[phi] Transfer merged_adam yaml to phi (#45367)

* add legacy_api.yaml

* set merged_momentum inplace only

* support inplace optional<vector<tensor>>

* add dygraph_mode api

* add merged_adam yaml

* add merged_adam python api

* change testcase of merged_adam and adam

* fix import of test_merged_adam_op

paddle/phi/api/yaml/legacy_api.yaml

@@ -1730,6 +1730,17 @@
     func : mean_all
   backward : mean_all_grad
 
+- api : merged_adam_
+  args : (Tensor[] param, Tensor[] grad, Tensor[] learning_rate, Tensor[] moment1, Tensor[] moment2, Tensor[] beta1_pow, Tensor[] beta2_pow, Tensor[] master_param, Scalar beta1, Scalar beta2, Scalar epsilon, bool multi_precision, bool use_global_beta_pow)
+  output : Tensor[](param_out){param.size()}, Tensor[](moment1_out){param.size()}, Tensor[](moment2_out){param.size()}, Tensor[](beta1_pow_out){param.size()}, Tensor[](beta2_pow_out){param.size()}, Tensor[](master_param_out){param.size()}
+  infer_meta :
+    func : MergedAdamInferMeta
+  optional: master_param
+  kernel :
+    func : merged_adam
+    data_type : param
+  inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_out)
+
 - api : merged_momentum_
   args : (Tensor[] param, Tensor[] grad, Tensor[] velocity, Tensor[] learning_rate, Tensor[] master_param, float mu, bool use_nesterov = false, str[] regularization_method = {}, float[] regularization_coeff = {}, bool multi_precision = false, float rescale_grad = 1.0f)
   output : Tensor[](param_out){param.size()}, Tensor[](velocity_out){param.size()}, Tensor[](master_param_out){param.size()}

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

@@ -1230,6 +1230,10 @@ class TestMultiTensorAdam(unittest.TestCase):
                 self._check_with_param_arrt(place, use_amp)
                 self._check_with_param_group(place, use_amp)
 
+    def test_api_eager_dygraph(self):
+        with _test_eager_guard():
+            self.test_main()
+
 
 if __name__ == "__main__":
     paddle.enable_static()

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

@@ -16,6 +16,7 @@ import unittest
 import paddle
 import numpy as np
 from paddle import _C_ops, _legacy_C_ops
+from paddle.fluid.framework import in_dygraph_mode
 
 
 def run_adam_op(params,
@@ -63,12 +64,18 @@ def run_adam_op(params,
                 master_param_vars[i], 'epsilon', epsilon, 'beta1', beta1,
                 'beta2', beta2, 'multi_precision', multi_precision)
     else:
-        _, _, _, _, _, _ = _legacy_C_ops.merged_adam(
-            param_vars, grad_vars, lr_vars, moment1_vars, moment2_vars,
-            beta1_pow_vars, beta2_pow_vars, master_param_vars, param_vars,
-            moment1_vars, moment2_vars, beta1_pow_vars, beta2_pow_vars,
-            master_param_vars, 'epsilon', epsilon, 'beta1', beta1, 'beta2',
-            beta2, 'multi_precision', multi_precision)
+        if in_dygraph_mode():
+            _, _, _, _, _, _ = _C_ops.merged_adam_(
+                param_vars, grad_vars, lr_vars, moment1_vars, moment2_vars,
+                beta1_pow_vars, beta2_pow_vars, master_param_vars, beta1, beta2,
+                epsilon, multi_precision, False)
+        else:
+            _, _, _, _, _, _ = _legacy_C_ops.merged_adam(
+                param_vars, grad_vars, lr_vars, moment1_vars, moment2_vars,
+                beta1_pow_vars, beta2_pow_vars, master_param_vars, param_vars,
+                moment1_vars, moment2_vars, beta1_pow_vars, beta2_pow_vars,
+                master_param_vars, 'epsilon', epsilon, 'beta1', beta1, 'beta2',
+                beta2, 'multi_precision', multi_precision)
 
     outputs = {
         'ParamOut': param_vars,

python/paddle/optimizer/adam.py

@@ -583,18 +583,28 @@ class Adam(Optimizer):
                     self._beta2, Variable) else self._beta2.numpy().item(0)
 
                 if framework._non_static_mode():
-                    _, _, _, _, _, _ = _legacy_C_ops.merged_adam(
-                        self._param_dict[key], grad_dict[key], lr_dict[key],
-                        self._moment1_dict[key], self._moment2_dict[key],
-                        self._beta1_pow_acc_dict[key],
-                        self._beta2_pow_acc_dict[key],
-                        self._master_weight_dict[key], self._param_dict[key],
-                        self._moment1_dict[key], self._moment2_dict[key],
-                        self._beta1_pow_acc_dict[key],
-                        self._beta2_pow_acc_dict[key],
-                        self._master_weight_dict[key], 'epsilon', self._epsilon,
-                        'beta1', _beta1, 'beta2', _beta2, 'multi_precision',
-                        find_master)
+                    if in_dygraph_mode():
+                        _, _, _, _, _, _ = _C_ops.merged_adam_(
+                            self._param_dict[key], grad_dict[key], lr_dict[key],
+                            self._moment1_dict[key], self._moment2_dict[key],
+                            self._beta1_pow_acc_dict[key],
+                            self._beta2_pow_acc_dict[key],
+                            self._master_weight_dict[key], _beta1, _beta2,
+                            self._epsilon, find_master, False)
+                    else:
+                        _, _, _, _, _, _ = _legacy_C_ops.merged_adam(
+                            self._param_dict[key], grad_dict[key], lr_dict[key],
+                            self._moment1_dict[key], self._moment2_dict[key],
+                            self._beta1_pow_acc_dict[key],
+                            self._beta2_pow_acc_dict[key],
+                            self._master_weight_dict[key],
+                            self._param_dict[key], self._moment1_dict[key],
+                            self._moment2_dict[key],
+                            self._beta1_pow_acc_dict[key],
+                            self._beta2_pow_acc_dict[key],
+                            self._master_weight_dict[key], 'epsilon',
+                            self._epsilon, 'beta1', _beta1, 'beta2', _beta2,
+                            'multi_precision', find_master)
                 else:
                     inputs = {
                         "Param": self._param_dict[key],