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From wang...@apache.org
Subject svn commit: r1723991 - in /incubator/singa/site/trunk/content: markdown/docs/general-rnn.md resources/images/char-rnn-net.jpg resources/images/char-rnn.png
Date Mon, 11 Jan 2016 09:23:34 GMT
Author: wangwei
Date: Mon Jan 11 09:23:34 2016
New Revision: 1723991

URL: http://svn.apache.org/viewvc?rev=1723991&view=rev
Log:
Add docs for char rnn using GRU.

Added:
    incubator/singa/site/trunk/content/markdown/docs/general-rnn.md
    incubator/singa/site/trunk/content/resources/images/char-rnn-net.jpg   (with props)
    incubator/singa/site/trunk/content/resources/images/char-rnn.png   (with props)

Added: incubator/singa/site/trunk/content/markdown/docs/general-rnn.md
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/markdown/docs/general-rnn.md?rev=1723991&view=auto
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--- incubator/singa/site/trunk/content/markdown/docs/general-rnn.md (added)
+++ incubator/singa/site/trunk/content/markdown/docs/general-rnn.md Mon Jan 11 09:23:34 2016
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+# RNN in SINGA
+
+---
+
+Recurrent neural networks (RNN) are widely used for modelling sequential data,
+e.g., natural language sentences. In this page, we describe how to implement a
+RNN application (or model) using SINGA built-in RNN layers. We will
+use the [char-rnn modle](https://github.com/karpathy/char-rnn) as an example,
+which trains over setences or source code, with each character as an input
+unit. Particularly, we will train a RNN using GRU over
+[Linux kernel source code](http://cs.stanford.edu/people/karpathy/char-rnn/).
+After training, we expect to generate meaningful code from the model, like the
+one shown by [Karpathy](http://karpathy.github.io/2015/05/21/rnn-effectiveness/).
+There is a [vanilla RNN example](rnn.html) for language modelling using user
+defined RNN layers, which is different to using built-in RNN layers discribed
+in this page.
+
+```
+/*
+ * If this error is set, we will need anything right after that BSD.
+ */
+static void action_new_function(struct s_stat_info *wb)
+{
+  unsigned long flags;
+  int lel_idx_bit = e->edd, *sys & ~((unsigned long) *FIRST_COMPAT);
+  buf[0] = 0xFFFFFFFF & (bit << 4);
+  min(inc, slist->bytes);
+  printk(KERN_WARNING "Memory allocated %02x/%02x, "
+      "original MLL instead\n"),
+    min(min(multi_run - s->len, max) * num_data_in),
+    frame_pos, sz + first_seg);
+  div_u64_w(val, inb_p);
+  spin_unlock(&disk->queue_lock);
+  mutex_unlock(&s->sock->mutex);
+  mutex_unlock(&func->mutex);
+  return disassemble(info->pending_bh);
+}
+```
+
+## User configuration
+
+The major diffences to the configuration of other models, e.g., feed-forward
+models include,
+
+1. the training algorithm should be changed to BPTT (back-propagation through time).
+2. the layers and their connections should be configured differently.
+
+The train one batch algorithm can be simply configured as
+
+    train_one_batch {
+      alg: kBPTT
+    }
+
+Next, we introduce the configuration of the neural net.
+
+<img src="../images/char-rnn.png" style="width: 550px"/>
+<p><strong> Fig.1 - Illustration of the structure of the Char-RNN model</strong></p>
+
+Fig.1 illustrates the net structure of the char-rnn model. The input layer
+buffers all training data (the Linux kernel code is about 6MB). For each
+iteration, it reads `unroll_len +1` (`unroll_len` is configured by users)
+successive characters, e.g., "int a;", and passes the first `unroll_len`
+characters to `OneHotLayer`s (one per layer). Every `OneHotLayer` converts its
+character into the one-hot vector representation. The input layer passes the
+last `unroll_len` characters as labels to the `RNNLabelLayer` (the label of the
+i-th character is the i+1 character, i.e., the objective is to predict the next
+character).  Each `GRULayer` receives an one-hot vector and the hidden feature
+vector from its precedent layer. After some feature transformation, its own
+feature vector is passed to an inner-product layer and its successive
+`GRULayer`. The i-th SoftmaxLossLayer measures the cross-entropy loss for
+predicting the i-th character. According to Karpathy, there could be another
+stack of `GRULayer`s connecting the first stack of `GRULayer`s, which improves
+the performance if there is enough training data. The layer configuration is
+similar to that for other models, e.g., feed-forward models. The major
+difference is on the connection configuration.
+
+### Unrolling length
+
+To model the long dependency, recurrent layers need to be unrolled many times,
+denoted as `unroll_len` (i.e., 50). According to our unified neural net
+representation, the neural net should have configurations for `unroll_len`
+recurrent layers. It is tedious
+to let users configure these layers manually. Hence, SINGA makes it a
+configuration field for each layer.  For example, to unroll the `GRULayer`,
+users just configure it as,
+
+    layer {
+      type: kGRU
+      unroll_len: 50
+    }
+
+Not only the `GRULayer` is unrolled, other layers like `InnerProductLayer` and
+`SoftmaxLossLayer`, are also unrolled. To simplify the configuration, SINGA
+provides a `unroll_len` field in the net configuration, which sets the
+`unroll_len` of each layer configuration if the `unroll_len` is not configured
+explicitly for that layer. For instance, SINGA would set the `unroll_len` of
+the `GRULayer` to 50 implicitly for the following configuration.
+
+    net {
+      unroll_len: 50
+       layer {
+         type: kCharRNNInput
+         unroll_len: 1  // configure it explicitly
+       }
+       layer {
+         type: kGRU
+         // no configuration for unroll_len
+        }
+     }
+
+### ConnectionType
+<img src="http://karpathy.github.io/assets/rnn/diags.jpeg" style="width: 550px"/>
+<p><strong> Fig.1 - Different RNN structures from [Karpathy](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)</strong></p>
+
+There would be many types of connections between layers in RNN models as shown
+by Karpathy in Fig.2.  For each `srclayer`, there is a connection_type for it.
+Taking the i-th `srclayer` as an example, if its connection type is,
+
+* kOneToOne, then each unrolled layer is connected with one unrolled layer from the i-th
`srclayer`.
+* kOneToALL, then each unrolled layer is connected with all unrolled layers from the i-th
`srclayer`.
+
+## Implementation
+
+### Neural net configuration preprocessing
+
+User configured neural net is preprocessed to unroll the recurrent layers,
+i.e., duplicating the configuration of the `GRULayer`s, renaming the name of
+each layer with unrolling index, and re-configuring the `srclayer` field. After
+preprocessing, each layer's name is changed to
+`<unrolling_index>#<user_configured_name>.`  Consequently, the (unrolled) neural
+net configuration passed to NeuralNet class includes all layers and their
+connections.  The NeuralNet class creates and setup each layer in the same way
+as for other models.  For example, after partitioning, each layer's name is
+changed to `<layer_name>@<partition_index>`. One difference is that it has some
+special code for sharing Param data and grad Blobs for layers unrolled from the
+same original layer.
+
+Users can visualize the neural net structure using the Python script `tool/graph.py`
+and the files in *WORKSPACE/visualization/*. For example, after the training program
+is started,
+
+    python tool/graph.py examples/char-rnn/visualization/train_net.json
+
+The generated image file is shown in Fig.3 for `unroll_len=5`,
+
+<img src="../images/char-rnn-net.jpg" style="width: 550px"/>
+<p><strong> Fig.3 - Net structure generated by SINGA</strong></p>
+
+### BPTTWorker
+
+The BPTT (back-propagation through time) algorithm is typically used to compute
+gradients of the objective loss w.r.t. parameters for RNN models. It forwards
+propagates through all unrolled layers (i.e., timepoints) to compute features
+of each layer, and backwards propagates to compute gradients of parameters. It
+is the same as the BP algorithm for feed-forward models if the recurrent layers
+are unrolled infinite times. In practice, due to the constraint of memory, the
+truncated BPTT is widely used.  It unrolls the recurrent layers a fixed
+(truncated) times (controlled by `unroll_len`).  In SINGA, a BPTTWorker is
+provided to run the truncated BPTT algorithm for each mini-batch (i.e.,
+iteration).  The pseudo code is
+
+```
+BPTTWorker::Forward(phase, net) {
+  for each layer in net
+    if layer.unroll_index() == 0
+      Get(layer.params());   // fetch params values from servers
+    srclayers = layer.srclayer();
+    if phase & kTest
+      srclayers.push_back(net->GetConextLayer(layer))
+    layer.ComputeFeature(phase, srclayers)
+}
+
+BPTTWorker::Backward(phase, net) {
+  for each layer in reverse(net.layers())
+    layer.ComputeGradient(layer.srclayers())
+    if layer.unroll_index() == 0
+      Update(layer.params());   // send params gradients to servers
+}
+```
+
+The testing phase is processed specially. Because the test phase may sample a
+long sequence of data (e.g., sampling a piece of Linux kernel code), which
+requires many unrolled layers (e.g., more than 1000 characters/layers). But we
+cannot unroll the recurrent layers too many times due to memory constraint.
+The special line add the 0-th unrolled layer as one of its own source layer.
+Consequently, it dynamically adds a recurrent connection to the recurrent layer
+(e.g., GRULayer). Then we can sample from the model for infinite times. Taking
+the char-rnn model as an example, the test job can be configured as
+
+    test_steps: 10000
+    train_one_batch {
+      Alg: kBPTT
+    }
+    net {
+      // do not set the unroll_len
+      layer {
+        // do not set the unroll_len
+      }
+      …
+    }
+
+The instructions for [running test](test.html) is the same for feed-forward
+models.

Added: incubator/singa/site/trunk/content/resources/images/char-rnn-net.jpg
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/content/resources/images/char-rnn-net.jpg?rev=1723991&view=auto
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