[0.9.1][Perf] Use fused ops npu_top_k_top_p

tests/ut/patch/worker/patch_common/test_patch_sampler.py

@@ -0,0 +1,44 @@
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+
+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):
+        # We have to patch and reload because the patch will take effect
+        # only after VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE is set.
+        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
 
@@ -46,11 +47,15 @@ def apply_min_p(
     return logits
 
 
-def _apply_top_k_top_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)
 
@@ -91,7 +96,7 @@ def topk_topp_forward_native(
 
     The logits tensor may be updated in-place.
     """
-    logits = _apply_top_k_top_p(logits, k, p)
+    logits = apply_top_k_top_p(logits, k, p)
     probs = logits.softmax(dim=-1, dtype=torch.float32)
     return random_sample(probs, generators)