6f7a81cd9f
1. speed up e2e light test.
2. create `2-cards` and `4-cards` folder in multicard
3. move ops to nightly
4. run test in Alphabetical Order
- vLLM version: v0.13.0
- vLLM main:
8be6432bda
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import pytest
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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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@pytest.mark.parametrize("model_name", ["deepseek-ai/DeepSeek-V2-Lite-Chat"])
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def test_deepseek_correctness_ep(model_name):
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example_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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max_tokens = 5
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# FIXME: Really strange that chunked prefill might lead to different results, investigate further
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with VllmRunner(model_name,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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tensor_parallel_size=2) as vllm_model:
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tp_output = vllm_model.generate_greedy(example_prompts, max_tokens)
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with VllmRunner(model_name,
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tensor_parallel_size=2,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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enable_expert_parallel=True) as vllm_model:
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ep_output = vllm_model.generate_greedy(example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=ep_output,
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outputs_1_lst=tp_output,
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name_0="ep_output",
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name_1="tp_output",
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)
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