native top_p_sampling (#2901)

fastdeploy/model_executor/layers/activation.py

@@ -63,7 +63,12 @@ class SiluAndMul(nn.Layer):
         """
         super().__init__()
 
-        if current_platform.is_cuda() or current_platform.is_xpu() or current_platform.is_iluvatar():
+        if (
+            current_platform.is_cuda()
+            or current_platform.is_xpu()
+            or current_platform.is_iluvatar()
+            or current_platform.is_dcu()
+        ):
             self.forward = self.forward_cuda
         elif current_platform.is_gcu():
             self.forward = self.forward_gcu

fastdeploy/model_executor/layers/attention/mla_attention_backend.py

@@ -32,7 +32,7 @@ from fastdeploy.model_executor.layers.attention.ops import (
 )
 from fastdeploy.platforms import current_platform
 
-if current_platform.is_cuda() and not current_platform.is_dcu():
+if current_platform.is_cuda():
     from fastdeploy.model_executor.ops.gpu import (
         decode_mla_write_cache,
         multi_head_latent_attention,

fastdeploy/model_executor/layers/attention/ops/append_attention.py

@@ -20,7 +20,7 @@ import paddle
 
 from fastdeploy.platforms import current_platform
 
-if current_platform.is_cuda() and not current_platform.is_dcu():
+if current_platform.is_cuda():
     from fastdeploy.model_executor.ops.gpu import (
         append_attention as append_attention_gpu,
     )

fastdeploy/model_executor/layers/backends/dcu/__init__.py

@@ -18,5 +18,6 @@ dcu backend methods
 
 from .fused_moe_triton_backends import DCUTritonWeightOnlyMoEMethod
 from .weight_only import DCUWeightOnlyLinearMethod
+from .top_p_sampling import native_top_p_sampling
 
-__all__ = ["DCUTritonWeightOnlyMoEMethod", "DCUWeightOnlyLinearMethod"]
+__all__ = ["DCUTritonWeightOnlyMoEMethod", "DCUWeightOnlyLinearMethod", "native_top_p_sampling"]

fastdeploy/model_executor/layers/backends/dcu/top_p_sampling.py

@@ -0,0 +1,40 @@
+"""
+# 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
+
+
+def native_top_p_sampling(
+    probs: paddle.Tensor,
+    top_p: paddle.Tensor
+) -> tuple[paddle.Tensor, paddle.Tensor]:
+    sorted_indices = paddle.argsort(probs, descending=True)
+    sorted_probs = paddle.sort(probs, descending=True)
+    cumulative_probs = paddle.cumsum(sorted_probs, axis=-1)
+    sorted_indices_to_remove = cumulative_probs > top_p
+    sorted_indices_to_remove = paddle.cast(sorted_indices_to_remove, dtype="int64")
+    sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+    sorted_indices_to_remove[:, 0] = 0
+    sorted_indices = sorted_indices + paddle.arange(probs.shape[0], dtype="int64").unsqueeze(-1) * probs.shape[-1]
+
+    condition = paddle.scatter(
+        sorted_indices_to_remove.flatten(), sorted_indices.flatten(), sorted_indices_to_remove.flatten()
+    )
+
+    condition = paddle.cast(condition, "bool").reshape(probs.shape)
+    probs = paddle.where(condition, paddle.full_like(probs, 0.0), probs)
+    next_tokens = paddle.multinomial(probs)
+
+    return None, next_tokens

fastdeploy/model_executor/layers/linear.py

@@ -61,6 +61,7 @@ class LinearBase(nn.Layer):
             or current_platform.is_xpu()
             or current_platform.is_iluvatar()
             or current_platform.is_gcu()
+            or current_platform.is_dcu()
         ):
             self.forward = self.forward_cuda
         else:

fastdeploy/model_executor/layers/moe/fused_moe_cutlass_backend.py

@@ -26,7 +26,7 @@ from fastdeploy.platforms import current_platform
 from ..utils import create_and_set_parameter, get_tensor
 from .fused_moe_backend_base import MoEMethodBase
 
-if current_platform.is_cuda() and not current_platform.is_dcu():
+if current_platform.is_cuda():
     from fastdeploy.model_executor.ops.gpu import (
         moe_expert_dispatch,
         moe_expert_reduce,

fastdeploy/model_executor/layers/sample/ops/apply_penalty_multi_scores.py

@@ -53,6 +53,23 @@ def apply_penalty_multi_scores(
             min_dec_lens,
             eos_token_ids,
         )
+    elif current_platform.is_dcu():
+        from fastdeploy.model_executor.ops.gpu import \
+            get_token_penalty_multi_scores
+        logits = get_token_penalty_multi_scores(
+            pre_token_ids,
+            prompt_ids,
+            prompt_lens,
+            logits,
+            repetition_penalties,
+            frequency_penalties,
+            presence_penalties,
+            temperature,
+            bad_words_token_ids,
+            step_idx,
+            min_dec_lens,
+            eos_token_ids,
+        )
     elif current_platform.is_xpu():
         from fastdeploy.model_executor.ops.xpu import get_token_penalty_multi_scores
 

fastdeploy/model_executor/layers/sample/ops/top_k_top_p_sampling.py

@@ -79,6 +79,9 @@ def top_k_top_p_sampling(
     else:
         if current_platform.is_gcu():
             _, ids = gcu_top_p_sampling(x, top_p)
+        elif current_platform.is_dcu():
+            from fastdeploy.model_executor.layers.backends import native_top_p_sampling
+            _, ids = native_top_p_sampling(x, top_p)
         else:
             _, ids = paddle.tensor.top_p_sampling(
                 x,

fastdeploy/model_executor/layers/sample/sampler.py

@@ -173,6 +173,7 @@ class Sampler(nn.Layer):
             or current_platform.is_xpu()
             or current_platform.is_iluvatar()
             or current_platform.is_gcu()
+            or current_platform.is_dcu()
         ):
             self.forward = self.forward_cuda
         else:

fastdeploy/model_executor/models/ernie4_5_vl/ernie4_5_vl_moe.py

@@ -44,7 +44,7 @@ from fastdeploy.model_executor.models.ernie4_5_moe import (
 from fastdeploy.model_executor.models.model_base import ModelForCasualLM
 from fastdeploy.platforms import current_platform
 
-if current_platform.is_cuda() and not current_platform.is_dcu():
+if current_platform.is_cuda():
     from fastdeploy.model_executor.ops.gpu import (
         extract_text_token_output,
         text_image_gather_scatter,

fastdeploy/model_executor/pre_and_post_process.py

@@ -479,6 +479,17 @@ def rebuild_padding(
     if current_platform.is_cuda():
         from fastdeploy.model_executor.ops.gpu import rebuild_padding
 
+        hidden_states = rebuild_padding(
+            tmp_out,
+            cum_offsets,
+            seq_len_this_time,
+            seq_lens_decoder,
+            seq_lens_encoder,
+            output_padding_offset,
+            max_input_length,
+        )
+    elif current_platform.is_dcu():
+        from fastdeploy.model_executor.ops.gpu import rebuild_padding
         hidden_states = rebuild_padding(
             tmp_out,
             cum_offsets,

fastdeploy/platforms/base.py

@@ -39,7 +39,7 @@ class Platform:
         """
         whether platform is cuda
         """
-        return paddle.is_compiled_with_cuda()
+        return paddle.is_compiled_with_cuda() and not paddle.is_compiled_with_rocm()
 
     def is_npu(self) -> bool:
         """

requirements_dcu.txt

@@ -28,3 +28,10 @@ use-triton-in-paddle
 crcmod
 fastsafetensors==0.1.14
 msgpack
+opentelemetry-api>=1.24.0
+opentelemetry-sdk>=1.24.0
+opentelemetry-instrumentation-redis
+opentelemetry-instrumentation-mysql
+opentelemetry-distro 
+opentelemetry-exporter-otlp
+opentelemetry-instrumentation-fastapi