2fda60464c
### 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>
29 lines
972 B
Python
29 lines
972 B
Python
import importlib
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import os
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import unittest
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from unittest import mock
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import torch
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from vllm.v1.sample.ops import topk_topp_sampler
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class TestTopKTopPSamplerOptimize(unittest.TestCase):
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@mock.patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE": "1"})
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@mock.patch("torch_npu.npu_top_k_top_p")
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def test_npu_topk_topp_called_when_optimized(self, mock_npu_op):
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import vllm_ascend.patch.worker.patch_common.patch_sampler
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importlib.reload(vllm_ascend.patch.worker.patch_common.patch_sampler)
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mock_npu_op.return_value = (torch.randn(1, 3))
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sampler = topk_topp_sampler.TopKTopPSampler()
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logits = torch.tensor([[1.0, 2.0, 3.0]])
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k = torch.tensor([2])
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p = torch.tensor([0.9])
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generators = {0: torch.Generator()}
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generators[0].manual_seed(42)
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sampler.forward_native(logits, generators, k, p)
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mock_npu_op.assert_called_once_with(logits, p, k)
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