import logging import threading import uuid from typing import Any, Dict, List, Optional, Tuple import torch from astrai.inference.core.cache import PagePool from astrai.inference.core.executor import Executor from astrai.inference.core.metrics import MetricsCollector from astrai.inference.core.task import STOP, Task, TaskManager, TaskStatus from astrai.model.automodel import AutoModel from astrai.tokenize.tokenizer import AutoTokenizer logger = logging.getLogger(__name__) class InferenceScheduler: """Continuous batching loop: cleanup -> refill -> prefill -> decode (all groups).""" def __init__( self, model: AutoModel, tokenizer: AutoTokenizer, max_batch_size: int = 16, max_seq_len: Optional[int] = None, device: Optional[str] = None, dtype: Optional[torch.dtype] = None, cache: Optional[PagePool] = None, ): config = model.config if max_seq_len is not None: self.max_seq_len = max_seq_len elif config.max_position_embeddings is not None: self.max_seq_len = config.max_position_embeddings else: raise ValueError( "max_seq_len must be provided either as argument " "or in model config (config.max_position_embeddings)" ) self.device = device or next(model.parameters()).device self.dtype = dtype or next(model.parameters()).dtype head_dim = config.hidden_size // config.num_attention_heads if cache is not None: self._cache = cache else: self._cache = PagePool( n_layers=config.num_hidden_layers, n_kv_heads=config.num_key_value_heads, head_dim=head_dim, max_batch_size=max_batch_size, max_seq_len=self.max_seq_len, device=self.device, dtype=self.dtype, ) self._metrics = MetricsCollector() self._task_mgr = TaskManager( tokenizer=tokenizer, max_batch_size=max_batch_size, max_seq_len=self.max_seq_len, metrics=self._metrics, ) self._executor = Executor( model=model, tokenizer=tokenizer, kv_cache=self._cache, device=self.device, dtype=self.dtype, ) self._stop_event = threading.Event() self._loop_thread: Optional[threading.Thread] = None def add_task(self, prompt: str, **kwargs) -> str: return self._task_mgr.add_task(prompt, **kwargs) def remove_task(self, task_id: str): for task in self._task_mgr.remove_task(task_id): self._cache.task_free(task.task_id) def get_stats(self) -> Dict[str, Any]: return self._task_mgr.get_stats() def _step( self, tasks: List[Task], return_logprobs: bool = False ) -> Tuple[List[Task], List[Task]]: """Advance every active task by one token (prefill + decode). Single shared primitive for both the continuous-batching loop and the synchronous ``run_batch`` path, so the two cannot drift. Tasks must already be allocated in the KV cache. Tasks without output are prefilled first and sample their first token from the final prompt position. Tasks with output extend the cache by one position and decode from their latest generated token. Args: tasks: Active tasks to advance by one token. return_logprobs: Forwarded to ``execute_decode``; per-token logprobs are recorded on each task's ``output_logprobs``. Returns: ``(decoded, aborted)``: tasks that produced a new token (its ID already appended to ``output_ids``) and tasks that hit the sequence cap and were marked ``ABORTED``. """ cache = self._cache to_prefill = [t for t in tasks if t.output_tokens == 0 and t.prompt_ids] prefilled_ids = set() produced: List[Task] = [] if to_prefill: for t in to_prefill: t.input_tokens = len(t.prompt_ids) groups: Dict[Tuple[int, int], List[Task]] = {} for t in to_prefill: start_pos = min(cache.task_cached(t.task_id), len(t.prompt_ids) - 1) groups.setdefault((len(t.prompt_ids), start_pos), []).append(t) for (prompt_len, start_pos), group in groups.items(): with self._metrics.record([t.task_id for t in group], "prefill"): prefilled, step_out = self._executor.execute_prefill( group, prompt_len, start_pos, return_logprobs=return_logprobs ) for t, out in zip(prefilled, step_out): t.output_ids.append(out[0] if return_logprobs else out) t.output_tokens += 1 prefilled_ids.add(t.task_id) produced.append(t) start_logical_page = start_pos // getattr(cache, "page_size", 64) for t in group: cache.task_record_hashes( t.task_id, t.prompt_ids, start_logical_page ) decoded: List[Task] = [] aborted: List[Task] = [] for t in tasks: if t.task_id in prefilled_ids: continue if cache.task_extend(t.task_id, t.next_pos): decoded.append(t) else: t.status = TaskStatus.ABORTED aborted.append(t) if decoded: with self._metrics.record([t.task_id for t in decoded], "decode"): step_out = self._executor.execute_decode( decoded, return_logprobs=return_logprobs ) for t, out in zip(decoded, step_out): t.output_ids.append(out[0] if return_logprobs else out) t.output_tokens += 1 produced.append(t) return produced, aborted def _run_generation_loop(self): stop_ids = self._task_mgr.tokenizer.stop_ids cache = self._cache try: while not self._stop_event.is_set(): finished = self._task_mgr.remove_finished_tasks(stop_ids) for task in finished: if task.status == TaskStatus.FINISHED: cache.task_record_hashes( task.task_id, cache.task_cacheable_ids( task.task_id, task.prompt_ids, task.output_ids ), ) cache.task_free(task.task_id) active = self._task_mgr.get_active_tasks() available = self._task_mgr.max_batch_size - len(active) if available > 0: candidates = self._task_mgr.pull_candidates(available) failed = [] for task in candidates: if cache.task_alloc(task.task_id, task.prompt_ids): self._task_mgr.activate(task) else: failed.append(task) if failed: self._task_mgr.return_to_waiting(failed) if not self._task_mgr.has_work(): self._task_mgr.wait_for_tasks(timeout=1.0) continue active = self._task_mgr.get_active_tasks() decoded, aborted = self._step(active) for t in aborted: self._task_mgr.invoke_callback(t.task_id, STOP) for t in decoded: new_text = t.decode_new_token(self._task_mgr.tokenizer) if new_text: self._task_mgr.invoke_callback(t.task_id, new_text) if t.is_finished(stop_ids): remaining = t.flush_remaining(self._task_mgr.tokenizer) if remaining: self._task_mgr.invoke_callback(t.task_id, remaining) self._task_mgr.invoke_callback(t.task_id, STOP) except Exception as e: self._stop_event.set() logger.error(f"Scheduler loop crashed: {e}", exc_info=True) for task in self._task_mgr.get_active_tasks(): self._task_mgr.invoke_callback(task.task_id, STOP) cache.task_free(task.task_id) for task in self._task_mgr.get_waiting_tasks(): self._task_mgr.invoke_callback(task.task_id, STOP) self._task_mgr.clear_queues() def start(self): if self._loop_thread is not None and self._loop_thread.is_alive(): return self._stop_event.clear() t = threading.Thread(target=self._run_generation_loop, daemon=True) t.start() self._loop_thread = t def stop(self): self._stop_event.set() self._task_mgr.wake() if self._loop_thread is not None: self._loop_thread.join(timeout=2.0) self._loop_thread = None for task in self._task_mgr.get_active_tasks(): self._task_mgr.invoke_callback(task.task_id, STOP) self._cache.task_free(task.task_id) for task in self._task_mgr.get_waiting_tasks(): self._task_mgr.invoke_callback(task.task_id, STOP) self._cache.task_free(task.task_id) self._task_mgr.clear_queues() if torch.cuda.is_available(): torch.cuda.empty_cache() def run_batch( self, prompt_ids_list: List[List[int]], *, max_tokens: Optional[int] = None, temperature: float = 1.0, top_p: float = 1.0, top_k: int = 50, frequency_penalty: float = 0.0, rep_window: int = 64, return_logprobs: bool = False, ) -> List[List[int]]: """Synchronous batch generation without the scheduler thread. Accepts already-tokenized prompts (no string round-trip) and runs prefill + decode to completion on the calling thread. Designed for RL rollout, where logprobs of the behaviour policy must be collected alongside generated tokens. Args: prompt_ids_list: ``B`` prompts, each a list of token IDs. max_tokens: Maximum tokens to generate per prompt. ``None`` uses ``self.max_seq_len - len(prompt_ids)``. temperature/top_p/top_k/frequency_penalty/rep_window: Sampling parameters (uniform across the batch). return_logprobs: If ``True``, return ``(token_ids, logprobs)`` tuples per prompt (logprobs aligned 1-to-1 with token_ids). Returns: ``List[List[int]]`` of generated token IDs per prompt, or — when ``return_logprobs`` is ``True`` — ``List[Tuple[List[int], List[float]]]``. """ stop_ids = self._task_mgr.tokenizer.stop_ids cache = self._cache seq_cap = self.max_seq_len tasks: List[Task] = [] for ids in prompt_ids_list: if len(ids) >= seq_cap: tasks.append(None) continue t_max = max_tokens if t_max is None: t_max = seq_cap - len(ids) else: t_max = min(t_max, seq_cap - len(ids)) if t_max <= 0: tasks.append(None) continue task = Task( task_id=f"batch_{uuid.uuid4().hex[:8]}", prompt_ids=list(ids), max_tokens=t_max, temperature=temperature, top_p=top_p, top_k=top_k, frequency_penalty=frequency_penalty, rep_window=rep_window, ) if not cache.task_alloc(task.task_id, task.prompt_ids): tasks.append(None) continue task.input_tokens = len(task.prompt_ids) self._metrics.register(task.task_id) tasks.append(task) try: live = [t for t in tasks if t is not None] while live: decoded, _ = self._step(live, return_logprobs=return_logprobs) live = [t for t in decoded if not t.is_finished(stop_ids)] finally: for t in tasks: if t is not None: self._metrics.mark_finished( t.task_id, t.input_tokens, t.output_tokens ) cache.task_free(t.task_id) results: List[Any] = [] for t in tasks: if t is None: results.append(([], []) if return_logprobs else []) elif return_logprobs: results.append((list(t.output_ids), list(t.output_logprobs))) else: results.append(list(t.output_ids)) return results