fix adam (#27343)

* fix adam

* rmsprop support double

paddle/fluid/operators/optimizers/rmsprop_op.cc

@@ -143,4 +143,5 @@ http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(rmsprop, ops::RmspropOp, ops::RmspropOpMaker);
 REGISTER_OP_CPU_KERNEL(
-    rmsprop, ops::RmspropOpKernel<paddle::platform::CPUDeviceContext, float>);
+    rmsprop, ops::RmspropOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::RmspropOpKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/optimizers/rmsprop_op.cu

@@ -15,4 +15,5 @@ limitations under the License. */
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
-    rmsprop, ops::RmspropOpKernel<paddle::platform::CUDADeviceContext, float>);
+    rmsprop, ops::RmspropOpKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::RmspropOpKernel<paddle::platform::CUDADeviceContext, double>);

python/paddle/optimizer/adam.py

@@ -282,14 +282,13 @@ class Adam(Optimizer):
         for param in self._parameter_list:
             if not param.trainable:
                 continue
-            if hasattr(
-                    param, "_is_sparse"
-            ) and param._is_sparse and self.regularization is not None:
+            if param._grad_ivar() is not None:
+                grad_var = param._grad_ivar()
+                if hasattr(grad_var, "_is_sparse") and grad_var._is_sparse(
+                ) and self.regularization is not None:
                     raise RuntimeError(
                         "Adam don't support weight_decay with sparse parameters, please set it to None."
                     )
-            if param._grad_ivar() is not None:
-                grad_var = param._grad_ivar()
                 params_grads.append((param, grad_var))
 
         optimize_ops = self._apply_optimize(