fluid API magration : Assert, increment, cond

python/paddle/distributed/fleet/utils/hybrid_parallel_inference.py

@@ -64,7 +64,7 @@ class HybridParallelInferenceHelper:
                 element_in_arr = layers.array_read(array=arr, i=step_idx)
                 # write placehold data to global lod_tensor_array,
                 # it need for send_v2 of lod_tensor_array
-                layers.increment(x=step_idx, value=1.0, in_place=True)
+                paddle.static.nn.increment(x=step_idx, value=1.0, in_place=True)
                 layers.array_write(element_in_arr, i=step_idx, array=arr)
 
             with paddle.fluid.device_guard(f'{device}:0'):
@@ -137,7 +137,7 @@ class HybridParallelInferenceHelper:
             with while_op.block():
                 with paddle.fluid.device_guard(f'{device}:all'):
                     input = layers.array_read(array=data, i=step_idx)
-                    layers.increment(x=step_idx, value=1.0, in_place=True)
+                    paddle.static.nn.increment(x=step_idx, value=1.0, in_place=True)
                     layers.array_write(input, i=step_idx, array=data)
 
                 with paddle.fluid.device_guard(f'{device}:0'):

python/paddle/distributed/passes/auto_parallel_gradient_merge.py

@@ -286,7 +286,7 @@ def true_apply_gradient():
             )
             new_grad.op._set_attr(OP_ROLE_KEY, op_maker.OpRole.Optimize)
 
-    layers.cond(cond_var, true_fn=true_apply_gradient, false_fn=None)
+    paddle.static.nn.cond(cond_var, true_fn=true_apply_gradient, false_fn=None)
     cond_op = main_program.global_block().ops[-1]
     cond_op._set_attr(OP_ROLE_KEY, OpRole.Optimize)
 

python/paddle/fluid/contrib/slim/quantization/adaround.py

@@ -83,7 +83,7 @@ def round_loss_fn():
 
             return round_loss
 
-        round_loss = fluid.layers.cond(
+        round_loss = paddle.static.nn.cond(
             warm_start,
             lambda: fluid.layers.fill_constant(
                 shape=[1], dtype='float32', value=0.0