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<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Tensor parallelism&quot;,&quot;local&quot;:&quot;tensor-parallelism&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Partitioning a model&quot;,&quot;local&quot;:&quot;partitioning-a-model&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Partitioning strategies&quot;,&quot;local&quot;:&quot;partitioning-strategies&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Packed strategies&quot;,&quot;local&quot;:&quot;packed-strategies&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3},{&quot;title&quot;:&quot;Local strategies&quot;,&quot;local&quot;:&quot;local-strategies&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Custom partitioning strategies&quot;,&quot;local&quot;:&quot;custom-partitioning-strategies&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Benchmarks&quot;,&quot;local&quot;:&quot;benchmarks&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Design implementation&quot;,&quot;local&quot;:&quot;design-implementation&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;DeviceMesh&quot;,&quot;local&quot;:&quot;devicemesh&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3},{&quot;title&quot;:&quot;DTensor&quot;,&quot;local&quot;:&quot;dtensor&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Resources&quot;,&quot;local&quot;:&quot;resources&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}">
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 <link rel="modulepreload" href="/docs/transformers/pr_41992/en/_app/immutable/chunks/HfOption.7b5c323c.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Tensor parallelism&quot;,&quot;local&quot;:&quot;tensor-parallelism&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Partitioning a model&quot;,&quot;local&quot;:&quot;partitioning-a-model&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Partitioning strategies&quot;,&quot;local&quot;:&quot;partitioning-strategies&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Packed strategies&quot;,&quot;local&quot;:&quot;packed-strategies&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3},{&quot;title&quot;:&quot;Local strategies&quot;,&quot;local&quot;:&quot;local-strategies&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Custom partitioning strategies&quot;,&quot;local&quot;:&quot;custom-partitioning-strategies&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Benchmarks&quot;,&quot;local&quot;:&quot;benchmarks&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Design implementation&quot;,&quot;local&quot;:&quot;design-implementation&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;DeviceMesh&quot;,&quot;local&quot;:&quot;devicemesh&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3},{&quot;title&quot;:&quot;DTensor&quot;,&quot;local&quot;:&quot;dtensor&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Resources&quot;,&quot;local&quot;:&quot;resources&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div> <h1 class="relative group"><a id="tensor-parallelism" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#tensor-parallelism"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Tensor parallelism</span></h1> <p data-svelte-h="svelte-1h3dybs"><a href="./perf_train_gpu_many#tensor-parallelism">Tensor parallelism</a> slices a model layer into pieces so multiple hardware accelerators work on it simultaneously. This lets you run models that exceed a single GPU’s memory capacity and achieve higher throughput. You’ll need fast intra-node communication because GPUs exchange partial results at each layer.</p> <p data-svelte-h="svelte-1t8hl7r">The list below shows models with native tensor parallelism support. Open a GitHub issue or pull request to add support for a model.</p> <details data-svelte-h="svelte-1w7okx8"><summary>Show supported models</summary> <ul><li><a href="./model_doc/cohere">Cohere</a> and <a href="./model_doc/cohere2">Cohere 2</a></li> <li><a href="./model_doc/gemma">Gemma</a> and <a href="./model_doc/gemma2">Gemma 2</a></li> <li><a href="./model_doc/glm">GLM</a></li> <li><a href="./model_doc/granite">Granite</a></li> <li><a href="./model_doc/llama">Llama</a></li> <li><a href="./model_doc/mistral">Mistral</a></li> <li><a href="./model_doc/mixtral">Mixtral</a></li> <li><a href="./model_doc/olmo">OLMo</a> and <a href="./model_doc/olmo2">OLMo2</a></li> <li><a href="./model_doc/phi">Phi</a> and <a href="./model_doc/phi3">Phi-3</a></li> <li><a href="./model_doc/qwen2">Qwen2</a>, <a href="./model_doc/qwen2_moe">Qwen2Moe</a>, and <a href="./model_doc/qwen2_5_vl">Qwen2-VL</a></li> <li><a href="./model_doc/starcoder2">Starcoder2</a></li></ul></details> <p data-svelte-h="svelte-1cnqv9m">This guide covers enabling tensor parallelism in Transformers and the available partitioning strategies.</p> <h2 class="relative group"><a id="partitioning-a-model" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#partitioning-a-model"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Partitioning a model</span></h2> <p data-svelte-h="svelte-1jje9f6">Transformers enables tensor parallelism when a model has a <code>tp_plan</code>. Choose from two partitioning methods.</p> <ul data-svelte-h="svelte-1k2hlag"><li>Set <code>tp_plan=&quot;auto&quot;</code> for an automatic plan based on the model’s predefined configuration.</li> <li>Define and pass a manual <code>tp_plan</code>.</li></ul> <div class="flex space-x-2 items-center my-1.5 mr-8 h-7 !pl-0 -mx-3 md:mx-0"><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd border-gray-800 bg-black dark:bg-gray-700 text-white">auto plan </div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">manual plan </div></div> <div class="language-select"><div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-py "><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> os
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer
<span class="hljs-comment"># model_id = &quot;meta-llama/Llama-4-Scout-17B-16E-Instruct&quot; # better to visualize all the possible strategies</span>
model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;meta-llama/Meta-Llama-3-8B-Instruct&quot;</span> , dtype=torch.bfloat16, tp_plan=<span class="hljs-string">&quot;auto&quot;</span>)
<span class="hljs-built_in">print</span>(model._tp_plan)
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;meta-llama/Meta-Llama-3-8B-Instruct&quot;</span>)
prompt = <span class="hljs-string">&quot;Can I help&quot;</span>
inputs = tokenizer(prompt, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids.to(model.device)
<span class="hljs-comment"># distributed run</span>
outputs = model(inputs)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-133xfp4">Launch the inference script with <a href="https://pytorch.org/docs/stable/elastic/run.html" rel="nofollow">torchrun</a>. Use 4 processes per GPU.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-bash "><!-- HTML_TAG_START -->torchrun --nproc-per-node 4 demo.py<!-- HTML_TAG_END --></pre></div> </div> <h2 class="relative group"><a id="partitioning-strategies" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#partitioning-strategies"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Partitioning strategies</span></h2> <p data-svelte-h="svelte-x2f3y8">The <code>ParallelInterface</code> class defines all partitioning strategies. It maps a string to the strategy implementation. You don’t need to interact with this class directly since you set strategies with <code>tp_plan</code> in <a href="/docs/transformers/pr_41992/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a>. It’s useful for checking available strategies.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-py "><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">ParallelInterface</span>(<span class="hljs-title class_ inherited__">MutableMapping</span>):
 <span class="hljs-string">&quot;&quot;&quot;
 Dict-like object keeping track of allowed attention functions. You can easily add a new attention function
 with a call to `register()`. If a model needs to locally overwrite an existing attention function, say `sdpa`,
 it needs to declare a new instance of this class inside the `modeling_&lt;model&gt;.py`, and declare it on that instance.
 &quot;&quot;&quot;</span>
 _global_mapping = {
 <span class="hljs-string">&quot;colwise&quot;</span>: ColwiseParallel(),
 <span class="hljs-string">&quot;rowwise&quot;</span>: RowwiseParallel(),
 <span class="hljs-string">&quot;colwise_rep&quot;</span>: ColwiseParallel(output_layouts=Replicate()),
 <span class="hljs-string">&quot;rowwise_rep&quot;</span>: RowwiseParallel(input_layouts=Replicate()),
 <span class="hljs-string">&quot;local_colwise&quot;</span>: ColwiseParallel(use_dtensor=<span class="hljs-literal">False</span>),
 <span class="hljs-string">&quot;local_rowwise&quot;</span>: RowwiseParallel(use_dtensor=<span class="hljs-literal">False</span>),
 <span class="hljs-string">&quot;local&quot;</span>: IsolatedParallel(),
 <span class="hljs-string">&quot;moe_tp_experts&quot;</span>: MoeTensorParalellExperts(),
 <span class="hljs-string">&quot;local_packed_rowwise&quot;</span>: PackedRowwiseParallel(use_dtensor=<span class="hljs-literal">False</span>),
 <span class="hljs-string">&quot;sequence_parallel&quot;</span>: SequenceParallel(),
 <span class="hljs-string">&quot;replicate&quot;</span>: ReplicateParallel(),
 }<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-dc71cs">The table below describes each strategy.</p> <table data-svelte-h="svelte-1ymixxk"><thead><tr><th>Strategy</th> <th>Description</th></tr></thead> <tbody><tr><td><code>ColwiseParallel</code></td> <td>Partitions weights and biases column-wise.</td></tr> <tr><td><code>RowwiseParallel</code></td> <td>Partitions weights and biases row-wise. Supports <code>nn.Embedding</code> modules partitioning.</td></tr> <tr><td><code>SequenceParallel</code></td> <td>Sequence parallel implementation to support <code>LayerNorm</code> and <code>Dropout</code> layers. Supports Python implementation of <a href="https://github.com/facebookresearch/llama/blob/main/llama/model.py#L34" rel="nofollow">RMSNorm</a>.</td></tr> <tr><td><code>PackedColwiseParallel</code></td> <td>A variant of <code>ColwiseParallel</code> that supports packed weights (for example, packing <code>up_proj</code> and <code>gate_proj</code> together). Refer to the <a href="https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/tensor_parallel.py#L79-#L108" rel="nofollow">code</a> for more details.</td></tr> <tr><td><code>PackedRowwiseParallel</code></td> <td>A variant of <code>RowwiseParallel</code> that supports packed weights (refer to the <a href="https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/tensor_parallel.py#L79-#L108" rel="nofollow">code</a> for more details).</td></tr> <tr><td><code>GatherParallel</code></td> <td>Gathers module outputs across devices.</td></tr> <tr><td><code>IsolatedParallel</code></td> <td>Isolates a module from other devices. Used for Experts in Mixture-of-Experts (MoE) layers.</td></tr> <tr><td><code>ReplicateParallel</code></td> <td>Replicates modules across all devices. Prevents <code>torch.distributed</code> APIs from breaking due to a partially sharded model.</td></tr></tbody></table> <h3 class="relative group"><a id="packed-strategies" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#packed-strategies"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Packed strategies</span></h3> <p data-svelte-h="svelte-2j1tdx">Weight packing combines multiple linear layers into a single, larger layer. The <code>PackedColwiseParallel</code> and <code>PackedRowwiseParallel</code> strategies shard packed weights correctly. Basic <code>ColwiseParallel</code> or <code>RowwiseParallel</code> strategies shard packed weights incorrectly.</p> <p data-svelte-h="svelte-1swuft2">The example below packs <code>up_proj</code> and <code>gate_proj</code> into a single <code>gate_up_proj</code> module and requires the <code>PackedRowwiseParallel</code> strategy to shard <code>gate_up_proj</code>.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">Llama4TextExperts</span>(nn.Module):
 ...
 self.gate_up_proj = nn.Parameter(torch.zeros(self.num_experts, self.hidden_size, <span class="hljs-number">2</span> * self.expert_dim))<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1vcz03b">Use batch matrix multiplication in the <code>forward</code> pass to compute the output of the <code>gate_up_proj</code> module.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, hidden_states</span>):
 ...
 gate_up = torch.bmm(hidden_states, self.gate_up_proj) <span class="hljs-comment"># Compute the output of the gate_up_proj module</span>
 gate, up = gate_up.chunk(<span class="hljs-number">2</span>, dim=-<span class="hljs-number">1</span>) <span class="hljs-comment"># Split the output into gate and up</span><!-- HTML_TAG_END --></pre></div> <blockquote class="tip" data-svelte-h="svelte-1ds8d8b"><p>See <a href="https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/tensor_parallel.py#L79-#L108" rel="nofollow">this comment</a> for a visual representation of why <code>Packed*</code> needs to be used.</p></blockquote> <h3 class="relative group"><a id="local-strategies" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#local-strategies"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Local strategies</span></h3> <p data-svelte-h="svelte-k4lmf4">Local strategies (<code>local_colwise</code>, <code>local_rowwise</code>, <code>local_packed_rowwise</code>) don’t use <a href="https://docs.pytorch.org/docs/stable/distributed.tensor.html" rel="nofollow">DTensor</a> because it lacks support for some operations like <a href="https://docs.pytorch.org/docs/stable/generated/torch.chunk.html" rel="nofollow">torch.chunk</a>. Instead, local strategies use the basic <a href="https://docs.pytorch.org/docs/stable/tensors.html" rel="nofollow">torch.Tensor</a> and perform distributed logic manually.</p> <h2 class="relative group"><a id="custom-partitioning-strategies" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#custom-partitioning-strategies"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Custom partitioning strategies</span></h2> <p data-svelte-h="svelte-1qlfo43">Inherit from <a href="https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/tensor_parallel.py" rel="nofollow">TensorParallelLayer</a> to create a custom partitioning strategy. Implement <code>partition_tensor</code>, <code>_prepare_input_fn</code> and <code>_prepare_output_fn</code>.</p> <p data-svelte-h="svelte-reiejm">Register the strategy in the <code>ParallelInterface</code> mapping so the dispatching logic finds it when specified in <code>tp_plan</code>.</p> <p data-svelte-h="svelte-okfrkp">The example below shows how to implement <code>ColwiseParallel</code> with this workflow.</p> <ol><li><p data-svelte-h="svelte-17kgupt">Inherit from <code>TensorParallelLayer</code>. In the <code>__init__</code> method, define <code>input_layouts</code> and <code>output_layouts</code> to describe how the input and output tensors should be placed on devices. The <code>desired_input_layouts</code> attribute is used to specify <em>how</em> the input should be placed on devices.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START --><span class="hljs-keyword">class</span> <span class="hljs-title class_">ColwiseParallel</span>(<span class="hljs-title class_ inherited__">TensorParallelLayer</span>):
 <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">
 self,
 *,
 input_layouts: <span class="hljs-type">Optional</span>[Placement] = <span class="hljs-literal">None</span>, <span class="hljs-comment"># The input layout coming from the previous layer</span>
 output_layouts: <span class="hljs-type">Optional</span>[Placement] = <span class="hljs-literal">None</span>, <span class="hljs-comment"># The output layout we want to achieve</span>
 use_local_output: <span class="hljs-built_in">bool</span> = <span class="hljs-literal">True</span>, <span class="hljs-comment"># Whether to use local output or not</span>
 use_dtensor=<span class="hljs-literal">True</span>, <span class="hljs-comment"># Whether to use DTensor or not</span>
 </span>):
 self.input_layouts = (input_layouts <span class="hljs-keyword">or</span> Replicate(),) <span class="hljs-comment"># The input sharding coming from the previous layer</span>
 self.output_layouts = (output_layouts <span class="hljs-keyword">or</span> Shard(-<span class="hljs-number">1</span>),) <span class="hljs-comment"># Desired output sharding</span>
 self.desired_input_layouts = (Replicate(),) <span class="hljs-comment"># Desired input sharding, inputs should be replicated across GPUs</span>
 self.use_local_output = use_local_output
 self.use_dtensor = use_dtensor<!-- HTML_TAG_END --></pre></div></li> <li><p data-svelte-h="svelte-1e1clij">Implement the <code>partition_tensor</code>, <code>_prepare_input_fn</code>, and <code>_prepare_output_fn</code> methods.</p> <p data-svelte-h="svelte-io1t4t">The <code>partition_tensor</code> method partitions the tensor and fills <code>empty_param</code> with the partitioned tensor. Use the utility function <code>get_tensor_shard</code> to help you get the correct shard of the original parameter for a given rank and <code>get_packed_weights</code> to help with packed weights.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">partition_tensor</span>(<span class="hljs-params">
 self,
 param, <span class="hljs-comment"># Full tensor of the parameter</span>
 empty_param, <span class="hljs-comment"># Empty tensor of the parameter, will be filled with the partitioned tensor</span>
 param_type, <span class="hljs-comment"># Type of the parameter, `bias` or `weight`</span>
 param_casting_dtype, <span class="hljs-comment"># The type to cast the parameter to</span>
 to_contiguous, <span class="hljs-comment"># Whether to convert the tensor to a contiguous memory layout</span>
 rank, <span class="hljs-comment"># The rank of the current device</span>
 device_mesh, <span class="hljs-comment"># The device mesh</span>
</span>) -&gt; nn.Parameter: <span class="hljs-comment"># Return the partitioned parameter</span>
 ...<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-493bdp">The <code>_prepare_input_fn</code> and <code>_prepare_output_fn</code> methods are used in the <a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.modules.module.register_module_forward_pre_hook.html" rel="nofollow">pre-forward</a> and <a href="https://docs.pytorch.org/docs/stable/generated/torch.nn.modules.module.register_module_forward_hook.html" rel="nofollow">forward</a> hooks. They redistribute the inputs and outputs to the desired layout as specified in the <code>__init__</code>.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">_prepare_input_fn</span>(<span class="hljs-params">input_layouts, desired_input_layouts, mod, inputs, device_mesh</span>):
 ...
 <span class="hljs-comment"># Do some custom logic, cast to DTensor etc.</span>
 ...
 <span class="hljs-keyword">return</span> inputs.redistribute(placements=desired_input_layouts, device_mesh=device_mesh)
<span class="hljs-keyword">def</span> <span class="hljs-title function_">_prepare_output_fn</span>(<span class="hljs-params">output_layouts, use_local_output, mod, outputs, device_mesh</span>):
 ...
 <span class="hljs-comment"># Do some custom logic, cast to DTensor etc.</span>
 ...
 <span class="hljs-keyword">return</span> outputs.redistribute(placements=output_layouts, device_mesh=device_mesh)<!-- HTML_TAG_END --></pre></div></li> <li><p data-svelte-h="svelte-fkwwr3">Register the strategy to <code>ParallelInterface</code> to enable it for use with <code>tp_plan</code>.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers.integrations.tensor_parallel <span class="hljs-keyword">import</span> ParallelInterface
ParallelInterface.register_strategy(<span class="hljs-string">&quot;colwise_custom&quot;</span>, ColwiseParallel)
tp_plan = {
 <span class="hljs-string">&quot;model.layers.*.self_attn.q_proj&quot;</span>: <span class="hljs-string">&quot;colwise_custom&quot;</span>,
 ...
}
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, tp_plan=tp_plan)<!-- HTML_TAG_END --></pre></div></li></ol> <h2 class="relative group"><a id="benchmarks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#benchmarks"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Benchmarks</span></h2> <p data-svelte-h="svelte-13b7shl">Tensor parallelism significantly speeds up inference, especially for large batch sizes or long sequences.</p> <p data-svelte-h="svelte-ek2cce">This chart shows the expected speedup for a single forward pass on <a href="./model_doc/llama">Llama</a> with a sequence length of 512.</p> <div style="text-align: center" data-svelte-h="svelte-1tp7cj2"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/Meta-Llama-3-8B-Instruct%2C%20seqlen%20%3D%20512%2C%20python%2C%20w_%20compile.png"></div> <h2 class="relative group"><a id="design-implementation" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#design-implementation"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Design implementation</span></h2> <p data-svelte-h="svelte-7povrh">Transformers implements tensor parallelism in a framework-agnostic way. It relies on <a href="https://docs.pytorch.org/tutorials/recipes/distributed_device_mesh.html" rel="nofollow">DeviceMesh</a> and <a href="https://docs.pytorch.org/docs/stable/distributed.tensor.html" rel="nofollow">DTensor</a> from <a href="https://docs.pytorch.org/tutorials/beginner/dist_overview.html" rel="nofollow">torch.distributed</a> to provide a simple, extensible interface.</p> <h3 class="relative group"><a id="devicemesh" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#devicemesh"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>DeviceMesh</span></h3> <p data-svelte-h="svelte-1ql6gz1"><code>DeviceMesh</code> creates a multi-dimensional grid of devices that communicate together. Different parallelization strategies require different communication patterns. Create a <code>DeviceMesh</code> with multiple sub-meshes to handle these patterns.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> torch.distributed.device_mesh <span class="hljs-keyword">import</span> init_device_mesh
<span class="hljs-comment"># Create a 1D mesh of 4 GPUs</span>
device_mesh = init_device_mesh(<span class="hljs-string">&quot;cuda&quot;</span>, (<span class="hljs-number">4</span>,), mesh_dim_names=[<span class="hljs-string">&quot;tp&quot;</span>])<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-ymumcn">Most <code>torch.distributed</code> parallelization strategies apply to the mesh itself or its sub-mesh. The mesh automatically handles communication patterns.</p> <h3 class="relative group"><a id="dtensor" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#dtensor"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>DTensor</span></h3> <p data-svelte-h="svelte-mpl0pm"><code>DTensor</code> (Distributed Tensor) handles distributed logic on top of usual tensor operations. Most model weights in tensor parallelism are stored as <code>DTensor</code>s.</p> <p data-svelte-h="svelte-1p3qk09">The <code>placement</code> attribute tells PyTorch how to place a tensor on devices in <code>DeviceMesh</code>. It accepts the following values:</p> <ul><li><p data-svelte-h="svelte-1qz2o5c"><code>Shard(dimension)</code> shards a <code>DTensor</code> across a given dimension over the <code>DeviceMesh</code> it was constructed under. The example below shows how to shard weights over different dimensions for column-wise partitioning.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START -->weight = ...
weight = DTensor.from_local(weight, device_mesh[<span class="hljs-string">&quot;tp&quot;</span>], placements=[Shard(<span class="hljs-number">0</span>)]) <span class="hljs-comment"># Shard across the 1st (column-wise) dimension</span>
bias = ...
bias = DTensor.from_local(bias, device_mesh[<span class="hljs-string">&quot;tp&quot;</span>], placements=[Shard(-<span class="hljs-number">1</span>)]) <span class="hljs-comment"># Shard across the ONLY dimension</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-10ug07g">This example shows how to shard weights over different dimensions for row-wise partitioning.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-python "><!-- HTML_TAG_START -->weight = ...
weight = DTensor.from_local(weight, device_mesh[<span class="hljs-string">&quot;tp&quot;</span>], placements=[Shard(<span class="hljs-number">1</span>)]) <span class="hljs-comment"># Shard across the 2nd (row-wise) dimension</span>
bias = ...
bias = DTensor.from_local(bias, device_mesh[<span class="hljs-string">&quot;tp&quot;</span>], placements=[Replicate()]) <span class="hljs-comment"># Replicate bias across all GPUs</span><!-- HTML_TAG_END --></pre></div></li> <li><p data-svelte-h="svelte-1m97lf6"><code>Replicate()</code> replicates a <code>DTensor</code> across the <code>DeviceMesh</code>. It creates a full copy of the tensor on each device.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class="language-py "><!-- HTML_TAG_START -->bias = ...
bias = DTensor.from_local(bias, device_mesh[<span class="hljs-string">&quot;tp&quot;</span>], placements=[Replicate()]) <span class="hljs-comment"># Replicate bias across all GPUs</span><!-- HTML_TAG_END --></pre></div></li> <li data-svelte-h="svelte-1wia58y"><p><code>Partial()</code> indicates a tensor is pending a reduction operation (not typically relevant for Transformers usage).</p></li></ul> <h2 class="relative group"><a id="resources" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#resources"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Resources</span></h2> <ul data-svelte-h="svelte-utuid1"><li><p>The <a href="https://huggingface.co/spaces/nanotron/ultrascale-playbook?section=tensor_parallelism" rel="nofollow">Ultra-Scale Playbook</a> section on tensor parallelism provides more details.</p></li> <li><p>Check the <a href="./expert_parallelism">expert parallelism</a> guide if you’re using a mixture-of-experts (MoE) model. These models support tensor parallelism and expert parallelism.</p></li> <li><p>Read the <a href="https://huggingface.co/blog/qgallouedec/tp" rel="nofollow">Tensor Parallelism (TP) in Transformers: 5 Minutes to Understand</a> blog post for a quick overview of tensor parallelism and learn how column and row parallel setups differ.</p></li> <li><p>See the <a href="./tensor_parallelism">Tensor parallelism</a> training guide to learn how to use it in a training setting.</p></li></ul> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/transformers/blob/main/docs/source/en/perf_infer_gpu_multi.md" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a> <p></p>
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