import torch._C import contextlib import ctypes import sys import types import torch.jit import torch._utils_internal # Query `hasattr` only once. _SET_GLOBAL_FLAGS = hasattr(sys, 'getdlopenflags') and hasattr(sys, 'setdlopenflags') @contextlib.contextmanager def dl_open_guard(): """ Context manager to set the RTLD_GLOBAL dynamic linker flag while we open a shared library to load custom operators. """ if _SET_GLOBAL_FLAGS: old_flags = sys.getdlopenflags() sys.setdlopenflags(old_flags | ctypes.RTLD_GLOBAL) yield if _SET_GLOBAL_FLAGS: sys.setdlopenflags(old_flags) # Each OpOverload object contains pointer to a a specific operator overload, a pointer to the parent `OpOverloadPacket` object. # You can obtain an OpOverload object through attribute query on OpOverloadPacket. class OpOverload: def __init__(self, overloadpacket, op, schema): self._op = op self._schema = schema self._overloadpacket = overloadpacket self._overloadname = 'default' if schema.overload_name == '' else schema.overload_name self.__name__ = "{}.{}".format(self._schema.name.split("::")[1], self._overloadname) self.__module__ = overloadpacket.__module__ op.__module__ = overloadpacket.__module__ # it's a no-op since OpOverload object is immutable and must be unique for a given op overload. def __deepcopy__(self, memo=None): return self def __repr__(self): return "".format(*self._schema.name.split("::"), self._overloadname) def __call__(self, *args, **kwargs): return self._op(*args, **kwargs or {}) def __getattr__(self, key): return getattr(self._op, key) def __hash__(self): return hash(self._op) # `my_namespace.my_op_name.overload_name` def __str__(self): return "{}.{}.{}".format(*self._schema.name.split("::"), self._overloadname) @property def overloadpacket(self): return self._overloadpacket @property def op(self): return self._op # TODO: add more methods to expose information about input and output arguments # OpOverloadPacket class contains pointer to a base unresolved operator that doesn't correspond to a specific operator # You can obtain an OpOverload object through attribute query. class OpOverloadPacket: def __init__(self, qualified_op_name, op_name, op): # These attributes are accessible on the object through the properties # defined below but are immutable self._qualified_op_name = qualified_op_name self.__name__ = op_name self._op = op # it's a no-op since OpOverloadPacket object is immutable and must be unique for a given op. def __deepcopy__(self, memo=None): return self def __repr__(self): return "".format(*self._qualified_op_name.split("::")) def __hash__(self): return hash(self._op) def __str__(self): return "{}.{}".format(*self._qualified_op_name.split("::")) @property def op(self): return self._op def __getattr__(self, key): # It is not a valid op_name when __file__ is passed in if key == '__file__': return 'torch.ops' # ensure that query for dunder attributes that does not exist on # opoverloadpacket but instead exists on the self._op object does not unnecessarily call # `_get_operation_overload` (which is an expensive operation). # This is done to prevent any potential slowdown. This list can be extended # if there exists other attributes like `__name__` that only exist on self._op and not on the # opoverloadpacket. # This is ok since we are guaranteed that an overload name for an aten op can't start with '__' try: if key.startswith('__'): return getattr(self._op, key) except AttributeError: # for consistency because it seems weird to # throw an attribute error with a message containing # an object name different from the one the attribute # query was performed on. raise AttributeError("'{}' can't have an overload name beginning with '__' and the " "underlying op {} has no attribute {} either." .format(str(self), str(self._op), key)) from None try: # This is ok since we are guaranteed that an overload name for an aten op can't be 'default' use_key = '' if key == 'default' else key # TODO: disallow access to overloads registered by JIT op_ = torch._C._get_operation_overload( self._qualified_op_name, use_key) schema = torch._C._get_schema(self._qualified_op_name, use_key) overload = OpOverload(self, op_, schema) # cache the overload object setattr(self, key, overload) return overload except RuntimeError: raise AttributeError( "The underlying op of '{}' has no overload name '{}'".format(str(self), key) ) from None def __call__(self, *args, **kwargs): # overloading __call__ to ensure torch.ops.foo.bar() # is still callable from JIT # We save the function ptr as the `op` attribute on # OpOverloadPacket to access it here. return self._op(*args, **kwargs or {}) # Resolution of torch.fn is different from torch.ops.aten.fn # torch.fn uses the Python argparser, matches with the # appropriate schema, and calls into the unboxed version of the method # torch.ops.aten.fn resolution is done via the mechanism defined in JIT. # JIT creates a stack of all the overloads and then tries to match the # correct one at runtime and always calls into the boxed version of the method # Autograd codegen creates VariableType, TracerType, # inplace or view type and python bindings. # Aten codegen generates tensor methods for the the tensor class. # _OpNamespace is a subclass of ModuleType because the torch script # allows attribute lookups on modules only. Since we want torch.ops.foo.bar() # to work from script, we need to ensure ops and foo are modules class _OpNamespace(types.ModuleType): """ An op namespace to dynamically bind Operators into Python. Say a user has created a custom Operator called "my_namespace::my_op". To call this op, the user will write torch.ops.my_namespace.my_op(...). At startup, this operation will not yet be bound into Python. Instead, the following sequence of magic tricks will occur: 1. `torch.ops.my_namespace` will invoke the `__getattr__` magic method on the `torch.ops` object, which will create a new `_OpNamespace` object called `my_namespace` and set it as an attribute on the `ops` object. 2. `torch.ops.my_namespace.my_op` will then invoke `__getattr__` on the `my_namespace` object, which will retrieve the operation via `torch.get_operation`, a function bound from C++, and then in a similar fashion bind this new object onto the `my_namespace` object. 3. `torch.ops.my_namespace.my_op(...)` then calls this new operation and subsequent accesses will incur no further lookup (the namespace and operation will already exist). """ def __init__(self, name): super(_OpNamespace, self).__init__('torch.ops.' + name) self.name = name def __getattr__(self, op_name): # It is not a valid op_name when __file__ is passed in if op_name == '__file__': return 'torch.ops' # Get the op `my_namespace::my_op` if available. This will also check # for overloads and raise an exception if there are more than one. namespace_name = self.name qualified_op_name = '{}::{}'.format(namespace_name, op_name) op = torch._C._jit_get_operation(qualified_op_name) # let the script frontend know that op is identical to the builtin op # with qualified_op_name torch.jit._builtins._register_builtin(op, qualified_op_name) op.__module__ = self.__module__ + "." + namespace_name opoverloadpacket = OpOverloadPacket(qualified_op_name, op_name, op) opoverloadpacket.__module__ = self.__module__ + "." + namespace_name # cache the opoverloadpacket to ensure that each op corresponds to # a unique OpOverloadPacket object setattr(self, op_name, opoverloadpacket) return opoverloadpacket class _Ops(types.ModuleType): __file__ = '_ops.py' def __init__(self): super(_Ops, self).__init__('torch.ops') self.loaded_libraries = set() def __getattr__(self, name): # Here we are creating `torch.ops.my_namespace` namespace = _OpNamespace(name) setattr(self, name, namespace) return namespace def load_library(self, path): """ Loads a shared library from the given path into the current process. The library being loaded may run global initialization code to register custom operators with the PyTorch JIT runtime. This allows dynamically loading custom operators. For this, you should compile your operator and the static registration code into a shared library object, and then call ``torch.ops.load_library('path/to/libcustom.so')`` to load the shared object. After the library is loaded, it is added to the ``torch.ops.loaded_libraries`` attribute, a set that may be inspected for the paths of all libraries loaded using this function. Args: path (str): A path to a shared library to load. """ if sys.executable == "torch_deploy": return path = torch._utils_internal.resolve_library_path(path) with dl_open_guard(): # Import the shared library into the process, thus running its # static (global) initialization code in order to register custom # operators with the JIT. ctypes.CDLL(path) self.loaded_libraries.add(path) # The ops "namespace" ops = _Ops()