[Perf] Use fused ops npu_top_k_top_p (#1308)

### What this PR does / why we need it?
Use fused ops torch_npu.npu_top_k_top_p(logits, p, k) when p and k are
not None, otherwise fallback to the original one. The replacement will
take place automatically when `VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE=1` .

This patch are using `npu_top_k_top_p` which required
torch_npu>=2.5.1.post1.dev20250619

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

No

### How was this patch tested?

Tested by DeepSeek R1 and UT passed

Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>

Author: Pr0Wh1teGivee

tests/ut/worker/patch_common/test_patch_sampler.py

@@ -0,0 +1,28 @@
+import importlib
+import os
+import unittest
+from unittest import mock
+
+import torch
+from vllm.v1.sample.ops import topk_topp_sampler
+
+
+class TestTopKTopPSamplerOptimize(unittest.TestCase):
+
+    @mock.patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE": "1"})
+    @mock.patch("torch_npu.npu_top_k_top_p")
+    def test_npu_topk_topp_called_when_optimized(self, mock_npu_op):
+        import vllm_ascend.patch.worker.patch_common.patch_sampler
+        importlib.reload(vllm_ascend.patch.worker.patch_common.patch_sampler)
+
+        mock_npu_op.return_value = (torch.randn(1, 3))
+        sampler = topk_topp_sampler.TopKTopPSampler()
+
+        logits = torch.tensor([[1.0, 2.0, 3.0]])
+        k = torch.tensor([2])
+        p = torch.tensor([0.9])
+        generators = {0: torch.Generator()}
+        generators[0].manual_seed(42)
+
+        sampler.forward_native(logits, generators, k, p)
+        mock_npu_op.assert_called_once_with(logits, p, k)

vllm_ascend/patch/worker/patch_common/patch_sampler.py

@@ -19,6 +19,7 @@
 from typing import Optional
 
 import torch
+import torch_npu
 from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler, random_sample
 from vllm.v1.sample.sampler import Sampler
 
@@ -48,9 +49,13 @@ def apply_min_p(
 
 def _apply_top_k_top_p(
     logits: torch.Tensor,
-    p: torch.Tensor,
     k: torch.Tensor,
+    p: torch.Tensor,
 ) -> torch.Tensor:
+    if p is not None and k is not None:
+        # npu_top_k_top_p's parameter order is (logits, p, k), not (logits, k, p)
+        return torch_npu.npu_top_k_top_p(logits, p, k)
+
     probs = logits.softmax(dim=-1)
     probs_sort, _ = probs.sort(dim=-1, descending=False)