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@@ -459,7 +459,6 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
<li><a href="#axolotl.utils.distributed.is_main_process" id="toc-axolotl.utils.distributed.is_main_process" class="nav-link" data-scroll-target="#axolotl.utils.distributed.is_main_process">is_main_process</a></li>
<li><a href="#axolotl.utils.distributed.reduce_and_broadcast" id="toc-axolotl.utils.distributed.reduce_and_broadcast" class="nav-link" data-scroll-target="#axolotl.utils.distributed.reduce_and_broadcast">reduce_and_broadcast</a></li>
<li><a href="#axolotl.utils.distributed.zero_first" id="toc-axolotl.utils.distributed.zero_first" class="nav-link" data-scroll-target="#axolotl.utils.distributed.zero_first">zero_first</a></li>
<li><a href="#axolotl.utils.distributed.zero_only" id="toc-axolotl.utils.distributed.zero_only" class="nav-link" data-scroll-target="#axolotl.utils.distributed.zero_only">zero_only</a></li>
</ul></li>
</ul></li>
</ul>
@@ -521,10 +520,6 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
<td><a href="#axolotl.utils.distributed.zero_first">zero_first</a></td>
<td>runs the wrapped context so that rank 0 runs first before other ranks</td>
</tr>
<tr class="even">
<td><a href="#axolotl.utils.distributed.zero_only">zero_only</a></td>
<td>Context manager that only runs the enclosed block on the main rank.</td>
</tr>
</tbody>
</table>
<section id="axolotl.utils.distributed.barrier" class="level3">
@@ -579,9 +574,13 @@ The value is then broadcasted to all other ranks.</p>
</section>
<section id="axolotl.utils.distributed.is_main_process" class="level3">
<h3 class="anchored" data-anchor-id="axolotl.utils.distributed.is_main_process">is_main_process</h3>
<div class="sourceCode" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>utils.distributed.is_main_process()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="sourceCode" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>utils.distributed.is_main_process(use_environ<span class="op">=</span><span class="va">False</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Check if the current process is the main process. If not in distributed mode,
always return <code>True</code>.</p>
<p>Args:
- use_environ (bool, optional): Use environment variable to determine main process.</p>
<p>Returns:
- bool: <code>True</code> if the current process is the main process, <code>False</code> otherwise.</p>
</section>
<section id="axolotl.utils.distributed.reduce_and_broadcast" class="level3">
<h3 class="anchored" data-anchor-id="axolotl.utils.distributed.reduce_and_broadcast">reduce_and_broadcast</h3>
@@ -599,11 +598,6 @@ and then broadcast the reduced result to all ranks.</p>
<h3 class="anchored" data-anchor-id="axolotl.utils.distributed.zero_first">zero_first</h3>
<div class="sourceCode" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>utils.distributed.zero_first(is_main)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>runs the wrapped context so that rank 0 runs first before other ranks</p>
</section>
<section id="axolotl.utils.distributed.zero_only" class="level3">
<h3 class="anchored" data-anchor-id="axolotl.utils.distributed.zero_only">zero_only</h3>
<div class="sourceCode" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>utils.distributed.zero_only()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Context manager that only runs the enclosed block on the main rank.</p>
</section>

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@@ -554,14 +554,14 @@
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"text": "Name\nDescription\n\n\n\n\nbarrier\nActs as a barrier to wait for all processes. This ensures that all processes\n\n\ncleanup_distributed\nDestroy process group if torch distributed is initialized. Called in training early\n\n\ncompute_and_broadcast\nCompute a value using the function ‘fn’ only on the specified rank (default is 0).\n\n\ngather_from_all_ranks\nRun a callable ‘fn’ on all ranks and gather the results on the specified rank.\n\n\ngather_scalar_from_all_ranks\nRun a callable ‘fn’ on all ranks and gather the results on the specified rank.\n\n\nis_distributed\nCheck if distributed training is initialized.\n\n\nis_main_process\nCheck if the current process is the main process. If not in distributed mode,\n\n\nreduce_and_broadcast\nRun a callable ‘fn1’ on all ranks, gather the results, reduce them using ‘fn2’,\n\n\nzero_first\nruns the wrapped context so that rank 0 runs first before other ranks\n\n\nzero_only\nContext manager that only runs the enclosed block on the main rank.\n\n\n\n\n\nutils.distributed.barrier()\nActs as a barrier to wait for all processes. This ensures that all processes\nreach the barrier before proceeding further.\n\n\n\nutils.distributed.cleanup_distributed()\nDestroy process group if torch distributed is initialized. Called in training early\ntermination or when training successfully completes.\n\n\n\nutils.distributed.compute_and_broadcast(fn)\nCompute a value using the function ‘fn’ only on the specified rank (default is 0).\nThe value is then broadcasted to all other ranks.\nArgs:\n- fn (callable): A function that computes the value. This should not have any side effects.\n- rank (int, optional): The rank that computes the value. Default is 0.\nReturns:\n- The computed value (int or float).\n\n\n\nutils.distributed.gather_from_all_ranks(fn, world_size=1)\nRun a callable ‘fn’ on all ranks and gather the results on the specified rank.\nArgs:\n- fn (callable): A function that computes the value. This should not have any side effects.\n- rank (int, optional): The rank that gathers the values. Default is 0.\n- world_size (int, optional): Total number of processes in the current distributed setup.\nReturns:\n- A list of computed values from all ranks if on the gathering rank, otherwise None.\n\n\n\nutils.distributed.gather_scalar_from_all_ranks(fn, world_size=1)\nRun a callable ‘fn’ on all ranks and gather the results on the specified rank.\nArgs:\n- fn (callable): A function that computes the value. This should not have any side effects.\n- rank (int, optional): The rank that gathers the values. Default is 0.\n- world_size (int, optional): Total number of processes in the current distributed setup.\nReturns:\n- A list of computed values from all ranks if on the gathering rank, otherwise None.\n\n\n\nutils.distributed.is_distributed()\nCheck if distributed training is initialized.\n\n\n\nutils.distributed.is_main_process()\nCheck if the current process is the main process. If not in distributed mode,\nalways return True.\n\n\n\nutils.distributed.reduce_and_broadcast(fn1, fn2)\nRun a callable ‘fn1’ on all ranks, gather the results, reduce them using ‘fn2’,\nand then broadcast the reduced result to all ranks.\nArgs:\n- fn1 (callable): A function that computes the value on each rank.\n- fn2 (callable): A reduction function that takes a list of values and returns a single value.\n- world_size (int, optional): Total number of processes in the current distributed setup.\nReturns:\n- The reduced and broadcasted value.\n\n\n\nutils.distributed.zero_first(is_main)\nruns the wrapped context so that rank 0 runs first before other ranks\n\n\n\nutils.distributed.zero_only()\nContext manager that only runs the enclosed block on the main rank."
"text": "Name\nDescription\n\n\n\n\nbarrier\nActs as a barrier to wait for all processes. This ensures that all processes\n\n\ncleanup_distributed\nDestroy process group if torch distributed is initialized. Called in training early\n\n\ncompute_and_broadcast\nCompute a value using the function ‘fn’ only on the specified rank (default is 0).\n\n\ngather_from_all_ranks\nRun a callable ‘fn’ on all ranks and gather the results on the specified rank.\n\n\ngather_scalar_from_all_ranks\nRun a callable ‘fn’ on all ranks and gather the results on the specified rank.\n\n\nis_distributed\nCheck if distributed training is initialized.\n\n\nis_main_process\nCheck if the current process is the main process. If not in distributed mode,\n\n\nreduce_and_broadcast\nRun a callable ‘fn1’ on all ranks, gather the results, reduce them using ‘fn2’,\n\n\nzero_first\nruns the wrapped context so that rank 0 runs first before other ranks\n\n\n\n\n\nutils.distributed.barrier()\nActs as a barrier to wait for all processes. This ensures that all processes\nreach the barrier before proceeding further.\n\n\n\nutils.distributed.cleanup_distributed()\nDestroy process group if torch distributed is initialized. Called in training early\ntermination or when training successfully completes.\n\n\n\nutils.distributed.compute_and_broadcast(fn)\nCompute a value using the function ‘fn’ only on the specified rank (default is 0).\nThe value is then broadcasted to all other ranks.\nArgs:\n- fn (callable): A function that computes the value. This should not have any side effects.\n- rank (int, optional): The rank that computes the value. Default is 0.\nReturns:\n- The computed value (int or float).\n\n\n\nutils.distributed.gather_from_all_ranks(fn, world_size=1)\nRun a callable ‘fn’ on all ranks and gather the results on the specified rank.\nArgs:\n- fn (callable): A function that computes the value. This should not have any side effects.\n- rank (int, optional): The rank that gathers the values. Default is 0.\n- world_size (int, optional): Total number of processes in the current distributed setup.\nReturns:\n- A list of computed values from all ranks if on the gathering rank, otherwise None.\n\n\n\nutils.distributed.gather_scalar_from_all_ranks(fn, world_size=1)\nRun a callable ‘fn’ on all ranks and gather the results on the specified rank.\nArgs:\n- fn (callable): A function that computes the value. This should not have any side effects.\n- rank (int, optional): The rank that gathers the values. Default is 0.\n- world_size (int, optional): Total number of processes in the current distributed setup.\nReturns:\n- A list of computed values from all ranks if on the gathering rank, otherwise None.\n\n\n\nutils.distributed.is_distributed()\nCheck if distributed training is initialized.\n\n\n\nutils.distributed.is_main_process(use_environ=False)\nCheck if the current process is the main process. If not in distributed mode,\nalways return True.\nArgs:\n- use_environ (bool, optional): Use environment variable to determine main process.\nReturns:\n- bool: True if the current process is the main process, False otherwise.\n\n\n\nutils.distributed.reduce_and_broadcast(fn1, fn2)\nRun a callable ‘fn1’ on all ranks, gather the results, reduce them using ‘fn2’,\nand then broadcast the reduced result to all ranks.\nArgs:\n- fn1 (callable): A function that computes the value on each rank.\n- fn2 (callable): A reduction function that takes a list of values and returns a single value.\n- world_size (int, optional): Total number of processes in the current distributed setup.\nReturns:\n- The reduced and broadcasted value.\n\n\n\nutils.distributed.zero_first(is_main)\nruns the wrapped context so that rank 0 runs first before other ranks"
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