wehub-resource-sync
100 lines
3.5 KiB
Python
100 lines
3.5 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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"""The quantizer layers should be traced by paddle.jit.save function."""
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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.quantization import QAT, QuantConfig
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from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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from paddle.quantization.quanters.abs_max import (
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FakeQuanterWithAbsMaxObserverLayer,
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)
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from paddle.vision.models import resnet18
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class TestPTQ(unittest.TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory(dir="./")
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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_qat(self):
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observer = FakeQuanterWithAbsMaxObserver()
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model = resnet18()
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model.train()
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q_config = QuantConfig(activation=None, weight=None)
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q_config.add_type_config(
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paddle.nn.Conv2D, activation=observer, weight=observer
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)
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qat = QAT(q_config)
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quant_model = qat.quantize(model)
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return quant_model, qat
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def _count_layers(self, model, layer_type):
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count = 0
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for _layer in model.sublayers(True):
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if isinstance(_layer, layer_type):
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count += 1
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return count
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def test_trace(self):
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quant_model, ptq = self._get_model_for_qat()
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image = paddle.rand([1, 3, 32, 32], dtype="float32")
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quantizer_count_in_dygraph = self._count_layers(
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quant_model, FakeQuanterWithAbsMaxObserverLayer
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)
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save_path = os.path.join(self.path, 'int8_infer')
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paddle.jit.save(quant_model, save_path, [image])
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print(f"quant_model is saved into {save_path}")
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paddle.enable_static()
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exe = paddle.static.Executor(paddle.CPUPlace())
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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[
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inference_program,
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feed_target_names,
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fetch_targets,
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] = paddle.static.load_inference_model(save_path, exe)
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quantizer_count_in_static_model = 0
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if paddle.base.framework.in_pir_mode():
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for _op in inference_program.global_block().ops:
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if (
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"fake_quantize_dequantize_moving_average_abs_max"
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in _op.name()
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):
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quantizer_count_in_static_model += 1
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else:
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for _op in inference_program.global_block().ops:
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if (
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_op.type
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== "fake_quantize_dequantize_moving_average_abs_max"
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):
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quantizer_count_in_static_model += 1
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self.assertEqual(
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quantizer_count_in_dygraph, quantizer_count_in_static_model
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)
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paddle.disable_static()
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if __name__ == '__main__':
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unittest.main()
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