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<div class="section" id="tensor-parallelism-torch-distributed-tensor-parallel">
<h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink" href="#tensor-parallelism-torch-distributed-tensor-parallel" title="Permalink to this heading">¶</a></h1>
<p>Tensor Parallelism(TP) is built on top of the PyTorch DistributedTensor
(<a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/torch/distributed/_tensor/README.md">DTensor</a>)
and provides different parallelism styles: Colwise and Rowwise Parallelism.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Tensor Parallelism APIs are experimental and subject to change.</p>
</div>
<p>The entrypoint to parallelize your <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> using Tensor Parallelism is:</p>
<span class="target" id="module-torch.distributed.tensor.parallel"></span><dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.parallelize_module">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">parallelize_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallelize_plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tp_mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/api.html#parallelize_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.parallelize_module" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply Tensor Parallelism in PyTorch by parallelizing modules or sub-modules based on a user-specified plan.</p>
<p>We parallelize module or sub_modules based on a parallelize_plan. The parallelize_plan contains
<code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code>, which indicates how user wants the module or sub_module
to be parallelized.</p>
<p>User can also specify different parallel style per module fully qualified name (FQN).</p>
<p>Note that <code class="docutils literal notranslate"><span class="pre">parallelize_module</span></code> only accepts a 1-D <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, if you have a 2-D or N-D <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>,
slice the DeviceMesh to a 1-D sub DeviceMesh first then pass to this API(i.e. <code class="docutils literal notranslate"><span class="pre">device_mesh["tp"]</span></code>)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>module</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code>) – Module to be parallelized.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>) – Object which describes the mesh topology
of devices for the DTensor.</p></li>
<li><p><strong>parallelize_plan</strong> (Union[<code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code>, Dict[str, <code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code>]]) – The plan used to parallelize the module. It can be either a
<code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code> object which contains how
we prepare input/output for Tensor Parallelism or it can be a
dict of module FQN and its corresponding <code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code> object.</p></li>
<li><p><strong>tp_mesh_dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>deprecated</em>) – The dimension of <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> where we perform
Tensor Parallelism on, this field is deprecated and will be removed in future.
If you have a 2-D or N-D <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, consider passing in device_mesh[“tp”]</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code> object parallelized.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><p id="torch.nn.Module"/><a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><em>Module</em></a></p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.tensor.parallel</span> <span class="kn">import</span> <span class="n">parallelize_module</span><span class="p">,</span> <span class="n">ColwiseParallel</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.device_mesh</span> <span class="kn">import</span> <span class="n">init_device_mesh</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># Define the module.</span>
<span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tp_mesh</span> <span class="o">=</span> <span class="n">init_device_mesh</span><span class="p">(</span><span class="s2">"cuda"</span><span class="p">,</span> <span class="p">(</span><span class="mi">8</span><span class="p">,))</span>
<span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">parallelize_module</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">tp_mesh</span><span class="p">,</span> <span class="p">{</span><span class="s2">"w1"</span><span class="p">:</span> <span class="n">ColwiseParallel</span><span class="p">(),</span> <span class="s2">"w2"</span><span class="p">:</span> <span class="n">RowwiseParallel</span><span class="p">()})</span>
<span class="go">>>></span>
</pre></div>
</div>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For complex module architecture like Attention, MLP layers, we recommend composing
different ParallelStyles together (i.e. <code class="docutils literal notranslate"><span class="pre">ColwiseParallel</span></code> and <code class="docutils literal notranslate"><span class="pre">RowwiseParallel</span></code>) and pass
as a parallelize_plan, to achieves the desired sharding computation.</p>
</div>
</dd></dl>
<p>Tensor Parallelism supports the following parallel styles:</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.ColwiseParallel">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">ColwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#ColwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.ColwiseParallel" title="Permalink to this definition">¶</a></dt>
<dd><p>Partition a compatible nn.Module in a column-wise fashion. Currently supports nn.Linear and nn.Embedding.
Users can compose it together with RowwiseParallel to achieve the sharding of more complicated modules.
(i.e. MLP, Attention)</p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input_layouts</strong> (<em>Placement</em><em>, </em><em>optional</em>) – The DTensor layout of input tensor for the nn.Module, this is used to annotate the input tensor to
become a DTensor. If not specified, we assume the input tensor to be replicated.</p></li>
<li><p><strong>output_layouts</strong> (<em>Placement</em><em>, </em><em>optional</em>) – The DTensor layout of the output for the nn.Module, this is used to ensure the output of the nn.Module
with the user desired layout. If not specified, the output tensor is sharded on the last dimension.</p></li>
<li><p><strong>use_local_output</strong> (<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><em>optional</em>) – Whether to use local <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> instead of <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> for the module output, default: True.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code> object that represents Colwise sharding of the nn.Module.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.tensor.parallel</span> <span class="kn">import</span> <span class="n">parallelize_module</span><span class="p">,</span> <span class="n">ColwiseParallel</span>
<span class="gp">>>> </span><span class="o">...</span>
<span class="gp">>>> </span><span class="c1"># By default, the input of the "w1" Linear will be annotated to Replicated DTensor</span>
<span class="gp">>>> </span><span class="c1"># and the output of "w1" will return :class:`torch.Tensor` that shards on the last dim.</span>
<span class="go">>>>></span>
<span class="gp">>>> </span><span class="n">parallelize_module</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">module</span><span class="o">=</span><span class="n">block</span><span class="p">,</span> <span class="c1"># this can be a submodule or module</span>
<span class="gp">>>> </span> <span class="o">...</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">parallelize_plan</span><span class="o">=</span><span class="p">{</span><span class="s2">"w1"</span><span class="p">:</span> <span class="n">ColwiseParallel</span><span class="p">()},</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="gp">>>> </span><span class="o">...</span>
</pre></div>
</div>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>By default <code class="docutils literal notranslate"><span class="pre">ColwiseParallel</span></code> output is sharded on the last dimension if the <code class="docutils literal notranslate"><span class="pre">output_layouts</span></code> not
specified, if there’re operators that require specific tensor shape (i.e. before the paired <code class="docutils literal notranslate"><span class="pre">RowwiseParallel</span></code>),
keep in mind that if the output is sharded the operator might need to be adjusted to the sharded size.</p>
</div>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.RowwiseParallel">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">RowwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#RowwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.RowwiseParallel" title="Permalink to this definition">¶</a></dt>
<dd><p>Partition a compatible nn.Module in a row-wise fashion. Currently supports nn.Linear only.
Users can compose it with ColwiseParallel to achieve the sharding of more complicated modules.
(i.e. MLP, Attention)</p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input_layouts</strong> (<em>Placement</em><em>, </em><em>optional</em>) – The DTensor layout of input tensor for the nn.Module, this is used to annotate the input tensor to
become a DTensor. If not specified, we assume the input tensor to be sharded on the last dimension.</p></li>
<li><p><strong>output_layouts</strong> (<em>Placement</em><em>, </em><em>optional</em>) – The DTensor layout of the output for the nn.Module, this is used to ensure the output of the nn.Module
with the user desired layout. If not specified, the output tensor is replicated.</p></li>
<li><p><strong>use_local_output</strong> (<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><em>optional</em>) – Whether to use local <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> instead of <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> for the module output, default: True.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code> object that represents Rowwise sharding of the nn.Module.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.tensor.parallel</span> <span class="kn">import</span> <span class="n">parallelize_module</span><span class="p">,</span> <span class="n">RowwiseParallel</span>
<span class="gp">>>> </span><span class="o">...</span>
<span class="gp">>>> </span><span class="c1"># By default, the input of the "w2" Linear will be annotated to DTensor that shards on the last dim</span>
<span class="gp">>>> </span><span class="c1"># and the output of "w2" will return a replicated :class:`torch.Tensor`.</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">parallelize_module</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">module</span><span class="o">=</span><span class="n">block</span><span class="p">,</span> <span class="c1"># this can be a submodule or module</span>
<span class="gp">>>> </span> <span class="o">...</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">parallelize_plan</span><span class="o">=</span><span class="p">{</span><span class="s2">"w2"</span><span class="p">:</span> <span class="n">RowwiseParallel</span><span class="p">()},</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="gp">>>> </span><span class="o">...</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<p>To simply configure the nn.Module’s inputs and outputs with DTensor layouts
and perform necessary layout redistributions, without distribute the module
parameters to DTensors, the following classes can be used in
the <code class="docutils literal notranslate"><span class="pre">parallelize_plan</span></code> of <code class="docutils literal notranslate"><span class="pre">parallelize_module</span></code>:</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.PrepareModuleInput">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleInput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleInput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleInput" title="Permalink to this definition">¶</a></dt>
<dd><p>Configure the nn.Module’s inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
<code class="docutils literal notranslate"><span class="pre">input_layouts</span></code>, and perform layout redistribution according to the <code class="docutils literal notranslate"><span class="pre">desired_input_layouts</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input_layouts</strong> (<em>Union</em><em>[</em><em>Placement</em><em>, </em><em>Tuple</em><em>[</em><em>Placement</em><em>]</em><em>]</em>) – The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to
DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, <code class="docutils literal notranslate"><span class="pre">None</span></code> need to be specified
as a placeholder.</p></li>
<li><p><strong>desired_input_layouts</strong> (<em>Union</em><em>[</em><em>Placement</em><em>, </em><em>Tuple</em><em>[</em><em>Placement</em><em>]</em><em>]</em>) – The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module
have the desired DTensor layouts. This argument needs to have the same length with <code class="docutils literal notranslate"><span class="pre">input_layouts</span></code>.</p></li>
<li><p><strong>use_local_output</strong> (<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><em>optional</em>) – Whether to use local <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> instead of <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> for the module inputs, default: False.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code> object that prepares the sharding layouts of the nn.Module’s inputs.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.tensor.parallel</span> <span class="kn">import</span> <span class="n">parallelize_module</span><span class="p">,</span> <span class="n">PrepareModuleInput</span>
<span class="gp">>>> </span><span class="o">...</span>
<span class="gp">>>> </span><span class="c1"># According to the style specified below, the first input of attn will be annotated to Sharded DTensor</span>
<span class="gp">>>> </span><span class="c1"># and then redistributed to Replicated DTensor.</span>
<span class="gp">>>> </span><span class="n">parallelize_module</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">module</span><span class="o">=</span><span class="n">block</span><span class="p">,</span> <span class="c1"># this can be a submodule or module</span>
<span class="gp">>>> </span> <span class="o">...</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">parallelize_plan</span><span class="o">=</span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s2">"attn"</span><span class="p">:</span> <span class="n">PrepareModuleInput</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">input_layouts</span><span class="o">=</span><span class="p">(</span><span class="n">Shard</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">),</span>
<span class="gp">>>> </span> <span class="n">desired_input_layouts</span><span class="o">=</span><span class="p">(</span><span class="n">Replicate</span><span class="p">(),</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="p">),</span>
<span class="gp">>>> </span> <span class="p">}</span>
<span class="gp">>>> </span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.PrepareModuleOutput">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleOutput" title="Permalink to this definition">¶</a></dt>
<dd><p>Configure the nn.Module’s outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
<code class="docutils literal notranslate"><span class="pre">output_layouts</span></code>, and perform layout redistribution according to the <code class="docutils literal notranslate"><span class="pre">desired_output_layouts</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output_layouts</strong> (<em>Union</em><em>[</em><em>Placement</em><em>, </em><em>Tuple</em><em>[</em><em>Placement</em><em>]</em><em>]</em>) – The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to
DTensors if they are <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>. If some outputs are not torch.Tensor or no need to convert to DTensors,
<code class="docutils literal notranslate"><span class="pre">None</span></code> need to be specified as a placeholder.</p></li>
<li><p><strong>desired_output_layouts</strong> (<em>Union</em><em>[</em><em>Placement</em><em>, </em><em>Tuple</em><em>[</em><em>Placement</em><em>]</em><em>]</em>) – The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module
have the desired DTensor layouts.</p></li>
<li><p><strong>use_local_output</strong> (<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><em>optional</em>) – Whether to use local <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> instead of <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> for the module outputs, default: False.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A ParallelStyle object that prepares the sharding layouts of the nn.Module’s outputs.</p>
</dd>
</dl>
<dl>
<dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.tensor.parallel</span> <span class="kn">import</span> <span class="n">parallelize_module</span><span class="p">,</span> <span class="n">PrepareModuleOutput</span>
<span class="gp">>>> </span><span class="o">...</span>
<span class="gp">>>> </span><span class="c1"># According to the style specified below, the first input of attn will be annotated to Sharded DTensor</span>
<span class="gp">>>> </span><span class="c1"># and then redistributed to Replicated DTensor.</span>
<span class="gp">>>> </span><span class="n">parallelize_module</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">module</span><span class="o">=</span><span class="n">block</span><span class="p">,</span> <span class="c1"># this can be a submodule or module</span>
<span class="gp">>>> </span> <span class="o">...</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">parallelize_plan</span><span class="o">=</span><span class="p">{</span>
<span class="gp">>>> </span> <span class="s2">"submodule"</span><span class="p">:</span> <span class="n">PrepareModuleOutput</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">output_layouts</span><span class="o">=</span><span class="n">Replicate</span><span class="p">(),</span>
<span class="gp">>>> </span> <span class="n">desired_output_layouts</span><span class="o">=</span><span class="n">Shard</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span> <span class="p">),</span>
<span class="gp">>>> </span> <span class="p">}</span>
<span class="gp">>>> </span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<p>For models like Transformer, we recommend users to use <code class="docutils literal notranslate"><span class="pre">ColwiseParallel</span></code>
and <code class="docutils literal notranslate"><span class="pre">RowwiseParallel</span></code> together in the parallelize_plan for achieve the desired
sharding for the entire model (i.e. Attention and MLP).</p>
</div>
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