Add SoftmaxGrad numba dispatch

aesara/link/numba/dispatch/elemwise.py

@@ -21,7 +21,7 @@ from aesara.link.utils import (
 )
 from aesara.tensor.elemwise import CAReduce, DimShuffle, Elemwise
 from aesara.tensor.math import MaxAndArgmax
-from aesara.tensor.nnet.basic import LogSoftmax, Softmax
+from aesara.tensor.nnet.basic import LogSoftmax, Softmax, SoftmaxGrad
 from aesara.tensor.type import tensor
 
 
@@ -424,6 +424,31 @@ def numba_funcify_Softmax(op, node, **kwargs):
     return softmax
 
 
+@numba_funcify.register(SoftmaxGrad)
+def numba_funcify_SoftmaxGrad(op, node, **kwargs):
+
+    sm_at = node.inputs[1]
+    sm_dtype = sm_at.type.numpy_dtype
+    sm_dtype = numba.np.numpy_support.from_dtype(sm_dtype)
+
+    axis = op.axis
+    if axis is not None:
+        reduce_sum = create_axis_reducer(
+            np.add, 0.0, axis, sm_at.ndim, sm_dtype, keepdims=True
+        )
+    else:
+        reduce_sum = np.sum
+
+    @numba.njit
+    def softmax_grad(dy, sm):
+        dy_times_sm = dy * sm
+        sum_dy_times_sm = reduce_sum(dy_times_sm)
+        dx = dy_times_sm - sum_dy_times_sm * sm
+        return dx
+
+    return softmax_grad
+
+
 @numba_funcify.register(LogSoftmax)
 def numba_funcify_LogSoftmax(op, node, **kwargs):
 

tests/link/test_numba.py

@@ -1893,6 +1893,51 @@ def test_Dot(x, y, exc):
         )
 
 
+@pytest.mark.parametrize(
+    "dy, sm, axis, exc",
+    [
+        (
+            set_test_value(
+                aet.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
+            ),
+            set_test_value(aet.matrix(), rng.random(size=(2, 3)).astype(config.floatX)),
+            None,
+            None,
+        ),
+        (
+            set_test_value(
+                aet.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
+            ),
+            set_test_value(aet.matrix(), rng.random(size=(2, 3)).astype(config.floatX)),
+            0,
+            None,
+        ),
+        (
+            set_test_value(
+                aet.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
+            ),
+            set_test_value(aet.matrix(), rng.random(size=(2, 3)).astype(config.floatX)),
+            1,
+            None,
+        ),
+    ],
+)
+def test_SoftmaxGrad(dy, sm, axis, exc):
+    g = nnetb.SoftmaxGrad(axis=axis)(dy, sm)
+    g_fg = FunctionGraph(outputs=[g])
+
+    cm = contextlib.suppress() if exc is None else pytest.warns(exc)
+    with cm:
+        compare_numba_and_py(
+            g_fg,
+            [
+                i.tag.test_value
+                for i in g_fg.inputs
+                if not isinstance(i, (SharedVariable, Constant))
+            ],
+        )
+
+
 @pytest.mark.parametrize(
     "x, axis, exc",
     [