Deprecated redefinition of np.allclose in _allclose

Author: Dhruvanshu-Joshi

pytensor/tensor/math.py

@@ -130,18 +130,6 @@ def _get_atol_rtol(a, b):
     return atol, rtol
 
 
-def _allclose(a, b, rtol=None, atol=None):
-    a = np.asarray(a)
-    b = np.asarray(b)
-    atol_, rtol_ = _get_atol_rtol(a, b)
-    if rtol is not None:
-        rtol_ = rtol
-    if atol is not None:
-        atol_ = atol
-
-    return np.allclose(a, b, atol=atol_, rtol=rtol_)
-
-
 class Argmax(COp):
     """
     Calculate the argmax over a given axis or over all axes.

pytensor/tensor/type.py

@@ -662,7 +662,12 @@ def values_eq_approx(
         if str(a.dtype) not in continuous_dtypes:
             return np.all(a == b)
         else:
-            cmp = pytensor.tensor.math._allclose(a, b, rtol=rtol, atol=atol)
+            atol_, rtol_ = pytensor.tensor.math._get_atol_rtol(a, b)
+            if rtol is not None:
+                rtol_ = rtol
+            if atol is not None:
+                atol_ = atol
+            cmp = np.allclose(np.asarray(a), np.asarray(a), rtol=rtol_, atol=atol_)
             if cmp:
                 # Numpy claims they are close, this is good enough for us.
                 return True

tests/graph/test_compute_test_value.py

@@ -10,7 +10,7 @@
 from pytensor.graph.op import Op
 from pytensor.graph.type import Type
 from pytensor.link.c.op import COp
-from pytensor.tensor.math import _allclose, dot
+from pytensor.tensor.math import _get_atol_rtol, dot
 from pytensor.tensor.type import fmatrix, iscalar, matrix, vector
 
 
@@ -85,7 +85,15 @@ def test_variable_only(self):
         z = dot(x, y)
         assert hasattr(z.tag, "test_value")
         f = pytensor.function([x, y], z)
-        assert _allclose(f(x.tag.test_value, y.tag.test_value), z.tag.test_value)
+        atol_, rtol_ = _get_atol_rtol(
+            f(x.tag.test_value, y.tag.test_value), z.tag.test_value
+        )
+        assert np.allclose(
+            f(x.tag.test_value, y.tag.test_value),
+            z.tag.test_value,
+            atol=atol_,
+            rtol=rtol_,
+        )
 
         # this test should fail
         y.tag.test_value = np.random.random((6, 5)).astype(config.floatX)
@@ -122,7 +130,16 @@ def test_string_var(self):
         out = dot(dot(x, y), z)
         assert hasattr(out.tag, "test_value")
         tf = pytensor.function([x, y], out)
-        assert _allclose(tf(x.tag.test_value, y.tag.test_value), out.tag.test_value)
+
+        atol_, rtol_ = _get_atol_rtol(
+            tf(x.tag.test_value, y.tag.test_value), out.tag.test_value
+        )
+        assert np.allclose(
+            tf(x.tag.test_value, y.tag.test_value),
+            out.tag.test_value,
+            atol=atol_,
+            rtol=rtol_,
+        )
 
         def f(x, y, z):
             return dot(dot(x, y), z)
@@ -141,7 +158,10 @@ def test_shared(self):
         z = dot(x, y)
         assert hasattr(z.tag, "test_value")
         f = pytensor.function([x], z)
-        assert _allclose(f(x.tag.test_value), z.tag.test_value)
+        atol_, rtol_ = _get_atol_rtol(f(x.tag.test_value), z.tag.test_value)
+        assert np.allclose(
+            f(x.tag.test_value), z.tag.test_value, atol=atol_, rtol=rtol_
+        )
 
         # this test should fail
         y.set_value(np.random.random((5, 6)).astype(config.floatX))
@@ -156,7 +176,8 @@ def test_ndarray(self):
         z = dot(x, y)
         assert hasattr(z.tag, "test_value")
         f = pytensor.function([], z)
-        assert _allclose(f(), z.tag.test_value)
+        atol_, rtol_ = _get_atol_rtol(f(), z.tag.test_value)
+        assert np.allclose(f(), z.tag.test_value, atol=atol_, rtol=rtol_)
 
         # this test should fail
         x = np.random.random((2, 4)).astype(config.floatX)
@@ -170,7 +191,8 @@ def test_empty_elemwise(self):
         z = (x + 2) * 3
         assert hasattr(z.tag, "test_value")
         f = pytensor.function([], z)
-        assert _allclose(f(), z.tag.test_value)
+        atol_, rtol_ = _get_atol_rtol(f(), z.tag.test_value)
+        assert np.allclose(f(), z.tag.test_value, atol=atol_, rtol=rtol_)
 
     def test_constant(self):
         x = pt.constant(np.random.random((2, 3)), dtype=config.floatX)
@@ -180,7 +202,8 @@ def test_constant(self):
         z = dot(x, y)
         assert hasattr(z.tag, "test_value")
         f = pytensor.function([], z)
-        assert _allclose(f(), z.tag.test_value)
+        atol_, rtol_ = _get_atol_rtol(f(), z.tag.test_value)
+        assert np.allclose(f(), z.tag.test_value, atol=atol_, rtol=rtol_)
 
         # this test should fail
         x = pt.constant(np.random.random((2, 4)), dtype=config.floatX)

tests/scan/test_rewriting.py

@@ -1270,7 +1270,7 @@ def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, y_tm3, W_in1):
 
         (pytensor_dump, pytensor_x, pytensor_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
 
-        np.testing.assert_allclose(pytensor_x, v_x[-1:][0])
+        np.testing.assert_allclose(pytensor_x, v_x[-1:].squeeze(0))
         np.testing.assert_allclose(pytensor_y, v_y[-1:])
 
     def test_save_mem_reduced_number_of_steps(self):

tests/sparse/test_basic.py

@@ -2091,7 +2091,6 @@ def test_op(self, op_type):
                 f = pytensor.function(variable, self.op(variable[0], axis=axis))
                 tested = f(*data)
                 expected = data[0].todense().sum(axis).ravel()
-
                 np.testing.assert_allclose(expected, [tested], atol=1e-08, rtol=1e-05)
 
     def test_infer_shape(self):

tests/tensor/conv/test_abstract_conv.py

@@ -38,9 +38,6 @@
 from tests.tensor.conv import c_conv3d_corr3d_ref, c_conv_corr_ref
 
 
-pytensor.config.mode = "FAST_COMPILE"
-
-
 def conv2d_corr(
     inputs,
     filters,
@@ -2414,7 +2411,6 @@ def test_fwd(self):
                 for j in range(0, kshp[2]):
                     single_kern = kern[:, i, j, ...].reshape(single_kshp)
                     ref_val = ref_func(img, single_kern)
-
                     np.testing.assert_allclose(
                         ref_val[:, :, i, j],
                         unshared_output[:, :, i, j],

tests/tensor/test_basic.py

@@ -8,7 +8,6 @@
 import pytensor
 import pytensor.scalar as ps
 import pytensor.tensor.basic as ptb
-import pytensor.tensor.math as ptm
 from pytensor import compile, config, function, shared
 from pytensor.compile import SharedVariable
 from pytensor.compile.io import In, Out
@@ -1258,11 +1257,6 @@ def test_cast_from_complex_to_real_raises_error(self, real_dtype, complex_dtype)
 #      gradient numerically
 
 
-def test_basic_allclose():
-    # This was raised by a user in https://github.com/Theano/Theano/issues/2975
-    assert ptm._allclose(-0.311023883434, -0.311022856884)
-
-
 def test_get_vector_length():
     # Test `Constant`s
     empty_tuple = as_tensor_variable(())

tests/tensor/test_math.py

@@ -45,8 +45,8 @@
     Prod,
     ProdWithoutZeros,
     Sum,
-    _allclose,
     _dot,
+    _get_atol_rtol,
     abs,
     add,
     allclose,
@@ -3608,7 +3608,8 @@ def setup_method(self):
     def _validate_output(self, a, b):
         pytensor_sol = self.op(a, b).eval()
         numpy_sol = np.matmul(a, b)
-        assert _allclose(numpy_sol, pytensor_sol)
+        atol_, rtol_ = _get_atol_rtol(numpy_sol, pytensor_sol)
+        assert np.allclose(numpy_sol, pytensor_sol, atol=atol_, rtol=rtol_)
 
     @pytest.mark.parametrize(
         "x1, x2",

tests/tensor/test_nlinalg.py

@@ -9,7 +9,7 @@
 from pytensor import function
 from pytensor.configdefaults import config
 from pytensor.tensor.basic import as_tensor_variable
-from pytensor.tensor.math import _allclose
+from pytensor.tensor.math import _get_atol_rtol
 from pytensor.tensor.nlinalg import (
     SVD,
     Eig,
@@ -60,7 +60,8 @@ def test_pseudoinverse_correctness():
     assert ri.dtype == r.dtype
     # Note that pseudoinverse can be quite imprecise so I prefer to compare
     # the result with what np.linalg returns
-    assert _allclose(ri, np.linalg.pinv(r))
+    atol_, rtol_ = _get_atol_rtol(ri, np.linalg.pinv(r))
+    assert np.allclose(ri, np.linalg.pinv(r), atol=atol_, rtol=rtol_)
 
 
 def test_pseudoinverse_grad():
@@ -92,8 +93,11 @@ def test_inverse_correctness(self):
         rir = np.dot(ri, r)
         rri = np.dot(r, ri)
 
-        assert _allclose(np.identity(4), rir), rir
-        assert _allclose(np.identity(4), rri), rri
+        atol_, rtol_ = _get_atol_rtol(np.identity(4), rir)
+        assert np.allclose(np.identity(4), rir, atol=atol_, rtol=rtol_), rir
+
+        atol_, rtol_ = _get_atol_rtol(np.identity(4), rri)
+        assert np.allclose(np.identity(4), rri, atol=atol_, rtol=rtol_), rri
 
     def test_infer_shape(self):
         r = self.rng.standard_normal((4, 4)).astype(config.floatX)
@@ -119,7 +123,8 @@ def test_matrix_dot():
     for r in rs[1:]:
         numpy_sol = np.dot(numpy_sol, r)
 
-    assert _allclose(numpy_sol, pytensor_sol)
+    atol_, rtol_ = _get_atol_rtol(numpy_sol, pytensor_sol)
+    assert np.allclose(numpy_sol, pytensor_sol, atol=atol_, rtol=rtol_)
 
 
 def test_qr_modes():
@@ -131,23 +136,34 @@ def test_qr_modes():
     f = function([A], qr(A))
     t_qr = f(a)
     n_qr = np.linalg.qr(a)
-    assert _allclose(n_qr, t_qr)
+    atol_, rtol_ = _get_atol_rtol(np.asarray(n_qr), np.asarray(t_qr))
+    assert np.allclose(np.asarray(n_qr), np.asarray(t_qr), atol=atol_, rtol=rtol_)
 
     for mode in ["reduced", "r", "raw"]:
         f = function([A], qr(A, mode))
         t_qr = f(a)
         n_qr = np.linalg.qr(a, mode)
         if isinstance(n_qr, list | tuple):
-            assert _allclose(n_qr[0], t_qr[0])
-            assert _allclose(n_qr[1], t_qr[1])
+            atol_, rtol_ = _get_atol_rtol(np.asarray(n_qr[0]), np.asarray(t_qr[0]))
+            assert np.allclose(
+                np.asarray(n_qr[0]), np.asarray(t_qr[0]), atol=atol_, rtol=rtol_
+            )
+            atol_, rtol_ = _get_atol_rtol(np.asarray(n_qr[1]), np.asarray(t_qr[1]))
+            assert np.allclose(
+                np.asarray(n_qr[1]), np.asarray(t_qr[1]), atol=atol_, rtol=rtol_
+            )
         else:
-            assert _allclose(n_qr, t_qr)
+            atol_, rtol_ = _get_atol_rtol(np.asarray(n_qr), np.asarray(t_qr))
+            assert np.allclose(
+                np.asarray(n_qr), np.asarray(t_qr), atol=atol_, rtol=rtol_
+            )
 
     try:
         n_qr = np.linalg.qr(a, "complete")
         f = function([A], qr(A, "complete"))
         t_qr = f(a)
-        assert _allclose(n_qr, t_qr)
+        atol_, rtol_ = _get_atol_rtol(np.asarray(n_qr), np.asarray(t_qr))
+        assert np.allclose(np.asarray(n_qr), np.asarray(t_qr), atol=atol_, rtol=rtol_)
     except TypeError as e:
         assert "name 'complete' is not defined" in str(e)
 
@@ -199,7 +215,8 @@ def test_svd(self, core_shape, full_matrix, compute_uv, batched, test_imag):
         np_outputs = np_outputs if isinstance(np_outputs, tuple) else [np_outputs]
 
         for np_val, pt_val in zip(np_outputs, pt_outputs):
-            assert _allclose(np_val, pt_val)
+            atol_, rtol_ = _get_atol_rtol(np_val, pt_val)
+            assert np.allclose(np_val, pt_val, atol=atol_, rtol=rtol_)
 
     def test_svd_infer_shape(self):
         self.validate_shape((4, 4), full_matrices=True, compute_uv=True)
@@ -306,7 +323,8 @@ def test_tensorsolve():
 
     n_x = np.linalg.tensorsolve(a, b)
     t_x = fn(a, b)
-    assert _allclose(n_x, t_x)
+    atol_, rtol_ = _get_atol_rtol(n_x, np.asarray(t_x))
+    assert np.allclose(n_x, t_x, atol=atol_, rtol=rtol_)
 
     # check the type upcast now
     C = tensor4("C", dtype="float32")
@@ -319,7 +337,8 @@ def test_tensorsolve():
     d = rng.random((2 * 3, 4)).astype("float64")
     n_y = np.linalg.tensorsolve(c, d)
     t_y = fn(c, d)
-    assert _allclose(n_y, t_y)
+    atol_, rtol_ = _get_atol_rtol(n_y, np.asarray(t_y))
+    assert np.allclose(n_y, t_y, atol=atol_, rtol=rtol_)
     assert n_y.dtype == Y.dtype
 
     # check the type upcast now
@@ -333,7 +352,8 @@ def test_tensorsolve():
     f = rng.random((2 * 3, 4)).astype("float64")
     n_z = np.linalg.tensorsolve(e, f)
     t_z = fn(e, f)
-    assert _allclose(n_z, t_z)
+    atol_, rtol_ = _get_atol_rtol(n_z, np.asarray(t_z))
+    assert np.allclose(n_z, t_z, atol=atol_, rtol=rtol_)
     assert n_z.dtype == Z.dtype
 
 
@@ -653,7 +673,8 @@ def test_eval(self):
         n_ainv = np.linalg.tensorinv(self.a)
         tf_a = function([A], [Ai])
         t_ainv = tf_a(self.a)
-        assert _allclose(n_ainv, t_ainv)
+        atol_, rtol_ = _get_atol_rtol(n_ainv, np.asarray(t_ainv))
+        assert np.allclose(n_ainv, t_ainv, atol=atol_, rtol=rtol_)
 
         B = self.B
         Bi = tensorinv(B)
@@ -664,8 +685,10 @@ def test_eval(self):
         tf_b1 = function([B], [Bi1])
         t_binv = tf_b(self.b)
         t_binv1 = tf_b1(self.b1)
-        assert _allclose(t_binv, n_binv)
-        assert _allclose(t_binv1, n_binv1)
+        atol_, rtol_ = _get_atol_rtol(np.asarray(t_binv), n_binv)
+        assert np.allclose(t_binv, n_binv, atol=atol_, rtol=rtol_)
+        atol_, rtol_ = _get_atol_rtol(np.asarray(t_binv1), n_binv1)
+        assert np.allclose(t_binv1, n_binv1, atol=atol_, rtol=rtol_)
 
 
 class TestKron(utt.InferShapeTester):