Refactor SoftmaxGrad numba patch

pytensor/link/numba/dispatch/elemwise.py

@@ -402,7 +402,9 @@ def {careduce_fn_name}({input_name}):
     return careduce_fn
 
 
-def jit_compile_reducer(node, fn, *, reduce_to_scalar=False, **kwds):
+def jit_compile_reducer(
+    node, fn, *, reduce_to_scalar=False, infer_signature=True, **kwds
+):
     """Compile Python source for reduction loops using additional optimizations.
 
     Parameters
@@ -411,6 +413,10 @@ def jit_compile_reducer(node, fn, *, reduce_to_scalar=False, **kwds):
         An node from which the signature can be derived.
     fn
         The Python function object to compile.
+    reduce_to_scalar: bool, default False
+        Whether to reduce output to a scalar (instead of 0d array)
+    infer_signature: bool: default True
+        Whether to try and infer the function signature from the Apply node.
     kwds
         Extra keywords to be added to the :func:`numba.njit` function.
 
@@ -419,13 +425,17 @@ def jit_compile_reducer(node, fn, *, reduce_to_scalar=False, **kwds):
     A :func:`numba.njit`-compiled function.
 
     """
+    if infer_signature:
         signature = create_numba_signature(node, reduce_to_scalar=reduce_to_scalar)
+        args = (signature,)
+    else:
+        args = ()
 
     # Eagerly compile the function using increased optimizations.  This should
     # help improve nested loop reductions.
     with use_optimized_cheap_pass():
         res = numba_basic.numba_njit(
-            signature,
+            *args,
             boundscheck=False,
             fastmath=config.numba__fastmath,
             **kwds,
@@ -926,11 +936,7 @@ def numba_funcify_SoftmaxGrad(op, node, **kwargs):
         return dx
 
     # The signature inferred by jit_compile_reducer is wrong when dy is a constant (readonly=True)
-    # softmax_grad = jit_compile_reducer(node, softmax_grad_py_fn)
-    softmax_grad = numba_njit(
-        boundscheck=False,
-        fastmath=config.numba__fastmath,
-    )(softmax_grad_py_fn)
+    softmax_grad = jit_compile_reducer(node, softmax_grad_py_fn, infer_signature=False)
 
     return softmax_grad