correct for_inference as training

Author: LiuChiachi

python/paddle/incubate/hapi/model.py

@@ -855,17 +855,17 @@ def save(self, path, training=True):
         """  
         This function saves parameters, optimizer information or model and 
         paramters only for inference to path. It depends on the parameter
-        `for_inference`.
+        `training`.
 
-        If `for_inference` is set to False, the parameters saved contain all 
+        If `training` is set to True, the parameters saved contain all 
         the trainable Variable, will save to a file with suffix ".pdparams".
         The optimizer information contains all the variable used by optimizer.
         For Adam optimizer, contains beta1, beta2, momentum etc. All the
         information will save to a file with suffix ".pdopt". (If the optimizer
         have no variable need to save (like SGD), the fill will not generated).
         This function will silently overwrite existing file at the target location.
 
-        If `for_inference` is set to True, only inference model will be saved. It 
+        If `training` is set to False, only inference model will be saved. It 
         should be noted that before using `save`, you should run the model, and 
         the shape of input you saved is as same as the input of its running.
         `@paddle.jit.to_static` must be added on `forward` function of your layer 
@@ -921,7 +921,7 @@ def forward(self, x):
         """
 
         if ParallelEnv().local_rank == 0:
-            if for_inference:
+            if not training:
                 self._save_inference_model(path)
             else:
                 self._adapter.save(path)