File size: 4,223 Bytes
d1ceb73 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
# mypy: allow-untyped-defs
from enum import Enum
from typing import Any, Callable, List, Optional, Set
import torch
from ._profiler import (
_ProfilerEvent,
ActiveProfilerType,
ProfilerActivity,
ProfilerConfig,
)
# Defined in torch/csrc/autograd/init.cpp
class DeviceType(Enum):
CPU = ...
CUDA = ...
XPU = ...
MKLDNN = ...
OPENGL = ...
OPENCL = ...
IDEEP = ...
HIP = ...
FPGA = ...
MAIA = ...
XLA = ...
MTIA = ...
MPS = ...
HPU = ...
Meta = ...
Vulkan = ...
Metal = ...
PrivateUse1 = ...
class ProfilerEvent:
def cpu_elapsed_us(self, other: ProfilerEvent) -> float: ...
def cpu_memory_usage(self) -> int: ...
def cuda_elapsed_us(self, other: ProfilerEvent) -> float: ...
def privateuse1_elapsed_us(self, other: ProfilerEvent) -> float: ...
def cuda_memory_usage(self) -> int: ...
def device(self) -> int: ...
def handle(self) -> int: ...
def has_cuda(self) -> bool: ...
def is_remote(self) -> bool: ...
def kind(self) -> int: ...
def name(self) -> str: ...
def node_id(self) -> int: ...
def sequence_nr(self) -> int: ...
def shapes(self) -> List[List[int]]: ...
def thread_id(self) -> int: ...
def flops(self) -> float: ...
def is_async(self) -> bool: ...
class _KinetoEvent:
def name(self) -> str: ...
def device_index(self) -> int: ...
def device_resource_id(self) -> int: ...
def start_ns(self) -> int: ...
def duration_ns(self) -> int: ...
def is_async(self) -> bool: ...
def linked_correlation_id(self) -> int: ...
def shapes(self) -> List[List[int]]: ...
def dtypes(self) -> List[str]: ...
def concrete_inputs(self) -> List[Any]: ...
def device_type(self) -> DeviceType: ...
def start_thread_id(self) -> int: ...
def end_thread_id(self) -> int: ...
def correlation_id(self) -> int: ...
def fwd_thread_id(self) -> int: ...
def stack(self) -> List[str]: ...
def scope(self) -> int: ...
def sequence_nr(self) -> int: ...
def flops(self) -> int: ...
def cuda_elapsed_us(self) -> int: ...
def privateuse1_elapsed_us(self) -> int: ...
class _ProfilerResult:
def events(self) -> List[_KinetoEvent]: ...
def legacy_events(self) -> List[List[ProfilerEvent]]: ...
def save(self, path: str) -> None: ...
def experimental_event_tree(self) -> List[_ProfilerEvent]: ...
def trace_start_ns(self) -> int: ...
class SavedTensor: ...
def _enable_profiler(
config: ProfilerConfig,
activities: Set[ProfilerActivity],
) -> None: ...
def _prepare_profiler(
config: ProfilerConfig,
activities: Set[ProfilerActivity],
) -> None: ...
def _disable_profiler() -> _ProfilerResult: ...
def _profiler_enabled() -> bool: ...
def _add_metadata_json(key: str, value: str) -> None: ...
def _kineto_step() -> None: ...
def _get_sequence_nr() -> int: ...
def kineto_available() -> bool: ...
def _record_function_with_args_enter(name: str, *args) -> torch.Tensor: ...
def _record_function_with_args_exit(handle: torch.Tensor) -> None: ...
def _supported_activities() -> Set[ProfilerActivity]: ...
def _enable_record_function(enable: bool) -> None: ...
def _set_empty_test_observer(is_global: bool, sampling_prob: float) -> None: ...
def _push_saved_tensors_default_hooks(
pack_hook: Callable[[torch.Tensor], Any],
unpack_hook: Callable[[Any], torch.Tensor],
) -> None: ...
def _pop_saved_tensors_default_hooks() -> None: ...
def _unsafe_set_version_counter(t: torch.Tensor, prev_version: int) -> None: ...
def _enable_profiler_legacy(config: ProfilerConfig) -> None: ...
def _disable_profiler_legacy() -> List[List[ProfilerEvent]]: ...
def _profiler_type() -> ActiveProfilerType: ...
def _saved_tensors_hooks_enable() -> None: ...
def _saved_tensors_hooks_disable(message: str) -> None: ...
def _saved_tensors_hooks_get_disabled_error_message() -> Optional[str]: ...
class CreationMeta(Enum):
DEFAULT = ...
IN_CUSTOM_FUNCTION = ...
MULTI_OUTPUT_NODE = ...
NO_GRAD_MODE = ...
INFERENCE_MODE = ...
def _set_creation_meta(t: torch.Tensor, creation_meta: CreationMeta) -> None: ...
def _get_creation_meta(t: torch.Tensor) -> CreationMeta: ...
|