312 lines
8.8 KiB
Python
312 lines
8.8 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 threading
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from dataclasses import dataclass
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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.inference.core.scheduler import InferenceScheduler
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from astrai.inference.core.task import STOP
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from astrai.tokenize import AutoTokenizer
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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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if not out:
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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) -> None:
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with self._cond:
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self._cond.wait_for(lambda: self._completed >= self._total)
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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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def _validate_params(top_k: int, top_p: float, temperature: float) -> None:
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if not (isinstance(top_k, int) and top_k >= 0):
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raise ValueError("top_k must be a non-negative integer")
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if not (0.0 <= top_p <= 1.0):
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raise ValueError("top_p must be a float between 0.0 and 1.0")
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if not (isinstance(temperature, (int, float)) and temperature >= 0):
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raise ValueError("temperature must be a non-negative number")
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@dataclass(frozen=True)
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class GenerationParams:
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"""Immutable value object for sampling hyperparameters."""
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top_k: int = 50
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top_p: float = 1.0
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temperature: float = 1.0
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max_tokens: int = 1024
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class GenerationRequest:
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"""Request parameters for text generation."""
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def __init__(
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self,
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messages: List[Dict[str, str]],
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top_k: int = 50,
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top_p: float = 1.0,
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temperature: float = 1.0,
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max_len: int = 1024,
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stream: bool = False,
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):
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self.messages = messages
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self.params = GenerationParams(
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top_k=top_k,
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top_p=top_p,
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temperature=temperature,
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max_tokens=max_len,
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)
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self.stream = stream
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_validate_params(top_k, top_p, temperature)
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@property
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def top_k(self) -> int:
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return self.params.top_k
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@property
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def top_p(self) -> float:
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return self.params.top_p
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@property
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def temperature(self) -> float:
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return self.params.temperature
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@property
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def max_len(self) -> int:
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return self.params.max_tokens
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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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max_prompt_len: int = 2048,
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page_size: int = 128,
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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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max_prompt_len=max_prompt_len,
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page_size=page_size,
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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: int = 1024,
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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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) -> Union[Generator, str, List[str]]:
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_validate_params(top_k, top_p, temperature)
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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 stream:
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return self._generate_streaming(
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prompts, is_batch, max_tokens, temperature, top_p, top_k
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)
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else:
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return self._generate_non_streaming(
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prompts, is_batch, max_tokens, temperature, top_p, top_k
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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: int = 1024,
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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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) -> AsyncGenerator[str, None]:
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sync_gen = self._generate_streaming(
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[prompt], False, max_tokens, temperature, top_p, top_k
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)
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async def _agen():
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loop = asyncio.get_event_loop()
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while True:
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token = await loop.run_in_executor(None, self._next_token, sync_gen)
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if token is None:
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break
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yield token
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return _agen()
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@staticmethod
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def _next_token(gen: Generator) -> Optional[str]:
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try:
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return next(gen)
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except StopIteration:
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return None
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def generate_with_request(
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self, request: GenerationRequest
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) -> Union[Generator[str, None, None], str, List[str]]:
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prompt = self.tokenizer.apply_chat_template(request.messages, tokenize=False)
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return self.generate(
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prompt=prompt,
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stream=request.stream,
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max_tokens=request.params.max_tokens,
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temperature=request.params.temperature,
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top_p=request.params.top_p,
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top_k=request.params.top_k,
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)
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def _submit_tasks(
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self,
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prompts: List[str],
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max_tokens: 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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) -> Tuple[GenerateResult, List[str]]:
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n = len(prompts)
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result = GenerateResult(count=n)
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task_ids = []
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for i, p in enumerate(prompts):
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cb = self._make_callback(result, i)
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task_id = 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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stream_callback=cb,
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)
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task_ids.append(task_id)
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return result, task_ids
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@staticmethod
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def _make_callback(result: GenerateResult, idx: int):
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def cb(token):
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result.append(token, idx)
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return cb
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def _generate_streaming(
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self,
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prompts: List[str],
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is_batch: bool,
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max_tokens: 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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) -> Generator:
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result, task_ids = self._submit_tasks(
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prompts, max_tokens, temperature, top_p, top_k
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)
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n = len(prompts)
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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 tid in task_ids:
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self.scheduler.remove_task(tid)
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return gen()
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def _generate_non_streaming(
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self,
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prompts: List[str],
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is_batch: bool,
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max_tokens: 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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) -> Union[str, List[str]]:
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result, task_ids = self._submit_tasks(
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prompts, max_tokens, temperature, top_p, top_k
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)
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result.wait_completion()
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for tid in task_ids:
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self.scheduler.remove_task(tid)
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res = result.get_results()
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return res if is_batch else res[0]
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def get_stats(self) -> Dict[str, Any]:
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return self.scheduler.get_stats()
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def shutdown(self) -> None:
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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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