set_excluded_layers¶

paddle.static.sparsity. set_excluded_layers ( main_program, param_names ) [source]

Set parameter name of layers which would not be pruned as sparse weights.

Parameters

• main_program (Program, optional) – Program with model definition and its parameters.

• param_names (list) – A list contains names of parameters.

Examples

import paddle
from paddle.static import sparsity

paddle.enable_static()

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, 128])
    label = paddle.static.data(name='label', shape=[None, 10])
    hidden = paddle.static.nn.fc(x=input_data, num_flatten_dims=-1, size=32, activation=None, name="need_sparse_fc")
    hidden = paddle.static.nn.fc(x=hidden, num_flatten_dims=-1, size=32, activation=None, name="need_dense_fc")
    prob = paddle.static.nn.fc(x=hidden, num_flatten_dims=-1, size=10, activation=None)
    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()`.
    sparsity.set_excluded_layers(main_program, ["need_dense_fc"])

    optimizer = paddle.optimizer.SGD(learning_rate=0.1)
    optimizer = paddle.static.amp.decorate(optimizer )
    # Calling sparsity.decorate() to wrap minimize() in optimizer, which
    # will insert necessary masking operations for ASP workflow.
    optimizer = sparsity.decorate(optimizer)
    optimizer.minimize(loss, startup_program)