add grad unittest

python/paddle/tests/test_model.py

@@ -728,18 +728,25 @@ class TestModelFunction(unittest.TestCase):
         inputs = [InputSpec([None, dim], 'float32', 'x')]
         labels = [InputSpec([None, 1], 'int64', 'label')]
 
-        model = Model(net, inputs, labels)
-        model.prepare(optim, loss=CrossEntropyLoss(reduction="sum"))
-        loss1, = model.train_batch([data], [label], update=False)
-        loss2, = model.train_batch([data], [label], update=True)
-        np.testing.assert_almost_equal(loss1, loss2, decimal=4)
-
-        model = Model(net, inputs, labels)
-        model.prepare(
-            optim, loss=CrossEntropyLoss(reduction="sum"), amp_configs='O1')
-        loss1, = model.train_batch([data], [label], update=False)
-        loss2, = model.train_batch([data], [label], update=True)
-        np.testing.assert_almost_equal(loss1, loss2, decimal=4)
+        for amp_cfg in [None, 'O1']:
+            model = Model(net, inputs, labels)
+            model.prepare(
+                optim,
+                loss=CrossEntropyLoss(reduction="sum"),
+                amp_configs=amp_cfg)
+            losses, grads = [], []
+            for stat in [False, False, True]:
+                loss, = model.train_batch([data], [label], update=stat)
+                losses.append(loss)
+                grads.append([p.grad.numpy() for p in net.parameters()])
+
+            for grad1, grad2, grad3 in zip(*grads):
+                np.testing.assert_almost_equal(grad1 * 2, grad2, decimal=4)
+                np.testing.assert_almost_equal(
+                    grad3, np.zeros_like(grad3), decimal=4)
+
+            np.testing.assert_almost_equal(losses[0], losses[1], decimal=4)
+            np.testing.assert_almost_equal(losses[0], losses[2], decimal=4)
 
 
 class TestModelWithLRScheduler(unittest.TestCase):