ViperEkura
88c06db096
- reject prompts that encode to zero tokens in add_task instead of admitting a task whose prefill can never run, and surface empty-id run_batch calls as prompt_empty errors - deliver the STOP stream callback when cancelling a live task so clients observe termination instead of hanging until socket timeout - strip the torch.compile _orig_mod. prefix at every unwrap_model site and when loading checkpoints so FSDP state dicts and saved weights no longer leak the wrapper name into downstream keys - reject online_* train strategies with nprocs > 1 at config validation time, explaining the NCCL all-gather deadlock they would otherwise hit mid-run - apply the frequency penalty before temperature scaling (OpenAI semantics) so the penalty survives temperature=0 instead of being annihilated by the 1e8 logit blowup, and exclude penalty pipelines from the greedy fast path - return logprobs from the raw pre-strategy distribution so they match training-side policy logprobs for PPO/GRPO importance ratios
536 lines
20 KiB
Python
536 lines
20 KiB
Python
import logging
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import threading
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import uuid
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from contextlib import nullcontext
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from functools import wraps
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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from astrai.extension import (
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ATTN_BACKEND,
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AttentionBackend,
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attn_backend,
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get_backend,
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)
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from astrai.inference.cache import PagePool, TaskCacheManager
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from astrai.inference.metrics import MetricsCollector
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from astrai.inference.runtime.executor import Executor
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from astrai.inference.task import (
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STOP,
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GenerationResult,
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Task,
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TaskManager,
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TaskStatus,
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)
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from astrai.model.automodel import AutoModel
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from astrai.tokenize.tokenizer import AutoTokenizer
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logger = logging.getLogger(__name__)
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def _with_weight_lock(method):
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@wraps(method)
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def synchronized(self, *args, **kwargs):
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with self._weight_lock:
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return method(self, *args, **kwargs)
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return synchronized
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class InferenceScheduler:
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"""Continuous batching loop: cleanup -> refill -> prefill -> decode (all groups)."""
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def __init__(
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self,
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model: AutoModel,
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tokenizer: AutoTokenizer,
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max_batch_size: int = 16,
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max_seq_len: Optional[int] = None,
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device: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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cache: Optional[PagePool] = None,
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enable_cuda_graph: bool = True,
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backend: Optional[Union[str, ATTN_BACKEND, AttentionBackend, type]] = None,
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policy_version: int = 0,
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):
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if (
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isinstance(policy_version, bool)
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or not isinstance(policy_version, int)
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or policy_version < 0
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):
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raise ValueError("policy_version must be a non-negative integer")
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config = model.config
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if max_seq_len is not None:
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self.max_seq_len = max_seq_len
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elif config.max_position_embeddings is not None:
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self.max_seq_len = config.max_position_embeddings
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else:
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raise ValueError(
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"max_seq_len must be provided either as argument "
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"or in model config (config.max_position_embeddings)"
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)
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self.device = device or next(model.parameters()).device
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self.dtype = dtype or next(model.parameters()).dtype
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head_dim = config.hidden_size // config.num_attention_heads
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if cache is not None:
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self._cache = cache
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else:
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self._cache = PagePool(
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n_layers=config.num_hidden_layers,
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n_kv_heads=config.num_key_value_heads,
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head_dim=head_dim,
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max_batch_size=max_batch_size,
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max_seq_len=self.max_seq_len,
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device=self.device,
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dtype=self.dtype,
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)
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self._metrics = MetricsCollector()
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self._task_cache = TaskCacheManager(self._cache)
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self._task_mgr = TaskManager(
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tokenizer=tokenizer,
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max_batch_size=max_batch_size,
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max_seq_len=self.max_seq_len,
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metrics=self._metrics,
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)
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if backend is None:
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self._backend = None
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active_backend = get_backend()
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else:
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active_backend = backend
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with attn_backend(active_backend):
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if backend is not None:
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self._backend = get_backend()
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self._backend_name = type(get_backend()).__name__
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self._executor = Executor(
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model=model,
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kv_cache=self._cache,
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task_cache=self._task_cache,
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device=self.device,
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dtype=self.dtype,
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enable_cuda_graph=enable_cuda_graph,
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)
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self._stop_event = threading.Event()
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self._loop_thread: Optional[threading.Thread] = None
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self._weight_lock = threading.RLock()
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self._policy_version = policy_version
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@property
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def policy_version(self) -> int:
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"""Version of the model weights used for subsequent generations."""
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return self._policy_version
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@_with_weight_lock
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def update_weights(self, policy_version: int) -> int:
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"""Acknowledge an in-place weight update and invalidate stale KV state.
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The scheduler owns the same model object as the in-process trainer, so
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weights have already changed when this method is called. The explicit
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version update makes that lifecycle visible and prevents prefix KV
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entries produced by older weights from being reused.
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"""
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if (
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isinstance(policy_version, bool)
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or not isinstance(policy_version, int)
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or policy_version < 0
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):
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raise ValueError("policy_version must be a non-negative integer")
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if policy_version < self._policy_version:
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raise ValueError(
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f"policy_version cannot move backwards from "
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f"{self._policy_version} to {policy_version}"
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)
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if policy_version == self._policy_version:
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return self._policy_version
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if self._loop_thread is not None and self._loop_thread.is_alive():
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raise RuntimeError("Stop the scheduler before updating model weights")
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if self._task_mgr.get_active_tasks() or self._task_mgr.get_waiting_tasks():
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raise RuntimeError("Cannot update model weights while tasks are queued")
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self._task_cache.invalidate_cache()
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self._policy_version = policy_version
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return self._policy_version
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def add_task(self, prompt: str, **kwargs) -> str:
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return self._task_mgr.add_task(prompt, **kwargs)
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def cancel_task(self, task_id: str) -> bool:
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"""Cancel a waiting or active task without freeing in-use KV state."""
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immediate, cancelled = self._task_mgr.cancel_task(task_id)
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for task in immediate:
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self._metrics.mark_finished(
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task.task_id, task.input_tokens, task.output_tokens
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)
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if cancelled:
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self._task_mgr.wake()
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return cancelled
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def remove_task(self, task_id: str) -> bool:
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"""Backward-compatible alias for cancellation."""
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return self.cancel_task(task_id)
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def get_stats(self) -> Dict[str, Any]:
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stats = self._task_mgr.get_stats()
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stats["kv_cache_tasks"] = self._task_cache.task_count
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stats["policy_version"] = self._policy_version
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return stats
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@property
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def backend_name(self) -> str:
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return self._backend_name
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@property
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def cuda_graph_enabled(self) -> bool:
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return self._executor.cuda_graph_enabled
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def _backend_context(self):
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if self._backend is None:
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return nullcontext()
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return attn_backend(self._backend)
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@staticmethod
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def _task_backend_groups(tasks: List[Task]):
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groups = {}
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for task in tasks:
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groups.setdefault(task.backend, (task.backend, []))[1].append(task)
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return groups.values()
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def _step(
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self, tasks: List[Task], return_logprobs: bool = False
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) -> Tuple[List[Task], List[Task]]:
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"""Advance every active task by one token (prefill + decode).
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Single shared primitive for both the continuous-batching loop and
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the synchronous ``run_batch`` path, so the two cannot drift.
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Tasks must already be allocated in the KV cache. Tasks without output
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are prefilled first and sample their first token from the final prompt
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position. Tasks with output extend the cache by one position and decode
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from their latest generated token.
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Args:
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tasks: Active tasks to advance by one token.
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return_logprobs: Forwarded to ``execute_decode``; per-token
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logprobs are recorded on each task's ``output_logprobs``.
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Returns:
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``(decoded, aborted)``: tasks that produced a new token (its ID
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already appended to ``output_ids``) and tasks that hit the
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sequence cap and were marked ``ABORTED``.
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"""
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to_prefill = [t for t in tasks if not t.prefill_done and t.prompt_ids]
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prefilled_ids = set()
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produced: List[Task] = []
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if to_prefill:
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for t in to_prefill:
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t.input_tokens = len(t.prompt_ids)
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groups: Dict[Tuple[int, Optional[AttentionBackend]], List[Task]] = {}
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for t in to_prefill:
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start_pos = min(
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self._task_cache.task_cached(t.task_id), len(t.prompt_ids) - 1
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)
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groups.setdefault((start_pos, t.backend), []).append(t)
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for (start_pos, _), group in groups.items():
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backend = group[0].backend
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backend_context = (
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attn_backend(backend) if backend is not None else nullcontext()
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)
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with (
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backend_context,
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self._metrics.record([t.task_id for t in group], "prefill"),
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):
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prefilled, step_out = self._executor.execute_prefill(
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group, start_pos=start_pos, return_logprobs=return_logprobs
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)
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for t, out in zip(prefilled, step_out):
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t.output_ids.append(out[0] if return_logprobs else out)
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t.output_tokens += 1
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t.mark_prefill_done()
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prefilled_ids.add(t.task_id)
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produced.append(t)
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start_logical_page = start_pos // self._cache.page_size
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for t in group:
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self._task_cache.task_record_hashes(
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t.task_id, t.prompt_ids, start_logical_page
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)
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decoded: List[Task] = []
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aborted: List[Task] = []
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for t in tasks:
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if t.task_id in prefilled_ids:
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continue
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if self._task_cache.task_extend(t.task_id, t.next_pos):
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decoded.append(t)
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else:
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t.status = TaskStatus.ABORTED
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aborted.append(t)
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for backend, group in self._task_backend_groups(decoded):
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backend_context = (
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attn_backend(backend) if backend is not None else nullcontext()
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)
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with (
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backend_context,
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self._metrics.record([t.task_id for t in group], "decode"),
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):
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step_out = self._executor.execute_decode(
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group, return_logprobs=return_logprobs
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)
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for t, out in zip(group, step_out):
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t.output_ids.append(out[0] if return_logprobs else out)
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t.output_tokens += 1
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t.advance_kv()
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produced.append(t)
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return produced, aborted
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def _run_generation_loop(self):
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stop_ids = self._task_mgr.tokenizer.stop_ids
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try:
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with self._backend_context():
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while not self._stop_event.is_set():
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finished = self._task_mgr.remove_finished_tasks(stop_ids)
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for task in finished:
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if task.status == TaskStatus.FINISHED:
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self._task_cache.task_record_hashes(
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task.task_id,
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self._task_cache.task_cacheable_ids(
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task.task_id, task.prompt_ids, task.output_ids
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),
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)
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self._task_cache.task_free(task.task_id)
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active = self._task_mgr.get_active_tasks()
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available = self._task_mgr.max_batch_size - len(active)
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if available > 0:
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candidates = self._task_mgr.pull_candidates(available)
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failed = []
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for task in candidates:
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if self._task_cache.task_alloc(
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task.task_id, task.prompt_ids
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):
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if not self._task_mgr.activate(task):
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self._task_cache.task_free(task.task_id)
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self._metrics.mark_finished(
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task.task_id,
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task.input_tokens,
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task.output_tokens,
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)
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else:
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failed.append(task)
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if failed:
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self._task_mgr.return_to_waiting(failed)
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if not self._task_mgr.has_work():
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self._task_mgr.wait_for_tasks(timeout=1.0)
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continue
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active = [
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task
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for task in self._task_mgr.get_active_tasks()
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if task.status != TaskStatus.ABORTED
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]
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decoded, aborted = self._step(active)
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for t in aborted:
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self._task_mgr.invoke_callback(t.task_id, STOP)
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for t in decoded:
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if t.status == TaskStatus.ABORTED:
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continue
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new_text = t.decode_new_token(self._task_mgr.tokenizer)
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if new_text:
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self._task_mgr.invoke_callback(t.task_id, new_text)
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if t.is_finished(stop_ids):
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self._task_mgr.invoke_callback(t.task_id, STOP)
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except Exception as e:
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self._stop_event.set()
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logger.error(f"Scheduler loop crashed: {e}", exc_info=True)
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self._abort_and_clear(free_waiting=False)
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def start(self):
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if self._loop_thread is not None and self._loop_thread.is_alive():
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return
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self._stop_event.clear()
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t = threading.Thread(target=self._run_generation_loop, daemon=True)
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t.start()
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self._loop_thread = t
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def stop(self):
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self._stop_event.set()
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self._task_mgr.wake()
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if self._loop_thread is not None:
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self._loop_thread.join(timeout=2.0)
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self._loop_thread = None
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self._abort_and_clear(free_waiting=True)
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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def _abort_and_clear(self, free_waiting: bool):
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"""Invoke STOP callbacks, release cache slots, and clear task queues."""
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active = self._task_mgr.get_active_tasks()
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waiting = self._task_mgr.get_waiting_tasks()
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for task in active:
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self._task_mgr.invoke_callback(task.task_id, STOP)
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self._task_cache.task_free(task.task_id)
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self._metrics.mark_finished(
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task.task_id, task.input_tokens, task.output_tokens
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)
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for task in waiting:
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self._task_mgr.invoke_callback(task.task_id, STOP)
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if free_waiting:
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self._task_cache.task_free(task.task_id)
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self._metrics.mark_finished(
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task.task_id, task.input_tokens, task.output_tokens
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)
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self._task_mgr.clear_queues()
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@_with_weight_lock
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def run_batch(
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self,
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prompt_ids_list: List[List[int]],
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*,
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max_tokens: Optional[int] = None,
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temperature: float = 1.0,
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top_p: float = 1.0,
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top_k: int = 50,
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frequency_penalty: float = 0.0,
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rep_window: int = 64,
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return_logprobs: bool = False,
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return_details: bool = False,
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) -> List[Any]:
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"""Synchronous batch generation without the scheduler thread.
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Accepts already-tokenized prompts (no string round-trip) and runs
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prefill + decode to completion on the calling thread. Designed for
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RL rollout, where logprobs of the behaviour policy must be collected
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alongside generated tokens.
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Args:
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prompt_ids_list: ``B`` prompts, each a list of token IDs.
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max_tokens: Maximum tokens to generate per prompt. ``None``
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uses ``self.max_seq_len - len(prompt_ids)``.
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temperature/top_p/top_k/frequency_penalty/rep_window: Sampling
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parameters (uniform across the batch).
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return_logprobs: If ``True``, return ``(token_ids, logprobs)``
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tuples per prompt (logprobs aligned 1-to-1 with token_ids).
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return_details: If ``True``, return a structured result per prompt
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with terminal and error reasons. Logprobs are populated when
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``return_logprobs`` is also ``True``.
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Returns:
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Structured results when ``return_details`` is ``True``;
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otherwise generated token IDs per prompt, or token/logprob tuples
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when ``return_logprobs`` is ``True``.
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"""
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stop_ids = self._task_mgr.tokenizer.stop_ids
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seq_cap = self.max_seq_len
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request_backend = get_backend(use_default=False)
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tasks: List[Optional[Task]] = []
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error_reasons: List[Optional[str]] = []
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for ids in prompt_ids_list:
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if not ids:
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tasks.append(None)
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error_reasons.append("prompt_empty")
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continue
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if len(ids) >= seq_cap:
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tasks.append(None)
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error_reasons.append("prompt_too_long")
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continue
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t_max = max_tokens
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if t_max is None:
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t_max = seq_cap - len(ids)
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else:
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t_max = min(t_max, seq_cap - len(ids))
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if t_max <= 0:
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tasks.append(None)
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error_reasons.append("max_tokens_non_positive")
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continue
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task = Task(
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task_id=f"batch_{uuid.uuid4().hex[:8]}",
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prompt_ids=list(ids),
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max_tokens=t_max,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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frequency_penalty=frequency_penalty,
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rep_window=rep_window,
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backend=request_backend,
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)
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if not self._task_cache.task_alloc(task.task_id, task.prompt_ids):
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tasks.append(None)
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error_reasons.append("kv_cache_allocation_failed")
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continue
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task.input_tokens = len(task.prompt_ids)
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self._metrics.register(task.task_id)
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tasks.append(task)
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error_reasons.append(None)
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runtime_errors: Dict[str, str] = {}
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try:
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live = [t for t in tasks if t is not None]
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with self._backend_context():
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while live:
|
|
decoded, aborted = self._step(live, return_logprobs=return_logprobs)
|
|
for task in aborted:
|
|
runtime_errors[task.task_id] = "kv_cache_extension_failed"
|
|
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
|
|
)
|
|
self._task_cache.task_free(t.task_id)
|
|
|
|
details: List[GenerationResult] = []
|
|
for t, setup_error in zip(tasks, error_reasons):
|
|
if t is None:
|
|
details.append(
|
|
GenerationResult(
|
|
token_ids=[],
|
|
logprobs=[],
|
|
finish_reason="rejected",
|
|
error_reason=setup_error,
|
|
)
|
|
)
|
|
else:
|
|
runtime_error = runtime_errors.get(t.task_id)
|
|
stopped = bool(t.output_ids and t.output_ids[-1] in stop_ids)
|
|
if runtime_error:
|
|
finish_reason = "rejected"
|
|
elif stopped:
|
|
finish_reason = "stop"
|
|
else:
|
|
finish_reason = "length"
|
|
details.append(
|
|
GenerationResult(
|
|
token_ids=list(t.output_ids),
|
|
logprobs=list(t.output_logprobs),
|
|
finish_reason=finish_reason,
|
|
error_reason=runtime_error,
|
|
)
|
|
)
|
|
|
|
if return_details:
|
|
return details
|
|
if return_logprobs:
|
|
return [(result.token_ids, result.logprobs) for result in details]
|
|
return [result.token_ids for result in details]
|