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<div class="section" id="torch-distributed-tensor">
<h1>torch.distributed.tensor<a class="headerlink" href="#torch-distributed-tensor" title="Permalink to this heading">¶</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">torch.distributed.tensor</span></code> is currently in alpha state and under
development, we are committing backward compatibility for the most APIs listed
in the doc, but there might be API changes if necessary.</p>
</div>
<div class="section" id="pytorch-dtensor-distributed-tensor">
<h2>PyTorch DTensor (Distributed Tensor)<a class="headerlink" href="#pytorch-dtensor-distributed-tensor" title="Permalink to this heading">¶</a></h2>
<p>PyTorch DTensor offers simple and flexible tensor sharding primitives that transparently handles distributed
logic, including sharded storage, operator computation and collective communications across devices/hosts.
<code class="docutils literal notranslate"><span class="pre">DTensor</span></code> could be used to build different paralleism solutions and support sharded state_dict representation
when working with multi-dimensional sharding.</p>
<p>Please see examples from the PyTorch native parallelism solutions that are built on top of <code class="docutils literal notranslate"><span class="pre">DTensor</span></code>:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://pytorch.org/docs/main/distributed.tensor.parallel.html">Tensor Parallel</a></p></li>
<li><p><a class="reference external" href="https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md">FSDP2</a></p></li>
</ul>
<span class="target" id="module-torch.distributed.tensor"></span><p><a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> follows the SPMD (single program, multiple data) programming model to empower users to
write distributed program as if it’s a <strong>single-device program with the same convergence property</strong>. It
provides a uniform tensor sharding layout (DTensor Layout) through specifying the <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>
and <code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code>:</p>
<ul class="simple">
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> represents the device topology and the communicators of the cluster using
an n-dimensional array.</p></li>
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code> describes the sharding layout of the logical tensor on the <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>.
DTensor supports three types of placements: <code class="xref py py-class docutils literal notranslate"><span class="pre">Shard</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Replicate</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">Partial</span></code>.</p></li>
</ul>
<div class="section" id="dtensor-class-apis">
<h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> is a <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> subclass. This means once a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> is created, it could be
used in very similar way to <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, including running different types of PyTorch operators as if
running them in a single device, allowing proper distributed computation for PyTorch operators.</p>
<p>In addition to existing <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> methods, it also offers a set of additional methods to interact with
<code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, <code class="docutils literal notranslate"><span class="pre">redistribute</span></code> the DTensor Layout to a new DTensor, get the full tensor content
on all devices, etc.</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.DTensor">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">DTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.distributed.tensor.DTensor" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">DTensor</span></code> (Distributed Tensor) is a subclass of <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> that provides single-device like
abstraction to program with multi-device <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>. It describes the distributed tensor sharding
layout (DTensor Layout) through the <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> and following types of <code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code>:</p>
<ul class="simple">
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Shard</span></code>: Tensor sharded on the tensor dimension <code class="docutils literal notranslate"><span class="pre">dim</span></code> on the devices of the <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension</p></li>
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Replicate</span></code>: Tensor replicated on the devices of the <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension</p></li>
<li><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Partial</span></code>: Tensor is pending reduction on the devices of the <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension</p></li>
</ul>
<p>When calling PyTorch operators, <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> overrides the PyTorch operators to perform sharded computation and issue
communications whenever necessary. Along with the operator computation, <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> will transform or propagate the
placements (DTensor Layout) properly (based on the operator semantic itself) and generate new <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> outputs.</p>
<p>To ensure numerical correctness of the <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> sharded computation when calling PyTorch operators, <code class="docutils literal notranslate"><span class="pre">DTensor</span></code>
requires every Tensor argument of the operator be DTensor.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Directly using the Tensor subclass constructor here is not the recommended way to create a <code class="docutils literal notranslate"><span class="pre">DTensor</span></code>
(i.e. it does not handle autograd correctly hence is not the public API). Please refer to the <a class="reference internal" href="#create-dtensor">create_dtensor</a>
section to see how to create a <code class="docutils literal notranslate"><span class="pre">DTensor</span></code>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor">DTensor</a></p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.from_local">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_tensor</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">placements</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</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">stride</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/_api.html#DTensor.from_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/_api.py#L352"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.from_local" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> from a local torch.Tensor on each rank
according to the <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and <code class="docutils literal notranslate"><span class="pre">placements</span></code> specified.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>local_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – local torch.Tensor on each rank.</p></li>
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – DeviceMesh to place the
tensor, if not specified, must be called under a DeviceMesh
context manager, default: None</p></li>
<li><p><strong>placements</strong> (List[<code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code>], optional) – the placements that
describes how to place the local torch.Tensor on DeviceMesh, must
have the same number of elements as <code class="docutils literal notranslate"><span class="pre">device_mesh.ndim</span></code>.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>run_check</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – at a cost of extra communications, perform
sanity check across ranks to check each local tensor’s meta information
to ensure correctness. If have <code class="xref py py-class docutils literal notranslate"><span class="pre">Replicate</span></code> in <code class="docutils literal notranslate"><span class="pre">placements</span></code>, the
data on first rank of the device mesh dimension will be broadcasted
to other ranks. default: False</p></li>
<li><p><strong>shape</strong> (<a class="reference internal" href="size.html#torch.Size" title="torch.Size"><em>torch.Size</em></a><em>, </em><em>optional</em>) – A List of int which specifies the size of
DTensor which build on top of <cite>local_tensor</cite>. Note this needs to be
provided if the shape of <code class="docutils literal notranslate"><span class="pre">local_tensor</span></code> are different across the ranks.
If not provided, <code class="docutils literal notranslate"><span class="pre">shape</span></code> will be computed assuming the given distributed
tensor is evenly sharded across ranks. default: None</p></li>
<li><p><strong>stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><em>tuple</em></a><em>, </em><em>optional</em>) – A List of int which specifies the stride of DTensor.
If not provided, <code class="docutils literal notranslate"><span class="pre">stride</span></code> will be computed assuming the given distributed
tensor is evenly sharded across ranks. default: None</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> object</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><em>DTensor</em></a></p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When <code class="docutils literal notranslate"><span class="pre">run_check=False</span></code>, it is the user’s responsibility to ensure the
local tensor passed in is correct across ranks (i.e. the tensor is sharded for
the <code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code> placement or replicated for the <code class="docutils literal notranslate"><span class="pre">Replicate()</span></code> placement).
If not, the behavior of the created DTensor is undefined.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">from_local</span></code> is differentiable, the <cite>requires_grad</cite> of the created
<cite>DTensor</cite> object will depend on if <cite>local_tensor</cite> requires_grad or not.</p>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.full_tensor">
<span class="sig-name descname"><span class="pre">full_tensor</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">grad_placements</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/_api.html#DTensor.full_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/_api.py#L546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.full_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the full tensor of this DTensor. It will perform necessary collectives
to gather the local tensors from other ranks in its DeviceMesh and concatenate
them together. It’s a syntatic sugar of the following code:</p>
<p><code class="docutils literal notranslate"><span class="pre">dtensor.redistribute(placements=[Replicate()]</span> <span class="pre">*</span> <span class="pre">mesh.ndim).to_local()</span></code></p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments</dt>
<dd class="field-odd"><p><strong>grad_placements</strong> (List[<code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code>], optional) – the placements describes
the future layout of any gradient layout of the full Tensor returned from this
function.
<cite>full_tensor</cite> converts DTensor to a full torch.Tensor and the returned torch.tensor
might not be used as the original replicated DTensor layout later in the code. This
argument is the hint that user can give to autograd in case the gradient
layout of the returned tensor does not match the original replicated DTensor layout.
If not specified, we will assume the gradient layout of the full tensor be replicated.</p>
</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 that represents the full tensor of this 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>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">full_tensor</span></code> is differentiable.</p>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.redistribute">
<span class="sig-name descname"><span class="pre">redistribute</span></span><span class="sig-paren">(</span><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">placements</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="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</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/_api.html#DTensor.redistribute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/_api.py#L474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.redistribute" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">redistribute</span></code> performs necessary collective operations that redistribute the current
DTensor from its current placements to a new placements, or from is current DeviceMesh
to a new DeviceMesh. i.e. we can turn a Sharded DTensor to a Replicated DTensor by
specifying a Replicate placement for each dimension of the DeviceMesh.</p>
<p>When redistributing from current to the new placements on one device mesh dimension, we
will perform the following operations including communication collective or local operation:</p>
<ol class="arabic simple">
<li><p><code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code> -> <code class="docutils literal notranslate"><span class="pre">Replicate()</span></code>: <code class="docutils literal notranslate"><span class="pre">all_gather</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Shard(src_dim)</span></code> -> <code class="docutils literal notranslate"><span class="pre">Shard(dst_dim)</span></code>: <code class="docutils literal notranslate"><span class="pre">all_to_all</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Replicate()</span></code> -> <code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code>: local chunking (i.e. <code class="docutils literal notranslate"><span class="pre">torch.chunk</span></code>)</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Partial()</span></code> -> <code class="docutils literal notranslate"><span class="pre">Replicate()</span></code>: <code class="docutils literal notranslate"><span class="pre">all_reduce</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Partial()</span></code> -> <code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code>: <code class="docutils literal notranslate"><span class="pre">reduce_scatter</span></code></p></li>
</ol>
<p><code class="docutils literal notranslate"><span class="pre">redistribute</span></code> would correctly figure out the necessary redistribute steps for DTensors
that are created either on 1-D or N-D DeviceMesh.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>device_mesh</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code>, optional) – DeviceMesh to place the
DTensor. If not specified, it would use the current DTensor’s DeviceMesh.
default: None</p></li>
<li><p><strong>placements</strong> (List[<code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code>], optional) – the new placements that
describes how to place the DTensor into the DeviceMesh, must
have the same number of elements as <code class="docutils literal notranslate"><span class="pre">device_mesh.ndim</span></code>.
default: replicate on all mesh dimensions</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><p><strong>async_op</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a><em>, </em><em>optional</em>) – whether to perform the DTensor redistribute operation
asynchronously or not. Default: False</p>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> object</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><em>DTensor</em></a></p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">redistribute</span></code> is differentiable, which means user do not need to worry about
the backward formula of the redistribute operation.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">redistribute</span></code> currently only supports redistributing DTensor on the same DeviceMesh,
Please file an issue if you need to redistribute DTensor to different DeviceMesh.</p>
</div>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.to_local">
<span class="sig-name descname"><span class="pre">to_local</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">grad_placements</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/_api.html#DTensor.to_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/_api.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.to_local" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the local tensor of this DTensor on its current rank. For sharding it returns
a local shard of the logical tensor view, for replication it returns the replica on
its current rank.</p>
<dl class="field-list simple">
<dt class="field-odd">Keyword Arguments</dt>
<dd class="field-odd"><p><strong>grad_placements</strong> (List[<code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code>], optional) – the placements describes
the future layout of any gradient layout of the Tensor returned from this
function.
<cite>to_local</cite> converts DTensor to local tensor and the returned local tensor
might not be used as the original DTensor layout later in the code. This
argument is the hint that user can give to autograd in case the gradient
layout of the returned tensor does not match the original DTensor layout.
If not specified, we will assume the gradient layout remains the same
as the original DTensor and use that for gradient computation.</p>
</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> or <code class="docutils literal notranslate"><span class="pre">AsyncCollectiveTensor</span></code> object. it represents the
local tensor on its current rank. When an <code class="docutils literal notranslate"><span class="pre">AsyncCollectiveTensor</span></code> object is returned,
it means the local tensor is not ready yet (i.e. communication is not finished). In this
case, user needs to call <code class="docutils literal notranslate"><span class="pre">wait</span></code> to wait the local tensor to be ready.</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>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">to_local</span></code> is differentiable, the <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> of the local tensor returned
will depend on if the <cite>DTensor</cite> requires_grad or not.</p>
</div>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.device_mesh">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">device_mesh</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference internal" href="distributed.html#torch.distributed.device_mesh.DeviceMesh" title="torch.distributed.device_mesh.DeviceMesh"><span class="pre">DeviceMesh</span></a></em><a class="headerlink" href="#torch.distributed.tensor.DTensor.device_mesh" title="Permalink to this definition">¶</a></dt>
<dd><p>The <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> attribute that associates with this DTensor object.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> is a read-only property, it can not be set.</p>
</div>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.placements">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">placements</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.13)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.tensor.placement_types.Placement" title="torch.distributed.tensor.placement_types.Placement"><span class="pre">Placement</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#torch.distributed.tensor.DTensor.placements" title="Permalink to this definition">¶</a></dt>
<dd><p>The placements attribute of this DTensor that describes the layout of this
DTensor on the its DeviceMesh.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p><code class="docutils literal notranslate"><span class="pre">placements</span></code> is a read-only property, it can not be set.</p>
</div>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="devicemesh-as-the-distributed-communicator">
<h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devicemesh-as-the-distributed-communicator" title="Permalink to this heading">¶</a></h3>
<p><a class="reference internal" href="distributed.html#torch.distributed.device_mesh.DeviceMesh" title="torch.distributed.device_mesh.DeviceMesh"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code></a> was built from DTensor as the abstraction to describe cluster’s device topology and represent
multi-dimensional communicators (on top of <code class="docutils literal notranslate"><span class="pre">ProcessGroup</span></code>). To see the details of how to create/use a DeviceMesh,
please refer to the <a class="reference external" href="https://pytorch.org/tutorials/recipes/distributed_device_mesh.html">DeviceMesh recipe</a>.</p>
</div>
<div class="section" id="module-torch.distributed.tensor.placement_types">
<span id="dtensor-placement-types"></span><h3>DTensor Placement Types<a class="headerlink" href="#module-torch.distributed.tensor.placement_types" title="Permalink to this heading">¶</a></h3>
<p>DTensor supports the following types of <a class="reference internal" href="#torch.distributed.tensor.placement_types.Placement" title="torch.distributed.tensor.placement_types.Placement"><code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code></a> on each <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension:</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Shard">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/placement_types.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Shard" title="Permalink to this definition">¶</a></dt>
<dd><p>The <code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code> placement describes the DTensor sharding on tensor dimension
<code class="docutils literal notranslate"><span class="pre">dim</span></code> over a corresponding <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the
DeviceMesh dimension only holds a shard/piece of the global Tensor. The
<code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code> placement follows the <code class="docutils literal notranslate"><span class="pre">torch.chunk(dim)</span></code> semantic, where the
last few shards on the DeviceMesh dimension might be empty when the tensor dimension
is not evenly divisible on the DeviceMesh dimension. The <code class="docutils literal notranslate"><span class="pre">Shard</span></code> placement can be
used by all DTensor APIs (i.e. distribute_tensor, from_local, etc.)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – The tensor dimension that describes the DTensor is sharded over its
corresponding DeviceMesh dimension.</p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>sharding on a tensor dimension where the tensor dimension size is not
evenly divisible on a DeviceMesh dimension is currently experimental and subject to change.</p>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Shard.dim">
<span class="sig-name descname"><span class="pre">dim</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></em><a class="headerlink" href="#torch.distributed.tensor.placement_types.Shard.dim" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Replicate">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Replicate</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/placement_types.py#L527"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Replicate" title="Permalink to this definition">¶</a></dt>
<dd><p>The <code class="docutils literal notranslate"><span class="pre">Replicate()</span></code> placement describes the DTensor replicating on a corresponding
<code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the DeviceMesh dimension holds a
replica of the global Tensor. The <code class="docutils literal notranslate"><span class="pre">Replicate</span></code> placement can be used by all
DTensor APIs (i.e. <code class="docutils literal notranslate"><span class="pre">distribute_tensor</span></code>, <code class="docutils literal notranslate"><span class="pre">DTensor.from_local</span></code>, etc.)</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Partial">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Partial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduce_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/placement_types.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Partial" title="Permalink to this definition">¶</a></dt>
<dd><p>The <code class="docutils literal notranslate"><span class="pre">Partial(reduce_op)</span></code> placement describes the DTensor that is pending
reduction on a specified <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the
DeviceMesh dimension holds the partial value of the global Tensor. User can
redistribute the <code class="docutils literal notranslate"><span class="pre">Partial</span></code> DTensor to a <code class="docutils literal notranslate"><span class="pre">Replicate</span></code> or <code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code>
placement on the specified <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension using <code class="docutils literal notranslate"><span class="pre">redistribute</span></code>,
which would trigger necessary communication operations under the hood (i.e.
<code class="docutils literal notranslate"><span class="pre">allreduce</span></code>, <code class="docutils literal notranslate"><span class="pre">reduce_scatter</span></code>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>reduce_op</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>optional</em>) – The reduction op to be used for the partial DTensor
to produce Replicated/Sharded DTensor. Only element-wise reduction operations
are supported, including: “sum”, “avg”, “product”, “max”, “min”, default: “sum”.</p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The <code class="docutils literal notranslate"><span class="pre">Partial</span></code> placement can be generated as a result of the DTensor operators,
and can only be used by the <code class="docutils literal notranslate"><span class="pre">DTensor.from_local</span></code> API.</p>
</div>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Partial.reduce_op">
<span class="sig-name descname"><span class="pre">reduce_op</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'sum'</span></em><a class="headerlink" href="#torch.distributed.tensor.placement_types.Partial.reduce_op" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Placement</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/placement_types.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement" title="Permalink to this definition">¶</a></dt>
<dd><p>The base class for the Placement type, where it describes how a DTensor is placed onto the
<code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code>. <code class="docutils literal notranslate"><span class="pre">Placement</span></code> and <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> together could describe the DTensor Layout.
It is the base class of the three main DTensor Placement types: <code class="docutils literal notranslate"><span class="pre">Shard</span></code>, <code class="docutils literal notranslate"><span class="pre">Replicate</span></code>,
and <code class="docutils literal notranslate"><span class="pre">Partial</span></code>.</p>
<p>This class is not meant to be used directly, mainly served as a typing stub.</p>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_partial">
<span class="sig-name descname"><span class="pre">is_partial</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/placement_types.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_partial" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_replicate">
<span class="sig-name descname"><span class="pre">is_replicate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/placement_types.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_replicate" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_shard">
<span class="sig-name descname"><span class="pre">is_shard</span></span><span class="sig-paren">(</span><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">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/placement_types.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_shard" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
</div>
<div class="section" id="different-ways-to-create-a-dtensor">
<span id="create-dtensor"></span><h2>Different ways to create a DTensor<a class="headerlink" href="#different-ways-to-create-a-dtensor" title="Permalink to this heading">¶</a></h2>
<dl class="simple">
<dt>There’re three ways to construct a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a>:</dt><dd><ul class="simple">
<li><p><a class="reference internal" href="#torch.distributed.tensor.distribute_tensor" title="torch.distributed.tensor.distribute_tensor"><code class="xref py py-meth docutils literal notranslate"><span class="pre">distribute_tensor()</span></code></a> creates a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> from a logical or “global” <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> on
each rank. This could be used to shard the leaf <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> s (i.e. model parameters/buffers
and inputs).</p></li>
<li><p><a class="reference internal" href="#torch.distributed.tensor.DTensor.from_local" title="torch.distributed.tensor.DTensor.from_local"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DTensor.from_local()</span></code></a> creates a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> from a local <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> on each rank, which can
be used to create <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> from a non-leaf <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> s (i.e. intermediate activation
tensors during forward/backward).</p></li>
<li><p>DTensor provides dedicated tensor factory functions (e.g. <a class="reference internal" href="#torch.distributed.tensor.empty" title="torch.distributed.tensor.empty"><code class="xref py py-meth docutils literal notranslate"><span class="pre">empty()</span></code></a>, <a class="reference internal" href="#torch.distributed.tensor.ones" title="torch.distributed.tensor.ones"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ones()</span></code></a>, <a class="reference internal" href="#torch.distributed.tensor.randn" title="torch.distributed.tensor.randn"><code class="xref py py-meth docutils literal notranslate"><span class="pre">randn()</span></code></a>, etc.)
to allow different <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> creations by directly specifying the <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> and
<code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code>. Compare to <a class="reference internal" href="#torch.distributed.tensor.distribute_tensor" title="torch.distributed.tensor.distribute_tensor"><code class="xref py py-meth docutils literal notranslate"><span class="pre">distribute_tensor()</span></code></a>, this could directly materializing the sharded memory
on device, instead of performing sharding after initializing the logical Tensor memory.</p></li>
</ul>
</dd>
</dl>
<div class="section" id="create-dtensor-from-a-logical-torch-tensor">
<h3>Create DTensor from a logical torch.Tensor<a class="headerlink" href="#create-dtensor-from-a-logical-torch-tensor" title="Permalink to this heading">¶</a></h3>
<p>The SPMD (single program, multiple data) programming model in <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> launches multiple processes
(i.e. via <code class="docutils literal notranslate"><span class="pre">torchrun</span></code>) to execute the same program, this means that the model inside the program would be
initialized on different processes first (i.e. the model might be initialized on CPU, or meta device, or directly
on GPU if enough memory).</p>
<p><code class="docutils literal notranslate"><span class="pre">DTensor</span></code> offers a <a class="reference internal" href="#torch.distributed.tensor.distribute_tensor" title="torch.distributed.tensor.distribute_tensor"><code class="xref py py-meth docutils literal notranslate"><span class="pre">distribute_tensor()</span></code></a> API that could shard the model weights or Tensors to <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> s,
where it would create a DTensor from the “logical” Tensor on each process. This would empower the created
<code class="docutils literal notranslate"><span class="pre">DTensor</span></code> s to comply with the single device semantic, which is critical for <strong>numerical correctness</strong>.</p>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.tensor.distribute_tensor">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</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">placements</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 external" href="https://github.com/pytorch/pytorch/blob/v2.6.0/torch/distributed/tensor/_api.py#L629"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Distribute a leaf <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> (i.e. nn.Parameter/buffers) to the <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> according
to the <code class="docutils literal notranslate"><span class="pre">placements</span></code> specified. The rank of <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and <code class="docutils literal notranslate"><span class="pre">placements</span></code> must be the
same. The <code class="docutils literal notranslate"><span class="pre">tensor</span></code> to distribute is the logical or “global” tensor, and the API would use
the <code class="docutils literal notranslate"><span class="pre">tensor</span></code> from first rank of the DeviceMesh dimension as the source of truth to preserve
the single-device semantic. If you want to construct a DTensor in the middle of the Autograd
computation, please use <a class="reference internal" href="#torch.distributed.tensor.DTensor.from_local" title="torch.distributed.tensor.DTensor.from_local"><code class="xref py py-meth docutils literal notranslate"><span class="pre">DTensor.from_local()</span></code></a> instead.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – torch.Tensor to be distributed. Note that if you
want to shard a tensor on a dimension that is not evenly divisible by
the number of devices in that mesh dimension, we use <code class="docutils literal notranslate"><span class="pre">torch.chunk</span></code>
semantic to shard the tensor and scatter the shards. The uneven sharding
behavior is experimental and subject to change.</p></li>