| | import contextlib |
| | import math |
| | import os |
| | import warnings |
| |
|
| |
|
| | try: |
| | import optuna |
| | _optuna_available = True |
| | except ImportError: |
| | _optuna_available = False |
| |
|
| |
|
| | from cupy._core import _optimize_config |
| | from cupyx import profiler |
| |
|
| |
|
| | def _optimize( |
| | optimize_config, target_func, suggest_func, |
| | default_best, ignore_error=()): |
| | assert isinstance(optimize_config, _optimize_config._OptimizationConfig) |
| | assert callable(target_func) |
| | assert callable(suggest_func) |
| |
|
| | def objective(trial): |
| | args = suggest_func(trial) |
| | max_total_time = optimize_config.max_total_time_per_trial |
| | try: |
| | perf = profiler.benchmark( |
| | target_func, args, max_duration=max_total_time) |
| | return perf.gpu_times.mean() |
| | except Exception as e: |
| | if isinstance(e, ignore_error): |
| | return math.inf |
| | else: |
| | raise e |
| |
|
| | study = optuna.create_study() |
| | study.enqueue_trial(default_best) |
| | study.optimize( |
| | objective, |
| | n_trials=optimize_config.max_trials, |
| | timeout=optimize_config.timeout) |
| | return study.best_trial |
| |
|
| |
|
| | @contextlib.contextmanager |
| | def optimize(*, key=None, path=None, readonly=False, **config_dict): |
| | """Context manager that optimizes kernel launch parameters. |
| | |
| | In this context, CuPy's routines find the best kernel launch parameter |
| | values (e.g., the number of threads and blocks). The found values are |
| | cached and reused with keys as the shapes, strides and dtypes of the |
| | given inputs arrays. |
| | |
| | Args: |
| | key (string or None): The cache key of optimizations. |
| | path (string or None): The path to save optimization cache records. |
| | When path is specified and exists, records will be loaded from |
| | the path. When readonly option is set to ``False``, optimization |
| | cache records will be saved to the path after the optimization. |
| | readonly (bool): See the description of ``path`` option. |
| | max_trials (int): The number of trials that defaults to 100. |
| | timeout (float): |
| | Stops study after the given number of seconds. Default is 1. |
| | max_total_time_per_trial (float): |
| | Repeats measuring the execution time of the routine for the |
| | given number of seconds. Default is 0.1. |
| | |
| | Examples |
| | -------- |
| | >>> import cupy |
| | >>> from cupyx import optimizing |
| | >>> |
| | >>> x = cupy.arange(100) |
| | >>> with optimizing.optimize(): |
| | ... cupy.sum(x) |
| | ... |
| | array(4950) |
| | |
| | .. note:: |
| | Optuna (https://optuna.org) installation is required. |
| | Currently it works for reduction operations only. |
| | """ |
| | if not _optuna_available: |
| | raise RuntimeError( |
| | 'Optuna is required to run optimization. ' |
| | 'See https://optuna.org/ for the installation instructions.') |
| |
|
| | old_context = _optimize_config.get_current_context() |
| | context = _optimize_config.get_new_context(key, _optimize, config_dict) |
| | _optimize_config.set_current_context(context) |
| |
|
| | if path is not None: |
| | if os.path.exists(path): |
| | context.load(path) |
| | elif readonly: |
| | warnings.warn(''' |
| | The specified path {} could not be found, and the readonly option is set. |
| | The optimization results will never be stored. |
| | '''.format(path)) |
| |
|
| | try: |
| | yield context |
| | if path is not None and not readonly: |
| | if context._is_dirty() or not os.path.exists(path): |
| | context.save(path) |
| | finally: |
| | _optimize_config.set_current_context(old_context) |
| |
|
