ViperEkura
47b3ed4e44
- InferenceEngine/Scheduler accept an explicit backend - capture request-level attn_backend context onto Task - split prefill/decode batches by backend instance - ASTR_BACKEND env overrides ContextVar as process-wide policy - report resolved backend and CUDA-graph state in benchmark
409 lines
15 KiB
Python
409 lines
15 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 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 STOP, Task, TaskManager, TaskStatus
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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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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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):
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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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default_backend = get_backend()
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self._backend_name = type(default_backend).__name__
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with attn_backend(default_backend):
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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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else:
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with attn_backend(backend):
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self._backend = get_backend()
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self._backend_name = type(self._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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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 remove_task(self, task_id: str):
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for task in self._task_mgr.remove_task(task_id):
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self._task_cache.task_free(task.task_id)
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def get_stats(self) -> Dict[str, Any]:
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return self._task_mgr.get_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, 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((len(t.prompt_ids), start_pos, t.backend), []).append(
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t
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)
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for (prompt_len, 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, prompt_len, 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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self._task_mgr.activate(task)
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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 = self._task_mgr.get_active_tasks()
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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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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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for task in self._task_mgr.get_active_tasks():
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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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for task in self._task_mgr.get_waiting_tasks():
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self._task_mgr.invoke_callback(task.task_id, STOP)
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self._task_mgr.clear_queues()
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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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for task in self._task_mgr.get_active_tasks():
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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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for task in self._task_mgr.get_waiting_tasks():
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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._task_mgr.clear_queues()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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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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) -> List[List[int]]:
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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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Returns:
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``List[List[int]]`` of generated token IDs per prompt, or —
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when ``return_logprobs`` is ``True`` —
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``List[Tuple[List[int], List[float]]]``.
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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[Task] = []
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for ids in prompt_ids_list:
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if len(ids) >= seq_cap:
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tasks.append(None)
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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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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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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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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:
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decoded, _ = self._step(live, return_logprobs=return_logprobs)
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live = [t for t in decoded if not t.is_finished(stop_ids)]
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finally:
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for t in tasks:
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if t is not None:
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self._metrics.mark_finished(
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t.task_id, t.input_tokens, t.output_tokens
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)
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self._task_cache.task_free(t.task_id)
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results: List[Any] = []
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for t in tasks:
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if t is None:
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results.append(([], []) if return_logprobs else [])
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elif return_logprobs:
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results.append((list(t.output_ids), list(t.output_logprobs)))
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else:
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results.append(list(t.output_ids))
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return results
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