Merge branch 'feature/experimental_feature_20250908' into yuanlehome-patch-2

custom_ops/gpu_ops/speculate_decoding/speculate_verify.cu

@@ -38,14 +38,20 @@ __device__ int64_t topp_sampling_kernel(const int64_t *candidate_ids,
   const int tid = threadIdx.x;
 
   float sum_scores = 0.0f;
-  float rand_top_p = curand_uniform(dev_curand_states + tid) * topp;
+  for (int i = 0; i < candidate_len; i++) {
+    sum_scores += candidate_scores[i];
+  }
+  float tgt_topp = sum_scores < topp ? sum_scores : topp;
+
+  sum_scores = 0.0f;
+  float rand_top_p = curand_uniform(dev_curand_states + tid) * tgt_topp;
   for (int i = 0; i < candidate_len; i++) {
     sum_scores += candidate_scores[i];
     if (rand_top_p <= sum_scores) {
-      return candidate_ids[i];
+            return candidate_ids[i];
     }
   }
-  return candidate_ids[0];
+    return candidate_ids[0];
 }
 
 __global__ void setup_kernel(curandState_t *state, const uint64_t seed,

custom_ops/gpu_ops/speculate_decoding/top_p_candidates.cu

@@ -467,6 +467,9 @@ __global__ void KeMatrixTopPBeamTopKFt(
                 break;
             }
         }
+        if (top_p_value == 1.0 && actual_candidates_lens[token_id] == 0){
+            actual_candidates_lens[token_id] = max_cadidate_len;
+        }
     }
 }
 

fastdeploy/entrypoints/openai/api_server.py

@@ -497,6 +497,7 @@ def control_scheduler(request: ControlSchedulerRequest):
         return JSONResponse(content=content.model_dump(), status_code=500)
 
     if request.reset:
+        llm_engine.engine.clear_data()
         llm_engine.engine.scheduler.reset()
 
     if request.load_shards_num or request.reallocate_shard:

fastdeploy/envs.py

@@ -95,6 +95,13 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "FD_FOR_TORCH_MODEL_FORMAT": lambda: bool(int(os.getenv("FD_FOR_TORCH_MODEL_FORMAT", "0"))),
     # force disable default chunked prefill
     "FD_DISABLE_CHUNKED_PREFILL": lambda: bool(int(os.getenv("FD_DISABLE_CHUNKED_PREFILL", "0"))),
+    # For separate setting of sampling parameters for speculative decoding
+    "FD_SPECULATE_SAMPLING_TOP_P": lambda: (
+        None if "FD_SPECULATE_SAMPLING_TOP_P" not in os.environ else float(os.environ["FD_SPECULATE_SAMPLING_TOP_P"])
+    ),
+    "FD_SPECULATE_SAMPLING_TOP_K": lambda: (
+        None if "FD_SPECULATE_SAMPLING_TOP_K" not in os.environ else float(os.environ["FD_SPECULATE_SAMPLING_TOP_K"])
+    ),
     "FD_ENABLE_INTERNAL_ADAPTER": lambda: int(os.getenv("FD_ENABLE_INTERNAL_ADAPTER", "0")),
     # LLMEngine recieve requests port, used when FD_ENABLE_INTERNAL_ADAPTER=1
     "FD_ZMQ_RECV_REQUEST_SERVER_PORT": lambda: os.getenv("FD_ZMQ_RECV_REQUEST_SERVER_PORT", "8200"),

fastdeploy/rl/dynamic_weight_manager.py

@@ -257,6 +257,7 @@ class DynamicWeightManager:
         while model_weights_status.value[0] != 0:
             if model_weights_status.value[0] == 1:
                 logger.info("infer engine stopped! start to load new checkpoint...")
+                model_runner.clear_requests()
                 model_runner.update_parameters(pid)
             elif model_weights_status.value[0] == -1:
                 logger.info("infer engine stopped! start to clear checkpoint...")

fastdeploy/spec_decode/mtp.py

@@ -303,8 +303,16 @@ class MTPProposer(Proposer):
         )
         # self.model_inputs["caches"] = self.cache_kvs
         # Inherit generation hyperparameters from the main model for consistency
-        self.model_inputs["top_p"] = self.target_model_inputs["top_p"]
+        self.model_inputs["top_p"] = (
-        self.model_inputs["top_k"] = self.target_model_inputs["top_k"]
+            self.target_model_inputs["top_p"]
+            if envs.FD_SPECULATE_SAMPLING_TOP_P is None
+            else paddle.full_like(self.target_model_inputs["top_p"], envs.FD_SPECULATE_SAMPLING_TOP_P)
+        )
+        self.model_inputs["top_k"] = (
+            self.target_model_inputs["top_k"]
+            if envs.FD_SPECULATE_SAMPLING_TOP_K is None
+            else paddle.full_like(self.target_model_inputs["top_k"], envs.FD_SPECULATE_SAMPLING_TOP_K)
+        )
         self.model_inputs["temperature"] = self.target_model_inputs["temperature"]
         self.model_inputs["eos_token_id"] = self.target_model_inputs["eos_token_id"]
         self.model_inputs["penalty_score"] = self.target_model_inputs["penalty_score"]

fastdeploy/worker/worker_process.py

@@ -315,6 +315,8 @@ class PaddleDisWorkerProc:
                         self.worker.model_runner,
                         self.parallel_config.engine_worker_queue_port,
                     )
+                    logger.info(f"current task queue data: {self.task_queue.num_tasks()}")
+                    self.task_queue.clear_data()
                     self.model_weights_signal[0] = 0
                     logger.info(f"Rank: {self.local_rank} has updated or cleared parameters.")