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<section id="irs">
<h1>IRs<a class="headerlink" href="#irs" title="Permalink to this heading">¶</a></h1>
<p>PyTorch 2.0 offers two set of IRs for backends to interface with: Core Aten IR and Prims IR.</p>
<section id="core-aten-ir">
<h2>Core Aten IR<a class="headerlink" href="#core-aten-ir" title="Permalink to this heading">¶</a></h2>
<p>Core aten ops is the core subset of aten operators that can be used to compose other operators.
Core aten IR is fully functional, and there is no <cite>inplace</cite> or <cite>_out</cite> variants in this opset.
In contrast to Prims IR, core aten ops reuses the existing aten ops in “native_functions.yaml”,
and it doesn’t further decompose ops into explicit type promotion and broadcasting ops.
This opset is designed to serve as the functional IR to interface with backends.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This opset is still under active development, more ops will be added in the future.</p>
</div>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Operator</p></th>
<th class="head"><p>Schema</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten._adaptive_avg_pool2d</span></code></p></td>
<td><p>_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten._adaptive_avg_pool2d_backward</span></code></p></td>
<td><p>_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten._log_softmax</span></code></p></td>
<td><p>_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten._native_batch_norm_legit.no_stats</span></code></p></td>
<td><p>_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten._softmax</span></code></p></td>
<td><p>_softmax(Tensor self, int dim, bool half_to_float) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten._to_copy</span></code></p></td>
<td><p>_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.abs</span></code></p></td>
<td><p>abs(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.acos</span></code></p></td>
<td><p>acos(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.acosh</span></code></p></td>
<td><p>acosh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.add.Scalar</span></code></p></td>
<td><p>add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.add.Tensor</span></code></p></td>
<td><p>add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.addmm</span></code></p></td>
<td><p>addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.alias</span></code></p></td>
<td><p>alias(Tensor(a) self) -> Tensor(a)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.amax</span></code></p></td>
<td><p>amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.amin</span></code></p></td>
<td><p>amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.arange.start_step</span></code></p></td>
<td><p>arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.argmax</span></code></p></td>
<td><p>argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.argmin</span></code></p></td>
<td><p>argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.as_strided</span></code></p></td>
<td><p>as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.asin</span></code></p></td>
<td><p>asin(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.asinh</span></code></p></td>
<td><p>asinh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.atan</span></code></p></td>
<td><p>atan(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.atanh</span></code></p></td>
<td><p>atanh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.avg_pool2d</span></code></p></td>
<td><p>avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.avg_pool2d_backward</span></code></p></td>
<td><p>avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.bitwise_and.Tensor</span></code></p></td>
<td><p>bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.bitwise_not</span></code></p></td>
<td><p>bitwise_not(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.bitwise_or.Tensor</span></code></p></td>
<td><p>bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.bitwise_xor.Tensor</span></code></p></td>
<td><p>bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.bmm</span></code></p></td>
<td><p>bmm(Tensor self, Tensor mat2) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.cat</span></code></p></td>
<td><p>cat(Tensor[] tensors, int dim=0) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.clamp</span></code></p></td>
<td><p>clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.clone</span></code></p></td>
<td><p>clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.col2im</span></code></p></td>
<td><p>col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.constant_pad_nd</span></code></p></td>
<td><p>constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.convolution</span></code></p></td>
<td><p>convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.convolution_backward</span></code></p></td>
<td><p>convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, int[] stride, SymInt[] padding, int[] dilation, bool transposed, SymInt[] output_padding, int groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.cos</span></code></p></td>
<td><p>cos(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.cosh</span></code></p></td>
<td><p>cosh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.div.Scalar</span></code></p></td>
<td><p>div.Scalar(Tensor self, Scalar other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.div.Tensor</span></code></p></td>
<td><p>div.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.embedding_dense_backward</span></code></p></td>
<td><p>embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.empty.memory_format</span></code></p></td>
<td><p>empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.empty_strided</span></code></p></td>
<td><p>empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.eq.Scalar</span></code></p></td>
<td><p>eq.Scalar(Tensor self, Scalar other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.eq.Tensor</span></code></p></td>
<td><p>eq.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.erf</span></code></p></td>
<td><p>erf(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.exp</span></code></p></td>
<td><p>exp(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.expand</span></code></p></td>
<td><p>expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.fill.Scalar</span></code></p></td>
<td><p>fill.Scalar(Tensor self, Scalar value) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.flip</span></code></p></td>
<td><p>flip(Tensor self, int[] dims) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.floor</span></code></p></td>
<td><p>floor(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.fmod.Tensor</span></code></p></td>
<td><p>fmod.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.full</span></code></p></td>
<td><p>full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.gather</span></code></p></td>
<td><p>gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.ge.Scalar</span></code></p></td>
<td><p>ge.Scalar(Tensor self, Scalar other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.ge.Tensor</span></code></p></td>
<td><p>ge.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.gelu</span></code></p></td>
<td><p>gelu(Tensor self, *, str approximate=’none’) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.grid_sampler_2d</span></code></p></td>
<td><p>grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.gt.Scalar</span></code></p></td>
<td><p>gt.Scalar(Tensor self, Scalar other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.gt.Tensor</span></code></p></td>
<td><p>gt.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.hardtanh</span></code></p></td>
<td><p>hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.index_select</span></code></p></td>
<td><p>index_select(Tensor self, int dim, Tensor index) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.isinf</span></code></p></td>
<td><p>isinf(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.isnan</span></code></p></td>
<td><p>isnan(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.le.Scalar</span></code></p></td>
<td><p>le.Scalar(Tensor self, Scalar other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.le.Tensor</span></code></p></td>
<td><p>le.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.leaky_relu</span></code></p></td>
<td><p>leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.log</span></code></p></td>
<td><p>log(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.logical_and</span></code></p></td>
<td><p>logical_and(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.logical_not</span></code></p></td>
<td><p>logical_not(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.logical_or</span></code></p></td>
<td><p>logical_or(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.lt.Scalar</span></code></p></td>
<td><p>lt.Scalar(Tensor self, Scalar other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.lt.Tensor</span></code></p></td>
<td><p>lt.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.max.dim</span></code></p></td>
<td><p>max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.max_pool2d_with_indices</span></code></p></td>
<td><p>max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.max_pool2d_with_indices_backward</span></code></p></td>
<td><p>max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.max_pool3d_with_indices</span></code></p></td>
<td><p>max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.maximum</span></code></p></td>
<td><p>maximum(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.mean.dim</span></code></p></td>
<td><p>mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.min.dim</span></code></p></td>
<td><p>min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.minimum</span></code></p></td>
<td><p>minimum(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.mm</span></code></p></td>
<td><p>mm(Tensor self, Tensor mat2) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.mul.Scalar</span></code></p></td>
<td><p>mul.Scalar(Tensor self, Scalar other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.mul.Tensor</span></code></p></td>
<td><p>mul.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.native_batch_norm</span></code></p></td>
<td><p>native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.native_dropout</span></code></p></td>
<td><p>native_dropout(Tensor input, float p, bool? train) -> (Tensor, Tensor)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.native_group_norm</span></code></p></td>
<td><p>native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.native_group_norm_backward</span></code></p></td>
<td><p>native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.native_layer_norm</span></code></p></td>
<td><p>native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.native_layer_norm_backward</span></code></p></td>
<td><p>native_layer_norm_backward(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.ne.Scalar</span></code></p></td>
<td><p>ne.Scalar(Tensor self, Scalar other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.ne.Tensor</span></code></p></td>
<td><p>ne.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.neg</span></code></p></td>
<td><p>neg(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.nonzero</span></code></p></td>
<td><p>nonzero(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.permute</span></code></p></td>
<td><p>permute(Tensor(a) self, int[] dims) -> Tensor(a)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.pow.Tensor_Scalar</span></code></p></td>
<td><p>pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.pow.Tensor_Tensor</span></code></p></td>
<td><p>pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.rand</span></code></p></td>
<td><p>rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.randn</span></code></p></td>
<td><p>randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.reciprocal</span></code></p></td>
<td><p>reciprocal(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.reflection_pad2d</span></code></p></td>
<td><p>reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.relu</span></code></p></td>
<td><p>relu(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.remainder.Tensor</span></code></p></td>
<td><p>remainder.Tensor(Tensor self, Tensor other) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.repeat</span></code></p></td>
<td><p>repeat(Tensor self, SymInt[] repeats) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.replication_pad2d</span></code></p></td>
<td><p>replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.replication_pad3d</span></code></p></td>
<td><p>replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.rsqrt</span></code></p></td>
<td><p>rsqrt(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.scalar_tensor</span></code></p></td>
<td><p>scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.scatter_add</span></code></p></td>
<td><p>scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.scatter_reduce.two</span></code></p></td>
<td><p>scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.select.int</span></code></p></td>
<td><p>select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.sigmoid</span></code></p></td>
<td><p>sigmoid(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.sign</span></code></p></td>
<td><p>sign(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.sin</span></code></p></td>
<td><p>sin(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.sinh</span></code></p></td>
<td><p>sinh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.slice.Tensor</span></code></p></td>
<td><p>slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.slice_scatter</span></code></p></td>
<td><p>slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.sqrt</span></code></p></td>
<td><p>sqrt(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.squeeze.dim</span></code></p></td>
<td><p>squeeze.dim(Tensor(a) self, int dim) -> Tensor(a)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.squeeze.dims</span></code></p></td>
<td><p>squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.sub.Scalar</span></code></p></td>
<td><p>sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.sub.Tensor</span></code></p></td>
<td><p>sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.sum.dim_IntList</span></code></p></td>
<td><p>sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.tanh</span></code></p></td>
<td><p>tanh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.topk</span></code></p></td>
<td><p>topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.unsqueeze</span></code></p></td>
<td><p>unsqueeze(Tensor(a) self, int dim) -> Tensor(a)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.upsample_bilinear2d.vec</span></code></p></td>
<td><p>upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.upsample_nearest2d.vec</span></code></p></td>
<td><p>upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.var.dim</span></code></p></td>
<td><p>var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">aten.view</span></code></p></td>
<td><p>view(Tensor(a) self, SymInt[] size) -> Tensor(a)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">aten.where.self</span></code></p></td>
<td><p>where.self(Tensor condition, Tensor self, Tensor other) -> Tensor</p></td>
</tr>
</tbody>
</table>
</section>
<section id="prims-ir">
<h2>Prims IR<a class="headerlink" href="#prims-ir" title="Permalink to this heading">¶</a></h2>
<p>Prims IR is a set of primitive operators that can be used to compose other operators.
Prims IR is a lower level opset than core aten IR, and it further decomposes ops into explicit
type promotion and broadcasting ops: prims.convert_element_type and prims.broadcast_in_dim.
This opset is designed to interface with compiler backends.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This opset is still under active development, more ops will be added in the future.</p>
</div>
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Operator</p></th>
<th class="head"><p>Schema</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.abs</span></code></p></td>
<td><p>abs(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.acos</span></code></p></td>
<td><p>acos(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.acosh</span></code></p></td>
<td><p>acosh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.asin</span></code></p></td>
<td><p>asin(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.asinh</span></code></p></td>
<td><p>asinh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.atan</span></code></p></td>
<td><p>atan(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.atanh</span></code></p></td>
<td><p>atanh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.cos</span></code></p></td>
<td><p>cos(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.cosh</span></code></p></td>
<td><p>cosh(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.bessel_i0</span></code></p></td>
<td><p>bessel_i0(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.bessel_i0e</span></code></p></td>
<td><p>bessel_i0e(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.bessel_i1</span></code></p></td>
<td><p>bessel_i1(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.bessel_i1e</span></code></p></td>
<td><p>bessel_i1e(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.bessel_j0</span></code></p></td>
<td><p>bessel_j0(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.bessel_j1</span></code></p></td>
<td><p>bessel_j1(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.bitwise_not</span></code></p></td>
<td><p>bitwise_not(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.cbrt</span></code></p></td>
<td><p>cbrt(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.ceil</span></code></p></td>
<td><p>ceil(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.conj_physical</span></code></p></td>
<td><p>conj_physical(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.digamma</span></code></p></td>
<td><p>digamma(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.erf</span></code></p></td>
<td><p>erf(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.erf_inv</span></code></p></td>
<td><p>erf_inv(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.erfc</span></code></p></td>
<td><p>erfc(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.erfcx</span></code></p></td>
<td><p>erfcx(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.exp</span></code></p></td>
<td><p>exp(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.expm1</span></code></p></td>
<td><p>expm1(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.exp2</span></code></p></td>
<td><p>exp2(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.fill</span></code></p></td>
<td><p>fill(Tensor self, Scalar value) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.floor</span></code></p></td>
<td><p>floor(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.imag</span></code></p></td>
<td><p>imag(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">prims.isfinite</span></code></p></td>
<td><p>isfinite(Tensor self) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">prims.lgamma</span></code></p></td>