ViperEkura
654e6eb0d1
- sample the first token from prefill logits without duplicating the prompt tail - reject incomplete multi-output records before preprocessing alignment - cover cached generation and partial DPO records with regression tests
242 lines
8.3 KiB
Python
242 lines
8.3 KiB
Python
import logging
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from dataclasses import dataclass
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from typing import List, Optional
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import torch
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from torch import Tensor
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from astrai.inference.core.cache import PagePool
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from astrai.inference.core.task import Task
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from astrai.inference.core.workspace import InferenceWorkspace
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from astrai.inference.sample import sample
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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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@dataclass
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class SamplingBatchInfo:
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"""Per-batch sampling parameters, cached across decode steps.
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Sampling params are constant for a given ordered task set, so they are
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built once (pinned-memory async H2D) and reused until the task set
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changes. ``top_ks`` is int32 to match the native consumers.
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"""
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temperatures: Tensor # float32 [B]
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top_ks: Tensor # int32 [B]
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top_ps: Tensor # float32 [B]
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freq_penalties: Tensor # float32 [B]
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has_freq: bool # any frequency_penalty != 0 (avoids per-step GPU .any())
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def _build_sampling_batch_info(tasks: List[Task], device) -> SamplingBatchInfo:
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pin = str(device).startswith("cuda")
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freq_penalties = torch.tensor(
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[t.frequency_penalty for t in tasks], dtype=torch.float32, pin_memory=pin
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).to(device, non_blocking=True)
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return SamplingBatchInfo(
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temperatures=torch.tensor(
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[t.temperature for t in tasks], dtype=torch.float32, pin_memory=pin
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).to(device, non_blocking=True),
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top_ks=torch.tensor(
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[t.top_k for t in tasks], dtype=torch.int32, pin_memory=pin
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).to(device, non_blocking=True),
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top_ps=torch.tensor(
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[t.top_p for t in tasks], dtype=torch.float32, pin_memory=pin
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).to(device, non_blocking=True),
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freq_penalties=freq_penalties,
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has_freq=bool((freq_penalties != 0).any()),
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)
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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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kv_cache: PagePool,
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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.kv_cache = kv_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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# Per-step decode cache for the steady-state case where the same
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# ordered task set decodes one token per step. Sampling params are
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# constant across steps; position_ids grows by exactly 1. Single-slot:
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# any task-set change is a cache miss.
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self._decode_cache: Optional[tuple] = None
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# Pre-allocated fixed-shape buffers for the decode hot path
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# (input_ids, decode mask, KV bind metadata). Eagerly sized at init
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# so the workspace is CUDA-graph-capture friendly — no allocation
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# during capture.
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self._workspace = InferenceWorkspace(
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max_batch_size=kv_cache.max_batch_size,
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max_seq_len=kv_cache.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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def _sample_logits(
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self,
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logits: Tensor,
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tasks: List[Task],
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return_logprobs: bool = False,
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info: Optional[SamplingBatchInfo] = None,
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):
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info = info or _build_sampling_batch_info(tasks, self.device)
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if info.has_freq:
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history_lists = [
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t.prompt_ids[-t.rep_window :] + t.output_ids for t in tasks
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]
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history_lens = [len(ids) for ids in history_lists]
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max_len = max(history_lens, default=0)
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padded_ids = torch.zeros(
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len(tasks), max_len, dtype=torch.long, device=self.device
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)
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padded_mask = torch.zeros(
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len(tasks), max_len, dtype=torch.bool, device=self.device
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)
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for i, ids in enumerate(history_lists):
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length = len(ids)
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padded_ids[i, :length] = torch.as_tensor(
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ids, dtype=torch.long, device=self.device
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)
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padded_mask[i, :length] = True
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else:
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padded_ids = None
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padded_mask = None
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result = sample(
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logits,
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temperature=info.temperatures,
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top_k=info.top_ks,
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top_p=info.top_ps,
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frequency_penalty=info.freq_penalties,
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input_ids=padded_ids,
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input_mask=padded_mask,
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return_logprobs=return_logprobs,
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)
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if not return_logprobs:
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return result.tolist()
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tokens, logprobs = result
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tokens_list = tokens.tolist()
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logprobs_list = logprobs.tolist()
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for task, logprob in zip(tasks, logprobs_list):
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task.output_logprobs.append(float(logprob))
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return list(zip(tokens_list, logprobs_list))
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def execute_prefill(
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self,
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tasks: List[Task],
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prompt_len: int,
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start_pos: int = 0,
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return_logprobs: bool = False,
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):
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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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input_ids = torch.tensor(
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[t.prompt_ids[start_pos:prompt_len] 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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position_ids = (
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torch.arange(start_pos, prompt_len, dtype=torch.long, device=self.device)
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.unsqueeze(0)
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.expand(batch_sz, -1)
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)
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input_mask = position_ids.unsqueeze(-1) >= torch.arange(
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prompt_len, device=self.device
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)
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with torch.inference_mode():
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outputs = self.model(
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input_ids,
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input_mask=input_mask,
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position_ids=position_ids,
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kv_cache=self.kv_cache.bind_tasks(
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task_ids,
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self._workspace,
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start_pos=start_pos,
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),
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)
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logits = outputs["logits"][:, -1, :]
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return tasks, self._sample_logits(logits, tasks, return_logprobs)
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def execute_decode(
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self, tasks: List[Task], return_logprobs: bool = False
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) -> List[int]:
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"""Decode next token for each task.
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Args:
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return_logprobs: When ``True``, also record (and return)
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the log-probability of each sampled token under the
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post-strategy sampling distribution. The logprob is
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appended to ``task.output_logprobs`` and the return
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list becomes ``List[Tuple[int, float]]``.
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Returns:
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``List[int]`` of sampled token IDs, or
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``List[Tuple[int, float]]`` of ``(token_id, logprob)`` when
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``return_logprobs`` is ``True``.
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"""
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if not tasks:
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return []
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input_ids = self._workspace.fill_input_ids(
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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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).unsqueeze(1)
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task_ids = [t.task_id for t in tasks]
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sig = tuple(task_ids)
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cur_positions = [t.next_pos for t in tasks]
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cached = self._decode_cache
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if (
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cached is not None
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and cached[0] == sig
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and cur_positions == [p + 1 for p in cached[1]]
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):
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_, _, info, position_ids = cached
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position_ids += 1
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self._decode_cache = (sig, cur_positions, info, position_ids)
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else:
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info = _build_sampling_batch_info(tasks, self.device)
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position_ids = torch.tensor(
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cur_positions, dtype=torch.long, device=self.device
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)
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self._decode_cache = (sig, cur_positions, info, position_ids)
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total_len = max(t.next_pos for t in tasks) + 1
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input_mask = self._workspace.decode_mask(position_ids, total_len)
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with torch.inference_mode():
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outputs = self.model(
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input_ids,
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input_mask=input_mask,
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kv_cache=self.kv_cache.bind_tasks(
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task_ids,
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self._workspace,
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),
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position_ids=position_ids.unsqueeze(1),
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)
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logits = outputs["logits"][:, -1, :]
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return self._sample_logits(logits, tasks, return_logprobs, info=info)
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