fix adamw lr_to_coeff is fixed when dygraph (#30526) (#30559)

python/paddle/fluid/tests/unittests/test_adamw_op.py

@@ -98,15 +98,26 @@ class TestAdamWOp(unittest.TestCase):
         value = np.arange(26).reshape(2, 13).astype("float32")
         a = paddle.to_tensor(value)
         linear = paddle.nn.Linear(13, 5)
+
+        lr = paddle.optimizer.lr.NoamDecay(d_model=0.01, warmup_steps=10)
+        wd = 0.1
         adam = paddle.optimizer.AdamW(
-            learning_rate=paddle.optimizer.lr.NoamDecay(
-                d_model=512, warmup_steps=4000),
+            learning_rate=lr,
             parameters=linear.parameters(),
             apply_decay_param_fun=lambda name: True,
-            weight_decay=0.01)
+            weight_decay=wd)
+
+        for _ in range(2):
             out = linear(a)
             out.backward()
+            lr_to_coeff = adam._lr_to_coeff
             adam.step()
+
+            for i, value in enumerate(lr_to_coeff.values()):
+                self.assertAlmostEqual(value.numpy()[0], 1.0 - lr() * wd)
+            self.assertEqual(len(adam._lr_to_coeff), 0)
+
+            lr.step()
             adam.clear_gradients()
 
 

python/paddle/optimizer/adamw.py

@@ -173,7 +173,10 @@ class AdamW(Adam):
             [param, grad]), framework.name_scope('weight decay'):
             self._params_name.add(param.name)
 
-            # If it has been calculated, the result will be reused
+            # If it has been calculated, the result will be reused.
+            # NOTE(wangxi): In dygraph mode, apply_gradient will be executed
+            # every step, so need clear _lr_to_coeff every step,
+            # we do this in _create_optimization_pass
             decay_coeff = self._lr_to_coeff.get(learning_rate, None)
             if decay_coeff is None:
                 decay_coeff = 1.0 - learning_rate * self._coeff
@@ -186,5 +189,12 @@ class AdamW(Adam):
         self._append_decoupled_weight_decay(block, param_and_grad)
         return super(AdamW, self)._append_optimize_op(block, param_and_grad)
 
+    def _create_optimization_pass(self, parameters_and_grads):
+        optimize_ops = super(
+            AdamW, self)._create_optimization_pass(parameters_and_grads)
+        # In dygraph mode, clear _lr_to_coeff after applied gradient
+        self._lr_to_coeff = dict()
+        return optimize_ops
+
     def __str__(self):
         return " ".join(["Weight Decay, params:", ",".join(self._params_name)])