wehub-resource-sync
232 lines
7.0 KiB
Python
232 lines
7.0 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import paddle
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestPruneBase(unittest.TestCase):
|
|
def run_net(self, net):
|
|
program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with paddle.static.program_guard(program, startup_program):
|
|
ret = net()
|
|
|
|
return ret, program
|
|
|
|
def check_prune_with_input(
|
|
self,
|
|
program,
|
|
feeded_vars,
|
|
targets,
|
|
ops_before_pruned,
|
|
ops_after_pruned,
|
|
):
|
|
block = program.global_block()
|
|
self.assertEqual(len(block.ops), len(ops_before_pruned))
|
|
self.assertEqual(
|
|
[op.name() for op in block.ops],
|
|
ops_before_pruned,
|
|
)
|
|
pruned_program = program._prune_with_input(
|
|
feeded_vars=feeded_vars, targets=targets
|
|
)
|
|
self.assertEqual(
|
|
len(pruned_program.global_block().ops), len(ops_after_pruned)
|
|
)
|
|
self.assertEqual(
|
|
[op.name() for op in pruned_program.global_block().ops],
|
|
ops_after_pruned,
|
|
)
|
|
|
|
def check_prune(
|
|
self, program, targets, ops_before_pruned, ops_after_pruned
|
|
):
|
|
block = program.global_block()
|
|
self.assertEqual(len(block.ops), len(ops_before_pruned))
|
|
self.assertEqual(
|
|
[op.name() for op in block.ops],
|
|
ops_before_pruned,
|
|
)
|
|
pruned_program = program._prune(targets=targets)
|
|
self.assertEqual(
|
|
len(pruned_program.global_block().ops), len(ops_after_pruned)
|
|
)
|
|
self.assertEqual(
|
|
[op.name() for op in pruned_program.global_block().ops],
|
|
ops_after_pruned,
|
|
)
|
|
|
|
def check_prune_target_not_list(
|
|
self, program, targets, ops_before_pruned, ops_after_pruned
|
|
):
|
|
block = program.global_block()
|
|
self.assertEqual(len(block.ops), len(ops_before_pruned))
|
|
self.assertEqual(
|
|
[op.name() for op in block.ops],
|
|
ops_before_pruned,
|
|
)
|
|
pruned_program = program._prune(targets=targets)
|
|
self.assertEqual(
|
|
len(pruned_program.global_block().ops), len(ops_after_pruned)
|
|
)
|
|
self.assertEqual(
|
|
[op.name() for op in pruned_program.global_block().ops],
|
|
ops_after_pruned,
|
|
)
|
|
|
|
def check_prune_target_none(self, program, ops_before_pruned):
|
|
block = program.global_block()
|
|
self.assertEqual(len(block.ops), len(ops_before_pruned))
|
|
self.assertEqual(
|
|
[op.name() for op in block.ops],
|
|
ops_before_pruned,
|
|
)
|
|
try:
|
|
pruned_program = program._prune(targets=None)
|
|
except TypeError as e:
|
|
self.assertIn(
|
|
"_prune(): incompatible function arguments. The following argument types are supported:",
|
|
str(e),
|
|
)
|
|
|
|
|
|
class TestPrune(TestPruneBase):
|
|
def net(self):
|
|
x = paddle.static.data(name='x', shape=[-1, 2], dtype='float32')
|
|
label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64")
|
|
y = paddle.static.nn.fc(x=[x], size=2, activation="softmax")
|
|
loss = paddle.nn.functional.cross_entropy(
|
|
input=y, label=label, reduction='none', use_softmax=False
|
|
)
|
|
loss = paddle.mean(x=loss)
|
|
return x, y, label, loss
|
|
|
|
def test_prune_with_input(self):
|
|
ops_before_pruned = [
|
|
"builtin.parameter",
|
|
"builtin.parameter",
|
|
"pd_op.data",
|
|
"pd_op.data",
|
|
"pd_op.matmul",
|
|
"pd_op.add",
|
|
"pd_op.softmax",
|
|
"pd_op.cross_entropy_with_softmax",
|
|
"pd_op.full_int_array",
|
|
"pd_op.mean",
|
|
]
|
|
|
|
ops_after_pruned = [
|
|
"pd_op.data",
|
|
"pd_op.data",
|
|
"pd_op.cross_entropy_with_softmax",
|
|
"pd_op.full_int_array",
|
|
"pd_op.mean",
|
|
]
|
|
(x, y, label, loss), program = self.run_net(self.net)
|
|
self.check_prune_with_input(
|
|
program,
|
|
[y, label],
|
|
[loss],
|
|
ops_before_pruned,
|
|
ops_after_pruned,
|
|
)
|
|
|
|
def test_prune(self):
|
|
ops_before_pruned = [
|
|
"builtin.parameter",
|
|
"builtin.parameter",
|
|
"pd_op.data",
|
|
"pd_op.data",
|
|
"pd_op.matmul",
|
|
"pd_op.add",
|
|
"pd_op.softmax",
|
|
"pd_op.cross_entropy_with_softmax",
|
|
"pd_op.full_int_array",
|
|
"pd_op.mean",
|
|
]
|
|
|
|
ops_after_pruned = [
|
|
"builtin.parameter",
|
|
"builtin.parameter",
|
|
"pd_op.data",
|
|
"pd_op.data",
|
|
"pd_op.matmul",
|
|
"pd_op.add",
|
|
"pd_op.softmax",
|
|
"pd_op.cross_entropy_with_softmax",
|
|
"pd_op.full_int_array",
|
|
"pd_op.mean",
|
|
]
|
|
|
|
(x, y, label, loss), program = self.run_net(self.net)
|
|
self.check_prune(program, [loss], ops_before_pruned, ops_after_pruned)
|
|
|
|
def test_prune_target_not_list(self):
|
|
ops_before_pruned = [
|
|
"builtin.parameter",
|
|
"builtin.parameter",
|
|
"pd_op.data",
|
|
"pd_op.data",
|
|
"pd_op.matmul",
|
|
"pd_op.add",
|
|
"pd_op.softmax",
|
|
"pd_op.cross_entropy_with_softmax",
|
|
"pd_op.full_int_array",
|
|
"pd_op.mean",
|
|
]
|
|
|
|
ops_after_pruned = [
|
|
"builtin.parameter",
|
|
"builtin.parameter",
|
|
"pd_op.data",
|
|
"pd_op.data",
|
|
"pd_op.matmul",
|
|
"pd_op.add",
|
|
"pd_op.softmax",
|
|
"pd_op.cross_entropy_with_softmax",
|
|
"pd_op.full_int_array",
|
|
"pd_op.mean",
|
|
]
|
|
|
|
(x, y, label, loss), program = self.run_net(self.net)
|
|
|
|
self.check_prune_target_not_list(
|
|
program, [loss], ops_before_pruned, ops_after_pruned
|
|
)
|
|
|
|
def test_prune_target_none(self):
|
|
ops_before_pruned = [
|
|
"builtin.parameter",
|
|
"builtin.parameter",
|
|
"pd_op.data",
|
|
"pd_op.data",
|
|
"pd_op.matmul",
|
|
"pd_op.add",
|
|
"pd_op.softmax",
|
|
"pd_op.cross_entropy_with_softmax",
|
|
"pd_op.full_int_array",
|
|
"pd_op.mean",
|
|
]
|
|
|
|
(x, y, label, loss), program = self.run_net(self.net)
|
|
self.check_prune_target_none(program, ops_before_pruned)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|