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<div class="section" id="torch-cond">
<h1>torch.cond<a class="headerlink" href="#torch-cond" title="Permalink to this heading">¶</a></h1>
<div class="section" id="cond-branch-class-method">
<h2>cond_branch_class_method<a class="headerlink" href="#cond-branch-class-method" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">class</span> <span class="nc">MySubModule</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="k">def</span> <span class="nf">foo</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="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">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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">foo</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">CondBranchClassMethod</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"> The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:</span>
<span class="sd"> - both branches must take the same args, which must also match the branch args passed to cond.</span>
<span class="sd"> - both branches must return a single tensor</span>
<span class="sd"> - returned tensor must have the same tensor metadata, e.g. shape and dtype</span>
<span class="sd"> - branch function can be free function, nested function, lambda, class methods</span>
<span class="sd"> - branch function can not have closure variables</span>
<span class="sd"> - no inplace mutations on inputs or global variables</span>
<span class="sd"> This example demonstrates using class method in cond().</span>
<span class="sd"> NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.</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="bp">self</span><span class="o">.</span><span class="n">subm</span> <span class="o">=</span> <span class="n">MySubModule</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">bar</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="k">return</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">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="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">2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">subm</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bar</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">])</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, l_x_: "f32[3]"):
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional = torch.ops.higher_order.cond(False, true_graph_0, false_graph_0, [l_x_]); true_graph_0 = false_graph_0 = l_x_ = None
getitem: "f32[3]" = conditional[0]; conditional = None
return (getitem,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[3]"):
cos: "f32[3]" = torch.ops.aten.cos.default(arg0_1); arg0_1 = None
return (cos,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[3]"):
sin: "f32[3]" = torch.ops.aten.sin.default(arg0_1); arg0_1 = None
return (sin,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}
Equality constraints: []
</pre></div>
</div>
</div>
<div class="section" id="cond-branch-nested-function">
<h2>cond_branch_nested_function<a class="headerlink" href="#cond-branch-nested-function" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">def</span> <span class="nf">cond_branch_nested_function</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:</span>
<span class="sd"> - both branches must take the same args, which must also match the branch args passed to cond.</span>
<span class="sd"> - both branches must return a single tensor</span>
<span class="sd"> - returned tensor must have the same tensor metadata, e.g. shape and dtype</span>
<span class="sd"> - branch function can be free function, nested function, lambda, class methods</span>
<span class="sd"> - branch function can not have closure variables</span>
<span class="sd"> - no inplace mutations on inputs or global variables</span>
<span class="sd"> This example demonstrates using nested function in cond().</span>
<span class="sd"> NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.</span>
<span class="sd"> """</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="k">def</span> <span class="nf">inner_true_fn</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="k">return</span> <span class="n">inner_true_fn</span><span class="p">(</span><span class="n">x</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="k">def</span> <span class="nf">inner_false_fn</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="n">y</span>
<span class="k">return</span> <span class="n">inner_false_fn</span><span class="p">(</span><span class="n">x</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">10</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>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, l_x_: "f32[3]"):
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional = torch.ops.higher_order.cond(True, true_graph_0, false_graph_0, [l_x_]); true_graph_0 = false_graph_0 = l_x_ = None
getitem: "f32[3]" = conditional[0]; conditional = None
return (getitem,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[3]"):
add: "f32[3]" = torch.ops.aten.add.Tensor(arg0_1, arg0_1); arg0_1 = None
return (add,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[3]"):
sub: "f32[3]" = torch.ops.aten.sub.Tensor(arg0_1, arg0_1); arg0_1 = None
return (sub,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}
Equality constraints: []
</pre></div>
</div>
</div>
<div class="section" id="cond-branch-nonlocal-variables">
<h2>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlocal-variables" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">def</span> <span class="nf">cond_branch_nonlocal_variables</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> The branch functions (`true_fn` and `false_fn`) passed to cond() must follow these rules:</span>
<span class="sd"> - both branches must take the same args, which must also match the branch args passed to cond.</span>
<span class="sd"> - both branches must return a single tensor</span>
<span class="sd"> - returned tensor must have the same tensor metadata, e.g. shape and dtype</span>
<span class="sd"> - branch function can be free function, nested function, lambda, class methods</span>
<span class="sd"> - branch function can not have closure variables</span>
<span class="sd"> - no inplace mutations on inputs or global variables</span>
<span class="sd"> This example demonstrates how to rewrite code to avoid capturing closure variables in branch functions.</span>
<span class="sd"> The code below will not work because capturing closure variables is not supported.</span>
<span class="sd"> ```</span>
<span class="sd"> my_tensor_var = x + 100</span>
<span class="sd"> my_primitive_var = 3.14</span>
<span class="sd"> def true_fn(y):</span>
<span class="sd"> nonlocal my_tensor_var, my_primitive_var</span>
<span class="sd"> return y + my_tensor_var + my_primitive_var</span>
<span class="sd"> def false_fn(y):</span>
<span class="sd"> nonlocal my_tensor_var, my_primitive_var</span>
<span class="sd"> return y - my_tensor_var - my_primitive_var</span>
<span class="sd"> return cond(x.shape[0] > 5, true_fn, false_fn, [x])</span>
<span class="sd"> ```</span>
<span class="sd"> NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.</span>
<span class="sd"> """</span>
<span class="n">my_tensor_var</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="mi">100</span>
<span class="n">my_primitive_var</span> <span class="o">=</span> <span class="mf">3.14</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">y</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span> <span class="o">+</span> <span class="n">z</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">y</span><span class="p">,</span> <span class="n">z</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="n">y</span> <span class="o">-</span> <span class="n">z</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">5</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">my_tensor_var</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">my_primitive_var</span><span class="p">)],</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, _lifted_tensor_constant0: "f32[]", l_x_: "f32[6]"):
add: "f32[6]" = torch.ops.aten.add.Tensor(l_x_, 100)
lift_fresh_copy: "f32[]" = torch.ops.aten.lift_fresh_copy.default(_lifted_tensor_constant0); _lifted_tensor_constant0 = None
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional = torch.ops.higher_order.cond(True, true_graph_0, false_graph_0, [l_x_, add, lift_fresh_copy]); true_graph_0 = false_graph_0 = l_x_ = add = lift_fresh_copy = None
getitem: "f32[6]" = conditional[0]; conditional = None
return (getitem,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[6]", arg1_1: "f32[6]", arg2_1: "f32[]"):
add: "f32[6]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
add_1: "f32[6]" = torch.ops.aten.add.Tensor(add, arg2_1); add = arg2_1 = None
return (add_1,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[6]", arg1_1: "f32[6]", arg2_1: "f32[]"):
sub: "f32[6]" = torch.ops.aten.sub.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
sub_1: "f32[6]" = torch.ops.aten.sub.Tensor(sub, arg2_1); sub = arg2_1 = None
return (sub_1,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.CONSTANT_TENSOR: 4>, arg=TensorArgument(name='_lifted_tensor_constant0'), target='_lifted_tensor_constant0'), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}
Equality constraints: []
</pre></div>
</div>
</div>
<div class="section" id="cond-closed-over-variable">
<h2>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">class</span> <span class="nc">CondClosedOverVariable</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"> torch.cond() supports branches closed over arbitrary variables.</span>
<span class="sd"> """</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">pred</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">true_fn</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="k">def</span> <span class="nf">false_fn</span><span class="p">(</span><span class="n">val</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="mi">2</span>
<span class="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">pred</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="o">+</span> <span class="mi">1</span><span class="p">])</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, l_pred_: "b8[]", l_x_: "f32[3, 2]"):
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional = torch.ops.higher_order.cond(l_pred_, true_graph_0, false_graph_0, [l_x_]); l_pred_ = true_graph_0 = false_graph_0 = l_x_ = None
getitem: "f32[3, 2]" = conditional[0]; conditional = None
return (getitem,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[3, 2]"):
mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(arg0_1, 2); arg0_1 = None
return (mul,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[3, 2]"):
sub: "f32[3, 2]" = torch.ops.aten.sub.Tensor(arg0_1, 2); arg0_1 = None
return (sub,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_pred_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}
Equality constraints: []
</pre></div>
</div>
</div>
<div class="section" id="cond-operands">
<h2>cond_operands<a class="headerlink" href="#cond-operands" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.export</span> <span class="kn">import</span> <span class="n">Dim</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="n">x</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="mi">2</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">dim0_x</span> <span class="o">=</span> <span class="n">Dim</span><span class="p">(</span><span class="s2">"dim0_x"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">cond_operands</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> The operands passed to cond() must be:</span>
<span class="sd"> - a list of tensors</span>
<span class="sd"> - match arguments of `true_fn` and `false_fn`</span>
<span class="sd"> NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.</span>
<span class="sd"> """</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">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</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">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="n">y</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">2</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">y</span><span class="p">])</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, l_x_: "f32[s0, 2]", l_y_: "f32[2]"):
sym_size_int: "Sym(s0)" = torch.ops.aten.sym_size.int(l_x_, 0)
gt: "Sym(s0 > 2)" = sym_size_int > 2; sym_size_int = None
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional = torch.ops.higher_order.cond(gt, true_graph_0, false_graph_0, [l_x_, l_y_]); gt = true_graph_0 = false_graph_0 = l_x_ = l_y_ = None
getitem: "f32[s0, 2]" = conditional[0]; conditional = None
return (getitem,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[s0, 2]", arg1_1: "f32[2]"):
add: "f32[s0, 2]" = torch.ops.aten.add.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
return (add,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[s0, 2]", arg1_1: "f32[2]"):
sub: "f32[s0, 2]" = torch.ops.aten.sub.Tensor(arg0_1, arg1_1); arg0_1 = arg1_1 = None
return (sub,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_y_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {s0: ValueRanges(lower=2, upper=oo, is_bool=False)}
Equality constraints: []
</pre></div>
</div>
</div>
<div class="section" id="cond-predicate">
<h2>cond_predicate<a class="headerlink" href="#cond-predicate" title="Permalink to this heading">¶</a></h2>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Tags: <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
<p>Support Level: SUPPORTED</p>
</div>
<p>Original source code:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">functorch.experimental.control_flow</span> <span class="kn">import</span> <span class="n">cond</span>
<span class="k">def</span> <span class="nf">cond_predicate</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""</span>
<span class="sd"> The conditional statement (aka predicate) passed to cond() must be one of the following:</span>
<span class="sd"> - torch.Tensor with a single element</span>
<span class="sd"> - boolean expression</span>
<span class="sd"> NOTE: If the `pred` is test on a dim with batch size < 2, it will be specialized.</span>
<span class="sd"> """</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">></span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">></span> <span class="mi">10</span>
<span class="k">return</span> <span class="n">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">cos</span><span class="p">(),</span> <span class="k">lambda</span> <span class="n">y</span><span class="p">:</span> <span class="n">y</span><span class="o">.</span><span class="n">sin</span><span class="p">(),</span> <span class="p">[</span><span class="n">x</span><span class="p">])</span>
</pre></div>
</div>
<p>Result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, l_x_: "f32[6, 4, 3]"):
true_graph_0 = self.true_graph_0
false_graph_0 = self.false_graph_0
conditional = torch.ops.higher_order.cond(False, true_graph_0, false_graph_0, [l_x_]); true_graph_0 = false_graph_0 = l_x_ = None
getitem: "f32[6, 4, 3]" = conditional[0]; conditional = None
return (getitem,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[6, 4, 3]"):
cos: "f32[6, 4, 3]" = torch.ops.aten.cos.default(arg0_1); arg0_1 = None
return (cos,)
class <lambda>(torch.nn.Module):
def forward(self, arg0_1: "f32[6, 4, 3]"):
sin: "f32[6, 4, 3]" = torch.ops.aten.sin.default(arg0_1); arg0_1 = None
return (sin,)
Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='getitem'), target=None)])
Range constraints: {}
Equality constraints: []
</pre></div>
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