From b78c0af336732f4e92ec872d9b8c20b0095de77c Mon Sep 17 00:00:00 2001
From: David Langerman <dev.onyxzerosoftware@gmail.com>
Date: Mon, 19 Jan 2026 15:49:53 -0500
Subject: [PATCH] Add experimental jax support

---
 README.md                            |   13 +-
 dltype/_lib/_core.py                 |    2 +-
 dltype/_lib/_dependency_utilities.py |   13 +-
 dltype/_lib/_dltype_context.py       |    2 +-
 dltype/_lib/_dtypes.py               |   32 +-
 dltype/_lib/_tensor_type_base.py     |    4 +-
 dltype/_lib/_universal_tensors.py    |    3 +-
 dltype/tests/dltype_test.py          |   71 +-
 dltype/tests/interop_test.py         |   23 +-
 pyproject.toml                       |    7 +-
 uv.lock                              | 1222 ++++++++++++++++++--------
 11 files changed, 986 insertions(+), 406 deletions(-)

diff --git a/README.md b/README.md
index 65ac2c3..91b210a 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # DL Type (Deep Learning Type Library)
 
-This typing library is intended to replace jaxtyping for runtime type checking of torch tensors and numpy arrays.
+This typing library is intended to replace jaxtyping for runtime type checking of torch tensors, Jax arrays, and numpy arrays.
 
 In particular, we support two functions that beartype/jaxtype do not:
 
@@ -23,7 +23,7 @@ pip3 install dltype
 ```
 
 > [!NOTE]
-> dltype does not depend explicitly on torch or numpy, but you must have at least one of them installed at import time otherwise the import will fail.
+> dltype does not depend explicitly on torch, jax, or numpy, but you must have at least one of them installed at import time otherwise the import will fail.
 
 ## Usage
 
@@ -195,18 +195,21 @@ def returned_tuple_func() -> tuple[Annotated[torch.Tensor, dltype.UInt8Tensor["b
     return torch.zeros(1, 3, 1080, 1920, dtype=torch.uint8), 8
 ```
 
-## Numpy and Tensor Mixing
+## Numpy, jax, and Tensor Mixing
 
 ```python
 from typing import Annotated
+
+import jax
 import torch
 import numpy as np
 from dltype import FloatTensor, dltyped
 
 @dltyped()
 def transform_tensors(
-    points: Annotated[np.ndarray, FloatTensor["N 3"]]
-    transform: Annotated[torch.Tensor, FloatTensor["3 3"]]
+    points: Annotated[np.ndarray, FloatTensor["N 3"]],
+    transform: Annotated[torch.Tensor, FloatTensor["3 3"]],
+    aux: Annotated[jax.Array, FloatTensor["N"]],
 ) -> Annotated[torch.Tensor, FloatTensor["N 3"]]:
     return torch.from_numpy(points) @ transform
 ```
diff --git a/dltype/_lib/_core.py b/dltype/_lib/_core.py
index 0e370e3..2976c0a 100644
--- a/dltype/_lib/_core.py
+++ b/dltype/_lib/_core.py
@@ -210,7 +210,7 @@ def _inner_dltyped(func: Callable[P, R]) -> Callable[P, R]:  # noqa: C901, PLR09
             return func
 
         @wraps(func)
-        @_dependency_utilities.torch_jit_unused  # pyright: ignore[reportUnknownMemberType]
+        @_dependency_utilities.torch_jit_unused
         def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:  # noqa: C901
             __tracebackhide__ = not _constants.DEBUG_MODE
             nonlocal signature
diff --git a/dltype/_lib/_dependency_utilities.py b/dltype/_lib/_dependency_utilities.py
index 2c07b45..d020b7d 100644
--- a/dltype/_lib/_dependency_utilities.py
+++ b/dltype/_lib/_dependency_utilities.py
@@ -18,7 +18,7 @@ def _empty_wrapper(fn: Callable[P, Ret]) -> Callable[P, Ret]:
     import torch
 
     # re-export for compatibility
-    torch_jit_unused = torch.jit.unused  # pyright: ignore[reportUnknownVariableType, reportUnknownMemberType]
+    torch_jit_unused = torch.jit.unused
 except ImportError:
     torch_jit_unused = _empty_wrapper
     torch = None
@@ -29,6 +29,11 @@ def _empty_wrapper(fn: Callable[P, Ret]) -> Callable[P, Ret]:
 except ImportError:
     np = None
 
+try:
+    import jax
+except ImportError:
+    jax = None
+
 
 @cache
 def is_torch_available() -> bool:
@@ -42,6 +47,12 @@ def is_numpy_available() -> bool:
     return np is not None
 
 
+@cache
+def is_jax_available() -> bool:
+    """Check if jax is available."""
+    return jax is not None
+
+
 @cache
 def is_np_float128_available() -> bool:
     float_128_available = False
diff --git a/dltype/_lib/_dltype_context.py b/dltype/_lib/_dltype_context.py
index 67d96a6..d73379c 100644
--- a/dltype/_lib/_dltype_context.py
+++ b/dltype/_lib/_dltype_context.py
@@ -140,7 +140,7 @@ def assert_context(self) -> None:
                         tensor_name=tensor_context.tensor_arg_name,
                     )
 
-                self.registered_tensor_dtypes[tensor_context.tensor_arg_name] = tensor_context.tensor.dtype
+                self.registered_tensor_dtypes[tensor_context.tensor_arg_name] = tensor_context.tensor.dtype  # pyright: ignore[reportUnknownMemberType] (jax doesn't parametrize the numpy dtype correctly)
                 expected_shape = tensor_context.get_expected_shape(
                     tensor_context.tensor,
                 )
diff --git a/dltype/_lib/_dtypes.py b/dltype/_lib/_dtypes.py
index fd05e0b..ec1bdb7 100644
--- a/dltype/_lib/_dtypes.py
+++ b/dltype/_lib/_dtypes.py
@@ -2,26 +2,36 @@
 
 import typing
 
-from dltype._lib import (
-    _dependency_utilities as _deps,
-)
+from dltype._lib import _dependency_utilities as _deps
 
 # NOTE: the order of these is important, pyright assumes the last branch is taken
 # so we get proper union type hint checking
-if _deps.is_numpy_available() and not _deps.is_torch_available():
+if _deps.is_numpy_available() and not _deps.is_torch_available() and not _deps.is_jax_available():
+    # numpy is here, jax and torch are not
     import numpy as np
     import numpy.typing as npt
 
     DLtypeTensorT: typing.TypeAlias = npt.NDArray[typing.Any]  # pyright: ignore[reportRedeclaration]
     DLtypeDtypeT: typing.TypeAlias = npt.DTypeLike  # pyright: ignore[reportRedeclaration]
     SUPPORTED_TENSOR_TYPES: typing.Final = {np.ndarray}
-elif _deps.is_torch_available() and not _deps.is_numpy_available():
+elif not _deps.is_numpy_available() and _deps.is_torch_available():
+    # If numpy is not available jax cannot be installed so we have only torch.
     import torch
 
     DLtypeTensorT: typing.TypeAlias = torch.Tensor  # pyright: ignore[reportRedeclaration]
     DLtypeDtypeT: typing.TypeAlias = torch.dtype  # pyright: ignore[reportRedeclaration]
     SUPPORTED_TENSOR_TYPES: typing.Final = {torch.Tensor}  # pyright: ignore[reportGeneralTypeIssues, reportConstantRedefinition]
-elif _deps.is_numpy_available() and _deps.is_torch_available():
+elif _deps.is_numpy_available() and not _deps.is_torch_available() and _deps.is_jax_available():
+    # we have numpy and jax but not torch
+    import jax
+    import numpy as np
+    import numpy.typing as npt
+
+    DLtypeTensorT: typing.TypeAlias = jax.Array | npt.NDArray[typing.Any]  # pyright: ignore[reportRedeclaration]
+    DLtypeDtypeT: typing.TypeAlias = npt.DTypeLike  # pyright: ignore[reportRedeclaration]
+    SUPPORTED_TENSOR_TYPES: typing.Final = {np.ndarray, jax.Array}  # pyright: ignore[reportGeneralTypeIssues, reportConstantRedefinition]
+elif _deps.is_numpy_available() and _deps.is_torch_available() and not _deps.is_jax_available():
+    # we have torch and numpy but not jax
     import numpy as np
     import numpy.typing as npt
     import torch
@@ -29,5 +39,15 @@
     DLtypeTensorT: typing.TypeAlias = torch.Tensor | npt.NDArray[typing.Any]  # pyright: ignore[reportRedeclaration]
     DLtypeDtypeT: typing.TypeAlias = torch.dtype | npt.DTypeLike  # pyright: ignore[reportRedeclaration]
     SUPPORTED_TENSOR_TYPES: typing.Final = {torch.Tensor, np.ndarray}  # pyright: ignore[reportGeneralTypeIssues, reportConstantRedefinition]
+elif _deps.is_numpy_available() and _deps.is_torch_available() and _deps.is_jax_available():
+    # we have all three
+    import jax
+    import numpy as np
+    import numpy.typing as npt
+    import torch
+
+    DLtypeTensorT: typing.TypeAlias = jax.Array | npt.NDArray[typing.Any] | torch.Tensor  # pyright: ignore[reportRedeclaration]
+    DLtypeDtypeT: typing.TypeAlias = npt.DTypeLike | torch.dtype  # pyright: ignore[reportRedeclaration]
+    SUPPORTED_TENSOR_TYPES: typing.Final = {np.ndarray, jax.Array, torch.Tensor}  # pyright: ignore[reportGeneralTypeIssues, reportConstantRedefinition]
 else:
     _deps.raise_for_missing_dependency()
diff --git a/dltype/_lib/_tensor_type_base.py b/dltype/_lib/_tensor_type_base.py
index 039dc9b..ddf4ec2 100644
--- a/dltype/_lib/_tensor_type_base.py
+++ b/dltype/_lib/_tensor_type_base.py
@@ -184,10 +184,10 @@ def check(
                 tensor_name=tensor_name,
             )
 
-        if self.DTYPES and tensor.dtype not in self.DTYPES:
+        if self.DTYPES and tensor.dtype not in self.DTYPES:  # pyright: ignore[reportUnknownMemberType] (jax doesn't parametrize the numpy dtype correctly)
             raise _errors.DLTypeDtypeError(
                 expected=self.DTYPES,
-                received={tensor.dtype},
+                received={tensor.dtype},  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType] (jax doesn't parametrize the numpy dtype correctly)
                 tensor_name=tensor_name,
             )
 
diff --git a/dltype/_lib/_universal_tensors.py b/dltype/_lib/_universal_tensors.py
index 9718aae..f616e23 100644
--- a/dltype/_lib/_universal_tensors.py
+++ b/dltype/_lib/_universal_tensors.py
@@ -3,12 +3,13 @@
 
 from dltype._lib import _tensor_type_base
 from dltype._lib._dependency_utilities import (
+    is_jax_available,
     is_numpy_available,
     is_torch_available,
     raise_for_missing_dependency,
 )
 
-if is_numpy_available():
+if is_numpy_available() or is_jax_available():
     from dltype._lib._numpy_tensors import (
         BoolTensor as NumPyBoolTensor,
     )
diff --git a/dltype/tests/dltype_test.py b/dltype/tests/dltype_test.py
index e3d73fa..8d30a63 100644
--- a/dltype/tests/dltype_test.py
+++ b/dltype/tests/dltype_test.py
@@ -10,6 +10,7 @@
 from typing import Annotated, Final, NamedTuple, TypeAlias
 from unittest.mock import patch
 
+import jax
 import numpy as np
 import numpy.typing as npt
 import pytest
@@ -537,18 +538,22 @@ def forward(
             f.name,
             input_names=["input"],
             output_names=["output"],
+            dynamo=False,
         )
 
         assert Path(f.name).exists()
         assert Path(f.name).stat().st_size > 0
 
-        with pytest.raises(dltype.DLTypeNDimsError):
+        with pytest.raises(
+            dltype.DLTypeNDimsError, match="Invalid number of dimensions, tensor=x expected ndims=4 actual=3"
+        ):
             torch.onnx.export(
                 _DummyModule(),
                 (torch.rand(1, 2, 3),),
                 f.name,
                 input_names=["input"],
                 output_names=["output"],
+                dynamo=False,
             )
 
 
@@ -1568,3 +1573,67 @@ def func(
 
     with pytest.raises(dltype.DLTypeShapeError):
         func((torch.zeros(1, 1, 3), torch.zeros(3, 2, 1), 1))
+
+
+def test_jax() -> None:
+    @dltype.dltyped()
+    def func(
+        arr: Annotated[jax.Array, dltype.FloatTensor["1 2 3"]],
+    ) -> Annotated[jax.Array, dltype.FloatTensor["3 2 1"]]:
+        return arr.transpose(2, 1, 0)
+
+    func(jax.numpy.zeros((1, 2, 3), dtype=np.float32))
+
+    with pytest.raises(dltype.DLTypeShapeError):
+        func(jax.numpy.zeros((1, 2, 4), dtype=np.float32))
+
+    with pytest.raises(dltype.DLTypeDtypeError):
+        func(jax.numpy.zeros((1, 2, 3), dtype=np.int8))
+
+    @dltype.dltyped_dataclass()
+    @dataclass(frozen=True, slots=True)
+    class JaxDataclass:
+        arr1: Annotated[jax.Array, dltype.UInt8Tensor["*batch chann feat"]]
+        arr2: Annotated[jax.Array, dltype.Float32Tensor["*batch chann"]]
+
+    JaxDataclass(
+        arr1=jax.numpy.zeros((4, 3, 2), dtype=np.uint8),
+        arr2=jax.numpy.zeros(
+            (4, 3),
+            dtype=np.float32,
+        ),
+    )
+
+    with pytest.raises(dltype.DLTypeShapeError):
+        JaxDataclass(
+            arr1=jax.numpy.zeros((4, 4, 2), dtype=np.uint8), arr2=jax.numpy.zeros((4, 3), dtype=np.float32)
+        )
+
+    JaxDataclass(
+        arr1=jax.numpy.zeros((8, 4, 3, 2), dtype=np.uint8), arr2=jax.numpy.zeros((8, 4, 3), dtype=np.float32)
+    )
+
+    class JaxBaseModel(BaseModel):
+        arr1: Annotated[jax.Array, dltype.FloatTensor["batch chann=3 feat"]]
+        arr2: Annotated[jax.Array, dltype.FloatTensor["batch chann=3"]]
+
+    JaxBaseModel(arr1=jax.numpy.zeros((1, 3, 2)), arr2=jax.numpy.zeros((1, 3)))
+
+    with pytest.raises(dltype.DLTypeShapeError):
+        JaxBaseModel(arr1=jax.numpy.zeros((4, 4, 2)), arr2=jax.numpy.zeros((4, 3)))
+
+    with pytest.raises(dltype.DLTypeDtypeError):
+        JaxBaseModel(arr1=jax.numpy.zeros((4, 3, 2)), arr2=jax.numpy.zeros((4, 3), dtype=np.uint8))
+
+    @dltype.dltyped_namedtuple()
+    class JaxNamedTuple(NamedTuple):
+        arr1: Annotated[jax.Array, dltype.FloatTensor["batch chann=3 feat"]]
+        arr2: Annotated[jax.Array, dltype.FloatTensor["batch chann=3"]]
+
+    JaxNamedTuple(arr1=jax.numpy.zeros((1, 3, 2)), arr2=jax.numpy.zeros((1, 3)))
+
+    with pytest.raises(dltype.DLTypeShapeError):
+        JaxNamedTuple(arr1=jax.numpy.zeros((4, 4, 2)), arr2=jax.numpy.zeros((4, 3)))
+
+    with pytest.raises(dltype.DLTypeDtypeError):
+        JaxNamedTuple(arr1=jax.numpy.zeros((4, 3, 2)), arr2=jax.numpy.zeros((4, 3), dtype=np.uint8))
diff --git a/dltype/tests/interop_test.py b/dltype/tests/interop_test.py
index 9cee57a..6697752 100644
--- a/dltype/tests/interop_test.py
+++ b/dltype/tests/interop_test.py
@@ -16,10 +16,11 @@
 def clear_cached_available_fns() -> None:
     """Clear cached functions to ensure fresh imports."""
     # Clear the cache for the dependency utilities
-    from dltype._lib._dependency_utilities import is_numpy_available, is_torch_available
+    from dltype._lib._dependency_utilities import is_jax_available, is_numpy_available, is_torch_available
 
     is_torch_available.cache_clear()
     is_numpy_available.cache_clear()
+    is_jax_available.cache_clear()
 
 
 @pytest.fixture(autouse=True)
@@ -46,6 +47,13 @@ def mock_missing_torch() -> Iterator[None]:
         yield
 
 
+@pytest.fixture
+def mock_missing_jax() -> Iterator[None]:
+    """Mock jax as missing."""
+    with patch("dltype._lib._dependency_utilities.jax", None):
+        yield
+
+
 def test_dltype_imports_without_torch(mock_missing_torch: None) -> None:
     """Test that dltype can be imported without torch."""
     del sys.modules["torch"]
@@ -70,6 +78,18 @@ def test_dltype_imports_without_numpy(mock_missing_numpy: None) -> None:
     assert (torch.bool,) == reloaded_dltype.BoolTensor.DTYPES
 
 
+def test_dltype_imports_without_torch_with_jax(mock_missing_torch: None) -> None:
+    """Test dltype works without jax and numpy."""
+    del sys.modules["torch"]
+
+    with pytest.raises(ImportError):
+        reload(torch)
+
+    reloaded_dltype = reload(dltype)
+
+    assert (np.bool,) == reloaded_dltype.BoolTensor.DTYPES
+
+
 def test_dltype_imports_with_both() -> None:
     """Test that dltype can be imported with both torch and numpy."""
     reloaded_dltype = reload(dltype)
@@ -82,6 +102,7 @@ def test_dltype_imports_with_both() -> None:
 def test_dltype_asserts_import_error_with_neither(
     mock_missing_numpy: None,
     mock_missing_torch: None,
+    mock_missing_jax: None,
 ) -> None:
     """Test that dltype raises ImportError if neither torch nor numpy is available."""
 
diff --git a/pyproject.toml b/pyproject.toml
index 5fe8788..5173c10 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,9 @@ dev = [
   "torch>=1.4.0",
   "setuptools>=60.0.0",
   "pyright>=1.1.407",
-  "pytest-cov>=7.0.0"
+  "pytest-cov>=7.0.0",
+  "jax>=0.6.2",
+  "onnxscript>=0.5.7"
 ]
 
 [project]
@@ -19,9 +21,10 @@ license-files = ["LICENSE"]
 name = "dltype"
 readme = "README.md"
 requires-python = ">=3.10"
-version = "0.8.0"
+version = "0.9.0"
 
 [project.optional-dependencies]
+jax = ["jax>=0.6.2"]
 numpy = ["numpy"]
 torch = ["torch>=1.11.0"]
 
diff --git a/uv.lock b/uv.lock
index 9954511..d9a313e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,7 +2,8 @@ version = 1
 revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.12'",
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
     "python_full_version == '3.11.*'",
     "python_full_version < '3.11'",
 ]
@@ -131,16 +132,20 @@ toml = [
 
 [[package]]
 name = "dltype"
-version = "0.8.0"
+version = "0.9.0"
 source = { virtual = "." }
 dependencies = [
     { name = "pydantic" },
 ]
 
 [package.optional-dependencies]
+jax = [
+    { name = "jax", version = "0.6.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "jax", version = "0.8.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+]
 numpy = [
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.4.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
 torch = [
     { name = "torch" },
@@ -148,9 +153,12 @@ torch = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "jax", version = "0.6.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "jax", version = "0.8.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.4.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "onnx" },
+    { name = "onnxscript" },
     { name = "pyright" },
     { name = "pytest" },
     { name = "pytest-cov" },
@@ -161,16 +169,19 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "jax", marker = "extra == 'jax'", specifier = ">=0.6.2" },
     { name = "numpy", marker = "extra == 'numpy'" },
     { name = "pydantic", specifier = ">=2.0" },
     { name = "torch", marker = "extra == 'torch'", specifier = ">=1.11.0" },
 ]
-provides-extras = ["numpy", "torch"]
+provides-extras = ["jax", "numpy", "torch"]
 
 [package.metadata.requires-dev]
 dev = [
+    { name = "jax", specifier = ">=0.6.2" },
     { name = "numpy", specifier = ">=2.2.6" },
     { name = "onnx", specifier = ">=1.18.0" },
+    { name = "onnxscript", specifier = ">=0.5.7" },
     { name = "pyright", specifier = ">=1.1.407" },
     { name = "pytest", specifier = ">=8.4.1" },
     { name = "pytest-cov", specifier = ">=7.0.0" },
@@ -181,41 +192,153 @@ dev = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.3.0"
+version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0b/9f/a65090624ecf468cdca03533906e7c69ed7588582240cfe7cc9e770b50eb/exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88", size = 29749, upload-time = "2025-05-10T17:42:51.123Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/36/f4/c6e662dade71f56cd2f3735141b265c3c79293c109549c1e6933b0651ffc/exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10", size = 16674, upload-time = "2025-05-10T17:42:49.33Z" },
+    { url = "https://files.pythonhosted.org/packages/8a/0e/97c33bf5009bdbac74fd2beace167cab3f978feb69cc36f1ef79360d6c4e/exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598", size = 16740, upload-time = "2025-11-21T23:01:53.443Z" },
 ]
 
 [[package]]
 name = "filelock"
-version = "3.18.0"
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