Fix autograd

brainpy/_src/math/object_transform/autograd.py

@@ -94,7 +94,6 @@ def __init__(
     self.target = target
 
     # transform
-    self._eval_dyn_vars = False
     self._grad_transform = transform
     self._dyn_vars = VariableStack()
     self._transform = None
@@ -198,20 +197,18 @@ def __call__(self, *args, **kwargs):
       )
       return self._return(rets)
 
-    elif not self._eval_dyn_vars:  # evaluate dynamical variables
-      stack = get_stack_cache(self.target)
-      if stack is None:
-        with VariableStack() as stack:
-          rets = eval_shape(self._transform,
-                            [v.value for v in self._grad_vars],  # variables for gradients
-                            {},  # dynamical variables
-                            *args,
-                            **kwargs)
-          cache_stack(self.target, stack)
-
+    # evaluate dynamical variables
+    stack = get_stack_cache(self.target)
+    if stack is None:
+      with VariableStack() as stack:
+        rets = eval_shape(self._transform,
+                          [v.value for v in self._grad_vars],  # variables for gradients
+                          {},  # dynamical variables
+                          *args,
+                          **kwargs)
+        cache_stack(self.target, stack)
       self._dyn_vars = stack
       self._dyn_vars.remove_by_id(*[id(v) for v in self._grad_vars])
-      self._eval_dyn_vars = True
 
       # if not the outermost transformation
       if not stack.is_first_stack():

brainpy/_src/math/object_transform/tests/test_autograd.py

@@ -86,6 +86,17 @@ def call(a, b, c):
     assert aux[1] == bm.exp(0.1)
 
 
+  def test_grad_jit(self):
+    def call(a, b, c): return bm.sum(a + b + c)
+
+    bm.random.seed(1)
+    a = bm.ones(10)
+    b = bm.random.randn(10)
+    c = bm.random.uniform(size=10)
+    f_grad = bm.jit(bm.grad(call))
+    assert (f_grad(a, b, c) == 1.).all()
+
+
 class TestObjectFuncGrad(unittest.TestCase):
   def test_grad_ob1(self):
     class Test(bp.BrainPyObject):