Initial tensorflow stubs (#8974)

Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>

stubs/tensorflow/@tests/stubtest_allowlist.txt

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+# Some methods are dynamically patched onto to instances as they 
+# may depend on whether code is executed in graph/eager/v1/v2/etc.
+# Tensorflow supports multiple modes of execution which changes some
+# of the attributes/methods/even class hierachies.
+tensorflow.Tensor.__int__
+tensorflow.Tensor.numpy
+tensorflow.Tensor.__index__
+# Incomplete
+tensorflow.sparse.SparseTensor.__getattr__
+tensorflow.SparseTensor.__getattr__
+tensorflow.TensorShape.__getattr__
+tensorflow.dtypes.DType.__getattr__
+tensorflow.RaggedTensor.__getattr__
+tensorflow.DType.__getattr__
+tensorflow.Graph.__getattr__
+tensorflow.Operation.__getattr__
+tensorflow.Variable.__getattr__
+# Internal undocumented API
+tensorflow.RaggedTensor.__init__
+# Has an undocumented extra argument that tf.Variable which acts like subclass 
+# (by dynamically patching tf.Tensor methods) does not preserve.
+tensorflow.Tensor.__getitem__

stubs/tensorflow/METADATA.toml

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+version = "2.10.*"
+# requires a version of numpy with a `py.typed` file
+requires = ["numpy>=1.20"]

stubs/tensorflow/tensorflow/__init__.pyi

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+from _typeshed import Incomplete, Self, Unused
+from abc import ABCMeta
+from builtins import bool as _bool
+from collections.abc import Callable, Iterable, Iterator, Sequence
+from contextlib import contextmanager
+from enum import Enum
+from typing import Any, NoReturn, overload
+from typing_extensions import TypeAlias
+
+import numpy
+from tensorflow.dtypes import *
+
+# Most tf.math functions are exported as tf, but sadly not all are.
+from tensorflow.math import abs as abs
+from tensorflow.sparse import SparseTensor
+
+# Tensors ideally should be a generic type, but properly typing data type/shape
+# will be a lot of work. Until we have good non-generic tensorflow stubs,
+# we will skip making Tensor generic. Also good type hints for shapes will
+# run quickly into many places where type system is not strong enough today.
+# So shape typing is probably not worth doing anytime soon.
+_Slice: TypeAlias = int | slice | None
+
+_FloatDataSequence: TypeAlias = Sequence[float] | Sequence[_FloatDataSequence]
+_StrDataSequence: TypeAlias = Sequence[str] | Sequence[_StrDataSequence]
+_ScalarTensorCompatible: TypeAlias = Tensor | str | float | numpy.ndarray[Any, Any] | numpy.number[Any]
+_TensorCompatible: TypeAlias = _ScalarTensorCompatible | Sequence[_TensorCompatible]
+_ShapeLike: TypeAlias = TensorShape | Iterable[_ScalarTensorCompatible | None] | int | Tensor
+_DTypeLike: TypeAlias = DType | str | numpy.dtype[Any]
+
+class Tensor:
+    def __init__(self, op: Operation, value_index: int, dtype: DType) -> None: ...
+    def consumers(self) -> list[Incomplete]: ...
+    @property
+    def shape(self) -> TensorShape: ...
+    def get_shape(self) -> TensorShape: ...
+    @property
+    def dtype(self) -> DType: ...
+    @property
+    def graph(self) -> Graph: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def op(self) -> Operation: ...
+    def numpy(self) -> numpy.ndarray[Any, Any]: ...
+    def __int__(self) -> int: ...
+    def __abs__(self, name: str | None = None) -> Tensor: ...
+    def __add__(self, other: _TensorCompatible) -> Tensor: ...
+    def __radd__(self, other: _TensorCompatible) -> Tensor: ...
+    def __sub__(self, other: _TensorCompatible) -> Tensor: ...
+    def __rsub__(self, other: _TensorCompatible) -> Tensor: ...
+    def __mul__(self, other: _TensorCompatible) -> Tensor: ...
+    def __rmul__(self, other: _TensorCompatible) -> Tensor: ...
+    def __pow__(self, other: _TensorCompatible) -> Tensor: ...
+    def __matmul__(self, other: _TensorCompatible) -> Tensor: ...
+    def __rmatmul__(self, other: _TensorCompatible) -> Tensor: ...
+    def __floordiv__(self, other: _TensorCompatible) -> Tensor: ...
+    def __rfloordiv__(self, other: _TensorCompatible) -> Tensor: ...
+    def __truediv__(self, other: _TensorCompatible) -> Tensor: ...
+    def __rtruediv__(self, other: _TensorCompatible) -> Tensor: ...
+    def __neg__(self, name: str | None = None) -> Tensor: ...
+    def __and__(self, other: _TensorCompatible) -> Tensor: ...
+    def __rand__(self, other: _TensorCompatible) -> Tensor: ...
+    def __or__(self, other: _TensorCompatible) -> Tensor: ...
+    def __ror__(self, other: _TensorCompatible) -> Tensor: ...
+    def __eq__(self, other: _TensorCompatible) -> Tensor: ...  # type: ignore[override]
+    def __ne__(self, other: _TensorCompatible) -> Tensor: ...  # type: ignore[override]
+    def __ge__(self, other: _TensorCompatible, name: str | None = None) -> Tensor: ...
+    def __gt__(self, other: _TensorCompatible, name: str | None = None) -> Tensor: ...
+    def __le__(self, other: _TensorCompatible, name: str | None = None) -> Tensor: ...
+    def __lt__(self, other: _TensorCompatible, name: str | None = None) -> Tensor: ...
+    def __bool__(self) -> NoReturn: ...
+    def __getitem__(self, slice_spec: _Slice | tuple[_Slice, ...]) -> Tensor: ...
+    def __len__(self) -> int: ...
+    # This only works for rank 0 tensors.
+    def __index__(self) -> int: ...
+    def __getattr__(self, name: str) -> Incomplete: ...
+
+class VariableSynchronization(Enum):
+    AUTO = 0
+    NONE = 1
+    ON_WRITE = 2
+    ON_READ = 3
+
+class VariableAggregation(Enum):
+    AUTO = 0
+    NONE = 1
+    ON_WRITE = 2
+    ON_READ = 3
+
+class _VariableMetaclass(type): ...
+
+# Variable class in intent/documentation is a Tensor. In implementation there's
+# TODO comment to make it Tensor. It is not actually Tensor type wise, but even
+# dynamically patches on most methods of tf.Tensor
+# https://github.com/tensorflow/tensorflow/blob/9524a636cae9ae3f0554203c1ba7ee29c85fcf12/tensorflow/python/ops/variables.py#L1086.
+class Variable(Tensor, metaclass=_VariableMetaclass):
+    def __init__(
+        self,
+        initial_value: Tensor | Callable[[], Tensor] | None = None,
+        trainable: _bool | None = None,
+        validate_shape: _bool = True,
+        # Valid non-None values are deprecated.
+        caching_device: None = None,
+        name: str | None = None,
+        # Real type is VariableDef protobuf type. Can be added after adding script
+        # to generate tensorflow protobuf stubs with mypy-protobuf.
+        variable_def: Incomplete | None = None,
+        dtype: _DTypeLike | None = None,
+        import_scope: str | None = None,
+        constraint: Callable[[Tensor], Tensor] | None = None,
+        synchronization: VariableSynchronization = VariableSynchronization.AUTO,
+        aggregation: VariableAggregation = VariableAggregation.NONE,
+        shape: _ShapeLike | None = None,
+    ) -> None: ...
+    def __getattr__(self, name: str) -> Incomplete: ...
+
+class RaggedTensor(metaclass=ABCMeta):
+    def bounding_shape(
+        self, axis: _TensorCompatible | None = None, name: str | None = None, out_type: _DTypeLike | None = None
+    ) -> Tensor: ...
+    @classmethod
+    def from_sparse(
+        cls, st_input: SparseTensor, name: str | None = None, row_splits_dtype: _DTypeLike = int64
+    ) -> RaggedTensor: ...
+    def to_sparse(self, name: str | None = None) -> SparseTensor: ...
+    def to_tensor(
+        self, default_value: float | str | None = None, name: str | None = None, shape: _ShapeLike | None = None
+    ) -> Tensor: ...
+    def __add__(self, other: RaggedTensor | float, name: str | None = None) -> RaggedTensor: ...
+    def __radd__(self, other: RaggedTensor | float, name: str | None = None) -> RaggedTensor: ...
+    def __sub__(self, other: RaggedTensor | float, name: str | None = None) -> RaggedTensor: ...
+    def __mul__(self, other: RaggedTensor | float, name: str | None = None) -> RaggedTensor: ...
+    def __rmul__(self, other: RaggedTensor | float, name: str | None = None) -> RaggedTensor: ...
+    def __floordiv__(self, other: RaggedTensor | float, name: str | None = None) -> RaggedTensor: ...
+    def __truediv__(self, other: RaggedTensor | float, name: str | None = None) -> RaggedTensor: ...
+    def __getitem__(self, slice_spec: _Slice | tuple[_Slice, ...]) -> RaggedTensor: ...
+    def __getattr__(self, name: str) -> Incomplete: ...
+
+class Operation:
+    def __init__(
+        self,
+        node_def: Incomplete,
+        g: Graph,
+        # isinstance is used so can not be Sequence/Iterable.
+        inputs: list[Tensor] | None = None,
+        output_types: Unused = None,
+        control_inputs: Iterable[Tensor | Operation] | None = None,
+        input_types: Iterable[DType] | None = None,
+        original_op: Operation | None = None,
+        op_def: Incomplete = None,
+    ) -> None: ...
+    @property
+    def inputs(self) -> list[Tensor]: ...
+    @property
+    def outputs(self) -> list[Tensor]: ...
+    @property
+    def device(self) -> str: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def type(self) -> str: ...
+    def __getattr__(self, name: str) -> Incomplete: ...
+
+class TensorShape(metaclass=ABCMeta):
+    def __init__(self, dims: _ShapeLike) -> None: ...
+    @property
+    def rank(self) -> int: ...
+    def as_list(self) -> list[int | None]: ...
+    def assert_has_rank(self, rank: int) -> None: ...
+    def assert_is_compatible_with(self, other: Iterable[int | None]) -> None: ...
+    def __bool__(self) -> _bool: ...
+    @overload
+    def __getitem__(self, key: int) -> int | None: ...
+    @overload
+    def __getitem__(self, key: slice) -> TensorShape: ...
+    def __iter__(self) -> Iterator[int | None]: ...
+    def __len__(self) -> int: ...
+    def __add__(self, other: Iterable[int | None]) -> TensorShape: ...
+    def __radd__(self, other: Iterable[int | None]) -> TensorShape: ...
+    def __getattr__(self, name: str) -> Incomplete: ...
+
+class Graph:
+    def add_to_collection(self, name: str, value: object) -> None: ...
+    def add_to_collections(self, names: Iterable[str] | str, value: object) -> None: ...
+    @contextmanager
+    def as_default(self: Self) -> Iterator[Self]: ...
+    def finalize(self) -> None: ...
+    def get_tensor_by_name(self, name: str) -> Tensor: ...
+    def get_operation_by_name(self, name: str) -> Operation: ...
+    def get_operations(self) -> list[Operation]: ...
+    def get_name_scope(self) -> str: ...
+    def __getattr__(self, name: str) -> Incomplete: ...
+
+def __getattr__(name: str) -> Incomplete: ...

stubs/tensorflow/tensorflow/dtypes.pyi

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+from _typeshed import Incomplete
+from abc import ABCMeta
+from builtins import bool as _bool
+from typing import Any
+
+import numpy as np
+from tensorflow import _DTypeLike
+
+class _DTypeMeta(ABCMeta): ...
+
+class DType(metaclass=_DTypeMeta):
+    @property
+    def name(self) -> str: ...
+    @property
+    def as_numpy_dtype(self) -> type[np.number[Any]]: ...
+    @property
+    def is_numpy_compatible(self) -> _bool: ...
+    @property
+    def is_bool(self) -> _bool: ...
+    @property
+    def is_floating(self) -> _bool: ...
+    @property
+    def is_integer(self) -> _bool: ...
+    @property
+    def is_quantized(self) -> _bool: ...
+    @property
+    def is_unsigned(self) -> _bool: ...
+    def __getattr__(self, name: str) -> Incomplete: ...
+
+bool: DType
+complex128: DType
+complex64: DType
+bfloat16: DType
+float16: DType
+half: DType
+float32: DType
+float64: DType
+double: DType
+int8: DType
+int16: DType
+int32: DType
+int64: DType
+uint8: DType
+uint16: DType
+uint32: DType
+uint64: DType
+qint8: DType
+qint16: DType
+qint32: DType
+quint8: DType
+quint16: DType
+string: DType
+
+def as_dtype(type_value: _DTypeLike) -> DType: ...
+def __getattr__(name: str) -> Incomplete: ...

stubs/tensorflow/tensorflow/math.pyi

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+from _typeshed import Incomplete
+from typing import overload
+
+from tensorflow import RaggedTensor, Tensor, _TensorCompatible
+from tensorflow.sparse import SparseTensor
+
+@overload
+def abs(x: _TensorCompatible, name: str | None = None) -> Tensor: ...
+@overload
+def abs(x: SparseTensor, name: str | None = None) -> SparseTensor: ...
+@overload
+def abs(x: RaggedTensor, name: str | None = None) -> RaggedTensor: ...
+def __getattr__(name: str) -> Incomplete: ...

stubs/tensorflow/tensorflow/sparse.pyi

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+from _typeshed import Incomplete
+from abc import ABCMeta
+from typing_extensions import TypeAlias
+
+from tensorflow import Tensor, TensorShape, _TensorCompatible
+from tensorflow.dtypes import DType
+
+_SparseTensorCompatible: TypeAlias = _TensorCompatible | SparseTensor
+
+class SparseTensor(metaclass=ABCMeta):
+    @property
+    def indices(self) -> Tensor: ...
+    @property
+    def values(self) -> Tensor: ...
+    @property
+    def dense_shape(self) -> Tensor: ...
+    @property
+    def shape(self) -> TensorShape: ...
+    @property
+    def dtype(self) -> DType: ...
+    name: str
+    def __init__(self, indices: _TensorCompatible, values: _TensorCompatible, dense_shape: _TensorCompatible) -> None: ...
+    def get_shape(self) -> TensorShape: ...
+    # Many arithmetic operations are not directly supported. Some have alternatives like tf.sparse.add instead of +.
+    def __div__(self, y: _SparseTensorCompatible) -> SparseTensor: ...
+    def __truediv__(self, y: _SparseTensorCompatible) -> SparseTensor: ...
+    def __mul__(self, y: _SparseTensorCompatible) -> SparseTensor: ...
+    def __getattr__(self, name: str) -> Incomplete: ...
+
+def __getattr__(name: str) -> Incomplete: ...