Bump version

* Fixes for some new failures. (Where did they come from?)

* Fixed isort?

.github/workflows/run_tests.yml

@@ -26,7 +26,7 @@ jobs:
   run-tests:
     strategy:
       matrix:
-        python-version: [ 3.7, 3.8, 3.9 ]
+        python-version: [ 3.8, 3.9 ]
         os: [ ubuntu-latest ]
       fail-fast: false
     runs-on: ${{ matrix.os }}

.isort.cfg

@@ -2,5 +2,6 @@
 force_alphabetical_sort_within_sections=true
 lines_after_imports=2
 profile=black
+combine_as_imports=True
 treat_comments_as_code=true
 extra_standard_library=typing_extensions

.pre-commit-config.yaml

@@ -23,13 +23,13 @@ repos:
     hooks:
     - id: black
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.2.3
+    rev: 1.6.3
     hooks:
     - id: nbqa-black
     - id: nbqa-isort 
     - id: nbqa-flake8
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.12.0
     hooks:
     - id: isort
   - repo: https://github.com/pycqa/flake8

API.md

@@ -62,6 +62,8 @@ Float32[Array, "some_shape"]
 
 The array should typically be a `jaxtyping.Array`, which is an alias for `jax.numpy.ndarray`.
 
+`jaxtyping.ArrayLike` is also available, which is an alias for `jax.typing.ArrayLike`. This is a union over JAX arrays and the builtin `bool`/`int`/`float`/`complex`.
+
 But you can use other types as well. `jaxtyping` has support for JAX, NumPy, TensorFlow, and PyTorch, e.g.:
 ```python
 Float[np.ndarray, "..."]
@@ -168,6 +170,8 @@ The import hook can be applied to multiple packages via
 install_import_hook(["foo", "bar.baz"], ...)
 ```
 
+The import hook will automatically decorate all functions, and the `__init__` method of dataclasses.
+
 **Example: writing an end-user script**
 
 ```python

README.md

@@ -2,9 +2,10 @@
 
 Type annotations **and runtime checking** for:
 
-1. shape and dtype of [JAX](https://github.com/google/jax) arrays;
+1. shape and dtype of [JAX](https://github.com/google/jax) arrays; *(Now also supports PyTorch, NumPy, and TensorFlow!)*
 2. [PyTrees](https://jax.readthedocs.io/en/latest/pytrees.html).
 
+
 **For example:**
 ```python
 from jaxtyping import Array, Float, PyTree
@@ -28,7 +29,9 @@ def accepts_pytree_of_arrays(x: PyTree[Float[Array, "batch c1 c2"]]):
 pip install jaxtyping
 ```
 
-Requires JAX 0.3.4+.
+Requires Python 3.8+.
+
+JAX is an optional dependency, required for `jaxtyping.{Array, ArrayLike, PyTree}`. If JAX is not installed then these types will not be available, but you may still use jaxtyping alongside PyTorch/NumPy/etc.
 
 Also install your favourite runtime type-checking package. The two most popular are [typeguard](https://github.com/agronholm/typeguard) (which exhaustively checks every argument) and [beartype](https://github.com/beartype/beartype) (which checks random pieces of arguments).
 
@@ -46,14 +49,10 @@ Neural networks: [Equinox](https://github.com/patrick-kidger/equinox).
 
 Numerical differential equation solvers: [Diffrax](https://github.com/patrick-kidger/diffrax).
 
+Computer vision models: [Eqxvision](https://github.com/paganpasta/eqxvision).
+
 SymPy<->JAX conversion; train symbolic expressions via gradient descent: [sympy2jax](https://github.com/google/sympy2jax).
 
-### Acknowledgements
-
-Shape annotations + runtime type checking is inspired by [TorchTyping](https://github.com/patrick-kidger/torchtyping).
-
-The concise syntax is partially inspired by [etils.array_types](https://github.com/google/etils/tree/main/etils/array_types).
-
 ### Disclaimer
 
 This is not an official Google product.

jaxtyping/__init__.py

@@ -20,47 +20,96 @@
 import typing
 
 
-if getattr(typing, "GENERATING_DOCUMENTATION", False):
-
-    class Array:
-        pass
-
-    Array.__module__ = "builtins"
+try:
+    import jax
+except ImportError:
+    has_jax = False
 else:
-    from jax.numpy import ndarray as Array
+    has_jax = True
+    del jax
+
+
+# Type checkers don't know which branch below will be executed.
+if typing.TYPE_CHECKING:
+    # For imports, we need to explicitly `import X as X` in order for Pyright to see
+    # them as public. See discussion at https://github.com/microsoft/pyright/issues/2277
+    from jax import Array as Array
+    from jax.typing import ArrayLike as ArrayLike
+elif has_jax:
+    if getattr(typing, "GENERATING_DOCUMENTATION", False):
+
+        class Array:
+            pass
+
+        Array.__module__ = "builtins"
+
+        class ArrayLike:
+            pass
+
+        ArrayLike.__module__ = "builtins"
+    else:
+        from jax import Array as Array
+
+        try:
+            from jax.typing import ArrayLike as ArrayLike
+        except (ModuleNotFoundError, ImportError):
+            pass
 
 from .array_types import (
-    AbstractArray,
-    AbstractDtype,
-    BFloat16,
-    Bool,
-    Complex,
-    Complex64,
-    Complex128,
-    Float,
-    Float16,
-    Float32,
-    Float64,
-    get_array_name_format,
-    Inexact,
-    Int,
-    Int8,
-    Int16,
-    Int32,
-    Int64,
-    Integer,
-    Num,
-    set_array_name_format,
-    Shaped,
-    UInt,
-    UInt8,
-    UInt16,
-    UInt32,
-    UInt64,
+    AbstractArray as AbstractArray,
+    AbstractDtype as AbstractDtype,
+    BFloat16 as BFloat16,
+    Bool as Bool,
+    Complex as Complex,
+    Complex64 as Complex64,
+    Complex128 as Complex128,
+    Float as Float,
+    Float16 as Float16,
+    Float32 as Float32,
+    Float64 as Float64,
+    get_array_name_format as get_array_name_format,
+    Inexact as Inexact,
+    Int as Int,
+    Int8 as Int8,
+    Int16 as Int16,
+    Int32 as Int32,
+    Int64 as Int64,
+    Integer as Integer,
+    Num as Num,
+    set_array_name_format as set_array_name_format,
+    Shaped as Shaped,
+    UInt as UInt,
+    UInt8 as UInt8,
+    UInt16 as UInt16,
+    UInt32 as UInt32,
+    UInt64 as UInt64,
 )
-from .decorator import jaxtyped
-from .import_hook import install_import_hook
-from .pytree_type import PyTree
+from .decorator import jaxtyped as jaxtyped
+from .import_hook import install_import_hook as install_import_hook
 
 
-__version__ = "0.2.5"
+if typing.TYPE_CHECKING:
+    # Set up to deliberately confuse a static type checker.
+    PyTree = getattr(typing, "foo" + "bar")
+    # What's going on with this madness?
+    #
+    # At static-type-checking-time, we want `PyTree` to be a type for which both
+    # `PyTree` and `PyTree[Foo]` are equivalent to `Any`.
+    # (The intention is that `PyTree` be a runtime-only type; there's no real way to
+    # do more with static type checkers.)
+    #
+    # Unfortunately, this isn't possible: `Any` isn't subscriptable. And there's no
+    # equivalent way we can fake this using typing annotations. (In some sense the
+    # closest thing would be a `Protocol[T]` with no methods, but that's actually the
+    # opposite of what we want: that ends up allowing nothing at all.)
+    #
+    # The good news for us is that static type checkers have an internal escape hatch.
+    # If they can't figure out what a type is, then they just give up and allow
+    # anything. (I believe this is sometimes called `Unknown`.) Thus, this odd-looking
+    # annotation, which static type checkers aren't smart enough to resolve.
+elif has_jax:
+    from .pytree_type import PyTree
+
+del has_jax
+
+__version__ = "0.2.13"

jaxtyping/array_types.py

@@ -20,8 +20,17 @@
 import enum
 import functools as ft
 import typing
-from typing import Any, Dict, List, NoReturn, Optional, Tuple, TYPE_CHECKING, Union
-from typing_extensions import Literal
+from typing import (
+    Any,
+    Dict,
+    List,
+    Literal,
+    NoReturn,
+    Optional,
+    Tuple,
+    TYPE_CHECKING,
+    Union,
+)
 
 import numpy as np
 
@@ -58,24 +67,72 @@ class _NamedDim:
         self.name = name
         self.broadcastable = broadcastable
 
+    def __eq__(self, other):
+        if type(self) is not type(other):
+            return False
+        if self.name != other.name:
+            return False
+        if self.broadcastable != other.broadcastable:
+            return False
+        return True
+
+    def __hash__(self):
+        return hash((self.name, self.broadcastable))
+
 
 class _NamedVariadicDim:
     def __init__(self, name, broadcastable):
         self.name = name
         self.broadcastable = broadcastable
 
+    def __eq__(self, other):
+        if type(self) is not type(other):
+            return False
+        if self.name != other.name:
+            return False
+        if self.broadcastable != other.broadcastable:
+            return False
+        return True
+
+    def __hash__(self):
+        return hash((self.name, self.broadcastable))
+
 
 class _FixedDim:
     def __init__(self, size, broadcastable):
         self.size = size
         self.broadcastable = broadcastable
 
+    def __eq__(self, other):
+        if type(self) is not type(other):
+            return False
+        if self.size != other.size:
+            return False
+        if self.broadcastable != other.broadcastable:
+            return False
+        return True
+
+    def __hash__(self):
+        return hash((self.size, self.broadcastable))
+
 
 class _SymbolicDim:
     def __init__(self, expr, broadcastable):
         self.expr = expr
         self.broadcastable = broadcastable
 
+    def __eq__(self, other):
+        if type(self) is not type(other):
+            return False
+        if self.expr != other.expr:
+            return False
+        if self.broadcastable != other.broadcastable:
+            return False
+        return True
+
+    def __hash__(self):
+        return hash((self.expr, self.broadcastable))
+
 
 _AbstractDimOrVariadicDim = Union[
     Literal[_anonymous_dim],
@@ -127,6 +184,22 @@ def _check_dims(
 
 
 class _MetaAbstractArray(type):
+    def __eq__(self, other):
+        if type(self) is not type(other):
+            return False
+        if self.array_type is not other.array_type:
+            return False
+        if self.dtypes != other.dtypes:
+            return False
+        if self.dims != other.dims:
+            return False
+        if self.index_variadic != other.index_variadic:
+            return False
+        return True
+
+    def __hash__(self):
+        return hash((self.array_type, self.dtypes, self.dims, self.index_variadic))
+
     def __instancecheck__(cls, obj):
         if not isinstance(obj, cls.array_type):
             return False
@@ -234,6 +307,12 @@ class _MetaAbstractArray(type):
             assert False
 
 
+def _check_scalar(dtype, dtypes, dims):
+    if len(dims) != 0:
+        return False
+    return (_any_dtype is dtypes) or any(d.startswith(dtype) for d in dtypes)
+
+
 class AbstractArray(metaclass=_MetaAbstractArray):
     array_type: Any
     dtypes: List[str]
@@ -383,26 +462,68 @@ class _MetaAbstractDtype(type):
                 elem = compile(elem, "<string>", "eval")
                 elem = _SymbolicDim(elem, broadcastable)
             dims.append(elem)
-        if _array_name_format == "dtype_and_shape":
-            name = f"{cls.__name__}[{array_type.__name__}, '{dim_str}']"
-        elif _array_name_format == "array":
-            name = array_type.__name__
+        dims = tuple(dims)
+
+        _not_made = object()
+
+        def _make(x):
+            # Allow Python built-in numeric types.
+            # TODO: do something more generic than this? Should we _make all types
+            # that have `shape` and `dtype` attributes or something?
+            if x is bool:
+                if _check_scalar("bool", cls.dtypes, dims):
+                    return x
+                else:
+                    return _not_made
+            elif x is int:
+                if _check_scalar("int", cls.dtypes, dims):
+                    return x
+                else:
+                    return _not_made
+            elif x is float:
+                if _check_scalar("float", cls.dtypes, dims):
+                    return x
+                else:
+                    return _not_made
+            elif x is complex:
+                if _check_scalar("complex", cls.dtypes, dims):
+                    return x
+                else:
+                    return _not_made
+            try:
+                type_str = x.__name__
+            except AttributeError:
+                type_str = repr(x)
+            if _array_name_format == "dtype_and_shape":
+                name = f"{cls.__name__}[{type_str}, '{dim_str}']"
+            elif _array_name_format == "array":
+                name = type_str
+            else:
+                raise ValueError(
+                    f"array_name_format {_array_name_format} not recognised"
+                )
+            out = _MetaAbstractArray(
+                name,
+                (AbstractArray,),
+                dict(
+                    array_type=x,
+                    dtypes=cls.dtypes,
+                    dims=dims,
+                    index_variadic=index_variadic,
+                ),
+            )
+            if getattr(typing, "GENERATING_DOCUMENTATION", False):
+                out.__module__ = "builtins"
+            else:
+                out.__module__ = "jaxtyping"
+            return out
+
+        if typing.get_origin(array_type) is typing.Union:
+            out = [_make(x) for x in typing.get_args(array_type)]
+            out = tuple(x for x in out if x is not _not_made)
+            out = Union[out]
         else:
-            raise ValueError(f"array_name_format {_array_name_format} not recognised")
-        out = _MetaAbstractArray(
-            name,
-            (AbstractArray,),
-            dict(
-                array_type=array_type,
-                dtypes=cls.dtypes,
-                dims=dims,
-                index_variadic=index_variadic,
-            ),
-        )
-        if getattr(typing, "GENERATING_DOCUMENTATION", False):
-            out.__module__ = "builtins"
-        else:
-            out.__module__ = "jaxtyping"
+            out = _make(array_type)
         return out
 
 
@@ -420,7 +541,9 @@ class AbstractDtype(metaclass=_MetaAbstractDtype):
 
         dtypes: Union[Literal[_any_dtype], str, List[str]] = cls.dtypes
         if isinstance(dtypes, str):
-            dtypes = [dtypes]
+            dtypes = (dtypes,)
+        elif dtypes is not _any_dtype:
+            dtypes = tuple(dtypes)
         cls.dtypes = dtypes
 
 
@@ -428,31 +551,34 @@ if TYPE_CHECKING:
     # Note that `from typing_extensions import Annotated; ... = Annotated`
     # does not work with static type checkers. `Annotated` is a typeform rather
     # than a type, meaning it cannot be assigned.
-    from typing_extensions import Annotated as BFloat16
-    from typing_extensions import Annotated as Bool
-    from typing_extensions import Annotated as Complex
-    from typing_extensions import Annotated as Complex64
-    from typing_extensions import Annotated as Complex128
-    from typing_extensions import Annotated as Float
-    from typing_extensions import Annotated as Float16
-    from typing_extensions import Annotated as Float32
-    from typing_extensions import Annotated as Float64
-    from typing_extensions import Annotated as Inexact
-    from typing_extensions import Annotated as Int
-    from typing_extensions import Annotated as Int8
-    from typing_extensions import Annotated as Int16
-    from typing_extensions import Annotated as Int32
-    from typing_extensions import Annotated as Int64
-    from typing_extensions import Annotated as Integer
-    from typing_extensions import Annotated as Num
-    from typing_extensions import Annotated as Shaped
-    from typing_extensions import Annotated as UInt
-    from typing_extensions import Annotated as UInt8
-    from typing_extensions import Annotated as UInt16
-    from typing_extensions import Annotated as UInt32
-    from typing_extensions import Annotated as UInt64
+    from typing_extensions import (
+        Annotated as BFloat16,
+        Annotated as Bool,
+        Annotated as Complex,
+        Annotated as Complex64,
+        Annotated as Complex128,
+        Annotated as Float,
+        Annotated as Float16,
+        Annotated as Float32,
+        Annotated as Float64,
+        Annotated as Inexact,
+        Annotated as Int,
+        Annotated as Int8,
+        Annotated as Int16,
+        Annotated as Int32,
+        Annotated as Int64,
+        Annotated as Integer,
+        Annotated as Num,
+        Annotated as Shaped,
+        Annotated as UInt,
+        Annotated as UInt8,
+        Annotated as UInt16,
+        Annotated as UInt32,
+        Annotated as UInt64,
+    )
 else:
-    _bool = "bool_"
+    _bool = "bool"
+    _bool_ = "bool_"
     _uint8 = "uint8"
     _uint16 = "uint16"
     _uint32 = "uint32"
@@ -495,6 +621,7 @@ else:
     Complex64 = _make_dtype(_complex64, "Complex64")
     Complex128 = _make_dtype(_complex128, "Complex128")
 
+    bools = [_bool, _bool_]
     uints = [_uint8, _uint16, _uint32, _uint64]
     ints = [_int8, _int16, _int32, _int64]
     floats = [_bfloat16, _float16, _float32, _float64]
@@ -503,7 +630,7 @@ else:
     # We match NumPy's type hierarachy in what types to provide. See the diagram at
     # https://numpy.org/doc/stable/reference/arrays.scalars.html#scalars
 
-    Bool = _make_dtype(_bool, "Bool")
+    Bool = _make_dtype(bools, "Bool")
     UInt = _make_dtype(uints, "UInt")
     Int = _make_dtype(ints, "Int")
     Integer = _make_dtype(uints + ints, "Integer")

jaxtyping/decorator.py

@@ -17,7 +17,9 @@
 # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+import dataclasses
 import functools as ft
+import inspect
 import threading
 
 
@@ -44,4 +46,31 @@ class _Jaxtyped:
 
 
 def jaxtyped(fn):
-    return ft.wraps(fn)(_Jaxtyped(fn))
+    if inspect.isclass(fn):  # allow decorators on class definitions
+        if dataclasses.is_dataclass(fn):
+            init = jaxtyped(fn.__init__)
+            fn.__init__ = init
+            return fn
+        else:
+            raise ValueError(
+                "jaxtyped may only be added as a class decorator to dataclasses"
+            )
+    else:
+        return ft.wraps(fn)(_Jaxtyped(fn))
+
+
+def _jaxtyped_typechecker(typechecker):
+    # typechecker is expected to probably be either `typeguard.typechecked`, or
+    # `beartype.beartype`, or `None`.
+
+    if typechecker is None:
+        typechecker = lambda x: x
+
+    def _wrapper(kls):
+        assert inspect.isclass(kls)
+        if dataclasses.is_dataclass(kls):
+            init = jaxtyped(typechecker(kls.__init__))
+            kls.__init__ = init
+        return kls
+
+    return _wrapper

jaxtyping/import_hook.py

@@ -65,7 +65,19 @@ def _call_with_frames_removed(f, *args, **kwargs):
 
 
 def _optimized_cache_from_source(path, debug_override=None):
-    return cache_from_source(path, debug_override, optimization="jaxtyping")
+    # Version 2: change the position of the `@jaxtyped` decorator, so need a
+    #     different name to avoid hitting old __pycache__.
+    # Version 3: now also annotating classes.
+    # Version 4: I'm honestly not sure, but bumping this fixed some kind of odd error.
+    #     Maybe I changed something with hte classes part way through version 3?
+    return cache_from_source(path, debug_override, optimization="jaxtyping4")
+
+
+def _dot_lookup(*elements):
+    out = ast.Name(id=elements[0], ctx=ast.Load())
+    for element in elements[1:]:
+        out = ast.Attribute(out, element, ctx=ast.Load())
+    return out
 
 
 class _JaxtypingTransformer(ast.NodeVisitor):
@@ -95,31 +107,37 @@ class _JaxtypingTransformer(ast.NodeVisitor):
         self._parents.pop()
         return node
 
+    def visit_ClassDef(self, node: ast.ClassDef):
+        func = _dot_lookup("jaxtyping", "decorator", "_jaxtyped_typechecker")
+        if self._typechecker is None:
+            args = [ast.Constant(None)]
+        else:
+            args = [_dot_lookup(*self._typechecker)]
+        node.decorator_list.insert(0, ast.Call(func, args, keywords=[]))
+        self._parents.append(node)
+        self.generic_visit(node)
+        self._parents.pop()
+        return node
+
     def visit_FunctionDef(self, node: ast.FunctionDef):
         has_annotated_args = any(arg for arg in node.args.args if arg.annotation)
         has_annotated_return = bool(node.returns)
         if has_annotated_args or has_annotated_return:
-            # Place at the start of the decorator list, in case a typechecking
-            # annotation has been manually applied; we need to be above that.
-            node.decorator_list.insert(
-                0,
-                ast.Attribute(
-                    ast.Name(id="jaxtyping", ctx=ast.Load()), "jaxtyped", ast.Load()
-                ),
-            )
+            # Place at the end of the decorator list, as otherwise we wrap e.g.
+            # `jax.custom_{jvp,vjp}` and lose the ability to `defjvp` etc.
+            #
+            # Note that the counter-argument here is that we'd like to place this
+            # at the start of the decorator list, in case a typechecking annotation
+            # has been manually applied, and we'd need to be above that. In this
+            # case we're just going to have to need to ask the user to remove their
+            # typechecking annotation (and let this decorator do it instead).
+            # It's more important we be compatible with normal JAX code.
+            node.decorator_list.append(_dot_lookup("jaxtyping", "jaxtyped"))
             if self._typechecker is not None:
                 # Place at the end of the decorator list, as decorators
                 # frequently remove annotations from functions and we'd like to
                 # use those annotations.
-                typechecker_module, typechecker_function = self._typechecker
-                node.decorator_list.append(
-                    ast.Attribute(
-                        ast.Name(id=typechecker_module, ctx=ast.Load()),
-                        typechecker_function,
-                        ast.Load(),
-                    )
-                )
-
+                node.decorator_list.append(_dot_lookup(*self._typechecker))
         self._parents.append(node)
         self.generic_visit(node)
         self._parents.pop()
@@ -230,8 +248,16 @@ def install_import_hook(
     - `typechecker`: the module and function of the typechecker you want to use, as a
         2-tuple of strings. For example `typechecker=("typeguard", "typechecked")` or
         `typechecker=("beartype", "beartype")`. You may pass `typechecker=None` if you
-        do not want to automatically decorate with a typechecker as well; e.g. if you
-        have a codebase that already has these decorators.
+        do not want to automatically decorate with a typechecker as well.
+
+    If the function already has any decorators on it, then both the `@jaxtyped` and the
+    typechecker decorators will go at the bottom of the decorator list, e.g.
+    ```python
+    @some_other_decorator
+    @jaxtyped
+    @beartype.beartype
+    def foo(...): ...
+    ```
 
     **Returns:**
 
@@ -243,8 +269,8 @@ def install_import_hook(
     ```python
     # entry_point.py
     from jaxtyped import install_import_hook
-    install_import_hook("main", ("beartype", "beartype"))
-    import main
+    with install_import_hook("main", ("beartype", "beartype"))
+        import main
     ...  # do whatever you're doing
 
     # main.py

jaxtyping/pytree_type.py

@@ -19,10 +19,9 @@
 
 import functools as ft
 import typing
-from typing import Generic, TYPE_CHECKING, TypeVar
-from typing_extensions import Protocol
+from typing import Generic, TypeVar
 
-import jax
+import jax.tree_util as jtu
 import typeguard
 
 
@@ -60,6 +59,14 @@ class _MetaPyTree(type):
         return out
 
 
+try:
+    # new typeguard
+    _TypeCheckError = (TypeError, typeguard.TypeCheckError)
+except AttributeError:
+    # old typeguard
+    _TypeCheckError = TypeError
+
+
 class _MetaSubscriptPyTree(type):
     def __call__(self, *args, **kwargs):
         raise RuntimeError("PyTree cannot be instantiated")
@@ -78,27 +85,20 @@ class _MetaSubscriptPyTree(type):
         def is_leaftype(x):
             try:
                 accepts_leaftype(x)
-            except TypeError:
+            except _TypeCheckError:
                 return False
             else:
                 return True
 
-        leaves = jax.tree_leaves(obj, is_leaf=is_leaftype)
+        leaves = jtu.tree_leaves(obj, is_leaf=is_leaftype)
         return all(map(is_leaftype, leaves))
 
 
-if TYPE_CHECKING:
-    # Work around pytype bug #1288
-    # pytype: skip-file
-    class PyTree(Protocol[_T]):
-        pass
-
-else:
-    PyTree = _MetaPyTree("PyTree", (), {})
-    if getattr(typing, "GENERATING_DOCUMENTATION", False):
-        PyTree.__module__ = "builtins"
-    else:
-        PyTree.__module__ = "jaxtyping"
 # Can't do `class PyTree(Generic[_T]): ...` because we need to override the
 # instancecheck for PyTree[foo], but subclassing
 # `type(Generic[int])`, i.e. `typing._GenericAlias` is disallowed.
+PyTree = _MetaPyTree("PyTree", (), {})
+if getattr(typing, "GENERATING_DOCUMENTATION", False):
+    PyTree.__module__ = "builtins"
+else:
+    PyTree.__module__ = "jaxtyping"

setup.py

@@ -63,15 +63,17 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Mathematics",
 ]
 
-python_requires = "~=3.7"
+python_requires = "~=3.8"
 
 # We use typeguard internally (in a fairly minimal way), but it's not required that
 # end users make the same choice.
+# For typing_extensions, we choose versions that match
+# https://github.com/explosion/confection/blob/main/setup.cfg#L33 used in colab
+
 install_requires = [
-    "jax>=0.3.4",
     "numpy>=1.20.0",
     "typeguard>=2.13.3",
-    "typing_extensions>=4.2.0",
+    "typing_extensions>=3.7.4.1",
 ]
 
 entry_points = dict(pytest11=["jaxtyping = jaxtyping.pytest_plugin"])

test/helpers.py

@@ -18,16 +18,31 @@
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
 import equinox as eqx
+import typeguard
 
 
+ParamError = []
+ReturnError = []
+ParamError.append(TypeError)  # old typeguard
+ReturnError.append(TypeError)  # old typeguard
+
+try:
+    # new typeguard
+    ParamError.append(typeguard.TypeCheckError)
+    ReturnError.append(typeguard.TypeCheckError)
+except AttributeError:
+    pass
+
 try:
     import beartype
 except ImportError:
-    ParamError = TypeError
-    ReturnError = TypeError
+    pass
 else:
-    ParamError = (TypeError, beartype.roar.BeartypeCallHintParamViolation)
-    ReturnError = (TypeError, beartype.roar.BeartypeCallHintReturnViolation)
+    ParamError.append(beartype.roar.BeartypeCallHintParamViolation)
+    ReturnError.append(beartype.roar.BeartypeCallHintReturnViolation)
+
+ParamError = tuple(ParamError)
+ReturnError = tuple(ReturnError)
 
 
 @eqx.filter_jit

test/import_hook_tester_beartype.py

@@ -17,6 +17,9 @@
 # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+import dataclasses
+
+import equinox as eqx
 import jax.numpy as jnp
 import pytest
 
@@ -32,3 +35,28 @@ def g(x: Float32[jnp.ndarray, " b"]):
 g(jnp.array([1.0]))
 with pytest.raises(ParamError):
     g(jnp.array(1))
+
+
+class M(eqx.Module):
+    foo: int
+    bar: Float32[jnp.ndarray, " a"]
+
+
+M(1, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    M(1.0, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    M(1, jnp.array(1.0))
+
+
+@dataclasses.dataclass
+class D:
+    foo: int
+    bar: Float32[jnp.ndarray, " a"]
+
+
+D(1, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    D(1.0, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    D(1, jnp.array(1.0))

test/import_hook_tester_broken_checker.py

@@ -17,6 +17,9 @@
 # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+import dataclasses
+
+import equinox as eqx
 import jax.numpy as jnp
 import pytest
 
@@ -32,3 +35,28 @@ def g(x: Float32[jnp.ndarray, " b"]):
 g(jnp.array([1.0]))
 with pytest.raises(ParamError):
     g(jnp.array(1))
+
+
+class M(eqx.Module):
+    foo: int
+    bar: Float32[jnp.ndarray, " a"]
+
+
+M(1, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    M(1.0, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    M(1, jnp.array(1.0))
+
+
+@dataclasses.dataclass
+class D:
+    foo: int
+    bar: Float32[jnp.ndarray, " a"]
+
+
+D(1, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    D(1.0, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    D(1, jnp.array(1.0))

test/import_hook_tester_transitive/another_file.py

@@ -17,6 +17,7 @@
 # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+import equinox as eqx
 import jax.numpy as jnp
 import pytest
 
@@ -32,3 +33,15 @@ def g(x: Float32[jnp.ndarray, " b"]):
 g(jnp.array([1.0]))
 with pytest.raises(ParamError):
     g(jnp.array(1))
+
+
+class M(eqx.Module):
+    foo: int
+    bar: Float32[jnp.ndarray, " a"]
+
+
+M(1, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    M(1.0, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    M(1, jnp.array(1.0))

test/import_hook_tester_typeguard.py

@@ -17,6 +17,9 @@
 # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+import dataclasses
+
+import equinox as eqx
 import jax.numpy as jnp
 import pytest
 
@@ -32,3 +35,28 @@ def g(x: Float32[jnp.ndarray, " b"]):
 g(jnp.array([1.0]))
 with pytest.raises(ParamError):
     g(jnp.array(1))
+
+
+class M(eqx.Module):
+    foo: int
+    bar: Float32[jnp.ndarray, " a"]
+
+
+M(1, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    M(1.0, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    M(1, jnp.array(1.0))
+
+
+@dataclasses.dataclass
+class D:
+    foo: int
+    bar: Float32[jnp.ndarray, " a"]
+
+
+D(1, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    D(1.0, jnp.array([1.0]))
+with pytest.raises(ParamError):
+    D(1, jnp.array(1.0))

test/test_array.py

@@ -17,11 +17,14 @@
 # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+from typing import get_args, get_origin, Union
+
 import jax.numpy as jnp
 import jax.random as jr
+import numpy as np
 import pytest
 
-from jaxtyping import AbstractDtype, Array, Float, Float32, jaxtyped, Shaped
+from jaxtyping import AbstractDtype, Array, ArrayLike, Float, Float32, jaxtyped, Shaped
 
 from .helpers import ParamError, ReturnError
 
@@ -409,3 +412,54 @@ def test_incomplete_symbolic(typecheck, getkey):
     x = jr.normal(getkey(), (4,))
     with pytest.raises(NameError):
         foo(x)
+
+
+def test_arraylike(typecheck, getkey):
+    floatlike1 = Float32[ArrayLike, ""]
+    floatlike2 = Float[ArrayLike, ""]
+    floatlike3 = Float32[ArrayLike, "4"]
+
+    assert get_origin(floatlike1) is Union
+    assert get_origin(floatlike2) is Union
+    assert get_origin(floatlike3) is Union
+    assert set(get_args(floatlike1)) == {
+        Float32[Array, ""],
+        Float32[np.ndarray, ""],
+        Float32[np.bool_, ""],
+        Float32[np.number, ""],
+        float,
+    }
+    assert set(get_args(floatlike2)) == {
+        Float[Array, ""],
+        Float[np.ndarray, ""],
+        Float[np.bool_, ""],
+        Float[np.number, ""],
+        float,
+    }
+    assert set(get_args(floatlike3)) == {
+        Float32[Array, "4"],
+        Float32[np.ndarray, "4"],
+        Float32[np.bool_, "4"],
+        Float32[np.number, "4"],
+    }
+
+    shaped1 = Shaped[ArrayLike, ""]
+    shaped2 = Shaped[ArrayLike, "4"]
+    assert get_origin(shaped1) is Union
+    assert get_origin(shaped2) is Union
+    assert set(get_args(shaped1)) == {
+        Shaped[Array, ""],
+        Shaped[np.ndarray, ""],
+        Shaped[np.bool_, ""],
+        Shaped[np.number, ""],
+        bool,
+        int,
+        float,
+        complex,
+    }
+    assert set(get_args(shaped2)) == {
+        Shaped[Array, "4"],
+        Shaped[np.ndarray, "4"],
+        Shaped[np.bool_, "4"],
+        Shaped[np.number, "4"],
+    }

test/test_decorator.py

@@ -1,13 +1,26 @@
+import abc
+
 from jaxtyping import jaxtyped
 
 
-class M:
+class M(metaclass=abc.ABCMeta):
     @jaxtyped
     @classmethod
     def f(cls):
         return 3
 
+    @jaxtyped
+    @abc.abstractmethod
+    def g(self):
+        ...
+
 
 # Check that the @jaxtyped decorator doesn't blat the __get__ of @classmethod
-def test_decorator():
+def test_classmethod():
     assert M.f() == 3
+
+
+# Check that the @jaxtyped decorator doesn't blat the __isabstractmethod__ of
+# @abstractmethod
+def test_abstractmethod():
+    assert M.g.__isabstractmethod__

test/test_import_hook.py

@@ -26,9 +26,8 @@ def test_import_hook_typeguard():
     hook = install_import_hook(
         "test.import_hook_tester_typeguard", ("typeguard", "typechecked")
     )
-    from . import import_hook_tester_typeguard  # noqa: F401
-
-    hook.uninstall()
+    with hook:
+        from . import import_hook_tester_typeguard  # noqa: F401
 
 
 def test_import_hook_beartype():
@@ -40,24 +39,21 @@ def test_import_hook_beartype():
         hook = install_import_hook(
             "test.import_hook_tester_beartype", ("beartype", "beartype")
         )
-        from . import import_hook_tester_beartype  # noqa: F401
-
-        hook.uninstall()
+        with hook:
+            from . import import_hook_tester_beartype  # noqa: F401
 
 
 def test_import_hook_transitive():
     hook = install_import_hook(
         "test.import_hook_tester_transitive", ("typeguard", "typechecked")
     )
-    from . import import_hook_tester_transitive  # noqa: F401
-
-    hook.uninstall()
+    with hook:
+        from . import import_hook_tester_transitive  # noqa: F401
 
 
 def test_import_hook_broken_checker():
     hook = install_import_hook(
         "test.import_hook_tester_broken_checker", ("jaxtyping", "does_not_exist")
     )
-    with pytest.raises(AttributeError):
+    with hook, pytest.raises(AttributeError):
         from . import import_hook_tester_broken_checker  # noqa: F401
-    hook.uninstall()

test/test_pytree.py

@@ -17,7 +17,7 @@
 # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
 # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
-from typing import Tuple, Union
+from typing import NamedTuple, Tuple, Union
 
 import equinox as eqx
 import jax
@@ -155,3 +155,33 @@ def test_pytree_tuple(typecheck):
         g([1, 1])
     with pytest.raises(ParamError):
         g([(1, 1), "hi"])
+
+
+def test_pytree_namedtuple(typecheck):
+    class CustomNamedTuple(NamedTuple):
+        x: Float[jnp.ndarray, "a b"]
+        y: Float[jnp.ndarray, "b c"]
+
+    class OtherCustomNamedTuple(NamedTuple):
+        x: Float[jnp.ndarray, "a b"]
+        y: Float[jnp.ndarray, "b c"]
+
+    @typecheck
+    def g(x: PyTree[CustomNamedTuple]):
+        ...
+
+    g(
+        CustomNamedTuple(
+            x=jax.random.normal(jax.random.PRNGKey(42), (3, 2)),
+            y=jax.random.normal(jax.random.PRNGKey(420), (2, 5)),
+        )
+    )
+    with pytest.raises(ParamError):
+        g(object())
+    with pytest.raises(ParamError):
+        g(
+            OtherCustomNamedTuple(
+                x=jax.random.normal(jax.random.PRNGKey(42), (3, 2)),
+                y=jax.random.normal(jax.random.PRNGKey(420), (2, 5)),
+            )
+        )