v2.18.3

Changes:

- Check for full completion;
- Add inference reward scale for streaming variance between chunks;
- Scale inference reward with cosine similarity with grouth truth logits;
- Add tests for inference;
- Add deps required for asyncio tests;
- Close all processes before exit.

Author: dbobrenko

neurons/validator.py

@@ -1,10 +1,14 @@
 import asyncio
+import atexit
 import os
 import signal
 import sys
+import time
 from multiprocessing.managers import AcquirerProxy
+from multiprocessing.synchronize import Event
 
 import netaddr
+import psutil
 import requests
 import torch
 import torch.multiprocessing as mp
@@ -102,6 +106,7 @@ def start_api(
     scoring_queue: list,
     reward_events: list,
     miners_dict: dict,
+    event_stop: Event,
 ):
     from prompting.api.api import start_scoring_api  # noqa: F401
 
@@ -124,7 +129,7 @@ async def start():
             logger.warning(f"Failed to serve scoring api to chain: {e}")
         await start_scoring_api(task_scorer, scoring_queue, reward_events, miners_dict)
 
-        while True:
+        while not event_stop.is_set():
             await asyncio.sleep(10)
 
     asyncio.run(start())
@@ -134,6 +139,7 @@ def start_task_sending_loop(
     task_queue: list,
     scoring_queue: list,
     miners_dict: dict,
+    event_stop: Event,
 ):
     async def spawn_loops(task_queue, scoring_queue, miners_dict: dict):
         from prompting.tasks.task_sending import TaskSender
@@ -142,7 +148,8 @@ async def spawn_loops(task_queue, scoring_queue, miners_dict: dict):
         task_sender = TaskSender()
         asyncio.create_task(task_sender.start(task_queue, scoring_queue, miners_dict, simultaneous_loops=1))
         logger.debug("Task sending loop started")
-        while True:
+
+        while not event_stop.is_set():
             await asyncio.sleep(5)
             logger.debug("Task sending loop is running")
 
@@ -155,13 +162,13 @@ async def spawn_loops(task_queue, scoring_queue, miners_dict: dict):
         raise
 
 
-def start_availability_checking_loop(miners_dict: dict):
+def start_availability_checking_loop(miners_dict: dict, event_stop: Event):
     async def spawn_loops(miners_dict: dict):
         from prompting.miner_availability.miner_availability import availability_checking_loop
 
         logger.info("Starting availability checking loop in validator...")
         asyncio.create_task(availability_checking_loop.start(miners_dict))
-        while True:
+        while not event_stop.is_set():
             await asyncio.sleep(5)
             logger.debug("Availability checking loop is running")
 
@@ -174,13 +181,13 @@ async def spawn_loops(miners_dict: dict):
         raise
 
 
-def start_weight_setter_loop(reward_events):
+def start_weight_setter_loop(reward_events, event_stop: Event):
     async def spawn_loops(reward_events):
         from prompting.weight_setting.weight_setter import weight_setter
 
         logger.info("Starting weight setter loop in validator...")
         asyncio.create_task(weight_setter.start(reward_events))
-        while True:
+        while not event_stop.is_set():
             await asyncio.sleep(5)
             logger.debug("Weight setter loop is running")
 
@@ -193,6 +200,34 @@ async def spawn_loops(reward_events):
         raise
 
 
+def health_check(parent_pid: int, event_stop: Event):
+    """Monitor parent process and kill all child processes in case of emergency."""
+    step = 0
+    while True:
+        try:
+            if not psutil.pid_exists(parent_pid):
+                event_stop.set()
+                logger.warning("Parent process died, killing all child processes")
+                os.killpg(0, signal.SIGKILL)
+
+            block = settings.shared_settings.block
+            if block - settings.shared_settings.METAGRAPH.last_update[settings.shared_settings.UID] > 320 and step > 60:
+                event_stop.set()
+                last_update_block = settings.shared_settings.METAGRAPH.last_update[settings.shared_settings.UID]
+                logger.warning(
+                    f"Metagraph hasn't been updated for {block - last_update_block} blocks. "
+                    f"Staled block: {block}, Last update: {last_update_block}"
+                )
+                os.killpg(0, signal.SIGKILL)
+            step += 1
+
+        except Exception as e:
+            logger.error(f"Failed to kill process group: {e}")
+        finally:
+            sys.exit(1)
+        time.sleep(60)
+
+
 async def main(
     cache_rewards: list | None = None,
     cache_scores: list | None = None,
@@ -208,6 +243,7 @@ async def main(
         mp_lock = manager.Lock()
         processes: list[mp.Process] = []
         tasks: list[asyncio.Task] = []
+        event_stop = mp.Event()
 
         model_scheduler = AsyncModelScheduler(llm_model_manager=ModelManager(), mp_lock=mp_lock, sync=True)
 
@@ -216,15 +252,19 @@ async def main(
             if settings.shared_settings.DEPLOY_SCORING_API and not settings.shared_settings.NEURON_DISABLE_SET_WEIGHTS:
                 # Use multiprocessing to bypass API blocking issue
                 api_process = mp.Process(
-                    target=start_api, args=(scoring_queue, reward_events, miners_dict), name="APIProcess"
+                    target=start_api,
+                    args=(scoring_queue, reward_events, miners_dict, event_stop),
+                    name="APIProcess",
+                    daemon=True,
                 )
                 api_process.start()
                 processes.append(api_process)
 
             availability_process = mp.Process(
                 target=start_availability_checking_loop,
-                args=(miners_dict,),
+                args=(miners_dict, event_stop),
                 name="AvailabilityProcess",
+                daemon=True,
             )
             availability_process.start()
             processes.append(availability_process)
@@ -243,62 +283,73 @@ async def main(
 
             sending_task = mp.Process(
                 target=start_task_sending_loop,
-                args=(task_queue, scoring_queue, miners_dict),
+                args=(task_queue, scoring_queue, miners_dict, event_stop),
                 name="SendingTaskProcess",
+                daemon=True,
             )
             sending_task.start()
             processes.append(sending_task)
 
             weight_setter_process = mp.Process(
                 target=start_weight_setter_loop,
-                args=(reward_events,),
+                args=(reward_events, event_stop),
                 name="WeightSetterProcess",
+                daemon=True,
             )
             weight_setter_process.start()
             processes.append(weight_setter_process)
 
-            GPUInfo.log_gpu_info()
+            health_check_process = mp.Process(
+                target=health_check,
+                args=(os.getpid(), event_stop),
+                name="HealthCheckProcess",
+                daemon=True,
+            )
+            health_check_process.start()
+            processes.append(health_check_process)
 
-            step = 0
+            GPUInfo.log_gpu_info()
             while True:
                 await asyncio.sleep(30)
-                block = settings.shared_settings.block
-                if (
-                    block - settings.shared_settings.METAGRAPH.last_update[settings.shared_settings.UID] > 500
-                    and step > 150
-                ):
-                    last_update_block = settings.shared_settings.METAGRAPH.last_update[settings.shared_settings.UID]
-                    logger.warning(
-                        f"Metagraph hasn't been updated for {block - last_update_block} blocks. "
-                        f"Staled block: {block}, Last update: {last_update_block}"
-                    )
-                    break
-                step += 1
 
         except KeyboardInterrupt:
+            event_stop.set()
             logger.info("KeyboardInterrupt detected. Shutting down gracefully...")
         except Exception as e:
             logger.error(f"Main loop error: {e}")
             raise
         finally:
-            logger.warning("🚨  Force‑killing entire process‑group")
+            logger.warning("🚨 Force‑killing entire process‑group")
 
             # 1. Cancel in‑process tasks so they stop touching the Manager.
             for t in tasks:
                 t.cancel()
             await asyncio.gather(*tasks, return_exceptions=True)
+            await asyncio.sleep(5)
 
             # 2. Manager cleanup *first* (so its socket vanishes).
             manager.shutdown()
 
             # 3. Sledgehammer.
-            if os.name == "posix":
+            try:
                 os.killpg(0, signal.SIGKILL)
-            else:
-                logger.error(f"Unsupported OS: {os.name}")
+            except Exception as e:
+                logger.error(f"Failed to kill process group: {e}")
     sys.exit(1)
 
 
+def kill_process_group():
+    try:
+        os.killpg(os.getpgid(0), signal.SIGKILL)
+    except Exception as e:
+        logger.error(f"Failed to kill process group: {e}")
+
+
 # The main function parses the configuration and runs the validator.
 if __name__ == "__main__":
+    try:
+        os.setpgrp()
+        atexit.register(kill_process_group)
+    except BaseException:
+        logger.warning("Failed to set process group; emergency termination may not work.")
     asyncio.run(main())

prompting/llms/model_manager.py

@@ -49,15 +49,12 @@ async def __aexit__(self, exc_type, exc, tb):
 
 class ModelManager(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    always_active_models: list[ModelConfig] = []
     total_ram: float = settings.shared_settings.LLM_MODEL_RAM
     active_models: dict[ModelConfig, ReproducibleVLLM] = {}
+    loading_tasks: dict[ModelConfig, asyncio.Future] = {}
     used_ram: float = 0.0
     lock: ClassVar[AsyncRLock] = AsyncRLock()
-
-    async def load_always_active_models(self):
-        for model_config in self.always_active_models:
-            await self.load_model(model_config=model_config)
+    # lock: ClassVar[AsyncRLock] = asyncio.Lock()
 
     async def load_model(self, model_config: ModelConfig, force: bool = True) -> ReproducibleVLLM:
         """Load model into GPU.
@@ -69,56 +66,40 @@ async def load_model(self, model_config: ModelConfig, force: bool = True) -> Rep
             force: If enabled, will unload all other models.
         """
         async with self.lock:
-            if model_config in self.active_models.keys():
+            # Copy active models, since they will be modified in the loop.
+            active_models = set(self.active_models.keys())
+
+            if model_config in active_models:
                 logger.debug(f"Model {model_config.llm_model_id} is already loaded.")
                 return self.active_models[model_config]
 
             if force:
                 logger.debug(f"Forcing model {model_config.llm_model_id} to load.")
-                for active_model in list(self.active_models.keys()):
-                    if active_model in self.always_active_models:
-                        continue
+                for active_model in active_models:
                     logger.debug(f"Unloading {active_model.llm_model_id} to make room for {model_config.llm_model_id}")
-
                     await self._unload_model(active_model)
                 await self.cleanup()
 
-            retries_max = 1
-            retry_counter = 0
-            retry_delay = 15
-            while True:
-                try:
-                    GPUInfo.log_gpu_info()
-                    model_class = model_factory(model_config.llm_model_id)
-                    model = model_class(
-                        model_id=model_config.llm_model_id,
-                        device=settings.shared_settings.NEURON_DEVICE,
-                        sampling_params=settings.shared_settings.SAMPLING_PARAMS,
-                    )
-                    self.used_ram += model_config.min_ram
-                    logger.info(
-                        f"Model {model_config.llm_model_id} has been successfully loaded. "
-                        f"Approx. used VRAM: {self.used_ram:.0f}GB"
-                    )
-                    self.active_models[model_config] = model
-                    await asyncio.sleep(1.0)
-                    return model
-                except BaseException as e:
-                    if retry_counter > retries_max:
-                        logger.error(f"Failed to load model after {retries_max} retries. Terminating process")
-                        await self.cleanup()
-                        # In case of VRAM leak, raise an exception to terminate the process.
-                        raise MemoryError
-
-                    retry_counter += 1
-                    retry_delay += retry_counter
-                    await self.cleanup()
-                    logger.error(
-                        f"Failed to load model {model_config.llm_model_id}. Retrying in {retry_delay} seconds. "
-                        f"Error: {str(e)}"
-                    )
-                    logger.debug(f"Current active models: {self.active_models}")
-                    await asyncio.sleep(retry_delay)
+            try:
+                GPUInfo.log_gpu_info()
+                model_class = model_factory(model_config.llm_model_id)
+                model = model_class(
+                    model_id=model_config.llm_model_id,
+                    device=settings.shared_settings.NEURON_DEVICE,
+                    sampling_params=settings.shared_settings.SAMPLING_PARAMS,
+                )
+                self.active_models[model_config] = model
+                self.used_ram += model_config.min_ram
+                logger.info(
+                    f"Model {model_config.llm_model_id} has been successfully loaded. "
+                    f"Approx. used VRAM: {self.used_ram:.0f}GB"
+                )
+                await asyncio.sleep(1.0)
+                return model
+            except BaseException as e:
+                await self.cleanup()
+                # In case of VRAM leak, raise an exception to terminate the process.
+                raise MemoryError(f"Failed to load model {model_config.llm_model_id}: {e}")
 
     async def _cleanup_model(self, model_instance: ReproducibleVLLM, cpu_offload: bool = False):
         """Free VRAM from given model."""
@@ -144,12 +125,10 @@ async def _unload_model(self, model_config: ModelConfig):
             return
 
         try:
-            model_instance = self.active_models.pop(model_config)
-
-            # Record initial memory state for debugging.
             initial_free_memory = GPUInfo.free_memory
             logger.debug(f"Initial free GPU memory before unloading: {initial_free_memory} GB")
-
+            # async with self.rlock:
+            model_instance = self.active_models.pop(model_config)
             await self._cleanup_model(model_instance, cpu_offload=False)
             await self.cleanup()
 
@@ -167,13 +146,13 @@ async def _unload_model(self, model_config: ModelConfig):
     async def get_model(self, llm_model: ModelConfig | str) -> ReproducibleVLLM:
         async with self.lock:
             if not llm_model:
-                llm_model = list(self.active_models.keys())[0] if self.active_models else ModelZoo.get_random()
+                llm_model = next(iter(self.active_models.keys())) if self.active_models else ModelZoo.get_random()
             if isinstance(llm_model, str):
                 llm_model = ModelZoo.get_model_by_id(llm_model)
             if llm_model in self.active_models:
                 return self.active_models[llm_model]
 
-        return await self.load_model(llm_model, force=True)
+        return await self.load_model(llm_model)
 
     async def generate(
         self,

prompting/llms/vllm_llm.py

@@ -191,21 +191,17 @@ def unload_model(self):
             if hasattr(self.model, "llm_engine") and hasattr(self.model.llm_engine, "driver_worker"):
                 del self.model.llm_engine.driver_worker
             if hasattr(self.model, "model"):
-                self.model = None
                 del self.model
             if hasattr(self.model, "tokenizer"):
-                self.tokenizer = None
                 del self.tokenizer
 
             gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
             if torch.distributed.is_initialized():
                 torch.distributed.destroy_process_group()
-
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
             logger.info("Successfully deleted the LLM pipeline and freed GPU memory")
-
-        except Exception as e:
+        except BaseException as e:
             logger.error(f"An error occurred during model unloading: {e}")
             gc.collect()
             if torch.cuda.is_available():