Add _get_parameter method to Lamb optimizer (#39416)

* add _get_parameter func to lamb

* remove duplicate code

python/paddle/fluid/tests/unittests/test_lambv2_op.py

@@ -195,32 +195,57 @@ class TestLambOpMultiPrecision(unittest.TestCase):
                 hidden = linear(x)
                 loss = paddle.mean(hidden)
 
-            optimizer = paddle.optimizer.Lamb(learning_rate=1e-3)
-            optimizer._multi_precision = multi_precision
+            original_optimizer = paddle.optimizer.Lamb(learning_rate=1e-3)
+            original_optimizer._multi_precision = multi_precision
             if multi_precision:
                 optimizer = paddle.static.amp.decorate(
-                    optimizer, use_pure_fp16=True, use_fp16_guard=True)
+                    original_optimizer, use_pure_fp16=True, use_fp16_guard=True)
+            else:
+                optimizer = original_optimizer
             optimizer.minimize(loss)
 
         weight, bias = linear.weight, linear.bias
-        scope = paddle.static.Scope()
         exe = paddle.static.Executor(place)
         scope = paddle.static.Scope()
         x = main_prog.global_block().var(x.name)
         if x.dtype == core.VarDesc.VarType.FP16:
             x_np = x_np.astype(np.float16)
 
+        def get_parameter(var):
+            name = var if isinstance(var, (str, bytes)) else var.name
+            params = original_optimizer._get_parameter(name, scope)
+            assert isinstance(params, (list, tuple))
+            params = list(params)
+            assert len(params) == 2
+            if multi_precision:
+                params[0] = np.array(params[0])
+                params[1] = np.array(params[1])
+                self.assertTrue(
+                    np.array_equal(params[0], params[1].astype(np.float16)))
+                return params[0].astype(np.float32)
+            else:
+                self.assertTrue(params[0] is not None)
+                self.assertTrue(params[1] is None)
+                params[0] = np.array(params[0])
+                return params[0]
+
         with paddle.static.scope_guard(scope):
             exe.run(startup_prog)
             if multi_precision:
                 optimizer.amp_init(place)
+
             weight_np, bias_np = None, None
             for i in range(n):
                 feed_dict = {x.name: x_np}
                 weight_np, bias_np = exe.run(main_prog,
                                              feed=feed_dict,
                                              fetch_list=[weight, bias])
-        return weight_np.astype('float32'), bias_np.astype('float32')
+                weight_np = weight_np.astype('float32')
+                bias_np = bias_np.astype('float32')
+                self.assertTrue(
+                    np.array_equal(weight_np, get_parameter(weight)))
+                self.assertTrue(np.array_equal(bias_np, get_parameter(bias)))
+            return weight_np, bias_np
 
     @switch_to_static_graph
     def test_main(self):

python/paddle/optimizer/lamb.py

@@ -20,6 +20,7 @@ from ..fluid import layers
 from ..fluid import unique_name
 from ..fluid.layer_helper import LayerHelper
 from paddle import _C_ops
+from paddle.fluid.executor import global_scope
 
 __all__ = []
 
@@ -131,9 +132,25 @@ class Lamb(Optimizer):
             'exclude_from_weight_decay_fn': exclude_from_weight_decay_fn,
         }
         self._master_weights = {}
+        self._used_master_weights = {}
         # TODO(zengjinle): expose API as soon as possible
         self._multi_precision = False
 
+    def _get_parameter(self, name, scope=None):
+        if scope is None:
+            scope = global_scope()
+
+        p_t = scope.find_var(name).get_tensor()
+
+        master_name = self._used_master_weights.get(name)
+        if master_name is not None:
+            master_p_t = scope.find_var(master_name).get_tensor()
+            assert master_p_t._dtype() != p_t._dtype()
+            assert master_p_t.shape() == p_t.shape()
+        else:
+            master_p_t = None
+        return p_t, master_p_t
+
     def _create_master_weight(self, param):
         assert self._multi_precision
         if param.name in self._master_weights:
@@ -243,8 +260,12 @@ class Lamb(Optimizer):
 
         find_master = self._multi_precision and param_and_grad[
             0].dtype == core.VarDesc.VarType.FP16
-        master_weight = self._master_weights[param_and_grad[0]
-                                             .name] if find_master else None
+        p_name = param_and_grad[0].name
+        if find_master:
+            master_weight = self._master_weights[p_name]
+            self._used_master_weights[p_name] = master_weight.name
+        else:
+            master_weight = None
         found_inf = self._get_auxiliary_var('found_inf')
 
         if framework.in_dygraph_mode():