ENH: support PyTorch device='meta'

src/array_api_extra/_lib/_funcs.py

@@ -153,7 +153,7 @@ def _apply_where(  # type: ignore[explicit-any]  # numpydoc ignore=PR01,RT01
 ) -> Array:
     """Helper of `apply_where`. On Dask, this runs on a single chunk."""
 
-    if not capabilities(xp)["boolean indexing"]:
+    if not capabilities(xp, device=_compat.device(cond))["boolean indexing"]:
         # jax.jit does not support assignment by boolean mask
         return xp.where(cond, f1(*args), f2(*args) if f2 is not None else fill_value)
 
@@ -716,7 +716,7 @@ def nunique(x: Array, /, *, xp: ModuleType | None = None) -> Array:
     # 2. backend has unique_counts and it returns a None-sized array;
     #    e.g. Dask, ndonnx
     # 3. backend does not have unique_counts; e.g. wrapped JAX
-    if capabilities(xp)["data-dependent shapes"]:
+    if capabilities(xp, device=_compat.device(x))["data-dependent shapes"]:
         # xp has unique_counts; O(n) complexity
         _, counts = xp.unique_counts(x)
         n = _compat.size(counts)

src/array_api_extra/_lib/_testing.py

@@ -100,7 +100,11 @@ def as_numpy_array(array: Array, *, xp: ModuleType) -> np.typing.NDArray[Any]:
         return array.todense()  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
 
     if is_torch_namespace(xp):
-        array = to_device(array, "cpu")
+        if array.device.type == "meta":  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
+            # Can't materialize; generate dummy data instead
+            array = xp.zeros_like(array, device="cpu")
+        else:
+            array = to_device(array, "cpu")
     if is_array_api_strict_namespace(xp):
         cpu: Device = xp.Device("CPU_DEVICE")
         array = to_device(array, cpu)

src/array_api_extra/_lib/_utils/_helpers.py

@@ -29,8 +29,9 @@
     is_jax_namespace,
     is_numpy_array,
     is_pydata_sparse_namespace,
+    is_torch_namespace,
 )
-from ._typing import Array
+from ._typing import Array, Device
 
 if TYPE_CHECKING:  # pragma: no cover
     # TODO import from typing (requires Python >=3.12 and >=3.13)
@@ -300,7 +301,7 @@ def meta_namespace(
     return array_namespace(*metas)
 
 
-def capabilities(xp: ModuleType) -> dict[str, int]:
+def capabilities(xp: ModuleType, *, device: Device | None = None) -> dict[str, int]:
     """
     Return patched ``xp.__array_namespace_info__().capabilities()``.
 
@@ -311,6 +312,8 @@ def capabilities(xp: ModuleType) -> dict[str, int]:
     ----------
     xp : array_namespace
         The standard-compatible namespace.
+    device : Device, optional
+        The device to use.
 
     Returns
     -------
@@ -326,6 +329,13 @@ def capabilities(xp: ModuleType) -> dict[str, int]:
         # Fixed in jax >=0.6.0
         out = out.copy()
         out["boolean indexing"] = False
+    if is_torch_namespace(xp):
+        # FIXME https://github.com/data-apis/array-api/issues/945
+        device = xp.get_default_device() if device is None else xp.device(device)
+        if cast(Any, device).type == "meta":  # type: ignore[explicit-any]
+            out = out.copy()
+            out["boolean indexing"] = False
+            out["data-dependent shapes"] = False
     return out
 
 

tests/conftest.py

@@ -211,7 +211,9 @@ def device(
     Where possible, return a device that is not the default one.
     """
     if library == Backend.ARRAY_API_STRICT:
-        d = xp.Device("device1")
-        assert get_device(xp.empty(0)) != d
-        return d
+        return xp.Device("device1")
+    if library == Backend.TORCH:
+        return xp.device("meta")
+    if library == Backend.TORCH_GPU:
+        return xp.device("cpu")
     return get_device(xp.empty(0))

tests/test_funcs.py

@@ -731,9 +731,6 @@ def test_device(self, xp: ModuleType, device: Device, equal_nan: bool):
         b = xp.asarray([1e-9, 1e-4, xp.nan], device=device)
         res = isclose(a, b, equal_nan=equal_nan)
         assert get_device(res) == device
-        xp_assert_equal(
-            isclose(a, b, equal_nan=equal_nan), xp.asarray([True, False, equal_nan])
-        )
 
 
 class TestKron:
@@ -996,6 +993,9 @@ def test_all_python_scalars(self, assume_unique: bool):
             _ = setdiff1d(0, 0, assume_unique=assume_unique)
 
     @assume_unique
+    @pytest.mark.skip_xp_backend(
+        Backend.TORCH, reason="device='meta' does not support unknown shapes"
+    )
     def test_device(self, xp: ModuleType, device: Device, assume_unique: bool):
         x1 = xp.asarray([3, 8, 20], device=device)
         x2 = xp.asarray([2, 3, 4], device=device)

tests/test_helpers.py

@@ -212,11 +212,31 @@ def test_xp(self, xp: ModuleType):
         assert meta_namespace(*args, xp=xp) in (xp, np_compat)
 
 
-def test_capabilities(xp: ModuleType):
-    expect = {"boolean indexing", "data-dependent shapes"}
-    if xp.__array_api_version__ >= "2024.12":
-        expect.add("max dimensions")
-    assert capabilities(xp).keys() == expect
+class TestCapabilities:
+    def test_basic(self, xp: ModuleType):
+        expect = {"boolean indexing", "data-dependent shapes"}
+        if xp.__array_api_version__ >= "2024.12":
+            expect.add("max dimensions")
+        assert capabilities(xp).keys() == expect
+
+    def test_device(self, xp: ModuleType, library: Backend, device: Device):
+        expect_keys = {"boolean indexing", "data-dependent shapes"}
+        if xp.__array_api_version__ >= "2024.12":
+            expect_keys.add("max dimensions")
+        assert capabilities(xp, device=device).keys() == expect_keys
+
+        if library.like(Backend.TORCH):
+            # The output of capabilities is device-specific.
+
+            # Test that device=None gets the current default device.
+            expect = capabilities(xp, device=device)
+            with xp.device(device):
+                actual = capabilities(xp)
+            assert actual == expect
+
+            # Test that we're accepting anything that is accepted by the
+            # device= parameter in other functions
+            actual = capabilities(xp, device=device.type)  # type: ignore[attr-defined]  # pyright: ignore[reportUnknownArgumentType,reportAttributeAccessIssue]
 
 
 class Wrapper(Generic[T]):

tests/test_lazy.py

@@ -278,6 +278,9 @@ def test_lazy_apply_none_shape_broadcast(xp: ModuleType):
                     Backend.ARRAY_API_STRICT, reason="device->host copy"
                 ),
                 pytest.mark.skip_xp_backend(Backend.CUPY, reason="device->host copy"),
+                pytest.mark.skip_xp_backend(
+                    Backend.TORCH, reason="materialize 'meta' device"
+                ),
                 pytest.mark.skip_xp_backend(
                     Backend.TORCH_GPU, reason="device->host copy"
                 ),

tests/test_testing.py

@@ -39,10 +39,22 @@
 )
 
 
-def test_as_numpy_array(xp: ModuleType, device: Device):
-    x = xp.asarray([1, 2, 3], device=device)
-    y = as_numpy_array(x, xp=xp)
-    assert isinstance(y, np.ndarray)
+class TestAsNumPyArray:
+    def test_basic(self, xp: ModuleType):
+        x = xp.asarray([1, 2, 3])
+        y = as_numpy_array(x, xp=xp)
+        xp_assert_equal(y, np.asarray([1, 2, 3]))  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
+
+    def test_device(self, xp: ModuleType, library: Backend, device: Device):
+        x = xp.asarray([1, 2, 3], device=device)
+        actual = as_numpy_array(x, xp=xp)
+        if library is Backend.TORCH:
+            assert device.type == "meta"  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
+            expect = np.asarray([0, 0, 0])
+        else:
+            expect = np.asarray([1, 2, 3])
+
+        xp_assert_equal(actual, expect)  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
 
 
 @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no isdtype", strict=False)