wehub-resource-sync
298 lines
10 KiB
Python
298 lines
10 KiB
Python
# copyright (c) 2023 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 os
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
import paddle.nn.functional as F
|
|
from paddle.nn import Conv2D, Linear, ReLU, Sequential
|
|
from paddle.nn.quant.format import LinearDequanter, LinearQuanter
|
|
from paddle.quantization import PTQ, QuantConfig
|
|
from paddle.quantization.observers import AbsmaxObserver
|
|
from paddle.quantization.observers.abs_max import AbsmaxObserverLayer
|
|
|
|
|
|
class LeNetDygraph(paddle.nn.Layer):
|
|
def __init__(self, num_classes=10):
|
|
super().__init__()
|
|
self.num_classes = num_classes
|
|
self.features = Sequential(
|
|
Conv2D(1, 6, 3, stride=1, padding=1),
|
|
ReLU(),
|
|
paddle.nn.MaxPool2D(2, 2),
|
|
Conv2D(6, 16, 5, stride=1, padding=0),
|
|
ReLU(),
|
|
paddle.nn.MaxPool2D(2, 2),
|
|
)
|
|
|
|
if num_classes > 0:
|
|
self.fc = Sequential(
|
|
Linear(576, 120), Linear(120, 84), Linear(84, 10)
|
|
)
|
|
|
|
def forward(self, inputs):
|
|
x = self.features(inputs)
|
|
if self.num_classes > 0:
|
|
x = paddle.flatten(x, 1)
|
|
x = self.fc(x)
|
|
out = F.relu(x)
|
|
return out
|
|
|
|
|
|
class TestPTQ(unittest.TestCase):
|
|
def setUp(self):
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
self.path = os.path.join(self.temp_dir.name, 'ptq')
|
|
|
|
def tearDown(self):
|
|
self.temp_dir.cleanup()
|
|
|
|
def _get_model_for_ptq(self):
|
|
observer = AbsmaxObserver(quant_bits=8)
|
|
model = LeNetDygraph()
|
|
model.eval()
|
|
q_config = QuantConfig(activation=observer, weight=observer)
|
|
ptq = PTQ(q_config)
|
|
quant_model = ptq.quantize(model)
|
|
return quant_model, ptq
|
|
|
|
def _count_layers(self, model, layer_type):
|
|
count = 0
|
|
for _layer in model.sublayers(True):
|
|
if isinstance(_layer, layer_type):
|
|
count += 1
|
|
return count
|
|
|
|
def test_quantize(self):
|
|
ptq_model, _ = self._get_model_for_ptq()
|
|
image = paddle.rand([1, 1, 32, 32], dtype="float32")
|
|
out = ptq_model(image)
|
|
self.assertIsNotNone(out)
|
|
|
|
observer_count = self._count_layers(ptq_model, AbsmaxObserverLayer)
|
|
self.assertEqual(observer_count, 14)
|
|
|
|
def test_convert(self):
|
|
quant_model, ptq = self._get_model_for_ptq()
|
|
|
|
image = paddle.rand([1, 1, 32, 32], dtype="float32")
|
|
out = quant_model(image)
|
|
converted_model = ptq.convert(quant_model)
|
|
out = converted_model(image)
|
|
self.assertIsNotNone(out)
|
|
|
|
observer_count = self._count_layers(
|
|
converted_model, AbsmaxObserverLayer
|
|
)
|
|
quanter_count = self._count_layers(converted_model, LinearQuanter)
|
|
dequanter_count = self._count_layers(converted_model, LinearDequanter)
|
|
self.assertEqual(observer_count, 0)
|
|
self.assertEqual(dequanter_count, 14)
|
|
self.assertEqual(quanter_count, 9)
|
|
|
|
save_path = os.path.join(self.temp_dir.name, 'int8_infer')
|
|
paddle.jit.save(converted_model, save_path, [image])
|
|
|
|
paddle.enable_static()
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
[
|
|
inference_program,
|
|
feed_target_names,
|
|
fetch_targets,
|
|
] = paddle.static.load_inference_model(save_path, exe)
|
|
tensor_img = np.array(
|
|
np.random.random((1, 1, 32, 32)), dtype=np.float32
|
|
)
|
|
results = exe.run(
|
|
inference_program,
|
|
feed={feed_target_names[0]: tensor_img},
|
|
fetch_list=fetch_targets,
|
|
)
|
|
self.assertIsNotNone(results)
|
|
paddle.disable_static()
|
|
|
|
def test_convert_2times(self):
|
|
quant_model, ptq = self._get_model_for_ptq()
|
|
|
|
image = paddle.rand([1, 1, 32, 32], dtype="float32")
|
|
out = quant_model(image)
|
|
converted_model = ptq.convert(quant_model)
|
|
converted_model = ptq.convert(converted_model)
|
|
out = converted_model(image)
|
|
self.assertIsNotNone(out)
|
|
|
|
observer_count = self._count_layers(
|
|
converted_model, AbsmaxObserverLayer
|
|
)
|
|
quanter_count = self._count_layers(converted_model, LinearQuanter)
|
|
dequanter_count = self._count_layers(converted_model, LinearDequanter)
|
|
self.assertEqual(observer_count, 0)
|
|
self.assertEqual(dequanter_count, 14)
|
|
self.assertEqual(quanter_count, 9)
|
|
|
|
save_path = os.path.join(self.temp_dir.name, 'int8_infer')
|
|
paddle.jit.save(converted_model, save_path, [image])
|
|
|
|
paddle.enable_static()
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
[
|
|
inference_program,
|
|
feed_target_names,
|
|
fetch_targets,
|
|
] = paddle.static.load_inference_model(save_path, exe)
|
|
tensor_img = np.array(
|
|
np.random.random((1, 1, 32, 32)), dtype=np.float32
|
|
)
|
|
results = exe.run(
|
|
inference_program,
|
|
feed={feed_target_names[0]: tensor_img},
|
|
fetch_list=fetch_targets,
|
|
)
|
|
self.assertIsNotNone(results)
|
|
paddle.disable_static()
|
|
|
|
|
|
class TestPTQFP8(unittest.TestCase):
|
|
def setUp(self):
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
self.path = os.path.join(self.temp_dir.name, 'ptq')
|
|
|
|
def tearDown(self):
|
|
self.temp_dir.cleanup()
|
|
|
|
def _get_model_for_ptq(self):
|
|
weight_observer = AbsmaxObserver(quant_bits=(4, 3))
|
|
act_observer = AbsmaxObserver(quant_bits=(5, 2))
|
|
model = LeNetDygraph()
|
|
model.eval()
|
|
q_config = QuantConfig(activation=act_observer, weight=weight_observer)
|
|
ptq = PTQ(q_config)
|
|
quant_model = ptq.quantize(model)
|
|
return quant_model, ptq
|
|
|
|
def _count_layers(self, model, layer_type):
|
|
count = 0
|
|
for _layer in model.sublayers(True):
|
|
if isinstance(_layer, layer_type):
|
|
count += 1
|
|
return count
|
|
|
|
def test_quantize(self):
|
|
ptq_model, _ = self._get_model_for_ptq()
|
|
image = paddle.rand([1, 1, 32, 32], dtype="float32")
|
|
out = ptq_model(image)
|
|
self.assertIsNotNone(out)
|
|
|
|
observer_count = self._count_layers(ptq_model, AbsmaxObserverLayer)
|
|
self.assertEqual(observer_count, 14)
|
|
|
|
def test_convert(self):
|
|
quant_model, ptq = self._get_model_for_ptq()
|
|
|
|
image = paddle.rand([1, 1, 32, 32], dtype="float32")
|
|
out = quant_model(image)
|
|
converted_model = ptq.convert(quant_model)
|
|
out = converted_model(image)
|
|
self.assertIsNotNone(out)
|
|
|
|
observer_count = self._count_layers(
|
|
converted_model, AbsmaxObserverLayer
|
|
)
|
|
quanter_count = self._count_layers(converted_model, LinearQuanter)
|
|
dequanter_count = self._count_layers(converted_model, LinearDequanter)
|
|
self.assertEqual(observer_count, 0)
|
|
self.assertEqual(dequanter_count, 14)
|
|
self.assertEqual(quanter_count, 9)
|
|
|
|
save_path = os.path.join(self.temp_dir.name, 'int8_infer')
|
|
paddle.jit.save(converted_model, save_path, [image])
|
|
|
|
paddle.enable_static()
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
[
|
|
inference_program,
|
|
feed_target_names,
|
|
fetch_targets,
|
|
] = paddle.static.load_inference_model(save_path, exe)
|
|
tensor_img = np.array(
|
|
np.random.random((1, 1, 32, 32)), dtype=np.float32
|
|
)
|
|
results = exe.run(
|
|
inference_program,
|
|
feed={feed_target_names[0]: tensor_img},
|
|
fetch_list=fetch_targets,
|
|
)
|
|
self.assertIsNotNone(results)
|
|
paddle.disable_static()
|
|
|
|
def test_convert_2times(self):
|
|
quant_model, ptq = self._get_model_for_ptq()
|
|
|
|
image = paddle.rand([1, 1, 32, 32], dtype="float32")
|
|
out = quant_model(image)
|
|
converted_model = ptq.convert(quant_model)
|
|
converted_model = ptq.convert(converted_model)
|
|
out = converted_model(image)
|
|
self.assertIsNotNone(out)
|
|
|
|
observer_count = self._count_layers(
|
|
converted_model, AbsmaxObserverLayer
|
|
)
|
|
quanter_count = self._count_layers(converted_model, LinearQuanter)
|
|
dequanter_count = self._count_layers(converted_model, LinearDequanter)
|
|
self.assertEqual(observer_count, 0)
|
|
self.assertEqual(dequanter_count, 14)
|
|
self.assertEqual(quanter_count, 9)
|
|
|
|
save_path = os.path.join(self.temp_dir.name, 'int8_infer')
|
|
paddle.jit.save(converted_model, save_path, [image])
|
|
|
|
paddle.enable_static()
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
main_program = paddle.static.Program()
|
|
startup_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program, startup_program):
|
|
[
|
|
inference_program,
|
|
feed_target_names,
|
|
fetch_targets,
|
|
] = paddle.static.load_inference_model(save_path, exe)
|
|
tensor_img = np.array(
|
|
np.random.random((1, 1, 32, 32)), dtype=np.float32
|
|
)
|
|
results = exe.run(
|
|
inference_program,
|
|
feed={feed_target_names[0]: tensor_img},
|
|
fetch_list=fetch_targets,
|
|
)
|
|
self.assertIsNotNone(results)
|
|
paddle.disable_static()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|