jnp.linalg.solve: deprecate batched 1D solves when b.ndim > 1

CHANGELOG.md

@@ -110,6 +110,9 @@ Remember to align the itemized text with the first line of an item within a list
     such as {func}`~jax.random.bits`, {func}`~jax.random.gamma`, and others, is deprecated
     and will emit a `FutureWarning`.  Use `jax.vmap` for explicit batching.
   * {func}`jax.lax.tie_in` is deprecated: it has been a no-op since JAX v0.2.0.
+  * {func}`jax.numpy.linalg.solve` now shows a deprecation warning for batched 1D
+    solves with `b.ndim > 1`. In the future these will be treated as batched 2D
+    solves.
 
 ## jaxlib 0.4.24
 

jax/_src/numpy/linalg.py

@@ -16,6 +16,7 @@
 
 from collections.abc import Sequence
 from functools import partial
+import warnings
 
 import numpy as np
 import textwrap
@@ -635,9 +636,15 @@ def qr(a: ArrayLike, mode: str = "reduced") -> Array | QRResult:
 def solve(a: ArrayLike, b: ArrayLike) -> Array:
   check_arraylike("jnp.linalg.solve", a, b)
   a, b = promote_dtypes_inexact(jnp.asarray(a), jnp.asarray(b))
-  # TODO(jakevdp): this condition matches the broadcasting behavior in numpy < 2.0.
-  # For the array API specification, we would check only if b.ndim == 1.
-  if b.ndim == 1 or a.ndim == b.ndim + 1:
+
+  if b.ndim == 1:
+    signature = "(m,m),(m)->(m)"
+  elif a.ndim == b.ndim + 1:
+    # Deprecation warning added 2024-02-06
+    warnings.warn("Batched 1D solves with b.ndim > 1 are deprecated, "
+                  "and in the future will be treated as a batched 2D solve. "
+                  "Use solve(a, b[..., None])[..., 0] to avoid this warning.",
+                  category=DeprecationWarning, stacklevel=2)
     signature = "(m,m),(m)->(m)"
   else:
     signature = "(m,m),(m,n)->(m,n)"

tests/linalg_test.py

@@ -1035,6 +1035,7 @@ def testSolve(self, lhs_shape, rhs_shape, dtype):
       lhs_shape=[(2, 2), (2, 2, 2), (2, 2, 2, 2), (2, 2, 2, 2, 2)],
       rhs_shape=[(2,), (2, 2), (2, 2, 2), (2, 2, 2, 2)]
   )
+  @jtu.ignore_warning(category=DeprecationWarning, message="Batched 1D solves")
   def testSolveBroadcasting(self, lhs_shape, rhs_shape):
     # Batched solve can involve some ambiguities; this test checks
     # that we match NumPy's convention in all cases.
@@ -1196,6 +1197,7 @@ def test(x):
     self.assertAllClose(xc, grad_test_jc(xc))
 
   @jtu.skip_on_flag("jax_skip_slow_tests", True)
+  @jtu.ignore_warning(category=DeprecationWarning, message="Batched 1D solves")
   def testIssue1151(self):
     rng = self.rng()
     A = jnp.array(rng.randn(100, 3, 3), dtype=jnp.float32)