Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets
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Kolp
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# mypy: allow-untyped-defs
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import itertools
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import warnings
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from typing_extensions import deprecated
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import torch
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import torch.cuda
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from torch.autograd import (
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_disable_profiler_legacy,
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_enable_profiler_legacy,
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DeviceType,
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ProfilerConfig,
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ProfilerState,
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)
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from torch.autograd.profiler_util import (
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_filter_name,
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_filter_stack_entry,
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_rewrite_name,
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EventList,
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FunctionEvent,
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MEMORY_EVENT_NAME,
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)
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__all__ = ["profile"]
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@deprecated(
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"`torch.autograd.profiler_legacy.profile` is deprecated and will be removed in a future release. "
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"Please use `torch.profiler` instead.",
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category=None, # TODO: change to `FutureWarning`
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)
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class profile:
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"""DEPRECATED: use torch.profiler instead."""
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def __init__(
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self,
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enabled=True,
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*,
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use_cuda=False,
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record_shapes=False,
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with_flops=False,
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profile_memory=False,
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with_stack=False,
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with_modules=False,
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):
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self.enabled: bool = enabled
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if not self.enabled:
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return
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self.use_cuda = use_cuda
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self.function_events = None
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self.entered = False
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self.record_shapes = record_shapes
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self.with_flops = with_flops
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self.record_shapes |= self.with_flops
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self.profile_memory = profile_memory
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self.with_stack = with_stack
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self.with_modules = with_modules
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if self.use_cuda and not torch.cuda.is_available():
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warnings.warn(
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"CUDA is not available, disabling CUDA profiling",
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stacklevel=2,
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)
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self.use_cuda = False
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if self.use_cuda:
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self.profiler_kind = ProfilerState.CUDA
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else:
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self.profiler_kind = ProfilerState.CPU
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def config(self):
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return ProfilerConfig(
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self.profiler_kind,
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self.record_shapes,
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self.profile_memory,
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self.with_stack,
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self.with_flops,
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self.with_modules,
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# avoid exposing _ExperimentalConfig this in legacy public API
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torch._C._profiler._ExperimentalConfig(),
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)
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def __enter__(self):
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if not self.enabled:
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return
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if self.entered:
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raise RuntimeError("Profiler context manager is not reentrant")
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self.entered = True
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self._start_trace()
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return self
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def _start_trace(self):
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_enable_profiler_legacy(self.config())
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def __exit__(self, exc_type, exc_val, exc_tb):
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if not self.enabled:
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return
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if self.use_cuda:
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torch.cuda.synchronize()
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records = _disable_profiler_legacy()
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parsed_results = _parse_legacy_records(records)
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# pyrefly: ignore [bad-assignment]
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self.function_events = EventList(
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parsed_results,
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use_device="cuda" if self.use_cuda else None,
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profile_memory=self.profile_memory,
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with_flops=self.with_flops,
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)
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# pyrefly: ignore [missing-attribute]
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self.function_events._build_tree()
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return False
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def __repr__(self):
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if self.function_events is None:
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return "<unfinished profiler_legacy.profile>"
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return repr(self.function_events)
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def __str__(self):
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if self.function_events is None:
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return "<unfinished profile.profiler_legacy.profile>"
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return str(self.function_events)
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def _check_finish(self):
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if self.function_events is None:
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raise RuntimeError("Profiler didn't finish running")
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def table(
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self,
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sort_by=None,
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row_limit=100,
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max_src_column_width=75,
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max_name_column_width=55,
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max_shapes_column_width=80,
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header=None,
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top_level_events_only=False,
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):
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self._check_finish()
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if self.function_events is None:
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raise AssertionError("Expected profiling results")
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return self.function_events.table(
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sort_by=sort_by,
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row_limit=row_limit,
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max_src_column_width=max_src_column_width,
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max_name_column_width=max_name_column_width,
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max_shapes_column_width=max_shapes_column_width,
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header=header,
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top_level_events_only=top_level_events_only,
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)
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table.__doc__ = EventList.table.__doc__
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def export_chrome_trace(self, path):
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self._check_finish()
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if self.function_events is None:
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raise AssertionError("Expected profiling results")
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return self.function_events.export_chrome_trace(path)
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export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__
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def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
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self._check_finish()
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if self.function_events is None:
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raise AssertionError("Expected profiling results")
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if not self.with_stack:
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raise AssertionError("export_stacks() requires with_stack=True")
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return self.function_events.export_stacks(path, metric)
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def key_averages(self, group_by_input_shape=False, group_by_stack_n=0):
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self._check_finish()
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if self.function_events is None:
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raise AssertionError("Expected profiling results")
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return self.function_events.key_averages(group_by_input_shape, group_by_stack_n)
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key_averages.__doc__ = EventList.key_averages.__doc__
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def total_average(self):
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self._check_finish()
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if self.function_events is None:
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raise AssertionError("Expected profiling results")
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return self.function_events.total_average()
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total_average.__doc__ = EventList.total_average.__doc__
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@property
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def self_cpu_time_total(self):
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"""Return CPU time as the sum of self times across all events."""
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self._check_finish()
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if self.function_events is None:
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raise AssertionError("Expected profiling results")
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return self.function_events.self_cpu_time_total
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def _parse_legacy_records(thread_records):
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def _get_record_key(record):
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"""Return a tuple for correlating start and end records in `_parse_legacy_records`."""
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return (record.handle(), record.node_id())
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start_record = None
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functions = []
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# '__start_profile' is not guaranteed to be first, so we must find it here
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for record in itertools.chain.from_iterable(thread_records):
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name = record.name()
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if start_record is None and name == "__start_profile":
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start_record = record
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if start_record is None or start_record.is_remote():
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raise AssertionError("Expected a valid local start_record")
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for thread_record_list in thread_records:
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# accumulated memory allocations per handle
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cpu_memory_allocs = {}
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cuda_memory_allocs = {}
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# ranges per handle
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range_starts = {}
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filtered_handles = set()
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prev_record = None
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for record in thread_record_list:
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record_key = _get_record_key(record)
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if _filter_name(record.name()) or record_key in filtered_handles:
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filtered_handles.add(record_key)
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continue
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if record.kind() == "push":
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# workaround to reduce double logging from operator
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# wrappers and redispatch
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if prev_record is not None:
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duplicate = (
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prev_record.name() == record.name()
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and prev_record.kind() == record.kind()
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and prev_record.node_id() == record.node_id()
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)
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if duplicate:
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filtered_handles.add(record_key)
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continue
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range_starts[record_key] = record
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cpu_memory_allocs[record_key] = 0
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cuda_memory_allocs[record_key] = 0
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elif record.kind() == "pop":
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if record_key not in range_starts:
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raise AssertionError(
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f"Expected record with key {record_key} to exist in range_starts. "
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"This means that the pop event did not have a corresponding push."
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)
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start = range_starts[record_key]
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cpu_memory_usage = cpu_memory_allocs[record_key]
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cuda_memory_usage = cuda_memory_allocs[record_key]
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is_async = start.is_async() or (start.thread_id() != record.thread_id())
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is_remote_event = record.is_remote()
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start_flops = start.flops()
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fe = FunctionEvent(
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id=record.handle(),
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node_id=record.node_id(),
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name=_rewrite_name(name=start.name(), with_wildcard=True),
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trace_name=_rewrite_name(name=start.name(), with_wildcard=False),
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thread=start.thread_id(),
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start_us=start_record.cpu_elapsed_us(start),
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end_us=start_record.cpu_elapsed_us(record),
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fwd_thread=start.fwd_thread_id(),
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input_shapes=start.shapes(),
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stack=[
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entry for entry in start.stack() if _filter_stack_entry(entry)
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],
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scope=start.scope(),
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use_device="cuda" if start.has_cuda() else None,
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cpu_memory_usage=cpu_memory_usage,
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device_memory_usage=cuda_memory_usage,
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is_async=is_async,
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is_remote=is_remote_event,
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sequence_nr=start.sequence_nr(),
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device_type=DeviceType.CPU,
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is_legacy=True,
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flops=start_flops,
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)
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# note: async events have only cpu total time
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if not is_async and start.has_cuda():
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duration = start.cuda_elapsed_us(record)
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if duration > 0:
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fe.append_kernel(start.name(), start.device(), duration)
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functions.append(fe)
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del range_starts[record_key]
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del cpu_memory_allocs[record_key]
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del cuda_memory_allocs[record_key]
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elif record.kind() == "memory_alloc":
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num_open_handles_cpu = len(cpu_memory_allocs)
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num_open_handles_cuda = len(cuda_memory_allocs)
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if num_open_handles_cpu != num_open_handles_cuda:
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raise AssertionError(
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f"Expected CPU and CUDA memory allocation handles to match, "
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f"but got {num_open_handles_cpu} CPU and {num_open_handles_cuda} CUDA"
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)
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for handle in cpu_memory_allocs:
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cpu_memory_allocs[handle] += record.cpu_memory_usage()
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for handle in cuda_memory_allocs:
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cuda_memory_allocs[handle] += record.cuda_memory_usage()
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if num_open_handles_cpu == 0:
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# output event as a top-level memory event
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fe = FunctionEvent(
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id=0,
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name=MEMORY_EVENT_NAME,
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trace_name=None,
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thread=0,
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start_us=0,
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end_us=0,
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stack=[],
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cpu_memory_usage=record.cpu_memory_usage(),
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device_memory_usage=record.cuda_memory_usage(),
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is_legacy=True,
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)
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functions.append(fe)
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prev_record = record
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# Sort functions by start time then by end time ascending.
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# This ensures that--in the case of nested events which
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# have the same start time (which may happen due to the
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# granularity of the given clock tick)--we always show
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# the outermost nested call first. This adds stability
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# in how FunctionEvents appear
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functions.sort(key=lambda evt: [evt.time_range.start, -evt.time_range.end])
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return functions
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