wehub-resource-sync
91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
# copyright (c) 2023 paddlepaddle authors. all rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import unittest
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import paddle
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from paddle.nn import Linear, Sequential
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from paddle.quantization import PTQ, QuantConfig
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from paddle.quantization.observers import (
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AbsmaxObserver,
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GroupWiseWeightObserver,
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)
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class LinearDygraph(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.fc = Sequential(
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Linear(128, 128), Linear(128, 128), Linear(128, 128)
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)
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def forward(self, inputs):
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out = self.fc(inputs)
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return out
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class TestPTQGroupWise(unittest.TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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self.path = os.path.join(self.temp_dir.name, 'ptq')
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def tearDown(self):
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self.temp_dir.cleanup()
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def _get_model_for_ptq_groupwise(self):
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observer = GroupWiseWeightObserver(quant_bits=4, group_size=128)
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model = LinearDygraph()
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model.eval()
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q_config = QuantConfig(activation=None, weight=observer)
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ptq = PTQ(q_config)
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quant_model = ptq.quantize(model)
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inputs = paddle.rand([128, 128], dtype="float32")
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out = model(inputs)
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return quant_model, ptq
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def _get_model_for_ptq_absmax(self):
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observer = AbsmaxObserver(quant_bits=8)
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model = LinearDygraph()
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model.eval()
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q_config = QuantConfig(activation=observer, weight=observer)
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ptq = PTQ(q_config)
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quant_model = ptq.quantize(model)
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inputs = paddle.rand([128, 128], dtype="float32")
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out = model(inputs)
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return quant_model, ptq
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def test_quantize(self):
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ptq_model, ptq = self._get_model_for_ptq_groupwise()
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inputs = paddle.rand([128, 128], dtype="float32")
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out = ptq_model(inputs)
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self.assertIsNotNone(out)
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converted_model = ptq.convert(ptq_model)
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out = converted_model(inputs)
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self.assertIsNotNone(out)
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def test_quantize_absmax(self):
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ptq_model, ptq = self._get_model_for_ptq_absmax()
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inputs = paddle.rand([128, 128], dtype="float32")
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out = ptq_model(inputs)
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self.assertIsNotNone(out)
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converted_model = ptq.convert(ptq_model)
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out = converted_model(inputs)
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self.assertIsNotNone(out)
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if __name__ == '__main__':
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unittest.main()
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