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torchxlabot2 committed Oct 30, 2024
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Expand Down Expand Up @@ -1722,10 +1722,9 @@ <h1>Source code for torch_xla.core.xla_model</h1><div class="highlight"><pre>
<span class="k">if</span> <span class="n">sharding</span> <span class="ow">and</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="o">%</span>
<span class="n">local_runtime_device_count</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">&quot;When minibatch is configured, batch dimension of the tensor &quot;</span> <span class="o">+</span>
<span class="s2">&quot;must be divisible by local runtime device count.input data shape &quot;</span>
<span class="o">+</span>
<span class="sa">f</span><span class="s2">&quot;=</span><span class="si">{</span><span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="si">}</span><span class="s2">, local_runtime_device_count = </span><span class="si">{</span><span class="n">local_runtime_device_count</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="s2">&quot;When minibatch is configured, the per-host batch size must be divisible &quot;</span>
<span class="o">+</span> <span class="s2">&quot;by local runtime device count. Per host input data shape &quot;</span> <span class="o">+</span>
<span class="sa">f</span><span class="s2">&quot;= </span><span class="si">{</span><span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="si">}</span><span class="s2">, local_runtime_device_count = </span><span class="si">{</span><span class="n">local_runtime_device_count</span><span class="si">}</span><span class="s2">&quot;</span>
<span class="p">)</span>

<span class="n">xtensors</span> <span class="o">=</span> <span class="n">torch_xla</span><span class="o">.</span><span class="n">_XLAC</span><span class="o">.</span><span class="n">_xla_tensors_from_aten</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">devices</span><span class="p">,</span>
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Expand Up @@ -77,8 +77,7 @@ import torch_xla.runtime as xr
import torch_xla.distributed.xla_backend

def setup(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '12355'
os.environ['PJRT_DEVICE'] = 'TPU'

# initialize the xla process group
dist.init_process_group("xla", rank=rank, world_size=world_size)
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Expand Down Expand Up @@ -486,8 +486,7 @@ <h2>How to use DistributedDataParallel<a class="headerlink" href="#how-to-use-di
<span class="kn">import</span> <span class="nn">torch_xla.distributed.xla_backend</span>

<span class="k">def</span> <span class="nf">setup</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;MASTER_ADDR&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;localhost&#39;</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;MASTER_PORT&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;12355&#39;</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;PJRT_DEVICE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;TPU&#39;</span>

<span class="c1"># initialize the xla process group</span>
<span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span><span class="s2">&quot;xla&quot;</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="n">world_size</span><span class="p">)</span>
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