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perf: cache per-step decode tensor construction

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- SamplingBatchInfo: sample params built once per task set (top_k int32, pinned async H2D)
- position_ids advances by +1 on steady-state decode instead of re-building
- DecodeBindCache: bind_tasks increments seq_lens/kv_indptr, reuses req_pool_indices
- saves ~240us of python/launch overhead per decode step
This commit is contained in:
ViperEkura
2026-08-02 20:32:53 +08:00
parent 8447f88f61
commit d033b2ef0f
2 changed files with 126 additions and 26 deletions
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astrai/inference/core/cache.py
+56 -5
View File
@@ -217,6 +217,24 @@ class KVCache:
kv_indptr: Optional[Tensor] = None
@dataclass
class DecodeBindCache:
"""Cached KV-addressing state for steady-state decode.
Valid for one ordered task set advancing every sequence by exactly one
token per step. ``seq_lens`` is the Python mirror used to validate the
+1 progression without a GPU round-trip; on any task-set change or
non-monotonic seq_lens the whole entry is rebuilt.
"""
sig: tuple
seq_lens: List[int]
req_pool_indices: Tensor
seq_lens_t: Tensor
kv_indptr: Tensor
inc: Tensor
class PagePool:
"""Top-level KV cache manager.
@@ -287,6 +305,12 @@ class PagePool:
self._task_pages: Dict[str, List[int]] = {}
self._lock = threading.Lock()
# Single-slot incremental cache for steady-state decode: the same
# ordered task set advances every sequence by exactly one token per
# step, so seq_lens_t and kv_indptr can be updated in-place instead
# of re-allocating + re-cumsumming. Any task-set change is a miss.
self._bind_cache: Optional[DecodeBindCache] = None
# ---- task lifecycle ----
def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
@@ -423,8 +447,38 @@ class PagePool:
start_pos: Optional[int] = None,
) -> KVCache:
req_indices = [self._task_req[tid] for tid in task_ids]
req_pool_indices = torch.tensor(req_indices, dtype=torch.long, device=device)
seq_lens_t = torch.tensor(seq_lens, dtype=torch.long, device=device)
sig = tuple(task_ids)
cache = self._bind_cache
incremental = (
start_pos is None
and cache is not None
and cache.sig == sig
and len(cache.seq_lens) == len(seq_lens)
and all(s == p + 1 for s, p in zip(seq_lens, cache.seq_lens))
)
if incremental:
req_pool_indices = cache.req_pool_indices
seq_lens_t = cache.seq_lens_t + 1
kv_indptr = cache.kv_indptr + cache.inc
inc = cache.inc
else:
req_pool_indices = torch.tensor(
req_indices, dtype=torch.long, device=device
)
seq_lens_t = torch.tensor(seq_lens, dtype=torch.long, device=device)
kv_indptr = torch.zeros(len(seq_lens) + 1, dtype=torch.int32, device=device)
kv_indptr[1:] = seq_lens_t.cumsum(0).to(torch.int32)
inc = torch.arange(len(seq_lens) + 1, dtype=torch.int32, device=device)
self._bind_cache = DecodeBindCache(
sig=sig,
seq_lens=list(seq_lens),
req_pool_indices=req_pool_indices,
seq_lens_t=seq_lens_t,
kv_indptr=kv_indptr,
inc=inc,
)
if start_pos is not None:
seq_len = seq_lens[0]
@@ -437,9 +491,6 @@ class PagePool:
req_pool_indices, write_pos
].unsqueeze(-1)
kv_indptr = torch.zeros(len(seq_lens) + 1, dtype=torch.int32, device=device)
kv_indptr[1:] = seq_lens_t.cumsum(0).to(torch.int32)
return KVCache(
k_buffer=self._storage.k_buffer,
v_buffer=self._storage.v_buffer,
astrai/inference/core/executor.py
+70 -21
View File
@@ -1,7 +1,9 @@
import logging
from dataclasses import dataclass
from typing import List, Optional
import torch
from torch import Tensor
from astrai.inference.core.cache import PagePool
from astrai.inference.core.task import Task
@@ -12,6 +14,39 @@ from astrai.tokenize.tokenizer import AutoTokenizer
logger = logging.getLogger(__name__)
@dataclass
class SamplingBatchInfo:
"""Per-batch sampling parameters, cached across decode steps.
Sampling params are constant for a given ordered task set, so they are
built once (pinned-memory async H2D) and reused until the task set
changes. ``top_ks`` is int32 to match the native consumers.
"""
temperatures: Tensor # float32 [B]
top_ks: Tensor # int32 [B]
top_ps: Tensor # float32 [B]
freq_penalties: Tensor # float32 [B]
def _build_sampling_batch_info(tasks: List[Task], device) -> SamplingBatchInfo:
pin = str(device).startswith("cuda")
return SamplingBatchInfo(
temperatures=torch.tensor(
[t.temperature for t in tasks], dtype=torch.float32, pin_memory=pin
).to(device, non_blocking=True),
top_ks=torch.tensor(
[t.top_k for t in tasks], dtype=torch.int32, pin_memory=pin
).to(device, non_blocking=True),
top_ps=torch.tensor(
[t.top_p for t in tasks], dtype=torch.float32, pin_memory=pin
).to(device, non_blocking=True),
freq_penalties=torch.tensor(
[t.frequency_penalty for t in tasks], dtype=torch.float32, pin_memory=pin
).to(device, non_blocking=True),
)
class Executor:
"""Model forward passes for prefill and decode phases."""
@@ -29,6 +64,12 @@ class Executor:
self.device = device or next(model.parameters()).device
self.dtype = dtype or next(model.parameters()).dtype
# Per-step decode cache for the steady-state case where the same
# ordered task set decodes one token per step. Sampling params are
# constant across steps; position_ids grows by exactly 1. Single-slot:
# any task-set change is a cache miss.
self._decode_cache: Optional[tuple] = None
def execute_prefill(self, tasks: List[Task], prompt_len: int, start_pos: int = 0):
if start_pos >= prompt_len:
return
@@ -88,24 +129,32 @@ class Executor:
device=self.device,
)
position_ids = torch.tensor(
[t.next_pos for t in tasks], dtype=torch.long, device=self.device
)
task_ids = [t.task_id for t in tasks]
sig = tuple(task_ids)
cur_positions = [t.next_pos for t in tasks]
cached = self._decode_cache
if (
cached is not None
and cached[0] == sig
and cur_positions == [p + 1 for p in cached[1]]
):
_, _, info, position_ids = cached
position_ids = position_ids + 1
self._decode_cache = (sig, cur_positions, info, position_ids)
else:
info = _build_sampling_batch_info(tasks, self.device)
position_ids = torch.tensor(
cur_positions, dtype=torch.long, device=self.device
)
self._decode_cache = (sig, cur_positions, info, position_ids)
total_len = max(t.next_pos for t in tasks) + 1
input_mask = position_ids[:, None, None] >= torch.arange(
total_len, device=self.device
)
task_ids = [t.task_id for t in tasks]
temperatures = torch.tensor([t.temperature for t in tasks], device=self.device)
top_ks = torch.tensor([t.top_k for t in tasks], device=self.device)
top_ps = torch.tensor([t.top_p for t in tasks], device=self.device)
freq_penalties = torch.tensor(
[t.frequency_penalty for t in tasks], device=self.device
)
has_freq = bool((freq_penalties != 0).any())
has_freq = bool((info.freq_penalties != 0).any())
if has_freq:
history_lists = []
history_lens = []
@@ -149,10 +198,10 @@ class Executor:
if return_logprobs:
tokens, logprobs = sample(
logits,
temperature=temperatures,
top_k=top_ks,
top_p=top_ps,
frequency_penalty=freq_penalties,
temperature=info.temperatures,
top_k=info.top_ks,
top_p=info.top_ps,
frequency_penalty=info.freq_penalties,
input_ids=padded_ids,
input_mask=padded_mask,
return_logprobs=True,
@@ -165,10 +214,10 @@ class Executor:
return sample(
logits,
temperature=temperatures,
top_k=top_ks,
top_p=top_ps,
frequency_penalty=freq_penalties,
temperature=info.temperatures,
top_k=info.top_ks,
top_p=info.top_ps,
frequency_penalty=info.freq_penalties,
input_ids=padded_ids,
input_mask=padded_mask,
).tolist()