wehub-resource-sync
128 lines
4.3 KiB
Python
128 lines
4.3 KiB
Python
# Copyright 2024 MIT Han Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import builtins
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import os
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import pickle
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import time
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import torch
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import torch.distributed as dist
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from triton.runtime.autotuner import Autotuner
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class CustomAutotuner(Autotuner):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# Use device name if CUDA is available, otherwise use a default name
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if torch.cuda.is_available():
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device_name = torch.cuda.get_device_name(0).replace(" ", "_")
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else:
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device_name = "cpu"
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self.best_config_cache_path = os.path.expanduser(
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os.path.join(
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"~",
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".triton",
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"best_config_cache",
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device_name,
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self.base_fn.__name__ + ".pkl",
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)
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)
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if os.path.exists(self.best_config_cache_path):
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with open(self.best_config_cache_path, "rb") as f:
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self.cache = pickle.load(f)
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def run(self, *args, **kwargs):
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self.nargs = dict(zip(self.arg_names, args))
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used_cached_result = True
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if len(self.configs) > 1:
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all_args = {**self.nargs, **kwargs}
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_args = []
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for name in self.arg_names:
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if name in all_args:
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_args.append(all_args[name])
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key = [_args[i] for i in self.key_idx]
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for arg in _args:
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if hasattr(arg, "dtype"):
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key.append(str(arg.dtype))
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key = tuple(key)
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if key not in self.cache:
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# prune configs
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used_cached_result = False
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pruned_configs = self.prune_configs(kwargs)
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bench_start = time.time()
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timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
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bench_end = time.time()
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self.bench_time = bench_end - bench_start
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self.cache[key] = builtins.min(timings, key=timings.get)
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self.pre_hook(args, reset_only=True)
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self.configs_timings = timings
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if not dist.is_initialized() or dist.get_rank() == 0:
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best_config_cache_dir = os.path.dirname(self.best_config_cache_path)
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os.makedirs(best_config_cache_dir, exist_ok=True)
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with open(self.best_config_cache_path, "wb") as f:
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pickle.dump(self.cache, f)
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config = self.cache[key]
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else:
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config = self.configs[0]
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self.best_config = config
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if os.getenv("TRITON_PRINT_AUTOTUNING", None) == "1" and not used_cached_result:
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print(
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f"Triton autotuning for function {self.base_fn.__name__} finished after "
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f"{self.bench_time:.2f}s; best config selected: {self.best_config};"
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)
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if config.pre_hook is not None:
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config.pre_hook({**self.nargs, **kwargs, **config.all_kwargs()})
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ret = self.fn.run(
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*args,
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**kwargs,
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**config.all_kwargs(),
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)
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self.nargs = None
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return ret
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def custom_autotune(
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configs,
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key,
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prune_configs_by=None,
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reset_to_zero=None,
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restore_value=None,
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pre_hook=None,
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post_hook=None,
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warmup=25,
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rep=100,
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use_cuda_graph=False,
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):
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def decorator(fn):
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return CustomAutotuner(
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fn,
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fn.arg_names,
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configs,
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key,
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reset_to_zero,
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restore_value,
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pre_hook=pre_hook,
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post_hook=post_hook,
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prune_configs_by=prune_configs_by,
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warmup=warmup,
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rep=rep,
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use_cuda_graph=use_cuda_graph,
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)
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return decorator
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