Workaround np.linalg.solve ambiguity

array_api_compat/numpy/linalg.py

@@ -1,5 +1,6 @@
 from numpy.linalg import * # noqa: F403
 from numpy.linalg import __all__ as linalg_all
+import numpy as _np
 
 from ..common import _linalg
 from .._internal import get_xp
@@ -27,14 +28,61 @@
 diagonal = get_xp(np)(_linalg.diagonal)
 trace = get_xp(np)(_linalg.trace)
 
+# Note: unlike np.linalg.solve, the array API solve() only accepts x2 as a
+# vector when it is exactly 1-dimensional. All other cases treat x2 as a stack
+# of matrices. The np.linalg.solve behavior of allowing stacks of both
+# matrices and vectors is ambiguous c.f.
+# https://github.com/numpy/numpy/issues/15349 and
+# https://github.com/data-apis/array-api/issues/285.
+
+# To workaround this, the below is the code from np.linalg.solve except
+# only calling solve1 in the exactly 1D case.
+
+# This code is here instead of in common because it is numpy specific. Also
+# note that CuPy's solve() does not currently support broadcasting (see
+# https://github.com/cupy/cupy/blob/main/cupy/cublas.py#L43).
+def solve(x1: _np.ndarray, x2: _np.ndarray, /) -> _np.ndarray:
+    try:
+        from numpy.linalg._linalg import (
+        _makearray, _assert_stacked_2d, _assert_stacked_square,
+        _commonType, isComplexType, _raise_linalgerror_singular
+        )
+    except ImportError:
+        from numpy.linalg.linalg import (
+        _makearray, _assert_stacked_2d, _assert_stacked_square,
+        _commonType, isComplexType, _raise_linalgerror_singular
+        )
+    from numpy.linalg import _umath_linalg
+
+    x1, _ = _makearray(x1)
+    _assert_stacked_2d(x1)
+    _assert_stacked_square(x1)
+    x2, wrap = _makearray(x2)
+    t, result_t = _commonType(x1, x2)
+
+    # This part is different from np.linalg.solve
+    if x2.ndim == 1:
+        gufunc = _umath_linalg.solve1
+    else:
+        gufunc = _umath_linalg.solve
+
+    # This does nothing currently but is left in because it will be relevant
+    # when complex dtype support is added to the spec in 2022.
+    signature = 'DD->D' if isComplexType(t) else 'dd->d'
+    with _np.errstate(call=_raise_linalgerror_singular, invalid='call',
+                      over='ignore', divide='ignore', under='ignore'):
+        r = gufunc(x1, x2, signature=signature)
+
+    return wrap(r.astype(result_t, copy=False))
+
 # These functions are completely new here. If the library already has them
 # (i.e., numpy 2.0), use the library version instead of our wrapper.
 if hasattr(np.linalg, 'vector_norm'):
     vector_norm = np.linalg.vector_norm
 else:
     vector_norm = get_xp(np)(_linalg.vector_norm)
 
-__all__ = linalg_all + _linalg.__all__
+__all__ = linalg_all + _linalg.__all__ + ['solve']
 
 del get_xp
 del np

numpy-xfails.txt

@@ -10,12 +10,6 @@ array_api_tests/test_has_names.py::test_has_names[array_method-to_device]
 array_api_tests/test_has_names.py::test_has_names[array_attribute-device]
 array_api_tests/test_has_names.py::test_has_names[array_attribute-mT]
 
-# linalg tests require https://github.com/data-apis/array-api-tests/pull/101
-# cleanups. Also some tests are using .mT
-array_api_tests/test_linalg.py::test_eigvalsh
-array_api_tests/test_linalg.py::test_solve
-array_api_tests/test_linalg.py::test_trace
-
 # Array methods and attributes not already on np.ndarray cannot be wrapped
 array_api_tests/test_signatures.py::test_array_method_signature[__array_namespace__]
 array_api_tests/test_signatures.py::test_array_method_signature[to_device]