Source code for torch.jit

import torch._C

from contextlib import contextmanager
from typing import Iterator

from torch.utils import set_module

# These are imported so users can access them from the `torch.jit` module
from torch._jit_internal import (
from torch.jit._script import (
from torch.jit._trace import (
from torch.jit._async import fork, wait
from torch.jit._serialization import save, load
from torch.jit._fuser import optimized_execution, fuser, last_executed_optimized_graph

from torch.jit._freeze import freeze, optimize_for_inference, run_frozen_optimizations

# For backwards compatibility
_fork = fork
_wait = wait

def export_opnames(m):
        Generates new bytecode for a Script module and 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
[docs]def annotate(the_type, the_value): """ 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 infernece can be wrong, including: - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`s - 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
[docs]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
[docs]def isinstance(obj, target_type): """ This function provides for conatiner 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(MyModule, self).__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)
# 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] if not torch._C._jit_init(): raise RuntimeError("JIT initialization failed")


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