[Dy2static] Refactor ProgramTranslator save_inference_model API (#24989)
* experimental refactoring, test=develop * add TranslatedLayer & remove StaticModelRunner, test=develop * revert tracedlayer change, test=develop * fix test_mnist unittest error, test=develop * add doc & examples, test=develop * polish doc details, test=develop * add imperative.jit module, test=develop * change TranslatedLayer pos, test=develop * adjust jit module import path, test=develop * polish doc based review result * add SaveLoadConfig.separate_params to save paraams separately * add Layer.buffer support, test=develop * polish doc details based review result, test=develop * polish details baesd review comments, test=develop * add empty str check for param, test=develop * add unittests, test=develop * polish details based review comment, test=develop * remove blanks in comment, test=develop * polish doc details, test=develop * update imperative doc link, test=develop * add api attr for load, test=developfix_copy_if_different
Chen Weihang
5 years ago
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GitHub
parent
43f9f180e5
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41d2247275
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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
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import paddle.fluid as fluid
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from paddle.fluid.dygraph import Linear
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from paddle.fluid.dygraph import declarative
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BATCH_SIZE = 32
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BATCH_NUM = 20
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SEED = 10
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def random_batch_reader():
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def _get_random_images_and_labels(image_shape, label_shape):
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np.random.seed(SEED)
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image = np.random.random(size=image_shape).astype('float32')
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label = np.random.random(size=label_shape).astype('int64')
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return image, label
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def __reader__():
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for _ in range(BATCH_NUM):
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batch_image, batch_label = _get_random_images_and_labels(
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[BATCH_SIZE, 784], [BATCH_SIZE, 1])
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yield batch_image, batch_label
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return __reader__
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class LinearNet(fluid.dygraph.Layer):
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def __init__(self, in_size, out_size):
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super(LinearNet, self).__init__()
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self._linear = Linear(in_size, out_size)
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@declarative
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def forward(self, x):
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return self._linear(x)
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class LinearNetNotDeclarative(fluid.dygraph.Layer):
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def __init__(self, in_size, out_size):
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super(LinearNetNotDeclarative, self).__init__()
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self._linear = Linear(in_size, out_size)
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def forward(self, x):
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return self._linear(x)
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class LinearNetReturnLoss(fluid.dygraph.Layer):
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def __init__(self, in_size, out_size):
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super(LinearNetReturnLoss, self).__init__()
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self._linear = Linear(in_size, out_size)
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@declarative
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def forward(self, x):
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y = self._linear(x)
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z = self._linear(y)
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loss = fluid.layers.mean(z)
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return z, loss
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def train(layer):
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# create optimizer
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adam = fluid.optimizer.AdamOptimizer(
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learning_rate=0.1, parameter_list=layer.parameters())
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# create data loader
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train_loader = fluid.io.DataLoader.from_generator(capacity=5)
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train_loader.set_batch_generator(random_batch_reader())
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# train
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for data in train_loader():
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img, label = data
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label.stop_gradient = True
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cost = layer(img)
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loss = fluid.layers.cross_entropy(cost, label)
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avg_loss = fluid.layers.mean(loss)
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avg_loss.backward()
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adam.minimize(avg_loss)
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layer.clear_gradients()
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return [img], layer, avg_loss
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def infer(layer):
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x = fluid.dygraph.to_variable(np.random.random((1, 784)).astype('float32'))
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return layer(x)
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class TestJitSaveLoad(unittest.TestCase):
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def setUp(self):
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self.model_path = "model.test_jit_save_load"
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# enable dygraph mode
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fluid.enable_dygraph()
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# config seed
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fluid.default_main_program().random_seed = SEED
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def train_and_save_model(self):
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layer = LinearNet(784, 1)
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example_inputs, layer, _ = train(layer)
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fluid.dygraph.jit.save(
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layer=layer, model_path=self.model_path, input_spec=example_inputs)
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return layer
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def test_save(self):
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# train and save model
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self.train_and_save_model()
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def test_load_infernece(self):
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# train and save model
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train_layer = self.train_and_save_model()
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# load model
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infer_layer = fluid.dygraph.jit.load(self.model_path)
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train_layer.eval()
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# inference & compare
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x = fluid.dygraph.to_variable(
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np.random.random((1, 784)).astype('float32'))
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self.assertTrue(
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np.array_equal(train_layer(x).numpy(), infer_layer(x).numpy()))
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def test_load_finetune(self):
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# train and save model
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train_layer = self.train_and_save_model()
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# load model
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load_train_layer = fluid.dygraph.jit.load(self.model_path)
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load_train_layer.train()
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# train & compare
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_, _, train_loss = train(train_layer)
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_, _, load_train_loss = train(load_train_layer)
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self.assertTrue(
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np.array_equal(train_loss.numpy(), load_train_loss.numpy()))
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def test_save_get_program_failed(self):
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layer = LinearNetNotDeclarative(784, 1)
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example_inputs, layer, _ = train(layer)
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with self.assertRaises(RuntimeError):
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fluid.dygraph.jit.save(
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layer=layer,
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model_path=self.model_path,
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input_spec=example_inputs)
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class TestJitSaveLoadConfig(unittest.TestCase):
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def setUp(self):
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# enable dygraph mode
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fluid.enable_dygraph()
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# config seed
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fluid.default_main_program().random_seed = SEED
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def basic_save_load(self, layer, model_path, configs):
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# 1. train & save
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example_inputs, train_layer, _ = train(layer)
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fluid.dygraph.jit.save(
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layer=train_layer,
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model_path=model_path,
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input_spec=example_inputs,
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configs=configs)
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# 2. load
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infer_layer = fluid.dygraph.jit.load(model_path, configs=configs)
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train_layer.eval()
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# 3. inference & compare
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x = fluid.dygraph.to_variable(
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np.random.random((1, 784)).astype('float32'))
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self.assertTrue(
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np.array_equal(train_layer(x).numpy(), infer_layer(x).numpy()))
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def test_model_filename(self):
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layer = LinearNet(784, 1)
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model_path = "model.save_load_config.output_spec"
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configs = fluid.dygraph.jit.SaveLoadConfig()
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configs.model_filename = "__simplenet__"
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self.basic_save_load(layer, model_path, configs)
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def test_params_filename(self):
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layer = LinearNet(784, 1)
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model_path = "model.save_load_config.params_filename"
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configs = fluid.dygraph.jit.SaveLoadConfig()
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configs.params_filename = "__params__"
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self.basic_save_load(layer, model_path, configs)
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def test_separate_params(self):
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layer = LinearNet(784, 1)
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model_path = "model.save_load_config.separate_params"
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configs = fluid.dygraph.jit.SaveLoadConfig()
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configs.separate_params = True
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self.basic_save_load(layer, model_path, configs)
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def test_output_spec(self):
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train_layer = LinearNetReturnLoss(8, 8)
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adam = fluid.optimizer.AdamOptimizer(
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learning_rate=0.1, parameter_list=train_layer.parameters())
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x = fluid.dygraph.to_variable(
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np.random.random((4, 8)).astype('float32'))
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for i in range(10):
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out, loss = train_layer(x)
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loss.backward()
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adam.minimize(loss)
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train_layer.clear_gradients()
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model_path = "model.save_load_config.output_spec"
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configs = fluid.dygraph.jit.SaveLoadConfig()
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configs.output_spec = [out]
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fluid.dygraph.jit.save(
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layer=train_layer,
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model_path=model_path,
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input_spec=[x],
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configs=configs)
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train_layer.eval()
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infer_layer = fluid.dygraph.jit.load(model_path, configs=configs)
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x = fluid.dygraph.to_variable(
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np.random.random((4, 8)).astype('float32'))
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self.assertTrue(
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np.array_equal(train_layer(x)[0].numpy(), infer_layer(x).numpy()))
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,18 @@
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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 ...fluid.dygraph.jit import save, load, SaveLoadConfig
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from ...fluid.dygraph.io import TranslatedLayer
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__all__ = ['save', 'load', 'SaveLoadConfig']
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