test_imperative_ptb_rnn.py 15.0 KB
Newer Older
J
JiabinYang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#   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.fluid as fluid
19
from paddle.fluid import Embedding
J
JiabinYang 已提交
20 21
import paddle.fluid.framework as framework
from paddle.fluid.optimizer import SGDOptimizer
L
lujun 已提交
22
from paddle.fluid.dygraph.base import to_variable
23
from test_imperative_base import new_program_scope
J
JiabinYang 已提交
24
import numpy as np
25
import six
J
JiabinYang 已提交
26 27


28
class SimpleLSTMRNN(fluid.Layer):
J
JiabinYang 已提交
29
    def __init__(self,
X
Xin Pan 已提交
30
                 name_scope,
J
JiabinYang 已提交
31 32 33 34 35
                 hidden_size,
                 num_steps,
                 num_layers=2,
                 init_scale=0.1,
                 dropout=None):
X
Xin Pan 已提交
36
        super(SimpleLSTMRNN, self).__init__(name_scope)
J
JiabinYang 已提交
37 38 39 40
        self._hidden_size = hidden_size
        self._num_layers = num_layers
        self._init_scale = init_scale
        self._dropout = dropout
41 42
        self._input = None
        self._num_steps = num_steps
43 44
        self.cell_array = []
        self.hidden_array = []
J
JiabinYang 已提交
45 46 47 48 49 50 51 52

    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):
53
            weight_1 = self.create_parameter(
54 55 56
                attr=fluid.ParamAttr(
                    initializer=fluid.initializer.UniformInitializer(
                        low=-self._init_scale, high=self._init_scale)),
J
JiabinYang 已提交
57 58 59 60
                shape=[self._hidden_size * 2, self._hidden_size * 4],
                dtype="float32",
                default_initializer=fluid.initializer.UniformInitializer(
                    low=-self._init_scale, high=self._init_scale))
61
            self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1))
62
            bias_1 = self.create_parameter(
63 64 65 66
                attr=fluid.ParamAttr(
                    initializer=fluid.initializer.UniformInitializer(
                        low=-self._init_scale, high=self._init_scale)),
                shape=[self._hidden_size * 4],
J
JiabinYang 已提交
67 68
                dtype="float32",
                default_initializer=fluid.initializer.Constant(0.0))
69
            self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1))
J
JiabinYang 已提交
70

71 72 73 74 75
    def forward(self, input_embedding, init_hidden=None, init_cell=None):
        self.cell_array = []
        self.hidden_array = []

        for i in range(self._num_layers):
J
JiabinYang 已提交
76 77 78 79 80 81 82 83 84 85 86 87
            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 = []
88 89
        for index in range(self._num_steps):
            self._input = fluid.layers.slice(
J
JiabinYang 已提交
90
                input_embedding, axes=[1], starts=[index], ends=[index + 1])
91 92
            self._input = fluid.layers.reshape(
                self._input, shape=[-1, self._hidden_size])
J
JiabinYang 已提交
93 94 95 96 97 98
            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]

99
                nn = fluid.layers.concat([self._input, pre_hidden], 1)
J
JiabinYang 已提交
100 101 102
                gate_input = fluid.layers.matmul(x=nn, y=weight_1)

                gate_input = fluid.layers.elementwise_add(gate_input, bias)
103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130
                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
J
JiabinYang 已提交
131 132


133
class PtbModel(fluid.Layer):
J
JiabinYang 已提交
134
    def __init__(self,
X
Xin Pan 已提交
135
                 name_scope,
J
JiabinYang 已提交
136 137 138 139 140 141
                 hidden_size,
                 vocab_size,
                 num_layers=2,
                 num_steps=20,
                 init_scale=0.1,
                 dropout=None):
X
Xin Pan 已提交
142
        super(PtbModel, self).__init__(name_scope)
J
JiabinYang 已提交
143 144 145 146 147 148 149
        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(
X
Xin Pan 已提交
150
            self.full_name(),
J
JiabinYang 已提交
151 152 153 154 155
            hidden_size,
            num_steps,
            num_layers=num_layers,
            init_scale=init_scale,
            dropout=dropout)
156
        self.embedding = Embedding(
X
Xin Pan 已提交
157
            self.full_name(),
J
JiabinYang 已提交
158 159 160 161 162 163 164
            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)))
165
        self.softmax_weight = self.create_parameter(
166 167
            attr=fluid.ParamAttr(),
            shape=[self.hidden_size, self.vocab_size],
J
JiabinYang 已提交
168 169 170
            dtype="float32",
            default_initializer=fluid.initializer.UniformInitializer(
                low=-self.init_scale, high=self.init_scale))
171
        self.softmax_bias = self.create_parameter(
172 173
            attr=fluid.ParamAttr(),
            shape=[self.vocab_size],
J
JiabinYang 已提交
174 175 176 177
            dtype="float32",
            default_initializer=fluid.initializer.UniformInitializer(
                low=-self.init_scale, high=self.init_scale))

178 179 180
    def _build_once(self, input, label, init_hidden, init_cell):
        pass

J
JiabinYang 已提交
181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199
    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])
200
        projection = fluid.layers.matmul(rnn_out, self.softmax_weight)
J
JiabinYang 已提交
201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
        projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
        projection = fluid.layers.reshape(
            projection, shape=[-1, self.vocab_size])
        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)
        loss.permissions = True

        return loss, last_hidden, last_cell


L
lujun 已提交
216
class TestDygraphPtbRnn(unittest.TestCase):
217
    def test_ptb_rnn_cpu_float32(self):
J
JiabinYang 已提交
218 219 220 221 222 223 224 225
        seed = 90
        hidden_size = 10
        vocab_size = 1000
        num_layers = 1
        num_steps = 3
        init_scale = 0.1
        batch_size = 4

L
lujun 已提交
226
        with fluid.dygraph.guard():
J
JiabinYang 已提交
227 228 229 230
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed
            # TODO: marsyang1993 Change seed to
            ptb_model = PtbModel(
X
Xin Pan 已提交
231
                "ptb_model",
J
JiabinYang 已提交
232 233 234 235 236 237 238
                hidden_size=hidden_size,
                vocab_size=vocab_size,
                num_layers=num_layers,
                num_steps=num_steps,
                init_scale=init_scale)

            sgd = SGDOptimizer(learning_rate=1e-3)
239 240
            dy_param_updated = dict()
            dy_param_init = dict()
J
JiabinYang 已提交
241 242 243
            dy_loss = None
            last_hidden = None
            last_cell = None
244
            batch_num = 200
245 246

            for i in range(batch_num):
J
JiabinYang 已提交
247 248 249 250 251 252 253 254 255 256 257 258 259 260 261
                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')
                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:
262
                    for param in ptb_model.parameters():
J
JiabinYang 已提交
263 264 265
                        dy_param_init[param.name] = param._numpy()
                dy_loss._backward()
                sgd.minimize(dy_loss)
266 267 268 269
                ptb_model.clear_gradients()
                if i == batch_num - 1:
                    for param in ptb_model.parameters():
                        dy_param_updated[param.name] = param._numpy()
270 271 272 273 274

        with new_program_scope():
            fluid.default_startup_program().random_seed = seed
            fluid.default_main_program().random_seed = seed
            ptb_model = PtbModel(
X
Xin Pan 已提交
275
                "ptb_model",
276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296
                hidden_size=hidden_size,
                vocab_size=vocab_size,
                num_layers=num_layers,
                num_steps=num_steps,
                init_scale=init_scale)

            exe = fluid.Executor(fluid.CPUPlace())
            sgd = SGDOptimizer(learning_rate=1e-3)
            x = fluid.layers.data(name="x", shape=[-1, 3, 1], 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()
            static_param_name_list = list()
297
            for param in ptb_model.parameters():
298 299 300 301 302 303
                static_param_name_list.append(param.name)

            out = exe.run(framework.default_startup_program(),
                          fetch_list=static_param_name_list)
            for i in range(len(static_param_name_list)):
                static_param_init[static_param_name_list[i]] = out[i]
J
JiabinYang 已提交
304 305 306
            static_loss_value = None
            static_last_cell_value = None
            static_last_hidden_value = None
307
            for i in range(batch_num):
308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326
                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]
                fetch_list.extend(static_param_name_list)
                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]
327 328
                static_last_hidden_value = out[1]
                static_last_cell_value = out[2]
J
JiabinYang 已提交
329

330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345
                if i == batch_num - 1:
                    for k in range(3, len(out)):
                        static_param_updated[static_param_name_list[k -
                                                                    3]] = out[k]

        self.assertTrue(np.allclose(static_loss_value, dy_loss._numpy()))
        self.assertTrue(np.allclose(static_last_cell_value, last_cell._numpy()))
        self.assertTrue(
            np.allclose(static_last_hidden_value, last_hidden._numpy()))
        for key, value in six.iteritems(static_param_init):
            # print("static_init name: {}, value {}".format(key, value))
            # print("dy_init name: {}, value {}".format(key, dy_param_init[key]))
            self.assertTrue(np.allclose(value, dy_param_init[key], atol=1e-5))
        for key, value in six.iteritems(static_param_updated):
            # print("static name: {}, value {}".format(key, value))
            # print("dy name: {}, value {}".format(key, dy_param_updated[key]))
346
            self.assertTrue(
347 348
                np.allclose(
                    value, dy_param_updated[key], atol=1e-5))
J
JiabinYang 已提交
349 350 351 352


if __name__ == '__main__':
    unittest.main()