# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pickle import shutil import tempfile import unittest import numpy as np import paddle import paddle.fluid as fluid from paddle.fluid import unique_name from paddle.fluid.dygraph.io import INFER_PARAMS_INFO_SUFFIX from paddle.fluid.layers.utils import flatten from paddle.jit.api import declarative from paddle.nn import Linear from paddle.static import InputSpec BATCH_SIZE = 32 BATCH_NUM = 10 SEED = 10 def random_batch_reader(input_size, label_size): def _get_random_inputs_and_labels(input_size, label_size): np.random.seed(SEED) input = np.random.random(size=input_size).astype('float32') label = np.random.random(size=label_size).astype('int64') return input, label def __reader__(): for _ in range(BATCH_NUM): batch_input, batch_label = _get_random_inputs_and_labels( [BATCH_SIZE, input_size], [BATCH_SIZE, label_size] ) yield batch_input, batch_label return __reader__ class LinearNet(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) @declarative def forward(self, x): return self._linear(x) class LinearNetWithInputSpec(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) @declarative(input_spec=[InputSpec(shape=[None, 784], dtype='float32')]) def forward(self, x): return self._linear(x) class LinearNetNotDeclarative(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) def forward(self, x): return self._linear(x) class LinerNetWithLabel(paddle.nn.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) @declarative( input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name="image"), InputSpec(shape=[None, 1], dtype='int64', name="label"), ] ) def forward(self, x, label): out = self._linear(x) loss = paddle.nn.functional.cross_entropy( out, label, reduction='none', use_softmax=False ) avg_loss = paddle.mean(loss) return out, avg_loss class LinerNetWithPruneInput(paddle.nn.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) @declarative( input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name="image"), InputSpec(shape=[None, 1], dtype='int64', name="label"), ] ) def forward(self, x, label): out = self._linear(x) loss = paddle.nn.functional.cross_entropy( out, label, reduction='none', use_softmax=False ) avg_loss = paddle.mean(loss) return out class LinerNetWithUselessInput(paddle.nn.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) @declarative( input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name="image"), InputSpec(shape=[None, 1], dtype='int64', name="label"), ] ) def forward(self, x, label): out = self._linear(x) return out class LinearNetReturnLoss(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) @declarative def forward(self, x): y = self._linear(x) z = self._linear(y) loss = paddle.mean(z) return z, loss class LinearNetMultiInput(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear1 = Linear(in_size, out_size) self._linear2 = Linear(in_size, out_size) @declarative( input_spec=[ InputSpec([None, 8], dtype='float32'), InputSpec([None, 8], dtype='float32'), ] ) def forward(self, x, y): x_out = self._linear1(x) y_out = self._linear2(y) loss = paddle.mean(x_out + y_out) return x_out, y_out, loss class LinearNetMultiInput1(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear1 = Linear(in_size, out_size) self._linear2 = Linear(in_size, out_size) @declarative( input_spec=( InputSpec([None, 8], dtype='float32'), InputSpec([None, 8], dtype='float32'), ) ) def forward(self, x, y): x_out = self._linear1(x) y_out = self._linear2(y) loss = paddle.mean(x_out + y_out) return x_out, y_out, loss class MultiLoadingLinearNet(fluid.dygraph.Layer): def __init__(self, size, model_path): super().__init__() self._linear = Linear(size, size) self._load_linear1 = paddle.jit.load(model_path) self._load_linear2 = paddle.jit.load(model_path) @declarative def forward(self, x): tmp1 = self._linear(x) tmp2 = self._load_linear1(tmp1) tmp3 = self._load_linear2(tmp2) y = self._linear(tmp3) return y class LinearNetReturnHidden(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear_1 = Linear(in_size, out_size) self._linear_2 = Linear(in_size, out_size) @declarative def forward(self, x): y = self._linear_1(x) z = self._linear_2(y) loss = paddle.mean(z) return y, loss class LinearNetWithNestOut(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear_1 = Linear(in_size, out_size) self._linear_2 = Linear(in_size, out_size) @declarative def forward(self, x): y = self._linear_1(x) z = self._linear_2(y) out = y + z loss = paddle.mean(out) return y, [(z, loss), out] class LinearNetWithDictInput(paddle.nn.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) @paddle.jit.to_static( input_spec=[ {'img': InputSpec(shape=[None, 8], dtype='float32', name='img')}, {'label': InputSpec(shape=[None, 1], dtype='int64', name='label')}, ] ) def forward(self, img, label): out = self._linear(img['img']) # not return loss to avoid prune output loss = paddle.nn.functional.cross_entropy(out, label['label']) return out class LinearNetWithDictInputNoPrune(paddle.nn.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear = Linear(in_size, out_size) def forward(self, img): out = self._linear(img['img'] + img['img2']) return out class EmptyLayer(paddle.nn.Layer): def __init__(self): super().__init__() @paddle.jit.to_static def forward(self, x): return x class NoParamLayer(paddle.nn.Layer): def __init__(self): super().__init__() @paddle.jit.to_static def forward(self, x, y): return x + y class LinearNetWithMultiStaticFunc(fluid.dygraph.Layer): def __init__(self, in_size, out_size): super().__init__() self._linear_0 = Linear(in_size, out_size) self._linear_1 = Linear(in_size, out_size) self._scale = paddle.to_tensor(9.9) @paddle.jit.to_static def forward(self, x): return self._linear_0(x) @paddle.jit.to_static def forward_no_param(self, x): return x @paddle.jit.to_static def forward_general(self, x): return self._linear_0(x) + self._linear_1(x) * self._scale def train(layer, input_size=784, label_size=1): # create optimizer sgd = fluid.optimizer.SGDOptimizer( learning_rate=0.01, parameter_list=layer.parameters() ) # create data loader train_loader = fluid.io.DataLoader.from_generator(capacity=5) train_loader.set_batch_generator( random_batch_reader(input_size, label_size) ) # train for data in train_loader(): img, label = data label.stop_gradient = True cost = layer(img) loss = paddle.nn.functional.cross_entropy( cost, label, reduction='none', use_softmax=False ) avg_loss = paddle.mean(loss) avg_loss.backward() sgd.minimize(avg_loss) layer.clear_gradients() return [img], layer, avg_loss def train_with_label(layer, input_size=784, label_size=1): # create optimizer sgd = fluid.optimizer.SGDOptimizer( learning_rate=0.01, parameter_list=layer.parameters() ) # create data loader train_loader = fluid.io.DataLoader.from_generator(capacity=5) train_loader.set_batch_generator( random_batch_reader(input_size, label_size) ) # train for data in train_loader(): img, label = data label.stop_gradient = True out, avg_loss = layer(img, label) avg_loss.backward() sgd.minimize(avg_loss) layer.clear_gradients() return out class TestJitSaveLoad(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() self.model_path = os.path.join( self.temp_dir.name, "test_jit_save_load/model" ) # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def tearDown(self): self.temp_dir.cleanup() def train_and_save_model(self, model_path=None): layer = LinearNet(784, 1) example_inputs, layer, _ = train(layer) final_model_path = model_path if model_path else self.model_path orig_input_types = [type(x) for x in example_inputs] paddle.jit.save( layer=layer, path=final_model_path, input_spec=example_inputs ) new_input_types = [type(x) for x in example_inputs] self.assertEqual(orig_input_types, new_input_types) return layer def test_save_load(self): # train and save model train_layer = self.train_and_save_model() # load model loaded_layer = paddle.jit.load(self.model_path) self.load_and_inference(train_layer, loaded_layer) self.load_dygraph_state_dict(train_layer) self.load_and_finetune(train_layer, loaded_layer) def load_and_inference(self, train_layer, infer_layer): train_layer.eval() infer_layer.eval() # inference & compare x = fluid.dygraph.to_variable( np.random.random((1, 784)).astype('float32') ) np.testing.assert_array_equal( train_layer(x).numpy(), infer_layer(x).numpy() ) def load_and_finetune(self, train_layer, load_train_layer): train_layer.train() load_train_layer.train() # train & compare img0, _, train_loss = train(train_layer) img1, _, load_train_loss = train(load_train_layer) np.testing.assert_array_equal( train_loss.numpy(), load_train_loss.numpy() ) def load_dygraph_state_dict(self, train_layer): train_layer.eval() # construct new model new_layer = LinearNet(784, 1) orig_state_dict = new_layer.state_dict() load_state_dict = paddle.load(self.model_path) for structured_name in orig_state_dict: self.assertTrue(structured_name in load_state_dict) new_layer.set_state_dict(load_state_dict) new_layer.eval() # inference & compare x = fluid.dygraph.to_variable( np.random.random((1, 784)).astype('float32') ) np.testing.assert_array_equal( train_layer(x).numpy(), new_layer(x).numpy() ) def test_load_dygraph_no_path(self): model_path = os.path.join( self.temp_dir.name, "test_jit_save_load.no_path/model_path" ) with self.assertRaises(ValueError): model_dict, _ = fluid.dygraph.load_dygraph(model_path) def test_jit_load_no_path(self): path = os.path.join( self.temp_dir.name, "test_jit_save_load.no_path/model_path" ) with self.assertRaises(ValueError): loaded_layer = paddle.jit.load(path) class TestSaveLoadWithNestOut(unittest.TestCase): def setUp(self): # enable dygraph mode fluid.enable_dygraph() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_nest_output(self): x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) net = LinearNetWithNestOut(8, 8) dy_outs = flatten(net(x)) net = declarative(net, input_spec=[InputSpec([None, 8], name='x')]) model_path = os.path.join(self.temp_dir.name, "net_with_nest_out/model") paddle.jit.save(net, model_path) load_net = paddle.jit.load(model_path) load_outs = flatten(load_net(x)) self.assertTrue(len(dy_outs) == 4) for dy_out, load_out in zip(dy_outs, load_outs): np.testing.assert_allclose( dy_out.numpy(), load_out.numpy(), rtol=1e-05 ) class TestSaveLoadWithDictInput(unittest.TestCase): def test_dict_input(self): # NOTE: This net cannot be executed, it is just # a special case for exporting models in model validation # We DO NOT recommend this writing way of Layer net = LinearNetWithDictInput(8, 8) # net.forward.concrete_program.inputs: # (<__main__.LinearNetWithDictInput object at 0x7f2655298a98>, # {'img': var img : fluid.VarType.LOD_TENSOR.shape(-1, 8).astype(VarType.FP32)}, # {'label': var label : fluid.VarType.LOD_TENSOR.shape(-1, 1).astype(VarType.INT64)}) self.assertEqual(len(net.forward.concrete_program.inputs), 3) temp_dir = tempfile.TemporaryDirectory() path = os.path.join( temp_dir.name, "test_jit_save_load_with_dict_input/model" ) # prune inputs paddle.jit.save( layer=net, path=path, input_spec=[ {'img': InputSpec(shape=[None, 8], dtype='float32', name='img')} ], ) img = paddle.randn(shape=[4, 8], dtype='float32') loaded_net = paddle.jit.load(path) loaded_out = loaded_net(img) # loaded_net._input_spec(): # [InputSpec(shape=(-1, 8), dtype=VarType.FP32, name=img)] self.assertEqual(len(loaded_net._input_spec()), 1) temp_dir.cleanup() class TestSaveLoadWithDictInputNoPrune(unittest.TestCase): def test_dict_input(self): net = LinearNetWithDictInputNoPrune(8, 8) temp_dir = tempfile.TemporaryDirectory() path = os.path.join( temp_dir.name, "test_jit_save_load_with_dict_input_no_prune/model" ) # prune inputs paddle.jit.save( layer=net, path=path, input_spec=[ { 'img': InputSpec( shape=[None, 8], dtype='float32', name='img' ), 'img2': InputSpec( shape=[None, 8], dtype='float32', name='img2' ), } ], ) img = paddle.randn(shape=[4, 8], dtype='float32') img2 = paddle.randn(shape=[4, 8], dtype='float32') loaded_net = paddle.jit.load(path) loaded_out = loaded_net(img, img2) self.assertEqual(len(loaded_net._input_spec()), 2) temp_dir.cleanup() class TestSaveLoadWithInputSpec(unittest.TestCase): def setUp(self): # enable dygraph mode fluid.enable_dygraph() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_with_input_spec(self): net = LinearNetReturnLoss(8, 8) # set x.shape = [None, 8] net.forward = declarative( net.forward, input_spec=[InputSpec([None, 8], name='x')] ) model_path = os.path.join( self.temp_dir.name, "input_spec.output_spec/model" ) # check inputs and outputs self.assertTrue(len(net.forward.inputs) == 1) input_x = net.forward.inputs[0] self.assertTrue(input_x.shape == (-1, 8)) self.assertTrue(input_x.name == 'x') # 1. prune loss output_spec = net.forward.outputs[:1] paddle.jit.save(net, model_path, output_spec=output_spec) # 2. load to infer infer_layer = paddle.jit.load(model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) pred = infer_layer(x) def test_multi_in_out(self): net = LinearNetMultiInput(8, 8) model_path = os.path.join( self.temp_dir.name, "multi_inout.output_spec1/model" ) # 1. check inputs and outputs self.assertTrue(len(net.forward.inputs) == 2) input_x = net.forward.inputs[0] input_y = net.forward.inputs[1] self.assertTrue(input_x.shape == (-1, 8)) self.assertTrue(input_y.shape == (-1, 8)) # 2. prune loss output_spec = net.forward.outputs[:2] paddle.jit.save(net, model_path, output_spec=output_spec) # 3. load to infer infer_layer = paddle.jit.load(model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) y = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) # 4. predict pred_x, pred_y = infer_layer(x, y) # 1. prune y and loss model_path = os.path.join( self.temp_dir.name, "multi_inout.output_spec2/model" ) output_spec = net.forward.outputs[:1] paddle.jit.save(net, model_path, [input_x], output_spec=output_spec) # 2. load again infer_layer2 = paddle.jit.load(model_path) # 3. predict pred_xx = infer_layer2(x) # 4. assert pred_x == pred_xx np.testing.assert_allclose(pred_x.numpy(), pred_xx.numpy(), rtol=1e-05) def test_multi_in_out1(self): net = LinearNetMultiInput1(8, 8) model_path = os.path.join( self.temp_dir.name, "multi_inout1.output_spec1/model" ) # 1. check inputs and outputs self.assertTrue(len(net.forward.inputs) == 2) input_x = net.forward.inputs[0] input_y = net.forward.inputs[1] self.assertTrue(input_x.shape == (-1, 8)) self.assertTrue(input_y.shape == (-1, 8)) # 2. prune loss output_spec = net.forward.outputs[:2] paddle.jit.save(net, model_path, output_spec=output_spec) # 3. load to infer infer_layer = paddle.jit.load(model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) y = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) # 4. predict pred_x, pred_y = infer_layer(x, y) # 1. prune y and loss model_path = os.path.join( self.temp_dir.name, "multi_inout1.output_spec2/model" ) output_spec = net.forward.outputs[:1] paddle.jit.save(net, model_path, (input_x,), output_spec=output_spec) # 2. load again infer_layer2 = paddle.jit.load(model_path) # 3. predict pred_xx = infer_layer2(x) # 4. assert pred_x == pred_xx np.testing.assert_allclose(pred_x.numpy(), pred_xx.numpy(), rtol=1e-05) class TestJitSaveLoadConfig(unittest.TestCase): def setUp(self): # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_output_spec(self): train_layer = LinearNetReturnLoss(8, 8) adam = fluid.optimizer.AdamOptimizer( learning_rate=0.1, parameter_list=train_layer.parameters() ) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) for i in range(10): out, loss = train_layer(x) loss.backward() adam.minimize(loss) train_layer.clear_gradients() model_path = os.path.join( self.temp_dir.name, "save_load_config.output_spec" ) output_spec = [out] paddle.jit.save( layer=train_layer, path=model_path, input_spec=[x], output_spec=output_spec, ) train_layer.eval() infer_layer = paddle.jit.load(model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) np.testing.assert_array_equal( train_layer(x)[0].numpy(), infer_layer(x).numpy() ) def test_save_no_support_config_error(self): layer = LinearNet(784, 1) path = os.path.join(self.temp_dir.name, "no_support_config_test") with self.assertRaises(ValueError): paddle.jit.save(layer=layer, path=path, model_filename="") def test_load_empty_model_filename_error(self): path = os.path.join(self.temp_dir.name, "error_model_filename_test") with self.assertRaises(ValueError): paddle.jit.load(path, model_filename="") def test_load_empty_params_filename_error(self): path = os.path.join(self.temp_dir.name, "error_params_filename_test") with self.assertRaises(ValueError): paddle.jit.load(path, params_filename="") def test_load_with_no_support_config(self): path = os.path.join(self.temp_dir.name, "no_support_config_test") with self.assertRaises(ValueError): paddle.jit.load(path, separate_params=True) class TestJitMultipleLoading(unittest.TestCase): def setUp(self): self.linear_size = 4 self.temp_dir = tempfile.TemporaryDirectory() self.model_path = os.path.join( self.temp_dir.name, "jit_multi_load/model" ) # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) # train and save base model self.train_and_save_orig_model() def tearDown(self): self.temp_dir.cleanup() def train_and_save_orig_model(self): layer = LinearNet(self.linear_size, self.linear_size) example_inputs, layer, _ = train(layer, self.linear_size, 1) paddle.jit.save( layer=layer, path=self.model_path, input_spec=example_inputs ) def test_load_model_retransform_inference(self): multi_loaded_layer = MultiLoadingLinearNet( self.linear_size, self.model_path ) state_dict = multi_loaded_layer.state_dict() name_set = set() for _, var in state_dict.items(): self.assertTrue(var.name not in name_set) name_set.add(var.name) class TestJitPruneModelAndLoad(unittest.TestCase): def setUp(self): self.linear_size = 4 self.temp_dir = tempfile.TemporaryDirectory() self.model_path = os.path.join( self.temp_dir.name, "jit_prune_model_and_load/model" ) # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) def tearDown(self): self.temp_dir.cleanup() def train_and_save(self): train_layer = LinearNetReturnHidden(8, 8) adam = fluid.optimizer.AdamOptimizer( learning_rate=0.1, parameter_list=train_layer.parameters() ) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) for i in range(10): hidden, loss = train_layer(x) loss.backward() adam.minimize(loss) train_layer.clear_gradients() output_spec = [hidden] paddle.jit.save( layer=train_layer, path=self.model_path, input_spec=[x], output_spec=output_spec, ) return train_layer def test_load_pruned_model(self): train_layer = self.train_and_save() train_layer.eval() infer_layer = paddle.jit.load(self.model_path) x = fluid.dygraph.to_variable( np.random.random((4, 8)).astype('float32') ) np.testing.assert_array_equal( train_layer(x)[0].numpy(), infer_layer(x).numpy() ) def test_load_var_not_in_extra_var_info(self): self.train_and_save() # chage extra var info var_info_path = self.model_path + INFER_PARAMS_INFO_SUFFIX with open(var_info_path, 'rb') as f: extra_var_info = pickle.load(f) extra_var_info.clear() with open(var_info_path, 'wb') as f: pickle.dump(extra_var_info, f, protocol=2) with self.assertRaises(RuntimeError): paddle.jit.load(self.model_path) class TestJitSaveMultiCases(unittest.TestCase): def setUp(self): # enable dygraph mode fluid.enable_dygraph() # config seed paddle.seed(SEED) paddle.framework.random._manual_program_seed(SEED) self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def verify_inference_correctness( self, layer, model_path, with_label_and_loss=False, with_label=False ): layer.eval() loaded_layer = paddle.jit.load(model_path) loaded_layer.eval() # inference & compare x = paddle.to_tensor(np.random.random((1, 784)).astype('float32')) if with_label_and_loss: y = paddle.to_tensor(np.random.random((1, 1)).astype('int64')) pred, _ = layer(x, y) pred = pred.numpy() elif with_label: y = paddle.to_tensor(np.random.random((1, 1)).astype('int64')) pred = layer(x, y) pred = pred.numpy() else: pred = layer(x).numpy() loaded_pred = loaded_layer(x).numpy() np.testing.assert_array_equal( pred, loaded_pred, err_msg='Result diff when load and inference:\nlayer result:\n{}\nloaded layer result:\n{}'.format( pred, loaded_pred ), ) def test_no_prune_to_static_after_train(self): layer = LinearNet(784, 1) train(layer) model_path = os.path.join( self.temp_dir.name, "test_no_prune_to_static_after_train/model" ) paddle.jit.save(layer, model_path) self.verify_inference_correctness(layer, model_path) def test_no_prune_to_static_no_train(self): layer = LinearNetWithInputSpec(784, 1) model_path = os.path.join( self.temp_dir.name, "test_no_prune_to_static_no_train/model" ) paddle.jit.save(layer, model_path) self.verify_inference_correctness(layer, model_path) def test_no_prune_no_to_static_after_train(self): layer = LinearNetNotDeclarative(784, 1) train(layer) model_path = os.path.join( self.temp_dir.name, "test_no_prune_no_to_static_after_train/model" ) paddle.jit.save( layer, model_path, input_spec=[InputSpec(shape=[None, 784], dtype='float32')], ) self.verify_inference_correctness(layer, model_path) def test_no_prune_no_to_static_after_train_with_examples(self): layer = LinearNetNotDeclarative(784, 1) example_inputs, _, _ = train(layer) model_path = os.path.join( self.temp_dir.name, "test_no_prune_no_to_static_after_train_with_examples/model", ) paddle.jit.save(layer=layer, path=model_path, input_spec=example_inputs) self.verify_inference_correctness(layer, model_path) def test_no_prune_no_to_static_no_train(self): layer = LinearNetNotDeclarative(784, 1) model_path = os.path.join( self.temp_dir.name, "test_no_prune_no_to_static_no_train/model" ) paddle.jit.save( layer, model_path, input_spec=[InputSpec(shape=[None, 784], dtype='float32')], ) self.verify_inference_correctness(layer, model_path) def test_prune_to_static_after_train(self): layer = LinerNetWithLabel(784, 1) out = train_with_label(layer) model_path = os.path.join( self.temp_dir.name, "test_prune_to_static_after_train/model" ) paddle.jit.save( layer, model_path, input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name="image") ], output_spec=[out], ) self.verify_inference_correctness( layer, model_path, with_label_and_loss=True ) def test_prune_to_static_no_train(self): layer = LinerNetWithLabel(784, 1) model_path = os.path.join( self.temp_dir.name, "test_prune_to_static_no_train/model" ) # TODO: no train, cannot get output_spec var here # now only can use index output_spec = layer.forward.outputs[:1] paddle.jit.save( layer, model_path, input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name="image") ], output_spec=output_spec, ) self.verify_inference_correctness( layer, model_path, with_label_and_loss=True ) def test_prune_input_to_static_no_train(self): layer = LinerNetWithPruneInput(784, 1) model_path = os.path.join( self.temp_dir.name, "test_prune_input_to_static_no_train/model" ) paddle.jit.save( layer, model_path, input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name="image") ], ) self.verify_inference_correctness(layer, model_path, with_label=True) def test_prune_useless_input_to_static_no_train(self): layer = LinerNetWithUselessInput(784, 1) model_path = os.path.join( self.temp_dir.name, "test_prune_useless_input_to_static_no_train/model", ) paddle.jit.save( layer, model_path, input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name="image") ], ) self.verify_inference_correctness(layer, model_path, with_label=True) def test_no_prune_input_spec_name_warning(self): layer = LinearNetWithInputSpec(784, 1) train(layer) model_path = os.path.join( self.temp_dir.name, "test_no_prune_input_spec_name_warning/model" ) paddle.jit.save( layer, model_path, input_spec=[InputSpec(shape=[None, 784], dtype='float32')], ) paddle.jit.save( layer, model_path, input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name='feed_input') ], ) self.verify_inference_correctness(layer, model_path) def test_not_prune_output_spec_name_warning(self): layer = LinearNet(784, 1) train(layer) model_path = os.path.join( self.temp_dir.name, "test_not_prune_output_spec_name_warning/model" ) out = paddle.to_tensor(np.random.random((1, 1)).astype('float')) paddle.jit.save(layer, model_path, output_spec=[out]) self.verify_inference_correctness(layer, model_path) def test_prune_input_spec_name_error(self): layer = LinerNetWithLabel(784, 1) model_path = os.path.join( self.temp_dir.name, "test_prune_input_spec_name_error/model" ) with self.assertRaises(ValueError): paddle.jit.save( layer, model_path, input_spec=[InputSpec(shape=[None, 784], dtype='float32')], ) with self.assertRaises(ValueError): paddle.jit.save( layer, model_path, input_spec=[ InputSpec( shape=[None, 784], dtype='float32', name='feed_input' ) ], ) def test_prune_output_spec_name_error(self): layer = LinerNetWithLabel(784, 1) train_with_label(layer) model_path = os.path.join( self.temp_dir.name, "test_prune_to_static_after_train/model" ) out = paddle.to_tensor(np.random.random((1, 1)).astype('float')) with self.assertRaises(ValueError): paddle.jit.save( layer, model_path, input_spec=[ InputSpec(shape=[None, 784], dtype='float32', name="image") ], output_spec=[out], ) class TestJitSaveLoadEmptyLayer(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() self.model_path = os.path.join( self.temp_dir.name, "jit_save_load_empty_layer/model" ) # enable dygraph mode paddle.disable_static() def tearDown(self): self.temp_dir.cleanup() def test_save_load_empty_layer(self): layer = EmptyLayer() x = paddle.to_tensor(np.random.random((10)).astype('float32')) out = layer(x) paddle.jit.save(layer, self.model_path) load_layer = paddle.jit.load(self.model_path) load_out = load_layer(x) np.testing.assert_array_equal(out, load_out) class TestJitSaveLoadNoParamLayer(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() self.model_path = os.path.join( self.temp_dir.name, "jit_save_load_no_param_layer/model" ) # enable dygraph mode paddle.disable_static() def tearDown(self): self.temp_dir.cleanup() def test_save_load_no_param_layer(self): layer = NoParamLayer() x = paddle.to_tensor(np.random.random((5)).astype('float32')) y = paddle.to_tensor(np.random.random((5)).astype('float32')) out = layer(x, y) paddle.jit.save(layer, self.model_path) load_layer = paddle.jit.load(self.model_path) load_out = load_layer(x, y) np.testing.assert_array_equal(out, load_out) class TestJitSaveLoadMultiMethods(unittest.TestCase): def setUp(self): # enable dygraph mode paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_save_load_inference(self): model_path_inference = os.path.join( self.temp_dir.name, "jit_save_load_multi_methods/model" ) IMAGE_SIZE = 224 layer = LinearNetWithMultiStaticFunc(IMAGE_SIZE, 10) inps = paddle.randn([1, IMAGE_SIZE]) result_origin = {} for func in dir(layer): if func.startswith('forward'): result_origin[func] = getattr(layer, func, None)(inps) paddle.jit.save(layer, model_path_inference) load_net = paddle.jit.load(model_path_inference) for func, result in result_origin.items(): self.assertTrue( float( (result - getattr(load_net, func, None)(inps)).abs().max() ) < 1e-5 ) def test_jit_save_load_multi_methods_inputspec(self): model_path = os.path.join( self.temp_dir.name, 'jit_save_load_multi_methods/model' ) layer = LinearNetWithMultiStaticFunc(784, 1) with self.assertRaises(ValueError): paddle.jit.save( layer, model_path, input_spec=[InputSpec(shape=[None, 784])] ) def test_parse_name(self): model_path_inference = os.path.join( self.temp_dir.name, "jit_save_load_parse_name/model" ) IMAGE_SIZE = 224 layer = LinearNet(IMAGE_SIZE, 1) inps = paddle.randn([1, IMAGE_SIZE]) layer(inps) paddle.jit.save(layer, model_path_inference) paddle.jit.save(layer, model_path_inference + '_v2') load_net = paddle.jit.load(model_path_inference) self.assertFalse(hasattr(load_net, 'v2')) class LayerSaved(paddle.nn.Layer): def __init__(self, in_size, out_size): super().__init__() self.hidden = 100 self._linear_0 = Linear(in_size, self.hidden) self._linear_1_0 = Linear(self.hidden, self.hidden) self._linear_1_1 = Linear(self.hidden, self.hidden) self._linear_2 = Linear(self.hidden, out_size) self._scale = paddle.to_tensor(9.9) @paddle.jit.to_static def forward(self, x): y = self._linear_0(x) # Multiple blocks if paddle.shape(x)[0] == 1: y = self._linear_1_0(y) else: y += self._linear_1_1(y + self._scale) return self._linear_2(y) class Net(paddle.nn.Layer): def __init__(self): super().__init__() self.fc1 = paddle.nn.Linear(4, 4) self.fc2 = paddle.nn.Linear(4, 4) self.bias = 0.4 self.flag = paddle.ones([2], dtype="int32") @paddle.jit.to_static(input_spec=[InputSpec([None, 4], dtype='float32')]) def log_softmax(self, input): return paddle.nn.functional.log_softmax(input, axis=-1) @paddle.jit.to_static(input_spec=[InputSpec([None, 4], dtype='float32')]) def forward(self, x): out = self.fc1(x) out = paddle.nn.functional.relu(out) out = paddle.mean(out) return out @paddle.jit.to_static(input_spec=[InputSpec([None, 4], dtype='float32')]) def infer(self, input): out = self.fc2(input) out = out + self.bias out = paddle.mean(out) return out # For extra Python float @paddle.jit.to_static(property=True) def fbias(self): return self.bias + 1 @paddle.jit.to_static(property=True) def down_sampling(self): return 4 @paddle.jit.to_static(property=True) def fstr(self): return "save str property" @paddle.jit.to_static(property=True) def ints(self): return [10, 20] @paddle.jit.to_static(property=True) def floats(self): return [1.1, 2.2] @paddle.jit.to_static(property=True) def strs(self): return ["hello", "world"] class NetTensor(paddle.nn.Layer): def __init__(self): super().__init__() self.fc1 = paddle.nn.Linear(4, 4) self.fc2 = paddle.nn.Linear(4, 4) self.bias = 0.4 self.flag = paddle.ones([2], dtype="int32") @paddle.jit.to_static(input_spec=[InputSpec([None, 4], dtype='float32')]) def forward(self, x): out = self.fc1(x) out = paddle.nn.functional.relu(out) out = paddle.mean(out) return out @paddle.jit.to_static(property=True) def fflag(self): return True class TestJitSaveCombineProperty(unittest.TestCase): def setUp(self): # enable dygraph mode paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_save_combine_property(self): model_path = os.path.join( self.temp_dir.name, "test_jit_save_combine/model" ) # Use new namespace with unique_name.guard(): net = Net() # save paddle.jit.save(net, model_path, combine_params=True) def test_jit_save_tensor_property(self): model_path = os.path.join( self.temp_dir.name, "test_jit_save_combine/model" ) # Use new namespace with unique_name.guard(): net = NetTensor() paddle.jit.save(net, model_path, combine_params=True) class LayerLoadFinetune(paddle.nn.Layer): def __init__(self, in_size, out_size, load_path): super().__init__() # Test duplicate name self._linear_0 = Linear(in_size, in_size) self._linear_1_0 = Linear(out_size, in_size) self._linear_1_1 = Linear(out_size, in_size) self._linear_2 = Linear(out_size, out_size) self._scale = paddle.to_tensor(9.9) # Load multiple times self._load_l1 = paddle.jit.load(load_path) self._load_l2 = paddle.jit.load(load_path) @paddle.jit.to_static def forward(self, x): y = self._linear_0(x) y = self._load_l1(y) # Multiple blocks if paddle.shape(x)[0] == 1: y = self._linear_1_0(y) y = self._load_l1(y) else: y += self._linear_1_1(x + self._scale) y = self._load_l2(y) y = self._linear_1_0(y) y = self._load_l1(y) y = self._linear_1_0(y) # Use the same layer multiple times. y = self._load_l1(y) return y class TestJitSaveLoadSaveWithoutRunning(unittest.TestCase): def setUp(self): # enable dygraph mode paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_save_load_finetune_load(self): model_path = os.path.join( self.temp_dir.name, "test_jit_save_load_save_without_running/model" ) IMAGE_SIZE = 224 inps0 = paddle.randn([1, IMAGE_SIZE]) inps1 = paddle.randn([2, IMAGE_SIZE]) # Use new namespace with unique_name.guard(): layer_save = LayerSaved(IMAGE_SIZE, IMAGE_SIZE) # save paddle.jit.save( layer_save, model_path, input_spec=[ paddle.static.InputSpec( shape=[None, IMAGE_SIZE], dtype='float32' ) ], ) result_00 = layer_save(inps0) result_01 = layer_save(inps1) # load and save without running with unique_name.guard(): layer_load = paddle.jit.load(model_path) paddle.jit.save( layer_load, model_path, input_spec=[ paddle.static.InputSpec( shape=[None, IMAGE_SIZE], dtype='float32' ) ], ) # reload layer_reload = paddle.jit.load(model_path) result_10 = layer_reload(inps0) result_11 = layer_reload(inps1) self.assertTrue(float((result_00 - result_10).abs().max()) < 1e-5) self.assertTrue(float((result_01 - result_11).abs().max()) < 1e-5) class TestJitSaveLoadFinetuneLoad(unittest.TestCase): def setUp(self): # enable dygraph mode paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_save_load_finetune_load(self): model_path = os.path.join( self.temp_dir.name, "test_jit_save_load_finetune_load/model" ) IMAGE_SIZE = 224 inps0 = paddle.randn([1, IMAGE_SIZE]) inps1 = paddle.randn([2, IMAGE_SIZE]) # Use new namespace with unique_name.guard(): layer_save = LayerSaved(IMAGE_SIZE, IMAGE_SIZE) layer_save(inps0) # save paddle.jit.save(layer_save, model_path) # load with unique_name.guard(): layer_load = LayerLoadFinetune(IMAGE_SIZE, IMAGE_SIZE, model_path) # train train(layer_load, input_size=IMAGE_SIZE) result_00 = layer_load(inps0) result_01 = layer_load(inps1) # save paddle.jit.save(layer_load, model_path) # load layer_finetune = paddle.jit.load(model_path) result_10 = layer_finetune(inps0) result_11 = layer_finetune(inps1) self.assertTrue(float((result_00 - result_10).abs().max()) < 1e-5) self.assertTrue(float(((result_01 - result_11)).abs().max()) < 1e-5) # NOTE(weixin): When there are multiple test functions in an # `unittest.TestCase`, functions will affect each other, # and there is a risk of random failure. # So divided into three TestCase: TestJitSaveLoadFunctionCase1, # TestJitSaveLoadFunctionCase2, TestJitSaveLoadFunctionCase3. class TestJitSaveLoadFunctionCase1(unittest.TestCase): def setUp(self): paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_save_load_static_function(self): @paddle.jit.to_static def fun(inputs): return paddle.tanh(inputs) path = os.path.join( self.temp_dir.name, 'test_jit_save_load_function_1/func' ) inps = paddle.rand([3, 6]) origin = fun(inps) paddle.jit.save(fun, path) load_func = paddle.jit.load(path) load_result = load_func(inps) self.assertTrue((load_result - origin).abs().max() < 1e-10) class TestJitSaveLoadFunctionCase2(unittest.TestCase): def setUp(self): paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_save_load_function_input_spec(self): @paddle.jit.to_static( input_spec=[ InputSpec(shape=[None, 6], dtype='float32', name='x'), ] ) def fun(inputs): return paddle.nn.functional.relu(inputs) path = os.path.join( self.temp_dir.name, 'test_jit_save_load_function_2/func' ) inps = paddle.rand([3, 6]) origin = fun(inps) paddle.jit.save(fun, path) load_func = paddle.jit.load(path) load_result = load_func(inps) self.assertTrue((load_result - origin).abs().max() < 1e-10) class TestJitSaveLoadFunctionCase3(unittest.TestCase): def setUp(self): paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_save_load_function_function(self): def fun(inputs): return paddle.tanh(inputs) path = os.path.join( self.temp_dir.name, 'test_jit_save_load_function_3/func' ) inps = paddle.rand([3, 6]) origin = fun(inps) paddle.jit.save( fun, path, input_spec=[ InputSpec(shape=[None, 6], dtype='float32', name='x'), ], ) load_func = paddle.jit.load(path) load_result = load_func(inps) self.assertTrue((load_result - origin).abs().max() < 1e-10) class TestJitSaveLoadFunctionWithParamCase1(unittest.TestCase): def setUp(self): paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_save_load_function(self): class LinearNet(paddle.nn.Layer): def __init__(self): super().__init__() self._linear = paddle.nn.Linear(5, 6) def forward(self, x): return paddle.tanh(x) def anothor_forward(self, x): return self._linear(x) layer = LinearNet() inps = paddle.rand([3, 5]) origin = layer.anothor_forward(inps) func = paddle.jit.to_static( layer.anothor_forward, [paddle.static.InputSpec(shape=[-1, 5])] ) path = os.path.join( self.temp_dir.name, 'test_jit_save_load_function_with_params_case1/func', ) paddle.jit.save(func, path) load_func = paddle.jit.load(path) load_result = load_func(inps) np.testing.assert_array_equal(load_result.numpy(), origin.numpy()) class TestJitSaveLoadFunctionWithParamCase2(unittest.TestCase): def setUp(self): paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_save_load_function(self): class LinearNet(paddle.nn.Layer): def __init__(self): super().__init__() self._linear = paddle.nn.Linear(5, 6) def forward(self, x): return paddle.tanh(x) @paddle.jit.to_static(input_spec=[InputSpec(shape=[-1, 5])]) def anothor_forward(self, x): return self._linear(x) layer = LinearNet() inps = paddle.rand([3, 5]) path = os.path.join( self.temp_dir.name, 'test_jit_save_load_function_with_params_case2/func', ) paddle.jit.save(layer.anothor_forward, path) origin_result = layer.anothor_forward(inps) load_func = paddle.jit.load(path) load_result = load_func(inps) np.testing.assert_array_equal( origin_result.numpy(), load_result.numpy() ) class TestJitSaveLoadFunctionWithParamCase3(unittest.TestCase): def setUp(self): paddle.disable_static() self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_save_load_function(self): class LinearNet(paddle.nn.Layer): def __init__(self): super().__init__() self._linear = paddle.nn.Linear(5, 6) def forward(self, x): return paddle.tanh(x) @paddle.jit.to_static def anothor_forward(self, x): return self._linear(x) layer = LinearNet() inps = paddle.rand([3, 5]) origin = layer.anothor_forward(inps) path = os.path.join( self.temp_dir.name, 'test_jit_save_load_function_with_params_case3/func', ) paddle.jit.save(layer.anothor_forward, path) load_func = paddle.jit.load(path) load_result = load_func(inps) np.testing.assert_array_equal(load_result.numpy(), origin.numpy()) class TestJitSaveLoadDataParallel(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def verify_inference_correctness(self, layer, path): layer.eval() loaded_layer = paddle.jit.load(path) loaded_layer.eval() # inference & compare x = paddle.to_tensor(np.random.random((1, 784)).astype('float32')) pred = layer(x).numpy() loaded_pred = loaded_layer(x).numpy() np.testing.assert_array_equal( pred, loaded_pred, err_msg='Result diff when load and inference:\nlayer result:\n{}\nloaded layer result:\n{}'.format( pred, loaded_pred ), ) def test_jit_save_data_parallel_with_inputspec(self): layer = LinearNetNotDeclarative(784, 1) layer = paddle.DataParallel(layer) path = os.path.join( self.temp_dir.name, "jit_save_data_parallel_with_inputspec/model" ) paddle.jit.save( layer=layer, path=path, input_spec=[InputSpec(shape=[None, 784])] ) self.verify_inference_correctness(layer, path) def test_jit_save_data_parallel_with_to_static(self): layer = LinearNetWithInputSpec(784, 1) layer = paddle.DataParallel(layer) path = os.path.join( self.temp_dir.name, "jit_save_data_parallel_with_to_static/model" ) paddle.jit.save(layer, path) self.verify_inference_correctness(layer, path) class InputSepcLayer(paddle.nn.Layer): ''' A layer with InputSpec to test InputSpec compatibility ''' @paddle.jit.to_static( input_spec=[ InputSpec(shape=[None, 8], dtype='float32', name='x'), InputSpec(shape=[None, 1], dtype='float64', name='y'), ] ) def forward(self, x, y): return x, y class TestInputSpecCompatibility(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def _assert_input_spec_layer_return(self, expect_layer, test_layer): input_x = paddle.uniform([8, 8], dtype='float32') input_y = paddle.uniform([8, 1], dtype='float64') expected_result = expect_layer(input_x, input_y) test_result = test_layer(input_x, input_y) np.testing.assert_allclose( expected_result[0].numpy(), test_result[0].numpy() ) np.testing.assert_allclose( expected_result[1].numpy(), test_result[1].numpy() ) def test_jit_save_compatible_input_sepc(self): layer = InputSepcLayer() save_dir = os.path.join( self.temp_dir.name, "jit_save_compatible_input_spec" ) path = save_dir + "/model" paddle.jit.save(layer=layer, path=path) no_input_spec_layer = paddle.jit.load(path) self._assert_input_spec_layer_return(layer, no_input_spec_layer) shutil.rmtree(save_dir) paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec(shape=[None, 8], dtype='float32', name='x'), InputSpec(shape=[None, 1], dtype='float64', name='y'), ], ) same_input_spec_layer = paddle.jit.load(path) self._assert_input_spec_layer_return(layer, same_input_spec_layer) shutil.rmtree(save_dir) paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec(shape=[8, 8], dtype='float32'), InputSpec(shape=[8, -1], dtype='float64'), ], ) compatible_input_spec_layer = paddle.jit.load(path) self._assert_input_spec_layer_return(layer, compatible_input_spec_layer) shutil.rmtree(save_dir) def test_jit_save_incompatible_input_sepc(self): layer = InputSepcLayer() save_dir = os.path.join( self.temp_dir.name, "jit_save_compatible_input_spec" ) path = save_dir + "/model" with self.assertRaises(ValueError): # type mismatch paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec(shape=[None, 8], dtype='float64'), InputSpec(shape=[None, 1], dtype='float64'), ], ) with self.assertRaises(ValueError): # shape len mismatch paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec(shape=[None, 8, 1], dtype='float32'), InputSpec(shape=[None, 1], dtype='float64'), ], ) with self.assertRaises(ValueError): # shape mismatch paddle.jit.save( layer=layer, path=path, input_spec=[ InputSpec(shape=[None, 8], dtype='float32'), InputSpec(shape=[None, 2], dtype='float64'), ], ) if os.path.exists(save_dir): shutil.rmtree(save_dir) class NotJitForward(paddle.nn.Layer): def __init__(self): super().__init__() def forward(self, x, y): return x + y class TestNotJitForward(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_jit_not_save_forward(self): layer = NotJitForward() save_dir = os.path.join(self.temp_dir.name, "jit_not_save_forward") path = save_dir + "/model" paddle.jit.save(layer=layer, path=path, skip_forward=True) self.assertTrue(not os.path.exists(path + ".pdmodel")) self.assertTrue(not os.path.exists(path + ".pdparam")) with self.assertRaises(ValueError): paddle.jit.load(path=path) shutil.rmtree(save_dir) if __name__ == '__main__': with fluid.framework._test_eager_guard(): unittest.main()