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1691 lines
63 KiB
1691 lines
63 KiB
5 months ago
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"""TorchScript.
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This module contains functionality to support the JIT's scripting frontend, notably:
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- torch.jit.script
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This is not intended to be imported directly; please use the exposed
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functionalities in `torch.jit`.
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"""
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import collections
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import copy
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import enum
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import functools
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import inspect
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import pickle
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import warnings
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from typing import Any, Callable, Dict, List, Set, Tuple, Union
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import torch
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import torch._jit_internal as _jit_internal
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from torch._classes import classes
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from torch._jit_internal import _qualified_name
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from torch.jit._builtins import _register_builtin
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from torch.jit._fuser import _graph_for, _script_method_graph_for
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from torch.jit._monkeytype_config import (
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JitTypeTraceConfig,
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JitTypeTraceStore,
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monkeytype_trace,
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)
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from torch.jit._recursive import (
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_compile_and_register_class,
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infer_methods_to_compile,
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ScriptMethodStub,
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wrap_cpp_module,
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)
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from torch.jit._state import (
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_enabled,
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_set_jit_function_cache,
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_set_jit_overload_cache,
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_try_get_jit_cached_function,
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_try_get_jit_cached_overloads,
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)
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from torch.jit.frontend import get_default_args, get_jit_class_def, get_jit_def
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from torch.nn import Module
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from torch.overrides import (
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has_torch_function,
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has_torch_function_unary,
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has_torch_function_variadic,
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)
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from torch.package import PackageExporter, PackageImporter
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from torch.utils import set_module
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from ._serialization import validate_map_location
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type_trace_db = JitTypeTraceStore() # DB to hold all call traces from MonkeyType
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torch._C.ScriptMethod.graph_for = _script_method_graph_for # type: ignore[attr-defined]
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torch._C.ScriptFunction.graph_for = _graph_for # type: ignore[attr-defined]
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ScriptFunction = torch._C.ScriptFunction
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ScriptFunction.__doc__ = """
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Functionally equivalent to a :class:`ScriptModule`, but represents a single
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function and does not have any attributes or Parameters.
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"""
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set_module(ScriptFunction, "torch.jit")
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# Throws an error if a jit function is pickled.
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# Helps to avoid Python crashes for Python versions 3.9.5 + when protocol 0 or 1 is given as an argument.
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def _reduce(cls):
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raise pickle.PickleError("ScriptFunction cannot be pickled")
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ScriptFunction.__reduce__ = _reduce # type: ignore[assignment]
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if _enabled:
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Attribute = collections.namedtuple("Attribute", ["value", "type"])
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else:
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def Attribute(value, type): # type: ignore[no-redef]
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return value
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Attribute.__doc__ = """
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This method is a pass-through function that returns `value`, mostly
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used to indicate to the TorchScript compiler that the left-hand side
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expression is a class instance attribute with type of `type`. Note that
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`torch.jit.Attribute` should only be used in `__init__` method of `jit.ScriptModule`
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subclasses.
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Though TorchScript can infer correct type for most Python expressions, there are some cases where
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type inference can be wrong, including:
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- Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`
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- Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume
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it is type `T` rather than `Optional[T]`
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In eager mode, it is simply a pass-through function that returns `value`
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without other implications.
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Example:
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.. testcode::
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import torch
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from typing import Dict
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class AttributeModule(torch.jit.ScriptModule):
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def __init__(self):
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super().__init__()
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self.foo = torch.jit.Attribute(0.1, float)
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# we should be able to use self.foo as a float here
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assert 0.0 < self.foo
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self.names_ages = torch.jit.Attribute({}, Dict[str, int])
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self.names_ages["someone"] = 20
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assert isinstance(self.names_ages["someone"], int)
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m = AttributeModule()
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# m will contain two attributes
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# 1. foo of type float
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# 2. names_ages of type Dict[str, int]
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.. testcleanup::
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del AttributeModule
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del m
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Note: it's now preferred to instead use type annotations instead of `torch.jit.Attribute`:
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.. testcode::
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import torch
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from typing import Dict
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class AttributeModule(torch.nn.Module):
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names: Dict[str, int]
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def __init__(self):
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super().__init__()
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self.names = {}
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m = AttributeModule()
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.. testcleanup::
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del AttributeModule
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del m
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Args:
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value: An initial value to be assigned to attribute.
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type: A Python type
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Returns:
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Returns `value`
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"""
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def _get_type_trace_db():
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# This is a private API. Use of this for external purposes is discouraged.
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return type_trace_db
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# Gets a function from the name of a method on a type
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def _get_function_from_type(cls, name):
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return getattr(cls, name, None)
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# ScriptClasses must be new-style classes because we construct them using their
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# __new__ method.
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def _is_new_style_class(cls):
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if hasattr(cls, "__class__"):
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return "__dict__" in dir(cls) or hasattr(cls, "__slots__")
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# These OrderedDictWrapper classes replace the actual OrderedDicts in
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# module with versions that get/set properties inside of Module.
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# This allows us to reuse most of nn.Module while still storing the
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# data in C++.
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# Each OrderedDict needs to support:
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# x not in view
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# x in view
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# view[name] = ...
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# view.values()
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# del view[name]
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# view.items()
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# view.keys()
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# len(view)
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class OrderedDictWrapper:
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def __init__(self, _c):
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self._c = _c
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def keys(self):
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return [k for k, v in self.items()]
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def values(self):
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return [v for k, v in self.items()]
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def __len__(self):
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return len(self.values())
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def __delitem__(self, k):
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raise RuntimeError("cannot delete methods or parameters of a script module")
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def items(self):
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return self._c.items()
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def __setitem__(self, k, v):
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if k not in self:
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raise RuntimeError(
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f"Can't add a new parameter after ScriptModule construction. Tried to add '{k}"
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)
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self._c.setattr(k, v)
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def __contains__(self, k):
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return self._c.contains(k)
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def __getitem__(self, k):
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if k not in self:
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raise KeyError(k)
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return self._c.getattr(k)
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class OrderedModuleDict(OrderedDictWrapper):
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def __init__(self, module, python_dict):
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super().__init__(torch._C.ModuleDict(module))
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# contains _both_ script modules and non-script python-only modules
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# because script modules are subclassed in python and the
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# C++ Module class will not hold references to them,
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# to ensure that you always get the same python value here
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# we store it in the python dict as well
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self._python_modules = python_dict
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def items(self):
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r = self._python_modules.items()
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return r
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def __contains__(self, k):
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return k in self._python_modules
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def __setitem__(self, k, v):
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# Cases where sub-module can be re-assigned after ScriptModule construction
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# 1. If the attr is an module interface type, it's guaranteed that the module is
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# not inlined in the graph, so it's safe to swap a new ScriptModule in.
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# 2. if the new value if a ScriptModule with the same JIT type, IR won't change
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# and it's legit to swap a new module in.
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# In these two cases we allow swapping a new scripted module and update the
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# corresponding python module dict to keep sync.
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# Note: the value to be swapped in has to be ScriptModule instead of nn.Module,
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# otherwise it's illegal and we throw error.
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if isinstance(v, ScriptModule):
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self._c.setattr(k, v)
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self._python_modules[k] = v
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else:
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raise RuntimeError(
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"Cannot re-assign modules in a ScriptModule with non-scripted "
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f"module, tried to replace existing module '{k}': {v}"
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)
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def __getitem__(self, k):
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return self._python_modules[k]
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# For each user-defined class that subclasses ScriptModule, this meta-class:
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# (1) finds all the methods annotated with @script_method in a ScriptModule and
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# removes them from the class attributes
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# (2) puts a wrapper around the class's __init__ method to recursively compile
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# all of the script_methods with the module after the original __init__ has
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# run. This has to occur after the user-defined __init__ so that submodules and
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# parameters are initialized _before_ the script compiler resolve references to
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# `self.param` or `self.module`.
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class ScriptMeta(type):
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def __init__(cls, name, bases, attrs): # noqa: B902
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# Aggregate all the ScriptMethods and constants from superclasses
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cls._methods: Dict[str, Any] = {}
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cls._constants_set = set(getattr(cls, "__constants__", ()))
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for base in reversed(bases):
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for k, v in getattr(base, "_methods", {}).items():
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cls._methods[k] = v
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base_constants: Set = getattr(base, "_constants_set", set())
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cls._constants_set = cls._constants_set.union(base_constants)
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# find all the script methods of the current class
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for k, v in sorted(attrs.items()):
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if isinstance(v, ScriptMethodStub):
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delattr(cls, k)
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cls._methods[v.original_method.__name__] = v
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if getattr(cls, "_disable_script_meta", False):
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# We leave built-in ScriptModule types alone, since this metaclass
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# is only for compiling user classes that inherit from
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# ScriptModule.
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return super().__init__(name, bases, attrs)
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original_init = getattr(cls, "__init__", lambda self: None)
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@functools.wraps(original_init)
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def init_then_script(self, *args, **kwargs):
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num_methods = len(cls._methods)
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original_init(self, *args, **kwargs)
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added_methods_in_init = len(cls._methods) > num_methods
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if type(self) == cls:
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def make_stubs(module):
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cls = type(module)
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if hasattr(cls, "_methods"):
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return [v for k, v in sorted(cls._methods.items())]
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else:
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return infer_methods_to_compile(module)
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self.__dict__[
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"_actual_script_module"
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] = torch.jit._recursive.create_script_module(
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self, make_stubs, share_types=not added_methods_in_init
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)
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# Delete the Python attributes that now shadow the ScriptModule
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# ones, so that __getattr__ and __setattr__ will properly find
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# the scripted versions.
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concrete_type = self._actual_script_module._concrete_type
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for name in concrete_type.get_attributes():
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delattr(self, name)
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for name, _ in concrete_type.get_modules():
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delattr(self, name)
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for name in ("_parameters", "_buffers", "_modules"):
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delattr(self, name)
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cls.__init__ = init_then_script # type: ignore[misc]
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super().__init__(name, bases, attrs)
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class _CachedForward:
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def __get__(self, obj, cls):
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return self.__getattr__("forward") # type: ignore[attr-defined]
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class ScriptWarning(Warning):
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pass
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def script_method(fn):
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if not _enabled:
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return fn
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# NOTE: we need to traverse two frames here because the meta-class frame
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# for ScriptModule will be present, as opposed to invoking @script on a
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# a function or invoking define() on a CompilationUnit.
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# The stack will look like:
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#
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# 0. createResolutionCallback()
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# 1. script_method()
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# 2. ScriptModule metaclass frame
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# 3. Surrounding scope
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#
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# createResolutionCallback internally adds 1 to get us to the scope of this
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# function (the calling function). Adding 2 gets us to the proper surrounding scope.
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||
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_rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=2)
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ast = get_jit_def(fn, fn.__name__, self_name="ScriptModule")
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return ScriptMethodStub(_rcb, ast, fn)
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||
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class ConstMap:
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||
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def __init__(self, const_mapping):
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self.const_mapping = const_mapping
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||
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def __getattr__(self, attr):
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return self.const_mapping[attr]
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||
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def unpackage_script_module(
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importer: PackageImporter, script_module_id: str
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||
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) -> torch.nn.Module:
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||
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"""
|
||
|
|
Call by ``torch.package.PackageImporter``'s Pickler's ``persistent_load`` function.
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||
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|
|
Performs work of loading and returning a ScriptModule from a ``torch.package`` archive.
|
||
|
|
"""
|
||
|
|
if not isinstance(importer.zip_reader, torch._C.PyTorchFileReader):
|
||
|
|
raise RuntimeError(
|
||
|
|
"Loading ScriptObjects from a PackageImporter created from a "
|
||
|
|
"directory is not supported. Use a package archive file instead."
|
||
|
|
)
|
||
|
|
cu = torch._C.CompilationUnit()
|
||
|
|
cpp_module = torch._C._import_ir_module_from_package(
|
||
|
|
cu,
|
||
|
|
importer.zip_reader,
|
||
|
|
importer.storage_context,
|
||
|
|
validate_map_location(importer.last_map_location),
|
||
|
|
script_module_id,
|
||
|
|
)
|
||
|
|
return wrap_cpp_module(cpp_module)
|
||
|
|
|
||
|
|
|
||
|
|
if _enabled:
|
||
|
|
_magic_methods = [
|
||
|
|
"__iter__",
|
||
|
|
"__len__",
|
||
|
|
"__neg__",
|
||
|
|
"__mul__",
|
||
|
|
"__contains__",
|
||
|
|
"__add__",
|
||
|
|
"__sub__",
|
||
|
|
"__pow__",
|
||
|
|
"__truediv__",
|
||
|
|
"__mod__",
|
||
|
|
"__ne__",
|
||
|
|
"__eq__",
|
||
|
|
"__lt__",
|
||
|
|
"__gt__",
|
||
|
|
"__le__",
|
||
|
|
"__ge__",
|
||
|
|
"__and__",
|
||
|
|
"__or__",
|
||
|
|
"__xor__",
|
||
|
|
"__getitem__",
|
||
|
|
"__setitem__",
|
||
|
|
"__call__",
|
||
|
|
"__int__",
|
||
|
|
"__float__",
|
||
|
|
"__bool__",
|
||
|
|
"__str__",
|
||
|
|
"__enter__",
|
||
|
|
"__exit__",
|
||
|
|
]
|
||
|
|
|
||
|
|
class RecursiveScriptClass:
|
||
|
|
"""Wrapper for a TorchScript class instance for use in Python.
|
||
|
|
|
||
|
|
An analogue of RecursiveScriptModule for regular objects that are not modules.
|
||
|
|
This class is a wrapper around a torch._C.ScriptObject that represents an instance
|
||
|
|
of a TorchScript class and allows it to be used in Python.
|
||
|
|
|
||
|
|
Attributes:
|
||
|
|
_c [torch._C.ScriptObject]: The C++ object to which attribute lookups and method
|
||
|
|
calls are forwarded.
|
||
|
|
_props [Dict[str, property]]: A dictionary of properties fetched from self._c and
|
||
|
|
exposed on this wrppaer.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, cpp_class):
|
||
|
|
super().__init__()
|
||
|
|
self.__dict__["_initializing"] = True
|
||
|
|
self._c = cpp_class
|
||
|
|
|
||
|
|
# Add wrapped object's properties to this class instance.
|
||
|
|
self._props = {
|
||
|
|
prop.name: property(prop.getter, prop.setter)
|
||
|
|
for prop in self._c._properties()
|
||
|
|
}
|
||
|
|
|
||
|
|
self.__dict__["_initializing"] = False
|
||
|
|
|
||
|
|
def __getattr__(self, attr):
|
||
|
|
if self.__dict__.get("_initializing"):
|
||
|
|
return super().__getattr__(attr) # type: ignore[misc]
|
||
|
|
|
||
|
|
if attr in self._props:
|
||
|
|
return self._props[attr].fget() # type: ignore[call-arg, misc]
|
||
|
|
|
||
|
|
return getattr(self._c, attr)
|
||
|
|
|
||
|
|
def __setattr__(self, attr, value):
|
||
|
|
if self.__dict__.get("_initializing"):
|
||
|
|
return super().__setattr__(attr, value)
|
||
|
|
|
||
|
|
if attr in self._props:
|
||
|
|
return self._props[attr].fset(value) # type: ignore[call-arg, misc]
|
||
|
|
|
||
|
|
setattr(self._c, attr, value)
|
||
|
|
|
||
|
|
# Delegate calls to magic methods like __len__ to the C++ module backing the
|
||
|
|
# RecursiveScriptClass.
|
||
|
|
def forward_magic_method(self, method_name, *args, **kwargs):
|
||
|
|
if not self._c._has_method(method_name):
|
||
|
|
raise TypeError()
|
||
|
|
|
||
|
|
self_method = self.__getattr__(method_name)
|
||
|
|
return self_method(*args, **kwargs)
|
||
|
|
|
||
|
|
def __getstate__(self):
|
||
|
|
raise pickle.PickleError("ScriptClasses cannot be pickled")
|
||
|
|
|
||
|
|
def __iadd__(self, other):
|
||
|
|
if self._c._has_method("__iadd__"):
|
||
|
|
return self.forward_magic_method("__iadd__", other)
|
||
|
|
else:
|
||
|
|
return self.forward_magic_method("__add__", other)
|
||
|
|
|
||
|
|
for method_name in _magic_methods:
|
||
|
|
|
||
|
|
def method_template(self, *args, **kwargs):
|
||
|
|
return self.forward_magic_method(method_name, *args, **kwargs)
|
||
|
|
|
||
|
|
setattr(RecursiveScriptClass, method_name, method_template)
|
||
|
|
|
||
|
|
# this is a Python 'non-data descriptor' that causes the first access
|
||
|
|
# to ScriptModule's forward to look up the forward method and stash
|
||
|
|
# it in the objects dict. Due to the standard rules for attribute lookup,
|
||
|
|
# subsequent lookups will just directly return the previously looked up method.
|
||
|
|
# This is necessary because nn.Module defines forward as a method. If we
|
||
|
|
# did nothing, __getattr__ would not be called. Instead we'd get nn.Module.forward
|
||
|
|
# which always throws an exception.
|
||
|
|
|
||
|
|
class ScriptModule(Module, metaclass=ScriptMeta):
|
||
|
|
r"""Wrapper for C++ torch::jit::Module with methods, attributes, and parameters.
|
||
|
|
|
||
|
|
A wrapper around C++ ``torch::jit::Module``. ``ScriptModule``\s
|
||
|
|
contain methods, attributes, parameters, and
|
||
|
|
constants. These can be accessed the same way as on a normal ``nn.Module``.
|
||
|
|
"""
|
||
|
|
|
||
|
|
__jit_unused_properties__ = [
|
||
|
|
"code",
|
||
|
|
"code_with_constants",
|
||
|
|
"graph",
|
||
|
|
"inlined_graph",
|
||
|
|
"original_name",
|
||
|
|
]
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
forward: Callable[..., Any] = _CachedForward() # type: ignore[assignment]
|
||
|
|
|
||
|
|
def __getattr__(self, attr):
|
||
|
|
if "_actual_script_module" not in self.__dict__:
|
||
|
|
return super().__getattr__(attr)
|
||
|
|
return getattr(self._actual_script_module, attr)
|
||
|
|
|
||
|
|
def __setattr__(self, attr, value):
|
||
|
|
if "_actual_script_module" not in self.__dict__:
|
||
|
|
# Unwrap torch.jit.Attribute into a regular setattr + record
|
||
|
|
# the provided type in __annotations__.
|
||
|
|
#
|
||
|
|
# This ensures that if we use the attr again in `__init__`, it
|
||
|
|
# will look like the actual value, not an instance of Attribute.
|
||
|
|
if isinstance(value, Attribute):
|
||
|
|
# NB: Ensure that we set __annotations__ on the specific
|
||
|
|
# class in question, and not on a superclass (which would
|
||
|
|
# be wrong wrong wrong!).
|
||
|
|
# See also https://github.com/pytorch/pytorch/issues/39463
|
||
|
|
if "__annotations__" not in self.__class__.__dict__:
|
||
|
|
self.__class__.__annotations__ = {}
|
||
|
|
self.__annotations__[attr] = value.type
|
||
|
|
value = value.value
|
||
|
|
return super().__setattr__(attr, value)
|
||
|
|
|
||
|
|
setattr(self._actual_script_module, attr, value)
|
||
|
|
|
||
|
|
def define(self, src):
|
||
|
|
if "_actual_script_module" in self.__dict__:
|
||
|
|
# If we have completed initialization, just defer to the
|
||
|
|
# backing RecursiveScriptModule to eagerly compile the provided
|
||
|
|
# source.
|
||
|
|
return self._actual_script_module.define(src)
|
||
|
|
|
||
|
|
# Otherwise, we are still in the object's __init__.
|
||
|
|
# In that case, add `src` as a stub to be compiled.
|
||
|
|
#
|
||
|
|
# We use frames_up=1 to get to the proper surrounding scope. The stack
|
||
|
|
# will look like:
|
||
|
|
# 0. createResolutionCallback
|
||
|
|
# 1. define()
|
||
|
|
# 2. surrounding scope.
|
||
|
|
#
|
||
|
|
# createResolutionCallback internally adds 1 to get us to our frame, then
|
||
|
|
# we add 1 to get to the proper surrounding scope.
|
||
|
|
rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
|
||
|
|
ast = torch._C._parse_source_def(src)
|
||
|
|
self._methods[ast.name().name] = ScriptMethodStub(rcb, ast, None)
|
||
|
|
|
||
|
|
def _replicate_for_data_parallel(self):
|
||
|
|
return self._actual_script_module._replicate_for_data_parallel()
|
||
|
|
|
||
|
|
def __reduce_package__(self, exporter: PackageExporter):
|
||
|
|
"""Save a ScriptModule inside of a ``torch.package`` archive.
|
||
|
|
|
||
|
|
Called by ``torch.package.PackageExporter``'s Pickler's ``persistent_id`` when
|
||
|
|
saving TorchScript objects. Performs act of saving a ScriptModule inside of
|
||
|
|
a ``torch.package`` archive.
|
||
|
|
|
||
|
|
Returns method to load the ScriptModule from a ``torch.package.PackageImporter``'s
|
||
|
|
Pickler's ``persistent_load`` function.
|
||
|
|
"""
|
||
|
|
script_module_id = exporter.get_unique_id()
|
||
|
|
exporter.script_module_serializer.serialize(self._c, int(script_module_id))
|
||
|
|
return (unpackage_script_module, (script_module_id,))
|
||
|
|
|
||
|
|
class RecursiveScriptModule(ScriptModule):
|
||
|
|
# XXX: RecursiveScriptModule inherits from ScriptModule for the sole
|
||
|
|
# reason that it retains the existing isinstance(ScriptModule)
|
||
|
|
# behavior.
|
||
|
|
r"""Retain the existing isinstance(ScriptModule) behavior.
|
||
|
|
|
||
|
|
The core data structure in TorchScript is the ``ScriptModule``. It is an
|
||
|
|
analogue of torch's ``nn.Module`` and represents an entire model as a tree of
|
||
|
|
submodules. Like normal modules, each individual module in a ``ScriptModule`` can
|
||
|
|
have submodules, parameters, and methods. In ``nn.Module``\s methods are implemented
|
||
|
|
as Python functions, but in ``ScriptModule``\s methods are implemented as
|
||
|
|
TorchScript functions, a statically-typed subset of Python that contains all
|
||
|
|
of PyTorch's built-in Tensor operations. This difference allows your
|
||
|
|
``ScriptModule``\s code to run without the need for a Python interpreter.
|
||
|
|
|
||
|
|
``ScriptModule``\s should not be created manually, instead use
|
||
|
|
either :func:`tracing <torch.jit.trace>` or :func:`scripting <torch.jit.script>`.
|
||
|
|
Tracing and scripting can be applied incrementally and :ref:`composed as necessary <Types>`.
|
||
|
|
|
||
|
|
* Tracing records the tensor operations as executed with a set of example inputs and uses these
|
||
|
|
operations to construct a computation graph. You can use the full dynamic behavior of Python with tracing,
|
||
|
|
but values other than Tensors and control flow aren't captured in the graph.
|
||
|
|
|
||
|
|
* Scripting inspects the Python code of the model
|
||
|
|
and compiles it to TorchScript. Scripting allows the use of many `types`_ of values and supports dynamic control flow.
|
||
|
|
Many, but not all features of Python are supported by the compiler, so changes to the source code may be necessary.
|
||
|
|
"""
|
||
|
|
|
||
|
|
_disable_script_meta = True
|
||
|
|
|
||
|
|
def __init__(self, cpp_module):
|
||
|
|
self.__dict__["_initializing"] = True
|
||
|
|
self._c = cpp_module
|
||
|
|
super().__init__()
|
||
|
|
# Delete the 'training' attribute set up by `Module.__init__`. It
|
||
|
|
# will get set on the underlying cpp module, so we delete it here
|
||
|
|
# to avoid this version shadowing the cpp module version.
|
||
|
|
delattr(self, "training")
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _construct(cpp_module, init_fn):
|
||
|
|
"""
|
||
|
|
Construct a RecursiveScriptModule that's ready for use.
|
||
|
|
|
||
|
|
PyTorch code should use this to construct a RecursiveScriptModule instead
|
||
|
|
of instead of calling `__init__` directly, as it makes sure the
|
||
|
|
object is properly finalized (and in the future, we may take
|
||
|
|
control of how the RecursiveScriptModule instance is created).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
cpp_module: The C++ Module that will hold the actual state of
|
||
|
|
this RecursiveScriptModule instance.
|
||
|
|
init_fn: Lambda that initializes the RecursiveScriptModule passed to it.
|
||
|
|
"""
|
||
|
|
script_module = RecursiveScriptModule(cpp_module)
|
||
|
|
init_fn(script_module)
|
||
|
|
|
||
|
|
# Finalize the ScriptModule: replace the nn.Module state with our
|
||
|
|
# custom implementations and flip the _initializing bit.
|
||
|
|
RecursiveScriptModule._finalize_scriptmodule(script_module)
|
||
|
|
return script_module
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _finalize_scriptmodule(script_module):
|
||
|
|
script_module._parameters = OrderedDictWrapper(
|
||
|
|
torch._C.ParameterDict(script_module._c)
|
||
|
|
)
|
||
|
|
script_module._buffers = OrderedDictWrapper(
|
||
|
|
torch._C.BufferDict(script_module._c)
|
||
|
|
)
|
||
|
|
script_module._modules = OrderedModuleDict(
|
||
|
|
script_module._c, script_module._modules
|
||
|
|
)
|
||
|
|
script_module._initializing = False
|
||
|
|
|
||
|
|
def _reconstruct(self, cpp_module):
|
||
|
|
"""
|
||
|
|
Re-construct an instance of RecursiveScriptModule using an instance of a C++ module.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
cpp_module: The C++ module that this RecursiveScriptModule will be rebuilt around.
|
||
|
|
"""
|
||
|
|
self.__init__(cpp_module) # type: ignore[misc]
|
||
|
|
|
||
|
|
# Copy the concrete type from the C++ module to this ScriptModule.
|
||
|
|
self._concrete_type = torch._C.ConcreteModuleType.from_jit_type(
|
||
|
|
self._c._type()
|
||
|
|
)
|
||
|
|
|
||
|
|
# Copy submodules from the C++ module to this ScriptModule.
|
||
|
|
modules = {}
|
||
|
|
for name, cpp_module in torch._C.ModuleDict(self._c).items():
|
||
|
|
modules[name] = wrap_cpp_module(cpp_module)
|
||
|
|
self._modules = OrderedModuleDict(self._c, modules) # type: ignore[assignment]
|
||
|
|
|
||
|
|
# Copy parameters and buffers.
|
||
|
|
self._parameters = OrderedDictWrapper(torch._C.ParameterDict(self._c)) # type: ignore[assignment]
|
||
|
|
self._buffers = OrderedDictWrapper(torch._C.BufferDict(self._c)) # type: ignore[assignment]
|
||
|
|
|
||
|
|
# Get rid of the functions from the old C++ module.
|
||
|
|
self.__dict__ = {
|
||
|
|
k: v
|
||
|
|
for k, v in self.__dict__.items()
|
||
|
|
if not isinstance(v, torch._C.ScriptMethod)
|
||
|
|
}
|
||
|
|
self.__dict__["_initializing"] = False
|
||
|
|
|
||
|
|
@property
|
||
|
|
def graph(self):
|
||
|
|
r"""Return a string representation of the internal graph for the ``forward`` method.
|
||
|
|
|
||
|
|
See :ref:`interpreting-graphs` for details.
|
||
|
|
"""
|
||
|
|
return self._c._get_method("forward").graph
|
||
|
|
|
||
|
|
@property
|
||
|
|
def inlined_graph(self):
|
||
|
|
r"""
|
||
|
|
Return a string representation of the internal graph for the ``forward`` method.
|
||
|
|
|
||
|
|
This graph will be preprocessed to inline all function and method calls.
|
||
|
|
See :ref:`interpreting-graphs` for details.
|
||
|
|
"""
|
||
|
|
return self.forward.inlined_graph # type: ignore[attr-defined]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def code(self):
|
||
|
|
r"""
|
||
|
|
Return a pretty-printed representation (as valid Python syntax) of the internal graph for the ``forward`` method.
|
||
|
|
|
||
|
|
See :ref:`inspecting-code` for details.
|
||
|
|
"""
|
||
|
|
return self.forward.code # type: ignore[attr-defined]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def code_with_constants(self):
|
||
|
|
r"""Return a tuple.
|
||
|
|
|
||
|
|
Returns a tuple of:
|
||
|
|
|
||
|
|
[0] a pretty-printed representation (as valid Python syntax) of
|
||
|
|
the internal graph for the ``forward`` method. See `code`.
|
||
|
|
[1] a ConstMap following the CONSTANT.cN format of the output in [0].
|
||
|
|
The indices in the [0] output are keys to the underlying constant's values.
|
||
|
|
|
||
|
|
See :ref:`inspecting-code` for details.
|
||
|
|
"""
|
||
|
|
r = self.forward.code_with_constants # type: ignore[attr-defined]
|
||
|
|
return (r[0], ConstMap(r[1]))
|
||
|
|
|
||
|
|
def save(self, f, **kwargs):
|
||
|
|
r"""Save with a file-like object.
|
||
|
|
|
||
|
|
save(f, _extra_files={})
|
||
|
|
|
||
|
|
See :func:`torch.jit.save <torch.jit.save>` which accepts a file-like object.
|
||
|
|
This function, torch.save(), converts the object to a string, treating it as a path.
|
||
|
|
DO NOT confuse these two functions when it comes to the 'f' parameter functionality.
|
||
|
|
"""
|
||
|
|
return self._c.save(str(f), **kwargs)
|
||
|
|
|
||
|
|
def _save_for_lite_interpreter(self, *args, **kwargs):
|
||
|
|
r"""Add (or update) the bytecode session to the script model.
|
||
|
|
|
||
|
|
_save_for_lite_interpreter(f)
|
||
|
|
|
||
|
|
The updated model is used
|
||
|
|
in lite interpreter for mobile applications.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
f: a string containing a file name.
|
||
|
|
_extra_files: Map from filename to contents which will be stored as part of 'f'.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return self._c._save_for_mobile(*args, **kwargs)
|
||
|
|
|
||
|
|
def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs):
|
||
|
|
return self._c._save_to_buffer_for_mobile(*args, **kwargs)
|
||
|
|
|
||
|
|
def save_to_buffer(self, *args, **kwargs):
|
||
|
|
return self._c.save_to_buffer(*args, **kwargs)
|
||
|
|
|
||
|
|
def get_debug_state(self, *args, **kwargs):
|
||
|
|
return self._c.get_debug_state()
|
||
|
|
|
||
|
|
def extra_repr(self):
|
||
|
|
return f"original_name={self.original_name}"
|
||
|
|
|
||
|
|
def graph_for(self, *args, **kwargs):
|
||
|
|
return self.forward.graph_for(self, *args, **kwargs) # type: ignore[attr-defined]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def original_name(self):
|
||
|
|
if type(self) == str(self._c._type().name()):
|
||
|
|
return ""
|
||
|
|
return str(self._c._type().name())
|
||
|
|
|
||
|
|
def define(self, src):
|
||
|
|
# We use frames_up=1 to get to the proper surrounding scope. The stack
|
||
|
|
# will look like:
|
||
|
|
# 0. createResolutionCallback
|
||
|
|
# 1. define()
|
||
|
|
# 2. surrounding scope.
|
||
|
|
#
|
||
|
|
# createResolutionCallback internally adds 1 to get us to our frame, then
|
||
|
|
# we add 1 to get to the proper surrounding scope.
|
||
|
|
rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
|
||
|
|
self._c._define(self._concrete_type, src, rcb)
|
||
|
|
|
||
|
|
def __getattr__(self, attr):
|
||
|
|
if "_initializing" not in self.__dict__:
|
||
|
|
raise RuntimeError(
|
||
|
|
"ScriptModule has not been initialized, did you forget to call super's init?"
|
||
|
|
)
|
||
|
|
|
||
|
|
if self._initializing:
|
||
|
|
return super().__getattr__(attr)
|
||
|
|
|
||
|
|
# _modules check is before hasattr since modules are included as attributes in _c,
|
||
|
|
# but we want to get the python wrapper from _modules instead of the raw _c object.
|
||
|
|
if attr in self._modules:
|
||
|
|
return self._modules[attr]
|
||
|
|
elif self._c.hasattr(attr):
|
||
|
|
return self._c.getattr(attr)
|
||
|
|
elif self._c._has_method(attr):
|
||
|
|
script_method = self._c._get_method(attr)
|
||
|
|
# cache method so future calls do not go through __getattr__
|
||
|
|
# to improve invocation performance
|
||
|
|
self.__dict__[attr] = script_method
|
||
|
|
return script_method
|
||
|
|
|
||
|
|
return super().__getattr__(attr)
|
||
|
|
|
||
|
|
def __setattr__(self, attr, value):
|
||
|
|
if self._initializing:
|
||
|
|
return super().__setattr__(attr, value)
|
||
|
|
|
||
|
|
if attr in self._modules:
|
||
|
|
self._modules[attr] = value
|
||
|
|
elif self._c.hasattr(attr):
|
||
|
|
self._c.setattr(attr, value)
|
||
|
|
elif (
|
||
|
|
hasattr(self, "_concrete_type")
|
||
|
|
and attr in self._concrete_type.get_constants().keys()
|
||
|
|
):
|
||
|
|
# TODO: we don't have _concrete_type set after load(), and in general we lose constant information.
|
||
|
|
# We should encode constants as class type attributes (or something) so it persists across save/load.
|
||
|
|
raise AttributeError(
|
||
|
|
f"Cannot mutate TorchScript constant value: '{attr}'. Value: '{value}'"
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
# We allow setting Python attributes on the ScriptModule, for
|
||
|
|
# when people want to stash some convenience info on it.
|
||
|
|
# TODO: it's possible that the following is confusing:
|
||
|
|
# s = torch.jit.script(...)
|
||
|
|
# s.python_attr = ...
|
||
|
|
# s.save() <--- this doesn't have `python_attr`
|
||
|
|
# It's fairly trivial to save enough info to warn in this case.
|
||
|
|
return super().__setattr__(attr, value)
|
||
|
|
|
||
|
|
def __copy__(self):
|
||
|
|
return torch.jit._recursive.wrap_cpp_module(copy.copy(self._c))
|
||
|
|
|
||
|
|
def __deepcopy__(self, memo):
|
||
|
|
return torch.jit._recursive.wrap_cpp_module(copy.deepcopy(self._c, memo))
|
||
|
|
|
||
|
|
# Python magic methods do method lookups on an object's class type, instead of looking up
|
||
|
|
# the method defines on the class instance. In order to continue to expose the magic methods
|
||
|
|
# of builtin-containers (ModuleList, Sequential, ModuleDict) to Python, we
|
||
|
|
# define magic methods here as a shim to the correct attribute.
|
||
|
|
def forward_magic_method(self, method_name, *args, **kwargs):
|
||
|
|
self_method = getattr(self, method_name)
|
||
|
|
if getattr(self_method, "__func__", None) == getattr(
|
||
|
|
RecursiveScriptModule, method_name
|
||
|
|
):
|
||
|
|
raise NotImplementedError()
|
||
|
|
return self_method(*args, **kwargs)
|
||
|
|
|
||
|
|
def __iter__(self):
|
||
|
|
return self.forward_magic_method("__iter__")
|
||
|
|
|
||
|
|
def __getitem__(self, idx):
|
||
|
|
return self.forward_magic_method("__getitem__", idx)
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.forward_magic_method("__len__")
|
||
|
|
|
||
|
|
def __contains__(self, key):
|
||
|
|
return self.forward_magic_method("__contains__", key)
|
||
|
|
|
||
|
|
# dir is defined by the base nn.Module, so instead of throwing if
|
||
|
|
# it is not overridden, we call into the nn.Module __dir__ method
|
||
|
|
def __dir__(self):
|
||
|
|
self_method = self.__dir__
|
||
|
|
if (
|
||
|
|
self_method.__func__ # type: ignore[attr-defined]
|
||
|
|
== _get_function_from_type(RecursiveScriptModule, "__dir__")
|
||
|
|
):
|
||
|
|
return super().__dir__()
|
||
|
|
return self_method()
|
||
|
|
|
||
|
|
# to resolve bool(value), Python looks if __bool__ is defined then __iter__
|
||
|
|
# is defined then returns true for classes. Since __iter__() on this
|
||
|
|
# class throws if it isn't overridden, we define __bool__ to preserve default behavior
|
||
|
|
def __bool__(self):
|
||
|
|
self_method = self.__bool__
|
||
|
|
if (
|
||
|
|
self_method.__func__ # type: ignore[attr-defined]
|
||
|
|
== _get_function_from_type(RecursiveScriptModule, "__bool__")
|
||
|
|
):
|
||
|
|
return True
|
||
|
|
return self_method()
|
||
|
|
|
||
|
|
def _replicate_for_data_parallel(self):
|
||
|
|
# we have to initialize ScriptModule properly so that
|
||
|
|
# it works with pybind11
|
||
|
|
def init_fn(script_module):
|
||
|
|
# Don't do anything here, we'll initialize the ScriptModule below
|
||
|
|
return
|
||
|
|
|
||
|
|
return RecursiveScriptModule._construct(
|
||
|
|
self._c._replicate_for_data_parallel(), init_fn
|
||
|
|
)
|
||
|
|
|
||
|
|
# Need to copy all RecursiveScriptModule methods to ScriptModule.
|
||
|
|
#
|
||
|
|
# This is because `super().foo()` does not use
|
||
|
|
# `__getattr__` to look up `foo`. So we need to make each method available on
|
||
|
|
# the ScriptModule manually.
|
||
|
|
for name, item in RecursiveScriptModule.__dict__.items():
|
||
|
|
if not callable(item) and not isinstance(item, property):
|
||
|
|
continue
|
||
|
|
if name.startswith("__") or hasattr(ScriptModule, name):
|
||
|
|
continue
|
||
|
|
# We can copy over the implementation wholesale because besides the
|
||
|
|
# `super()` thing above, ScriptModule behaves exactly like
|
||
|
|
# RecursiveScriptModule
|
||
|
|
setattr(ScriptModule, name, item)
|
||
|
|
|
||
|
|
def _get_methods(cls):
|
||
|
|
import inspect
|
||
|
|
|
||
|
|
# In Python 3 unbound methods are functions, but in Python 2 they are methods
|
||
|
|
return inspect.getmembers(
|
||
|
|
cls, predicate=lambda x: inspect.isfunction(x) or inspect.ismethod(x)
|
||
|
|
)
|
||
|
|
|
||
|
|
_compiled_methods_allowlist = {
|
||
|
|
"forward",
|
||
|
|
"register_buffer",
|
||
|
|
"register_parameter",
|
||
|
|
"register_module",
|
||
|
|
"add_module",
|
||
|
|
"_apply",
|
||
|
|
"apply",
|
||
|
|
"cuda",
|
||
|
|
"cpu",
|
||
|
|
"to",
|
||
|
|
"type",
|
||
|
|
"float",
|
||
|
|
"double",
|
||
|
|
"half",
|
||
|
|
"state_dict",
|
||
|
|
"_save_to_state_dict",
|
||
|
|
"load_state_dict",
|
||
|
|
"_load_from_state_dict",
|
||
|
|
"_named_members",
|
||
|
|
"parameters",
|
||
|
|
"named_parameters",
|
||
|
|
"buffers",
|
||
|
|
"named_buffers",
|
||
|
|
"children",
|
||
|
|
"named_children",
|
||
|
|
"modules",
|
||
|
|
"named_modules",
|
||
|
|
"zero_grad",
|
||
|
|
"share_memory",
|
||
|
|
"_get_name",
|
||
|
|
"extra_repr",
|
||
|
|
"_slow_forward",
|
||
|
|
"_tracing_name",
|
||
|
|
"eval",
|
||
|
|
"train",
|
||
|
|
"get_extra_state",
|
||
|
|
"set_extra_state",
|
||
|
|
}
|
||
|
|
|
||
|
|
def _make_fail(name):
|
||
|
|
def fail(self, *args, **kwargs):
|
||
|
|
raise RuntimeError(name + " is not supported on ScriptModules")
|
||
|
|
|
||
|
|
return fail
|
||
|
|
|
||
|
|
for name, method in _get_methods(torch.nn.Module):
|
||
|
|
if name.startswith("__") or name.endswith("_call_impl"):
|
||
|
|
continue
|
||
|
|
if (
|
||
|
|
name not in RecursiveScriptModule.__dict__
|
||
|
|
and name not in _compiled_methods_allowlist
|
||
|
|
):
|
||
|
|
setattr(RecursiveScriptModule, method.__name__, _make_fail(name))
|
||
|
|
|
||
|
|
|
||
|
|
else:
|
||
|
|
# TODO MAKE SURE THAT DISABLING WORKS
|
||
|
|
class RecursiveScriptClass: # type: ignore[no-redef]
|
||
|
|
pass
|
||
|
|
|
||
|
|
class ScriptModule(torch.nn.Module): # type: ignore[no-redef]
|
||
|
|
def __init__(self, arg=None):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
class RecursiveScriptModule(ScriptModule): # type: ignore[no-redef]
|
||
|
|
def __init__(self, arg=None):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
|
||
|
|
def call_prepare_scriptable_func_impl(obj, memo):
|
||
|
|
if not isinstance(obj, torch.nn.Module):
|
||
|
|
return obj
|
||
|
|
|
||
|
|
obj_id = id(obj)
|
||
|
|
|
||
|
|
# If obj_id is in memo, obj has already been prepared or is being
|
||
|
|
# prepared in another call up the stack.
|
||
|
|
if obj_id in memo:
|
||
|
|
return memo[id(obj)]
|
||
|
|
|
||
|
|
obj = obj.__prepare_scriptable__() if hasattr(obj, "__prepare_scriptable__") else obj # type: ignore[operator]
|
||
|
|
# Record obj in memo to avoid infinite recursion in the case of cycles in the module
|
||
|
|
# hierarchy when recursing below.
|
||
|
|
memo[obj_id] = obj
|
||
|
|
|
||
|
|
new_obj_dict = {}
|
||
|
|
|
||
|
|
for name, sub_module in obj.__dict__.items():
|
||
|
|
if name == "_modules":
|
||
|
|
for k, v in sub_module.items():
|
||
|
|
sub_module[k] = call_prepare_scriptable_func_impl(v, memo)
|
||
|
|
new_obj_dict[name] = sub_module
|
||
|
|
elif isinstance(sub_module, torch.nn.Module) and not isinstance(
|
||
|
|
sub_module, ScriptModule
|
||
|
|
):
|
||
|
|
new_obj_dict[name] = call_prepare_scriptable_func_impl(sub_module, memo)
|
||
|
|
else:
|
||
|
|
new_obj_dict[name] = sub_module
|
||
|
|
|
||
|
|
for k, v in new_obj_dict.items():
|
||
|
|
obj.__dict__[name] = v
|
||
|
|
|
||
|
|
return obj
|
||
|
|
|
||
|
|
|
||
|
|
def call_prepare_scriptable_func(obj):
|
||
|
|
memo: Dict[int, torch.nn.Module] = {}
|
||
|
|
return call_prepare_scriptable_func_impl(obj, memo)
|
||
|
|
|
||
|
|
|
||
|
|
def create_script_dict(obj):
|
||
|
|
"""
|
||
|
|
Create a ``torch._C.ScriptDict`` instance with the data from ``obj``.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
obj (dict): The Python dictionary that is used to initialize the ``ScriptDict``
|
||
|
|
returned by this function.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
An instance of ``torch._C.ScriptDict`` that has the same data as ``obj``
|
||
|
|
and can be passed between Python and TorchScript with reference semantics and
|
||
|
|
zero copy overhead.
|
||
|
|
"""
|
||
|
|
return torch._C.ScriptDict(obj) # type: ignore[attr-defined]
|
||
|
|
|
||
|
|
|
||
|
|
def create_script_list(obj, type_hint=None):
|
||
|
|
"""
|
||
|
|
Create a ``torch._C.ScriptList`` instance with the data from ``obj``.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
obj (dict): The Python list that is used to initialize the ``ScriptList``
|
||
|
|
returned by this function.
|
||
|
|
Returns:
|
||
|
|
An instance of ``torch._C.ScriptList`` that has the same data as ``obj``
|
||
|
|
and can be passed between Python and TorchScript with reference semantics and
|
||
|
|
zero copy overhead.
|
||
|
|
"""
|
||
|
|
return torch._C.ScriptList(obj) # type: ignore[attr-defined]
|
||
|
|
|
||
|
|
|
||
|
|
def script(
|
||
|
|
obj,
|
||
|
|
optimize=None,
|
||
|
|
_frames_up=0,
|
||
|
|
_rcb=None,
|
||
|
|
example_inputs: Union[List[Tuple], Dict[Callable, List[Tuple]], None] = None,
|
||
|
|
):
|
||
|
|
r"""Script the function.
|
||
|
|
|
||
|
|
Scripting a function or ``nn.Module`` will inspect the source code, compile
|
||
|
|
it as TorchScript code using the TorchScript compiler, and return a :class:`ScriptModule` or
|
||
|
|
:class:`ScriptFunction`. TorchScript itself is a subset of the Python language, so not all
|
||
|
|
features in Python work, but we provide enough functionality to compute on
|
||
|
|
tensors and do control-dependent operations. For a complete guide, see the
|
||
|
|
:ref:`language-reference`.
|
||
|
|
|
||
|
|
Scripting a dictionary or list copies the data inside it into a TorchScript instance than can be
|
||
|
|
subsequently passed by reference between Python and TorchScript with zero copy overhead.
|
||
|
|
|
||
|
|
``torch.jit.script`` can be used as a function for modules, functions, dictionaries and lists
|
||
|
|
and as a decorator ``@torch.jit.script`` for :ref:`torchscript-classes` and functions.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
obj (Callable, class, or nn.Module): The ``nn.Module``, function, class type,
|
||
|
|
dictionary, or list to compile.
|
||
|
|
example_inputs (Union[List[Tuple], Dict[Callable, List[Tuple]], None]): Provide example inputs
|
||
|
|
to annotate the arguments for a function or ``nn.Module``.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
If ``obj`` is ``nn.Module``, ``script`` returns
|
||
|
|
a :class:`ScriptModule` object. The returned :class:`ScriptModule` will
|
||
|
|
have the same set of sub-modules and parameters as the
|
||
|
|
original ``nn.Module``. If ``obj`` is a standalone function,
|
||
|
|
a :class:`ScriptFunction` will be returned. If ``obj`` is a ``dict``, then
|
||
|
|
``script`` returns an instance of `torch._C.ScriptDict`. If ``obj`` is a ``list``,
|
||
|
|
then ``script`` returns an instance of `torch._C.ScriptList`.
|
||
|
|
|
||
|
|
**Scripting a function**
|
||
|
|
The ``@torch.jit.script`` decorator will construct a :class:`ScriptFunction`
|
||
|
|
by compiling the body of the function.
|
||
|
|
|
||
|
|
Example (scripting a function):
|
||
|
|
|
||
|
|
.. testcode::
|
||
|
|
|
||
|
|
import torch
|
||
|
|
|
||
|
|
@torch.jit.script
|
||
|
|
def foo(x, y):
|
||
|
|
if x.max() > y.max():
|
||
|
|
r = x
|
||
|
|
else:
|
||
|
|
r = y
|
||
|
|
return r
|
||
|
|
|
||
|
|
print(type(foo)) # torch.jit.ScriptFunction
|
||
|
|
|
||
|
|
# See the compiled graph as Python code
|
||
|
|
print(foo.code)
|
||
|
|
|
||
|
|
# Call the function using the TorchScript interpreter
|
||
|
|
foo(torch.ones(2, 2), torch.ones(2, 2))
|
||
|
|
|
||
|
|
.. testoutput::
|
||
|
|
:hide:
|
||
|
|
|
||
|
|
...
|
||
|
|
|
||
|
|
****Scripting a function using example_inputs**
|
||
|
|
Example inputs can be used to annotate a function arguments.
|
||
|
|
|
||
|
|
Example (annotating a function before scripting):
|
||
|
|
|
||
|
|
.. testcode::
|
||
|
|
|
||
|
|
import torch
|
||
|
|
|
||
|
|
def test_sum(a, b):
|
||
|
|
return a + b
|
||
|
|
|
||
|
|
# Annotate the arguments to be int
|
||
|
|
scripted_fn = torch.jit.script(test_sum, example_inputs=[(3, 4)])
|
||
|
|
|
||
|
|
print(type(scripted_fn)) # torch.jit.ScriptFunction
|
||
|
|
|
||
|
|
# See the compiled graph as Python code
|
||
|
|
print(scripted_fn.code)
|
||
|
|
|
||
|
|
# Call the function using the TorchScript interpreter
|
||
|
|
scripted_fn(20, 100)
|
||
|
|
|
||
|
|
.. testoutput::
|
||
|
|
:hide:
|
||
|
|
|
||
|
|
...
|
||
|
|
|
||
|
|
**Scripting an nn.Module**
|
||
|
|
Scripting an ``nn.Module`` by default will compile the ``forward`` method and recursively
|
||
|
|
compile any methods, submodules, and functions called by ``forward``. If a ``nn.Module`` only uses
|
||
|
|
features supported in TorchScript, no changes to the original module code should be necessary. ``script``
|
||
|
|
will construct :class:`ScriptModule` that has copies of the attributes, parameters, and methods of
|
||
|
|
the original module.
|
||
|
|
|
||
|
|
Example (scripting a simple module with a Parameter):
|
||
|
|
|
||
|
|
.. testcode::
|
||
|
|
|
||
|
|
import torch
|
||
|
|
|
||
|
|
class MyModule(torch.nn.Module):
|
||
|
|
def __init__(self, N, M):
|
||
|
|
super().__init__()
|
||
|
|
# This parameter will be copied to the new ScriptModule
|
||
|
|
self.weight = torch.nn.Parameter(torch.rand(N, M))
|
||
|
|
|
||
|
|
# When this submodule is used, it will be compiled
|
||
|
|
self.linear = torch.nn.Linear(N, M)
|
||
|
|
|
||
|
|
def forward(self, input):
|
||
|
|
output = self.weight.mv(input)
|
||
|
|
|
||
|
|
# This calls the `forward` method of the `nn.Linear` module, which will
|
||
|
|
# cause the `self.linear` submodule to be compiled to a `ScriptModule` here
|
||
|
|
output = self.linear(output)
|
||
|
|
return output
|
||
|
|
|
||
|
|
scripted_module = torch.jit.script(MyModule(2, 3))
|
||
|
|
|
||
|
|
Example (scripting a module with traced submodules):
|
||
|
|
|
||
|
|
.. testcode::
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
class MyModule(nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
super().__init__()
|
||
|
|
# torch.jit.trace produces a ScriptModule's conv1 and conv2
|
||
|
|
self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
|
||
|
|
self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))
|
||
|
|
|
||
|
|
def forward(self, input):
|
||
|
|
input = F.relu(self.conv1(input))
|
||
|
|
input = F.relu(self.conv2(input))
|
||
|
|
return input
|
||
|
|
|
||
|
|
scripted_module = torch.jit.script(MyModule())
|
||
|
|
|
||
|
|
To compile a method other than ``forward`` (and recursively compile anything it calls), add
|
||
|
|
the :func:`@torch.jit.export <torch.jit.export>` decorator to the method. To opt out of compilation
|
||
|
|
use :func:`@torch.jit.ignore <torch.jit.ignore>` or :func:`@torch.jit.unused <torch.jit.unused>`.
|
||
|
|
|
||
|
|
Example (an exported and ignored method in a module)::
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
|
||
|
|
class MyModule(nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
@torch.jit.export
|
||
|
|
def some_entry_point(self, input):
|
||
|
|
return input + 10
|
||
|
|
|
||
|
|
@torch.jit.ignore
|
||
|
|
def python_only_fn(self, input):
|
||
|
|
# This function won't be compiled, so any
|
||
|
|
# Python APIs can be used
|
||
|
|
import pdb
|
||
|
|
pdb.set_trace()
|
||
|
|
|
||
|
|
def forward(self, input):
|
||
|
|
if self.training:
|
||
|
|
self.python_only_fn(input)
|
||
|
|
return input * 99
|
||
|
|
|
||
|
|
scripted_module = torch.jit.script(MyModule())
|
||
|
|
print(scripted_module.some_entry_point(torch.randn(2, 2)))
|
||
|
|
print(scripted_module(torch.randn(2, 2)))
|
||
|
|
|
||
|
|
Example ( Annotating forward of nn.Module using example_inputs)::
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
from typing import NamedTuple
|
||
|
|
|
||
|
|
class MyModule(NamedTuple):
|
||
|
|
result: List[int]
|
||
|
|
|
||
|
|
class TestNNModule(torch.nn.Module):
|
||
|
|
def forward(self, a) -> MyModule:
|
||
|
|
result = MyModule(result=a)
|
||
|
|
return result
|
||
|
|
|
||
|
|
pdt_model = TestNNModule()
|
||
|
|
|
||
|
|
# Runs the pdt_model in eager model with the inputs provided and annotates the arguments of forward
|
||
|
|
scripted_model = torch.jit.script(pdt_model, example_inputs={pdt_model: [([10, 20, ], ), ], })
|
||
|
|
|
||
|
|
# Run the scripted_model with actual inputs
|
||
|
|
print(scripted_model([20]))
|
||
|
|
"""
|
||
|
|
global type_trace_db
|
||
|
|
if not _enabled:
|
||
|
|
return obj
|
||
|
|
|
||
|
|
if optimize is not None:
|
||
|
|
warnings.warn(
|
||
|
|
"`optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead"
|
||
|
|
)
|
||
|
|
|
||
|
|
# No-op for modules, functions, class instances that are already scripted
|
||
|
|
if isinstance(obj, RecursiveScriptClass):
|
||
|
|
return obj
|
||
|
|
if isinstance(obj, ScriptModule):
|
||
|
|
return obj
|
||
|
|
if isinstance(obj, ScriptFunction):
|
||
|
|
return obj
|
||
|
|
|
||
|
|
if example_inputs:
|
||
|
|
# If MonkeyType is installed, enable profile directed type annotation
|
||
|
|
# Check if example_inputs are defined and generate call traces
|
||
|
|
# for the method by running eager mode version of the method with
|
||
|
|
# the provide example inputs. This logs all the traces in type_trace_db
|
||
|
|
type_trace_db = JitTypeTraceStore()
|
||
|
|
if monkeytype_trace:
|
||
|
|
monkeytype_config = JitTypeTraceConfig(type_trace_db)
|
||
|
|
with monkeytype_trace(monkeytype_config):
|
||
|
|
if isinstance(example_inputs, Dict):
|
||
|
|
# If the obj is an nn.Module or a class, then each method is
|
||
|
|
# executed with the arguments provided in the example inputs.
|
||
|
|
# example inputs here will be of type Dict(class.method, (arguments))
|
||
|
|
# This is used to infer type annotations for those methods
|
||
|
|
# which are not called directly under the hood of monkeytype.
|
||
|
|
for module, example_input in example_inputs.items():
|
||
|
|
for example in example_input:
|
||
|
|
module(*example)
|
||
|
|
elif isinstance(example_inputs, List):
|
||
|
|
for examples in example_inputs:
|
||
|
|
obj(*examples)
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"Error: Unable to infer types. Please format the inputs to type `List[Tuple]`"
|
||
|
|
" or `Dict[Callable, List[Tuple]]` to be run with MonkeyType."
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
warnings.warn(
|
||
|
|
"Warning: monkeytype is not installed. Please install https://github.com/Instagram/MonkeyType "
|
||
|
|
"to enable Profile-Directed Typing in TorchScript. Refer to "
|
||
|
|
"https://github.com/Instagram/MonkeyType/blob/master/README.rst to install MonkeyType. "
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(obj, torch.nn.Module):
|
||
|
|
obj = call_prepare_scriptable_func(obj)
|
||
|
|
return torch.jit._recursive.create_script_module(
|
||
|
|
obj, torch.jit._recursive.infer_methods_to_compile
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
obj = obj.__prepare_scriptable__() if hasattr(obj, "__prepare_scriptable__") else obj # type: ignore[operator]
|
||
|
|
|
||
|
|
if isinstance(obj, dict):
|
||
|
|
return create_script_dict(obj)
|
||
|
|
if isinstance(obj, list):
|
||
|
|
return create_script_list(obj)
|
||
|
|
|
||
|
|
if inspect.isclass(obj):
|
||
|
|
qualified_name = _qualified_name(obj)
|
||
|
|
# If this type is a `nn.Module` subclass, they probably meant to pass
|
||
|
|
# an instance instead of a Module
|
||
|
|
if issubclass(obj, torch.nn.Module):
|
||
|
|
raise RuntimeError(
|
||
|
|
f"Type '{obj}' cannot be compiled since it inherits from nn.Module, pass an instance instead"
|
||
|
|
)
|
||
|
|
|
||
|
|
# Enums are automatically usable in TorchScript, explicitly scripting
|
||
|
|
# is not necessary, but not harmful either.
|
||
|
|
if issubclass(obj, enum.Enum):
|
||
|
|
return obj
|
||
|
|
|
||
|
|
if not _is_new_style_class(obj):
|
||
|
|
raise RuntimeError(
|
||
|
|
"TorchScript classes must be new-style classes. "
|
||
|
|
"Please inherit from 'object'."
|
||
|
|
)
|
||
|
|
if len(obj.mro()) > 2:
|
||
|
|
raise RuntimeError(
|
||
|
|
"TorchScript classes does not support inheritance yet. "
|
||
|
|
"Please directly inherit from 'object'."
|
||
|
|
)
|
||
|
|
if _rcb is None:
|
||
|
|
_rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
|
||
|
|
_compile_and_register_class(obj, _rcb, qualified_name)
|
||
|
|
return obj
|
||
|
|
elif inspect.isfunction(obj) or inspect.ismethod(obj):
|
||
|
|
qualified_name = _qualified_name(obj)
|
||
|
|
# this is a decorated fn, and we need to the underlying fn and its rcb
|
||
|
|
if hasattr(obj, "__script_if_tracing_wrapper"):
|
||
|
|
obj = obj.__original_fn # type: ignore[union-attr]
|
||
|
|
_rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
|
||
|
|
|
||
|
|
# some functions are explicitly marked as not supported in script mode
|
||
|
|
if hasattr(obj, "__script_unsupported"):
|
||
|
|
raise RuntimeError("TorchScript error: " + obj.__script_unsupported)
|
||
|
|
|
||
|
|
_check_directly_compile_overloaded(obj)
|
||
|
|
maybe_already_compiled_fn = _try_get_jit_cached_function(obj)
|
||
|
|
if maybe_already_compiled_fn:
|
||
|
|
return maybe_already_compiled_fn
|
||
|
|
ast = get_jit_def(obj, obj.__name__)
|
||
|
|
if _rcb is None:
|
||
|
|
_rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
|
||
|
|
fn = torch._C._jit_script_compile(
|
||
|
|
qualified_name, ast, _rcb, get_default_args(obj)
|
||
|
|
)
|
||
|
|
# Forward docstrings
|
||
|
|
fn.__doc__ = obj.__doc__
|
||
|
|
# Allow torch.compile() to inline
|
||
|
|
fn._torchdynamo_inline = obj # type: ignore[attr-defined]
|
||
|
|
_set_jit_function_cache(obj, fn)
|
||
|
|
return fn
|
||
|
|
else:
|
||
|
|
return torch.jit._recursive.create_script_class(obj)
|
||
|
|
|
||
|
|
|
||
|
|
# overloads are registered in _jit_internal and compiled here so that _overload
|
||
|
|
# can be used in nn/functional.py without an import cycle
|
||
|
|
|
||
|
|
|
||
|
|
def _check_overload_defaults(impl_defaults, overload_defaults, loc):
|
||
|
|
for name, overload_value in overload_defaults.items():
|
||
|
|
if name not in impl_defaults or impl_defaults[name] != overload_value:
|
||
|
|
raise torch.jit.frontend.FrontendError(
|
||
|
|
loc,
|
||
|
|
"Default parameters on overloads do not affect the runtime so they "
|
||
|
|
"must equal to the default parameter on the implementation function. Found on "
|
||
|
|
f"parameter {name}",
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _compile_function_with_overload(overload_fn, qual_name, impl_fn):
|
||
|
|
overload_decl = get_jit_def(overload_fn, overload_fn.__name__).decl()
|
||
|
|
overload_signature = torch.jit.annotations.get_signature(
|
||
|
|
overload_fn, None, None, inspect.ismethod(overload_fn)
|
||
|
|
)
|
||
|
|
impl_ast = get_jit_def(impl_fn, impl_fn.__name__)
|
||
|
|
overload_defaults = get_default_args(overload_fn)
|
||
|
|
implementation_defaults = get_default_args(impl_fn)
|
||
|
|
_rcb = _jit_internal.createResolutionCallbackFromClosure(impl_fn)
|
||
|
|
_check_overload_defaults(
|
||
|
|
implementation_defaults, overload_defaults, overload_decl.range()
|
||
|
|
)
|
||
|
|
fn = torch._C._jit_script_compile_overload(
|
||
|
|
qual_name,
|
||
|
|
overload_decl,
|
||
|
|
impl_ast,
|
||
|
|
_rcb,
|
||
|
|
implementation_defaults,
|
||
|
|
overload_signature,
|
||
|
|
)
|
||
|
|
return fn
|
||
|
|
|
||
|
|
|
||
|
|
def _get_overloads(obj):
|
||
|
|
# check for cached compiled fns
|
||
|
|
existing_compiled_fns = _try_get_jit_cached_overloads(obj)
|
||
|
|
qual_name = _qualified_name(obj)
|
||
|
|
uncompiled_overloads = _jit_internal._get_fn_overloads(qual_name)
|
||
|
|
if uncompiled_overloads is None:
|
||
|
|
return existing_compiled_fns
|
||
|
|
|
||
|
|
if obj in uncompiled_overloads:
|
||
|
|
raise RuntimeError(
|
||
|
|
_jit_internal.get_overload_no_implementation_error_message("function", obj)
|
||
|
|
)
|
||
|
|
|
||
|
|
compiled_fns = []
|
||
|
|
for overload_fn in uncompiled_overloads:
|
||
|
|
compiled_fns.append(
|
||
|
|
_compile_function_with_overload(overload_fn, qual_name, obj)
|
||
|
|
)
|
||
|
|
|
||
|
|
if existing_compiled_fns:
|
||
|
|
compiled_fns = existing_compiled_fns + compiled_fns
|
||
|
|
|
||
|
|
# cache compilation, remove information stored to do compilation
|
||
|
|
_set_jit_overload_cache(obj, compiled_fns)
|
||
|
|
_jit_internal._clear_fn_overloads(qual_name)
|
||
|
|
return compiled_fns
|
||
|
|
|
||
|
|
|
||
|
|
def _check_directly_compile_overloaded(obj):
|
||
|
|
qual_name = _qualified_name(obj)
|
||
|
|
if _jit_internal._get_fn_overloads(qual_name) or _try_get_jit_cached_overloads(obj):
|
||
|
|
raise RuntimeError(
|
||
|
|
f"Function {qual_name} cannot be directly compiled because it"
|
||
|
|
" is overloaded. It must be used in a context of a function"
|
||
|
|
" where its inputs can determine which overload to call."
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def interface(obj):
|
||
|
|
r"""Decorate to annotate classes or modules of different types.
|
||
|
|
|
||
|
|
This decorator can be used to define an interface that can be used to annotate
|
||
|
|
classes or modules of different types. This can be used for to annotate a submodule
|
||
|
|
or attribute class that could have different types that implement the same
|
||
|
|
interface, or which could be swapped at runtime; or to store a list of modules or
|
||
|
|
classes of varying types.
|
||
|
|
|
||
|
|
It is sometimes used to implement "Callables" - functions or modules that implement
|
||
|
|
an interface but whose implementations differ and which can be swapped out.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
.. testcode::
|
||
|
|
|
||
|
|
import torch
|
||
|
|
from typing import List
|
||
|
|
|
||
|
|
@torch.jit.interface
|
||
|
|
class InterfaceType:
|
||
|
|
def run(self, x: torch.Tensor) -> torch.Tensor:
|
||
|
|
pass
|
||
|
|
|
||
|
|
# implements InterfaceType
|
||
|
|
@torch.jit.script
|
||
|
|
class Impl1:
|
||
|
|
def run(self, x: torch.Tensor) -> torch.Tensor:
|
||
|
|
return x.relu()
|
||
|
|
|
||
|
|
class Impl2(torch.nn.Module):
|
||
|
|
def __init__(self):
|
||
|
|
super().__init__()
|
||
|
|
self.val = torch.rand(())
|
||
|
|
|
||
|
|
@torch.jit.export
|
||
|
|
def run(self, x: torch.Tensor) -> torch.Tensor:
|
||
|
|
return x + self.val
|
||
|
|
|
||
|
|
def user_fn(impls: List[InterfaceType], idx: int, val: torch.Tensor) -> torch.Tensor:
|
||
|
|
return impls[idx].run(val)
|
||
|
|
|
||
|
|
user_fn_jit = torch.jit.script(user_fn)
|
||
|
|
|
||
|
|
impls = [Impl1(), torch.jit.script(Impl2())]
|
||
|
|
val = torch.rand(4, 4)
|
||
|
|
user_fn_jit(impls, 0, val)
|
||
|
|
user_fn_jit(impls, 1, val)
|
||
|
|
"""
|
||
|
|
if not inspect.isclass(obj):
|
||
|
|
raise RuntimeError("interface must be applied to a class")
|
||
|
|
if not _is_new_style_class(obj):
|
||
|
|
raise RuntimeError("TorchScript interfaces must inherit from 'object'")
|
||
|
|
|
||
|
|
# Expected MRO is:
|
||
|
|
# User module
|
||
|
|
# torch.nn.modules.module.Module
|
||
|
|
# object
|
||
|
|
is_module_interface = issubclass(obj, torch.nn.Module) and len(obj.mro()) == 3
|
||
|
|
|
||
|
|
if not is_module_interface and len(obj.mro()) > 2:
|
||
|
|
raise RuntimeError(
|
||
|
|
"TorchScript interface does not support inheritance yet. "
|
||
|
|
"Please directly inherit from 'object' or 'nn.Module'."
|
||
|
|
)
|
||
|
|
|
||
|
|
qualified_name = _qualified_name(obj)
|
||
|
|
rcb = _jit_internal.createResolutionCallbackFromFrame(1)
|
||
|
|
# if this type is a `nn.Module` subclass, generate a module interface type
|
||
|
|
# instead of a class interface type; a module interface type only compiles
|
||
|
|
# the user provided methods as part of the interface
|
||
|
|
ast = get_jit_class_def(obj, obj.__name__)
|
||
|
|
mangled_classname = torch._C._jit_script_interface_compile(
|
||
|
|
qualified_name, ast, rcb, is_module_interface
|
||
|
|
)
|
||
|
|
obj.__torch_script_interface__ = mangled_classname
|
||
|
|
return obj
|
||
|
|
|
||
|
|
|
||
|
|
def _recursive_compile_class(obj, loc):
|
||
|
|
_qual_name = _qualified_name(obj)
|
||
|
|
# We're starting a new compilation, so update the error call stack in
|
||
|
|
# case it fails
|
||
|
|
error_stack = torch._C.CallStack(_qual_name, loc)
|
||
|
|
rcb = _jit_internal.createResolutionCallbackForClassMethods(obj)
|
||
|
|
return _compile_and_register_class(obj, rcb, _qual_name)
|
||
|
|
|
||
|
|
|
||
|
|
CompilationUnit = torch._C.CompilationUnit
|
||
|
|
set_module(CompilationUnit, "torch.jit")
|
||
|
|
|
||
|
|
|
||
|
|
def pad(s: str, padding: int, offset: int = 0, char: str = " "):
|
||
|
|
if padding >= len(s):
|
||
|
|
padding -= len(s)
|
||
|
|
return "".join([char for _ in range(padding + offset)]) + s
|
||
|
|
|
||
|
|
|
||
|
|
class _ScriptProfileColumn:
|
||
|
|
def __init__(self, header: str, alignment: int = 4, offset: int = 0):
|
||
|
|
self.header = header
|
||
|
|
self.alignment = alignment
|
||
|
|
self.offset = offset
|
||
|
|
self.rows: Dict[int, Any] = {}
|
||
|
|
|
||
|
|
def add_row(self, lineno: int, value: Any):
|
||
|
|
self.rows[lineno] = value
|
||
|
|
|
||
|
|
def materialize(self):
|
||
|
|
max_length = len(self.header)
|
||
|
|
rows: List[Tuple[int, str]] = []
|
||
|
|
for key, value in self.rows.items():
|
||
|
|
cell = str(value)
|
||
|
|
rows.append((key, cell))
|
||
|
|
max_length = max(len(cell), max_length)
|
||
|
|
|
||
|
|
if self.alignment > 0:
|
||
|
|
padding = max_length + self.alignment
|
||
|
|
padding -= padding % self.alignment
|
||
|
|
else:
|
||
|
|
padding = 0
|
||
|
|
|
||
|
|
rows = [(key, pad(cell, padding, self.offset)) for key, cell in rows]
|
||
|
|
return pad(self.header, padding, self.offset), rows
|
||
|
|
|
||
|
|
|
||
|
|
class _ScriptProfileTable:
|
||
|
|
def __init__(self, cols: List[_ScriptProfileColumn], source_range: List[int]):
|
||
|
|
self.cols = cols
|
||
|
|
self.source_range = source_range
|
||
|
|
|
||
|
|
def dump_string(self):
|
||
|
|
outputs: List[str] = []
|
||
|
|
cells: List[Tuple[str, Dict[int, str]]] = []
|
||
|
|
header_buffer = ""
|
||
|
|
for col in self.cols:
|
||
|
|
header, rows = col.materialize()
|
||
|
|
header_buffer += header
|
||
|
|
cells.append((header, dict(rows)))
|
||
|
|
|
||
|
|
outputs.append(header_buffer)
|
||
|
|
outputs.append(pad("", len(header_buffer), 0, "="))
|
||
|
|
for line in self.source_range:
|
||
|
|
row_buffer = ""
|
||
|
|
for header, rows in cells:
|
||
|
|
cell = rows.get(line)
|
||
|
|
if cell is None:
|
||
|
|
row_buffer += pad("", len(header))
|
||
|
|
else:
|
||
|
|
row_buffer += cell
|
||
|
|
outputs.append(row_buffer)
|
||
|
|
return "\n".join(outputs)
|
||
|
|
|
||
|
|
|
||
|
|
class _ScriptProfile:
|
||
|
|
def __init__(self):
|
||
|
|
self.profile = classes.profiling._ScriptProfile()
|
||
|
|
|
||
|
|
def enable(self):
|
||
|
|
self.profile.enable()
|
||
|
|
|
||
|
|
def disable(self):
|
||
|
|
self.profile.disable()
|
||
|
|
|
||
|
|
def dump_string(self) -> str:
|
||
|
|
outputs: List[str] = []
|
||
|
|
for source_stats in self.profile._dump_stats():
|
||
|
|
source_ref = source_stats.source()
|
||
|
|
source_lines = source_ref.text().splitlines()
|
||
|
|
dedent = min([len(line) - len(line.lstrip(" ")) for line in source_lines])
|
||
|
|
source_lines = [line[dedent:] for line in source_lines]
|
||
|
|
|
||
|
|
start_line = source_ref.starting_lineno()
|
||
|
|
end_line = start_line + len(source_lines)
|
||
|
|
source_range = range(start_line, end_line)
|
||
|
|
lineno = _ScriptProfileColumn("Line #")
|
||
|
|
hits = _ScriptProfileColumn("Hits")
|
||
|
|
time_ns = _ScriptProfileColumn("Time (ns)")
|
||
|
|
line_contents = _ScriptProfileColumn("Line Contents", 0, 1)
|
||
|
|
stats = source_stats.line_map()
|
||
|
|
for line in source_range:
|
||
|
|
lineno.add_row(line, line)
|
||
|
|
line_contents.add_row(line, source_lines[line - start_line])
|
||
|
|
stat = stats.get(line)
|
||
|
|
if stat is not None:
|
||
|
|
hits.add_row(line, stat.count())
|
||
|
|
time_ns.add_row(line, stat.duration_ns())
|
||
|
|
|
||
|
|
table = _ScriptProfileTable(
|
||
|
|
[lineno, hits, time_ns, line_contents], list(source_range)
|
||
|
|
)
|
||
|
|
outputs.append(table.dump_string())
|
||
|
|
return "\n\n".join(outputs)
|
||
|
|
|
||
|
|
def dump(self):
|
||
|
|
print(self.dump_string())
|
||
|
|
|
||
|
|
|
||
|
|
def _unwrap_optional(x):
|
||
|
|
assert x is not None, "Unwrapping null optional"
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
_register_builtin(_unwrap_optional, "aten::_unwrap_optional")
|
||
|
|
_register_builtin(_jit_internal.is_scripting, "aten::is_scripting")
|
||
|
|
_register_builtin(has_torch_function, "aten::has_torch_function")
|
||
|
|
_register_builtin(has_torch_function_unary, "aten::has_torch_function")
|
||
|
|
_register_builtin(has_torch_function_variadic, "aten::has_torch_function")
|