Fix numpy v2 breaking changes

autograd/numpy/numpy_boxes.py

@@ -66,7 +66,13 @@ def __hash__(self): return id(self)
 # Flatten has no function, only a method.
 setattr(ArrayBox, 'flatten', anp.__dict__['ravel'])
 
-if np.__version__ >= '1.25':
+if np.lib.NumpyVersion(np.__version__) >= '2.0.0':
+    SequenceBox.register(np.linalg._linalg.EigResult)
+    SequenceBox.register(np.linalg._linalg.EighResult)
+    SequenceBox.register(np.linalg._linalg.QRResult)
+    SequenceBox.register(np.linalg._linalg.SlogdetResult)
+    SequenceBox.register(np.linalg._linalg.SVDResult)
+elif np.__version__ >= '1.25':
     SequenceBox.register(np.linalg.linalg.EigResult)
     SequenceBox.register(np.linalg.linalg.EighResult)
     SequenceBox.register(np.linalg.linalg.QRResult)

autograd/numpy/numpy_jvps.py

@@ -1,3 +1,4 @@
+import numpy as onp
 from . import numpy_wrapper as anp
 from .numpy_vjps import (untake, balanced_eq, match_complex, replace_zero,
                          dot_adjoint_0, dot_adjoint_1, tensordot_adjoint_0,
@@ -210,7 +211,8 @@ def fwd_grad_sort(g, ans, x, axis=-1, kind='quicksort', order=None):
     sort_perm = anp.argsort(x, axis, kind, order)
     return g[sort_perm]
 defjvp(anp.sort, fwd_grad_sort)
-defjvp(anp.msort, lambda g, ans, x: fwd_grad_sort(g, ans, x, axis=0))
+if onp.lib.NumpyVersion(onp.__version__) < '2.0.0':
+    defjvp(anp.msort, lambda g, ans, x: fwd_grad_sort(g, ans, x, axis=0))
 
 def fwd_grad_partition(g, ans, x, kth, axis=-1, kind='introselect', order=None):
     partition_perm = anp.argpartition(x, kth, axis, kind, order)

autograd/numpy/numpy_vjps.py

@@ -559,7 +559,8 @@ def grad_sort(ans, x, axis=-1, kind='quicksort', order=None):
     sort_perm = anp.argsort(x, axis, kind, order)
     return lambda g: unpermuter(g, sort_perm)
 defvjp(anp.sort, grad_sort)
-defvjp(anp.msort, grad_sort)  # Until multi-D is allowed, these are the same.
+if onp.lib.NumpyVersion(onp.__version__) < '2.0.0':
+    defvjp(anp.msort, grad_sort)  # Until multi-D is allowed, these are the same.
 
 def grad_partition(ans, x, kth, axis=-1, kind='introselect', order=None):
     #TODO: Cast input with np.asanyarray()

autograd/numpy/numpy_vspaces.py

@@ -4,7 +4,7 @@
 
 class ArrayVSpace(VSpace):
     def __init__(self, value):
-        value = np.array(value, copy=False)
+        value = np.asarray(value)
         self.shape = value.shape
         self.dtype = value.dtype
 
@@ -66,7 +66,19 @@ def _covector(self, x):
     ComplexArrayVSpace.register(type_)
 
 
-if np.__version__ >= '1.25':
+if np.lib.NumpyVersion(np.__version__) >= '2.0.0':
+    class EigResultVSpace(NamedTupleVSpace):     seq_type = np.linalg._linalg.EigResult
+    class EighResultVSpace(NamedTupleVSpace):    seq_type = np.linalg._linalg.EighResult
+    class QRResultVSpace(NamedTupleVSpace):      seq_type = np.linalg._linalg.QRResult
+    class SlogdetResultVSpace(NamedTupleVSpace): seq_type = np.linalg._linalg.SlogdetResult
+    class SVDResultVSpace(NamedTupleVSpace):     seq_type = np.linalg._linalg.SVDResult
+
+    EigResultVSpace.register(np.linalg._linalg.EigResult)
+    EighResultVSpace.register(np.linalg._linalg.EighResult)
+    QRResultVSpace.register(np.linalg._linalg.QRResult)
+    SlogdetResultVSpace.register(np.linalg._linalg.SlogdetResult)
+    SVDResultVSpace.register(np.linalg._linalg.SVDResult)
+elif np.__version__ >= '1.25':
     class EigResultVSpace(NamedTupleVSpace):     seq_type = np.linalg.linalg.EigResult
     class EighResultVSpace(NamedTupleVSpace):    seq_type = np.linalg.linalg.EighResult
     class QRResultVSpace(NamedTupleVSpace):      seq_type = np.linalg.linalg.QRResult

pyproject.toml

@@ -44,7 +44,7 @@ keywords = [
   "SciPy",
 ]
 dependencies = [
-  "numpy<2",
+  "numpy",
 ]
 # dynamic = ["version"]
 

tests/test_linalg.py

@@ -1,6 +1,7 @@
 from __future__ import absolute_import
 from builtins import range
 import itertools
+import numpy as onp
 import autograd.numpy as np
 import autograd.numpy.random as npr
 from autograd.test_util import check_grads
@@ -94,7 +95,10 @@ def test_solve_arg1_3d():
     D = 4
     A = npr.randn(D+1, D, D) + 5*np.eye(D)
     B = npr.randn(D+1, D)
-    fun = lambda A: np.linalg.solve(A, B)
+    if onp.lib.NumpyVersion(onp.__version__) < '2.0.0':
+        fun = lambda A: np.linalg.solve(A, B)
+    else:
+        fun = lambda A: np.linalg.solve(A, B[..., None])[..., 0]
     check_grads(fun)(A)
 
 def test_solve_arg1_3d_3d():

tests/test_systematic.py

@@ -1,4 +1,5 @@
 from __future__ import absolute_import
+import numpy as onp
 import autograd.numpy.random as npr
 import autograd.numpy as np
 import operator as op
@@ -43,7 +44,7 @@ def test_log1p():   unary_ufunc_check(np.log1p, lims=[0.2, 2.0])
 def test_log2():    unary_ufunc_check(np.log2,  lims=[0.2, 2.0])
 def test_rad2deg(): unary_ufunc_check(lambda x : np.rad2deg(x)/50.0, test_complex=False)
 def test_radians(): unary_ufunc_check(np.radians, test_complex=False)
-def test_sign():    unary_ufunc_check(np.sign)
+def test_sign():    unary_ufunc_check(np.sign, test_complex=False)
 def test_sin():     unary_ufunc_check(np.sin)
 def test_sinh():    unary_ufunc_check(np.sinh)
 def test_sqrt():    unary_ufunc_check(np.sqrt, lims=[1.0, 3.0])
@@ -155,7 +156,8 @@ def test_fmin(): combo_check(np.fmin, [0, 1])(
                             [R(1), R(1,4), R(3, 4)])
 
 def test_sort():       combo_check(np.sort, [0])([R(1), R(7)])
-def test_msort():     combo_check(np.msort, [0])([R(1), R(7)])
+if onp.lib.NumpyVersion(onp.__version__) < '2.0.0':
+    def test_msort():     combo_check(np.msort, [0])([R(1), R(7)])
 def test_partition(): combo_check(np.partition, [0])(
                                   [R(7), R(14)], kth=[0, 3, 6])