name is", "The capital of the United States is",
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"The capital of France is", "The future of AI is"
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]
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sampling_params = SamplingParams(max_tokens=10, temperature=0.0)
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with VllmRunner(
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model,
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max_model_len=1024,
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tensor_parallel_size=2,
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compilation_config={
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"cudagraph_capture_sizes": [2, 4],
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"cudagraph_mode": "FULL_DECODE_ONLY",
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"pass_config": {"enable_sp": False}
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},
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additional_config={"npugraph_ex_config": {"enable": False}}
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) as runner:
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no_sp_outputs = runner.model.generate(prompts, sampling_params)
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with VllmRunner(
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model,
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max_model_len=1024,
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tensor_parallel_size=2,
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compilation_config={
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"cudagraph_capture_sizes": [2, 4],
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"cudagraph_mode": "FULL_DECODE_ONLY",
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"pass_config": {"enable_sp": True}
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},
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additional_config={"sp_threshold": 10, "npugraph_ex_config": {"enable": False}}
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) as runner:
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sp_outputs = runner.model.generate(
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prompts, sampling_params)
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no_sp_outputs_list = []
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for output in no_sp_outputs:
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no_sp_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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sp_outputs_list = []
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for output in sp_outputs:
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sp_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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check_outputs_equal(
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outputs_0_lst=no_sp_outputs_list,
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outputs_1_lst=sp_outputs_list,
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name_0="no_sp_outputs",
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name_1="sp_outputs",
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)
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