Remove deprecation warning on softmax functions

Author: ricardoV94

pytensor/tensor/special.py

@@ -1,4 +1,3 @@
-import warnings
 from textwrap import dedent
 
 import numpy as np
@@ -483,25 +482,8 @@ def c_code_cache_version():
         return (4,)
 
 
-UNSET_AXIS = object()
-
-
-def softmax(c, axis=UNSET_AXIS):
-    if axis is UNSET_AXIS:
-        warnings.warn(
-            "Softmax now accepts an axis argument. For backwards-compatibility it defaults to -1 when not specified, "
-            "but in the future the default will be `None`.\nTo suppress this warning specify axis explicitly.",
-            FutureWarning,
-        )
-        axis = -1
-
+def softmax(c, axis=None):
     c = as_tensor_variable(c)
-    if c.ndim == 1:
-        # TODO: Create Specific warning type that can be suppressed?
-        warnings.warn(
-            "Softmax no longer converts a vector to a row matrix.",
-            UserWarning,
-        )
     return Softmax(axis=axis)(c)
 
 
@@ -749,22 +731,8 @@ def c_code_cache_version():
         return (1,)
 
 
-def log_softmax(c, axis=UNSET_AXIS):
-    if axis is UNSET_AXIS:
-        warnings.warn(
-            "logsoftmax now accepts an axis argument. For backwards-compatibility it defaults to -1 when not specified, "
-            "but in the future the default will be `None`.\nTo suppress this warning specify axis explicitly.",
-            FutureWarning,
-        )
-        axis = -1
-
+def log_softmax(c, axis=None):
     c = as_tensor_variable(c)
-    if c.ndim == 1:
-        # TODO: Create Specific warning type that can be suppressed?
-        warnings.warn(
-            "Softmax no longer converts a vector to a row matrix.",
-            UserWarning,
-        )
     return LogSoftmax(axis=axis)(c)
 
 

tests/d3viz/models.py

@@ -25,7 +25,7 @@ def __init__(self, nfeatures=100, noutputs=10, nhiddens=50, rng=None):
 
         wy = shared(self.rng.normal(0, 1, (nhiddens, noutputs)))
         by = shared(np.zeros(noutputs), borrow=True)
-        y = softmax(at.dot(h, wy) + by)
+        y = softmax(at.dot(h, wy) + by, axis=-1)
         self.inputs = [x]
         self.outputs = [y]
 

tests/tensor/rewriting/test_special.py

@@ -72,7 +72,7 @@ def test_logsoftmax_grad_true_div_elemwise(self):
         """
 
         x = matrix("x")
-        y = log(softmax(x))
+        y = log(softmax(x, axis=-1))
         g = pytensor.tensor.grad(y.sum(), x)
 
         softmax_grad_node = g.owner
@@ -96,7 +96,7 @@ def test_log_softmax_stabilization():
     mode = mode.including("local_log_softmax", "specialize")
 
     x = matrix()
-    y = softmax(x)
+    y = softmax(x, axis=-1)
     z = log(y)
 
     fgraph = FunctionGraph([x], [z])

tests/test_rop.py

@@ -272,7 +272,9 @@ def test_sum(self):
         self.check_mat_rop_lop(self.mx.sum(axis=1), (self.mat_in_shape[0],))
 
     def test_softmax(self):
-        self.check_rop_lop(pytensor.tensor.special.softmax(self.x), self.in_shape)
+        self.check_rop_lop(
+            pytensor.tensor.special.softmax(self.x, axis=-1), self.in_shape
+        )
 
     def test_alloc(self):
         # Alloc of the sum of x into a vector