Add custom black variable name set in amp interface. (#20875)

* add custom black varname test=develop

* fix dtype test=develop

* fix num test=develop

* fix ut test=develop

* fix coverage test=develop

* fix blackvar names test=develop

python/paddle/fluid/contrib/mixed_precision/fp16_lists.py

@@ -29,12 +29,16 @@ class AutoMixedPrecisionLists(object):
         custom_black_list (set): Users' custom black list.
     """
 
-    def __init__(self, custom_white_list=None, custom_black_list=None):
+    def __init__(self,
+                 custom_white_list=None,
+                 custom_black_list=None,
+                 custom_black_varnames=None):
         self._custom_white_list = custom_white_list
         self._custom_black_list = custom_black_list
         self.white_list = copy.copy(white_list)
         self.black_list = copy.copy(black_list)
         self.gray_list = copy.copy(gray_list)
+        self.black_varnames = copy.copy(custom_black_varnames)
         self._update_list()
 
     def _update_list(self):

python/paddle/fluid/contrib/mixed_precision/fp16_utils.py

@@ -85,6 +85,7 @@ def _insert_cast_op(block, op, idx, src_dtype, dest_dtype):
         core.VarDesc.VarType.LOD_TENSOR, core.VarDesc.VarType.SELECTED_ROWS,
         core.VarDesc.VarType.LOD_TENSOR_ARRAY
     ]
+
     for in_name in op.input_names:
         if src_dtype == core.VarDesc.VarType.FP32 and op.type == 'batch_norm':
             if in_name != 'X':
@@ -94,12 +95,15 @@ def _insert_cast_op(block, op, idx, src_dtype, dest_dtype):
             if in_var.type not in valid_types:
                 continue
             if in_var.dtype == src_dtype:
+                cast_name = in_var.name + '.cast_' + _dtype_to_str(dest_dtype)
+                out_var = block.vars.get(cast_name)
+                if out_var is None or out_var.dtype != dest_dtype:
                     out_var = block.create_var(
-                    name=in_var.name + \
-                            '.cast_' + _dtype_to_str(dest_dtype),
+                        name=cast_name,
                         dtype=dest_dtype,
                         persistable=False,
                         stop_gradient=False)
+
                     block._insert_op(
                         idx,
                         type="cast",
@@ -155,6 +159,18 @@ def find_true_prev_op(ops, cur_op, var_name):
     return None
 
 
+def _is_in_black_varnames(op, amp_lists):
+    for in_name in op.input_arg_names:
+        if in_name in amp_lists.black_varnames:
+            return True
+
+    for out_name in op.output_arg_names:
+        if out_name in amp_lists.black_varnames:
+            return True
+
+    return False
+
+
 def rewrite_program(main_prog, amp_lists):
     """
     Traverse all ops in current block and insert cast op according to 
@@ -180,6 +196,11 @@ def rewrite_program(main_prog, amp_lists):
     white_op_set = set()
     black_op_set = set()
     for op in ops:
+        if amp_lists.black_varnames is not None and _is_in_black_varnames(
+                op, amp_lists):
+            black_op_set.add(op)
+            continue
+
         if op.type in amp_lists.black_list:
             black_op_set.add(op)
         elif op.type in amp_lists.white_list:

python/paddle/fluid/contrib/tests/test_image_classification_fp16.py

@@ -134,8 +134,11 @@ def train(net_type, use_cuda, save_dirname, is_local):
 
         optimizer = fluid.optimizer.Lamb(learning_rate=0.001)
 
+        amp_lists = fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
+            custom_black_varnames={"loss", "conv2d_0.w_0"})
         mp_optimizer = fluid.contrib.mixed_precision.decorate(
             optimizer=optimizer,
+            amp_lists=amp_lists,
             init_loss_scaling=8.0,
             use_dynamic_loss_scaling=True)