Change trainable to stop_gradient in optimizer

python/paddle/optimizer/adam.py

@@ -351,7 +351,7 @@ def step(self):
         """
         params_grads = []
         for param in self._parameter_list:
-            if not param.trainable:
+            if param.stop_gradient:
                 continue
             if param._grad_ivar() is not None:
                 grad_var = param._grad_ivar()

python/paddle/optimizer/adamax.py

@@ -184,7 +184,7 @@ def _finish_update(self, block, parameters_and_grads):
         """
         assert isinstance(block, framework.Block)
         for param, grad in parameters_and_grads:
-            if grad is None or param.trainable is False:
+            if grad is None or param.stop_gradient is True:
                 continue
             with param.block.program._optimized_guard(
                 [param, grad]), name_scope('adamax'):

python/paddle/optimizer/optimizer.py

@@ -542,7 +542,7 @@ def _get_accumulator(self, name, param):
 
     def _update_param_device_map(self, parameters_and_grads, target_block):
         for param_and_grad in parameters_and_grads:
-            if param_and_grad[0].trainable is True:
+            if param_and_grad[0].stop_gradient is False:
                 param_name = param_and_grad[0].name
                 ops = target_block.ops
                 device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName(
@@ -598,22 +598,22 @@ def _create_optimization_pass(self, parameters_and_grads):
         self._update_param_device_map(parameters_and_grads, target_block)
         self._create_accumulators(
             target_block,
-            [p[0] for p in parameters_and_grads if p[0].trainable])
+            [p[0] for p in parameters_and_grads if not p[0].stop_gradient])
         self._create_global_learning_rate()
 
         if framework.in_dygraph_mode():
             for param_and_grad in parameters_and_grads:
                 if param_and_grad[1] is None:
                     continue
-                if param_and_grad[0].trainable is True:
+                if param_and_grad[0].stop_gradient is False:
                     self._append_optimize_op(target_block, param_and_grad)
         else:
             for param_and_grad in parameters_and_grads:
                 if param_and_grad[1] is None:
                     continue
                 with param_and_grad[0].block.program._optimized_guard(
                         param_and_grad), name_scope("optimizer"):
-                    if param_and_grad[0].trainable is True:
+                    if param_and_grad[0].stop_gradient is False:
                         device = self._get_device_for_param(param_and_grad[0]
                                                             .name)
                         with device_guard(device):
@@ -689,7 +689,7 @@ def backward(self,
 
             params_grads = []
             for param in parameter_list:
-                if not param.trainable:
+                if param.stop_gradient:
                     continue
                 if param._grad_ivar() is not None:
                     # create gradient tensor
@@ -789,8 +789,9 @@ def _apply_optimize(self, loss, startup_program, params_grads):
     def _get_no_grad_set(self, loss, no_grad_set=None):
         no_grad_set = _get_no_grad_set_name(no_grad_set)
         parameters = loss.block.program.global_block().all_parameters()
-        param_no_trainable = set(
-            [param.name for param in parameters if param.trainable is False])
+        param_no_trainable = set([
+            param.name for param in parameters if param.stop_gradient is True
+        ])
         # If the parameter is no trainable, it should not have a gradient.
         no_grad_set.update(param_no_trainable)
 
@@ -825,7 +826,7 @@ def clear_grad(self):
 
         """
         for p in self._parameter_list:
-            if p.trainable:
+            if not p.stop_gradient:
                 p.clear_gradient()
 
     @imperative_base.no_grad
@@ -920,7 +921,7 @@ def step(self):
         """
         params_grads = []
         for param in self._parameter_list:
-            if not param.trainable:
+            if param.stop_gradient:
                 continue
             if param._grad_ivar() is not None:
                 grad_var = param._grad_ivar()