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<div class="section" id="tensor-basics">
<h1>Tensor Basics<a class="headerlink" href="#tensor-basics" title="Permalink to this headline">¶</a></h1>
<p>The ATen tensor library backing PyTorch is a simple tensor library thats exposes
the Tensor operations in Torch directly in C++14. ATen’s API is auto-generated
from the same declarations PyTorch uses so the two APIs will track each other
over time.</p>
<p>Tensor types are resolved dynamically, such that the API is generic and does not
include templates. That is, there is one <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> type. It can hold a CPU or
CUDA Tensor, and the tensor may have Doubles, Float, Ints, etc. This design
makes it easy to write generic code without templating everything.</p>
<p>See <a class="reference external" href="https://pytorch.org/cppdocs/api/namespace_at.html#functions">https://pytorch.org/cppdocs/api/namespace_at.html#functions</a> for the provided
API. Excerpt:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">Tensor</span> <span class="nf">atan2</span><span class="p">(</span><span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">other</span><span class="p">)</span> <span class="k">const</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="o">&</span> <span class="n">atan2_</span><span class="p">(</span><span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">other</span><span class="p">);</span>
<span class="n">Tensor</span> <span class="nf">pow</span><span class="p">(</span><span class="n">Scalar</span> <span class="n">exponent</span><span class="p">)</span> <span class="k">const</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="nf">pow</span><span class="p">(</span><span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">exponent</span><span class="p">)</span> <span class="k">const</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="o">&</span> <span class="n">pow_</span><span class="p">(</span><span class="n">Scalar</span> <span class="n">exponent</span><span class="p">);</span>
<span class="n">Tensor</span> <span class="o">&</span> <span class="n">pow_</span><span class="p">(</span><span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">exponent</span><span class="p">);</span>
<span class="n">Tensor</span> <span class="nf">lerp</span><span class="p">(</span><span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">end</span><span class="p">,</span> <span class="n">Scalar</span> <span class="n">weight</span><span class="p">)</span> <span class="k">const</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="o">&</span> <span class="n">lerp_</span><span class="p">(</span><span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">end</span><span class="p">,</span> <span class="n">Scalar</span> <span class="n">weight</span><span class="p">);</span>
<span class="n">Tensor</span> <span class="nf">histc</span><span class="p">()</span> <span class="k">const</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="nf">histc</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">bins</span><span class="p">)</span> <span class="k">const</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="nf">histc</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">bins</span><span class="p">,</span> <span class="n">Scalar</span> <span class="n">min</span><span class="p">)</span> <span class="k">const</span><span class="p">;</span>
<span class="n">Tensor</span> <span class="nf">histc</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">bins</span><span class="p">,</span> <span class="n">Scalar</span> <span class="n">min</span><span class="p">,</span> <span class="n">Scalar</span> <span class="n">max</span><span class="p">)</span> <span class="k">const</span><span class="p">;</span>
</pre></div>
</div>
<p>In place operations are also provided, and always suffixed by <cite>_</cite> to indicate
they will modify the Tensor.</p>
<div class="section" id="efficient-access-to-tensor-elements">
<h2>Efficient Access to Tensor Elements<a class="headerlink" href="#efficient-access-to-tensor-elements" title="Permalink to this headline">¶</a></h2>
<p>When using Tensor-wide operations, the relative cost of dynamic dispatch is very
small. However, there are cases, especially in your own kernels, where efficient
element-wise access is needed, and the cost of dynamic dispatch inside the
element-wise loop is very high. ATen provides <em>accessors</em> that are created with
a single dynamic check that a Tensor is the type and number of dimensions.
Accessors then expose an API for accessing the Tensor elements efficiently.</p>
<p>Accessors are temporary views of a Tensor. They are only valid for the lifetime
of the tensor that they view and hence should only be used locally in a
function, like iterators.</p>
<p>Note that accessors are not compatible with CUDA tensors inside kernel functions.
Instead, you will have to use a <em>packed accessor</em> which behaves the same way but
copies tensor metadata instead of pointing to it.</p>
<p>It is thus recommended to use <em>accessors</em> for CPU tensors and <em>packed accessors</em>
for CUDA tensors.</p>
<div class="section" id="cpu-accessors">
<h3>CPU accessors<a class="headerlink" href="#cpu-accessors" title="Permalink to this headline">¶</a></h3>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">foo</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">({</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">});</span>
<span class="c1">// assert foo is 2-dimensional and holds floats.</span>
<span class="k">auto</span> <span class="n">foo_a</span> <span class="o">=</span> <span class="n">foo</span><span class="p">.</span><span class="n">accessor</span><span class="o"><</span><span class="kt">float</span><span class="p">,</span><span class="mi">2</span><span class="o">></span><span class="p">();</span>
<span class="kt">float</span> <span class="n">trace</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
<span class="k">for</span><span class="p">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">foo_a</span><span class="p">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">);</span> <span class="n">i</span><span class="o">++</span><span class="p">)</span> <span class="p">{</span>
<span class="c1">// use the accessor foo_a to get tensor data.</span>
<span class="n">trace</span> <span class="o">+=</span> <span class="n">foo_a</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">i</span><span class="p">];</span>
<span class="p">}</span>
</pre></div>
</div>
</div>
<div class="section" id="cuda-accessors">
<h3>CUDA accessors<a class="headerlink" href="#cuda-accessors" title="Permalink to this headline">¶</a></h3>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">__global__</span> <span class="kt">void</span> <span class="nf">packed_accessor_kernel</span><span class="p">(</span>
<span class="n">PackedTensorAccessor64</span><span class="o"><</span><span class="kt">float</span><span class="p">,</span> <span class="mi">2</span><span class="o">></span> <span class="n">foo</span><span class="p">,</span>
<span class="kt">float</span><span class="o">*</span> <span class="n">trace</span><span class="p">)</span> <span class="p">{</span>
<span class="kt">int</span> <span class="n">i</span><span class="o">=</span><span class="n">threadIdx</span><span class="p">.</span><span class="n">x</span>
<span class="n">atomicAdd</span><span class="p">(</span><span class="n">trace</span><span class="p">,</span> <span class="n">foo</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>
<span class="p">}</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">foo</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">({</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">});</span>
<span class="c1">// assert foo is 2-dimensional and holds floats.</span>
<span class="k">auto</span> <span class="n">foo_a</span> <span class="o">=</span> <span class="n">foo</span><span class="p">.</span><span class="n">packed_accessor64</span><span class="o"><</span><span class="kt">float</span><span class="p">,</span><span class="mi">2</span><span class="o">></span><span class="p">();</span>
<span class="kt">float</span> <span class="n">trace</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
<span class="n">packed_accessor_kernel</span><span class="o"><<<</span><span class="mi">1</span><span class="p">,</span> <span class="mi">12</span><span class="o">>>></span><span class="p">(</span><span class="n">foo_a</span><span class="p">,</span> <span class="o">&</span><span class="n">trace</span><span class="p">);</span>
</pre></div>
</div>
<p>In addition to <code class="docutils literal notranslate"><span class="pre">PackedTensorAccessor64</span></code> and <code class="docutils literal notranslate"><span class="pre">packed_accessor64</span></code> there are
also the corresponding <code class="docutils literal notranslate"><span class="pre">PackedTensorAccessor32</span></code> and <code class="docutils literal notranslate"><span class="pre">packed_accessor32</span></code>
which use 32-bit integers for indexing. This can be quite a bit faster on CUDA
but may lead to overflows in the indexing calculations.</p>
<p>Note that the template can hold other parameters such as the pointer restriction
and the integer type for indexing. See documentation for a thorough template
description of <em>accessors</em> and <em>packed accessors</em>.</p>
</div>
</div>
<div class="section" id="using-externally-created-data">
<h2>Using Externally Created Data<a class="headerlink" href="#using-externally-created-data" title="Permalink to this headline">¶</a></h2>
<p>If you already have your tensor data allocated in memory (CPU or CUDA),
you can view that memory as a <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> in ATen:</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="kt">float</span> <span class="n">data</span><span class="p">[]</span> <span class="o">=</span> <span class="p">{</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span>
<span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span> <span class="p">};</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">f</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">from_blob</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">{</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">});</span>
</pre></div>
</div>
<p>These tensors cannot be resized because ATen does not own the memory, but
otherwise behave as normal tensors.</p>
</div>
<div class="section" id="scalars-and-zero-dimensional-tensors">
<h2>Scalars and zero-dimensional tensors<a class="headerlink" href="#scalars-and-zero-dimensional-tensors" title="Permalink to this headline">¶</a></h2>
<p>In addition to the <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> objects, ATen also includes <code class="docutils literal notranslate"><span class="pre">Scalar</span></code>s that
represent a single number. Like a Tensor, Scalars are dynamically typed and can
hold any one of ATen’s number types. Scalars can be implicitly constructed from
C++ number types. Scalars are needed because some functions like <code class="docutils literal notranslate"><span class="pre">addmm</span></code> take
numbers along with Tensors and expect these numbers to be the same dynamic type
as the tensor. They are also used in the API to indicate places where a function
will <em>always</em> return a Scalar value, like <code class="docutils literal notranslate"><span class="pre">sum</span></code>.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">namespace</span> <span class="n">torch</span> <span class="p">{</span>
<span class="n">Tensor</span> <span class="n">addmm</span><span class="p">(</span><span class="n">Scalar</span> <span class="n">beta</span><span class="p">,</span> <span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">self</span><span class="p">,</span>
<span class="n">Scalar</span> <span class="n">alpha</span><span class="p">,</span> <span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">mat1</span><span class="p">,</span>
<span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">mat2</span><span class="p">);</span>
<span class="n">Scalar</span> <span class="nf">sum</span><span class="p">(</span><span class="k">const</span> <span class="n">Tensor</span> <span class="o">&</span> <span class="n">self</span><span class="p">);</span>
<span class="p">}</span> <span class="c1">// namespace torch</span>
<span class="c1">// Usage.</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">a</span> <span class="o">=</span> <span class="p">...</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">b</span> <span class="o">=</span> <span class="p">...</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">c</span> <span class="o">=</span> <span class="p">...</span>
<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">r</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">addmm</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="mf">.5</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">);</span>
</pre></div>
</div>
<p>In addition to <code class="docutils literal notranslate"><span class="pre">Scalar</span></code>s, ATen also allows <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> objects to be
zero-dimensional. These Tensors hold a single value and they can be references
to a single element in a larger <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>. They can be used anywhere a
<code class="docutils literal notranslate"><span class="pre">Tensor</span></code> is expected. They are normally created by operators like <cite>select</cite>
which reduce the dimensions of a <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">two</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">({</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">});</span>
<span class="n">two</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span><span class="p">;</span>
<span class="c1">// ^^^^^^ <- zero-dimensional Tensor</span>
</pre></div>
</div>
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