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Add the compile time metric retrieved from the subprocess
Summary: Add the compile_time metric to measure the triton compilation time. Reviewed By: chenyang78 Differential Revision: D55943230 fbshipit-source-id: 5a1aa0a11d840a88642ec46b9c7d9598c7113534
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,182 @@ | ||
| from torchbenchmark import Worker | ||
| from torchbenchmark._components._impl.tasks import base as base_task | ||
| from torchbenchmark._components._impl.workers import subprocess_worker | ||
| import threading | ||
| import os | ||
| import torch | ||
| import dataclasses | ||
| from pathlib import Path | ||
| import gc | ||
|
|
||
| from typing import Optional, Dict, Any, List | ||
|
|
||
| @dataclasses.dataclass(frozen=True) | ||
| class OpDetails: | ||
| """Static description of what a particular TritonBench operator supports. | ||
| When parameterizing tests, we only want to generate sensible ones. | ||
| (e.g. Those where an operator can be imported and supports the feature to be | ||
| tested or benchmarked.) This requires us to import the operator; however many | ||
| of the operators are EXTREMELY stateful, and even importing them consumes | ||
| significant system resources. As a result, we only want one (or a few) | ||
| alive at any given time. | ||
| Note that affinity cannot be solved by simply calling `torch.set_num_threads` | ||
| in the child process; this will cause PyTorch to use all of the cores but | ||
| at a much lower efficiency. | ||
| This class describes what a particular operator does and does not support, so | ||
| that we can release the underlying subprocess but retain any pertinent | ||
| metadata. | ||
| """ | ||
|
|
||
| name: str | ||
| exists: bool | ||
| metadata: Dict[str, Any] | ||
|
|
||
|
|
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| class OpTask(base_task.TaskBase): | ||
|
|
||
| # The worker may (and often does) consume significant system resources. | ||
| # In order to ensure that runs do not interfere with each other, we only | ||
| # allow a single OpTask to exist at a time. | ||
| _lock = threading.Lock() | ||
|
|
||
| def __init__( | ||
| self, | ||
| name: str, | ||
| timeout: Optional[float] = None, | ||
| extra_env: Optional[Dict[str, str]] = None, | ||
| save_output_dir: Optional[Path] = None, | ||
| ) -> None: | ||
| gc.collect() # Make sure previous task has a chance to release the lock | ||
| assert self._lock.acquire(blocking=False), "Failed to acquire lock." | ||
|
|
||
| self._op_name = name | ||
| self._worker = Worker( | ||
| timeout=timeout, extra_env=extra_env, save_output_dir=save_output_dir | ||
| ) | ||
|
|
||
| self.worker.run("import torch") | ||
| self._details: OpDetails = OpDetails( | ||
| **self._maybe_import_operator( | ||
| package=__name__, | ||
| op_name=name, | ||
| ) | ||
| ) | ||
| # ========================================================================= | ||
| # == Import Operator in the child process ==================================== | ||
| # ========================================================================= | ||
|
|
||
| @property | ||
| def worker(self) -> subprocess_worker.SubprocessWorker: | ||
| return self._worker | ||
|
|
||
| @base_task.run_in_worker(scoped=True) | ||
| @staticmethod | ||
| def _maybe_import_operator(package: str, op_name: str) -> Dict[str, Any]: | ||
| import importlib | ||
| import os | ||
| import traceback | ||
| from torchbenchmark.operators import load_opbench_by_name | ||
|
|
||
| Operator = load_opbench_by_name(op_name) | ||
|
|
||
| # Populate global namespace so subsequent calls to worker.run can access `Operator` | ||
| globals()["Operator"] = Operator | ||
|
|
||
| # This will be used to populate a `OpDetails` instance in the parent. | ||
| return { | ||
| "name": op_name, | ||
| "exists": Operator is not None, | ||
| "metadata": {}, | ||
| } | ||
|
|
||
| # ========================================================================= | ||
| # == Instantiate a concrete `op` instance ============================== | ||
| # ========================================================================= | ||
|
|
||
| @base_task.run_in_worker(scoped=True) | ||
| @staticmethod | ||
| def make_operator_instance( | ||
| mode: str, | ||
| device: str, | ||
| extra_args: Optional[List[str]] = None, | ||
| ) -> None: | ||
| Operator = globals()["Operator"] | ||
| op = Operator( | ||
| mode=mode, | ||
| device=device, | ||
| extra_args=extra_args, | ||
| ) | ||
|
|
||
| import gc | ||
| gc.collect() | ||
|
|
||
| if device == "cuda": | ||
| torch.cuda.empty_cache() | ||
| maybe_sync = torch.cuda.synchronize | ||
| else: | ||
| maybe_sync = lambda: None | ||
|
|
||
| globals().update( | ||
| { | ||
| "op": op, | ||
| "maybe_sync": maybe_sync, | ||
| } | ||
| ) | ||
|
|
||
| # ========================================================================= | ||
| # == Forward calls to `op` from parent to worker ======================= | ||
| # ========================================================================= | ||
| def run(self) -> None: | ||
| self.worker.run( | ||
| """ | ||
| op.run() | ||
| maybe_sync() | ||
| """ | ||
| ) | ||
|
|
||
|
|
||
| # ========================================================================= | ||
| # == Get Operator attribute in the child process ============================= | ||
| # ========================================================================= | ||
| @base_task.run_in_worker(scoped=True) | ||
| @staticmethod | ||
| def get_attribute( | ||
| attr: str, | ||
| field: Optional[str] = None, | ||
| classattr: bool = False | ||
| ) -> Any: | ||
| if classattr: | ||
| op = globals()["Operator"] | ||
| else: | ||
| op = globals()["op"] | ||
| if hasattr(op, attr): | ||
| if field: | ||
| op_attr = getattr(op, attr) | ||
| return getattr(op_attr, field) | ||
| else: | ||
| return getattr(op, attr) | ||
| else: | ||
| return None | ||
|
|
||
| def del_op_instance(self): | ||
| self.worker.run( | ||
| """ | ||
| del op | ||
| del maybe_sync | ||
| """ | ||
| ) | ||
| self.gc_collect() | ||
|
|
||
| def gc_collect(self) -> None: | ||
| self.worker.run( | ||
| """ | ||
| import gc | ||
| gc.collect() | ||
| """ | ||
| ) | ||
|
|
||
| def __del__(self) -> None: | ||
| self._lock.release() |
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