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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/extending.func.html">Extending torch.func with autograd.Function</a></li>
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<div class="section" id="module-torch.backends">
<span id="torch-backends"></span><h1>torch.backends<a class="headerlink" href="#module-torch.backends" title="Permalink to this heading">¶</a></h1>
<p><cite>torch.backends</cite> controls the behavior of various backends that PyTorch supports.</p>
<p>These backends include:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cpu</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.mps</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.mkl</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.mkldnn</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.openmp</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.opt_einsum</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.xeon</span></code></p></li>
</ul>
<div class="section" id="module-torch.backends.cpu">
<span id="torch-backends-cpu"></span><h2>torch.backends.cpu<a class="headerlink" href="#module-torch.backends.cpu" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cpu.get_cpu_capability">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cpu.</span></span><span class="sig-name descname"><span class="pre">get_cpu_capability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cpu.html#get_cpu_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cpu.get_cpu_capability" title="Permalink to this definition">¶</a></dt>
<dd><p>Return cpu capability as a string value.</p>
<p>Possible values:
- “DEFAULT”
- “VSX”
- “Z VECTOR”
- “NO AVX”
- “AVX2”
- “AVX512”</p>
<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/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="module-torch.backends.cuda">
<span id="torch-backends-cuda"></span><h2>torch.backends.cuda<a class="headerlink" href="#module-torch.backends.cuda" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.is_built">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_built" title="Permalink to this definition">¶</a></dt>
<dd><p>Return whether PyTorch is built with CUDA support.</p>
<p>Note that this doesn’t necessarily mean CUDA is available; just that if this PyTorch
binary were run on a machine with working CUDA drivers and devices, we would be able to use it.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cuda.matmul.allow_tf32">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.matmul.</span></span><span class="sig-name descname"><span class="pre">allow_tf32</span></span><a class="headerlink" href="#torch.backends.cuda.matmul.allow_tf32" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that controls whether TensorFloat-32 tensor cores may be used in matrix
multiplications on Ampere or newer GPUs. See <a class="reference internal" href="notes/cuda.html#tf32-on-ampere"><span class="std std-ref">TensorFloat-32 (TF32) on Ampere (and later) devices</span></a>.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.matmul.</span></span><span class="sig-name descname"><span class="pre">allow_fp16_reduced_precision_reduction</span></span><a class="headerlink" href="#torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that controls whether reduced precision reductions (e.g., with fp16 accumulation type) are allowed with fp16 GEMMs.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.matmul.</span></span><span class="sig-name descname"><span class="pre">allow_bf16_reduced_precision_reduction</span></span><a class="headerlink" href="#torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that controls whether reduced precision reductions are allowed with bf16 GEMMs.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cuda.cufft_plan_cache">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">cufft_plan_cache</span></span><a class="headerlink" href="#torch.backends.cuda.cufft_plan_cache" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">cufft_plan_cache</span></code> contains the cuFFT plan caches for each CUDA device.
Query a specific device <cite>i</cite>’s cache via <cite>torch.backends.cuda.cufft_plan_cache[i]</cite>.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cuda.cufft_plan_cache.size">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.cufft_plan_cache.</span></span><span class="sig-name descname"><span class="pre">size</span></span><a class="headerlink" href="#torch.backends.cuda.cufft_plan_cache.size" title="Permalink to this definition">¶</a></dt>
<dd><p>A readonly <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a> that shows the number of plans currently in a cuFFT plan cache.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cuda.cufft_plan_cache.max_size">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.cufft_plan_cache.</span></span><span class="sig-name descname"><span class="pre">max_size</span></span><a class="headerlink" href="#torch.backends.cuda.cufft_plan_cache.max_size" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a> that controls the capacity of a cuFFT plan cache.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.backends.cuda.cufft_plan_cache.clear">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.cufft_plan_cache.</span></span><span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.backends.cuda.cufft_plan_cache.clear" title="Permalink to this definition">¶</a></dt>
<dd><p>Clears a cuFFT plan cache.</p>
</dd></dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.preferred_linalg_library">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_linalg_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</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/backends/cuda.html#preferred_linalg_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_linalg_library" title="Permalink to this definition">¶</a></dt>
<dd><p>Override the heuristic PyTorch uses to choose between cuSOLVER and MAGMA for CUDA linear algebra operations.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This flag is experimental and subject to change.</p>
</div>
<p>When PyTorch runs a CUDA linear algebra operation it often uses the cuSOLVER or MAGMA libraries,
and if both are available it decides which to use with a heuristic.
This flag (a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) allows overriding those heuristics.</p>
<ul class="simple">
<li><p>If <cite>“cusolver”</cite> is set then cuSOLVER will be used wherever possible.</p></li>
<li><p>If <cite>“magma”</cite> is set then MAGMA will be used wherever possible.</p></li>
<li><p>If <cite>“default”</cite> (the default) is set then heuristics will be used to pick between
cuSOLVER and MAGMA if both are available.</p></li>
<li><p>When no input is given, this function returns the currently preferred library.</p></li>
<li><p>User may use the environment variable TORCH_LINALG_PREFER_CUSOLVER=1 to set the preferred library to cuSOLVER
globally.
This flag only sets the initial value of the preferred library and the preferred library
may still be overridden by this function call later in your script.</p></li>
</ul>
<p>Note: When a library is preferred other libraries may still be used if the preferred library
doesn’t implement the operation(s) called.
This flag may achieve better performance if PyTorch’s heuristic library selection is incorrect
for your application’s inputs.</p>
<p>Currently supported linalg operators:</p>
<ul class="simple">
<li><p><a class="reference internal" href="generated/torch.linalg.inv.html#torch.linalg.inv" title="torch.linalg.inv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.inv()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.inv_ex.html#torch.linalg.inv_ex" title="torch.linalg.inv_ex"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.inv_ex()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.cholesky.html#torch.linalg.cholesky" title="torch.linalg.cholesky"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.cholesky()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.cholesky_ex.html#torch.linalg.cholesky_ex" title="torch.linalg.cholesky_ex"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.cholesky_ex()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.cholesky_solve.html#torch.cholesky_solve" title="torch.cholesky_solve"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cholesky_solve()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.cholesky_inverse.html#torch.cholesky_inverse" title="torch.cholesky_inverse"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cholesky_inverse()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.lu_factor.html#torch.linalg.lu_factor" title="torch.linalg.lu_factor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.lu_factor()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.lu.html#torch.linalg.lu" title="torch.linalg.lu"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.lu()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.lu_solve.html#torch.linalg.lu_solve" title="torch.linalg.lu_solve"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.lu_solve()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.qr.html#torch.linalg.qr" title="torch.linalg.qr"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.qr()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.eigh.html#torch.linalg.eigh" title="torch.linalg.eigh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.eigh()</span></code></a></p></li>
<li><p><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.eighvals()</span></code></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.svd.html#torch.linalg.svd" title="torch.linalg.svd"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.svd()</span></code></a></p></li>
<li><p><a class="reference internal" href="generated/torch.linalg.svdvals.html#torch.linalg.svdvals" title="torch.linalg.svdvals"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.linalg.svdvals()</span></code></a></p></li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>_LinalgBackend</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cuda.SDPBackend">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">SDPBackend</span></span><a class="headerlink" href="#torch.backends.cuda.SDPBackend" title="Permalink to this definition">¶</a></dt>
<dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_SDPBackend</span></code></p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cuda.SDPAParams">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">SDPAParams</span></span><a class="headerlink" href="#torch.backends.cuda.SDPAParams" title="Permalink to this definition">¶</a></dt>
<dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_SDPAParams</span></code></p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.flash_sdp_enabled">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">flash_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#flash_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.flash_sdp_enabled" title="Permalink to this definition">¶</a></dt>
<dd><div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This flag is beta and subject to change.</p>
</div>
<p>Returns whether flash scaled dot product attention is enabled or not.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.enable_mem_efficient_sdp">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_mem_efficient_sdp</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 internal" href="_modules/torch/backends/cuda.html#enable_mem_efficient_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_mem_efficient_sdp" title="Permalink to this definition">¶</a></dt>
<dd><div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This flag is beta and subject to change.</p>
</div>
<p>Enables or disables memory efficient scaled dot product attention.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.mem_efficient_sdp_enabled">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_efficient_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#mem_efficient_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.mem_efficient_sdp_enabled" title="Permalink to this definition">¶</a></dt>
<dd><div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This flag is beta and subject to change.</p>
</div>
<p>Returns whether memory efficient scaled dot product attention is enabled or not.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.enable_flash_sdp">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_flash_sdp</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 internal" href="_modules/torch/backends/cuda.html#enable_flash_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_flash_sdp" title="Permalink to this definition">¶</a></dt>
<dd><div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This flag is beta and subject to change.</p>
</div>
<p>Enables or disables flash scaled dot product attention.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.math_sdp_enabled">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">math_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#math_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.math_sdp_enabled" title="Permalink to this definition">¶</a></dt>
<dd><div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This flag is beta and subject to change.</p>
</div>
<p>Returns whether math scaled dot product attention is enabled or not.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.enable_math_sdp">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_math_sdp</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 internal" href="_modules/torch/backends/cuda.html#enable_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_math_sdp" title="Permalink to this definition">¶</a></dt>
<dd><div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This flag is beta and subject to change.</p>
</div>
<p>Enables or disables math scaled dot product attention.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.can_use_flash_attention">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_flash_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</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/backends/cuda.html#can_use_flash_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_flash_attention" title="Permalink to this definition">¶</a></dt>
<dd><p>Check if FlashAttention can be utilized in scaled_dot_product_attention.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>params</strong> (<em>_SDPAParams</em>) – An instance of SDPAParams containing the tensors for query,
key, value, an optional attention mask, dropout rate, and
a flag indicating if the attention is causal.</p></li>
<li><p><strong>debug</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to logging.warn debug information as to why FlashAttention could not be run.
Defaults to False.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True if FlashAttention can be used with the given parameters; otherwise, False.</p>
</dd>
<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 v3.12)">bool</a></p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function is dependent on a CUDA-enabled build of PyTorch. It will return False
in non-CUDA environments.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.can_use_efficient_attention">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_efficient_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</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/backends/cuda.html#can_use_efficient_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_efficient_attention" title="Permalink to this definition">¶</a></dt>
<dd><p>Check if efficient_attention can be utilized in scaled_dot_product_attention.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>params</strong> (<em>_SDPAParams</em>) – An instance of SDPAParams containing the tensors for query,
key, value, an optional attention mask, dropout rate, and
a flag indicating if the attention is causal.</p></li>
<li><p><strong>debug</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to logging.warn with information as to why efficient_attention could not be run.
Defaults to False.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True if efficient_attention can be used with the given parameters; otherwise, False.</p>
</dd>
<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 v3.12)">bool</a></p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function is dependent on a CUDA-enabled build of PyTorch. It will return False
in non-CUDA environments.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cuda.sdp_kernel">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">sdp_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_flash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_math</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_mem_efficient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#sdp_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.sdp_kernel" title="Permalink to this definition">¶</a></dt>
<dd><div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This flag is beta and subject to change.</p>
</div>
<p>This context manager can be used to temporarily enable or disable any of the three backends for scaled dot product attention.
Upon exiting the context manager, the previous state of the flags will be restored.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
</div>
<div class="section" id="module-torch.backends.cudnn">
<span id="torch-backends-cudnn"></span><h2>torch.backends.cudnn<a class="headerlink" href="#module-torch.backends.cudnn" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cudnn.version">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.version" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the version of cuDNN.</p>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.cudnn.is_available">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a bool indicating if CUDNN is currently available.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cudnn.enabled">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">enabled</span></span><a class="headerlink" href="#torch.backends.cudnn.enabled" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that controls whether cuDNN is enabled.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cudnn.allow_tf32">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">allow_tf32</span></span><a class="headerlink" href="#torch.backends.cudnn.allow_tf32" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that controls where TensorFloat-32 tensor cores may be used in cuDNN
convolutions on Ampere or newer GPUs. See <a class="reference internal" href="notes/cuda.html#tf32-on-ampere"><span class="std std-ref">TensorFloat-32 (TF32) on Ampere (and later) devices</span></a>.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cudnn.deterministic">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">deterministic</span></span><a class="headerlink" href="#torch.backends.cudnn.deterministic" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that, if True, causes cuDNN to only use deterministic convolution algorithms.
See also <a class="reference internal" href="generated/torch.are_deterministic_algorithms_enabled.html#torch.are_deterministic_algorithms_enabled" title="torch.are_deterministic_algorithms_enabled"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.are_deterministic_algorithms_enabled()</span></code></a> and
<a class="reference internal" href="generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a>.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cudnn.benchmark">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">benchmark</span></span><a class="headerlink" href="#torch.backends.cudnn.benchmark" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a> that, if True, causes cuDNN to benchmark multiple convolution algorithms
and select the fastest.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.cudnn.benchmark_limit">
<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">benchmark_limit</span></span><a class="headerlink" href="#torch.backends.cudnn.benchmark_limit" title="Permalink to this definition">¶</a></dt>
<dd><p>A <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a> that specifies the maximum number of cuDNN convolution algorithms to try when
<cite>torch.backends.cudnn.benchmark</cite> is True. Set <cite>benchmark_limit</cite> to zero to try every
available algorithm. Note that this setting only affects convolutions dispatched via the
cuDNN v8 API.</p>
</dd></dl>
<span class="target" id="module-torch.backends.cudnn.rnn"></span></div>
<div class="section" id="module-torch.backends.mps">
<span id="torch-backends-mps"></span><h2>torch.backends.mps<a class="headerlink" href="#module-torch.backends.mps" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.mps.is_available">
<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a bool indicating if MPS is currently available.</p>
<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 v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.mps.is_built">
<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_built" title="Permalink to this definition">¶</a></dt>
<dd><p>Return whether PyTorch is built with MPS support.</p>
<p>Note that this doesn’t necessarily mean MPS is available; just that
if this PyTorch binary were run a machine with working MPS drivers
and devices, we would be able to use it.</p>
<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 v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="module-torch.backends.mkl">
<span id="torch-backends-mkl"></span><h2>torch.backends.mkl<a class="headerlink" href="#module-torch.backends.mkl" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.mkl.is_available">
<span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Return whether PyTorch is built with MKL support.</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.backends.mkl.verbose">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.verbose" title="Permalink to this definition">¶</a></dt>
<dd><p>On-demand oneMKL verbosing functionality.</p>
<p>To make it easier to debug performance issues, oneMKL can dump verbose
messages containing execution information like duration while executing
the kernel. The verbosing functionality can be invoked via an environment
variable named <cite>MKL_VERBOSE</cite>. However, this methodology dumps messages in
all steps. Those are a large amount of verbose messages. Moreover, for
investigating the performance issues, generally taking verbose messages
for one single iteration is enough. This on-demand verbosing functionality
makes it possible to control scope for verbose message dumping. In the
following example, verbose messages will be dumped out for the second
inference only.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mkl</span><span class="o">.</span><span class="n">verbose</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mkl</span><span class="o">.</span><span class="n">VERBOSE_ON</span><span class="p">):</span>
<span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>level</strong> – Verbose level
- <code class="docutils literal notranslate"><span class="pre">VERBOSE_OFF</span></code>: Disable verbosing
- <code class="docutils literal notranslate"><span class="pre">VERBOSE_ON</span></code>: Enable verbosing</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="module-torch.backends.mkldnn">
<span id="torch-backends-mkldnn"></span><h2>torch.backends.mkldnn<a class="headerlink" href="#module-torch.backends.mkldnn" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.mkldnn.is_available">
<span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Return whether PyTorch is built with MKL-DNN support.</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.backends.mkldnn.verbose">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.verbose" title="Permalink to this definition">¶</a></dt>
<dd><p>On-demand oneDNN (former MKL-DNN) verbosing functionality.</p>
<p>To make it easier to debug performance issues, oneDNN can dump verbose
messages containing information like kernel size, input data size and
execution duration while executing the kernel. The verbosing functionality
can be invoked via an environment variable named <cite>DNNL_VERBOSE</cite>. However,
this methodology dumps messages in all steps. Those are a large amount of
verbose messages. Moreover, for investigating the performance issues,
generally taking verbose messages for one single iteration is enough.
This on-demand verbosing functionality makes it possible to control scope
for verbose message dumping. In the following example, verbose messages
will be dumped out for the second inference only.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mkldnn</span><span class="o">.</span><span class="n">verbose</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">mkldnn</span><span class="o">.</span><span class="n">VERBOSE_ON</span><span class="p">):</span>
<span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>level</strong> – Verbose level
- <code class="docutils literal notranslate"><span class="pre">VERBOSE_OFF</span></code>: Disable verbosing
- <code class="docutils literal notranslate"><span class="pre">VERBOSE_ON</span></code>: Enable verbosing
- <code class="docutils literal notranslate"><span class="pre">VERBOSE_ON_CREATION</span></code>: Enable verbosing, including oneDNN kernel creation</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="module-torch.backends.openmp">
<span id="torch-backends-openmp"></span><h2>torch.backends.openmp<a class="headerlink" href="#module-torch.backends.openmp" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.openmp.is_available">
<span class="sig-prename descclassname"><span class="pre">torch.backends.openmp.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/openmp.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.openmp.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Return whether PyTorch is built with OpenMP support.</p>
</dd></dl>
<span class="target" id="module-torch.backends.quantized"></span><span class="target" id="module-torch.backends.xnnpack"></span></div>
<div class="section" id="module-torch.backends.opt_einsum">
<span id="torch-backends-opt-einsum"></span><h2>torch.backends.opt_einsum<a class="headerlink" href="#module-torch.backends.opt_einsum" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.opt_einsum.is_available">
<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.is_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Return a bool indicating if opt_einsum is currently available.</p>
<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 v3.12)">bool</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.backends.opt_einsum.get_opt_einsum">
<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">get_opt_einsum</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#get_opt_einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.get_opt_einsum" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the opt_einsum package if opt_einsum is currently available, else None.</p>
<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/typing.html#typing.Any" title="(in Python v3.12)"><em>Any</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.opt_einsum.enabled">
<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">enabled</span></span><a class="headerlink" href="#torch.backends.opt_einsum.enabled" title="Permalink to this definition">¶</a></dt>
<dd><p>A :class:<code class="docutils literal notranslate"><span class="pre">bool</span></code> that controls whether opt_einsum is enabled (<code class="docutils literal notranslate"><span class="pre">True</span></code> by default). If so,
torch.einsum will use opt_einsum (<a class="reference external" href="https://optimized-einsum.readthedocs.io/en/stable/path_finding.html">https://optimized-einsum.readthedocs.io/en/stable/path_finding.html</a>)
if available to calculate an optimal path of contraction for faster performance.</p>
<p>If opt_einsum is not available, torch.einsum will fall back to the default contraction path
of left to right.</p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="torch.backends.opt_einsum.strategy">
<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">strategy</span></span><a class="headerlink" href="#torch.backends.opt_einsum.strategy" title="Permalink to this definition">¶</a></dt>
<dd><p>A :class:<code class="docutils literal notranslate"><span class="pre">str</span></code> that specifies which strategies to try when <code class="docutils literal notranslate"><span class="pre">torch.backends.opt_einsum.enabled</span></code>
is <code class="docutils literal notranslate"><span class="pre">True</span></code>. By default, torch.einsum will try the “auto” strategy, but the “greedy” and “optimal”
strategies are also supported. Note that the “optimal” strategy is factorial on the number of
inputs as it tries all possible paths. See more details in opt_einsum’s docs
(<a class="reference external" href="https://optimized-einsum.readthedocs.io/en/stable/path_finding.html">https://optimized-einsum.readthedocs.io/en/stable/path_finding.html</a>).</p>
</dd></dl>
</div>
<div class="section" id="module-torch.backends.xeon">
<span id="torch-backends-xeon"></span><h2>torch.backends.xeon<a class="headerlink" href="#module-torch.backends.xeon" title="Permalink to this heading">¶</a></h2>
<span class="target" id="module-torch.backends.xeon.run_cpu"></span></div>
</div>
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