MLPY/Lib/site-packages/torch/jit/__init__.py

import warnings

from contextlib import contextmanager
from typing import Any, Iterator

import torch._C

# These are imported so users can access them from the `torch.jit` module
from torch._jit_internal import (
    _Await,
    _drop,
    _IgnoreContextManager,
    _isinstance,
    _overload,
    _overload_method,
    export,
    Final,
    Future,
    ignore,
    is_scripting,
    unused,
)
from torch.jit._async import fork, wait
from torch.jit._await import _awaitable, _awaitable_nowait, _awaitable_wait
from torch.jit._decomposition_utils import _register_decomposition
from torch.jit._freeze import freeze, optimize_for_inference, run_frozen_optimizations
from torch.jit._fuser import (
    fuser,
    last_executed_optimized_graph,
    optimized_execution,
    set_fusion_strategy,
)
from torch.jit._ir_utils import _InsertPoint
from torch.jit._script import (
    _ScriptProfile,
    _unwrap_optional,
    Attribute,
    CompilationUnit,
    interface,
    RecursiveScriptClass,
    RecursiveScriptModule,
    script,
    script_method,
    ScriptFunction,
    ScriptModule,
    ScriptWarning,
)
from torch.jit._serialization import (
    jit_module_from_flatbuffer,
    load,
    save,
    save_jit_module_to_flatbuffer,
)
from torch.jit._trace import (
    _flatten,
    _get_trace_graph,
    _script_if_tracing,
    _unique_state_dict,
    is_tracing,
    ONNXTracedModule,
    TopLevelTracedModule,
    trace,
    trace_module,
    TracedModule,
    TracerWarning,
    TracingCheckError,
)

from torch.utils import set_module

__all__ = [
    "Attribute",
    "CompilationUnit",
    "Error",
    "Future",
    "ScriptFunction",
    "ScriptModule",
    "annotate",
    "enable_onednn_fusion",
    "export",
    "export_opnames",
    "fork",
    "freeze",
    "ignore",
    "isinstance",
    "load",
    "onednn_fusion_enabled",
    "optimize_for_inference",
    "save",
    "script",
    "script_if_tracing",
    "set_fusion_strategy",
    "strict_fusion",
    "trace",
    "trace_module",
    "unused",
    "wait",
]

# For backwards compatibility
_fork = fork
_wait = wait
_set_fusion_strategy = set_fusion_strategy


def export_opnames(m):
    r"""

    Generate new bytecode for a Script module.



    Returns what the op list would be for a Script Module based off the current code base.



    If you have a LiteScriptModule and want to get the currently present

    list of ops call _export_operator_list instead.

    """
    return torch._C._export_opnames(m._c)


# torch.jit.Error
Error = torch._C.JITException
set_module(Error, "torch.jit")
# This is not perfect but works in common cases
Error.__name__ = "Error"
Error.__qualname__ = "Error"


# for use in python if using annotate
def annotate(the_type, the_value):
    """Use to give type of `the_value` in TorchScript compiler.



    This method is a pass-through function that returns `the_value`, used to hint TorchScript

    compiler the type of `the_value`. It is a no-op when running outside of TorchScript.



    Though TorchScript can infer correct type for most Python expressions, there are some cases where

    type inference can be wrong, including:



    - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`

    - Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume

      it is type `T` rather than `Optional[T]`



    Note that `annotate()` does not help in `__init__` method of `torch.nn.Module` subclasses because it

    is executed in eager mode. To annotate types of `torch.nn.Module` attributes,

    use :meth:`~torch.jit.Annotate` instead.



    Example:



    .. testcode::



        import torch

        from typing import Dict



        @torch.jit.script

        def fn():

            # Telling TorchScript that this empty dictionary is a (str -> int) dictionary

            # instead of default dictionary type of (str -> Tensor).

            d = torch.jit.annotate(Dict[str, int], {})



            # Without `torch.jit.annotate` above, following statement would fail because of

            # type mismatch.

            d["name"] = 20



    .. testcleanup::



        del fn



    Args:

        the_type: Python type that should be passed to TorchScript compiler as type hint for `the_value`

        the_value: Value or expression to hint type for.



    Returns:

        `the_value` is passed back as return value.

    """
    return the_value


def script_if_tracing(fn):
    """

    Compiles ``fn`` when it is first called during tracing.



    ``torch.jit.script`` has a non-negligible start up time when it is first called due to

    lazy-initializations of many compiler builtins. Therefore you should not use

    it in library code. However, you may want to have parts of your library work

    in tracing even if they use control flow. In these cases, you should use

    ``@torch.jit.script_if_tracing`` to substitute for

    ``torch.jit.script``.



    Args:

        fn: A function to compile.



    Returns:

        If called during tracing, a :class:`ScriptFunction` created by `torch.jit.script` is returned.

        Otherwise, the original function `fn` is returned.

    """
    return _script_if_tracing(fn)


# for torch.jit.isinstance
def isinstance(obj, target_type):
    """

    Provide container type refinement in TorchScript.



    It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``,

    ``Dict[str, List[torch.Tensor]]``, ``Optional[Tuple[int,str,int]]``. It can also

    refine basic types such as bools and ints that are available in TorchScript.



    Args:

        obj: object to refine the type of

        target_type: type to try to refine obj to

    Returns:

        ``bool``: True if obj was successfully refined to the type of target_type,

            False otherwise with no new type refinement





    Example (using ``torch.jit.isinstance`` for type refinement):

    .. testcode::



        import torch

        from typing import Any, Dict, List



        class MyModule(torch.nn.Module):

            def __init__(self):

                super().__init__()



            def forward(self, input: Any): # note the Any type

                if torch.jit.isinstance(input, List[torch.Tensor]):

                    for t in input:

                        y = t.clamp(0, 0.5)

                elif torch.jit.isinstance(input, Dict[str, str]):

                    for val in input.values():

                        print(val)



        m = torch.jit.script(MyModule())

        x = [torch.rand(3,3), torch.rand(4,3)]

        m(x)

        y = {"key1":"val1","key2":"val2"}

        m(y)

    """
    return _isinstance(obj, target_type)


class strict_fusion:
    """

    Give errors if not all nodes have been fused in inference, or symbolically differentiated in training.



    Example:

    Forcing fusion of additions.



    .. code-block:: python



        @torch.jit.script

        def foo(x):

            with torch.jit.strict_fusion():

                return x + x + x



    """

    def __init__(self):
        if not torch._jit_internal.is_scripting():
            warnings.warn("Only works in script mode")
        pass

    def __enter__(self):
        pass

    def __exit__(self, type: Any, value: Any, tb: Any) -> None:
        pass


# Context manager for globally hiding source ranges when printing graphs.
# Note that these functions are exposed to Python as static members of the
# Graph class, so mypy checks need to be skipped.
@contextmanager
def _hide_source_ranges() -> Iterator[None]:
    old_enable_source_ranges = torch._C.Graph.global_print_source_ranges  # type: ignore[attr-defined]
    try:
        torch._C.Graph.set_global_print_source_ranges(False)  # type: ignore[attr-defined]
        yield
    finally:
        torch._C.Graph.set_global_print_source_ranges(old_enable_source_ranges)  # type: ignore[attr-defined]


def enable_onednn_fusion(enabled: bool):
    """Enable or disables onednn JIT fusion based on the parameter `enabled`."""
    torch._C._jit_set_llga_enabled(enabled)


def onednn_fusion_enabled():
    """Return whether onednn JIT fusion is enabled."""
    return torch._C._jit_llga_enabled()


del Any

if not torch._C._jit_init():
    raise RuntimeError("JIT initialization failed")