Merge pull request #1345 from gzrp/dev-postgresql

Commit message: Add the sequence model for the peft example

Author: moazreyad

examples/singa_peft/examples/model/char_rnn.py

@@ -0,0 +1,89 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# =============================================================================
+'''Train a Char-RNN model using plain text files.
+The model is created following https://github.com/karpathy/char-rnn
+The train file could be any text file,
+e.g., http://cs.stanford.edu/people/karpathy/char-rnn/
+'''
+
+from __future__ import division
+from __future__ import print_function
+from builtins import range
+import numpy as np
+import sys
+import argparse
+from tqdm import tqdm
+
+from singa import device
+from singa import tensor
+from singa import autograd
+from singa import layer
+from singa import model
+from singa import opt
+
+
+class CharRNN(model.Model):
+
+    def __init__(self, vocab_size, hidden_size=32):
+        super(CharRNN, self).__init__()
+        self.rnn = layer.LSTM(vocab_size, hidden_size)
+        self.cat = layer.Cat()
+        self.reshape1 = layer.Reshape()
+        self.dense = layer.Linear(hidden_size, vocab_size)
+        self.reshape2 = layer.Reshape()
+        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+        self.optimizer = opt.SGD(0.01)
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+
+    def reset_states(self, dev):
+        self.hx.to_device(dev)
+        self.cx.to_device(dev)
+        self.hx.set_value(0.0)
+        self.cx.set_value(0.0)
+
+    def initialize(self, inputs):
+        batchsize = inputs[0].shape[0]
+        self.hx = tensor.Tensor((batchsize, self.hidden_size))
+        self.cx = tensor.Tensor((batchsize, self.hidden_size))
+        self.reset_states(inputs[0].device)
+
+    def forward(self, inputs):
+        x, hx, cx = self.rnn(inputs, (self.hx, self.cx))
+        self.hx.copy_data(hx)
+        self.cx.copy_data(cx)
+        x = self.cat(x)
+        x = self.reshape1(x, (-1, self.hidden_size))
+        return self.dense(x)
+
+    def train_one_batch(self, x, y):
+        out = self.forward(x)
+        y = self.reshape2(y, (-1, 1))
+        loss = self.softmax_cross_entropy(out, y)
+        self.optimizer(loss)
+        return out, loss
+
+    def get_states(self):
+        ret = super().get_states()
+        ret[self.hx.name] = self.hx
+        ret[self.cx.name] = self.cx
+        return ret
+
+    def set_states(self, states):
+        self.hx.copy_from(states[self.hx.name])
+        self.hx.copy_from(states[self.hx.name])
+        super().set_states(states)