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<div class="section" id="control-flow-cond">
<span id="cond"></span><h1>Control Flow - Cond<a class="headerlink" href="#control-flow-cond" title="Permalink to this heading">¶</a></h1>
<p><cite>torch.cond</cite> is a structured control flow operator. It can be used to specify if-else like control flow
and can logically be seen as implemented as follows.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cond</span><span class="p">(</span>
<span class="n">pred</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">bool</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
<span class="n">true_fn</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="n">false_fn</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="n">operands</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span>
<span class="p">):</span>
<span class="k">if</span> <span class="n">pred</span><span class="p">:</span>
<span class="k">return</span> <span class="n">true_fn</span><span class="p">(</span><span class="o">*</span><span class="n">operands</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">false_fn</span><span class="p">(</span><span class="o">*</span><span class="n">operands</span><span class="p">)</span>
</pre></div>
</div>
<p>Its unique power lies in its ability of expressing <strong>data-dependent control flow</strong>: it lowers to a conditional
operator (<cite>torch.ops.higher_order.cond</cite>), which preserves predicate, true function and false functions.
This unlocks great flexibilty in writing and deploying models that change model architecture based on
the <strong>value</strong> or <strong>shape</strong> of inputs or intermediate outputs of tensor operations.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><cite>torch.cond</cite> is a prototype feature in PyTorch. It has limited support for input and output types and
doesn’t support training currently. Please look forward to a more stable implementation in a future version of PyTorch.
Read more about feature classification at: <a class="reference external" href="https://pytorch.org/blog/pytorch-feature-classification-changes/#prototype">https://pytorch.org/blog/pytorch-feature-classification-changes/#prototype</a></p>
</div>
<div class="section" id="examples">
<h2>Examples<a class="headerlink" href="#examples" title="Permalink to this heading">¶</a></h2>
<p>Below is an example that uses cond to branch based on input shape:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="k">def</span> <span class="nf">true_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">cos</span><span class="p">()</span> <span class="o">+</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">false_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="k">class</span> <span class="nc">DynamicShapeCondPredicate</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> A basic usage of cond based on dynamic shape predicate.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">true_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">cos</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">false_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cond</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="mi">4</span><span class="p">,</span> <span class="n">true_fn</span><span class="p">,</span> <span class="n">false_fn</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,))</span>
<span class="n">dyn_shape_mod</span> <span class="o">=</span> <span class="n">DynamicShapeCondPredicate</span><span class="p">()</span>
</pre></div>
</div>
<p>We can eagerly run the model and expect the results vary based on input shape:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">inp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">inp2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">dyn_shape_mod</span><span class="p">(</span><span class="n">inp</span><span class="p">),</span> <span class="n">false_fn</span><span class="p">(</span><span class="n">inp</span><span class="p">))</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">dyn_shape_mod</span><span class="p">(</span><span class="n">inp2</span><span class="p">),</span> <span class="n">true_fn</span><span class="p">(</span><span class="n">inp2</span><span class="p">))</span>
</pre></div>
</div>
<p>We can export the model for further transformations and deployment:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">inp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">dim_batch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">Dim</span><span class="p">(</span><span class="s2">"batch"</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ep</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">DynamicShapeCondPredicate</span><span class="p">(),</span> <span class="p">(</span><span class="n">inp</span><span class="p">,),</span> <span class="p">{},</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="p">{</span><span class="s2">"x"</span><span class="p">:</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="n">dim_batch</span><span class="p">}})</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ep</span><span class="p">)</span>
</pre></div>
</div>
<p>This gives us an exported program as shown below:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[s0, 3]):
sym_size: Sym(s0) = torch.ops.aten.sym_size.int(arg0_1, 0)
gt: Sym(s0 > 4) = sym_size > 4; sym_size = None
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional: f32[s0, 3] = torch.ops.higher_order.cond(gt, true_graph_0, false_graph_0, [arg0_1]); gt = true_graph_0 = false_graph_0 = arg0_1 = None
return (conditional,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: f32[s0, 3]):
cos: f32[s0, 3] = torch.ops.aten.cos.default(arg0_1)
sin: f32[s0, 3] = torch.ops.aten.sin.default(arg0_1); arg0_1 = None
add: f32[s0, 3] = torch.ops.aten.add.Tensor(cos, sin); cos = sin = None
return add
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: f32[s0, 3]):
sin: f32[s0, 3] = torch.ops.aten.sin.default(arg0_1); arg0_1 = None
return sin
</pre></div>
</div>
<p>Notice that <cite>torch.cond</cite> is lowered to <cite>torch.ops.higher_order.cond</cite>, its predicate becomes a Symbolic expression over the shape of input,
and branch functions becomes two sub-graph attributes of the top level graph module.</p>
<p>Here is another exmaple that showcases how to express a data-dependet control flow:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DataDependentCondPredicate</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> A basic usage of cond based on data dependent predicate.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cond</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">></span> <span class="mf">4.0</span><span class="p">,</span> <span class="n">true_fn</span><span class="p">,</span> <span class="n">false_fn</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,))</span>
</pre></div>
</div>
<p>The exported program we get after export:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[s0, 3]):
sum_1: f32[] = torch.ops.aten.sum.default(arg0_1)
gt: b8[] = torch.ops.aten.gt.Scalar(sum_1, 4.0); sum_1 = None
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional: f32[s0, 3] = torch.ops.higher_order.cond(gt, true_graph_0, false_graph_0, [arg0_1]); gt = true_graph_0 = false_graph_0 = arg0_1 = None
return (conditional,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: f32[s0, 3]):
cos: f32[s0, 3] = torch.ops.aten.cos.default(arg0_1)
sin: f32[s0, 3] = torch.ops.aten.sin.default(arg0_1); arg0_1 = None
add: f32[s0, 3] = torch.ops.aten.add.Tensor(cos, sin); cos = sin = None
return add
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: f32[s0, 3]):
sin: f32[s0, 3] = torch.ops.aten.sin.default(arg0_1); arg0_1 = None
return sin
</pre></div>
</div>
</div>
<div class="section" id="invariants-of-torch-ops-higher-order-cond">
<h2>Invariants of torch.ops.higher_order.cond<a class="headerlink" href="#invariants-of-torch-ops-higher-order-cond" title="Permalink to this heading">¶</a></h2>
<p>There are several useful invariants for <cite>torch.ops.higher_order.cond</cite>:</p>
<ul class="simple">
<li><dl class="simple">
<dt>For predicate:</dt><dd><ul>
<li><p>Dynamicness of predicate is preserved (e.g. <cite>gt</cite> shown in the above example)</p></li>
<li><p>If the predicate in user-program is constant (e.g. a python bool constant), the <cite>pred</cite> of the operator will be a constant.</p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>For branches:</dt><dd><ul>
<li><p>The input and output signature will be a flattened tuple.</p></li>
<li><p>They are <cite>torch.fx.GraphModule</cite>.</p></li>
<li><p>Closures in original function becomes explicit inputs. No closures.</p></li>
<li><p>No mutations on inputs or globals are allowed.</p></li>
</ul>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>For operands:</dt><dd><ul>
<li><p>It will also be a flat tuple.</p></li>
</ul>
</dd>
</dl>
</li>
<li><p>Nesting of <cite>torch.cond</cite> in user program becomes nested graph modules.</p></li>
</ul>
<div class="section" id="api-reference">
<h3>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h3>
<dl class="py function">
<dt class="sig sig-object py" id="torch._higher_order_ops.cond.cond">
<span class="sig-prename descclassname"><span class="pre">torch._higher_order_ops.cond.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch._higher_order_ops.cond.cond" title="Permalink to this definition">¶</a></dt>
<dd><p>Conditionally applies <cite>true_fn</cite> or <cite>false_fn</cite>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><cite>torch.cond</cite> is a prototype feature in PyTorch. It has limited support for input and output types and
doesn’t support training currently. Please look forward to a more stable implementation in a future version of PyTorch.
Read more about feature classification at: <a class="reference external" href="https://pytorch.org/blog/pytorch-feature-classification-changes/#prototype">https://pytorch.org/blog/pytorch-feature-classification-changes/#prototype</a></p>
</div>
<p><cite>cond</cite> is structured control flow operator. That is, it is like a Python if-statement,
but has restrictions on <cite>true_fn</cite>, <cite>false_fn</cite>, and <cite>operands</cite> that enable it to be
capturable using torch.compile and torch.export.</p>
<p>Assuming the constraints on <cite>cond</cite>’s arguments are met, <cite>cond</cite> is equivalent to the following:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">true_branch</span><span class="p">,</span> <span class="n">false_branch</span><span class="p">,</span> <span class="n">operands</span><span class="p">):</span>
<span class="k">if</span> <span class="n">pred</span><span class="p">:</span>
<span class="k">return</span> <span class="n">true_branch</span><span class="p">(</span><span class="o">*</span><span class="n">operands</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">false_branch</span><span class="p">(</span><span class="o">*</span><span class="n">operands</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>pred</strong> (<em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>]</em>) – A boolean expression or a tensor with one element,
indicating which branch function to apply.</p></li>
<li><p><strong>true_fn</strong> (<em>Callable</em>) – A callable function (a -> b) that is within the
scope that is being traced.</p></li>
<li><p><strong>false_fn</strong> (<em>Callable</em>) – A callable function (a -> b) that is within the
scope that is being traced. The true branch and false branch must
have consistent input and outputs, meaning the inputs have to be
the same, and the outputs have to be the same type and shape.</p></li>
<li><p><strong>operands</strong> (<em>Tuple</em><em> of </em><em>possibly nested dict/list/tuple</em><em> of </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – A tuple of inputs to the true/false functions.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">true_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">cos</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">false_fn</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">sin</span><span class="p">()</span>
<span class="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="mi">4</span><span class="p">,</span> <span class="n">true_fn</span><span class="p">,</span> <span class="n">false_fn</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,))</span>
</pre></div>
</div>
<dl class="simple">
<dt>Restrictions:</dt><dd><ul class="simple">
<li><p>The conditional statement (aka <cite>pred</cite>) must meet one of the following constraints:</p>
<ul>
<li><p>It’s a <cite>torch.Tensor</cite> with only one element, and torch.bool dtype</p></li>
<li><p>It’s a boolean expression, e.g. <cite>x.shape[0] > 10</cite> or <cite>x.dim() > 1 and x.shape[1] > 10</cite></p></li>
</ul>
</li>
<li><p>The branch function (aka <cite>true_fn</cite>/<cite>false_fn</cite>) must meet all of the following constraints:</p>
<ul>
<li><p>The function signature must match with operands.</p></li>
<li><p>The function must return a tensor with the same metadata, e.g. shape,
dtype, etc.</p></li>
<li><p>The function cannot have in-place mutations on inputs or global variables.
(Note: in-place tensor operations such as <cite>add_</cite> for intermediate results
are allowed in a branch)</p></li>
</ul>
</li>
</ul>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Temporal Limitations:</p>
<ul class="simple">
<li><p><cite>cond</cite> only supports <strong>inference</strong> right now. Autograd will be supported in the future.</p></li>
<li><p>The <strong>output</strong> of branches must be a <strong>single Tensor</strong>. Pytree of tensors will be supported in the future.</p></li>
</ul>
</div>
</dd></dl>
</div>
</div>
</div>
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<a href="https://pytorch.org/tutorials/beginner/basics/intro.html">Learn the Basics</a>
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<a href="https://pytorch.org/tutorials/recipes/recipes_index.html">PyTorch Recipes</a>
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