118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
import logging
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from typing import List, Optional
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import torch
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from astrai.inference.cache import PagedCache
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from astrai.inference.sample import sample
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from astrai.inference.task import Task
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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 Executor:
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"""Model forward passes for prefill and decode phases."""
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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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page_cache: PagedCache,
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device: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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):
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self.model = model
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self.tokenizer = tokenizer
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self.page_cache = page_cache
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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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def execute_prefill(
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self, tasks: List[Task], prompt_len: int, start_pos: int = 0
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) -> None:
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if start_pos >= prompt_len:
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return
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tasks = sorted(tasks, key=lambda t: t.task_id)
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batch_sz = len(tasks)
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seq_len = prompt_len - start_pos
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input_ids = torch.empty(batch_sz, seq_len, dtype=torch.long, device=self.device)
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for i, t in enumerate(tasks):
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input_ids[i] = torch.tensor(
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t.prompt_ids[start_pos:prompt_len], device=self.device
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)
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task_ids = [t.task_id for t in tasks]
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page_tables = self.page_cache.make_table_tensor(task_ids, self.device)
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with torch.inference_mode():
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self.model(
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input_ids,
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position_ids=torch.arange(
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start_pos, prompt_len, dtype=torch.long, device=self.device
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)
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.unsqueeze(0)
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.expand(batch_sz, -1),
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paged_cache=self.page_cache.bind(page_tables, total_len=prompt_len),
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)
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for i, t in enumerate(tasks):
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input_ids[i] = torch.tensor(
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t.prompt_ids[start_pos:prompt_len], device=self.device
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)
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task_ids = [t.task_id for t in tasks]
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page_tables = self.page_cache.make_table_tensor(task_ids, self.device)
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with torch.inference_mode():
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self.model(
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input_ids,
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position_ids=torch.arange(
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start_pos, prompt_len, dtype=torch.long, device=self.device
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)
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.unsqueeze(0)
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.expand(batch_sz, -1),
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paged_cache=self.page_cache.bind(page_tables, total_len=prompt_len),
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)
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def execute_decode(self, tasks: List[Task], start_pos: int) -> List[int]:
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if not tasks:
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return []
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batch_sz = len(tasks)
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input_ids = torch.tensor(
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[t.output_ids[-1] if t.output_ids else t.prompt_ids[-1] for t in tasks],
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dtype=torch.long,
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device=self.device,
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)
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task_ids = [t.task_id for t in tasks]
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page_tables = self.page_cache.make_table_tensor(task_ids, self.device)
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total_len = start_pos + 1
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temperatures = torch.tensor([t.temperature for t in tasks], device=self.device)
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top_ks = torch.tensor([t.top_k for t in tasks], device=self.device)
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top_ps = torch.tensor([t.top_p for t in tasks], device=self.device)
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with torch.inference_mode():
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outputs = self.model(
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input_ids.unsqueeze(1),
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paged_cache=self.page_cache.bind(page_tables, total_len=total_len),
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position_ids=torch.full(
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(batch_sz, 1), start_pos, dtype=torch.long, device=self.device
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),
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)
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logits = outputs["logits"][:, -1, :]
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return sample(
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logits,
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temperature=temperatures,
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top_k=top_ks,
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top_p=top_ps,
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).tolist()
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