add pbt lm; test=develop (#2287)

implement the ptb model under dygraph mode

dygraph/pbt_lm/args.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import distutils.util
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--model_type",
+        type=str,
+        default="small",
+        help="model_type [test|small|medium|large]")
+    parser.add_argument(
+        "--rnn_model",
+        type=str,
+        default="static",
+        help="model_type [static|padding|cudnn]")
+    parser.add_argument(
+        "--data_path", type=str, help="all the data for train,valid,test")
+    parser.add_argument('--para_init', action='store_true')
+    parser.add_argument(
+        '--use_gpu', type=bool, default=False, help='whether using gpu')
+    parser.add_argument(
+        '--log_path',
+        help='path of the log file. If not set, logs are printed to console')
+    parser.add_argument('--enable_ce', action='store_true')
+    args = parser.parse_args()
+    return args

dygraph/pbt_lm/data/download_data.sh

+
+wget http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
+
+tar -xzvf simple-examples.tgz

dygraph/pbt_lm/debug.sh

+
+export CUDA_VISIBLE_DEVICES=0
+
+#export FLAGS_fraction_of_gpu_memory_to_use=0.0
+python  ptb_dy.py  --data_path data/simple-examples/data/ \
+           --model_type small 
+

dygraph/pbt_lm/ptb_dy.py

+#   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
+import paddle.fluid.core as core
+from paddle.fluid.dygraph.nn import Embedding
+import paddle.fluid.framework as framework
+from paddle.fluid.optimizer import SGDOptimizer
+from paddle.fluid.dygraph.base import to_variable
+import numpy as np
+import six
+
+import reader
+import time
+
+from args import *
+
+#import fluid.dygraph_grad_clip as dygraph_clip
+#from fluid.dygraph_grad_clip  import *
+
+import sys
+if sys.version[0] == '2':
+    reload(sys)
+    sys.setdefaultencoding("utf-8")
+
+
+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 = []
+
+        #build_once()
+        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 build_once(self, input_embedding, init_hidden=None, init_cell=None):
+
+        pass
+        '''
+        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(
+            self.full_name(),
+            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 build_once(self, input, label, init_hidden, init_cell):
+        pass
+
+    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)
+
+        #print( self.x_emb.numpy() )
+        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.dropout,
+                dropout_implementation='upscale_in_train')
+        rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h,
+                                                               init_c)
+
+        #print( "rnn_out", rnn_out.numpy() )
+        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)
+        loss.permissions = True
+
+        return loss, last_hidden, last_cell
+
+    def debug_emb(self):
+        #print("1111", self.x_emb.gradient() )
+
+        np.save("emb_grad", self.x_emb.gradient())
+
+
+def train_ptb_lm():
+
+    args = parse_args()
+    model_type = args.model_type
+
+    vocab_size = 10000
+    if model_type == "test":
+        num_layers = 1
+        batch_size = 2
+        hidden_size = 10
+        num_steps = 3
+        init_scale = 0.1
+        max_grad_norm = 5.0
+        epoch_start_decay = 1
+        max_epoch = 1
+        dropout = 0.0
+        lr_decay = 0.5
+        base_learning_rate = 1.0
+    elif model_type == "small":
+        num_layers = 2
+        batch_size = 20
+        hidden_size = 200
+        num_steps = 20
+        init_scale = 0.1
+        max_grad_norm = 5.0
+        epoch_start_decay = 4
+        max_epoch = 13
+        dropout = 0.0
+        lr_decay = 0.5
+        base_learning_rate = 1.0
+    elif model_type == "medium":
+        num_layers = 2
+        batch_size = 20
+        hidden_size = 650
+        num_steps = 35
+        init_scale = 0.05
+        max_grad_norm = 5.0
+        epoch_start_decay = 6
+        max_epoch = 39
+        dropout = 0.5
+        lr_decay = 0.8
+        base_learning_rate = 1.0
+    elif model_type == "large":
+        num_layers = 2
+        batch_size = 20
+        hidden_size = 1500
+        num_steps = 35
+        init_scale = 0.04
+        max_grad_norm = 10.0
+        epoch_start_decay = 14
+        max_epoch = 55
+        dropout = 0.65
+        lr_decay = 1.0 / 1.15
+        base_learning_rate = 1.0
+    else:
+        print("model type not support")
+        return
+
+    with fluid.dygraph.guard(core.CUDAPlace(0)):
+        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,
+            dropout=dropout)
+
+        dy_param_updated = dict()
+        dy_param_init = dict()
+        dy_loss = None
+        last_hidden = None
+        last_cell = None
+
+        data_path = args.data_path
+        print("begin to load data")
+        raw_data = reader.ptb_raw_data(data_path)
+        print("finished load data")
+        train_data, valid_data, test_data, _ = raw_data
+
+        batch_len = len(train_data) // batch_size
+        total_batch_size = (batch_len - 1) // num_steps
+        log_interval = total_batch_size // 10
+
+        bd = []
+        lr_arr = [1.0]
+        for i in range(1, max_epoch):
+            bd.append(total_batch_size * i)
+            new_lr = base_learning_rate * (lr_decay**
+                                           max(i + 1 - epoch_start_decay, 0.0))
+            lr_arr.append(new_lr)
+
+        sgd = SGDOptimizer(learning_rate=fluid.layers.piecewise_decay(
+            boundaries=bd, values=lr_arr))
+
+        def eval(model, data):
+            print("begion to eval")
+            total_loss = 0.0
+            iters = 0.0
+            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')
+
+            model.eval()
+            train_data_iter = reader.get_data_iter(data, batch_size, num_steps)
+            for batch_id, batch in enumerate(train_data_iter):
+                x_data, y_data = batch
+                x_data = x_data.reshape((-1, num_steps, 1))
+                y_data = y_data.reshape((-1, 1))
+                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)
+
+                out_loss = dy_loss.numpy()
+
+                init_hidden_data = last_hidden.numpy()
+                init_cell_data = last_cell.numpy()
+
+                total_loss += out_loss
+                iters += num_steps
+
+            print("eval finished")
+            ppl = np.exp(total_loss / iters)
+            print("ppl ", batch_id, ppl[0])
+
+        grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(max_grad_norm)
+        for epoch_id in range(max_epoch):
+            ptb_model.train()
+            total_loss = 0.0
+            iters = 0.0
+            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')
+
+            train_data_iter = reader.get_data_iter(train_data, batch_size,
+                                                   num_steps)
+
+            start_time = time.time()
+            for batch_id, batch in enumerate(train_data_iter):
+                x_data, y_data = batch
+                x_data = x_data.reshape((-1, num_steps, 1))
+                y_data = y_data.reshape((-1, 1))
+                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)
+
+                out_loss = dy_loss.numpy()
+
+                init_hidden_data = last_hidden.numpy()
+                init_cell_data = last_cell.numpy()
+                dy_loss.backward()
+                sgd.minimize(dy_loss, grad_clip=grad_clip)
+
+                ptb_model.clear_gradients()
+                total_loss += out_loss
+                iters += num_steps
+
+                if batch_id > 0 and batch_id % log_interval == 0:
+                    ppl = np.exp(total_loss / iters)
+                    print(epoch_id, "ppl ", batch_id, ppl[0],
+                          sgd._global_learning_rate().numpy())
+
+            print("one ecpoh finished", epoch_id)
+            print("time cost ", time.time() - start_time)
+            ppl = np.exp(total_loss / iters)
+            print("ppl ", epoch_id, ppl[0])
+
+        eval(ptb_model, test_data)
+
+
+train_ptb_lm()

dygraph/pbt_lm/reader.py

+# Copyright 2015 The TensorFlow 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.
+# ==============================================================================
+"""Utilities for parsing PTB text files."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import os
+import sys
+import numpy as np
+
+Py3 = sys.version_info[0] == 3
+
+
+def _read_words(filename):
+    data = []
+    with open(filename, "r") as f:
+        if Py3:
+            return f.read().replace("\n", "<eos>").split()
+        else:
+            return f.read().decode("utf-8").replace("\n", "<eos>").split()
+
+
+def _build_vocab(filename):
+    data = _read_words(filename)
+
+    counter = collections.Counter(data)
+    count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))
+
+    words, _ = list(zip(*count_pairs))
+
+    print("vocab word num", len(words))
+    word_to_id = dict(zip(words, range(len(words))))
+
+    return word_to_id
+
+
+def _file_to_word_ids(filename, word_to_id):
+    data = _read_words(filename)
+    return [word_to_id[word] for word in data if word in word_to_id]
+
+
+def ptb_raw_data(data_path=None):
+    """Load PTB raw data from data directory "data_path".
+
+  Reads PTB text files, converts strings to integer ids,
+  and performs mini-batching of the inputs.
+
+  The PTB dataset comes from Tomas Mikolov's webpage:
+
+  http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
+
+  Args:
+    data_path: string path to the directory where simple-examples.tgz has
+      been extracted.
+
+  Returns:
+    tuple (train_data, valid_data, test_data, vocabulary)
+    where each of the data objects can be passed to PTBIterator.
+  """
+
+    train_path = os.path.join(data_path, "ptb.train.txt")
+    #train_path = os.path.join(data_path, "train.fake")
+    valid_path = os.path.join(data_path, "ptb.valid.txt")
+    test_path = os.path.join(data_path, "ptb.test.txt")
+
+    word_to_id = _build_vocab(train_path)
+    train_data = _file_to_word_ids(train_path, word_to_id)
+    valid_data = _file_to_word_ids(valid_path, word_to_id)
+    test_data = _file_to_word_ids(test_path, word_to_id)
+    vocabulary = len(word_to_id)
+    return train_data, valid_data, test_data, vocabulary
+
+
+def get_data_iter(raw_data, batch_size, num_steps):
+    data_len = len(raw_data)
+    raw_data = np.asarray(raw_data, dtype="int64")
+
+    #print( "raw", raw_data[:20] )
+
+    batch_len = data_len // batch_size
+
+    data = raw_data[0:batch_size * batch_len].reshape((batch_size, batch_len))
+
+    #h = data.reshape( (-1))
+    #print( "h", h[:20])
+
+    epoch_size = (batch_len - 1) // num_steps
+    for i in range(epoch_size):
+        start = i * num_steps
+        #print( i * num_steps )
+        x = np.copy(data[:, i * num_steps:(i + 1) * num_steps])
+        y = np.copy(data[:, i * num_steps + 1:(i + 1) * num_steps + 1])
+
+        yield (x, y)