Add Decorator 'dygraph_to_static_program' and ProgramTranslator.save_inference_model (#23227)
1. Add Decorator 'dygraph_to_static_program' 2. Add corresponding ProgramTranslator.get_program 3. Add ProgramTranslator.save_inference_model 4. Modified some warning information of dy2stat 5. Change program cache to contain startup_program because for users who gets program to run, they may like to initialize startup programrevert-23830-2.0-beta
Huihuang Zheng
5 years ago
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GitHub
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a647bcd355
commit
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# Copyright (c) 2020 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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from __future__ import print_function
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import os
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.fluid.dygraph.dygraph_to_static import ProgramTranslator
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from paddle.fluid.dygraph.jit import dygraph_to_static_output
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np.random.seed(2020)
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place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace(
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)
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class SimpleFcLayer(fluid.dygraph.Layer):
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def __init__(self, fc_size):
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super(SimpleFcLayer, self).__init__()
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self._linear = fluid.dygraph.Linear(fc_size, fc_size)
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@dygraph_to_static_output
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def forward(self, x):
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x = fluid.dygraph.to_variable(x)
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y = self._linear(x)
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z = self._linear(y)
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out = fluid.layers.mean(z, name='mean')
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return out
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class TestDyToStaticSaveInferenceModel(unittest.TestCase):
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def test_save_inference_model(self):
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fc_size = 20
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x = np.random.random((fc_size, fc_size)).astype('float32')
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layer = SimpleFcLayer(fc_size)
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program_translator = ProgramTranslator.get_instance()
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program_cache = ProgramTranslator().get_program_cache
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adam = fluid.optimizer.SGD(learning_rate=0.001)
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program_translator.set_optimizer(adam, 'mean')
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for i in range(5):
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out = layer(x)
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main_program = ProgramTranslator.get_instance().main_program
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expected_persistable_vars = set(
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[layer._linear.weight.name, layer._linear.bias.name])
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infer_model_dir = "./test_dy2stat_save_inference_model"
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ProgramTranslator.get_instance().save_inference_model(infer_model_dir)
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saved_var_names = set([
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filename for filename in os.listdir(infer_model_dir)
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if filename != '__model__'
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])
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self.assertEqual(saved_var_names, expected_persistable_vars)
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if __name__ == '__main__':
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unittest.main()
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# Copyright (c) 2020 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.framework as framework
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from paddle.fluid.dygraph.jit import dygraph_to_static_program
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from paddle.fluid.dygraph.nn import Linear
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np.random.seed(2020)
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place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda() else fluid.CPUPlace(
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)
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def simple_func(x, weight_numpy):
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weight_initalizer = fluid.initializer.NumpyArrayInitializer(weight_numpy)
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linear = Linear(32, 64, param_attr=weight_initalizer)
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x = fluid.dygraph.to_variable(x)
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y = linear(x)
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z = linear(x)
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return z
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@dygraph_to_static_program
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def decorated_simple_func(x, weight_numpy):
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weight_initalizer = fluid.initializer.NumpyArrayInitializer(weight_numpy)
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linear = Linear(32, 64, param_attr=weight_initalizer)
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x = fluid.dygraph.to_variable(x)
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y = linear(x)
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z = linear(x)
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return z
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class TestDyToStaticSaveLoad(unittest.TestCase):
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def test_save_load_same_result(self):
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x = np.random.randn(30, 10, 32).astype('float32')
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weight = np.random.randn(32, 64).astype('float32')
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with fluid.dygraph.guard(place):
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dygraph_result = simple_func(x, weight)
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main_program, startup_program, inputs, outputs = decorated_simple_func(
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x, weight)
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exe = fluid.Executor(place)
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exe.run(startup_program)
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fluid.save(main_program, "./test_dy2stat_save_load")
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# set vars to zero so that we can test load in same file
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for var in main_program.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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tensor = fluid.global_scope().find_var(var.name).get_tensor()
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tensor.set(np.zeros_like(np.array(tensor)), place)
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# make sure all the paramerter or optimizer var have been set to zero
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tensor_np = np.array(fluid.global_scope().find_var(var.name)
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.get_tensor())
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self.assertEqual(0, np.sum(np.abs(tensor_np)))
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fluid.load(main_program, "./test_dy2stat_save_load")
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static_result = exe.run(main_program,
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feed={inputs[0].name: x},
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fetch_list=outputs)
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self.assertTrue(np.allclose(dygraph_result.numpy(), static_result))
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if __name__ == '__main__':
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unittest.main()
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