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# mypy: allow-untyped-defs
from enum import Enum
from typing import Any, Callable
import torch
from torch._C._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 end_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 kwinputs(self) -> dict[str, 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: ...
def is_user_annotation(self) -> bool: ...
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 _toggle_collection_dynamic(
enable: bool,
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_current_graph_task_keep_graph() -> bool: ...
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() -> str | None: ...
def _saved_tensors_hooks_set_tracing(is_tracing: bool) -> bool: ...
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: ...
|
