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 functools
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import logging
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import os
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import sys
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import tempfile
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import typing_extensions
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from collections.abc import Callable
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from typing import Any, TypeVar
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from typing_extensions import ParamSpec
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import torch
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from torch._strobelight.compile_time_profiler import StrobelightCompileTimeProfiler
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_T = TypeVar("_T")
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_P = ParamSpec("_P")
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log = logging.getLogger(__name__)
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if os.environ.get("TORCH_COMPILE_STROBELIGHT", False):
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import shutil
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if not shutil.which("strobeclient"):
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log.info(
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"TORCH_COMPILE_STROBELIGHT is true, but seems like you are not on a FB machine."
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)
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else:
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log.info("Strobelight profiler is enabled via environment variable")
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StrobelightCompileTimeProfiler.enable()
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# this arbitrary-looking assortment of functionality is provided here
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# to have a central place for overridable behavior. The motivating
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# use is the FB build environment, where this source file is replaced
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# by an equivalent.
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if os.path.basename(os.path.dirname(__file__)) == "shared":
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torch_parent = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
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else:
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torch_parent = os.path.dirname(os.path.dirname(__file__))
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def get_file_path(*path_components: str) -> str:
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return os.path.join(torch_parent, *path_components)
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def get_file_path_2(*path_components: str) -> str:
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return os.path.join(*path_components)
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def get_writable_path(path: str) -> str:
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if os.access(path, os.W_OK):
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return path
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return tempfile.mkdtemp(suffix=os.path.basename(path))
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def prepare_multiprocessing_environment(path: str) -> None:
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pass
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def resolve_library_path(path: str) -> str:
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return os.path.realpath(path)
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def throw_abstract_impl_not_imported_error(opname, module, context):
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if module in sys.modules:
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raise NotImplementedError(
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f"{opname}: We could not find the fake impl for this operator. "
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)
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else:
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raise NotImplementedError(
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f"{opname}: We could not find the fake impl for this operator. "
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f"The operator specified that you may need to import the '{module}' "
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f"Python module to load the fake impl. {context}"
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)
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# NB! This treats "skip" kwarg specially!!
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def compile_time_strobelight_meta(
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phase_name: str,
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) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
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def compile_time_strobelight_meta_inner(
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function: Callable[_P, _T],
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) -> Callable[_P, _T]:
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@functools.wraps(function)
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def wrapper_function(*args: _P.args, **kwargs: _P.kwargs) -> _T:
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if "skip" in kwargs and isinstance(
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skip := kwargs["skip"],
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int,
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):
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kwargs["skip"] = skip + 1
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# This is not needed but we have it here to avoid having profile_compile_time
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# in stack traces when profiling is not enabled.
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if not StrobelightCompileTimeProfiler.enabled:
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return function(*args, **kwargs)
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return StrobelightCompileTimeProfiler.profile_compile_time(
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function, phase_name, *args, **kwargs
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)
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return wrapper_function
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return compile_time_strobelight_meta_inner
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# Meta only, see
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# https://www.internalfb.com/intern/wiki/ML_Workflow_Observability/User_Guides/Adding_instrumentation_to_your_code/
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#
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# This will cause an event to get logged to Scuba via the signposts API. You
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# can view samples on the API at https://fburl.com/scuba/workflow_signpost/zh9wmpqs
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# we log to subsystem "torch", and the category and name you provide here.
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# Each of the arguments translate into a Scuba column. We're still figuring
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# out local conventions in PyTorch, but category should be something like
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# "dynamo" or "inductor", and name should be a specific string describing what
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# kind of event happened.
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#
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# Killswitch is at
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# https://www.internalfb.com/intern/justknobs/?name=pytorch%2Fsignpost#event
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def signpost_event(category: str, name: str, parameters: dict[str, Any]):
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log.info("%s %s: %r", category, name, parameters)
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def add_mlhub_insight(category: str, insight: str, insight_description: str):
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pass
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def log_compilation_event(metrics):
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log.info("%s", metrics)
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def upload_graph(graph):
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pass
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def set_pytorch_distributed_envs_from_justknobs():
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pass
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def log_export_usage(**kwargs):
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pass
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def log_draft_export_usage(**kwargs):
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pass
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def log_trace_structured_event(*args, **kwargs) -> None:
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pass
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def log_cache_bypass(*args, **kwargs) -> None:
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pass
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def log_torchscript_usage(api: str, **kwargs):
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_ = api
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return
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def check_if_torch_exportable():
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return False
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def export_training_ir_rollout_check() -> bool:
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return True
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def full_aoti_runtime_assert() -> bool:
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return True
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def log_torch_jit_trace_exportability(
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api: str,
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type_of_export: str,
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export_outcome: str,
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result: str,
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):
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_, _, _, _ = api, type_of_export, export_outcome, result
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return
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DISABLE_JUSTKNOBS = True
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def justknobs_check(name: str, default: bool = True) -> bool:
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"""
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This function can be used to killswitch functionality in FB prod,
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where you can toggle this value to False in JK without having to
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do a code push. In OSS, we always have everything turned on all
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the time, because downstream users can simply choose to not update
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PyTorch. (If more fine-grained enable/disable is needed, we could
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potentially have a map we lookup name in to toggle behavior. But
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the point is that it's all tied to source code in OSS, since there's
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no live server to query.)
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This is the bare minimum functionality I needed to do some killswitches.
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We have a more detailed plan at
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https://docs.google.com/document/d/1Ukerh9_42SeGh89J-tGtecpHBPwGlkQ043pddkKb3PU/edit
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In particular, in some circumstances it may be necessary to read in
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a knob once at process start, and then use it consistently for the
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rest of the process. Future functionality will codify these patterns
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into a better high level API.
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WARNING: Do NOT call this function at module import time, JK is not
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fork safe and you will break anyone who forks the process and then
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hits JK again.
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"""
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return default
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def justknobs_getval_int(name: str) -> int:
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"""
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Read warning on justknobs_check
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"""
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return 0
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def is_fb_unit_test() -> bool:
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return False
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@functools.cache
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def max_clock_rate():
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"""
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unit: MHz
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"""
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if not torch.version.hip:
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from triton.testing import nvsmi
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return nvsmi(["clocks.max.sm"])[0]
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else:
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# Manually set max-clock speeds on ROCm until equivalent nvmsi
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# functionality in triton.testing or via pyamdsmi enablement. Required
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# for test_snode_runtime unit tests.
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gcn_arch = str(torch.cuda.get_device_properties(0).gcnArchName.split(":", 1)[0])
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if "gfx94" in gcn_arch:
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return 1700
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elif "gfx90a" in gcn_arch:
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return 1700
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elif "gfx908" in gcn_arch:
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return 1502
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elif "gfx12" in gcn_arch:
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return 1700
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elif "gfx11" in gcn_arch:
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return 1700
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elif "gfx103" in gcn_arch:
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return 1967
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elif "gfx101" in gcn_arch:
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return 1144
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elif "gfx95" in gcn_arch:
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return 1700 # TODO: placeholder, get actual value
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else:
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return 1100
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def get_mast_job_name_version() -> tuple[str, int] | None:
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return None
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TEST_MASTER_ADDR = "127.0.0.1"
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TEST_MASTER_PORT = 29500
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# USE_GLOBAL_DEPS controls whether __init__.py tries to load
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# libtorch_global_deps, see Note [Global dependencies]
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USE_GLOBAL_DEPS = True
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# USE_RTLD_GLOBAL_WITH_LIBTORCH controls whether __init__.py tries to load
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# _C.so with RTLD_GLOBAL during the call to dlopen.
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USE_RTLD_GLOBAL_WITH_LIBTORCH = False
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# If an op was defined in C++ and extended from Python using the
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# torch.library.register_fake, returns if we require that there be a
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# m.set_python_module("mylib.ops") call from C++ that associates
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# the C++ op with a python module.
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REQUIRES_SET_PYTHON_MODULE = False
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def maybe_upload_prof_stats_to_manifold(profile_path: str) -> str | None:
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print("Uploading profile stats (fb-only otherwise no-op)")
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return None
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def log_chromium_event_internal(
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event: dict[str, Any],
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stack: list[str],
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logger_uuid: str,
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start_time_ns: int,
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):
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return None
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def record_chromium_event_internal(
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event: dict[str, Any],
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):
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return None
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def profiler_allow_cudagraph_cupti_lazy_reinit_cuda12():
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return True
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def deprecated():
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"""
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When we deprecate a function that might still be in use, we make it internal
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by adding a leading underscore. This decorator is used with a private function,
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and creates a public alias without the leading underscore, but has a deprecation
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warning. This tells users "THIS FUNCTION IS DEPRECATED, please use something else"
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without breaking them, however, if they still really really want to use the
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deprecated function without the warning, they can do so by using the internal
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function name.
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"""
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def decorator(func: Callable[_P, _T]) -> Callable[_P, _T]:
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# Validate naming convention - single leading underscore, not dunder
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if not (func.__name__.startswith("_")):
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raise ValueError(
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"@deprecate must decorate a function whose name "
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"starts with a single leading underscore (e.g. '_foo') as the api should be considered internal for deprecation."
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)
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public_name = func.__name__[1:] # drop exactly one leading underscore
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module = sys.modules[func.__module__]
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# Don't clobber an existing symbol accidentally.
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if hasattr(module, public_name):
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raise RuntimeError(
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f"Cannot create alias '{public_name}' -> symbol already exists in {module.__name__}. \
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Please rename it or consult a pytorch developer on what to do"
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)
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warning_msg = f"{func.__name__[1:]} is DEPRECATED, please consider using an alternative API(s). "
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# public deprecated alias
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alias = typing_extensions.deprecated(
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# pyrefly: ignore [bad-argument-type]
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warning_msg,
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category=UserWarning,
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stacklevel=1,
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)(func)
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alias.__name__ = public_name
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# Adjust qualname if nested inside a class or another function
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if "." in func.__qualname__:
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alias.__qualname__ = func.__qualname__.rsplit(".", 1)[0] + "." + public_name
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else:
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alias.__qualname__ = public_name
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setattr(module, public_name, alias)
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return func
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return decorator
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def get_default_numa_options():
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"""
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When using elastic agent, if no numa options are provided, we will use these
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as the default.
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For external use cases, we return None, i.e. no numa binding. If you would like
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to use torch's automatic numa binding capabilities, you should provide
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NumaOptions to your launch config directly or use the numa binding option
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available in torchrun.
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Must return None or NumaOptions, but not specifying to avoid circular import.
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"""
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return None
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def log_triton_builds(fail: str | None):
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pass
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def find_compile_subproc_binary() -> str | None:
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"""
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Allows overriding the binary used for subprocesses
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"""
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return None
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def get_torch_source_version() -> str:
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"""Return the source commit hash for the current PyTorch build."""
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import torch.version as torch_version
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return getattr(torch_version, "git_version", "")
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