support kwarg maximize

Author: algorithm1832

python/paddle/optimizer/optimizer.py

@@ -161,6 +161,9 @@ class Optimizer:
             For more information, please refer to :ref:`api_guide_Name`.
             The default value is None.
 
+    Keyword Args:
+        maximize (bool, optional): Maximize the objective with respect to the params, instead of minimizing. The default value is False.
+
     Returns:
        Base class for optimizer.
 
@@ -218,6 +221,8 @@ def __init__(
         weight_decay: float | WeightDecayRegularizer | None = None,
         grad_clip: GradientClipBase | None = None,
         name: str | None = None,
+        *,
+        maximize: bool = False,
     ) -> None:
         if parameters is not None:
             # paddle.Tensor is also iterable, so here we don't check whether
@@ -274,6 +279,7 @@ def __init__(
             self.regularization = weight_decay
         self._grad_clip = grad_clip
         self._learning_rate = learning_rate
+        self._maximize = maximize
 
         self._dtype = None
         # Infer the dtype form parameter
@@ -2037,7 +2043,10 @@ def _declarative_step(self):
                 parameters,
             )
         )
-        params_grads = [(param, param.grad) for param in parameters]
+        if self._maximize is True:
+            params_grads = [(param, -param.grad) for param in parameters]
+        else:
+            params_grads = [(param, param.grad) for param in parameters]
         optimize_ops = self.apply_gradients(params_grads)
 
     @imperative_base.no_grad()
@@ -2110,15 +2119,24 @@ def step(
                         hasattr(param, "main_grad")
                         and param.main_grad is not None
                     ):
-                        params_grads.append((param, param.main_grad))
+                        if self._maximize is True:
+                            params_grads.append((param, -param.main_grad))
+                        else:
+                            params_grads.append((param, param.main_grad))
                 elif (
                     hasattr(param, "main_grad") and param.main_grad is not None
                 ):
-                    params_grads.append((param, param.main_grad))
+                    if self._maximize is True:
+                        params_grads.append((param, -param.main_grad))
+                    else:
+                        params_grads.append((param, param.main_grad))
                 else:
                     if param._grad_ivar() is not None:
                         grad_var = param._grad_ivar()
-                        params_grads.append((param, grad_var))
+                        if self._maximize is True:
+                            params_grads.append((param, -grad_var))
+                        else:
+                            params_grads.append((param, grad_var))
 
             self._apply_optimize(
                 loss=None,
@@ -2136,7 +2154,10 @@ def step(
                         continue
                     if param._grad_ivar() is not None:
                         grad_var = param._grad_ivar()
-                        params_grads['params'].append((param, grad_var))
+                        if self._maximize is True:
+                            params_grads['params'].append((param, -grad_var))
+                        else:
+                            params_grads['params'].append((param, grad_var))
                 params_grads.update(
                     {k: v for k, v in param_group.items() if k != 'params'}
                 )