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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 several parallelism styles: Rowwise, Colwise and Pairwise 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>The API to apply Tensor Parallelism (TP) in PyTorch. 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).
The API supports 2D parallelism natively by accepting an n-dimension device_mesh
and users just need to specify the dimension where we perform tensor parallelism on.</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>) – The dimension of <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> where we perform
Tensor Parallelism on.</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">PairwiseParallel</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">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">PairwiseParallel</span><span class="p">())</span>
<span class="go">>>></span>
</pre></div>
</div>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><code class="docutils literal notranslate"><span class="pre">PairwiseParallel</span></code> comes with constraints for now. If you need finer
granularity, you need to pass in a dict of module FQN and parallel style instead.</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.style.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.style.</span></span><span class="sig-name descname"><span class="pre">RowwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_prepare_input=<function</span> <span class="pre">make_input_shard_1d_last_dim></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_prepare_output=<function</span> <span class="pre">make_output_tensor></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.style.RowwiseParallel" title="Permalink to this definition">¶</a></dt>
<dd><p>Partitioning the row of a module.
We assume the input to be a sharded <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> and output to be a <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>.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.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.style.</span></span><span class="sig-name descname"><span class="pre">ColwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_prepare_input=<function</span> <span class="pre">make_input_replicate_1d></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_prepare_output=<function</span> <span class="pre">make_sharded_output_tensor></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.style.ColwiseParallel" title="Permalink to this definition">¶</a></dt>
<dd><p>Partitioning the column of a tensor or module.
We assume the input to be a replicated <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> and output to be a sharded <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>.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.PairwiseParallel">
<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.style.</span></span><span class="sig-name descname"><span class="pre">PairwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_prepare_input</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">_prepare_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PairwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.PairwiseParallel" title="Permalink to this definition">¶</a></dt>
<dd><p>PairwiseParallel concatenate colwise and rowwise styles as a fixed
pair like what Megatron-LM(<a class="reference external" href="https://arxiv.org/abs/1909.08053">https://arxiv.org/abs/1909.08053</a>) is doing.
We assume both input and output need to be replicate DTensors.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>PairwiseParallel does not support <code class="docutils literal notranslate"><span class="pre">nn.MultiheadAttention</span></code>,
<code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> well at this moment. One workaround is to apply
<code class="docutils literal notranslate"><span class="pre">ColwiseParallel</span></code> and <code class="docutils literal notranslate"><span class="pre">RowwiseParallel</span></code> to the components of
transformer. We recommend to use <code class="docutils literal notranslate"><span class="pre">PairwiseParallel</span></code> only
for even-number-layer MLP for now.</p>
</div>
<dl class="field-list simple">
</dl>
</dd></dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Sequence Parallelism are still in experimental and no evaluation has been done.</p>
</div>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.SequenceParallel">
<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.style.</span></span><span class="sig-name descname"><span class="pre">SequenceParallel</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#SequenceParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.SequenceParallel" title="Permalink to this definition">¶</a></dt>
<dd><p>SequenceParallel concatenate colwise and rowwise styles as a fixed
pair together with sequence parallel like what Megatron-LM Sequence parallel
(<a class="reference external" href="https://arxiv.org/pdf/2205.05198.pdf">https://arxiv.org/pdf/2205.05198.pdf</a>) is doing.
We assume both input and output need to be sharded DTensors.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>SequenceParallel does not support <code class="docutils literal notranslate"><span class="pre">nn.MultiheadAttention</span></code>,
<code class="docutils literal notranslate"><span class="pre">nn.Transformer</span></code> well at this moment. One workaround is to apply
<code class="docutils literal notranslate"><span class="pre">ColwiseParallel</span></code> and <code class="docutils literal notranslate"><span class="pre">RowwiseParallel</span></code> to the components of
transformer. We recommend to use <code class="docutils literal notranslate"><span class="pre">SequenceParallel</span></code> only
for even-number-layer MLP for now.</p>
</div>
<dl class="field-list simple">
</dl>
</dd></dl>
<p>Since Tensor Parallelism is built on top of DTensor, we need to specify the
input and output placement of the module with DTensors so it can expectedly
interacts with the module before and after. The followings are functions
used for input/output preparation:</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.make_input_replicate_1d">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.style.</span></span><span class="sig-name descname"><span class="pre">make_input_replicate_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#make_input_replicate_1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.make_input_replicate_1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Replicate input tensor over an 1-D device mesh. This function will be used in ParallelStyle.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (Union[<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>, <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>]) – This input tensor will be replicated over the 1-D <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – The 1-D device mesh where <code class="docutils literal notranslate"><span class="pre">input</span></code> will be replicated.
If no <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> is passed and <code class="docutils literal notranslate"><span class="pre">input</span></code> is a <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>,
<code class="docutils literal notranslate"><span class="pre">input.device_mesh</span></code> will be used.
If <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> is not 1-D, an exception will be thrown.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></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">DTensor</span></code> replicated over <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code>.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>DTensor</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.make_input_reshard_replicate">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.style.</span></span><span class="sig-name descname"><span class="pre">make_input_reshard_replicate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#make_input_reshard_replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.make_input_reshard_replicate" title="Permalink to this definition">¶</a></dt>
<dd><p>To construct a Sharded DTensor from a tensor on different ranks
and then convert to a replicate DTensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<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>) – The input tensor on each rank which consists of a global DTensor
sharded on dimension <code class="docutils literal notranslate"><span class="pre">0</span></code> over the 1-D <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>
and then the sharded DTensor is converted to a replicate DTensor.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – The 1-D device mesh where <code class="docutils literal notranslate"><span class="pre">input</span></code> will be sharded.
If <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> is not 1-D, an exception will be thrown.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><dl class="simple">
<dt>A <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> sharded on dimension <code class="docutils literal notranslate"><span class="pre">0</span></code> over <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code></dt><dd><p>and then converted to replicate.</p>
</dd>
</dl>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>DTensor</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.make_input_shard_1d">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.style.</span></span><span class="sig-name descname"><span class="pre">make_input_shard_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</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">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/style.html#make_input_shard_1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.make_input_shard_1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Shard input tensor on <code class="docutils literal notranslate"><span class="pre">dim</span></code> over an 1-D device mesh. This function will be used in ParallelStyle.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (Union[<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>, <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>]) – Single tensor will be sharded on dimension <code class="docutils literal notranslate"><span class="pre">dim</span></code>
over the 1-D <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – The 1-D device mesh where <code class="docutils literal notranslate"><span class="pre">input</span></code> will be sharded.
If no <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> is passed and <code class="docutils literal notranslate"><span class="pre">input</span></code> is a <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>,
<cite>input.device_mesh</cite> will be used.
If <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> is not 1-D, an exception will be thrown.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
<li><p><strong>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>optional</em>) – The sharding dimension of <code class="docutils literal notranslate"><span class="pre">input</span></code> tensor.
Default: 0</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">DTensor</span></code> sharded on dimension <code class="docutils literal notranslate"><span class="pre">dim</span></code> over <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code>.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>DTensor</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.make_input_shard_1d_last_dim">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.style.</span></span><span class="sig-name descname"><span class="pre">make_input_shard_1d_last_dim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#make_input_shard_1d_last_dim"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.make_input_shard_1d_last_dim" title="Permalink to this definition">¶</a></dt>
<dd><p>Wrapper func of <code class="docutils literal notranslate"><span class="pre">make_input_shard_1d</span></code> with <code class="docutils literal notranslate"><span class="pre">dim</span></code> = -1.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (Union[<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>, <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>]) – This single tensor will be sharded on the last dimension
over the 1-D <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – The 1-D device mesh where <code class="docutils literal notranslate"><span class="pre">input</span></code> will be sharded.
If no <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> is passed and <code class="docutils literal notranslate"><span class="pre">input</span></code> is a <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>,
<cite>input.device_mesh</cite> will be used.
If <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> is not 1-D, an exception will be thrown.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></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">DTensor</span></code> sharded on the last dimension over <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code>.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>DTensor</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.make_output_replicate_1d">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.style.</span></span><span class="sig-name descname"><span class="pre">make_output_replicate_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#make_output_replicate_1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.make_output_replicate_1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert Output DTensor to a replicated DTensor. This will be used in ParallelStyle.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>) – Output of module to be converted.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – Object needed to replicate the output and it needs to be a 1D <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code>
and we will throw exceptions if a non-1D <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is passed in.
If no <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is passed in, we will reuse the one from output.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></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">DTensor</span></code> object made replicate.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>DTensor</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.make_output_reshard_tensor">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.style.</span></span><span class="sig-name descname"><span class="pre">make_output_reshard_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#make_output_reshard_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.make_output_reshard_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert Output DTensor to a sharded DTensor and return the local tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>) – Output of module to be converted.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – Object needed to shard the output and it needs to be a 1D <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code>
and we will throw exceptions if a non-1D <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is passed in.
If no <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is passed in, we will reuse the one from output.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <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> object converted from output DTensor.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.make_output_shard_1d">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.style.</span></span><span class="sig-name descname"><span class="pre">make_output_shard_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</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">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/style.html#make_output_shard_1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.make_output_shard_1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert Output DTensor to a sharded DTensor. This will be used in ParallelStyle.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>) – Output of module to be converted.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – Object needed to shard the output and it needs to be a 1D <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code>
and we will throw exceptions if a non-1D <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is passed in.
If no <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is passed in, we will reuse the one from output.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
<li><p><strong>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>) – Sharding dim for output. Default: 0</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">DTensor</span></code> object sharded on the given dim.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>DTensor</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.style.make_output_tensor">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.style.</span></span><span class="sig-name descname"><span class="pre">make_output_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#make_output_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.style.make_output_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert Output DTensor to a replicated DTensor first and then convert it to Tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>) – Output of module to be converted.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – Object which is needed to replicate the output and it needs to be
a 1D <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and we will throw exceptions if a non-1D
<code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is passed in. If no <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is passed in,
we will reuse the one from output.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <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> object converted from output DTensor.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
</dd></dl>
<p>Currently, there are some constraints which makes it hard for the <code class="docutils literal notranslate"><span class="pre">MultiheadAttention</span></code>
module to work out of box for Tensor Parallelism, so we recommend users to try <code class="docutils literal notranslate"><span class="pre">ColwiseParallel</span></code>
and <code class="docutils literal notranslate"><span class="pre">RowwiseParallel</span></code> for each parameter. There might be some code changes needed now
since we are parallelizing on the head dim of the <code class="docutils literal notranslate"><span class="pre">MultiheadAttention</span></code> module.</p>
<p>We also support 2D parallelism, where we compose tensor parallelism with data parallelism.
To integrate with <code class="docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code>,
users just need to call the following API explicitly:</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.fsdp.enable_2d_with_fsdp">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.fsdp.</span></span><span class="sig-name descname"><span class="pre">enable_2d_with_fsdp</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/fsdp.html#enable_2d_with_fsdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.fsdp.enable_2d_with_fsdp" title="Permalink to this definition">¶</a></dt>
<dd><p>The API registers the extension which is needed for Tensor Parallelism (TP)
to work with FullyShardedDataParallel (FSDP). We first parallelize parameters
within one module or sub_modules based on a parallelize_plan and will let FSDP
reshard the local tensor of distributed parameter which is essentially a DTensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <cite>bool</cite> indicated whether extension registration succeeds or not.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
<p>To integrate with <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code>,
users just need to call the following API explicitly:</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.parallel.ddp.pre_dp_module_transform">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.ddp.</span></span><span class="sig-name descname"><span class="pre">pre_dp_module_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/ddp.html#pre_dp_module_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.ddp.pre_dp_module_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Enable the composability between Tensor Parallelism (TP) and Data
Parallelism(DP) in PyTorch when using DDP. We need to convert Parameters which
are DTensors to local tensors before wrapping with data parallelism API.
We then register two hooks, one for converting local tensors back to DTensor
preforward and one to convert DTensors back to tensors after Forward. By
integrating this way, we avoid any special handling of DTensor parameters by DDP
and get DTensor’s gradients propagated back to DP, e.g. gradient buckets of DDP.</p>
<p>For now, this API only works with <code class="docutils literal notranslate"><span class="pre">DistributedDataParallel</span></code>. It will later support
other DP methods such as FSDP.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>module</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code>) – Module which has been applied TP on.</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">PairwiseParallel</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.nn.parallel</span> <span class="kn">import</span> <span class="n">DistributedDataParallel</span> <span class="k">as</span> <span class="n">DDP</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">torch.distributed.tensor.parallel.ddp</span> <span class="kn">import</span> <span class="n">pre_dp_module_transform</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">module</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">parallelize_module</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">PairwiseParallel</span><span class="p">())</span>
<span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">pre_dp_module_transform</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">DDP</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="go">>>></span>
</pre></div>
</div>
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