Files
vLLM/vllm/model_executor/warmup/flashinfer_autotune_cache.py
T
71c20f73f2 [FlashInfer] Migrate autotune warmup to flashinfer.autotune_v2 managed cache (#55671)
Signed-off-by: Yang Xu <yanxu@nvidia.com>
Signed-off-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com>
Signed-off-by: YangXu <yanxu@nvidia.com>
Co-authored-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com>
Co-authored-by: Kimi Code <noreply@moonshot.cn>
Co-authored-by: Codex <noreply@openai.com>
Co-authored-by: Misha Goin <mgoin64@gmail.com>
Co-authored-by: vllm-agent <claw@inferact.ai>
2026-10-17 02:08:28 +00:00

159 lines
6.0 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""FlashInfer autotune cache helpers."""
import hashlib
import os
import tempfile
from contextlib import suppress
from pathlib import Path
from typing import TYPE_CHECKING
import vllm.envs as envs
from vllm.config import CacheConfig, VllmConfig, replace
from vllm.logger import init_logger
if TYPE_CHECKING:
from vllm.distributed.parallel_state import GroupCoordinator
from vllm.v1.worker.gpu_model_runner import GPUModelRunner
logger = init_logger(__name__)
def _normalize_cache_config_for_hash(cache_config: CacheConfig) -> CacheConfig:
"""Normalize cache config for hashing"""
if cache_config.mamba_block_size is None and cache_config.kv_cache_layout is None:
return cache_config
normalized = replace(cache_config, mamba_block_size=None)
assert normalized.kv_cache_layout is None
return normalized
def flashinfer_autotune_cache_hash(runner: "GPUModelRunner") -> str:
vllm_config: VllmConfig = runner.vllm_config
cache_config = _normalize_cache_config_for_hash(vllm_config.cache_config)
if cache_config is not vllm_config.cache_config:
vllm_config = replace(vllm_config, cache_config=cache_config)
config_hash = vllm_config.compute_hash(include_version=False)
return hashlib.sha256(config_hash.encode()).hexdigest()
def resolve_flashinfer_autotune_file(runner: "GPUModelRunner") -> Path:
override_dir = envs.VLLM_FLASHINFER_AUTOTUNE_CACHE_DIR
if override_dir:
root = Path(override_dir).expanduser()
else:
from flashinfer.jit import env as flashinfer_jit_env
flashinfer_workspace = flashinfer_jit_env.FLASHINFER_WORKSPACE_DIR
root = (
Path(envs.VLLM_CACHE_ROOT)
/ "flashinfer_autotune_cache"
/ flashinfer_workspace.parent.name
/ flashinfer_workspace.name
)
output_dir = root / flashinfer_autotune_cache_hash(runner)
output_dir.mkdir(parents=True, exist_ok=True)
return output_dir / "autotune_configs.json"
def use_flashinfer_autotune_v2(runner: "GPUModelRunner") -> bool:
from vllm.distributed.parallel_state import get_node_count, get_world_group
from vllm.utils.flashinfer import has_flashinfer_autotune_v2
# Elastic EP transfers tuning state to joining workers with v1
# save_configs/load_configs, which do not export managed-store winners.
if runner.vllm_config.parallel_config.enable_elastic_ep:
return False
# PP stages profile independently. Keep their group-scoped v1 files until
# managed-cache reads and publishes can be isolated across tuning groups.
if runner.vllm_config.parallel_config.pipeline_parallel_size > 1:
return False
if not has_flashinfer_autotune_v2():
return False
# Use the initialized topology: Ray may span nodes with config.nnodes=1.
# A cache hit skips profiling collectives, so node-local stores with
# different contents cannot safely participate in synchronized tuning.
if get_node_count() != 1:
logger.info_once(
"Using legacy FlashInfer autotune cache synchronization for "
"multi-node deployments; managed-cache synchronization is "
"currently supported only within one node."
)
return False
# Lift this gate after pinning a release with the fix below and validating
# distributed managed-cache tuning: https://github.com/flashinfer-ai/flashinfer/pull/6185
if get_world_group().world_size > 1:
logger.info_once(
"Using legacy FlashInfer autotune cache synchronization for "
"multi-rank deployments until FlashInfer coordinates cache hits."
)
return False
return True
def resolve_flashinfer_autotune_v2_root() -> Path | None:
"""Placement root for FlashInfer's managed autotune store.
Unlike :func:`resolve_flashinfer_autotune_file`, no vLLM-side identity
(config hash, flashinfer version, arch) is folded into the path: the
managed store keys entries per op and hashes the software/hardware
environment itself. ``None`` selects FlashInfer's default root.
"""
override_dir = envs.VLLM_FLASHINFER_AUTOTUNE_CACHE_DIR
return Path(override_dir).expanduser() if override_dir else None
def sync_flashinfer_autotune_cache(
runner: "GPUModelRunner",
group: "GroupCoordinator",
) -> None:
cache: bytes | str | None = None
if (
group.rank_in_group == 0
and runner.vllm_config.kernel_config.enable_flashinfer_autotune
):
try:
from vllm.platforms import current_platform
from vllm.utils.flashinfer import has_flashinfer
if has_flashinfer() and current_platform.has_device_capability(90):
from flashinfer.autotuner import AutoTuner
with tempfile.TemporaryDirectory() as temp_dir:
path = Path(temp_dir) / "autotune_configs.json"
AutoTuner.get().save_configs(str(path))
cache = path.read_bytes()
except Exception as exc:
cache = f"{type(exc).__name__}: {exc}"
cache = group.broadcast_object(cache)
if isinstance(cache, str):
raise RuntimeError(f"Failed to serialize FlashInfer autotune state: {cache}")
if cache is None or group.rank_in_group == 0:
return
from flashinfer.autotuner import AutoTuner
with tempfile.NamedTemporaryFile() as f:
f.write(cache)
f.flush()
if not AutoTuner.get().load_configs(f.name):
raise RuntimeError("FlashInfer autotune cache is incompatible")
def write_flashinfer_autotune_cache(cache_path: Path, contents: bytes) -> None:
cache_path.parent.mkdir(parents=True, exist_ok=True)
fd, tmp_path = tempfile.mkstemp(
dir=cache_path.parent, suffix=".tmp", prefix=f".{cache_path.name}."
)
try:
with os.fdopen(fd, "wb") as f:
f.write(contents)
os.replace(tmp_path, cache_path)
except BaseException:
with suppress(OSError):
os.unlink(tmp_path)
raise