# Copyright (c) 2018 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 errno import os import pickle import tempfile import unittest import numpy as np from test_imperative_base import new_program_scope import paddle import paddle.fluid as fluid import paddle.fluid.core as core import paddle.fluid.framework as framework from paddle.fluid.optimizer import Adam paddle.enable_static() class SimpleLSTMRNN(fluid.Layer): def __init__( self, name_scope, hidden_size, num_steps, num_layers=2, init_scale=0.1, dropout=None, ): super().__init__() self._hidden_size = hidden_size self._num_layers = num_layers self._init_scale = init_scale self._dropout = dropout self._input = None self._num_steps = num_steps self.cell_array = [] self.hidden_array = [] self.weight_1_arr = [] self.weight_2_arr = [] self.bias_arr = [] self.mask_array = [] for i in range(self._num_layers): weight_1 = self.create_parameter( attr=fluid.ParamAttr( initializer=fluid.initializer.UniformInitializer( low=-self._init_scale, high=self._init_scale ) ), shape=[self._hidden_size * 2, self._hidden_size * 4], dtype="float32", default_initializer=fluid.initializer.UniformInitializer( low=-self._init_scale, high=self._init_scale ), ) self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1)) bias_1 = self.create_parameter( attr=fluid.ParamAttr( initializer=fluid.initializer.UniformInitializer( low=-self._init_scale, high=self._init_scale ) ), shape=[self._hidden_size * 4], dtype="float32", default_initializer=fluid.initializer.Constant(0.0), ) self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1)) def forward(self, input_embedding, init_hidden=None, init_cell=None): self.cell_array = [] self.hidden_array = [] for i in range(self._num_layers): pre_hidden = paddle.slice( init_hidden, axes=[0], starts=[i], ends=[i + 1] ) pre_cell = paddle.slice( init_cell, axes=[0], starts=[i], ends=[i + 1] ) pre_hidden = paddle.reshape( pre_hidden, shape=[-1, self._hidden_size] ) pre_cell = paddle.reshape(pre_cell, shape=[-1, self._hidden_size]) self.hidden_array.append(pre_hidden) self.cell_array.append(pre_cell) res = [] for index in range(self._num_steps): self._input = paddle.slice( input_embedding, axes=[1], starts=[index], ends=[index + 1] ) self._input = paddle.reshape( self._input, shape=[-1, self._hidden_size] ) for k in range(self._num_layers): pre_hidden = self.hidden_array[k] pre_cell = self.cell_array[k] weight_1 = self.weight_1_arr[k] bias = self.bias_arr[k] nn = fluid.layers.concat([self._input, pre_hidden], 1) gate_input = paddle.matmul(x=nn, y=weight_1) gate_input = paddle.add(gate_input, bias) i, j, f, o = fluid.layers.split( gate_input, num_or_sections=4, dim=-1 ) c = pre_cell * paddle.nn.functional.sigmoid( f ) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j) m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o) self.hidden_array[k] = m self.cell_array[k] = c self._input = m if self._dropout is not None and self._dropout > 0.0: self._input = paddle.nn.functional.dropout( self._input, p=self._dropout, mode='upscale_in_train', ) res.append( paddle.reshape(self._input, shape=[1, -1, self._hidden_size]) ) real_res = fluid.layers.concat(res, 0) real_res = paddle.transpose(x=real_res, perm=[1, 0, 2]) last_hidden = fluid.layers.concat(self.hidden_array, 1) last_hidden = paddle.reshape( last_hidden, shape=[-1, self._num_layers, self._hidden_size] ) last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2]) last_cell = fluid.layers.concat(self.cell_array, 1) last_cell = paddle.reshape( last_cell, shape=[-1, self._num_layers, self._hidden_size] ) last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2]) return real_res, last_hidden, last_cell class PtbModel(fluid.Layer): def __init__( self, name_scope, hidden_size, vocab_size, num_layers=2, num_steps=20, init_scale=0.1, dropout=None, ): super().__init__() self.hidden_size = hidden_size self.vocab_size = vocab_size self.init_scale = init_scale self.num_layers = num_layers self.num_steps = num_steps self.dropout = dropout self.simple_lstm_rnn = SimpleLSTMRNN( self.full_name(), hidden_size, num_steps, num_layers=num_layers, init_scale=init_scale, dropout=dropout, ) self.embedding = paddle.nn.Embedding( num_embeddings=vocab_size, embedding_dim=hidden_size, weight_attr=fluid.ParamAttr( name='embedding_para', initializer=fluid.initializer.UniformInitializer( low=-init_scale, high=init_scale ), ), ) self.softmax_weight = self.create_parameter( attr=fluid.ParamAttr(), shape=[self.hidden_size, self.vocab_size], dtype="float32", default_initializer=fluid.initializer.UniformInitializer( low=-self.init_scale, high=self.init_scale ), ) self.softmax_bias = self.create_parameter( attr=fluid.ParamAttr(), shape=[self.vocab_size], dtype="float32", default_initializer=fluid.initializer.UniformInitializer( low=-self.init_scale, high=self.init_scale ), ) def forward(self, input, label, init_hidden, init_cell): init_h = paddle.reshape( init_hidden, shape=[self.num_layers, -1, self.hidden_size] ) init_c = paddle.reshape( init_cell, shape=[self.num_layers, -1, self.hidden_size] ) # NPU 'tok_k' kernel only support `int32` dtype, so cast `input` from `int64` to `int32`. input = fluid.layers.cast(input, "int32") x_emb = self.embedding(input) x_emb = paddle.reshape( x_emb, shape=[-1, self.num_steps, self.hidden_size] ) if self.dropout is not None and self.dropout > 0.0: x_emb = paddle.nn.functional.dropout( x_emb, p=self.drop_out, mode='upscale_in_train', ) rnn_out, last_hidden, last_cell = self.simple_lstm_rnn( x_emb, init_h, init_c ) rnn_out = paddle.reshape( rnn_out, shape=[-1, self.num_steps, self.hidden_size] ) projection = paddle.matmul(rnn_out, self.softmax_weight) projection = paddle.add(projection, self.softmax_bias) projection = paddle.reshape(projection, shape=[-1, self.vocab_size]) loss = paddle.nn.functional.softmax_with_cross_entropy( logits=projection, label=label, soft_label=False ) loss = paddle.reshape(loss, shape=[-1, self.num_steps]) loss = paddle.mean(loss, axis=[0]) loss = paddle.sum(loss) return loss, last_hidden, last_cell class TestSaveLoadBase(unittest.TestCase): def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def test_ptb_rnn_cpu_float32(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 temp_dir = tempfile.TemporaryDirectory() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t fluid.save(main_program, os.path.join(temp_dir.name, "test_1")) # set var to zero for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.load( main_program, os.path.join(temp_dir.name, "test_1.pdparams"), exe, ) for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) temp_dir.cleanup() class TestSaveLoadPartial(unittest.TestCase): def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def test_ptb_rnn_cpu_float32(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 temp_dir = tempfile.TemporaryDirectory() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) test_program = fluid.default_main_program().clone(for_test=True) add_1 = fluid.layers.fc( static_last_hidden, size=hidden_size, num_flatten_dims=2, bias_attr=False, ) sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t fluid.save(main_program, os.path.join(temp_dir.name, "test_1")) # set var to zero for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.load( test_program, os.path.join(temp_dir.name, "test_1.pdopt"), None ) for var in test_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) fluid.load( test_program, os.path.join(temp_dir.name, "test_1.pdmodel"), None, ) temp_dir.cleanup() class TestSaveLoadSetStateDict(unittest.TestCase): def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def test_ptb_rnn_cpu_float32(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 temp_dir = tempfile.TemporaryDirectory() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t fluid.save(main_program, os.path.join(temp_dir.name, "test_1")) # set var to zero for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.load(main_program, os.path.join(temp_dir.name, "test_1"), exe) for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) temp_dir.cleanup() class TestProgramStatePartial(unittest.TestCase): def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def test_ptb_rnn_cpu_float32(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 temp_dir = tempfile.TemporaryDirectory() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) test_program = fluid.default_main_program().clone(for_test=True) add_1 = fluid.layers.fc( static_last_hidden, size=hidden_size, num_flatten_dims=2, bias_attr=False, ) sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t fluid.save(main_program, os.path.join(temp_dir.name, 'test_1')) # set var to zero for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) # fluid.load(test_program, "./test_1", None ) program_state = fluid.load_program_state( os.path.join(temp_dir.name, 'test_1') ) program_state_1 = fluid.load_program_state( os.path.join(temp_dir.name, 'test_1.pdparams') ) program_state_2 = fluid.load_program_state( os.path.join(temp_dir.name, 'test_1.pdopt') ) program_state_3 = fluid.load_program_state( os.path.join(temp_dir.name, 'test_1.pdmodel') ) fluid.set_program_state(test_program, program_state) for var in test_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) # check 1 for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.set_program_state(test_program, program_state_1) for var in test_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) # check 2 for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.set_program_state(test_program, program_state_2) for var in test_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) # check 3 for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.set_program_state(test_program, program_state_3) for var in test_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) temp_dir.cleanup() class TestVariableInit(unittest.TestCase): def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def test_variable_init(self): x = fluid.data(name="x", shape=[10, 10], dtype='float32') y = fluid.layers.fc(x, 10) z = fluid.layers.fc(y, 10) place = self.set_place() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) temp_dir = tempfile.TemporaryDirectory() fluid.save( fluid.default_main_program(), os.path.join(temp_dir.name, "test_path"), ) def set_var(var, ndarray): t = var.get_tensor() p = t._place() if p.is_cpu_place(): place = paddle.fluid.CPUPlace() elif p.is_cuda_pinned_place(): place = paddle.fluid.CUDAPinnedPlace() else: p = paddle.fluid.core.Place() p.set_place(t._place()) place = paddle.fluid.CUDAPlace(p.gpu_device_id()) t.set(ndarray, place) program = fluid.default_main_program() new_scope = fluid.core.Scope() place = self.set_place() exe = fluid.Executor(place) parameter_list = list( filter(fluid.io.is_parameter, program.list_vars()) ) fluid.core._create_loaded_parameter( parameter_list, new_scope, exe._default_executor ) parameter_file_name = os.path.join(temp_dir.name, "test_path.pdparams") with open(parameter_file_name, 'rb') as f: load_dict = pickle.load(f) for v in parameter_list: assert ( v.name in load_dict ), "Can not find [{}] in model file [{}]".format( v.name, parameter_file_name ) new_v = new_scope.find_var(v.name) set_var(new_v, load_dict[v.name]) opt_list = list( filter(fluid.io.is_belong_to_optimizer, program.list_vars()) ) fluid.core._create_loaded_parameter( opt_list, new_scope, exe._default_executor ) opt_file_name = os.path.join(temp_dir.name, "test_path.pdopt") with open(opt_file_name, 'rb') as f: load_dict = pickle.load(f) for v in opt_list: assert ( v.name in load_dict ), "Can not find [{}] in model file [{}]".format( v.name, opt_file_name ) new_v = new_scope.find_var(v.name) set_var(new_v, load_dict[v.name]) base_map = {} for var in program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update base_map[var.name] = t for var in program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array(new_scope.find_var(var.name).get_tensor()) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) temp_dir.cleanup() class TestLoadFromOldInterface(unittest.TestCase): def setUp(self): if os.path.exists("test_path.pdparams"): os.remove("test_path.pdparams") if os.path.exists("test_static_load_var_list.pdparams"): os.remove("test_static_load_var_list.pdparams") self.temp_dir = tempfile.TemporaryDirectory() def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def tearDown(self): self.temp_dir.cleanup() def test_load_from_old_interface(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) test_clone_program = fluid.default_main_program().clone() sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t # fluid.save(main_program, "./test_1") paddle.distributed.io.save_persistables( exe, os.path.join(self.temp_dir.name, "test_path"), main_program ) # set var to zero for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.load( main_program, os.path.join(self.temp_dir.name, "test_path"), exe ) for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() old_shape = np.array(ten).shape new_shape = [e + 10 for e in old_shape] var.desc.set_shape(new_shape) with self.assertRaises(RuntimeError): fluid.load( main_program, os.path.join(self.temp_dir.name, "test_path"), exe, ) # check unused parameter fluid.load( test_clone_program, os.path.join(self.temp_dir.name, "test_path"), exe, ) def test_load_from_old_interface_var_list(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) test_clone_program = fluid.default_main_program().clone() sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t # fluid.save(main_program, "./test_1") paddle.distributed.io.save_persistables( exe, os.path.join(self.temp_dir.name, "test_static_load_var_list"), main_program, ) # set var to zero var_list = [] for i, var in enumerate(main_program.list_vars()): if isinstance(var, framework.Parameter) or var.persistable: if i % 2 == 0: var_list.append(var) ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.load( main_program, os.path.join(self.temp_dir.name, "test_static_load_var_list"), exe, var_list, ) var_list_names = [var.name for var in var_list] for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) if var.name in var_list_names: # loaded vars base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) else: # not loaded vars self.assertTrue(np.sum(np.abs(new_t)) == 0) class TestLoadFromOldInterfaceSingleFile(unittest.TestCase): def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def test_load_from_old_interface(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 temp_dir = tempfile.TemporaryDirectory() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t save_dir = os.path.join(temp_dir.name, "test_path") # fluid.save(main_program, "./test_1") paddle.distributed.io.save_persistables( exe, save_dir, main_program, filename="model_single" ) # set var to zero for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) file_model_path = os.path.join(save_dir, "model_single") fluid.load( main_program, file_model_path, exe, fluid.io.get_program_persistable_vars(main_program), ) for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) # test exception # change shape for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() old_shape = np.array(ten).shape new_shape = [e + 10 for e in old_shape] var.desc.set_shape(new_shape) with self.assertRaises(RuntimeError): fluid.load( main_program, file_model_path, exe, fluid.io.get_program_persistable_vars(main_program), ) fluid.io.save_params( exe, "test_path", main_program, filename="model_single" ) with self.assertRaises(RuntimeError): fluid.load( main_program, file_model_path, exe, fluid.io.get_program_persistable_vars(main_program), ) # check when executor is None with self.assertRaises(ValueError): fluid.load( main_program, file_model_path, None, fluid.io.get_program_persistable_vars(main_program), ) # check when var list is None with self.assertRaises(ValueError): fluid.load(main_program, file_model_path, exe, None) # check save params, load var_list = get_program_persistable_vars with self.assertRaises(RuntimeError): temp_var = framework.Variable( main_program.global_block(), shape=[1], name="test_temp_var" ) all_var_list = list(main_program.list_vars()) fluid.load( main_program, file_model_path, exe, all_var_list + [temp_var], ) temp_dir.cleanup() class TestProgramStateOldSave(unittest.TestCase): def setUp(self): self.test_dygraph = True self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def test_ptb_rnn_cpu_float32(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) test_program = fluid.default_main_program().clone(for_test=True) add_1 = fluid.layers.fc( static_last_hidden, size=hidden_size, num_flatten_dims=2, bias_attr=False, ) sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t save_dir = os.path.join(self.temp_dir.name, "test_program_1") paddle.distributed.io.save_persistables(exe, save_dir, main_program) # set var to zero for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) # case 1: load basic program_state = fluid.load_program_state(save_dir) fluid.set_program_state(main_program, program_state) self.check_in_static(main_program, base_map) # case 2: load with no need file def symlink_force(target, link_name): try: self.create_symlink(target, link_name) except OSError as e: if e.errno == errno.EEXIST: os.remove(link_name) self.create_symlink(target, link_name) else: raise e program_state = fluid.load_program_state(save_dir) fluid.set_program_state(main_program, program_state) self.check_in_static(main_program, base_map) # case 3: load with var_list program_state = fluid.load_program_state( save_dir, main_program.all_parameters() ) fluid.set_program_state(main_program, program_state) self.check_in_static(main_program, base_map) if self.test_dygraph: # make sure `load_program_state` can be used in dynamic graph mode with fluid.dygraph.guard(place): load_state = fluid.load_program_state(save_dir) for k, v in load_state.items(): np.testing.assert_array_equal(base_map[k], v) def create_symlink(self, target, link_name): try: os.symlink(target, link_name) except AttributeError: import ctypes kernel_dll = ctypes.windll.LoadLibrary("kernel32.dll") kernel_dll.CreateSymbolicLinkA(target, link_name, 0) def check_in_static(self, main_program, base_map): for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) class TestProgramStateOldSaveSingleModel(unittest.TestCase): def set_place(self): return ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) def test_ptb_rnn_cpu_float32(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 batch_num = 200 temp_dir = tempfile.TemporaryDirectory() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed ptb_model = PtbModel( "ptb_model", hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) place = self.set_place() exe = fluid.Executor(place) sgd = Adam(learning_rate=1e-3) x = fluid.layers.data( name="x", shape=[-1, num_steps], dtype='int64' ) y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32') init_hidden = fluid.layers.data( name="init_hidden", shape=[1], dtype='float32' ) init_cell = fluid.layers.data( name="init_cell", shape=[1], dtype='float32' ) static_loss, static_last_hidden, static_last_cell = ptb_model( x, y, init_hidden, init_cell ) test_program = fluid.default_main_program().clone(for_test=True) add_1 = fluid.layers.fc( static_last_hidden, size=hidden_size, num_flatten_dims=2, bias_attr=False, ) sgd.minimize(static_loss) static_param_updated = dict() static_param_init = dict() out = exe.run(framework.default_startup_program()) static_loss_value = None static_last_cell_value = None static_last_hidden_value = None for i in range(batch_num): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32' ) fetch_list = [static_loss, static_last_hidden, static_last_cell] out = exe.run( fluid.default_main_program(), feed={ "x": x_data, "y": y_data, "init_hidden": init_hidden_data, "init_cell": init_cell_data, }, fetch_list=fetch_list, ) static_loss_value = out[0] static_last_hidden_value = out[1] static_last_cell_value = out[2] # get value before save main_program = framework.default_main_program() base_map = {} for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t save_dir = os.path.join(temp_dir.name, "test_program_2") paddle.distributed.io.save_persistables( exe, save_dir, main_program, filename="model_1" ) # set var to zero for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = fluid.global_scope().find_var(var.name).get_tensor() ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) # fluid.load(test_program, "./test_1", None ) program_state = fluid.load_program_state( os.path.join(save_dir, "model_1"), var_list=fluid.io.get_program_persistable_vars(main_program), ) fluid.set_program_state(main_program, program_state) for var in main_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) with self.assertRaises(ValueError): fluid.load_program_state(os.path.join(save_dir, "model_1")) with self.assertRaises(TypeError): fluid.load_program_state( os.path.join(save_dir, "model_1"), var_list=["str"] ) with self.assertRaises(RuntimeError): fluid.load_program_state( os.path.join(save_dir, "model_1"), var_list=[ main_program.global_block().create_var( name="fake_var_name", persistable=True ) ], ) temp_dir.cleanup() class TestStaticSaveLoadPickle(unittest.TestCase): def test_pickle_protocol(self): # enable static mode paddle.enable_static() with new_program_scope(): # create network x = paddle.static.data( name="static_save_load_large_x", shape=[None, 10], dtype='float32', ) z = paddle.static.nn.fc(x, 10, bias_attr=False) place = paddle.CPUPlace() exe = paddle.static.Executor(place) exe.run(paddle.static.default_startup_program()) prog = paddle.static.default_main_program() base_map = {} for var in prog.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) # make sure all the paramerter or optimizer var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t temp_dir = tempfile.TemporaryDirectory() path = os.path.join( temp_dir.name, "test_static_save_load_pickle", "pickle_protocol" ) with self.assertRaises(ValueError): paddle.fluid.save(prog, path, 2.0) with self.assertRaises(ValueError): paddle.fluid.save(prog, path, 1) with self.assertRaises(ValueError): paddle.fluid.save(prog, path, 5) protocols = [2, 3, 4] for protocol in protocols: paddle.fluid.save(prog, path, protocol) # set var to zero for var in prog.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: ten = ( fluid.global_scope().find_var(var.name).get_tensor() ) ten.set(np.zeros_like(np.array(ten)), place) new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) self.assertTrue(np.sum(np.abs(new_t)) == 0) paddle.fluid.load(prog, path) for var in prog.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: new_t = np.array( fluid.global_scope().find_var(var.name).get_tensor() ) base_t = base_map[var.name] np.testing.assert_array_equal(new_t, base_t) class TestSaveLoadInferenceModel(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() self.model_path = os.path.join(self.temp_dir.name, 'no_params') def tearDown(self): self.temp_dir.cleanup() def test_no_params(self): main_program = framework.Program() with framework.program_guard(main_program): x = paddle.static.data(name="x", shape=[10, 10], dtype='float32') y = x + x place = paddle.CPUPlace() exe = paddle.static.Executor(place) paddle.static.save_inference_model(self.model_path, [x], [y], exe) [ inference_program, feed_target_names, fetch_targets, ] = paddle.static.load_inference_model(self.model_path, exe) self.assertEqual(feed_target_names, ['x']) self.assertEqual(fetch_targets[0].shape, (10, 10)) ops = [op.type for op in inference_program.block(0).ops] self.assertEqual(ops, ['feed', 'elementwise_add', 'scale', 'fetch']) if __name__ == '__main__': paddle.enable_static() unittest.main()