pytorch/torch/_utils_internal.py

# mypy: allow-untyped-defs
import functools
import logging
import os
import sys
import tempfile
from typing import Any, Dict, List, Optional

import torch
from torch._strobelight.compile_time_profiler import StrobelightCompileTimeProfiler


log = logging.getLogger(__name__)

if os.environ.get("TORCH_COMPILE_STROBELIGHT", False):
    import shutil

    if not shutil.which("strobeclient"):
        log.info(
            "TORCH_COMPILE_STROBELIGHT is true, but seems like you are not on a FB machine."
        )
    else:
        log.info("Strobelight profiler is enabled via environment variable")
        StrobelightCompileTimeProfiler.enable()

# this arbitrary-looking assortment of functionality is provided here
# to have a central place for overrideable behavior. The motivating
# use is the FB build environment, where this source file is replaced
# by an equivalent.

if torch._running_with_deploy():
    # __file__ is meaningless in the context of frozen torch used in torch deploy.
    # setting empty torch_parent should allow below functions to operate without crashing,
    # but it's unclear if there is a valid use case for them in the context of deploy.
    torch_parent = ""
else:
    if os.path.basename(os.path.dirname(__file__)) == "shared":
        torch_parent = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
    else:
        torch_parent = os.path.dirname(os.path.dirname(__file__))


def get_file_path(*path_components: str) -> str:
    return os.path.join(torch_parent, *path_components)


def get_file_path_2(*path_components: str) -> str:
    return os.path.join(*path_components)


def get_writable_path(path: str) -> str:
    if os.access(path, os.W_OK):
        return path
    return tempfile.mkdtemp(suffix=os.path.basename(path))


def prepare_multiprocessing_environment(path: str) -> None:
    pass


def resolve_library_path(path: str) -> str:
    return os.path.realpath(path)


def throw_abstract_impl_not_imported_error(opname, module, context):
    if module in sys.modules:
        raise NotImplementedError(
            f"{opname}: We could not find the fake impl for this operator. "
        )
    else:
        raise NotImplementedError(
            f"{opname}: We could not find the fake impl for this operator. "
            f"The operator specified that you may need to import the '{module}' "
            f"Python module to load the fake impl. {context}"
        )


# NB!  This treats "skip" kwarg specially!!
def compile_time_strobelight_meta(phase_name):
    def compile_time_strobelight_meta_inner(function):
        @functools.wraps(function)
        def wrapper_function(*args, **kwargs):
            if "skip" in kwargs:
                kwargs["skip"] = kwargs["skip"] + 1

            if not StrobelightCompileTimeProfiler.enabled:
                return function(*args, **kwargs)

            return StrobelightCompileTimeProfiler.profile_compile_time(
                function, phase_name, *args, **kwargs
            )

        return wrapper_function

    return compile_time_strobelight_meta_inner


# Meta only, see
# https://www.internalfb.com/intern/wiki/ML_Workflow_Observability/User_Guides/Adding_instrumentation_to_your_code/
#
# This will cause an event to get logged to Scuba via the signposts API.  You
# can view samples on the API at https://fburl.com/scuba/workflow_signpost/zh9wmpqs
# we log to subsystem "torch", and the category and name you provide here.
# Each of the arguments translate into a Scuba column.  We're still figuring
# out local conventions in PyTorch, but category should be something like
# "dynamo" or "inductor", and name should be a specific string describing what
# kind of event happened.
#
# Killswitch is at
# https://www.internalfb.com/intern/justknobs/?name=pytorch%2Fsignpost#event
def signpost_event(category: str, name: str, parameters: Dict[str, Any]):
    log.info("%s %s: %r", category, name, parameters)


def log_compilation_event(metrics):
    log.info("%s", metrics)


def upload_graph(graph):
    pass


def set_pytorch_distributed_envs_from_justknobs():
    pass


def log_export_usage(**kwargs):
    pass


def log_trace_structured_event(*args, **kwargs) -> None:
    pass


def log_cache_bypass(*args, **kwargs) -> None:
    pass


def log_torchscript_usage(api: str, **kwargs):
    _ = api
    return


def check_if_torch_exportable():
    return False


def export_training_ir_rollout_check() -> bool:
    return False


def log_torch_jit_trace_exportability(
    api: str,
    type_of_export: str,
    export_outcome: str,
    result: str,
):
    _, _, _, _ = api, type_of_export, export_outcome, result
    return


def capture_pre_autograd_graph_using_training_ir() -> bool:
    return False


class JustKnobsConfig:
    """Represents a lazily loaded config

    This is designed to be used to specify a value in a config.

    i.e. foo.bar = JustknobsConfig(name="//foo:bar", env_name="FORCE_FOO_BAR")

    Call .get() in order to access the value
    i.e. if foo.bar.get():

    Note that the value is fetched once, and then not allowed to change. This
    means less suprises, at the downside that you may have to restart a job
    to pick up an update.

    It can also be set explicitly via set - i.e.
    foo.bar = JustknobsConfig(name="//foo:bar")
    foo.bar.set(True)

    Note that this does allow for no JK name (so that you can use this to replace old configurations).
    """

    def __init__(
        self, *, name: Optional[str] = None, env_name=None, default: bool = True
    ):
        self.name = name
        self.env_name = env_name
        self.default = default
        self.value: Optional[bool] = None
        self.executed_value = None

    def set(self, value: bool):
        self.value = value

    def get(self):
        if self.executed_value is None:
            self.executed_value = justknobs_feature(
                self.name,
                config_value=self.value,
                env_name=self.env_name,
                default=self.default,
            )
        return self.executed_value

    def __str__(self):
        v = bool(self)
        return f"JustknobsConfig(name={self.name}, env_name={self.env_name}, default={self.default} - evals_to={v})"

    def __bool__(self):
        return self.get()


def justknobs_feature(
    name: Optional[str], config_value=None, env_name=None, default: bool = True
):
    """Returns whether or not a specific justknob feature is enabled.

    This is a slightly higher level API then justknobs_check, designed to make it "easy" to do the right thing.
    The primary thing it does, is allow configuration to override JK by default, while retaining some features to force this
    the other way during sevs.

    The preference order (i.e. who wins first) in OSS (and FB) is
    - Config if specified
    - Environment Variable if specified
    - JK (FB), or default (OSS)


    Quickstart
    Have a config variable
    Make a JK which is set to your "enabled" value (generally true).
    Use this feature to check it (if you set the JK to be false, change the default).
    If you have an env variable, also use the function to check it.

    Arguments:
        name - This should correspond 1:1 to a JK name internally to FB.
        env_name - If this is set, we'll try and read the value from environment variables
        config_value - If this is set to anything other than None, we'll use this value by
            default. Note that within FB, there is some functionality to force override these
            configs
        default - This is the value to return in OSS. This avoids having to write weird double
            negatives within justknobs and the config code, if you just want to have the
            killswitch work by having feature return True to turn off features

    Requirements:
        WARNING - Don't use this at import time - Simply pass in the existing config.
        If you want to use this at config time, use JustKnobsConfig
    """
    if config_value is not None:
        return config_value
    if env_name is not None and ((env := os.getenv(env_name)) is not None):
        env = env.upper()
        if env in ("1", "TRUE"):
            return True
        if env in ("0", "FALSE"):
            return False
        log.error(
            "Difficulty parsing env variable %s=%s for feature %s - Assuming env variable means true and returning True",
            env_name,
            env,
            name,
        )
        # We could return default here, but that was confusing to log.
        return True
    if name is None:
        return True
    if not default:
        return not justknobs_check(name)
    return justknobs_check(name)


def justknobs_check(name: str) -> bool:
    """
    This function can be used to killswitch functionality in FB prod,
    where you can toggle this value to False in JK without having to
    do a code push.  In OSS, we always have everything turned on all
    the time, because downstream users can simply choose to not update
    PyTorch.  (If more fine-grained enable/disable is needed, we could
    potentially have a map we lookup name in to toggle behavior.  But
    the point is that it's all tied to source code in OSS, since there's
    no live server to query.)

    This is the bare minimum functionality I needed to do some killswitches.
    We have a more detailed plan at
    https://docs.google.com/document/d/1Ukerh9_42SeGh89J-tGtecpHBPwGlkQ043pddkKb3PU/edit
    In particular, in some circumstances it may be necessary to read in
    a knob once at process start, and then use it consistently for the
    rest of the process.  Future functionality will codify these patterns
    into a better high level API.

    WARNING: Do NOT call this function at module import time, JK is not
    fork safe and you will break anyone who forks the process and then
    hits JK again.
    """
    return True


def justknobs_getval_int(name: str) -> int:
    """
    Read warning on justknobs_check
    """
    return 0


def is_fb_unit_test() -> bool:
    return False


@functools.lru_cache(None)
def max_clock_rate():
    if not torch.version.hip:
        from triton.testing import nvsmi

        return nvsmi(["clocks.max.sm"])[0]
    else:
        # Manually set max-clock speeds on ROCm until equivalent nvmsi
        # functionality in triton.testing or via pyamdsmi enablement. Required
        # for test_snode_runtime unit tests.
        gcn_arch = str(torch.cuda.get_device_properties(0).gcnArchName.split(":", 1)[0])
        if "gfx94" in gcn_arch:
            return 1700
        elif "gfx90a" in gcn_arch:
            return 1700
        elif "gfx908" in gcn_arch:
            return 1502
        elif "gfx11" in gcn_arch:
            return 1700
        elif "gfx103" in gcn_arch:
            return 1967
        elif "gfx101" in gcn_arch:
            return 1144
        else:
            return 1100


TEST_MASTER_ADDR = "127.0.0.1"
TEST_MASTER_PORT = 29500
# USE_GLOBAL_DEPS controls whether __init__.py tries to load
# libtorch_global_deps, see Note [Global dependencies]
USE_GLOBAL_DEPS = True
# USE_RTLD_GLOBAL_WITH_LIBTORCH controls whether __init__.py tries to load
# _C.so with RTLD_GLOBAL during the call to dlopen.
USE_RTLD_GLOBAL_WITH_LIBTORCH = False
# If an op was defined in C++ and extended from Python using the
# torch.library.register_fake, returns if we require that there be a
# m.set_python_module("mylib.ops") call from C++ that associates
# the C++ op with a python module.
REQUIRES_SET_PYTHON_MODULE = False


def maybe_upload_prof_stats_to_manifold(profile_path: str) -> Optional[str]:
    print("Uploading profile stats (fb-only otherwise no-op)")
    return None


def log_chromium_event_internal(
    event: Dict[str, Any],
    stack: List[str],
    compile_id: Optional[str],
    logger_uuid: str,
    start_time_ns: int,
):
    return None