e56dba9b0d
### What this PR does / why we need it?
This PR refactors the E2E multicard test suite to improve test case
identification and maintainability. Specifically, it renames various
test functions to be more descriptive (explicitly indicating model
families like Qwen/DeepSeek and parallelism strategies like DP/TP/PP/EP)
and cleans up outdated or redundant test configurations in the offline
distributed inference tests.
**Key Changes:**
1. Test Function Renaming (Standardization): Renamed multiple test
functions across **`tests/e2e/multicard/`** to include clear
suffixes/prefixes regarding the model and parallel strategy. This helps
differentiate test cases in CI logs and prevents naming collisions.
**`test_aclgraph_capture_replay.py`:**
- `test_aclgraph_capture_replay_dp2` ->
`test_aclgraph_capture_replay_metrics_dp2`
**`test_data_parallel.py`:**
- `test_data_parallel_inference` -> `test_qwen_inference_dp2`
**`test_data_parallel_tp2.py`:**
- `test_data_parallel_inference` -> `test_qwen_inference_dp2_tp2`
**`test_expert_parallel.py`:**
- `test_e2e_ep_correctness` -> `test_deepseek_correctness_ep`
**`test_external_launcher.py`:**
- `test_external_launcher` -> `test_qwen_external_launcher`
- `test_moe_external_launcher` -> `test_qwen_moe_external_launcher_ep`
- `test_external_launcher_and_sleepmode` ->
`test_qwen_external_launcher_with_sleepmode`
- `test_external_launcher_and_sleepmode_level2` ->
`test_qwen_external_launcher_with_sleepmode_level2`
- `test_mm_allreduce` ->
`test_qwen_external_launcher_with_matmul_allreduce`
**`test_full_graph_mode.py`:**
- `test_models_distributed_Qwen3_MOE_TP2_WITH_FULL_DECODE_ONLY` ->
`test_qwen_moe_with_full_decode_only`
- `test_models_distributed_Qwen3_MOE_TP2_WITH_FULL` ->
`test_qwen_moe_with_full`
**`test_fused_moe_allgather_ep.py`:**
- `test_generate_with_allgather `->
`test_deepseek_moe_fused_allgather_ep`
- `test_generate_with_alltoall` -> `test_deepseek_moe_fused_alltoall_ep`
**`test_offline_weight_load.py`:**
- `test_offline_weight_load_and_sleepmode` ->
`test_qwen_offline_weight_load_and_sleepmode`
**`test_pipeline_parallel.py`:**
- `test_models` -> `test_models_pp2`
2. Distributed Inference Cleanup
(**`test_offline_inference_distributed.py`**):
**model list changes:**
```
QWEN_DENSE_MODELS = [
- "vllm-ascend/Qwen3-8B-W8A8", "vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8"
+ "vllm-ascend/Qwen3-8B-W8A8",
]
```
```
- QWEN_W4A8_OLD_VERSION_MODELS = [
- "vllm-ascend/Qwen3-8B-W4A8",
- ]
- QWEN_W4A8_NEW_VERSION_MODELS = [
- "vllm-ascend/DeepSeek-V3-W4A8-Pruing",
- "vllm-ascend/DeepSeek-V3.1-W4A8-puring",
- ]
+ DEEPSEEK_W4A8_MODELS = [
+ "vllm-ascend/DeepSeek-V3.1-W4A8-puring",
+ ]
```
**Test Function Changes:**
- removed `test_models_distributed_QwQ`
- removed `test_models_distributed_Qwen3_W8A8`
- removed `test_models_distributed_Qwen3_W4A8DYNAMIC_old_version`
- `test_models_distributed_Qwen3_W4A8DYNAMIC_new_version` ->
`test_models_distributed_Qwen3_W4A8DYNAMIC`
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
34 lines
1.1 KiB
Python
34 lines
1.1 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, tensor_parallel_size=2,
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enforce_eager=False) 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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enable_expert_parallel=True,
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enforce_eager=False) 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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