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https://github.com/zebrajr/pytorch.git
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4753100a3b
Summary: Generally wildcard imports are bad for the reasons described here: https://www.flake8rules.com/rules/F403.html This PR replaces wildcard imports with an explicit list of imported items where possible, and adds a `# noqa: F403` comment in the other cases (mostly re-exports in `__init__.py` files). This is a prerequisite for https://github.com/pytorch/pytorch/issues/55816, because currently [`tools/codegen/dest/register_dispatch_key.py` simply fails if you sort its imports](https://github.com/pytorch/pytorch/actions/runs/742505908). Pull Request resolved: https://github.com/pytorch/pytorch/pull/55838 Test Plan: CI. You can also run `flake8` locally. Reviewed By: jbschlosser Differential Revision: D27724232 Pulled By: samestep fbshipit-source-id: 269fb09cb4168f8a51fd65bfaacc6cda7fb87c34
313 lines
12 KiB
Python
313 lines
12 KiB
Python
from tools.codegen.model import (Argument, Arguments, BaseTy, BaseType,
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FunctionSchema, ListType, NativeFunction,
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OptionalType, Return, SelfArgument,
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TensorOptionsArguments, Type, assert_never)
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from tools.codegen.api.types import (ArgName, BaseCType, Binding,
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ConstRefCType, CType, MutRefCType,
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OptionalCType, SpecialArgName)
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from typing import Optional, Sequence, Union, List, Set
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# This file describes the translation of JIT schema to the public C++
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# API, which is what people use when they call functions like at::add.
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#
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# Prominent characteristics of the C++ API:
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#
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# - dtype, layout, device and pin_memory are collected into
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# a single C++ type TensorOptions (the native functions API
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# also has this, but tensor options is really most relevant
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# for the C++ API; it makes calling kwarg factory functions
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# pleasant)
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#
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# - defaulting lives here (in fact, the dispatcher is completely
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# oblivious of defaults!)
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#
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# BTW: policy on name collisions: we try not to have types with
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# collisions, but functions are fair game to collide
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def name(func: FunctionSchema, *, faithful_name_for_out_overloads: bool = False) -> str:
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name = str(func.name.name)
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if func.is_out_fn():
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if faithful_name_for_out_overloads:
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name += '_outf'
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else:
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name += '_out'
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return name
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# Translation of "value types" in JIT schema to C++ API type. Value
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# types look the same no matter if they are argument types or return
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# types. Returns None if the type in question is not a value type.
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def valuetype_type(t: Type, *, binds: ArgName) -> Optional[CType]:
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if isinstance(t, BaseType):
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if t.name == BaseTy.Tensor or t.name == BaseTy.Scalar:
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return None
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elif t.name == BaseTy.int:
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return BaseCType('int64_t', binds)
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elif t.name == BaseTy.float:
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return BaseCType('double', binds)
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elif t.name == BaseTy.str:
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return BaseCType('std::string', binds)
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elif t.name in [BaseTy.bool, BaseTy.QScheme, BaseTy.Scalar,
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BaseTy.ScalarType, BaseTy.Generator, BaseTy.Storage,
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BaseTy.Layout, BaseTy.Device, BaseTy.MemoryFormat,
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BaseTy.Dimname, BaseTy.Stream, BaseTy.ConstQuantizerPtr]:
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# These C++ names line up with their schema names
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return BaseCType(t.name.name, binds)
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else:
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raise AssertionError(f"unsupported type: {t}")
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elif isinstance(t, OptionalType):
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elem = valuetype_type(t.elem, binds=binds)
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if elem is None:
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return None
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return OptionalCType(elem)
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elif isinstance(t, ListType):
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if str(t.elem) == 'bool':
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assert t.size is not None
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return BaseCType(f"std::array<bool,{t.size}>", binds)
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else:
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return None
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else:
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raise AssertionError(f"unrecognized type {repr(t)}")
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# Translation of types occuring in JIT arguments to a C++ argument type.
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def argumenttype_type(t: Type, *, mutable: bool, binds: ArgName) -> CType:
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# If it's a value type, do the value type translation
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r = valuetype_type(t, binds=binds)
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if r is not None:
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return r
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if isinstance(t, BaseType):
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if t.name == BaseTy.Tensor:
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if mutable:
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return MutRefCType(BaseCType('Tensor', binds))
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else:
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return ConstRefCType(BaseCType('Tensor', binds))
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elif t.name == BaseTy.Scalar:
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return ConstRefCType(BaseCType('Scalar', binds))
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else:
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raise AssertionError(f"base type should have been value type {t}")
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elif isinstance(t, OptionalType):
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if str(t.elem) == 'Tensor':
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if mutable:
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return MutRefCType(BaseCType('Tensor', binds)) # TODO: fix this discrepancy
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else:
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return ConstRefCType(OptionalCType(BaseCType('Tensor', binds)))
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elif str(t.elem) == 'Scalar':
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return ConstRefCType(OptionalCType(BaseCType('Scalar', binds)))
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elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
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return OptionalCType(elem)
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elif isinstance(t, ListType):
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# TODO: remove these special cases, ArrayRef fallthrough works fine
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# NB: CType throws away ArrayRef structure because it is not currently
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# relevant in translation. When it becomes relevant, need to add back
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if str(t.elem) == 'int':
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return BaseCType("IntArrayRef", binds)
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elif str(t.elem) == 'Tensor':
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return BaseCType("TensorList", binds)
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elif str(t.elem) == 'Scalar':
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return BaseCType("ArrayRef<Scalar>", binds)
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elif str(t.elem) == 'Dimname':
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return BaseCType("DimnameList", binds)
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elif str(t.elem) == 'Tensor?':
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return ConstRefCType(BaseCType("c10::List<c10::optional<Tensor>>", binds))
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elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
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# TODO: explicitly qualify namespace here
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return BaseCType(f"ArrayRef<{elem.cpp_type()}>", binds)
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else:
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raise AssertionError(f"unrecognized type {repr(t)}")
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# Translate a JIT argument into its C++ type
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def argument_type(a: Argument, *, binds: ArgName) -> CType:
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return argumenttype_type(a.type, mutable=a.is_write, binds=binds)
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# Translation of a (non-multi) return type from JIT to C++
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# NB: if need translations on return types, make this return CType too. Need to
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# take care; ArgName is misnomer now, and inputs are permitted to conflict with outputs
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# so need to make sure you don't have trouble
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def returntype_type(t: Type, *, mutable: bool) -> str:
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# placeholder is ignored
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r = valuetype_type(t, binds="__placeholder__")
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if r is not None:
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return r.cpp_type()
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if isinstance(t, BaseType):
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if t.name == BaseTy.Tensor:
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if mutable:
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return 'Tensor &'
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else:
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return 'Tensor'
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elif t.name == BaseTy.Scalar:
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return 'Scalar'
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elif isinstance(t, ListType):
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elem = returntype_type(t.elem, mutable=mutable)
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assert t.size is None, f"fixed size list returns not supported: {t}"
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return f"std::vector<{elem}>"
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raise AssertionError(f"unrecognized return type {t}")
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# Translation of a single return to its C++ type
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def return_type(r: Return) -> str:
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return returntype_type(r.type, mutable=r.is_write)
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# Translation of a full (possibly multi) return from JIT to its C++ type
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def returns_type(rs: Sequence[Return]) -> str:
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if len(rs) == 0:
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return 'void'
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elif len(rs) == 1:
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return return_type(rs[0])
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else:
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args = ','.join(map(return_type, rs))
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return f'std::tuple<{args}>'
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def return_names(f: NativeFunction) -> Sequence[str]:
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returns: List[str] = []
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for i, r in enumerate(f.func.returns):
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# If we have an inplace function, the return argument is
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# implicitly named self.
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# TODO: Consider incorporating this into the data model
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if f.func.name.name.inplace:
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assert i == 0, "illegal inplace function with multiple returns"
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name = 'self'
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# If we are out function, the name is the name of the
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# corresponding output function (r.name will get recorded
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# in field_name later.)
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elif f.func.is_out_fn():
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name = f.func.arguments.out[i].name
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# If the return argument is explicitly named...
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elif r.name:
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name_conflict = any(r.name == a.name for a in f.func.schema_order_arguments())
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if name_conflict and not f.func.is_out_fn():
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name = f'{r.name}_return'
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else:
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name = r.name
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# If there is no explicit name, we just name the output result,
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# unless it's a multi-return, in which case it's result0,
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# result1, etc (zero-indexed)
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else:
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name = 'result' if len(f.func.returns) == 1 else f'result{i}'
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returns.append(name)
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return returns
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JIT_TO_CPP_DEFAULT = {
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'False': 'false',
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'True': 'true',
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'None': 'c10::nullopt', # UGH this one is type directed
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'Mean': 'at::Reduction::Mean',
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'[]': '{}',
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'contiguous_format': 'MemoryFormat::Contiguous',
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'long': 'at::kLong',
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}
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# Convert a JIT default into C++ expression representing the default
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def default_expr(d: str, t: Type) -> str:
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if d == 'None' and str(t) == 'Tensor?':
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return '{}'
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if isinstance(t, BaseType) and t.name is BaseTy.str:
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# Schema allows single quotes but C++ needs double
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if len(d) >= 2 and d[0] == "'" and d[-1] == "'":
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s = ''
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i = 1
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while i + 1 < len(d):
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if d[i] != '\\':
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if d[i] == '"':
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s += '\\"'
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else:
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s += d[i]
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i += 1
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else:
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if d[i + 1] == "'":
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s += "'"
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else:
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s += d[i:i + 2]
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i += 2
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return f'"{s}"'
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if isinstance(t, OptionalType):
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if d == 'None':
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return 'c10::nullopt'
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return default_expr(d, t.elem)
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if isinstance(t, ListType):
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if (d.startswith('[') and d.endswith(']')):
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return '{' + d[1:-1] + '}'
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elif t.size is None:
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# NOTE: Sized lists can have scalar defaults
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raise ValueError(f"Expected a list default '[...]' but found: '{d}'")
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return JIT_TO_CPP_DEFAULT.get(d, d)
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# Convert an argument into its C++ API form
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def argument(
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a: Union[Argument, TensorOptionsArguments, SelfArgument],
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*, cpp_no_default_args: Set[str], method: bool, faithful: bool,
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has_tensor_options: bool
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) -> List[Binding]:
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def sub_argument(a: Union[Argument, TensorOptionsArguments, SelfArgument]) -> List[Binding]:
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return argument(
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a, cpp_no_default_args=cpp_no_default_args, method=method, faithful=faithful,
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has_tensor_options=has_tensor_options)
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if isinstance(a, Argument):
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binds: ArgName
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if a.name == "memory_format" and has_tensor_options:
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binds = SpecialArgName.possibly_redundant_memory_format
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else:
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binds = a.name
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default: Optional[str] = None
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if a.name not in cpp_no_default_args and a.default is not None:
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default = default_expr(a.default, a.type)
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return [Binding(
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ctype=argument_type(a, binds=binds),
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name=a.name,
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default=default,
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argument=a,
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)]
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elif isinstance(a, TensorOptionsArguments):
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if faithful:
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return sub_argument(a.dtype) + sub_argument(a.layout) + \
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sub_argument(a.device) + sub_argument(a.pin_memory)
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else:
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default = None
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# Enforced by NativeFunction.__post_init__
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assert 'options' not in cpp_no_default_args
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if all(x.default == "None" for x in a.all()):
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default = '{}'
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elif a.dtype.default == "long":
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default = 'at::kLong' # TODO: this is wrong
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return [Binding(
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ctype=BaseCType('TensorOptions', 'options'),
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name='options',
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default=default,
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argument=a,
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)]
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elif isinstance(a, SelfArgument):
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if method:
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# Caller is responsible for installing implicit this in context!
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return []
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else:
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return sub_argument(a.argument)
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else:
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assert_never(a)
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def arguments(
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arguments: Arguments,
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*, faithful: bool, method: bool, cpp_no_default_args: Set[str]
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) -> List[Binding]:
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args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
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if faithful:
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args.extend(arguments.non_out)
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args.extend(arguments.out)
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else:
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args.extend(arguments.out)
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args.extend(arguments.non_out)
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return [
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r.no_default() if faithful else r for a in args
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for r in argument(
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a, faithful=faithful, method=method,
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has_tensor_options=arguments.tensor_options is not None,
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cpp_no_default_args=cpp_no_default_args)
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]
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