[CI] cleanup single/multi-card test (#5623)

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>

tests/e2e/singlecard/test_aclgraph_accuracy.py

@@ -36,7 +36,7 @@ MODELS = [
 
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("max_tokens", [32])
-def test_models_output_between_eager_and_aclgraph(
+def test_models_output(
     model: str,
     max_tokens: int,
 ) -> None:

tests/e2e/singlecard/test_fused_sigmoid_gating_delta_rule.py

@@ -1,65 +0,0 @@
-import torch
-from vllm.model_executor.layers.fla.ops import fused_recurrent_gated_delta_rule
-from vllm.model_executor.models.qwen3_next import fused_gdn_gating
-
-from vllm_ascend.ops.triton.fla.sigmoid_gating import \
-    fused_sigmoid_gating_delta_rule_update
-
-
-def test_triton_fusion_ops():
-    q = torch.randn(1, 1, 4, 128, dtype=torch.bfloat16).npu()
-    k = torch.randn(1, 1, 4, 128, dtype=torch.bfloat16).npu()
-    v = torch.randn(1, 1, 8, 128, dtype=torch.bfloat16).npu()
-    a = torch.tensor([[
-        -2.6094, -0.2617, -0.3848, 2.2656, 3.6250, -0.7383, -1.0938, -0.0505
-    ]]).bfloat16().npu()
-    b = torch.tensor(
-        [[0.4277, 0.8906, 1.6875, 2.3750, 4.1562, 0.3809, 1.0625,
-          3.6719]]).bfloat16().npu()
-    ssm_state = torch.randn(1, 8, 128, 128, dtype=torch.bfloat16).npu()
-    non_spec_state_indices_tensor = torch.tensor([2]).int().npu()
-    non_spec_query_start_loc = torch.tensor([0, 1]).int().npu()
-    a_log = torch.tensor([
-        -2.6875, -3.2031, -3.3438, -2.7812, -3.0625, -4.0312, -5.3750, 5.7188
-    ]).bfloat16().npu()
-    dt_bias = torch.tensor(
-        [-4.7812, -5.0938, -5.5000, 9.4375, 7.6250, -4.3750, -3.0938,
-         0.9688]).bfloat16().npu()
-
-    core_attn_out_non_spec_fused = fused_sigmoid_gating_delta_rule_update(
-        A_log=a_log.contiguous(),
-        dt_bias=dt_bias.contiguous(),
-        q=q.contiguous(),
-        k=k.contiguous(),
-        v=v.contiguous(),
-        a=a.contiguous(),
-        b=b.contiguous(),
-        initial_state_source=ssm_state,
-        initial_state_indices=non_spec_state_indices_tensor,
-        cu_seqlens=non_spec_query_start_loc,
-        use_qk_l2norm_in_kernel=True,
-        softplus_beta=1.0,
-        softplus_threshold=20.0,
-    )
-
-    g, beta = fused_gdn_gating(a_log, a, b, dt_bias)
-    g_non_spec = g
-    beta_non_spec = beta
-    core_attn_out_non_spec_split, last_recurrent_state = (
-        fused_recurrent_gated_delta_rule(
-            q=q,
-            k=k,
-            v=v,
-            g=g_non_spec,
-            beta=beta_non_spec,
-            initial_state=ssm_state,
-            inplace_final_state=True,
-            cu_seqlens=non_spec_query_start_loc,
-            ssm_state_indices=non_spec_state_indices_tensor,
-            use_qk_l2norm_in_kernel=True,
-        ))
-    torch.testing.assert_close(core_attn_out_non_spec_fused,
-                               core_attn_out_non_spec_split,
-                               rtol=1e-02,
-                               atol=1e-02,
-                               equal_nan=True)

tests/e2e/singlecard/test_quantization.py

@@ -46,7 +46,7 @@ def test_qwen3_w8a8_quant():
         outputs_0_lst=vllm_target_outputs,
         outputs_1_lst=vllm_quant_w8a8_outputs,
         name_0="vllm_target_outputs",
-        name_1="vllm_w8a16_outputs",
+        name_1="vllm_quant_w8a8_outputs",
     )
 
 
@@ -75,5 +75,5 @@ def test_qwen3_dense_w8a16():
         outputs_0_lst=vllm_target_outputs,
         outputs_1_lst=vllm_quant_w8a16_outputs,
         name_0="vllm_target_outputs",
-        name_1="vllm_w8a16_outputs",
+        name_1="vllm_quant_w8a16_outputs",
     )