Merge pull request #57 from pytorch/fix-links-to-2.9

Fix links to sources for 2.9.0

Author: svekars

2.9/amp.html

@@ -4426,7 +4426,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.checkpoint">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_fn=&lt;function</span> <span class="pre">noop_context_fn&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">determinism_check='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stop=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/utils/checkpoint.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_fn=&lt;function</span> <span class="pre">noop_context_fn&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">determinism_check='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stop=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/utils/checkpoint.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint" title="Permalink to this definition">#</a></dt>
 <dd><p>Checkpoint a model or part of the model.</p>
 <p>Activation checkpointing is a technique that trades compute for memory.
 Instead of keeping tensors needed for backward alive until they are used in
@@ -4547,7 +4547,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.checkpoint_sequential">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint_sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">segments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant</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><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/utils/checkpoint.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint_sequential" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint_sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">segments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant</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><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/utils/checkpoint.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint_sequential" title="Permalink to this definition">#</a></dt>
 <dd><p>Checkpoint a sequential model to save memory.</p>
 <p>Sequential models execute a list of modules/functions in order
 (sequentially). Therefore, we can divide such a model in various segments
@@ -4597,7 +4597,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.set_checkpoint_debug_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">set_checkpoint_debug_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/utils/checkpoint.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.set_checkpoint_debug_enabled" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">set_checkpoint_debug_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/utils/checkpoint.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.set_checkpoint_debug_enabled" title="Permalink to this definition">#</a></dt>
 <dd><p>Context manager that sets whether checkpoint should print additional debug
 information when running. See the <code class="docutils literal notranslate"><span class="pre">debug</span></code> flag for
 <a class="reference internal" href="#torch.utils.checkpoint.checkpoint" title="torch.utils.checkpoint.checkpoint"><code class="xref py py-func docutils literal notranslate"><span class="pre">checkpoint()</span></code></a> for more information. Note that
@@ -4613,7 +4613,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.CheckpointPolicy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">CheckpointPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/utils/checkpoint.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.CheckpointPolicy" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">CheckpointPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/utils/checkpoint.py#L1251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.CheckpointPolicy" title="Permalink to this definition">#</a></dt>
 <dd><p>Enum for specifying the policy for checkpointing during backpropagation.</p>
 <p>The following policies are supported:</p>
 <ul class="simple">
@@ -4637,7 +4637,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.SelectiveCheckpointContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SelectiveCheckpointContext</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">is_recompute</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/utils/checkpoint.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.SelectiveCheckpointContext" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SelectiveCheckpointContext</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">is_recompute</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/utils/checkpoint.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.SelectiveCheckpointContext" title="Permalink to this definition">#</a></dt>
 <dd><p>Context passed to policy function during selective checkpointing.</p>
 <p>This class is used to pass relevant metadata to the policy function during
 selective checkpointing. The metadata includes whether the current invocation
@@ -4660,7 +4660,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.create_selective_checkpoint_contexts">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">create_selective_checkpoint_contexts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">policy_fn_or_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_cache_entry_mutation</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 external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/utils/checkpoint.py#L1365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.create_selective_checkpoint_contexts" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">create_selective_checkpoint_contexts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">policy_fn_or_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_cache_entry_mutation</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 external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/utils/checkpoint.py#L1365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.create_selective_checkpoint_contexts" title="Permalink to this definition">#</a></dt>
 <dd><p>Helper to avoid recomputing certain ops during activation checkpointing.</p>
 <p>Use this with <cite>torch.utils.checkpoint.checkpoint</cite> to control which
 operations are recomputed during the backward pass.</p>

2.9/autograd.html

@@ -4564,7 +4564,7 @@ <h2>Invariants of torch.ops.higher_order.cond<a class="headerlink" href="#invari
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">#</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch._higher_order_ops.cond.cond">
-<span class="sig-prename descclassname"><span class="pre">torch._higher_order_ops.cond.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/_higher_order_ops/cond.py#L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._higher_order_ops.cond.cond" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch._higher_order_ops.cond.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/_higher_order_ops/cond.py#L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._higher_order_ops.cond.cond" title="Permalink to this definition">#</a></dt>
 <dd><p>Conditionally applies <cite>true_fn</cite> or <cite>false_fn</cite>.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>

2.9/backends.html

@@ -4396,7 +4396,7 @@
 <p class="date-info-last-verified" style="color: #6c6c6d; font-size: small;">Created On: Apr 09, 2019 | Last Updated On: Jun 13, 2025</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__config__.show">
-<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">show</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/__config__.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.show" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">show</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/__config__.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.show" title="Permalink to this definition">#</a></dt>
 <dd><p>Return a human-readable string with descriptions of the
 configuration of PyTorch.</p>
 <dl class="field-list simple">
@@ -4408,7 +4408,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__config__.parallel_info">
-<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">parallel_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.1/torch/__config__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.parallel_info" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">parallel_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/v2.9.0/torch/__config__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.parallel_info" title="Permalink to this definition">#</a></dt>
 <dd><p>Returns detailed string with parallelization settings</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>