move adhoc executor first for priority in autograd (#1569)

thunder/__init__.py

@@ -335,7 +335,7 @@ def jit(
     # Resolve names of executors
     executors = resolve_executors(executors)
     ad_hoc_executor = extend.AdHocExecutor()
-    executors = (*executors, ad_hoc_executor)
+    executors = (ad_hoc_executor, *executors)
 
     # TODO: verify that tutorials don't have false positives and enable warning by default
     # # Make sharp_edges == warn default if not supplied and if in the general jit

thunder/tests/test_grad.py

@@ -1853,3 +1853,36 @@ def forward(x):
     actual_grad = torch.autograd.grad(actual, x, grad_o)
     expected_grad = torch.autograd.grad(expected, x, grad_o)
     torch.testing.assert_close(actual_grad, expected_grad)
+
+
+@instantiate(
+    dtypes=NOTHING,
+)
+def test_adhoc_executor_grad(executor, device, _):
+    import torch
+    import thunder
+
+    x = torch.ones(2, device=device, requires_grad=True)
+
+    class Sin(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, x):
+            ctx.save_for_backward(x)
+            return torch.sin(x)
+
+        @staticmethod
+        def backward(ctx, g):
+            (x,) = ctx.saved_tensors
+            return g * torch.cos(x) * 200
+
+    def func(x):
+        return Sin.apply(x)
+
+    cfunc = thunder.jit(func)
+    actual = cfunc(x)
+    (actual_gr,) = torch.autograd.grad(actual.sum(), x)
+    expected = func(x)
+    (expected_gr,) = torch.autograd.grad(expected.sum(), x)
+
+    torch.testing.assert_close(actual, expected)
+    torch.testing.assert_close(actual_gr, expected_gr)