ViperEkura
e13fe53475
- add build_engine() to astrai.inference.engine as the single load-place-wire path for InferenceEngine, accepting a checkpoint path or live model/tokenizer plus passthrough engine kwargs - migrate the server lifespan, generate CLI, humaneval/ifeval evals, and all three demos to build_engine; app._create_engine collapses into a direct call - export build_engine from astrai and astrai.inference - parameterize the autoregressive demo with --prompt one-shot continuation plus model path and sampling knobs, exiting cleanly on !exit or EOF - cover the composition root with unit tests for live-object assembly, kwargs passthrough, and argument validation
309 lines
9.5 KiB
Python
309 lines
9.5 KiB
Python
"""Unified inference engine for continuous batching."""
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import asyncio
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import gc
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import logging
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import threading
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from pathlib import Path
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from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from astrai.extension import ATTN_BACKEND, AttentionBackend, get_backend
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from astrai.inference.cache import PagePool
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from astrai.inference.scheduler import InferenceScheduler
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from astrai.inference.task import STOP
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from astrai.model import AutoModel
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from astrai.tokenize import AutoTokenizer
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logger = logging.getLogger(__name__)
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class GenerateResult:
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"""Thread-safe token accumulator for streaming and non-streaming modes."""
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def __init__(self, count: int = 1):
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self._cond = threading.Condition()
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self._event = threading.Event()
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self.tokens: List[Tuple[int, str]] = []
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self.results: List[str] = [""] * count
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self._done: List[bool] = [False] * count
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self._completed = 0
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self._total = count
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def append(self, token: str, idx: int = 0):
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with self._cond:
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self.tokens.append((idx, token))
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if token is not STOP:
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self.results[idx] += token
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else:
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if not self._done[idx]:
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self._done[idx] = True
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self._completed += 1
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self._cond.notify_all()
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self._event.set()
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def pop_all(self) -> List[Tuple[int, str]]:
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with self._cond:
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out = self.tokens.copy()
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self.tokens.clear()
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self._event.clear()
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return out
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def wait(self, timeout: Optional[float] = None) -> bool:
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return self._event.wait(timeout=timeout)
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def wait_completion(self, timeout: float = 300.0):
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with self._cond:
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if not self._cond.wait_for(
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lambda: self._completed >= self._total, timeout=timeout
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):
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raise TimeoutError(
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f"Generation timeout after {timeout}s "
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f"({self._completed}/{self._total} completed)"
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)
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def get_results(self) -> List[str]:
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with self._cond:
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return self.results.copy()
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class InferenceEngine:
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"""Unified inference engine backed by continuous-batching scheduler."""
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def __init__(
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self,
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model: nn.Module,
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tokenizer: AutoTokenizer,
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max_batch_size: int = 1,
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max_seq_len: Optional[int] = 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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self.model = model
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self.tokenizer = tokenizer
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self.scheduler = InferenceScheduler(
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model=self.model,
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tokenizer=self.tokenizer,
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max_batch_size=max_batch_size,
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max_seq_len=max_seq_len,
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cache=cache,
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enable_cuda_graph=enable_cuda_graph,
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backend=backend,
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)
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self.scheduler.start()
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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self.shutdown()
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return False
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def generate(
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self,
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prompt: Union[str, List[str]],
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stream: bool = False,
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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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) -> Union[Generator, str, List[str]]:
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is_batch = isinstance(prompt, list)
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prompts = prompt if is_batch else [prompt]
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if max_tokens is not None and max_tokens <= 0:
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if stream:
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return iter(())
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results = [""] * len(prompts)
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return results if is_batch else results[0]
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return self._generate(
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prompts,
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is_batch,
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stream,
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max_tokens,
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temperature,
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top_p,
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top_k,
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frequency_penalty,
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rep_window,
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)
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def generate_async(
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self,
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prompt: str,
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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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) -> AsyncGenerator[str, None]:
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request_backend = get_backend(use_default=False)
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result = GenerateResult()
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task_id = self.scheduler.add_task(
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prompt=prompt,
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max_tokens=max_tokens,
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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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stream_callback=result.append,
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)
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async def _agen():
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finished = False
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try:
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while not finished:
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for _idx, token in result.pop_all():
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if token is STOP:
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finished = True
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break
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yield token
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if not finished:
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await asyncio.to_thread(result.wait, 0.05)
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finally:
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if not finished:
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self.scheduler.cancel_task(task_id)
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return _agen()
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def _generate(
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self,
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prompts: List[str],
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is_batch: bool,
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stream: bool,
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max_tokens: Optional[int],
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temperature: float,
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top_p: float,
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top_k: int,
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frequency_penalty: float,
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rep_window: int,
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) -> Union[Generator, str, List[str]]:
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n = len(prompts)
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request_backend = get_backend(use_default=False)
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result = GenerateResult(count=n)
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task_ids = [
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self.scheduler.add_task(
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prompt=p,
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max_tokens=max_tokens,
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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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stream_callback=lambda token, idx=i: result.append(token, idx),
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)
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for i, p in enumerate(prompts)
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]
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if not stream:
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try:
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result.wait_completion()
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except TimeoutError:
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for tid in task_ids:
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self.scheduler.cancel_task(tid)
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raise
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res = result.get_results()
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return res if is_batch else res[0]
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remaining = n
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finished = [False] * n
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def gen():
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nonlocal remaining
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try:
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while remaining > 0:
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items = result.pop_all()
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for idx, token in items:
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if token is STOP:
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if not finished[idx]:
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finished[idx] = True
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remaining -= 1
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else:
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yield (idx, token) if is_batch else token
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if remaining > 0:
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result.wait(timeout=0.05)
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finally:
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for idx, task_id in enumerate(task_ids):
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if not finished[idx]:
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self.scheduler.cancel_task(task_id)
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return gen()
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def get_stats(self) -> Dict[str, Any]:
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return self.scheduler.get_stats()
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@property
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def backend_name(self) -> str:
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return self.scheduler.backend_name
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@property
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def cuda_graph_enabled(self) -> bool:
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return self.scheduler.cuda_graph_enabled
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def shutdown(self):
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self.scheduler.stop()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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def build_engine(
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param_path: Optional[Union[str, Path]] = None,
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*,
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model: Optional[nn.Module] = None,
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tokenizer: Optional[AutoTokenizer] = None,
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device: Optional[str] = "cuda",
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dtype: Optional[torch.dtype] = torch.bfloat16,
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max_batch_size: int = 16,
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max_seq_len: Optional[int] = None,
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**engine_kwargs: Any,
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) -> InferenceEngine:
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"""Composition root for inference assembly.
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Loads model and tokenizer from *param_path*, or accepts preloaded
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objects, places the model, and returns a started InferenceEngine.
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Extra *engine_kwargs* (cache, enable_cuda_graph, backend) pass
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through to InferenceEngine. Placement parts left as None are skipped.
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"""
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if param_path is not None:
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if model is not None or tokenizer is not None:
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raise ValueError("pass either param_path or model+tokenizer, not both")
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path = Path(param_path)
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if not path.exists():
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raise FileNotFoundError(f"Parameter directory not found: {path}")
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tokenizer = AutoTokenizer.from_pretrained(path)
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model = AutoModel.from_pretrained(path)
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elif model is None or tokenizer is None:
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raise ValueError("build_engine requires param_path or both model and tokenizer")
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placement: Dict[str, Any] = {}
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if device is not None:
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placement["device"] = device
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if dtype is not None:
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placement["dtype"] = dtype
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if placement:
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model.to(**placement)
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logger.info(
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f"Model placed on {placement.get('device')} "
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f"with dtype {placement.get('dtype')}"
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)
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return InferenceEngine(
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model=model,
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tokenizer=tokenizer,
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max_batch_size=max_batch_size,
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max_seq_len=max_seq_len,
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**engine_kwargs,
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)
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