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import inspect
import textwrap
import torch.jit
from torch.jit._builtins import _find_builtin
# this file is for generating documentation using sphinx autodoc
# > help(torch.jit.supported_ops) will also give a nice listed of the
# supported ops programmatically
def _hidden(name):
return name.startswith("_") and not name.startswith("__")
def _emit_type(type):
return str(type)
def _emit_arg(indent, i, arg):
v = f"{arg.name} : {_emit_type(arg.type)}"
default = arg.default_value
if default is not None:
v = f"{v}={str(default)}"
if i > 0:
v = f"\n{' ' * indent}{v}"
return v
def _emit_args(indent, arguments):
return ",".join(_emit_arg(indent, i, arg) for i, arg in enumerate(arguments))
def _emit_ret(ret):
return _emit_type(ret.type)
def _emit_rets(returns):
if len(returns) == 1:
return _emit_ret(returns[0])
return f"Tuple[{', '.join(_emit_ret(r) for r in returns)}]"
def _emit_schema(mod, name, schema, arg_start=0, padding=4):
if mod is None:
qualified_name = name
else:
qualified_name = f"{mod}.{name}"
schema_str = "{}({}) -> {}".format(
qualified_name,
_emit_args(len(qualified_name) + 1 + padding, schema.arguments[arg_start:]),
_emit_rets(schema.returns),
)
return schema_str
def _get_tensor_ops():
def is_tensor_method(schema):
if len(schema.arguments) == 0:
return False
self = schema.arguments[0]
if self.name != "self":
return False
if not self.type.isSubtypeOf(torch._C.TensorType.get()):
return False
return True
methods = []
# discover methods
for elem in dir(torch.Tensor):
if not _hidden(elem):
schemas = torch._C._jit_get_schemas_for_operator("aten::" + elem)
for schema in schemas:
if is_tensor_method(schema):
methods.append(_emit_schema("Tensor", elem, schema, arg_start=1))
return "Supported Tensor Methods", methods
def _get_nn_functional_ops():
functions = []
# Iterate over torch.nn.functional
mod = torch.nn.functional
name = mod.__name__
for elem in dir(torch.nn.functional):
attr = getattr(mod, elem)
if not inspect.isfunction(attr) or _hidden(elem[0]):
# Ignore non-functions and internal methods
continue
attr_module = inspect.getmodule(attr)
if not attr_module:
raise RuntimeError(f"Module for {attr} not found")
if "torch.nn.functional" not in attr_module.__name__:
# Ignore functions from outside torch.nn.functional
continue
try:
# compile fn, get schema
scripted = torch.jit.script(attr)
scripted_schema = scripted.schema
functions.append(_emit_schema(name, elem, scripted_schema))
except: # noqa: B001,E722
# Skip interpolate / boolean dispatched things
pass
# Iterate over modules that we know contain a lot of builtins
for mod in torch.jit._builtins._modules_containing_builtins:
name = mod.__name__
for elem in dir(mod):
builtin = _find_builtin(getattr(mod, elem))
if builtin is not None:
schemas = torch._C._jit_get_schemas_for_operator(builtin)
for schema in schemas:
# remove _tan but not __and__
if not _hidden(elem):
functions.append(_emit_schema(name, elem, schema))
return "Supported PyTorch Functions", functions
def _get_builtins_helper():
builtins = []
for fn, _builtin_name in torch.jit._builtins._builtin_ops:
mod = inspect.getmodule(fn)
if not hasattr(fn, "__name__"):
# typing classes
continue
if not mod:
continue
if _hidden(fn.__name__) or _hidden(fn.__qualname__) or _hidden(mod.__name__):
# skip internal-only methods
continue
if "torch._C" in mod.__name__:
continue
builtins.append((fn, _builtin_name))
return builtins
def _is_math_fn(fn):
mod = inspect.getmodule(fn)
if not mod:
raise RuntimeError(f"Module for {fn} not found")
return mod.__name__ == "math"
def _get_torchscript_builtins():
functions = []
builtins = filter(lambda fn: not _is_math_fn(fn[0]), _get_builtins_helper())
builtins_list = list(builtins)
# Iterate over the specially added builtins
for fn, _builtin_name in builtins_list:
mod = inspect.getmodule(fn)
if not mod:
raise RuntimeError(f"Module for {fn} not found")
builtin = _find_builtin(fn)
if builtin is not None:
schemas = torch._C._jit_get_schemas_for_operator(builtin)
for schema in schemas:
functions.append(_emit_schema(mod.__name__, fn.__name__, schema))
pass
return "TorchScript Builtin Functions", functions
def _get_math_builtins():
functions = []
builtins = filter(lambda fn: _is_math_fn(fn[0]), _get_builtins_helper())
builtins_list = list(builtins)
# Iterate over the specially added builtins
for fn, _builtin_name in builtins_list:
mod = inspect.getmodule(fn)
if not mod:
raise RuntimeError(f"Module for {fn} not found")
builtin = _find_builtin(fn)
if builtin is not None:
schemas = torch._C._jit_get_schemas_for_operator(builtin)
for schema in schemas:
schema_str = _emit_schema(mod.__name__, fn.__name__, schema)
if "Tensor" in schema_str:
# Skip Tensor ops that have the same name as math functions
# (they will show up in the tensor methods section)
continue
functions.append(schema)
pass
return "``math`` Module", functions
def _get_global_builtins():
# Taken from the 'globals' map in torch/csrc/jit/frontend/ir_emitter.cpp
supported_builtins = [
"print",
"tuple",
"float",
"complex",
"int",
"bool",
"str",
"getattr",
"hasattr",
"isinstance",
"len",
"hex",
"oct",
"round",
"hash",
"min",
"max",
"abs",
"all",
"divmod",
"list",
"ord",
"chr",
"bin",
"range",
"zip",
"enumerate",
"sorted",
]
op_renames = {
"bool": "aten::Bool",
"int": "aten::Int",
"float": "aten::Float",
"complex": "aten::Complex",
"abs": "prim::abs",
"max": "prim::max",
"min": "prim::min",
"range": "fake::does_not_exist",
}
schemaless_op_explanations = {
"print": "Print any value",
"tuple": "Lists cannot be converted to tuples with this method since their size is not statically known",
"getattr": "Attribute name must be a literal string",
"hasattr": "Attribute name must be a literal string",
"isinstance": "Result is static",
"zip": "Arguments must be iterable. See :ref:`Iterables <jit_iterables>` for details.",
"enumerate": "Arguments must be iterable. See :ref:`Iterables <jit_iterables>` for details.",
"range": "Can only be used as an iterator in a for loop",
}
magic_methods = [
("complex", "__complex__"),
("float", "__float__"),
("int", "__int__"),
("bool", "__bool__"),
("str", "__str__"),
("len", "__len__"),
("hex", "__hex__"),
("oct", "__oct__"),
]
magic_methods_rows = []
for fn, magic_method in magic_methods:
magic_methods_rows.append(f'"{fn}", "``{magic_method}``"')
schematized_ops = []
schemaless_ops = []
for fn in supported_builtins:
op_name = f"aten::{fn}"
if fn in op_renames:
op_name = op_renames[fn]
schemas = torch._C._jit_get_schemas_for_operator(op_name)
for s in schemas:
schematized_ops.append(_emit_schema(None, fn, s, padding=0))
if len(schemas) > 0:
schematized_ops.append("")
else:
table_row = f'":any:`{fn}`", "{schemaless_op_explanations[fn]}"'
schemaless_ops.append(table_row)
schematized_ops_str = "\n".join(schematized_ops)
schemaless_ops_str = "\n".join(schemaless_ops)
magic_methods_rows_str = "\n".join(magic_methods_rows)
schematized_ops_str = textwrap.indent(schematized_ops_str, "\t")
schemaless_ops_str = textwrap.indent(schemaless_ops_str, "\t")
magic_methods_rows_str = textwrap.indent(magic_methods_rows_str, "\t")
section = f"""
The functions in the following table are supported but do not have a static schema
.. csv-table::
:header: "Function", "Note"
{schemaless_ops_str}
The following functions will use the corresponding magic method on :any:`TorchScript classes`
.. csv-table::
:header: "Function", "Magic Method"
{magic_methods_rows_str}
These built-in functions use the schema
.. rst-class:: codeblock-height-limiter
::
{schematized_ops_str}
"""
return "Python Built-in Functions", section
def _list_supported_ops():
def emit_block(decls):
return "\n.. rst-class:: codeblock-height-limiter\n\n::\n\n{}\n".format(
"".join(f" {d}\n\n" for d in decls)
)
body = ""
op_gathering_fns = (
_get_tensor_ops,
_get_nn_functional_ops,
_get_torchscript_builtins,
_get_global_builtins,
_get_math_builtins,
)
for fn in op_gathering_fns:
header, items = fn()
link_target = header.replace("`", "").replace("-", "").lower().replace(" ", "-")
if isinstance(items, str):
section = f"{header}\n{'~' * len(header)}\n{items}\n"
else:
section = f"{header}\n{'~' * len(header)}\n{emit_block(items)}"
section = f".. _{link_target}:" + "\n\n" + section
body += section
return body
__doc__ = _list_supported_ops()
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