[FIX 2.0.2] Topk topp sampling fix (#2805)

* fix topk-topp

* fix

docs/usage/environment_variables.md

@@ -52,7 +52,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "FD_ATTENTION_BACKEND":
     lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),
 
-    # Sampling class ("base", "air", or "rejection")
+    # Sampling class ("base", "base_non_truncated", "air", or "rejection")
     "FD_SAMPLING_CLASS":
     lambda: os.getenv("FD_SAMPLING_CLASS", "base"),
 
@@ -67,6 +67,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # Switch from standalone PD to centralized inference (0 or 1)
     "FD_PD_CHANGEABLE":
     lambda: os.getenv("FD_PD_CHANGEABLE", "1"),
-  
+
 }
-```
+```

docs/zh/usage/environment_variables.md

@@ -1,5 +1,6 @@
 # FastDeploy 环境变量说明
 FastDeploy 的环境变量保存在了代码库根目录下 fastdeploy/envs.py 文件中，以下是其对应的中文版说明：
+
 ```python
 environment_variables: dict[str, Callable[[], Any]] = {
     # 构建 FastDeploy 时使用的 CUDA 架构版本，这是一个字符串列表，例如[80,90]
@@ -50,7 +51,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "FD_ATTENTION_BACKEND":
     lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),
 
-    # 设置采样类别，当前可设置为 "base"、"air" 或 "rejection"
+    # 设置采样类别，当前可设置为 "base"、"base_non_truncated"、"air" 或 "rejection"
     "FD_SAMPLING_CLASS":
     lambda: os.getenv("FD_SAMPLING_CLASS", "base"),
 
@@ -65,6 +66,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # 是否从单机 PD 分离转换为集中式推理
     "FD_PD_CHANGEABLE":
     lambda: os.getenv("FD_PD_CHANGEABLE", "1"),
-  
+
 }
-```
+```

fastdeploy/envs.py

@@ -74,7 +74,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "FD_ATTENTION_BACKEND":
     lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),
 
-    # Set sampling class. "base", "air" and "rejection" can be set currently.
+    # Set sampling class. "base", "base_non_truncated",  "air" and "rejection" can be set currently.
     "FD_SAMPLING_CLASS":
     lambda: os.getenv("FD_SAMPLING_CLASS", "base"),
 

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

@@ -64,9 +64,20 @@ def top_k_top_p_sampling(
                                     seed=seed,
                                     k=k,
                                     mode=mode)
+    # rejection
     elif top_p_class == "rejection":
         ids = rejection_top_p_sampling(x, top_p, top_k, seed, order)
         _ = None
+    # base non-truncated
+    elif top_p_class == "base_non_truncated":
+        _, ids = paddle.tensor.top_p_sampling(x,
+                                              top_p,
+                                              threshold=threshold,
+                                              topp_seed=topp_seed,
+                                              seed=seed,
+                                              k=k,
+                                              mode="non-truncated")
+    # base truncated
     else:
         _, ids = paddle.tensor.top_p_sampling(x,
                                               top_p,
@@ -74,7 +85,7 @@ def top_k_top_p_sampling(
                                               topp_seed=topp_seed,
                                               seed=seed,
                                               k=k,
-                                              mode=mode)
+                                              mode="truncated")
     return _, ids
 
 
@@ -102,26 +113,25 @@ def air_top_p_sampling(
 def rejection_top_p_sampling(
     x: paddle.Tensor,
     top_p: paddle.Tensor,
-    top_k: Optional[paddle.Tensor] = None,
+    top_k: paddle.Tensor,
     seed: int = -1,
     order: Literal['top_k_first', 'joint'] = "top_k_first",
 ) -> paddle.Tensor:
     """
     rejection_top_p_sampling
     """
-    assert top_p is not None, "Top_p should not be none when FD_SAMPLING_CLASS is rejection"
     try:
         from fastdeploy.model_executor.ops.gpu import (
             rejection_top_p_sampling, top_k_renorm_probs)
 
-        if top_k is None:
+        if paddle.count_nonzero(top_k) == 0:
             ids = rejection_top_p_sampling(
                 x,
                 top_p,
                 None,
                 seed,
             )
-        elif top_k is not None and top_p is not None:
+        else:
             if order == "top_k_first":
                 renorm_probs = top_k_renorm_probs(x, top_k)
                 ids = rejection_top_p_sampling(
@@ -137,10 +147,6 @@ def rejection_top_p_sampling(
                     top_k,
                     seed,
                 )
-        else:
-            raise ValueError(
-                "Top_p cannot be none."
-            )
     except ImportError:
         raise RuntimeError("Cannot import rejection_top_p_sampling op.")
     return ids

fastdeploy/worker/gpu_model_runner.py

@@ -164,28 +164,12 @@ class GPUModelRunner(ModelRunnerBase):
                 -1].disaggregate_info["role"] == "prefill":
             os.environ['PREFILL_NODE_ONE_STEP_STOP'] = "1"
 
-        top_k_reqs = []
-        top_p_reqs = []
-        max_num_seqs = self.parallel_config.max_num_seqs
-        top_p_buffer = paddle.full([max_num_seqs, 1],
-                                    self.model_config.top_p,
-                                    dtype='float32')
-        top_k_buffer = paddle.full([max_num_seqs, 1],
-                                                0,
-                                                dtype='int64')
         req_len = len(req_dicts)
         for i in range(req_len):
             request = req_dicts[i]
             idx = request.idx
             length = len(request.prompt_token_ids)
 
-            if sampling_params := request.sampling_params:
-                if sampling_params.top_p < 1:
-                    top_p_reqs.append(idx)
-                top_k = sampling_params.top_k
-                if top_k > 0:
-                    top_k_reqs.append(idx)
-
             prefill_tokens = []
             if (request.guided_json is not None
                     or request.guided_regex is not None
@@ -260,8 +244,8 @@ class GPUModelRunner(ModelRunnerBase):
                 request.eos_token_ids.append(request.eos_token_ids[0])
             self.share_inputs["eos_token_id"][:] = np.array(
                 request.eos_token_ids, dtype="int64").reshape(-1, 1)
-            top_p_buffer[idx:idx + 1] = request.get("top_p", 1.0)
+            self.share_inputs["top_p"][idx:idx + 1] = request.get("top_p", 1.0)
-            top_k_buffer[idx:idx + 1] = request.get("top_k", 0)
+            self.share_inputs["top_k"][idx:idx + 1] = request.get("top_k", 0)
             self.share_inputs["temperature"][idx:idx + 1] = request.get(
                 "temperature", 0.95)
             self.share_inputs["penalty_score"][idx:idx + 1] = request.get(
@@ -312,16 +296,6 @@ class GPUModelRunner(ModelRunnerBase):
         if self.speculative_method in ["mtp"]:
             self.proposer.insert_prefill_inputs(req_dicts)
 
-        if len(top_k_reqs) == 0:
-            self.share_inputs["top_k"] = None
-        else:
-            self.share_inputs["top_k"] = top_k_buffer
-
-        if len(top_p_reqs) == 0:
-            self.share_inputs["top_p"] = None
-        else:
-            self.share_inputs["top_p"] = top_p_buffer
-
     def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int,
                               expected_decode_len: int):
         """ Set dummy prefill inputs to share_inputs """

fastdeploy/worker/xpu_model_runner.py

@@ -266,26 +266,11 @@ class XPUModelRunner(ModelRunnerBase):
 
     def process_prefill_inputs(self, req_dicts: List[Request]):
         """ Process inputs for prefill tasks and update share_inputs buffer """
-        top_k_reqs = []
-        top_p_reqs = []
-        max_num_seqs = self.parallel_config.max_num_seqs
-        top_p_buffer = paddle.full([max_num_seqs, 1],
-                                    self.model_config.top_p,
-                                    dtype='float32')
-        top_k_buffer = paddle.full([max_num_seqs, 1],
-                                                0,
-                                                dtype='int64')
         req_len = len(req_dicts)
         for i in range(req_len):
             request = req_dicts[i]
             idx = request.idx
             length = request.prompt_token_ids_len
-            if sampling_params := request.sampling_params:
-                if sampling_params.top_p < 1:
-                    top_p_reqs.append(idx)
-                top_k = sampling_params.top_k
-                if top_k > 0:
-                    top_k_reqs.append(idx)
             self.share_inputs["input_ids"][idx:idx + 1, :length] = np.array(
                 request.prompt_token_ids)
             if len(request.eos_token_ids
@@ -294,8 +279,8 @@ class XPUModelRunner(ModelRunnerBase):
             self.share_inputs["eos_token_id"][:] = np.array(
                 request.eos_token_ids, dtype="int64").reshape(-1, 1)
             self.share_inputs["pre_ids"][idx:idx + 1] = -1
-            top_p_buffer[idx:idx + 1] = request.get("top_p", 1.0)
+            self.share_inputs["top_p"][idx:idx + 1] = request.get("top_p", 1.0)
-            top_k_buffer[idx:idx + 1] = request.get("top_k", 0)
+            self.share_inputs["top_k"][idx:idx + 1] = request.get("top_k", 0)
             self.share_inputs["temperature"][idx:idx + 1] = request.get(
                 "temperature", 0.95)
             self.share_inputs["penalty_score"][idx:idx + 1] = request.get(
@@ -344,15 +329,6 @@ class XPUModelRunner(ModelRunnerBase):
                         request.get("stop_token_ids"), dtype="int64")
 
         self.share_inputs["not_need_stop"][0] = True
-        if len(top_k_reqs) == 0:
-            self.share_inputs["top_k"] = None
-        else:
-            self.share_inputs["top_k"] = top_k_buffer
-
-        if len(top_p_reqs) == 0:
-            self.share_inputs["top_p"] = None
-        else:
-            self.share_inputs["top_p"] = top_p_buffer
 
     def _init_share_inputs(self, max_num_seqs: int):
         """Initialize all share buffers for model inputs.