### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `tests/e2e/310p/multicard/test_vl_model_multicard.py` |
| `tests/e2e/310p/singlecard/test_vl_model_singlecard.py` |
| `tests/e2e/310p/test_utils.py` |
| `tests/e2e/conftest.py` |
| `tests/e2e/model_utils.py` |
| `tests/e2e/models/conftest.py` |
| `tests/e2e/models/test_lm_eval_correctness.py` |
| `tests/e2e/multicard/2-cards/spec_decode/test_spec_decode.py` |
| `tests/e2e/multicard/2-cards/test_aclgraph_capture_replay.py` |
| `tests/e2e/multicard/2-cards/test_data_parallel.py` |
| `tests/e2e/multicard/2-cards/test_disaggregated_encoder.py` |
| `tests/e2e/multicard/2-cards/test_expert_parallel.py` |
| `tests/e2e/multicard/2-cards/test_external_launcher.py` |
| `tests/e2e/multicard/2-cards/test_full_graph_mode.py` |
| `tests/e2e/multicard/2-cards/test_ilama_lora_tp2.py` |
| `tests/e2e/multicard/2-cards/test_offline_inference_distributed.py` |
| `tests/e2e/multicard/2-cards/test_offline_weight_load.py` |
| `tests/e2e/multicard/2-cards/test_pipeline_parallel.py` |
| `tests/e2e/multicard/2-cards/test_prefix_caching.py` |
| `tests/e2e/multicard/2-cards/test_quantization.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_moe.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_moe_routing_replay.py` |
| `tests/e2e/multicard/2-cards/test_qwen3_performance.py` |
| `tests/e2e/multicard/2-cards/test_shared_expert_dp.py` |
| `tests/e2e/multicard/2-cards/test_single_request_aclgraph.py` |
| `tests/e2e/multicard/2-cards/test_sp_pass.py` |
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
9562912cea
Signed-off-by: MrZ20 <2609716663@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
31 lines
1.0 KiB
Python
31 lines
1.0 KiB
Python
import os
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from unittest.mock import patch
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from vllm import SamplingParams
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from vllm.sampling_params import RequestOutputKind
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from tests.e2e.conftest import VllmRunner
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@patch.dict(os.environ, {"OMP_NUM_THREADS": "1"})
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def test_qwen3_moe_routing_replay():
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prompts = [
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"Hello, please introduce yourself.",
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]
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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tensor_parallel_size=2,
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enable_expert_parallel=True,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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distributed_executor_backend="mp",
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enable_return_routed_experts=True,
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) as vllm_model:
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sampling_params = SamplingParams(
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max_tokens=5, temperature=0.8, top_p=0.95, output_kind=RequestOutputKind.FINAL_ONLY
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)
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inputs = vllm_model.get_inputs(prompts=prompts)
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outputs = vllm_model.model.generate(prompts=inputs, sampling_params=sampling_params)
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assert outputs[0].finished
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assert len(outputs[0].outputs[0].text) > 0
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assert outputs[0].outputs[0].routed_experts.size > 0
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