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Summary: Kineto traces use microsecond level granularity because of chrome tracing defaults to that precision. Fix by adding preprocessor flag to TARGETS and BUCK files. Also remove any unnecessary ns to us conversions made in the profiler itself. This diff contains profiler changes only. Libkineto changes found in D54964435. Test Plan: Check JSON and chrome tracing to make sure values are as expected. Tracing with flags enabled should have ns precision. Tracings without flags should be same as master. Zoomer: https://www.internalfb.com/intern/zoomer/?profiling_run_fbid=796886748550189 Ran key_averages() to make sure FunctionEvent code working as expected: -- ------------ ------------ Name Self CPU % Self CPU CPU total % CPU total CPU time avg Self CUDA Self CUDA % CUDA total CUDA time avg # of Calls ProfilerStep* 0.74% 3.976ms 64.40% 346.613ms 69.323ms 0.000us 0.00% 61.710ms 12.342ms 5 Optimizer.zero_grad#SGD.zero_grad 0.76% 4.109ms 0.76% 4.109ms 821.743us 0.000us 0.00% 0.000us 0.000us 5 ## forward ## 6.89% 37.057ms 27.19% 146.320ms 29.264ms 0.000us 0.00% 58.708ms 11.742ms 5 aten::conv2d 0.22% 1.176ms 7.74% 41.658ms 157.199us 0.000us 0.00% 27.550ms 103.962us 265 aten::convolution 0.79% 4.273ms 7.52% 40.482ms 152.762us 0.000us 0.00% 27.550ms 103.962us 265 aten::_convolution 0.69% 3.688ms 6.73% 36.209ms 136.637us 0.000us 0.00% 27.550ms 103.962us 265 aten::cudnn_convolution 6.04% 32.520ms 6.04% 32.520ms 122.719us 27.550ms 8.44% 27.550ms 103.962us 265 aten::add_ 2.42% 13.045ms 2.42% 13.045ms 30.694us 12.700ms 3.89% 12.700ms 29.882us 425 aten::batch_norm 0.19% 1.027ms 8.12% 43.717ms 164.971us 0.000us 0.00% 16.744ms 63.185us 265 aten::_batch_norm_impl_index 0.31% 1.646ms 7.93% 42.691ms 161.096us 0.000us 0.00% 16.744ms 63.185us 265 ------------------------------------------------------- ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ ------------ Differential Revision: D55925068 Pull Request resolved: https://github.com/pytorch/pytorch/pull/123650 Approved by: https://github.com/aaronenyeshi
125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
from enum import Enum
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from typing import Any, Callable, List, Optional, Set
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import torch
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from ._profiler import (
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_ProfilerEvent,
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ActiveProfilerType,
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ProfilerActivity,
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ProfilerConfig,
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)
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# Defined in tools/autograd/init.cpp
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class DeviceType(Enum):
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CPU = ...
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CUDA = ...
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MKLDNN = ...
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OPENGL = ...
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OPENCL = ...
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IDEEP = ...
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HIP = ...
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FPGA = ...
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ORT = ...
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XLA = ...
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MPS = ...
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HPU = ...
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Meta = ...
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Vulkan = ...
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Metal = ...
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PrivateUse1 = ...
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class ProfilerEvent:
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def cpu_elapsed_us(self, other: ProfilerEvent) -> float: ...
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def cpu_memory_usage(self) -> int: ...
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def cuda_elapsed_us(self, other: ProfilerEvent) -> float: ...
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def privateuse1_elapsed_us(self, other: ProfilerEvent) -> float: ...
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def cuda_memory_usage(self) -> int: ...
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def device(self) -> int: ...
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def handle(self) -> int: ...
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def has_cuda(self) -> bool: ...
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def is_remote(self) -> bool: ...
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def kind(self) -> int: ...
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def name(self) -> str: ...
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def node_id(self) -> int: ...
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def sequence_nr(self) -> int: ...
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def shapes(self) -> List[List[int]]: ...
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def thread_id(self) -> int: ...
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def flops(self) -> float: ...
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def is_async(self) -> bool: ...
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class _KinetoEvent:
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def name(self) -> str: ...
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def device_index(self) -> int: ...
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def device_resource_id(self) -> int: ...
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def start_ns(self) -> int: ...
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def duration_ns(self) -> int: ...
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def is_async(self) -> bool: ...
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def linked_correlation_id(self) -> int: ...
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def shapes(self) -> List[List[int]]: ...
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def dtypes(self) -> List[str]: ...
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def concrete_inputs(self) -> List[Any]: ...
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def device_type(self) -> DeviceType: ...
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def start_thread_id(self) -> int: ...
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def end_thread_id(self) -> int: ...
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def correlation_id(self) -> int: ...
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def fwd_thread_id(self) -> int: ...
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def stack(self) -> List[str]: ...
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def scope(self) -> int: ...
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def sequence_nr(self) -> int: ...
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def flops(self) -> int: ...
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def cuda_elapsed_us(self) -> int: ...
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def privateuse1_elapsed_us(self) -> int: ...
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class _ProfilerResult:
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def events(self) -> List[_KinetoEvent]: ...
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def legacy_events(self) -> List[List[ProfilerEvent]]: ...
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def save(self, path: str) -> None: ...
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def experimental_event_tree(self) -> List[_ProfilerEvent]: ...
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def trace_start_ns(self) -> int: ...
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class SavedTensor: ...
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def _enable_profiler(
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config: ProfilerConfig,
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activities: Set[ProfilerActivity],
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) -> None: ...
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def _prepare_profiler(
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config: ProfilerConfig,
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activities: Set[ProfilerActivity],
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) -> None: ...
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def _disable_profiler() -> _ProfilerResult: ...
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def _profiler_enabled() -> bool: ...
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def _add_metadata_json(key: str, value: str) -> None: ...
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def _kineto_step() -> None: ...
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def _get_sequence_nr() -> int: ...
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def kineto_available() -> bool: ...
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def _record_function_with_args_enter(name: str, *args) -> torch.Tensor: ...
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def _record_function_with_args_exit(handle: torch.Tensor) -> None: ...
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def _supported_activities() -> Set[ProfilerActivity]: ...
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def _enable_record_function(enable: bool) -> None: ...
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def _set_empty_test_observer(is_global: bool, sampling_prob: float) -> None: ...
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def _push_saved_tensors_default_hooks(
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pack_hook: Callable[[torch.Tensor], Any],
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unpack_hook: Callable[[Any], torch.Tensor],
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) -> None: ...
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def _pop_saved_tensors_default_hooks() -> None: ...
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def _unsafe_set_version_counter(t: torch.Tensor, prev_version: int) -> None: ...
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def _enable_profiler_legacy(config: ProfilerConfig) -> None: ...
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def _disable_profiler_legacy() -> List[List[ProfilerEvent]]: ...
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def _profiler_type() -> ActiveProfilerType: ...
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def _saved_tensors_hooks_enable() -> None: ...
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def _saved_tensors_hooks_disable(message: str) -> None: ...
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def _saved_tensors_hooks_get_disabled_error_message() -> Optional[str]: ...
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class CreationMeta(Enum):
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DEFAULT = ...
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IN_CUSTOM_FUNCTION = ...
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MULTI_OUTPUT_NODE = ...
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NO_GRAD_MODE = ...
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INFERENCE_MODE = ...
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def _set_creation_meta(t: torch.Tensor, creation_meta: CreationMeta) -> None: ...
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def _get_creation_meta(t: torch.Tensor) -> CreationMeta: ...
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