[LLM] First commit the llm deployment code

fastdeploy/model_executor/layers/sample/sampler.py

@@ -0,0 +1,72 @@
+"""
+# 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 paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from fastdeploy.distributed.parallel_state import \
+    get_tensor_model_parallel_world_size
+from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
+from fastdeploy.model_executor.layers.sample.ops import \
+    apply_penalty_multi_scores
+from fastdeploy.platforms import current_platform
+
+
+class Sampler(nn.Layer):
+    """
+    """
+
+    def __init__(self):
+        """
+        """
+        super().__init__()
+        if current_platform.is_cuda():
+            self.nranks = get_tensor_model_parallel_world_size()
+            self.forward = self.forward_cuda
+        else:
+            raise NotImplementedError()
+
+    def forward_cuda(
+        self,
+        logits: paddle.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> paddle.Tensor:
+        """
+        """
+
+        logits = apply_penalty_multi_scores(
+            sampling_metadata.prompt_token_ids,
+            logits,
+            sampling_metadata.repetition_penalties,
+            sampling_metadata.frequency_penalties,
+            sampling_metadata.presence_penalties,
+            sampling_metadata.temperature,
+            sampling_metadata.bad_words_token_ids,
+            sampling_metadata.step_idx,
+            sampling_metadata.min_dec_lens,
+            sampling_metadata.eos_token_ids,
+        )
+
+        probs = F.softmax(logits)
+
+        _, next_tokens = paddle.tensor.top_p_sampling(probs,
+                                                      sampling_metadata.top_p)
+
+        if self.nranks > 1:
+            paddle.distributed.broadcast(next_tokens, 0)
+
+        return next_tokens