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| import itertools | |
| from warnings import warn | |
| import torch | |
| import torch.cuda | |
| from torch.autograd import ( | |
| _disable_profiler_legacy, | |
| _enable_profiler_legacy, | |
| DeviceType, | |
| ProfilerConfig, | |
| ProfilerState, | |
| ) | |
| from torch.autograd.profiler_util import ( | |
| _filter_name, | |
| _filter_stack_entry, | |
| _rewrite_name, | |
| EventList, | |
| FunctionEvent, | |
| MEMORY_EVENT_NAME, | |
| ) | |
| __all__ = ["profile"] | |
| class profile: | |
| """DEPRECATED: use torch.profiler instead.""" | |
| def __init__( | |
| self, | |
| enabled=True, | |
| *, | |
| use_cuda=False, | |
| record_shapes=False, | |
| with_flops=False, | |
| profile_memory=False, | |
| with_stack=False, | |
| with_modules=False, | |
| ): | |
| self.enabled: bool = enabled | |
| if not self.enabled: | |
| return | |
| self.use_cuda = use_cuda | |
| self.function_events = None | |
| self.entered = False | |
| self.record_shapes = record_shapes | |
| self.with_flops = with_flops | |
| self.record_shapes |= self.with_flops | |
| self.profile_memory = profile_memory | |
| self.with_stack = with_stack | |
| self.with_modules = with_modules | |
| if self.use_cuda and not torch.cuda.is_available(): | |
| warn("CUDA is not available, disabling CUDA profiling") | |
| self.use_cuda = False | |
| if self.use_cuda: | |
| self.profiler_kind = ProfilerState.CUDA | |
| else: | |
| self.profiler_kind = ProfilerState.CPU | |
| def config(self): | |
| return ProfilerConfig( | |
| self.profiler_kind, | |
| self.record_shapes, | |
| self.profile_memory, | |
| self.with_stack, | |
| self.with_flops, | |
| self.with_modules, | |
| # avoid exposing _ExperimentalConfig this in legacy public API | |
| torch._C._profiler._ExperimentalConfig(), | |
| ) | |
| def __enter__(self): | |
| if not self.enabled: | |
| return | |
| if self.entered: | |
| raise RuntimeError("Profiler context manager is not reentrant") | |
| self.entered = True | |
| self._start_trace() | |
| return self | |
| def _start_trace(self): | |
| _enable_profiler_legacy(self.config()) | |
| def __exit__(self, exc_type, exc_val, exc_tb): | |
| if not self.enabled: | |
| return | |
| if self.use_cuda: | |
| torch.cuda.synchronize() | |
| records = _disable_profiler_legacy() | |
| parsed_results = _parse_legacy_records(records) | |
| self.function_events = EventList( | |
| parsed_results, | |
| use_cuda=self.use_cuda, | |
| profile_memory=self.profile_memory, | |
| with_flops=self.with_flops, | |
| ) | |
| self.function_events._build_tree() | |
| return False | |
| def __repr__(self): | |
| if self.function_events is None: | |
| return "<unfinished profiler_legacy.profile>" | |
| return repr(self.function_events) | |
| def __str__(self): | |
| if self.function_events is None: | |
| return "<unfinished profile.profiler_legacy.profile>" | |
| return str(self.function_events) | |
| def _check_finish(self): | |
| if self.function_events is None: | |
| raise RuntimeError("Profiler didn't finish running") | |
| def table( | |
| self, | |
| sort_by=None, | |
| row_limit=100, | |
| max_src_column_width=75, | |
| max_name_column_width=55, | |
| max_shapes_column_width=80, | |
| header=None, | |
| top_level_events_only=False, | |
| ): | |
| self._check_finish() | |
| assert self.function_events is not None | |
| return self.function_events.table( | |
| sort_by=sort_by, | |
| row_limit=row_limit, | |
| max_src_column_width=max_src_column_width, | |
| max_name_column_width=max_name_column_width, | |
| max_shapes_column_width=max_shapes_column_width, | |
| header=header, | |
| top_level_events_only=top_level_events_only, | |
| ) | |
| table.__doc__ = EventList.table.__doc__ | |
| def export_chrome_trace(self, path): | |
| self._check_finish() | |
| assert self.function_events is not None | |
| return self.function_events.export_chrome_trace(path) | |
| export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__ | |
| def export_stacks(self, path: str, metric: str = "self_cpu_time_total"): | |
| self._check_finish() | |
| assert self.function_events is not None, "Expected profiling results" | |
| assert self.with_stack, "export_stacks() requires with_stack=True" | |
| return self.function_events.export_stacks(path, metric) | |
| def key_averages(self, group_by_input_shape=False, group_by_stack_n=0): | |
| self._check_finish() | |
| assert self.function_events is not None, "Expected profiling results" | |
| return self.function_events.key_averages(group_by_input_shape, group_by_stack_n) | |
| key_averages.__doc__ = EventList.key_averages.__doc__ | |
| def total_average(self): | |
| self._check_finish() | |
| assert self.function_events is not None, "Expected profiling results" | |
| return self.function_events.total_average() | |
| total_average.__doc__ = EventList.total_average.__doc__ | |
| def self_cpu_time_total(self): | |
| """Return CPU time as the sum of self times across all events.""" | |
| self._check_finish() | |
| assert self.function_events is not None | |
| return self.function_events.self_cpu_time_total | |
| def _parse_legacy_records(thread_records): | |
| def _get_record_key(record): | |
| """Return a tuple for correlating start and end records in `_parse_legacy_records`.""" | |
| return (record.handle(), record.node_id()) | |
| next_id = 0 | |
| start_record = None | |
| functions = [] | |
| record_stack = [] | |
| # '__start_profile' is not guaranteed to be first, so we must find it here | |
| for record in itertools.chain(*thread_records): | |
| name = record.name() | |
| if start_record is None and name == "__start_profile": | |
| start_record = record | |
| assert start_record is not None and not start_record.is_remote() | |
| for thread_record_list in thread_records: | |
| # accumulated memory allocations per handle | |
| cpu_memory_allocs = {} | |
| cuda_memory_allocs = {} | |
| # ranges per handle | |
| range_starts = {} | |
| filtered_handles = set() | |
| prev_record = None | |
| for record in thread_record_list: | |
| record_key = _get_record_key(record) | |
| if _filter_name(record.name()) or record_key in filtered_handles: | |
| filtered_handles.add(record_key) | |
| continue | |
| if record.kind() == "push": | |
| # workaround to reduce double logging from operator | |
| # wrappers and redispatch | |
| if prev_record is not None: | |
| duplicate = ( | |
| prev_record.name() == record.name() | |
| and prev_record.kind() == record.kind() | |
| and prev_record.node_id() == record.node_id() | |
| ) | |
| if duplicate: | |
| filtered_handles.add(record_key) | |
| continue | |
| range_starts[record_key] = record | |
| cpu_memory_allocs[record_key] = 0 | |
| cuda_memory_allocs[record_key] = 0 | |
| elif record.kind() == "pop": | |
| assert ( | |
| record_key in range_starts | |
| ), f"""Expected record with key {record_key} to exist in range_starts. | |
| This means that the pop event did not have a corresponding push.""" | |
| start = range_starts[record_key] | |
| cpu_memory_usage = cpu_memory_allocs[record_key] | |
| cuda_memory_usage = cuda_memory_allocs[record_key] | |
| is_async = start.is_async() or (start.thread_id() != record.thread_id()) | |
| is_remote_event = record.is_remote() | |
| start_flops = start.flops() | |
| fe = FunctionEvent( | |
| id=record.handle(), | |
| node_id=record.node_id(), | |
| name=_rewrite_name(name=start.name(), with_wildcard=True), | |
| trace_name=_rewrite_name(name=start.name(), with_wildcard=False), | |
| thread=start.thread_id(), | |
| start_us=start_record.cpu_elapsed_us(start), | |
| end_us=start_record.cpu_elapsed_us(record), | |
| fwd_thread=start.fwd_thread_id(), | |
| input_shapes=start.shapes(), | |
| stack=[ | |
| entry for entry in start.stack() if _filter_stack_entry(entry) | |
| ], | |
| scope=start.scope(), | |
| cpu_memory_usage=cpu_memory_usage, | |
| cuda_memory_usage=cuda_memory_usage, | |
| is_async=is_async, | |
| is_remote=is_remote_event, | |
| sequence_nr=start.sequence_nr(), | |
| device_type=DeviceType.CPU, | |
| is_legacy=True, | |
| flops=start_flops, | |
| ) | |
| # note: async events have only cpu total time | |
| if not is_async and start.has_cuda(): | |
| duration = start.cuda_elapsed_us(record) | |
| if duration > 0: | |
| fe.append_kernel(start.name(), start.device(), duration) | |
| functions.append(fe) | |
| del range_starts[record_key] | |
| del cpu_memory_allocs[record_key] | |
| del cuda_memory_allocs[record_key] | |
| elif record.kind() == "memory_alloc": | |
| num_open_handles_cpu = len(cpu_memory_allocs) | |
| num_open_handles_cuda = len(cuda_memory_allocs) | |
| assert num_open_handles_cpu == num_open_handles_cuda | |
| for handle in cpu_memory_allocs.keys(): | |
| cpu_memory_allocs[handle] += record.cpu_memory_usage() | |
| for handle in cuda_memory_allocs.keys(): | |
| cuda_memory_allocs[handle] += record.cuda_memory_usage() | |
| if num_open_handles_cpu == 0: | |
| # output event as a top-level memory event | |
| fe = FunctionEvent( | |
| id=0, | |
| name=MEMORY_EVENT_NAME, | |
| trace_name=None, | |
| thread=0, | |
| start_us=0, | |
| end_us=0, | |
| stack=[], | |
| cpu_memory_usage=record.cpu_memory_usage(), | |
| cuda_memory_usage=record.cuda_memory_usage(), | |
| is_legacy=True, | |
| ) | |
| functions.append(fe) | |
| prev_record = record | |
| # Sort functions by start time then by end time ascending. | |
| # This ensures that--in the case of nested events which | |
| # have the same start time (which may happen due to the | |
| # granularity of the given clock tick)--we always show | |
| # the outermost nested call first. This adds stability | |
| # in how FunctionEvents appear | |
| functions.sort(key=lambda evt: [evt.time_range.start, -evt.time_range.end]) | |
| return functions | |