Merge branch 'main' into typ_v4

Author: crusaderky

array_api_compat/common/_helpers.py

@@ -8,6 +8,7 @@
 
 from __future__ import annotations
 
+import enum
 import inspect
 import math
 import sys
@@ -481,6 +482,86 @@ def _check_api_version(api_version: str | None) -> None:
         )
 
 
+class _ClsToXPInfo(enum.Enum):
+    SCALAR = 0
+    MAYBE_JAX_ZERO_GRADIENT = 1
+
+
+@lru_cache(100)
+def _cls_to_namespace(
+    cls: type,
+    api_version: str | None,
+    use_compat: bool | None,
+) -> tuple[Namespace | None, _ClsToXPInfo | None]:
+    if use_compat not in (None, True, False):
+        raise ValueError("use_compat must be None, True, or False")
+    _use_compat = use_compat in (None, True)
+    cls_ = cast(Hashable, cls)  # Make mypy happy
+
+    if (
+        _issubclass_fast(cls_, "numpy", "ndarray") 
+        or _issubclass_fast(cls_, "numpy", "generic")
+    ):
+        if use_compat is True:
+            _check_api_version(api_version)
+            from .. import numpy as xp
+        elif use_compat is False:
+            import numpy as xp  # type: ignore[no-redef]
+        else:
+            # NumPy 2.0+ have __array_namespace__; however they are not
+            # yet fully array API compatible.
+            from .. import numpy as xp  # type: ignore[no-redef]
+        return xp, _ClsToXPInfo.MAYBE_JAX_ZERO_GRADIENT
+
+    # Note: this must happen _after_ the test for np.generic,
+    # because np.float64 and np.complex128 are subclasses of float and complex.
+    if issubclass(cls, int | float | complex | type(None)):
+        return None, _ClsToXPInfo.SCALAR
+
+    if _issubclass_fast(cls_, "cupy", "ndarray"):
+        if _use_compat:
+            _check_api_version(api_version)
+            from .. import cupy as xp  # type: ignore[no-redef]
+        else:
+            import cupy as xp  # type: ignore[no-redef]
+        return xp, None
+
+    if _issubclass_fast(cls_, "torch", "Tensor"):
+        if _use_compat:
+            _check_api_version(api_version)
+            from .. import torch as xp  # type: ignore[no-redef]
+        else:
+            import torch as xp  # type: ignore[no-redef]
+        return xp, None
+
+    if _issubclass_fast(cls_, "dask.array", "Array"):
+        if _use_compat:
+            _check_api_version(api_version)
+            from ..dask import array as xp  # type: ignore[no-redef]
+        else:
+            import dask.array as xp  # type: ignore[no-redef]
+        return xp, None
+
+    # Backwards compatibility for jax<0.4.32
+    if _issubclass_fast(cls_, "jax", "Array"):
+        return _jax_namespace(api_version, use_compat), None
+
+    return None, None
+
+
+def _jax_namespace(api_version: str | None, use_compat: bool | None) -> Namespace:
+    if use_compat:
+        raise ValueError("JAX does not have an array-api-compat wrapper")
+    import jax.numpy as jnp
+    if not hasattr(jnp, "__array_namespace_info__"):
+        # JAX v0.4.32 and newer implements the array API directly in jax.numpy.
+        # For older JAX versions, it is available via jax.experimental.array_api.
+        # jnp.Array objects gain the __array_namespace__ method.
+        import jax.experimental.array_api  # noqa: F401
+    # Test api_version
+    return jnp.empty(0).__array_namespace__(api_version=api_version)
+
+
 def array_namespace(
     *xs: Array | complex | None,
     api_version: str | None = None,
@@ -549,105 +630,40 @@ def your_function(x, y):
     is_pydata_sparse_array
 
     """
-    if use_compat not in [None, True, False]:
-        raise ValueError("use_compat must be None, True, or False")
-
-    _use_compat = use_compat in [None, True]
-
     namespaces: set[Namespace] = set()
     for x in xs:
-        if is_numpy_array(x):
-            import numpy as np
-
-            from .. import numpy as numpy_namespace
-
-            if use_compat is True:
-                _check_api_version(api_version)
-                namespaces.add(numpy_namespace)
-            elif use_compat is False:
-                namespaces.add(np)
-            else:
-                # numpy 2.0+ have __array_namespace__, however, they are not yet fully array API
-                # compatible.
-                namespaces.add(numpy_namespace)
-        elif is_cupy_array(x):
-            if _use_compat:
-                _check_api_version(api_version)
-                from .. import cupy as cupy_namespace
-
-                namespaces.add(cupy_namespace)
-            else:
-                import cupy as cp
-
-                namespaces.add(cp)
-        elif is_torch_array(x):
-            if _use_compat:
-                _check_api_version(api_version)
-                from .. import torch as torch_namespace
-
-                namespaces.add(torch_namespace)
-            else:
-                import torch
-
-                namespaces.add(torch)
-        elif is_dask_array(x):
-            if _use_compat:
-                _check_api_version(api_version)
-                from ..dask import array as dask_namespace
-
-                namespaces.add(dask_namespace)
-            else:
-                import dask.array as da
-
-                namespaces.add(da)
-        elif is_jax_array(x):
-            if use_compat is True:
-                _check_api_version(api_version)
-                raise ValueError("JAX does not have an array-api-compat wrapper")
-            elif use_compat is False:
-                import jax.numpy as jnp
-            else:
-                # JAX v0.4.32 and newer implements the array API directly in jax.numpy.
-                # For older JAX versions, it is available via jax.experimental.array_api.
-                import jax.numpy
-
-                if hasattr(jax.numpy, "__array_api_version__"):
-                    jnp = jax.numpy
-                else:
-                    import jax.experimental.array_api as jnp  # type: ignore[no-redef]
-            namespaces.add(jnp)
-        elif is_pydata_sparse_array(x):
-            if use_compat is True:
-                _check_api_version(api_version)
-                raise ValueError("`sparse` does not have an array-api-compat wrapper")
-            else:
-                import sparse
-            # `sparse` is already an array namespace. We do not have a wrapper
-            # submodule for it.
-            namespaces.add(sparse)
-        elif hasattr(x, "__array_namespace__"):
-            if use_compat is True:
+        xp, info = _cls_to_namespace(cast(Hashable, type(x)), api_version, use_compat)
+        if info is _ClsToXPInfo.SCALAR:
+            continue
+
+        if (
+            info is _ClsToXPInfo.MAYBE_JAX_ZERO_GRADIENT
+            and _is_jax_zero_gradient_array(x)
+        ):
+            xp = _jax_namespace(api_version, use_compat)
+
+        if xp is None:
+            get_ns = getattr(x, "__array_namespace__", None)
+            if get_ns is None:
+                raise TypeError(f"{type(x).__name__} is not a supported array type")
+            if use_compat:
                 raise ValueError(
                     "The given array does not have an array-api-compat wrapper"
                 )
-            x = cast("SupportsArrayNamespace[Any]", x)
-            namespaces.add(x.__array_namespace__(api_version=api_version))
-        elif isinstance(x, int | float | complex) or x is None:
-            continue
-        else:
-            # TODO: Support Python scalars?
-            raise TypeError(f"{type(x).__name__} is not a supported array type")
+            xp = get_ns(api_version=api_version)
 
-    if not namespaces:
-        raise TypeError("Unrecognized array input")
+        namespaces.add(xp)
 
-    if len(namespaces) != 1:
+    try:
+        (xp,) = namespaces
+        return xp
+    except ValueError:
+        if not namespaces:
+            raise TypeError(
+                "array_namespace requires at least one non-scalar array input"
+            )
         raise TypeError(f"Multiple namespaces for array inputs: {namespaces}")
 
-    (xp,) = namespaces
-
-    return xp
-
 
 # backwards compatibility alias
 get_namespace = array_namespace