# 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 os import tempfile import unittest import numpy as np import paddle from paddle import fluid from paddle.fluid import core from paddle.fluid.dygraph.base import to_variable from paddle.nn import Embedding from paddle.optimizer import Adam from paddle.optimizer.lr import LRScheduler class SimpleLSTMRNN(paddle.nn.Layer): def __init__( self, 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=paddle.nn.initializer.Uniform( low=-self._init_scale, high=self._init_scale ) ), shape=[self._hidden_size * 2, self._hidden_size * 4], dtype="float32", default_initializer=paddle.nn.initializer.Uniform( 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=paddle.nn.initializer.Uniform( low=-self._init_scale, high=self._init_scale ) ), shape=[self._hidden_size * 4], dtype="float32", default_initializer=paddle.nn.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 = paddle.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 = paddle.split( gate_input, num_or_sections=4, axis=-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 = paddle.concat(res, 0) real_res = paddle.transpose(x=real_res, perm=[1, 0, 2]) last_hidden = paddle.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 = paddle.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(paddle.nn.Layer): def __init__( self, 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( hidden_size, num_steps, num_layers=num_layers, init_scale=init_scale, dropout=dropout, ) self.embedding = Embedding( vocab_size, hidden_size, sparse=False, weight_attr=fluid.ParamAttr( name='embedding_para', initializer=paddle.nn.initializer.Uniform( 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=paddle.nn.initializer.Uniform( 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=paddle.nn.initializer.Uniform( 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] ) 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 TestDygraphPtbRnn(unittest.TestCase): def setUp(self): self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def func_setUp(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 fluid.dygraph.guard(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) bd = [] lr_arr = [1.0] # this a fake lr decay strategy for i in range(1, 10): bd.append(100 * i) new_lr = 1.0 lr_arr.append(new_lr) place = ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) scheduler = paddle.optimizer.lr.PiecewiseDecay( boundaries=bd, values=lr_arr ) adam = Adam( learning_rate=scheduler, parameters=ptb_model.parameters() ) dy_param_updated = {} dy_param_init = {} dy_loss = None last_hidden = None last_cell = 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') 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' ) x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model( x, y, init_hidden, init_cell ) if i == 0: for param in ptb_model.parameters(): dy_param_init[param.name] = param.numpy() dy_loss.backward() adam.minimize(dy_loss) scheduler.step() ptb_model.clear_gradients() if i == batch_num - 1: for param in ptb_model.parameters(): dy_param_updated[param.name] = param.numpy() # check optimizer self.opti_dict = adam.state_dict() self.base_opti = {} for k, v in self.opti_dict.items(): if isinstance(v, core.eager.Tensor): self.base_opti[v.name] = v.numpy() self.assertTrue(np.sum(np.abs(v.numpy())) != 0) else: self.base_opti[k] = v paddle.save( self.opti_dict, os.path.join(self.temp_dir.name, "test_dy_v2.pdopt"), ) self.state_dict = ptb_model.state_dict() self.model_base = {} for k, v in self.state_dict.items(): np_t = v.numpy() self.model_base[k] = np_t paddle.save( self.state_dict, os.path.join(self.temp_dir.name, "test_dy_v2.pdparams"), ) def func_testLoadAndSetVarBase(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 fluid.dygraph.guard(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) bd = [] lr_arr = [1.0] # this a fake lr decay strategy for i in range(1, 10): bd.append(100 * i) new_lr = 1.0 lr_arr.append(new_lr) place = ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) scheduler = paddle.optimizer.lr.PiecewiseDecay( boundaries=bd, values=lr_arr ) adam = Adam( learning_rate=scheduler, parameters=ptb_model.parameters() ) dy_param_updated = {} dy_param_init = {} dy_loss = None last_hidden = None last_cell = 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') 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' ) x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model( x, y, init_hidden, init_cell ) if i == 0: for param in ptb_model.parameters(): dy_param_init[param.name] = param.numpy() dy_loss.backward() adam.minimize(dy_loss) scheduler.step() ptb_model.clear_gradients() if i == batch_num - 1: for param in ptb_model.parameters(): dy_param_updated[param.name] = param.numpy() # check optimizer opti_dict = adam.state_dict() # set to zero for k, v in opti_dict.items(): if isinstance(v, core.eager.Tensor): np_t = v.numpy() var = v.value().get_tensor() var.set(np.zeros_like(np_t), place) self.assertTrue(np.sum(np.abs(v.numpy())) == 0) para_state_dict = paddle.load( os.path.join(self.temp_dir.name, "test_dy_v2.pdparams") ) opti_state_dict = paddle.load( os.path.join(self.temp_dir.name, "test_dy_v2.pdopt") ) adam.set_state_dict(opti_state_dict) opti_dict = adam.state_dict() for k, v in opti_dict.items(): if isinstance(v, core.eager.Tensor): np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] ) else: self.assertEqual(v, self.base_opti[k]) # check parameter state_dict = ptb_model.state_dict() for k, v in state_dict.items(): np_t = v.numpy() var = v.value().get_tensor() var.set(np.zeros_like(np_t), place) ptb_model.set_dict(para_state_dict) state_dict = ptb_model.state_dict() for k, v in state_dict.items(): new_t = v.numpy() base_t = self.model_base[k] np.testing.assert_array_equal(new_t, base_t) def func_testSetVariable(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 fluid.dygraph.guard(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) bd = [] lr_arr = [1.0] # this a fake lr decay strategy for i in range(1, 10): bd.append(100 * i) new_lr = 1.0 lr_arr.append(new_lr) place = ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) scheduler = paddle.optimizer.lr.PiecewiseDecay( boundaries=bd, values=lr_arr ) adam = Adam( learning_rate=scheduler, parameters=ptb_model.parameters() ) dy_param_updated = {} dy_param_init = {} dy_loss = None last_hidden = None last_cell = 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') 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' ) x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model( x, y, init_hidden, init_cell ) if i == 0: for param in ptb_model.parameters(): dy_param_init[param.name] = param.numpy() dy_loss.backward() adam.minimize(dy_loss) scheduler.step() ptb_model.clear_gradients() if i == batch_num - 1: for param in ptb_model.parameters(): dy_param_updated[param.name] = param.numpy() # check optimizer opti_dict = adam.state_dict() # set to zero for k, v in opti_dict.items(): if isinstance(v, core.eager.Tensor): np_t = v.numpy() var = v.value().get_tensor() var.set(np.zeros_like(np_t), place) self.assertTrue(np.sum(np.abs(v.numpy())) == 0) if isinstance(adam._learning_rate, LRScheduler): adam._learning_rate.step_num = 0 adam.set_state_dict(self.opti_dict) opti_dict = adam.state_dict() for k, v in opti_dict.items(): if isinstance(v, core.eager.Tensor): np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] ) else: self.assertEqual(v, self.base_opti[k]) # check parameter state_dict = ptb_model.state_dict() for k, v in state_dict.items(): np_t = v.numpy() var = v.value().get_tensor() var.set(np.zeros_like(np_t), place) ptb_model.set_dict(self.state_dict) state_dict = ptb_model.state_dict() for k, v in state_dict.items(): new_t = v.numpy() base_t = self.model_base[k] np.testing.assert_array_equal(new_t, base_t) def func_testSetNumpy(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 fluid.dygraph.guard(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) bd = [] lr_arr = [1.0] # this a fake lr decay strategy for i in range(1, 10): bd.append(100 * i) new_lr = 1.0 lr_arr.append(new_lr) place = ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) scheduler = paddle.optimizer.lr.PiecewiseDecay( boundaries=bd, values=lr_arr ) adam = Adam( learning_rate=scheduler, parameters=ptb_model.parameters() ) dy_param_updated = {} dy_param_init = {} dy_loss = None last_hidden = None last_cell = 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') 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' ) x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model( x, y, init_hidden, init_cell ) if i == 0: for param in ptb_model.parameters(): dy_param_init[param.name] = param.numpy() dy_loss.backward() adam.minimize(dy_loss) scheduler.step() ptb_model.clear_gradients() if i == batch_num - 1: for param in ptb_model.parameters(): dy_param_updated[param.name] = param.numpy() # check optimizer opti_dict = adam.state_dict() np_opti_dict = {} # set to zero for k, v in opti_dict.items(): if isinstance(v, core.eager.Tensor): np_t = v.numpy() np_opti_dict[v.name] = np_t var = v.value().get_tensor() var.set(np.zeros_like(np_t), place) self.assertTrue(np.sum(np.abs(v.numpy())) == 0) else: np_opti_dict[k] = v if isinstance(adam._learning_rate, LRScheduler): adam._learning_rate.step_num = 0 adam.set_state_dict(np_opti_dict) opti_dict = adam.state_dict() for k, v in opti_dict.items(): if isinstance(v, core.eager.Tensor): np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] ) else: self.assertEqual(v, self.base_opti[k]) # check parameter state_dict = ptb_model.state_dict() np_state_dict = {} for k, v in state_dict.items(): np_t = v.numpy() np_state_dict[k] = np_t var = v.value().get_tensor() var.set(np.zeros_like(np_t), place) ptb_model.set_dict(np_state_dict) state_dict = ptb_model.state_dict() for k, v in state_dict.items(): new_t = v.numpy() base_t = self.model_base[k] np.testing.assert_array_equal(new_t, base_t) def func_testSetVariableBeforeTrain(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 fluid.dygraph.guard(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( 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) ) adam = Adam( learning_rate=0.0, beta1=0.8, beta2=0.6, parameters=ptb_model.parameters(), ) dy_param_updated = {} dy_param_init = {} dy_loss = None last_hidden = None last_cell = None adam.set_state_dict(self.opti_dict) ptb_model.set_dict(self.state_dict) for i in range(1): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') 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' ) x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model( x, y, init_hidden, init_cell ) dy_loss.backward() adam.minimize(dy_loss) ptb_model.clear_gradients() opti_dict = adam.state_dict() for k, v in opti_dict.items(): if k == "global_step": np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] + 1 ) if k.find("beta1_pow_acc_0") > 0: np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] * adam._beta1 ) if k.find("beta2_pow_acc_0") > 0: np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] * adam._beta2 ) state_dict = ptb_model.state_dict() for k, v in state_dict.items(): new_t = v.numpy() base_t = self.model_base[k] np.testing.assert_array_equal(new_t, base_t) def func_testLoadAndSetVarBaseBeforeTrain(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 fluid.dygraph.guard(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) bd = [] lr_arr = [0.0] # this a fake lr decay strategy for i in range(1, 10): bd.append(100 * i) # set lr to zero not update parameter new_lr = 0.0 lr_arr.append(new_lr) place = ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) adam = Adam( learning_rate=0.0, beta1=0.8, beta2=0.6, parameters=ptb_model.parameters(), ) dy_param_updated = {} dy_param_init = {} dy_loss = None last_hidden = None last_cell = None model_prefix = os.path.join(self.temp_dir.name, "test_dy_v2") state_dict = paddle.load(model_prefix + '.pdparams') opti_dict = paddle.load(model_prefix + '.pdopt') adam.set_state_dict(opti_dict) ptb_model.set_dict(state_dict) for i in range(1): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') 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' ) x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model( x, y, init_hidden, init_cell ) dy_loss.backward() adam.minimize(dy_loss) ptb_model.clear_gradients() opti_dict = adam.state_dict() for k, v in opti_dict.items(): if k == "global_step": np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] + 1 ) if k.find("beta1_pow_acc_0") > 0: np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] * adam._beta1 ) if k.find("beta2_pow_acc_0") > 0: np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] * adam._beta2 ) # check parameter state_dict = ptb_model.state_dict() for k, v in state_dict.items(): new_t = v.numpy() base_t = self.model_base[k] np.testing.assert_array_equal(new_t, base_t) def func_testSetNumpyBeforeTrain(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 fluid.dygraph.guard(): paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) # TODO: marsyang1993 Change seed to ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale, ) bd = [] lr_arr = [0.0] # this a fake lr decay strategy for i in range(1, 10): bd.append(100 * i) # set lr to 0.0, not update parameter new_lr = 0.0 lr_arr.append(new_lr) place = ( fluid.CPUPlace() if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0) ) scheduler = paddle.optimizer.lr.PiecewiseDecay( boundaries=bd, values=lr_arr ) adam = Adam( learning_rate=scheduler, beta1=0.8, beta2=0.6, parameters=ptb_model.parameters(), ) dy_param_updated = {} dy_param_init = {} dy_loss = None last_hidden = None last_cell = None np_opti_dict = {} np_state_dict = {} for k, v in self.opti_dict.items(): if isinstance(v, core.eager.Tensor): np_opti_dict[v.name] = v.numpy() else: np_opti_dict[k] = v for k, v in self.state_dict.items(): np_state_dict[k] = v.numpy() adam.set_state_dict(np_opti_dict) ptb_model.set_dict(np_state_dict) for i in range(1): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') 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' ) x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model( x, y, init_hidden, init_cell ) dy_loss.backward() scheduler.step() adam.minimize(dy_loss) ptb_model.clear_gradients() opti_dict = adam.state_dict() for k, v in opti_dict.items(): if k == "LR_Scheduler": np.testing.assert_array_equal( v['last_epoch'], self.base_opti[k]['last_epoch'] + 1 ) if k.find("beta1_pow_acc_0") > 0: np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] * adam._beta1 ) if k.find("beta2_pow_acc_0") > 0: np.testing.assert_array_equal( v.numpy(), self.base_opti[v.name] * adam._beta2 ) # check parameter state_dict = ptb_model.state_dict() for k, v in state_dict.items(): new_t = v.numpy() base_t = self.model_base[k] np.testing.assert_array_equal(new_t, base_t) def func_testOnlyLoadParams(self): with fluid.dygraph.guard(): emb = paddle.nn.Embedding(10, 10) state_dict = emb.state_dict() paddle.save( state_dict, os.path.join(self.temp_dir.name, 'saved_dy', 'emb_dy.pdparams'), ) para_state_dict = paddle.load( os.path.join(self.temp_dir.name, 'saved_dy', 'emb_dy.pdparams') ) def func_test_no_state_in_input_dict(self): with fluid.dygraph.guard(): emb = paddle.nn.Embedding(10, 10) state_dict = emb.state_dict() paddle.save( state_dict, os.path.join(self.temp_dir.name, 'saved_dy', 'emb_dy.pdparams'), ) para_state_dict = paddle.load( os.path.join(self.temp_dir.name, 'saved_dy', 'emb_dy.pdparams') ) para_state_dict.pop('weight') emb.set_state_dict(para_state_dict) def func_test_state_shape_mismatch(self): with fluid.dygraph.guard(): emb = paddle.nn.Embedding(10, 10) state_dict = emb.state_dict() paddle.save( state_dict, os.path.join(self.temp_dir.name, 'saved_dy', 'emb_dy.pdparams'), ) para_state_dict = paddle.load( os.path.join(self.temp_dir.name, 'saved_dy', 'emb_dy.pdparams'), return_numpy=True, ) para_state_dict['weight'] = np.expand_dims( para_state_dict['weight'], axis=-1 ) emb.set_state_dict(para_state_dict) def test_main(self): self.func_setUp() self.func_testLoadAndSetVarBase() self.func_testSetVariable() self.func_testSetNumpy() self.func_testSetVariableBeforeTrain() self.func_testLoadAndSetVarBaseBeforeTrain() self.func_testSetNumpyBeforeTrain() self.func_testOnlyLoadParams() self.func_test_no_state_in_input_dict() self.func_test_state_shape_mismatch() if __name__ == '__main__': unittest.main()