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# Design for TensorArray
TensorArray as a new concept is borrowed from TensorFlow,
it is meant to be used with dynamic iteration primitives such as `while_loop` and `map_fn`.
This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers,
such as `RecurrentGradientMachine`.
In [our design for dynamic RNN](https://github.com/PaddlePaddle/Paddle/pull/4401),
`TensorArray` is used to segment inputs and store states in all time steps.
By providing some methods similar to a C++ array,
the definition of some state-based dynamic models such as RNN could be more natural and highly flexible.
## Dynamic-Related Methods
Some basic methods should be proposed as follows:
### stack()
Pack the values in a `TensorArray` into a tensor with rank one higher than each tensor in `values`.
### unstack(axis=0)
Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
### concat()
Return the values in the `TensorArray` as a concatenated Tensor.
### write(index, value, data_shared=true)
Write value into index of the TensorArray.
### read(index)
Read the value at location `index` in the `TensorArray`.
### size()
Return the number of values.
## LoDTensor-related Supports
The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes variant length sequences as input,
because each step of RNN could only take a tensor-represented batch of data as input,
some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches.
Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`.
With these two methods, a variant-sentence-RNN can be implemented like
```c++
// input is the varient-length data
LodTensor sentence_input(xxx);
TensorArray ta;
Tensor indice_map;
Tensor boot_state = xxx; // to initialize rnn's first state
TensorArray::unpack(input, 1/*level*/, true/*sort_by_length*/, &ta, &indice_map);
TessorArray step_outputs;
TensorArray states;
for (int step = 0; step = ta.size(); step++) {
auto state = states.read(step);
// rnnstep is a function which acts like a step of RNN
auto step_input = ta.read(step);
auto step_output = rnnstep(step_input, state);
step_outputs.write(step_output, true/*data_shared*/);
}
// rnn_output is the final output of an rnn
LoDTensor rnn_output = ta.pack(ta, indice_map);
```
the code above shows that by embedding the LoDTensor-related preprocess operations into `TensorArray`,
the implementation of a RNN that supports varient-length sentences is far more concise than `RecurrentGradientMachine` because the latter mixes all the codes together, hard to read and extend.
some details are as follows.
### unpack(level, sort_by_length)
Split LodTensor in some `level` and generate batches, if set `sort_by_length`, will sort by length.
Returns:
- a new `TensorArray`, whose values are LodTensors and represents batches of data.
- an int32 Tensor, which stores the map from the new batch's indices to original LoDTensor
### pack(level, indices_map)
Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` and `level` and `indices_map`.
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<div class="section" id="design-for-tensorarray">
<span id="design-for-tensorarray"></span><h1>Design for TensorArray<a class="headerlink" href="#design-for-tensorarray" title="Permalink to this headline"></a></h1>
<p>TensorArray as a new concept is borrowed from TensorFlow,
it is meant to be used with dynamic iteration primitives such as <code class="docutils literal"><span class="pre">while_loop</span></code> and <code class="docutils literal"><span class="pre">map_fn</span></code>.</p>
<p>This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers,
such as <code class="docutils literal"><span class="pre">RecurrentGradientMachine</span></code>.</p>
<p>In <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/pull/4401">our design for dynamic RNN</a>,
<code class="docutils literal"><span class="pre">TensorArray</span></code> is used to segment inputs and store states in all time steps.
By providing some methods similar to a C++ array,
the definition of some state-based dynamic models such as RNN could be more natural and highly flexible.</p>
<div class="section" id="dynamic-related-methods">
<span id="dynamic-related-methods"></span><h2>Dynamic-Related Methods<a class="headerlink" href="#dynamic-related-methods" title="Permalink to this headline"></a></h2>
<p>Some basic methods should be proposed as follows:</p>
<div class="section" id="stack">
<span id="stack"></span><h3>stack()<a class="headerlink" href="#stack" title="Permalink to this headline"></a></h3>
<p>Pack the values in a <code class="docutils literal"><span class="pre">TensorArray</span></code> into a tensor with rank one higher than each tensor in <code class="docutils literal"><span class="pre">values</span></code>.</p>
</div>
<div class="section" id="unstack-axis-0">
<span id="unstack-axis-0"></span><h3>unstack(axis=0)<a class="headerlink" href="#unstack-axis-0" title="Permalink to this headline"></a></h3>
<p>Unpacks the given dimension of a rank-<code class="docutils literal"><span class="pre">R</span></code> tensor into rank-<code class="docutils literal"><span class="pre">(R-1)</span></code> tensors.</p>
</div>
<div class="section" id="concat">
<span id="concat"></span><h3>concat()<a class="headerlink" href="#concat" title="Permalink to this headline"></a></h3>
<p>Return the values in the <code class="docutils literal"><span class="pre">TensorArray</span></code> as a concatenated Tensor.</p>
</div>
<div class="section" id="write-index-value-data-shared-true">
<span id="write-index-value-data-shared-true"></span><h3>write(index, value, data_shared=true)<a class="headerlink" href="#write-index-value-data-shared-true" title="Permalink to this headline"></a></h3>
<p>Write value into index of the TensorArray.</p>
</div>
<div class="section" id="read-index">
<span id="read-index"></span><h3>read(index)<a class="headerlink" href="#read-index" title="Permalink to this headline"></a></h3>
<p>Read the value at location <code class="docutils literal"><span class="pre">index</span></code> in the <code class="docutils literal"><span class="pre">TensorArray</span></code>.</p>
</div>
<div class="section" id="size">
<span id="size"></span><h3>size()<a class="headerlink" href="#size" title="Permalink to this headline"></a></h3>
<p>Return the number of values.</p>
</div>
</div>
<div class="section" id="lodtensor-related-supports">
<span id="lodtensor-related-supports"></span><h2>LoDTensor-related Supports<a class="headerlink" href="#lodtensor-related-supports" title="Permalink to this headline"></a></h2>
<p>The <code class="docutils literal"><span class="pre">RecurrentGradientMachine</span></code> in Paddle serves as a flexible RNN layer; it takes variant length sequences as input,
because each step of RNN could only take a tensor-represented batch of data as input,
some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches.</p>
<p>Such cut-like operations can be embedded into <code class="docutils literal"><span class="pre">TensorArray</span></code> as general methods called <code class="docutils literal"><span class="pre">unpack</span></code> and <code class="docutils literal"><span class="pre">pack</span></code>.</p>
<p>With these two methods, a variant-sentence-RNN can be implemented like</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="c1">// input is the varient-length data</span>
<span class="n">LodTensor</span> <span class="nf">sentence_input</span><span class="p">(</span><span class="n">xxx</span><span class="p">);</span>
<span class="n">TensorArray</span> <span class="n">ta</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="n">indice_map</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="n">boot_state</span> <span class="o">=</span> <span class="n">xxx</span><span class="p">;</span> <span class="c1">// to initialize rnn&#39;s first state</span>
<span class="n">TensorArray</span><span class="o">::</span><span class="n">unpack</span><span class="p">(</span><span class="n">input</span><span class="p">,</span> <span class="mi">1</span><span class="cm">/*level*/</span><span class="p">,</span> <span class="nb">true</span><span class="cm">/*sort_by_length*/</span><span class="p">,</span> <span class="o">&amp;</span><span class="n">ta</span><span class="p">,</span> <span class="o">&amp;</span><span class="n">indice_map</span><span class="p">);</span>
<span class="n">TessorArray</span> <span class="n">step_outputs</span><span class="p">;</span>
<span class="n">TensorArray</span> <span class="n">states</span><span class="p">;</span>
<span class="k">for</span> <span class="p">(</span><span class="kt">int</span> <span class="n">step</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">step</span> <span class="o">=</span> <span class="n">ta</span><span class="p">.</span><span class="n">size</span><span class="p">();</span> <span class="n">step</span><span class="o">++</span><span class="p">)</span> <span class="p">{</span>
<span class="k">auto</span> <span class="n">state</span> <span class="o">=</span> <span class="n">states</span><span class="p">.</span><span class="n">read</span><span class="p">(</span><span class="n">step</span><span class="p">);</span>
<span class="c1">// rnnstep is a function which acts like a step of RNN</span>
<span class="k">auto</span> <span class="n">step_input</span> <span class="o">=</span> <span class="n">ta</span><span class="p">.</span><span class="n">read</span><span class="p">(</span><span class="n">step</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">step_output</span> <span class="o">=</span> <span class="n">rnnstep</span><span class="p">(</span><span class="n">step_input</span><span class="p">,</span> <span class="n">state</span><span class="p">);</span>
<span class="n">step_outputs</span><span class="p">.</span><span class="n">write</span><span class="p">(</span><span class="n">step_output</span><span class="p">,</span> <span class="nb">true</span><span class="cm">/*data_shared*/</span><span class="p">);</span>
<span class="p">}</span>
<span class="c1">// rnn_output is the final output of an rnn</span>
<span class="n">LoDTensor</span> <span class="n">rnn_output</span> <span class="o">=</span> <span class="n">ta</span><span class="p">.</span><span class="n">pack</span><span class="p">(</span><span class="n">ta</span><span class="p">,</span> <span class="n">indice_map</span><span class="p">);</span>
</pre></div>
</div>
<p>the code above shows that by embedding the LoDTensor-related preprocess operations into <code class="docutils literal"><span class="pre">TensorArray</span></code>,
the implementation of a RNN that supports varient-length sentences is far more concise than <code class="docutils literal"><span class="pre">RecurrentGradientMachine</span></code> because the latter mixes all the codes together, hard to read and extend.</p>
<p>some details are as follows.</p>
<div class="section" id="unpack-level-sort-by-length">
<span id="unpack-level-sort-by-length"></span><h3>unpack(level, sort_by_length)<a class="headerlink" href="#unpack-level-sort-by-length" title="Permalink to this headline"></a></h3>
<p>Split LodTensor in some <code class="docutils literal"><span class="pre">level</span></code> and generate batches, if set <code class="docutils literal"><span class="pre">sort_by_length</span></code>, will sort by length.</p>
<p>Returns:</p>
<ul class="simple">
<li>a new <code class="docutils literal"><span class="pre">TensorArray</span></code>, whose values are LodTensors and represents batches of data.</li>
<li>an int32 Tensor, which stores the map from the new batch&#8217;s indices to original LoDTensor</li>
</ul>
</div>
<div class="section" id="pack-level-indices-map">
<span id="pack-level-indices-map"></span><h3>pack(level, indices_map)<a class="headerlink" href="#pack-level-indices-map" title="Permalink to this headline"></a></h3>
<p>Recover the original LoD-arranged LoDTensor with the values in a <code class="docutils literal"><span class="pre">TensorArray</span></code> and <code class="docutils literal"><span class="pre">level</span></code> and <code class="docutils literal"><span class="pre">indices_map</span></code>.</p>
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# Design for TensorArray
TensorArray as a new concept is borrowed from TensorFlow,
it is meant to be used with dynamic iteration primitives such as `while_loop` and `map_fn`.
This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers,
such as `RecurrentGradientMachine`.
In [our design for dynamic RNN](https://github.com/PaddlePaddle/Paddle/pull/4401),
`TensorArray` is used to segment inputs and store states in all time steps.
By providing some methods similar to a C++ array,
the definition of some state-based dynamic models such as RNN could be more natural and highly flexible.
## Dynamic-Related Methods
Some basic methods should be proposed as follows:
### stack()
Pack the values in a `TensorArray` into a tensor with rank one higher than each tensor in `values`.
### unstack(axis=0)
Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
### concat()
Return the values in the `TensorArray` as a concatenated Tensor.
### write(index, value, data_shared=true)
Write value into index of the TensorArray.
### read(index)
Read the value at location `index` in the `TensorArray`.
### size()
Return the number of values.
## LoDTensor-related Supports
The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes variant length sequences as input,
because each step of RNN could only take a tensor-represented batch of data as input,
some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches.
Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`.
With these two methods, a variant-sentence-RNN can be implemented like
```c++
// input is the varient-length data
LodTensor sentence_input(xxx);
TensorArray ta;
Tensor indice_map;
Tensor boot_state = xxx; // to initialize rnn's first state
TensorArray::unpack(input, 1/*level*/, true/*sort_by_length*/, &ta, &indice_map);
TessorArray step_outputs;
TensorArray states;
for (int step = 0; step = ta.size(); step++) {
auto state = states.read(step);
// rnnstep is a function which acts like a step of RNN
auto step_input = ta.read(step);
auto step_output = rnnstep(step_input, state);
step_outputs.write(step_output, true/*data_shared*/);
}
// rnn_output is the final output of an rnn
LoDTensor rnn_output = ta.pack(ta, indice_map);
```
the code above shows that by embedding the LoDTensor-related preprocess operations into `TensorArray`,
the implementation of a RNN that supports varient-length sentences is far more concise than `RecurrentGradientMachine` because the latter mixes all the codes together, hard to read and extend.
some details are as follows.
### unpack(level, sort_by_length)
Split LodTensor in some `level` and generate batches, if set `sort_by_length`, will sort by length.
Returns:
- a new `TensorArray`, whose values are LodTensors and represents batches of data.
- an int32 Tensor, which stores the map from the new batch's indices to original LoDTensor
### pack(level, indices_map)
Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` and `level` and `indices_map`.
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<div class="section" id="design-for-tensorarray">
<span id="design-for-tensorarray"></span><h1>Design for TensorArray<a class="headerlink" href="#design-for-tensorarray" title="永久链接至标题"></a></h1>
<p>TensorArray as a new concept is borrowed from TensorFlow,
it is meant to be used with dynamic iteration primitives such as <code class="docutils literal"><span class="pre">while_loop</span></code> and <code class="docutils literal"><span class="pre">map_fn</span></code>.</p>
<p>This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers,
such as <code class="docutils literal"><span class="pre">RecurrentGradientMachine</span></code>.</p>
<p>In <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/pull/4401">our design for dynamic RNN</a>,
<code class="docutils literal"><span class="pre">TensorArray</span></code> is used to segment inputs and store states in all time steps.
By providing some methods similar to a C++ array,
the definition of some state-based dynamic models such as RNN could be more natural and highly flexible.</p>
<div class="section" id="dynamic-related-methods">
<span id="dynamic-related-methods"></span><h2>Dynamic-Related Methods<a class="headerlink" href="#dynamic-related-methods" title="永久链接至标题"></a></h2>
<p>Some basic methods should be proposed as follows:</p>
<div class="section" id="stack">
<span id="stack"></span><h3>stack()<a class="headerlink" href="#stack" title="永久链接至标题"></a></h3>
<p>Pack the values in a <code class="docutils literal"><span class="pre">TensorArray</span></code> into a tensor with rank one higher than each tensor in <code class="docutils literal"><span class="pre">values</span></code>.</p>
</div>
<div class="section" id="unstack-axis-0">
<span id="unstack-axis-0"></span><h3>unstack(axis=0)<a class="headerlink" href="#unstack-axis-0" title="永久链接至标题"></a></h3>
<p>Unpacks the given dimension of a rank-<code class="docutils literal"><span class="pre">R</span></code> tensor into rank-<code class="docutils literal"><span class="pre">(R-1)</span></code> tensors.</p>
</div>
<div class="section" id="concat">
<span id="concat"></span><h3>concat()<a class="headerlink" href="#concat" title="永久链接至标题"></a></h3>
<p>Return the values in the <code class="docutils literal"><span class="pre">TensorArray</span></code> as a concatenated Tensor.</p>
</div>
<div class="section" id="write-index-value-data-shared-true">
<span id="write-index-value-data-shared-true"></span><h3>write(index, value, data_shared=true)<a class="headerlink" href="#write-index-value-data-shared-true" title="永久链接至标题"></a></h3>
<p>Write value into index of the TensorArray.</p>
</div>
<div class="section" id="read-index">
<span id="read-index"></span><h3>read(index)<a class="headerlink" href="#read-index" title="永久链接至标题"></a></h3>
<p>Read the value at location <code class="docutils literal"><span class="pre">index</span></code> in the <code class="docutils literal"><span class="pre">TensorArray</span></code>.</p>
</div>
<div class="section" id="size">
<span id="size"></span><h3>size()<a class="headerlink" href="#size" title="永久链接至标题"></a></h3>
<p>Return the number of values.</p>
</div>
</div>
<div class="section" id="lodtensor-related-supports">
<span id="lodtensor-related-supports"></span><h2>LoDTensor-related Supports<a class="headerlink" href="#lodtensor-related-supports" title="永久链接至标题"></a></h2>
<p>The <code class="docutils literal"><span class="pre">RecurrentGradientMachine</span></code> in Paddle serves as a flexible RNN layer; it takes variant length sequences as input,
because each step of RNN could only take a tensor-represented batch of data as input,
some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches.</p>
<p>Such cut-like operations can be embedded into <code class="docutils literal"><span class="pre">TensorArray</span></code> as general methods called <code class="docutils literal"><span class="pre">unpack</span></code> and <code class="docutils literal"><span class="pre">pack</span></code>.</p>
<p>With these two methods, a variant-sentence-RNN can be implemented like</p>
<div class="highlight-c++"><div class="highlight"><pre><span></span><span class="c1">// input is the varient-length data</span>
<span class="n">LodTensor</span> <span class="nf">sentence_input</span><span class="p">(</span><span class="n">xxx</span><span class="p">);</span>
<span class="n">TensorArray</span> <span class="n">ta</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="n">indice_map</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="n">boot_state</span> <span class="o">=</span> <span class="n">xxx</span><span class="p">;</span> <span class="c1">// to initialize rnn&#39;s first state</span>
<span class="n">TensorArray</span><span class="o">::</span><span class="n">unpack</span><span class="p">(</span><span class="n">input</span><span class="p">,</span> <span class="mi">1</span><span class="cm">/*level*/</span><span class="p">,</span> <span class="nb">true</span><span class="cm">/*sort_by_length*/</span><span class="p">,</span> <span class="o">&amp;</span><span class="n">ta</span><span class="p">,</span> <span class="o">&amp;</span><span class="n">indice_map</span><span class="p">);</span>
<span class="n">TessorArray</span> <span class="n">step_outputs</span><span class="p">;</span>
<span class="n">TensorArray</span> <span class="n">states</span><span class="p">;</span>
<span class="k">for</span> <span class="p">(</span><span class="kt">int</span> <span class="n">step</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">step</span> <span class="o">=</span> <span class="n">ta</span><span class="p">.</span><span class="n">size</span><span class="p">();</span> <span class="n">step</span><span class="o">++</span><span class="p">)</span> <span class="p">{</span>
<span class="k">auto</span> <span class="n">state</span> <span class="o">=</span> <span class="n">states</span><span class="p">.</span><span class="n">read</span><span class="p">(</span><span class="n">step</span><span class="p">);</span>
<span class="c1">// rnnstep is a function which acts like a step of RNN</span>
<span class="k">auto</span> <span class="n">step_input</span> <span class="o">=</span> <span class="n">ta</span><span class="p">.</span><span class="n">read</span><span class="p">(</span><span class="n">step</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">step_output</span> <span class="o">=</span> <span class="n">rnnstep</span><span class="p">(</span><span class="n">step_input</span><span class="p">,</span> <span class="n">state</span><span class="p">);</span>
<span class="n">step_outputs</span><span class="p">.</span><span class="n">write</span><span class="p">(</span><span class="n">step_output</span><span class="p">,</span> <span class="nb">true</span><span class="cm">/*data_shared*/</span><span class="p">);</span>
<span class="p">}</span>
<span class="c1">// rnn_output is the final output of an rnn</span>
<span class="n">LoDTensor</span> <span class="n">rnn_output</span> <span class="o">=</span> <span class="n">ta</span><span class="p">.</span><span class="n">pack</span><span class="p">(</span><span class="n">ta</span><span class="p">,</span> <span class="n">indice_map</span><span class="p">);</span>
</pre></div>
</div>
<p>the code above shows that by embedding the LoDTensor-related preprocess operations into <code class="docutils literal"><span class="pre">TensorArray</span></code>,
the implementation of a RNN that supports varient-length sentences is far more concise than <code class="docutils literal"><span class="pre">RecurrentGradientMachine</span></code> because the latter mixes all the codes together, hard to read and extend.</p>
<p>some details are as follows.</p>
<div class="section" id="unpack-level-sort-by-length">
<span id="unpack-level-sort-by-length"></span><h3>unpack(level, sort_by_length)<a class="headerlink" href="#unpack-level-sort-by-length" title="永久链接至标题"></a></h3>
<p>Split LodTensor in some <code class="docutils literal"><span class="pre">level</span></code> and generate batches, if set <code class="docutils literal"><span class="pre">sort_by_length</span></code>, will sort by length.</p>
<p>Returns:</p>
<ul class="simple">
<li>a new <code class="docutils literal"><span class="pre">TensorArray</span></code>, whose values are LodTensors and represents batches of data.</li>
<li>an int32 Tensor, which stores the map from the new batch&#8217;s indices to original LoDTensor</li>
</ul>
</div>
<div class="section" id="pack-level-indices-map">
<span id="pack-level-indices-map"></span><h3>pack(level, indices_map)<a class="headerlink" href="#pack-level-indices-map" title="永久链接至标题"></a></h3>
<p>Recover the original LoD-arranged LoDTensor with the values in a <code class="docutils literal"><span class="pre">TensorArray</span></code> and <code class="docutils literal"><span class="pre">level</span></code> and <code class="docutils literal"><span class="pre">indices_map</span></code>.</p>
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