# 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. from __future__ import print_function import unittest import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.dygraph.nn import Embedding import paddle.fluid.framework as framework from paddle.fluid.optimizer import Adam from paddle.fluid.dygraph.base import to_variable from test_imperative_base import new_program_scope from paddle.fluid.executor import global_scope import numpy as np import six import pickle class SimpleLSTMRNN(fluid.Layer): def __init__(self, name_scope, hidden_size, num_steps, num_layers=2, init_scale=0.1, dropout=None): super(SimpleLSTMRNN, self).__init__(name_scope) 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 = [] def _build_once(self, input_embedding, init_hidden=None, init_cell=None): 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 = fluid.layers.slice( init_hidden, axes=[0], starts=[i], ends=[i + 1]) pre_cell = fluid.layers.slice( init_cell, axes=[0], starts=[i], ends=[i + 1]) pre_hidden = fluid.layers.reshape( pre_hidden, shape=[-1, self._hidden_size]) pre_cell = fluid.layers.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 = fluid.layers.slice( input_embedding, axes=[1], starts=[index], ends=[index + 1]) self._input = fluid.layers.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 = fluid.layers.matmul(x=nn, y=weight_1) gate_input = fluid.layers.elementwise_add(gate_input, bias) i, j, f, o = fluid.layers.split( gate_input, num_or_sections=4, dim=-1) c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid( i) * fluid.layers.tanh(j) m = fluid.layers.tanh(c) * fluid.layers.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 = fluid.layers.dropout( self._input, dropout_prob=self._dropout, dropout_implementation='upscale_in_train') res.append( fluid.layers.reshape( self._input, shape=[1, -1, self._hidden_size])) real_res = fluid.layers.concat(res, 0) real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2]) last_hidden = fluid.layers.concat(self.hidden_array, 1) last_hidden = fluid.layers.reshape( last_hidden, shape=[-1, self._num_layers, self._hidden_size]) last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2]) last_cell = fluid.layers.concat(self.cell_array, 1) last_cell = fluid.layers.reshape( last_cell, shape=[-1, self._num_layers, self._hidden_size]) last_cell = fluid.layers.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(PtbModel, self).__init__(name_scope) 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 = Embedding( size=[vocab_size, hidden_size], dtype='float32', is_sparse=False, param_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 = fluid.layers.reshape( init_hidden, shape=[self.num_layers, -1, self.hidden_size]) init_c = fluid.layers.reshape( init_cell, shape=[self.num_layers, -1, self.hidden_size]) x_emb = self.embedding(input) x_emb = fluid.layers.reshape( x_emb, shape=[-1, self.num_steps, self.hidden_size]) if self.dropout is not None and self.dropout > 0.0: x_emb = fluid.layers.dropout( x_emb, dropout_prob=self.drop_out, dropout_implementation='upscale_in_train') rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h, init_c) rnn_out = fluid.layers.reshape( rnn_out, shape=[-1, self.num_steps, self.hidden_size]) projection = fluid.layers.matmul(rnn_out, self.softmax_weight) projection = fluid.layers.elementwise_add(projection, self.softmax_bias) projection = fluid.layers.reshape( projection, shape=[-1, self.vocab_size]) loss = fluid.layers.softmax_with_cross_entropy( logits=projection, label=label, soft_label=False) loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps]) loss = fluid.layers.reduce_mean(loss, dim=[0]) loss = fluid.layers.reduce_sum(loss) return loss, last_hidden, last_cell class TestSaveLoadBase(unittest.TestCase): 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 = fluid.CPUPlace() if not core.is_compiled_with_cuda( ) else fluid.CUDAPlace(0) 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 optimzier var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t fluid.save(main_program, "./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 optimzier var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.load(main_program, "./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] self.assertTrue(np.array_equal(new_t, base_t)) class TestSaveLoadPartial(unittest.TestCase): 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 = fluid.CPUPlace() if not core.is_compiled_with_cuda( ) else fluid.CUDAPlace(0) 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 optimzier var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t fluid.save(main_program, "./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 optimzier var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.load(test_program, "./test_1", None) for var in test_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: print(var.name) new_t = np.array(fluid.global_scope().find_var(var.name) .get_tensor()) base_t = base_map[var.name] self.assertTrue(np.array_equal(new_t, base_t)) class TestSaveLoadSetStateDict(unittest.TestCase): 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 = fluid.CPUPlace() if not core.is_compiled_with_cuda( ) else fluid.CUDAPlace(0) 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 optimzier var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t fluid.save(main_program, "./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 optimzier var have been set to zero self.assertTrue(np.sum(np.abs(new_t)) == 0) fluid.load(main_program, "./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] self.assertTrue(np.array_equal(new_t, base_t)) class TestProgramStatePartial(unittest.TestCase): 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 = fluid.CPUPlace() if not core.is_compiled_with_cuda( ) else fluid.CUDAPlace(0) 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 optimzier var have been update self.assertTrue(np.sum(np.abs(t)) != 0) base_map[var.name] = t fluid.save(main_program, "./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 optimzier 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("./test_1") fluid.set_program_state(test_program, program_state) for var in test_program.list_vars(): if isinstance(var, framework.Parameter) or var.persistable: print(var.name) new_t = np.array(fluid.global_scope().find_var(var.name) .get_tensor()) base_t = base_map[var.name] self.assertTrue(np.array_equal(new_t, base_t)) class TestVariableInit(unittest.TestCase): 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 = fluid.CPUPlace() if not core.is_compiled_with_cuda( ) else fluid.CUDAPlace(0) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) fluid.save(fluid.default_main_program(), "./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 = fluid.CPUPlace() if not core.is_compiled_with_cuda( ) else fluid.CUDAPlace(0) 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 = "./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 = "./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 optimzier 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] self.assertTrue(np.array_equal(new_t, base_t)) if __name__ == '__main__': unittest.main()