[CI] refect e2e ci test (#5246)

### What this PR does / why we need it?
efect e2e ci test：
1. tests/e2e/singlecard/pooling/test_embedding.py: remove the eager
parameter and rename test case
2. tests/e2e/singlecard/pooling/test_scoring.py: Rename test cases
3. tests/e2e/singlecard/pooling/test_classification.py: Rename test case
4. tests/e2e/singlecard/test_quantization.py: remove the eager parameter
and chage model to vllm-ascend/Qwen2.5-0.6B-W8A8 and Rename test case
5. tests/e2e/multicard/test_shared_expert_dp.py: Rename test cases
6. tests/e2e/singlecard/test_sampler.py: Rename test cases
7. tests/e2e/singlecard/test_aclgraph_accuracy.py: Rename test cases
8. tests/e2e/multicard/test_offline_inference_distributed.py: Rename
test cases and remove the eager parameter
9. tests/e2e/multicard/long_sequence/test_accuracy.py: Rename test cases
and remove the eager parameter
10. tests/e2e/multicard/long_sequence/test_basic.py: Rename test cases
and remove the eager parameter
11.tests/e2e/multicard/test_expert_parallel.py:remove the eager
parameter
12.tests/e2e/multicard/test_full_graph_mode.py:remove the eager
parameter
13.tests/e2e/multicard/test_ilama_lora_tp2.py:remove the eager parameter

14.tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py:remove
the eager parameter
15.tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py:remove the
eager parameter
16.tests/e2e/singlecard/test_aclgraph_accuracy.py:remove the eager
parameter
17.tests/e2e/singlecard/test_camem.py:remove the eager parameter
18.tests/e2e/singlecard/test_ilama_lora.py:remove the eager parameter

19.tests/e2e/singlecard/test_multistream_overlap_shared_expert.py:remove
the eager parameter
20.tests/e2e/singlecard/test_vlm.py:remove the eager parameter
21.tests/e2e/singlecard/test_xli:remove the eager parameter

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

Signed-off-by: hfadzxy <starmoon_zhang@163.com>

tests/e2e/singlecard/pooling/test_classification.py

@@ -5,7 +5,7 @@ from transformers import AutoModelForSequenceClassification
 from tests.e2e.conftest import HfRunner, VllmRunner
 
 
-def test_classify_correctness() -> None:
+def test_qwen_pooling_classify_correctness() -> None:
 
     model_name = snapshot_download("Howeee/Qwen2.5-1.5B-apeach")
 

tests/e2e/singlecard/pooling/test_embedding.py

@@ -36,7 +36,6 @@ def test_embed_models_correctness(model: str):
     with VllmRunner(
             model_name,
             runner="pooling",
-            enforce_eager=False,
             max_model_len=None,
             cudagraph_capture_sizes=[4],
     ) as vllm_runner:
@@ -58,14 +57,13 @@ def test_embed_models_correctness(model: str):
     )
 
 
-def test_bge_model_correctness():
+def test_bge_m3_correctness():
     queries = ['What is the capital of China?', 'Explain gravity']
 
     model_name = snapshot_download("BAAI/bge-m3")
     with VllmRunner(
             model_name,
             runner="pooling",
-            enforce_eager=False,
     ) as vllm_aclgraph_runner:
         vllm_aclgraph_outputs = vllm_aclgraph_runner.embed(queries)
 

tests/e2e/singlecard/pooling/test_scoring.py

@@ -34,7 +34,7 @@ def model_name(request):
     yield snapshot_download(request.param)
 
 
-def test_cross_encoder_1_to_1(model_name):
+def test_cross_encoder_score_1_to_1(model_name):
     text_pair = [TEXTS_1[0], TEXTS_2[0]]
 
     with HfRunner(model_name, dtype=DTYPE, is_cross_encoder=True) as hf_model:
@@ -53,7 +53,7 @@ def test_cross_encoder_1_to_1(model_name):
     assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
 
 
-def test_cross_encoder_1_to_N(model_name):
+def test_cross_encoder_score_1_to_N(model_name):
     text_pairs = [
         [TEXTS_1[0], TEXTS_2[0]],
         [TEXTS_1[0], TEXTS_2[1]],
@@ -76,7 +76,7 @@ def test_cross_encoder_1_to_N(model_name):
     assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
 
 
-def test_cross_encoder_N_to_N(model_name):
+def test_cross_encoder_score_N_to_N(model_name):
     text_pairs = [
         [TEXTS_1[0], TEXTS_2[0]],
         [TEXTS_1[1], TEXTS_2[1]],
@@ -104,7 +104,7 @@ def emb_model_name(request):
     yield snapshot_download(request.param)
 
 
-def test_embedding_1_to_1(emb_model_name):
+def test_embedding_score_1_to_1(emb_model_name):
     text_pair = [TEXTS_1[0], TEXTS_2[0]]
 
     with HfRunner(emb_model_name, dtype=DTYPE,
@@ -127,7 +127,7 @@ def test_embedding_1_to_1(emb_model_name):
     assert hf_outputs[0] == pytest.approx(vllm_outputs[0], rel=0.01)
 
 
-def test_embedding_1_to_N(emb_model_name):
+def test_embedding_score_1_to_N(emb_model_name):
     text_pairs = [
         [TEXTS_1[0], TEXTS_2[0]],
         [TEXTS_1[0], TEXTS_2[1]],
@@ -157,7 +157,7 @@ def test_embedding_1_to_N(emb_model_name):
     assert hf_outputs[1] == pytest.approx(vllm_outputs[1], rel=0.01)
 
 
-def test_embedding_N_to_N(emb_model_name):
+def test_embedding_score_N_to_N(emb_model_name):
     text_pairs = [
         [TEXTS_1[0], TEXTS_2[0]],
         [TEXTS_1[1], TEXTS_2[1]],