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<h1>Source code for torch.mps</h1><div class="highlight"><pre>
<span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd">This package enables an interface for accessing MPS (Metal Performance Shaders) backend in Python.</span>
<span class="sd">Metal is Apple's API for programming metal GPU (graphics processor unit). Using MPS means that increased</span>
<span class="sd">performance can be achieved, by running work on the metal GPU(s).</span>
<span class="sd">See https://developer.apple.com/documentation/metalperformanceshaders for more details.</span>
<span class="sd">"""</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="n">_is_in_bad_fork</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="p">,</span> <span class="s2">"_mps_is_in_bad_fork"</span><span class="p">,</span> <span class="k">lambda</span><span class="p">:</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">_default_mps_generator</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Generator</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># type: ignore[assignment]</span>
<span class="c1"># local helper function (not public or exported)</span>
<span class="k">def</span> <span class="nf">_get_default_mps_generator</span><span class="p">()</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">Generator</span><span class="p">:</span>
<span class="k">global</span> <span class="n">_default_mps_generator</span>
<span class="k">if</span> <span class="n">_default_mps_generator</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">_default_mps_generator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_get_default_generator</span><span class="p">()</span>
<span class="k">return</span> <span class="n">_default_mps_generator</span>
<div class="viewcode-block" id="device_count"><a class="viewcode-back" href="../../generated/torch.mps.device_count.html#torch.mps.device_count">[docs]</a><span class="k">def</span> <span class="nf">device_count</span><span class="p">()</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the number of available MPS devices."""</span>
<span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_has_mps</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_is_available</span><span class="p">())</span></div>
<div class="viewcode-block" id="synchronize"><a class="viewcode-back" href="../../generated/torch.mps.synchronize.html#torch.mps.synchronize">[docs]</a><span class="k">def</span> <span class="nf">synchronize</span><span class="p">()</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Waits for all kernels in all streams on a MPS device to complete."""</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_deviceSynchronize</span><span class="p">()</span></div>
<div class="viewcode-block" id="get_rng_state"><a class="viewcode-back" href="../../generated/torch.mps.get_rng_state.html#torch.mps.get_rng_state">[docs]</a><span class="k">def</span> <span class="nf">get_rng_state</span><span class="p">(</span><span class="n">device</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"mps"</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the random number generator state as a ByteTensor.</span>
<span class="sd"> Args:</span>
<span class="sd"> device (torch.device or int, optional): The device to return the RNG state of.</span>
<span class="sd"> Default: ``'mps'`` (i.e., ``torch.device('mps')``, the current MPS device).</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">_get_default_mps_generator</span><span class="p">()</span><span class="o">.</span><span class="n">get_state</span><span class="p">()</span></div>
<div class="viewcode-block" id="set_rng_state"><a class="viewcode-back" href="../../generated/torch.mps.set_rng_state.html#torch.mps.set_rng_state">[docs]</a><span class="k">def</span> <span class="nf">set_rng_state</span><span class="p">(</span>
<span class="n">new_state</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"mps"</span>
<span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Sets the random number generator state.</span>
<span class="sd"> Args:</span>
<span class="sd"> new_state (torch.ByteTensor): The desired state</span>
<span class="sd"> device (torch.device or int, optional): The device to set the RNG state.</span>
<span class="sd"> Default: ``'mps'`` (i.e., ``torch.device('mps')``, the current MPS device).</span>
<span class="sd"> """</span>
<span class="n">new_state_copy</span> <span class="o">=</span> <span class="n">new_state</span><span class="o">.</span><span class="n">clone</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">)</span>
<span class="n">_get_default_mps_generator</span><span class="p">()</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">new_state_copy</span><span class="p">)</span></div>
<div class="viewcode-block" id="manual_seed"><a class="viewcode-back" href="../../generated/torch.mps.manual_seed.html#torch.mps.manual_seed">[docs]</a><span class="k">def</span> <span class="nf">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Sets the seed for generating random numbers.</span>
<span class="sd"> Args:</span>
<span class="sd"> seed (int): The desired seed.</span>
<span class="sd"> """</span>
<span class="c1"># the torch.mps.manual_seed() can be called from the global</span>
<span class="c1"># torch.manual_seed() in torch/random.py. So we need to make</span>
<span class="c1"># sure mps is available (otherwise we just return without</span>
<span class="c1"># erroring out)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_has_mps</span><span class="p">:</span>
<span class="k">return</span>
<span class="n">seed</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
<span class="n">_get_default_mps_generator</span><span class="p">()</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span></div>
<div class="viewcode-block" id="seed"><a class="viewcode-back" href="../../generated/torch.mps.seed.html#torch.mps.seed">[docs]</a><span class="k">def</span> <span class="nf">seed</span><span class="p">()</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Sets the seed for generating random numbers to a random number."""</span>
<span class="n">_get_default_mps_generator</span><span class="p">()</span><span class="o">.</span><span class="n">seed</span><span class="p">()</span></div>
<div class="viewcode-block" id="empty_cache"><a class="viewcode-back" href="../../generated/torch.mps.empty_cache.html#torch.mps.empty_cache">[docs]</a><span class="k">def</span> <span class="nf">empty_cache</span><span class="p">()</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Releases all unoccupied cached memory currently held by the caching</span>
<span class="sd"> allocator so that those can be used in other GPU applications.</span>
<span class="sd"> """</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_emptyCache</span><span class="p">()</span></div>
<div class="viewcode-block" id="set_per_process_memory_fraction"><a class="viewcode-back" href="../../generated/torch.mps.set_per_process_memory_fraction.html#torch.mps.set_per_process_memory_fraction">[docs]</a><span class="k">def</span> <span class="nf">set_per_process_memory_fraction</span><span class="p">(</span><span class="n">fraction</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Set memory fraction for limiting process's memory allocation on MPS device.</span>
<span class="sd"> The allowed value equals the fraction multiplied by recommended maximum device memory</span>
<span class="sd"> (obtained from Metal API device.recommendedMaxWorkingSetSize).</span>
<span class="sd"> If trying to allocate more than the allowed value in a process, it will raise an out of</span>
<span class="sd"> memory error in allocator.</span>
<span class="sd"> Args:</span>
<span class="sd"> fraction(float): Range: 0~2. Allowed memory equals total_memory * fraction.</span>
<span class="sd"> .. note::</span>
<span class="sd"> Passing 0 to fraction means unlimited allocations</span>
<span class="sd"> (may cause system failure if out of memory).</span>
<span class="sd"> Passing fraction greater than 1.0 allows limits beyond the value</span>
<span class="sd"> returned from device.recommendedMaxWorkingSetSize.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fraction</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Invalid type for fraction argument, must be `float`"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">fraction</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">fraction</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Invalid fraction value: </span><span class="si">{</span><span class="n">fraction</span><span class="si">}</span><span class="s2">. Allowed range: 0~2"</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_setMemoryFraction</span><span class="p">(</span><span class="n">fraction</span><span class="p">)</span></div>
<div class="viewcode-block" id="current_allocated_memory"><a class="viewcode-back" href="../../generated/torch.mps.current_allocated_memory.html#torch.mps.current_allocated_memory">[docs]</a><span class="k">def</span> <span class="nf">current_allocated_memory</span><span class="p">()</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns the current GPU memory occupied by tensors in bytes.</span>
<span class="sd"> .. note::</span>
<span class="sd"> The returned size does not include cached allocations in</span>
<span class="sd"> memory pools of MPSAllocator.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_currentAllocatedMemory</span><span class="p">()</span></div>
<div class="viewcode-block" id="driver_allocated_memory"><a class="viewcode-back" href="../../generated/torch.mps.driver_allocated_memory.html#torch.mps.driver_allocated_memory">[docs]</a><span class="k">def</span> <span class="nf">driver_allocated_memory</span><span class="p">()</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns total GPU memory allocated by Metal driver for the process in bytes.</span>
<span class="sd"> .. note::</span>
<span class="sd"> The returned size includes cached allocations in MPSAllocator pools</span>
<span class="sd"> as well as allocations from MPS/MPSGraph frameworks.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_driverAllocatedMemory</span><span class="p">()</span></div>
<div class="viewcode-block" id="recommended_max_memory"><a class="viewcode-back" href="../../generated/torch.mps.recommended_max_memory.html#torch.mps.recommended_max_memory">[docs]</a><span class="k">def</span> <span class="nf">recommended_max_memory</span><span class="p">()</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Returns recommended max Working set size for GPU memory in bytes.</span>
<span class="sd"> .. note::</span>
<span class="sd"> Recommended max working set size for Metal.</span>
<span class="sd"> returned from device.recommendedMaxWorkingSetSize.</span>
<span class="sd"> """</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_recommendedMaxMemory</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_compile_shader</span><span class="p">(</span><span class="n">source</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""Compiles compute shader from source and allows one to invoke kernels</span>
<span class="sd"> defined there from the comfort of Python runtime</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS)</span>
<span class="sd"> >>> lib = torch.mps._compile_shader(</span>
<span class="sd"> ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }"</span>
<span class="sd"> ... )</span>
<span class="sd"> >>> x = torch.zeros(16, device="mps")</span>
<span class="sd"> >>> lib.full(x, 3.14)</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="p">,</span> <span class="s2">"_mps_compileShader"</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"MPS is not available"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_mps_compileShader</span><span class="p">(</span><span class="n">source</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">is_available</span><span class="p">()</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">return</span> <span class="n">device_count</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">profiler</span>
<span class="kn">from</span> <span class="nn">.event</span> <span class="kn">import</span> <span class="n">Event</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">"device_count"</span><span class="p">,</span>
<span class="s2">"get_rng_state"</span><span class="p">,</span>
<span class="s2">"manual_seed"</span><span class="p">,</span>
<span class="s2">"seed"</span><span class="p">,</span>
<span class="s2">"set_rng_state"</span><span class="p">,</span>
<span class="s2">"synchronize"</span><span class="p">,</span>
<span class="s2">"empty_cache"</span><span class="p">,</span>
<span class="s2">"set_per_process_memory_fraction"</span><span class="p">,</span>
<span class="s2">"current_allocated_memory"</span><span class="p">,</span>
<span class="s2">"driver_allocated_memory"</span><span class="p">,</span>
<span class="s2">"Event"</span><span class="p">,</span>
<span class="s2">"profiler"</span><span class="p">,</span>
<span class="s2">"recommended_max_memory"</span><span class="p">,</span>
<span class="s2">"is_available"</span><span class="p">,</span>
<span class="p">]</span>
</pre></div>
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