[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/nightly/single_node/ops/singlecard_ops/triton/test_chunk_gated_delta_rule.py

@@ -0,0 +1,33 @@
+import torch
+
+from tests.ut.base import PytestBase
+from vllm_ascend.ops.triton.fla.chunk import chunk_gated_delta_rule
+
+
+class TestChunkGatedDeltaRule(PytestBase):
+
+    def test_triton_fusion_ops(self, mock_moe_env):
+        q = torch.randn(1, 17, 4, 128, dtype=torch.bfloat16).npu()
+        k = torch.randn(1, 17, 4, 128, dtype=torch.bfloat16).npu()
+        v = torch.randn(1, 17, 8, 128, dtype=torch.bfloat16).npu()
+        g = torch.randn(1, 17, 8, dtype=torch.float32).npu()
+        beta = torch.randn(1, 17, 8, dtype=torch.bfloat16).npu()
+        initial_state = torch.randn(3, 8, 128, 128, dtype=torch.bfloat16).npu()
+        q_start_loc = torch.range(0, 3, dtype=torch.int).npu()
+
+        (
+            core_attn_out_non_spec,
+            last_recurrent_state,
+        ) = chunk_gated_delta_rule(q=q,
+                                   k=k,
+                                   v=v,
+                                   g=g,
+                                   beta=beta,
+                                   initial_state=initial_state,
+                                   output_final_state=True,
+                                   cu_seqlens=q_start_loc,
+                                   head_first=False,
+                                   use_qk_l2norm_in_kernel=True)
+
+        assert core_attn_out_non_spec.shape == (1, 17, 8, 128)
+        assert last_recurrent_state.shape == (3, 8, 128, 128)

tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_fused_sigmoid_gating_delta_rule.py

@@ -0,0 +1,65 @@
+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)