[Feature] support min_p_sampling (#2872)

* Fastdeploy support min_p

* add test_min_p

* fix

* min_p_sampling

* update

* delete vl_gpu_model_runner.py

* fix

* Align usage of min_p with vLLM

* fix

* modified unit test

* fix test_min_sampling

* pre-commit all files

* fix

* fix

* fix

* fix xpu_model_runner.py

test/layers/test_min_sampling.py

@@ -0,0 +1,113 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.nn.functional as F
+
+from fastdeploy.model_executor.ops.gpu import min_p_sampling
+
+
+class TestMinPSampling(unittest.TestCase):
+    def setUp(self):
+        self.sample_time = 1000000
+        self.vocab_size = 1000
+        self.min_p_value = 0.5
+        self.batch_size = 3
+        self.batch_min_p_values = [0.1, 0.0, 0.9]
+        self.additional_batch_min_p_values = [0.1, 0.0, 0.3]
+
+    # min_p:0.5：FastDeploy
+    def min_p_sampling_cpu(self, min_p):
+        logits = paddle.ones(shape=[1, self.vocab_size], dtype="float32")
+        logits[0][0] = 10
+        logits[0][1] = 8
+        low_prob_tensor = paddle.linspace(2.0, 0.0, self.vocab_size - 2)
+        logits[0][2:] = low_prob_tensor
+
+        probs = F.softmax(logits)
+        max_probabilities = paddle.amax(probs, axis=-1, keepdim=True)
+        adjusted_min_p = max_probabilities * min_p.reshape([-1, 1])
+        invalid_token_mask = probs < adjusted_min_p
+        probs = paddle.where(invalid_token_mask, paddle.full_like(probs, 0.0), probs)
+        return probs
+
+    # min_p:0.5：FastDeploy
+    def fastdeploy_min_p_sampling(self, min_p):
+        logits = paddle.ones(shape=[1, self.vocab_size], dtype="float32")
+        logits[0][0] = 10
+        logits[0][1] = 8
+        low_prob_tensor = paddle.linspace(2.0, 0.0, self.vocab_size - 2)
+        logits[0][2:] = low_prob_tensor
+
+        probs = F.softmax(logits)
+        probs = min_p_sampling(probs, min_p)
+        return probs
+
+    # batch:[0.1.0.0,0.9]：FastDeploy
+    def fastdeploy_batch_min_p_sampling(self, batch_size, min_p_values):
+        logits = paddle.ones(shape=[batch_size, self.vocab_size], dtype="float32")
+        for b in range(batch_size):
+            logits[b][0] = 10
+            logits[b][1] = 8
+            logits[b][2:] = paddle.linspace(2.0, 0.0, self.vocab_size - 2)
+
+        probs = F.softmax(logits, axis=-1)
+        min_p_arr = paddle.to_tensor(min_p_values, dtype="float32")
+
+        probs = min_p_sampling(probs, min_p_arr)
+
+        return probs
+
+    def compare_results(self, probs, probs_cpu, atol=1e-6, rtol=1e-6):
+        probs_np = probs.numpy()
+        probs_cpu_np = probs_cpu.numpy()
+        try:
+            np.testing.assert_allclose(
+                probs_np,
+                probs_cpu_np,
+                rtol=rtol,
+                atol=atol,
+            )
+            print("The results are same between fastdeploy_min_p_sampling and min_p_sampling_cpu")
+        except AssertionError as e:
+            raise AssertionError(
+                f"The results are different between fastdeploy_min_p_sampling and min_p_sampling_cpu:\n{str(e)}"
+            )
+
+    def test_single_min_p_sampling(self):
+        min_p = paddle.to_tensor([self.min_p_value], dtype="float32")
+        probs = self.fastdeploy_min_p_sampling(min_p)
+        probs_cpu = self.min_p_sampling_cpu(min_p)
+        self.compare_results(probs, probs_cpu)
+
+    def test_batch_min_p_sampling(self):
+        batch_min_p = paddle.to_tensor(self.batch_min_p_values, dtype="float32")
+        batch_probs = self.fastdeploy_batch_min_p_sampling(self.batch_size, batch_min_p)
+        batch_probs_cpu = self.min_p_sampling_cpu(batch_min_p)
+        self.compare_results(batch_probs, batch_probs_cpu)
+
+    def test_additional_batch_min_p_sampling(self):
+        additional_batch_min_p = paddle.to_tensor(self.additional_batch_min_p_values, dtype="float32")
+        additional_batch_probs = self.fastdeploy_batch_min_p_sampling(self.batch_size, additional_batch_min_p)
+        additional_batch_probs_cpu = self.min_p_sampling_cpu(additional_batch_min_p)
+        self.compare_results(additional_batch_probs, additional_batch_probs_cpu)
+
+
+if __name__ == "__main__":
+    if paddle.is_compiled_with_cuda():
+        unittest.main()