Rename top_p_sampling to top_k_top_p_sampling (#2791)

docs/offline_inference.md

@@ -4,6 +4,7 @@
 FastDeploy supports offline inference by loading models locally and processing user data. Usage examples:
 
 ### Chat Interface (LLM.chat)
+
 ```python
 from fastdeploy import LLM, SamplingParams
 
@@ -77,10 +78,12 @@ for output in outputs:
     prompt = output.prompt
     generated_text = output.outputs.text
 ```
+
 > Note: Text completion interface, suitable for scenarios where users have predefined the context input and expect the model to output only the continuation content. No additional `prompt` concatenation will be added during the inference process.
 > For the `chat` model, it is recommended to use the Chat Interface (`LLM.chat`).
 
 For multimodal models, such as `baidu/ERNIE-4.5-VL-28B-A3B-Paddle`, when calling the `generate interface`, you need to provide a prompt that includes images. The usage is as follows:
+
 ```python
 import io
 import os
@@ -141,6 +144,7 @@ for output in outputs:
     reasoning_text = output.outputs.reasoning_content
 
 ```
+
 >Note: The `generate interface` does not currently support passing parameters to control the thinking function (on/off). It always uses the model's default parameters.
 
 ## 2. API Documentation
@@ -176,6 +180,7 @@ For ```LLM``` configuration, refer to [Parameter Documentation](parameters.md).
 * repetition_penalty(float): Direct penalty for repeated tokens (>1 penalizes, <1 encourages)
 * temperature(float): Controls randomness (higher = more random)
 * top_p(float): Probability threshold for token selection
+* top_k(int): Number of tokens considered for sampling
 * max_tokens(int): Maximum generated tokens (input + output)
 * min_tokens(int): Minimum forced generation length
 

docs/zh/offline_inference.md

@@ -78,10 +78,12 @@ for output in outputs:
     prompt = output.prompt
     generated_text = output.outputs.text
 ```
+
 > 注： 续写接口, 适应于用户自定义好上下文输入, 并希望模型仅输出续写内容的场景; 推理过程不会增加其他 `prompt`拼接。
 > 对于 `chat`模型, 建议使用对话接口(LLM.chat)。
 
 对于多模模型, 例如`baidu/ERNIE-4.5-VL-28B-A3B-Paddle`, 在调用`generate接口`时, 需要提供包含图片的prompt, 使用方式如下:
+
 ```python
 import io
 import os
@@ -142,6 +144,7 @@ for output in outputs:
     reasoning_text = output.outputs.reasoning_content
 
 ```
+
 > 注： `generate` 接口, 暂时不支持思考开关参数控制, 均使用模型默认思考能力。
 
 ## 2. 接口说明
@@ -155,7 +158,6 @@ for output in outputs:
 > 2. 模型服务启动后，会在日志文件log/fastdeploy.log中打印如 `Doing profile, the total_block_num:640` 的日志，其中640即表示自动计算得到的KV Cache block数量，将它乘以block_size(默认值64)，即可得到部署后总共可以在KV Cache中缓存的Token数。
 > 3. `max_num_seqs` 用于配置decode阶段最大并发处理请求数，该参数可以基于第1点中缓存的Token数来计算一个较优值，例如线上统计输入平均token数800, 输出平均token数500，本次计>算得到KV Cache block为640， block_size为64。那么我们可以配置 `kv_cache_ratio = 800 / (800 + 500) = 0.6` , 配置 `max_seq_len = 640 * 64 / (800 + 500) = 31`。
 
-
 ### 2.2 fastdeploy.LLM.chat
 
 * messages(list[dict],list[list[dict]]): 输入的message, 支持batch message 输入
@@ -178,7 +180,7 @@ for output in outputs:
 * repetition_penalty(float): 直接对重复生成的token进行惩罚的系数（>1时惩罚重复，<1时鼓励重复）
 * temperature(float): 控制生成随机性的参数，值越高结果越随机，值越低结果越确定
 * top_p(float): 概率累积分布截断阈值，仅考虑累计概率达到此阈值的最可能token集合
-* top_k(int): 采样概率最高的的token数量，考虑概率最高的k个token进行采样
+* top_k(int): 采样概率最高的token数量，考虑概率最高的k个token进行采样
 * max_tokens(int): 限制模型生成的最大token数量（包括输入和输出）
 * min_tokens(int): 强制模型生成的最少token数量，避免过早结束
 

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

@@ -16,10 +16,10 @@
 
 from .apply_penalty_multi_scores import (
     apply_penalty_multi_scores, apply_speculative_penalty_multi_scores)
-from .top_p_sampling import top_p_sampling
+from .top_k_top_p_sampling import top_k_top_p_sampling
 
 __all__ = [
     "apply_penalty_multi_scores",
     "apply_speculative_penalty_multi_scores",
-    "top_p_sampling",
+    "top_k_top_p_sampling",
 ]

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

@@ -25,7 +25,7 @@ if current_platform.is_gcu():
     from fastdeploy.model_executor.ops.gcu import \
         top_p_sampling as gcu_top_p_sampling
 
-def top_p_sampling(
+def top_k_top_p_sampling(
     x: paddle.Tensor,
     top_p: paddle.Tensor,
     top_k: Optional[paddle.Tensor] = None,

fastdeploy/model_executor/layers/sample/sampler.py

@@ -27,7 +27,7 @@ from fastdeploy.model_executor.guided_decoding.base_guided_decoding import \
 from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
 from fastdeploy.model_executor.layers.sample.ops import (
     apply_penalty_multi_scores, apply_speculative_penalty_multi_scores,
-    top_p_sampling)
+    top_k_top_p_sampling)
 from fastdeploy.platforms import current_platform
 
 
@@ -214,7 +214,7 @@ class Sampler(nn.Layer):
 
         probs = F.softmax(logits)
 
-        _, next_tokens = top_p_sampling(probs, sampling_metadata.top_p, sampling_metadata.top_k)
+        _, next_tokens = top_k_top_p_sampling(probs, sampling_metadata.top_p, sampling_metadata.top_k)
 
         self.processor.update_output_tokens(next_tokens, skip_idx_list)
         return next_tokens
@@ -367,5 +367,5 @@ class MTPSampler(nn.Layer):
         )
         probs = F.softmax(logits)
 
-        _, next_tokens = top_p_sampling(probs, sampling_metadata.top_p, sampling_metadata.top_k)
+        _, next_tokens = top_k_top_p_sampling(probs, sampling_metadata.top_p, sampling_metadata.top_k)
         return next_tokens