[BugFix] Fix implementation bug of triton rope_siso (#7082)

### What this PR does / why we need it?
Previously implemention of triton rope_siso missing the storage of
second half of rope results, which will result in:

1. accuracy problem in neox-style scenario
2. ub overflow in non neox-style scenario

This PR fixes it and supplement nightly test case for it.

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e

Signed-off-by: whx-sjtu <2952154980@qq.com>

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

@@ -3,7 +3,7 @@ import gc
 import pytest
 import torch
 
-from vllm_ascend.ops.triton.rope import rope_forward_triton
+from vllm_ascend.ops.triton.rope import rope_forward_triton, rope_forward_triton_siso
 from vllm_ascend.ops.triton.triton_utils import init_device_properties_triton
 
 IS_NEOX_STYLE = [True, False]
@@ -65,6 +65,33 @@ def _rope_pytorch_native(
         key = key_rot.to(orig_dtype)
     return query, key
 
+def _rope_siso_pytorch_native(
+        query, cos, sin, rope_dim,
+        is_neox_style) -> tuple[torch.Tensor, torch.Tensor | None]:
+    """PyTorch-native implementation equivalent to forward()."""
+    assert query is not None
+    orig_dtype = query.dtype
+    query_rot = query[..., :rope_dim].to(torch.float32)
+    head_size = query.shape[-1]
+    if rope_dim < head_size:
+        query_pass = query[..., rope_dim:]
+
+    if is_neox_style:
+        cos = cos.repeat(1, 2).unsqueeze(-2).to(torch.float32)
+        sin = sin.repeat(1, 2).unsqueeze(-2).to(torch.float32)
+    else:
+        cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2).to(torch.float32)
+        sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2).to(torch.float32)
+
+    rotate_fn = rotate_neox if is_neox_style else rotate_gptj
+    query_rot = query_rot * cos + rotate_fn(query_rot) * sin
+
+    if rope_dim < head_size:
+        query = torch.cat((query_rot.to(orig_dtype), query_pass), dim=-1)
+    else:
+        query = query_rot.to(orig_dtype)
+    return query
+
 
 @pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@@ -220,3 +247,60 @@ def test_rotary_embedding_triton_kernel_with_cos_sin_cache(
     gc.collect()
     torch.npu.empty_cache()
     torch.npu.reset_peak_memory_stats()
+
+
+@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("num_q_heads", NUM_Q_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", DEVICES)
+@torch.inference_mode()
+def test_rotary_embedding_triton_kernel_siso(
+    is_neox_style: bool,
+    num_tokens: int,
+    num_q_heads: int,
+    head_size: int,
+    rotary_dim: int,
+    dtype: torch.dtype,
+    seed: int,
+    device: str,
+) -> None:
+    torch.manual_seed(seed)
+    torch.set_default_device(device)
+    init_device_properties_triton()
+    if rotary_dim == -1:
+        rotary_dim = head_size
+    sin = torch.randn(num_tokens, rotary_dim // 2, dtype=dtype, device=device)
+    cos = torch.randn(num_tokens, rotary_dim // 2, dtype=dtype, device=device)
+    q_trt = torch.randn(num_tokens,
+                        num_q_heads,
+                        head_size,
+                        dtype=dtype,
+                        device=device)
+    q_gold = torch.randn(num_tokens,
+                         num_q_heads,
+                         head_size,
+                         dtype=dtype,
+                         device=device)
+    q_trt.copy_(q_gold)
+    q_trt = rope_forward_triton_siso(q_trt,
+                                       cos,
+                                       sin,
+                                       rope_dim=rotary_dim,
+                                       is_neox_style=is_neox_style)
+    q_gold = _rope_siso_pytorch_native(q_gold,
+                                          cos,
+                                          sin,
+                                          rope_dim=rotary_dim,
+                                          is_neox_style=is_neox_style)
+    # Compare the results.
+    torch.testing.assert_close(q_trt.view(q_gold.size()),
+                               q_gold,
+                               atol=DEFAULT_ATOL,
+                               rtol=DEFAULT_RTOL)
+    gc.collect()
+    torch.npu.empty_cache()
+    torch.npu.reset_peak_memory_stats()

vllm_ascend/ops/triton/rope.py

@@ -218,6 +218,9 @@ def _triton_rope_siso(
         new_qk_tile_1 = qk_tile_1 * cos_row - qk_tile_2 * sin_row
         tl.store(qk_start_ptr + first_half_offsets, new_qk_tile_1, mask=first_mask)
 
+        new_qk_tile_2 = qk_tile_2 * cos_row + qk_tile_1 * sin_row
+        tl.store(qk_start_ptr + second_half_offsets, new_qk_tile_2, mask=second_mask)
+
 
 def rope_forward_triton(
     q: torch.Tensor,