Fix memory allocator strategy flag. (#2308)

As the title.

doc/fluid/advanced_guide/flags/memory_cn.rst

@@ -11,13 +11,14 @@ FLAGS_allocator_strategy
 
 取值范围
 ---------------
-String型，['naive_best_fit', 'auto_growth']中的一个。缺省值为'naive_best_fit'。
+String型，['naive_best_fit', 'auto_growth']中的一个。缺省值如果编译Paddle CMake时使用-DON_INFER=ON为'naive_best_fit'。
+其他默认情况为'auto_growth'。PaddlePaddle pip安装包的默认策略也是'auto_growth'
 
 示例
 --------
-FLAGS_allocator_strategy=naive_best_fit - 使用预分配best fit分配器。
+FLAGS_allocator_strategy=naive_best_fit - 使用预分配best fit分配器，PaddlePaddle会先占用大多比例的可用内存/显存，在Paddle具体数据使用时分配，这种方式预占空间较大，但内存/显存碎片较少(比如能够支持模型的最大batch size会变大)。
 
-FLAGS_allocator_strategy=auto_growth - 使用auto growth分配器。
+FLAGS_allocator_strategy=auto_growth - 使用auto growth分配器。PaddlePaddle会随着真实数据需要再占用内存/显存，但内存/显存可能会产生碎片（比如能够支持模型的最大batch size会变小）。
 
 
 FLAGS_eager_delete_scope

doc/fluid/advanced_guide/flags/memory_en.rst

@@ -11,13 +11,13 @@ Use to choose allocator strategy of PaddlePaddle.
 
 Values accepted
 ---------------
-String, enum in ['naive_best_fit', 'auto_growth']. The default value is 'naive_best_fit'.
+String, enum in ['naive_best_fit', 'auto_growth']. The default value will be 'naive_best_fit' if users compile PaddlePaddle with -DON_INFER=ON CMake flag, otherwise is 'auto_growth'. The default PaddlePaddle pip package uses 'auto_growth'.
 
 Example
 --------
-FLAGS_allocator_strategy=naive_best_fit would use the pre-allocated best fit allocator.
+FLAGS_allocator_strategy=naive_best_fit would use the pre-allocated best fit allocator. 'naive_best_fit' strategy would occupy almost all GPU memory by default but leads to less memory fragmentation (i.e., maximum batch size of models may be larger).
 
-FLAGS_allocator_strategy=auto_growth would use the auto growth allocator.
+FLAGS_allocator_strategy=auto_growth would use the auto growth allocator. 'auto_growth' strategy would allocate GPU memory on demand but may lead to more memory fragmentation (i.e., maximum batch size of models may be smaller).