wehub-resource-sync
238 lines
7.8 KiB
Python
238 lines
7.8 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 numpy as np
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import paddle
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from paddle.inference import Config, PrecisionType, create_predictor
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from paddle.jit import to_static
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from paddle.static import InputSpec
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from paddle.vision.models import alexnet
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class TestSaveOptimizedModelPass:
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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net = alexnet(True)
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model = to_static(
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net,
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input_spec=[InputSpec(shape=[None, 3, 224, 224], name='x')],
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full_graph=True,
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)
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paddle.jit.save(
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model, os.path.join(self.temp_dir.name, 'alexnet/inference')
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)
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def tearDown(self):
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self.temp_dir.cleanup()
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def get_baseline(self):
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predictor = self.init_predictor(save_optimized_model=True)
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inputs = [
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paddle.to_tensor(0.1 * np.ones([1, 3, 224, 224]).astype(np.float32))
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]
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outputs = predictor.run(inputs)
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return outputs[0]
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def get_test_output(self):
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predictor = self.init_predictor(save_optimized_model=False)
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inputs = [
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paddle.to_tensor(0.1 * np.ones([1, 3, 224, 224]).astype(np.float32))
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]
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outputs = predictor.run(inputs)
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return outputs[0]
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def test_output(self):
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if paddle.is_compiled_with_cuda():
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baseline = self.get_baseline()
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test_output = self.get_test_output()
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np.testing.assert_allclose(
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baseline.numpy().flatten(),
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test_output.numpy().flatten(),
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)
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class TestSaveOptimizedModelPassWithGPU(
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TestSaveOptimizedModelPass, unittest.TestCase
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):
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def init_predictor(self, save_optimized_model: bool):
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if save_optimized_model is True:
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config = Config(
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os.path.join(self.temp_dir.name, 'alexnet/inference.json'),
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os.path.join(self.temp_dir.name, 'alexnet/inference.pdiparams'),
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)
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config.enable_use_gpu(256, 0, PrecisionType.Half)
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config.enable_memory_optim()
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config.switch_ir_optim(True)
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config.set_optim_cache_dir(
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os.path.join(self.temp_dir.name, 'alexnet')
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)
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config.enable_save_optim_model(True)
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else:
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config = Config(
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os.path.join(self.temp_dir.name, 'alexnet/_optimized.json'),
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os.path.join(
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self.temp_dir.name, 'alexnet/_optimized.pdiparams'
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),
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)
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config.enable_use_gpu(256, 0, PrecisionType.Half)
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config.enable_memory_optim()
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config.switch_ir_optim(False)
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predictor = create_predictor(config)
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return predictor
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class TestSavePirOptimizedModelPassWithGPU(unittest.TestCase):
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def setUp(self):
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self.temp_dir = "./"
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net = alexnet(True)
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with paddle.pir_utils.DygraphPirGuard():
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model = to_static(
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net,
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input_spec=[InputSpec(shape=[None, 3, 224, 224], name='x')],
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full_graph=True,
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)
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paddle.jit.save(
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model, os.path.join(self.temp_dir, 'alexnet/inference')
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)
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def tearDown(self):
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# self.temp_dir.cleanup()
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pass
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def get_baseline(self):
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predictor = self.init_predictor(save_optimized_model=True)
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inputs = [
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paddle.to_tensor(0.1 * np.ones([1, 3, 224, 224]).astype(np.float32))
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]
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outputs = predictor.run(inputs)
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return outputs[0]
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def get_test_output(self):
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predictor = self.init_predictor(save_optimized_model=False)
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inputs = [
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paddle.to_tensor(0.1 * np.ones([1, 3, 224, 224]).astype(np.float32))
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]
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outputs = predictor.run(inputs)
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return outputs[0]
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def test_output(self):
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if paddle.is_compiled_with_cuda():
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baseline = self.get_baseline()
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test_output = self.get_test_output()
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np.testing.assert_allclose(
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baseline.numpy().flatten(),
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test_output.numpy().flatten(),
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)
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def init_predictor(self, save_optimized_model: bool):
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if save_optimized_model is True:
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config = Config(
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os.path.join(self.temp_dir, 'alexnet/inference.json'),
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os.path.join(self.temp_dir, 'alexnet/inference.pdiparams'),
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)
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config.enable_use_gpu(256, 0, PrecisionType.Half)
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config.enable_memory_optim()
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config.switch_ir_optim(True)
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config.set_optim_cache_dir(os.path.join(self.temp_dir, 'alexnet'))
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config.enable_save_optim_model(True)
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else:
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config = Config(
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os.path.join(self.temp_dir, 'alexnet/_optimized.json'),
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os.path.join(self.temp_dir, 'alexnet/_optimized.pdiparams'),
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)
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config.enable_use_gpu(256, 0, PrecisionType.Half)
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config.enable_memory_optim()
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config.switch_ir_optim(False)
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config.enable_new_executor()
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config.enable_new_ir()
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predictor = create_predictor(config)
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return predictor
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class TestSaveOptimizedModelPassWithTRT(
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TestSaveOptimizedModelPass, unittest.TestCase
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):
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def init_predictor(self, save_optimized_model: bool):
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if save_optimized_model is True:
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config = Config(
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os.path.join(self.temp_dir.name, 'alexnet/inference.json'),
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os.path.join(self.temp_dir.name, 'alexnet/inference.pdiparams'),
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)
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config.enable_use_gpu(256, 0)
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config.enable_tensorrt_engine(
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workspace_size=1 << 30,
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max_batch_size=1,
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min_subgraph_size=3,
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precision_mode=PrecisionType.Half,
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use_static=True,
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use_calib_mode=False,
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)
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config.set_trt_dynamic_shape_info(
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{"x": [1, 3, 224, 224], "flatten_0.tmp_0": [1, 9216]},
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{"x": [1, 3, 224, 224], "flatten_0.tmp_0": [1, 9216]},
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{"x": [1, 3, 224, 224], "flatten_0.tmp_0": [1, 9216]},
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)
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config.exp_disable_tensorrt_ops(["flatten_contiguous_range"])
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config.enable_memory_optim()
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config.switch_ir_optim(True)
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config.set_optim_cache_dir(
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os.path.join(self.temp_dir.name, 'alexnet')
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)
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config.enable_save_optim_model(True)
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else:
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config = Config(
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os.path.join(self.temp_dir.name, 'alexnet/_optimized.json'),
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os.path.join(
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self.temp_dir.name, 'alexnet/_optimized.pdiparams'
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),
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)
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config.enable_use_gpu(256, 0)
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config.enable_tensorrt_engine(
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workspace_size=1 << 30,
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max_batch_size=1,
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min_subgraph_size=3,
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precision_mode=PrecisionType.Half,
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use_static=True,
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use_calib_mode=False,
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)
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config.enable_memory_optim()
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config.switch_ir_optim(False)
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predictor = create_predictor(config)
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return predictor
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if __name__ == '__main__':
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unittest.main()
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