reset_excluded_layers¶

paddle.incubate.asp. reset_excluded_layers ( main_program=None ) [source]

Reset exculded layers setting corresponding to main_program. If main_program is None, then all configurations of excluded_layers would be cleaned.

Parameters

main_program (Program, optional) – Program with model definition and its parameters. If None is given, then this function would reset all excluded_layers. Default is None.

Examples

1. Usage of Dynamic Graph

   import paddle
   
   class MyLayer(paddle.nn.Layer):
       def __init__(self):
           super().__init__()
           self.conv1 = paddle.nn.Conv2D(
               in_channels=3, out_channels=4, kernel_size=3, padding=2)
           self.linear1 = paddle.nn.Linear(4624, 100)
   
       def forward(self, img):
           hidden = self.conv1(img)
           hidden = paddle.flatten(hidden, start_axis=1)
           prediction = self.linear1(hidden)
           return prediction
   
   my_layer = MyLayer()
   optimizer = paddle.optimizer.SGD(
       learning_rate=0.01, parameters=my_layer.parameters())
   
   # Need to set excluded layers before calling decorate
   paddle.incubate.asp.set_excluded_layers([my_layer.linear1.full_name()])
   # Reset excluded_layers, all supported layers would be included into Automatic SParsity's workflow.
   # Please note, reset_excluded_layers also must be called before calling asp.decorate().
   paddle.incubate.asp.reset_excluded_layers()
   
   optimizer = paddle.incubate.asp.decorate(optimizer)
   

2. Usage of Static Graph

   import paddle
   
   paddle.enable_static()
   
   class MyLayer(paddle.nn.Layer):
       def __init__(self):
           super().__init__()
           self.conv1 = paddle.nn.Conv2D(
               in_channels=3, out_channels=4, kernel_size=3, padding=2)
           self.linear1 = paddle.nn.Linear(4624, 100)
   
       def forward(self, img):
           hidden = self.conv1(img)
           hidden = paddle.flatten(hidden, start_axis=1)
           prediction = self.linear1(hidden)
           return prediction
   
   main_program = paddle.static.Program()
   startup_program = paddle.static.Program()
   
   with paddle.static.program_guard(main_program, startup_program):
       input_data = paddle.static.data(name='data', shape=[None, 3, 224, 224])
       label = paddle.static.data(name='label', shape=[None, 100])
       my_layer = MyLayer()
       prob = my_layer(input_data)
       loss = paddle.mean(paddle.nn.functional.square_error_cost(prob, label))
   
       # Setup exluded layers out from ASP workflow.
       # Please note, excluded_layers must be set before calling optimizer.minimize().
       paddle.incubate.asp.set_excluded_layers([my_layer.linear1.full_name()], main_program)
       # Reset excluded_layers, all supported layers would be included into Automatic SParsity's workflow.
       # Please note, reset_excluded_layers also must be called before calling optimizer.minimize().
       paddle.incubate.asp.reset_excluded_layers(main_program)
   
       optimizer = paddle.optimizer.SGD(learning_rate=0.1)
       optimizer = paddle.static.amp.decorate(optimizer )
       # Calling paddle.incubate.asp.decorate() to wrap minimize() in optimizer, which
       # will insert necessary masking operations for ASP workflow.
       optimizer = paddle.incubate.asp.decorate(optimizer)
       optimizer.minimize(loss, startup_program)