File size: 4,207 Bytes
d643072 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
# Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import builtins
import json
import os
import pickle
import time
import ipdb
import torch
import torch.distributed as dist
from triton.runtime.autotuner import Autotuner
class CustomAutotuner(Autotuner):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.best_config_cache_path = os.path.expanduser(
os.path.join(
"~",
".triton",
"best_config_cache",
torch.cuda.get_device_name(0).replace(" ", "_"),
self.base_fn.__name__ + ".pkl",
)
)
if os.path.exists(self.best_config_cache_path):
with open(self.best_config_cache_path, "rb") as f:
self.cache = pickle.load(f)
def run(self, *args, **kwargs):
self.nargs = dict(zip(self.arg_names, args))
used_cached_result = True
if len(self.configs) > 1:
all_args = {**self.nargs, **kwargs}
_args = []
for name in self.arg_names:
if name in all_args:
_args.append(all_args[name])
key = [_args[i] for i in self.key_idx]
for arg in _args:
if hasattr(arg, "dtype"):
key.append(str(arg.dtype))
key = tuple(key)
if key not in self.cache:
# prune configs
used_cached_result = False
pruned_configs = self.prune_configs(kwargs)
bench_start = time.time()
timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
bench_end = time.time()
self.bench_time = bench_end - bench_start
self.cache[key] = builtins.min(timings, key=timings.get)
self.pre_hook(args, reset_only=True)
self.configs_timings = timings
if not dist.is_initialized() or dist.get_rank() == 0:
best_config_cache_dir = os.path.dirname(self.best_config_cache_path)
os.makedirs(best_config_cache_dir, exist_ok=True)
with open(self.best_config_cache_path, "wb") as f:
pickle.dump(self.cache, f)
config = self.cache[key]
else:
config = self.configs[0]
self.best_config = config
if os.getenv("TRITON_PRINT_AUTOTUNING", None) == "1" and not used_cached_result:
print(
f"Triton autotuning for function {self.base_fn.__name__} finished after "
f"{self.bench_time:.2f}s; best config selected: {self.best_config};"
)
if config.pre_hook is not None:
config.pre_hook({**self.nargs, **kwargs, **config.all_kwargs()})
ret = self.fn.run(
*args,
**kwargs,
**config.all_kwargs(),
)
self.nargs = None
return ret
def custom_autotune(
configs,
key,
prune_configs_by=None,
reset_to_zero=None,
restore_value=None,
pre_hook=None,
post_hook=None,
warmup=25,
rep=100,
use_cuda_graph=False,
):
def decorator(fn):
return CustomAutotuner(
fn,
fn.arg_names,
configs,
key,
reset_to_zero,
restore_value,
pre_hook=pre_hook,
post_hook=post_hook,
prune_configs_by=prune_configs_by,
warmup=warmup,
rep=rep,
use_cuda_graph=use_cuda_graph,
)
return decorator
|